diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 0000000000000000000000000000000000000000..bf5ea2d27bbdc6bd4c244ccaed5adfbf7706fdb5
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,74 @@
+; see http://editorconfig.org/ for docs on this file
+
+root = true
+
+[*]
+ignore_if_in_header = This code was generated by a tool|<auto-generated>
+indent_style = space
+indent_size = 4
+; uncomment to help with sharing files across os's (i.e. network share or through local vm)
+#end_of_line = lf
+; avoid a bom, which causes endless problems with naive text tooling
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+; keeping auto-format enabled helps avoid merge hell for projects without CI-based format validation
+#disable_auto_format = true
+
+[*.cs]
+; uncomment to enable full formatting of c# files
+formatters = generic, uncrustify
+
+[*.asmdef]
+scrape_api = true
+
+[**/Tests/**.asmdef]
+scrape_api = false
+
+[*.Tests.asmdef]
+scrape_api = false
+
+[*.md]
+indent_size = 2
+; trailing whitespace is unfortunately significant in markdown
+trim_trailing_whitespace = false
+; uncomment to enable basic formatting of markdown files
+#formatters = generic
+
+[{Makefile,makefile}]
+; tab characters are part of the Makefile format
+indent_style = tab
+
+[*.asmdef]
+indent_size = 4
+
+[*.json]
+indent_size = 2
+
+[*.{vcproj,bat,cmd,xaml,tt,t4,ttinclude}]
+end_of_line = crlf
+
+; this VS-specific stuff is based on experiments to see how VS will modify a file after it has been manually edited.
+; the settings are meant to closely match what VS does to minimize unnecessary diffs.
+[*.{vcxproj,vcxproj.filters}]
+indent_style = space
+indent_size = 2
+end_of_line = crlf
+charset = utf-8-bom
+trim_trailing_whitespace = true
+insert_final_newline = false
+; must be broken out because of 51-char bug (https://github.com/editorconfig/editorconfig-visualstudio/issues/21)
+[*.{csproj,pyproj,props,targets}]
+indent_style = space
+indent_size = 2
+end_of_line = crlf
+charset = utf-8-bom
+trim_trailing_whitespace = true
+insert_final_newline = false
+[*.{sln,sln.template}]
+indent_style = tab
+indent_size = 4
+end_of_line = crlf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = false
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..e32645fa4eda9e8cc5fbb6ce24c3cb806271be82 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,35 +1,88 @@
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-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.asset binary
+*.unity binary
+*.prefab binary
+*.meta binary
+*/CommunicatorObjects/* binary
+*/communicator_objects/* binary
+*.md text
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+com.unity.ml-agents/Plugins/ProtoBuffer/runtimes/linux/native/libgrpc_csharp_ext.x86.so filter=lfs diff=lfs merge=lfs -text
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diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000000000000000000000000000000000..e5fe730e6c571b22aff36a5899f2da307cf3db7b
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,33 @@
+---
+name: Bug report
+about: Report a bug with ML-Agents
+title: ''
+labels: bug
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Console logs / stack traces**
+Please wrap in triple backticks (```) to make it easier to read.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Environment (please complete the following information):**
+- Unity Version: [e.g. Unity 2022.3f1]
+- OS + version: [e.g. Windows 10]
+- _ML-Agents version_: (e.g. ML-Agents v0.8, or latest `develop` branch from source)
+- _Torch version_: (you can run `pip3 show torch` to get this)
+- _Environment_: (which example environment you used to reproduce the error)
+
+**NOTE:** We are unable to help reproduce bugs with custom environments.  Please attempt to reproduce your issue with one of the example environments, or provide a minimal patch to one of the environments needed to reproduce the issue.
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..2fa1c22dcf32935c676cfd872325a675c856bbeb
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,11 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Discussion / General Questions
+    url: https://forum.unity.com/forums/ml-agents.453/
+    about: Discussion about ML-Agents, RL algorithms, or game integrations.
+  - name: Installation / Setup
+    url: https://forum.unity.com/forums/ml-agents.453/
+    about: Questions about python installation, initial connection between Unity and training, etc.
+  - name: ML-Agents Unity Forum
+    url: https://forum.unity.com/forums/ml-agents.453/
+    about: Please ask other questions in the ML-Agents Unity Forum.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000000000000000000000000000000000000..3228bca0413373bb96794271a0a2278a73ee5bac
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: request
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000000000000000000000000000000000000..47c73ebf07cf4a4a6313cb6f0ba6149d036624a9
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,24 @@
+### Proposed change(s)
+
+Describe the changes made in this PR.
+
+### Useful links (Github issues, JIRA tickets, ML-Agents forum threads etc.)
+
+
+
+### Types of change(s)
+
+- [ ] Bug fix
+- [ ] New feature
+- [ ] Code refactor
+- [ ] Breaking change
+- [ ] Documentation update
+- [ ] Other (please describe)
+
+### Checklist
+- [ ] Added tests that prove my fix is effective or that my feature works
+- [ ] Updated the changelog (if applicable)
+- [ ] Updated the documentation (if applicable)
+- [ ] Updated the migration guide (if applicable)
+
+### Other comments
diff --git a/.github/stale.yml b/.github/stale.yml
new file mode 100644
index 0000000000000000000000000000000000000000..88e27662486b3e5338f987b2cfee4b939cd580fb
--- /dev/null
+++ b/.github/stale.yml
@@ -0,0 +1,26 @@
+# Configuration for probot-stale - https://github.com/probot/stale
+
+# General configuration
+# Label to use when marking as stale
+staleLabel: stale
+
+# Only run on issues
+only: issues
+
+# Issue specific configuration
+issues:
+  limitPerRun: 5
+  daysUntilStale: 90
+  daysUntilClose: 30
+  markComment: >
+    This issue has been automatically marked as stale because it has not had activity in the
+    last 90 days. It will be closed in the next 30 days if no further activity occurs.
+    Thank you for your contributions.
+  closeComment: >
+    This issue has been automatically closed because it has not had activity in the
+    last 120 days. If this issue is still valid, please ping a maintainer.
+    Thank you for your contributions.
+  exemptLabels:
+    - request
+    - help-wanted
+    - announcement
diff --git a/.github/workflows/colab.yml b/.github/workflows/colab.yml
new file mode 100644
index 0000000000000000000000000000000000000000..2af462ec9f20a29a5fd22f68f2bbc0af2164d2c1
--- /dev/null
+++ b/.github/workflows/colab.yml
@@ -0,0 +1,65 @@
+name: colab
+
+on:
+  pull_request:
+    paths:  # This action will only run if the PR modifies a file in one of these directories
+    - 'ml-agents-envs/**'
+    - 'gym-unity/**'
+    - 'colab/**'
+    - '.github/workflows/colab.yml'
+  push:
+    branches:
+     - main
+     - develop
+     - 'release/**'
+  workflow_dispatch:
+
+jobs:
+  colab:
+    runs-on: ubuntu-latest
+    env:
+        COLAB_ALWAYS_INSTALL_XVFB: 1
+        QLEARNING_NUM_TRAINING_STEPS: 5
+        QLEARNING_NUM_NEW_EXP: 64
+        QLEARNING_BUFFER_SIZE: 64
+    strategy:
+      fail-fast: false
+      matrix:
+        notebook_path: [Colab_UnityEnvironment_1_Run.ipynb, Colab_UnityEnvironment_2_Train.ipynb, Colab_UnityEnvironment_3_SideChannel.ipynb]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.9.x
+    - uses: actions/setup-dotnet@v1
+      with:
+        dotnet-version: '6.0.x'
+    - name: Cache pip
+      uses: actions/cache@v2
+      with:
+        # This path is specific to Ubuntu
+        path: ~/.cache/pip
+        # Look to see if there is a cache hit for the corresponding requirements file
+        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'gym-unity/setup.py', 'colab_requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+          ${{ runner.os }}-
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install --upgrade setuptools
+        # Install the local checkouts of ml-agents. This will prevent the colab notebooks from installing a released version.
+        python -m pip install --progress-bar=off -e ./ml-agents-envs
+        python -m pip install --progress-bar=off -e ./ml-agents
+        python -m pip install --progress-bar=off -r colab_requirements.txt
+    - name: Execute notebook
+      run: jupyter nbconvert --to notebook --execute --log-level=DEBUG --ExecutePreprocessor.kernel_name=python3 --output output-${{ matrix.notebook_path }} colab/${{ matrix.notebook_path }}
+    - name: Upload colab results
+      uses: actions/upload-artifact@v2
+      with:
+        name: artifacts
+        path: |
+          colab/output-${{ matrix.notebook_path }}
+      # Use always() to always run this step to publish execution results when there are failures
+      if: ${{ always() }}
diff --git a/.github/workflows/lock.yml b/.github/workflows/lock.yml
new file mode 100644
index 0000000000000000000000000000000000000000..6133cf8596e16cb14deec8dd3a9836635d0520c1
--- /dev/null
+++ b/.github/workflows/lock.yml
@@ -0,0 +1,23 @@
+name: 'Lock Threads'
+
+on:
+  schedule:
+    - cron: '0 0/4 * * *'
+  workflow_dispatch:
+
+jobs:
+  lock:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: dessant/lock-threads@v2
+        with:
+          github-token: ${{ github.token }}
+          issue-lock-inactive-days: '30'
+          issue-exclude-created-before: ''
+          issue-exclude-labels: ''
+          issue-lock-labels: ''
+          issue-lock-comment: >
+            This thread has been automatically locked since there has not been
+            any recent activity after it was closed. Please open a new issue for
+            related bugs.
+          issue-lock-reason: 'resolved'
diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d15b4415fd9de6097d6d9d30a8f7d61e7ddfff65
--- /dev/null
+++ b/.github/workflows/nightly.yml
@@ -0,0 +1,92 @@
+name: nightly
+
+on:
+  schedule:
+    - cron: '0 7 * * *' # run at 7 AM UTC == midnight PST
+  workflow_dispatch:
+
+jobs:
+  markdown-link-check-full:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - uses: actions/setup-node@v2
+      with:
+        node-version: '12'
+    - name: Install manual dependencies
+      run: |
+        sudo npm install -g markdown-link-check@3.8.7
+        python -m pip install pre-commit
+        pre-commit install
+    - name: Run markdown checker
+      run: |
+        pre-commit run --hook-stage manual markdown-link-check-full --all-files
+  full-pytest:
+    runs-on: ubuntu-latest
+#   TODO: Re-use pytest workflow once https://github.com/github/roadmap/issues/257 is done.
+#    steps:
+#    - uses: actions/checkout@v2
+#    - uses: ./.github/workflows/pytest.yml
+#      with:
+#        # Run all tests.
+#        pytest_markers: "not slow or slow"
+    env:
+        TEST_ENFORCE_BUFFER_KEY_TYPES: 1
+    strategy:
+      # If one test in the matrix fails we still want to run the others.
+      fail-fast: false
+      matrix:
+        python-version: [3.8.x, 3.9.x, 3.10.x]
+        include:
+          - python-version: 3.8.x
+            pip_constraints: test_constraints_min_version.txt
+          - python-version: 3.9.x
+            pip_constraints: test_constraints_mid_version.txt
+          - python-version: 3.10.x
+            pip_constraints: test_constraints_max_version.txt
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Cache pip
+      uses: actions/cache@v2
+      with:
+        # This path is specific to Ubuntu
+        path: ~/.cache/pip
+        # Look to see if there is a cache hit for the corresponding requirements file
+        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'test_requirements.txt', matrix.pip_constraints) }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+          ${{ runner.os }}-
+    - name: Display Python version
+      run: python -c "import sys; print(sys.version)"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install --upgrade setuptools
+        python -m pip install --progress-bar=off -e ./ml-agents-envs -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -r test_requirements.txt -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents-plugin-examples -c ${{ matrix.pip_constraints }}
+    - name: Save python dependencies
+      run: |
+        pip freeze > pip_versions-${{ matrix.python-version }}.txt
+        cat pip_versions-${{ matrix.python-version }}.txt
+    - name: Run pytest
+      run: |
+        pytest --cov=ml-agents --cov=ml-agents-envs \
+        --cov-report=html --junitxml=junit/test-results-${{ matrix.python-version }}.xml \
+        -p no:warnings -v -n 8
+    - name: Upload pytest test results
+      uses: actions/upload-artifact@v2
+      with:
+        name: artifacts-${{ matrix.python-version }}
+        path: |
+          htmlcov
+          pip_versions-${{ matrix.python-version }}.txt
+          junit/test-results-${{ matrix.python-version }}.xml
+      # Use always() to always run this step to publish test results when there are test failures
+      if: ${{ always() }}
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
new file mode 100644
index 0000000000000000000000000000000000000000..81677bb16e74ce0f37aa54075068590f91717467
--- /dev/null
+++ b/.github/workflows/pre-commit.yml
@@ -0,0 +1,57 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+    branches:
+     - main
+     - develop
+     - 'release/**'
+  workflow_dispatch:
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: 3.8.x
+    - uses: actions/setup-ruby@v1
+      env:
+        ImageOS: ubuntu20
+      with:
+        ruby-version: '2.6'
+    - uses: actions/setup-dotnet@v3
+      with:
+        dotnet-version: '7.0.100'
+    - name: Install manual dependencies
+      run: |
+        python -m pip install pre-commit
+        pre-commit install
+    - name: Run pre-commit
+      run: |
+        pre-commit run --all-files
+  markdown-link-check:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - uses: actions/setup-node@v2
+      with:
+        node-version: '12'
+    - name: Install manual dependencies
+    # pin markdown-link-check version to support multi-level reference link
+      run: |
+        sudo npm install -g markdown-link-check@3.8.7
+        python -m pip install pre-commit
+        pre-commit install
+    - name: Run markdown checker
+      run: |
+        pre-commit run --hook-stage manual markdown-link-check --all-files
+  validate-meta-files:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+    - run: python utils/validate_meta_files.py
diff --git a/.github/workflows/publish_docs.yaml b/.github/workflows/publish_docs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d7139e3f639a2246d8092be8e98edf135dd300c1
--- /dev/null
+++ b/.github/workflows/publish_docs.yaml
@@ -0,0 +1,24 @@
+name: Publish HTML Docs
+
+on:
+  workflow_dispatch:
+
+jobs:
+  publish:
+    name: Publish Docs to GH Pages
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          token: ${{ secrets.PUBLIC_GH_TOKEN }}
+      - name: Setup Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+      - name: Publish docs
+        run: |
+          pip install mkdocs
+          git remote add public git@github.com:Unity-Technologies/ml-agents.git
+          mkdocs gh-deploy --clean -r public
+
diff --git a/.github/workflows/publish_pypi.yaml b/.github/workflows/publish_pypi.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9ef3d0f31535281492561d0afad0456bc370f84e
--- /dev/null
+++ b/.github/workflows/publish_pypi.yaml
@@ -0,0 +1,48 @@
+# Adapted from https://packaging.python.org/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/
+name: Publish to PyPI (or TestPyPI)
+
+on:
+  push:
+    tags:
+      - "release_[0-9]+_test[0-9]+"
+      - "release_[0-9]+"
+  workflow_dispatch:
+
+jobs:
+  build-and-publish:
+    name: publish to PyPI (or TestPyPI)
+    runs-on: ubuntu-latest
+    strategy:
+        matrix:
+            package-path: [ml-agents, ml-agents-envs]
+
+    steps:
+    - uses: actions/checkout@main
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: pip install setuptools wheel twine --user
+    - name: verify git tag vs. version
+      run: |
+        cd ${{ matrix.package-path }}
+        python setup.py verify
+    - name: Build package
+      run: |
+        cd ${{ matrix.package-path }}
+        python setup.py sdist
+        python setup.py bdist_wheel
+    - name: Publish distribution 📦 to Test PyPI
+      if: startsWith(github.ref, 'refs/tags') && contains(github.ref, 'test')
+      uses: actions/gh-action-pypi-publish@717ba43cfbb0387f6ce311b169a825772f54d295
+      with:
+        password: ${{ secrets.TEST_PYPI_PASSWORD }}
+        repository_url: https://test.pypi.org/legacy/
+        packages_dir: ${{ matrix.package-path }}/dist/
+    - name: Publish distribution 📦 to Production PyPI
+      if: startsWith(github.ref, 'refs/tags') && !contains(github.ref, 'test')
+      uses: actions/gh-action-pypi-publish@717ba43cfbb0387f6ce311b169a825772f54d295
+      with:
+        password: ${{ secrets.PYPI_PASSWORD }}
+        packages_dir: ${{ matrix.package-path }}/dist/
diff --git a/.github/workflows/publish_pypi_python_api.yaml b/.github/workflows/publish_pypi_python_api.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..062c2d6fee9e37d747aa11daa9a16dda591ed392
--- /dev/null
+++ b/.github/workflows/publish_pypi_python_api.yaml
@@ -0,0 +1,50 @@
+# Adapted from https://packaging.python.org/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/
+name: Publish Python APIs to PyPI (or TestPyPI)
+
+on:
+  push:
+    tags:
+      - "api_release_[0-9]+_test[0-9]+"
+      - "api_release_[0-9]+"
+  workflow_dispatch:
+
+jobs:
+  build-and-publish:
+    name: publish to PyPI (or TestPyPI)
+    runs-on: ubuntu-latest
+    strategy:
+        matrix:
+            package-path: [ml-agents-envs]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: pip install setuptools wheel twine --user
+    - name: verify git tag vs. version
+      run: |
+        cd ${{ matrix.package-path }}
+        python setup.py verify
+    - name: Build package
+      run: |
+        cd ${{ matrix.package-path }}
+        python setup.py sdist
+        python setup.py bdist_wheel
+    - name: Publish distribution 📦 to Test PyPI
+      if: startsWith(github.ref, 'refs/tags') && contains(github.ref, 'test')
+      uses: actions/gh-action-pypi-publish@717ba43cfbb0387f6ce311b169a825772f54d295
+      with:
+        user: __token__
+        password: ${{ secrets.TEST_PYPI_PASSWORD }}
+        repository_url: https://test.pypi.org/legacy/
+        packages_dir: ${{ matrix.package-path }}/dist/
+    - name: Publish distribution 📦 to Production PyPI
+      if: startsWith(github.ref, 'refs/tags') && !contains(github.ref, 'test')
+      uses: actions/gh-action-pypi-publish@717ba43cfbb0387f6ce311b169a825772f54d295
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_PASSWORD }}
+        packages_dir: ${{ matrix.package-path }}/dist/
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
new file mode 100644
index 0000000000000000000000000000000000000000..4e84daab104439180a86e398fb1b4bb42fd2a201
--- /dev/null
+++ b/.github/workflows/pytest.yml
@@ -0,0 +1,100 @@
+name: pytest
+
+on:
+  pull_request:
+    paths:  # This action will only run if the PR modifies a file in one of these directories
+    - 'ml-agents/**'
+    - 'ml-agents-envs/**'
+    - 'test_constraints*.txt'
+    - 'test_requirements.txt'
+    - '.github/workflows/pytest.yml'
+  push:
+    branches:
+     - main
+     - develop
+     - 'release/**'
+  workflow_dispatch:
+    inputs:
+      pytest_markers:
+        description: "Restrict which tests to run based on pytest markers"
+        required: false
+        default: "not slow"
+        type: string
+  workflow_call:
+    inputs:
+      pytest_markers:
+        required: false
+        # Hacky way to make sure we run all tests
+        default: "slow or not slow"
+        type: string
+
+jobs:
+  pytest:
+    runs-on: ubuntu-latest
+    env:
+        TEST_ENFORCE_BUFFER_KEY_TYPES: 1
+    strategy:
+      # If one test in the matrix fails we still want to run the others.
+      fail-fast: false
+      matrix:
+        python-version: [3.8.x, 3.9.x, 3.10.x]
+        include:
+          - python-version: 3.8.x
+            pip_constraints: test_constraints_min_version.txt
+          - python-version: 3.9.x
+            pip_constraints: test_constraints_mid_version.txt
+          - python-version: 3.10.x
+            pip_constraints: test_constraints_max_version.txt
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Cache pip
+      uses: actions/cache@v2
+      with:
+        # This path is specific to Ubuntu
+        path: ~/.cache/pip
+        # Look to see if there is a cache hit for the corresponding requirements file
+        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'test_requirements.txt', matrix.pip_constraints) }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+          ${{ runner.os }}-
+    - name: Display Python version
+      run: python -c "import sys; print(sys.version)"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install --upgrade setuptools
+        python -m pip install --progress-bar=off -e ./ml-agents-envs -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -r test_requirements.txt -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents-plugin-examples -c ${{ matrix.pip_constraints }}
+    - name: Save python dependencies
+      run: |
+        pip freeze > pip_versions-${{ matrix.python-version }}.txt
+        cat pip_versions-${{ matrix.python-version }}.txt
+    - name: Get pytest marker
+      id: pytest_marker
+      run: |
+        if [ "${{ github.event.inputs.pytest_markers }}" != "" ]; then
+          echo "::set-output name=markers::${{ github.event.inputs.pytest_markers }}"
+        else
+          echo "::set-output name=markers::not slow"
+        fi
+    - name: Run pytest
+      run: |
+        pytest --cov=ml-agents --cov=ml-agents-envs \
+        --cov-report=html --junitxml=junit/test-results-${{ matrix.python-version }}.xml \
+        -p no:warnings -v -m "${{ steps.pytest_marker.outputs.markers }}" -n 8
+    - name: Upload pytest test results
+      uses: actions/upload-artifact@v2
+      with:
+        name: artifacts-${{ matrix.python-version }}
+        path: |
+          htmlcov
+          pip_versions-${{ matrix.python-version }}.txt
+          junit/test-results-${{ matrix.python-version }}.xml
+      # Use always() to always run this step to publish test results when there are test failures
+      if: ${{ always() }}
diff --git a/.github/workflows/validate_catalog.yaml b/.github/workflows/validate_catalog.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4166fd06c89f2f87c0826f65474c45957952f5ca
--- /dev/null
+++ b/.github/workflows/validate_catalog.yaml
@@ -0,0 +1,21 @@
+# Validate catalog-info.yaml file
+name: Validate Catalog-info.yaml
+on:
+  pull_request:
+    paths:
+      - 'catalog-info.yaml'
+  push:
+    paths:
+      - 'catalog-info.yaml'
+    branches: [ main ]
+
+jobs:
+  validate_catalog:
+    runs-on: [ubuntu-latest]
+    steps:
+      - id: 'Checkout'
+        uses: actions/checkout@v2
+
+      - id: 'Validate'
+        uses: 'RoadieHQ/backstage-entity-validator@v0.3.2'
+  
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..31877c1cb6c70805e72ee7b3e2039d72de16683a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,97 @@
+# Output Artifacts (Legacy)
+/models
+/summaries
+# Output Artifacts
+/results
+# Output Builds
+/Builds
+
+# Training environments
+/envs
+
+# Environemnt logfile
+*Project.log
+
+# Custom settings asset
+*.settings.asset*
+
+# Visual Studio 2015 cache directory
+/Project/.vs/
+
+# Autogenerated VS/MD/Consulo solution and project files
+/com.unity.ml-agentsExportedObj/
+/com.unity.ml-agents.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+
+# Unity3D generated meta files
+*.pidb.meta
+
+# Unity3D Generated File On Crash Reports
+/com.unity.ml-agents/sysinfo.txt
+
+# Builds
+*.apk
+*.unitypackage
+*.app
+*.exe
+*.x86_64
+*.x86
+
+# Plugins
+/com.unity.ml-agents/VideoRecorder*
+
+# Generated doc folders
+/docs/html
+
+# Mac hidden files
+*.DS_Store
+*/.ipynb_checkpoints
+*/.idea
+*.pyc
+*.idea/misc.xml
+*.idea/modules.xml
+*.idea/
+*.iml
+*.cache
+*/build/
+*/dist/
+*.egg-info*
+*.eggs*
+*.gitignore.swp
+
+# VSCode hidden files
+*.vscode/
+
+.DS_Store
+.ipynb_checkpoints
+
+# pytest cache
+*.pytest_cache/
+
+# Ignore compiled protobuf files.
+*Grpc.Tools*
+
+# Ignore PyPi build files.
+dist/
+build/
+
+# Python virtual environment
+venv/
+.mypy_cache/
+
+# Code coverage report
+.coverage
+coverage.xml
+/htmlcov/
+
+**/UserSettings/*
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4330316d37aaa006c020d0fc49defa02586e772d
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,138 @@
+repos:
+-   repo: https://github.com/python/black
+    rev: 22.3.0
+    hooks:
+    -   id: black
+        exclude: >
+            (?x)^(
+                .*_pb2.py|
+                .*_pb2.pyi|
+                .*_pb2_grpc.py
+            )$
+
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.991
+    hooks:
+    -   id: mypy
+        name: mypy-ml-agents
+        files: "ml-agents/.*"
+        args: [--ignore-missing-imports, --disallow-incomplete-defs, --no-strict-optional]
+        additional_dependencies: [types-PyYAML, types-attrs, types-protobuf, types-setuptools, types-filelock]
+    -   id: mypy
+        name: mypy-ml-agents-envs
+        files: "ml-agents-envs/.*"
+        # Exclude protobuf files and don't follow them when imported
+        exclude: ".*_pb2.py"
+        args: [--ignore-missing-imports, --disallow-incomplete-defs, --no-strict-optional]
+        additional_dependencies: [types-PyYAML, types-attrs, types-protobuf, types-setuptools, types-filelock]
+-   repo: https://github.com/PyCQA/flake8
+    rev: 3.9.2
+    hooks:
+    -   id: flake8
+        exclude: >
+            (?x)^(
+                .*_pb2.py|
+                .*_pb2_grpc.py
+            )$
+        # flake8-tidy-imports is used for banned-modules, not actually tidying
+        additional_dependencies: [flake8-comprehensions==3.2.2, flake8-tidy-imports==4.1.0, flake8-bugbear==20.1.4]
+
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v2.31.0
+    hooks:
+    -   id: pyupgrade
+        args: [--py3-plus, --py36-plus]
+        exclude: >
+            (?x)^(
+                .*_pb2.py|
+                .*_pb2_grpc.py
+            )$
+
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.1.0
+    hooks:
+    -   id: mixed-line-ending
+        exclude: >
+            (?x)^(
+                .*cs.meta|
+                .*.css|
+                .*.meta
+            )$
+        args: [--fix=lf]
+
+    -   id: trailing-whitespace
+        name: trailing-whitespace-markdown
+        types: [markdown]
+    -   id: check-merge-conflict
+        args: [--assume-in-merge]
+    -   id: check-yaml
+        # Won't handle the templating in yamato
+        exclude: \.yamato/.*
+
+-   repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.9.0
+    hooks:
+    -   id: python-check-mock-methods
+
+- repo: https://github.com/mattlqx/pre-commit-search-and-replace
+  rev: v1.0.5
+  hooks:
+  - id: search-and-replace
+    types: [markdown]
+    exclude: ".*localized.*"
+
+#- repo: https://github.com/dotnet/format
+#  rev: v5.1.225507
+#  hooks:
+#  - id: dotnet-format
+#    entry: dotnet-format whitespace
+#    args: [--folder, --include]
+
+# "Local" hooks, see https://pre-commit.com/#repository-local-hooks
+-   repo: local
+    hooks:
+    -   id: dotnet-format
+        name: dotnet-format
+        language: system
+        entry: dotnet format whitespace --folder --include
+        types_or: ["c#"]
+    -   id: markdown-link-check
+        name: markdown-link-check
+        entry: utils/run_markdown_link_check.py
+        language: script
+        types: [markdown]
+        # Don't check localized files since their target might not be localized.
+        exclude: ".*localized.*"
+        # Only run manually, e.g. pre-commit run --hook-stage manual markdown-link-check
+        stages: [manual]
+    -   id: markdown-link-check-full
+        name: markdown-link-check-full
+        entry: utils/run_markdown_link_check.py
+        language: script
+        types: [markdown]
+        # Don't check localized files since their target might not be localized.
+        exclude: ".*localized.*"
+        # Only run manually, e.g. pre-commit run --hook-stage manual markdown-link-check-full
+        stages: [manual]
+        args: [--check-remote]
+    -   id: validate-versions
+        name: validate library versions
+        language: script
+        entry: utils/validate_versions.py
+        files: ".*/__init__.py"
+    -   id: validate-init-py
+        name: validate __init__.py files
+        language: script
+        entry: utils/validate_inits.py
+        types: [python]
+    -   id: validate-release-links-py
+        name: validate release links
+        language: script
+        entry: utils/validate_release_links.py
+        require_serial: true
+    -   id: generate-markdown-docs
+        name: generate markdown docs
+        language: python
+        entry: ./utils/generate_markdown_docs.py --package_dirs ml-agents-envs ml-agents
+        pass_filenames: false
+        additional_dependencies: [pyyaml, pydoc-markdown==3.10.1]
diff --git a/.pre-commit-search-and-replace.yaml b/.pre-commit-search-and-replace.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5621e6547003f388e9db7a3c53a135d327e06409
--- /dev/null
+++ b/.pre-commit-search-and-replace.yaml
@@ -0,0 +1,8 @@
+- description: Replace "ML agents toolkit", "ML-Agents toolkit" etc
+  search: /ML[ -]Agents toolkit/
+  replacement: ML-Agents Toolkit
+  insensitive: true
+- description: Replace "the the"
+  search: /the the/
+  replacement: the
+  insensitive: true
diff --git a/.yamato/com.unity.ml-agents-coverage.yml b/.yamato/com.unity.ml-agents-coverage.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f46612541b3cba4965206c1e17167b7ae8f097a3
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-coverage.yml
@@ -0,0 +1,43 @@
+{% metadata_file .yamato/coverage_tests.metafile %}
+---
+{% for package in coverage_test_packages %}
+{% for editor in coverage_test_editors %}
+{% for platform in coverage_test_platforms %}
+{% capture coverageOptions %} --enable-code-coverage --code-coverage-options 'generateHtmlReport;assemblyFilters:+{{ package.assembly }}'{% endcapture %}
+
+test_coverage_{{ package.name }}_{{ platform.name }}_{{ editor.version }}:
+    name : Coverage {{ package.name }} test {{ editor.version }} on {{ platform.name }}
+    agent:
+        type: {{ platform.type }}
+        image: {{ platform.image }}
+        flavor: {{ platform.flavor}}
+    commands:
+        - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+        - upm-ci project test -u {{ editor.version }} --type project-tests --project-path {{ editor.testProject }} --package-filter {{ package.name }} {{ coverageOptions }} --extra-utr-arg "reruncount=2"
+        - |
+          conda activate python3.8
+          python3 ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ package.minCoveragePct }}
+    artifacts:
+        logs:
+            paths:
+                - "upm-ci~/test-results/**/*"
+    dependencies:
+        - .yamato/com.unity.ml-agents-pack.yml#pack
+    triggers:
+        cancel_old_ci: true
+      {% if platform.name == "linux" %}
+        expression: |
+            (pull_request.target eq "main" OR
+            pull_request.target eq "develop" OR
+            pull_request.target match "release.+") AND
+            NOT pull_request.draft AND
+            (pull_request.changes.any match "com.unity.ml-agents/**" OR
+             pull_request.changes.any match " {{ editor.testProject }}/**" OR
+            {% if package.name == "com.unity.ml-agents.extensions" %}
+            pull_request.changes.any match "com.unity.ml-agents.extensions/**" OR
+            {% endif %}
+            pull_request.changes.any match ".yamato/com.unity.ml-agents-coverage.yml")
+      {% endif %}
+{% endfor %}
+{% endfor %}
+{% endfor %}
diff --git a/.yamato/com.unity.ml-agents-pack.yml b/.yamato/com.unity.ml-agents-pack.yml
new file mode 100644
index 0000000000000000000000000000000000000000..521881da4e06aff20769649941521c08fea108a3
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-pack.yml
@@ -0,0 +1,21 @@
+pack:
+  name: Pack
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.small
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+      unity-downloader-cli -u 2022.3 -c editor --wait --fast
+      ./.Editor/Unity -projectPath Project -batchMode -executeMethod Unity.MLAgents.SampleExporter.ExportCuratedSamples -logFile -
+      npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+      upm-ci project pack --project-path Project
+  artifacts:
+    packages:
+      paths:
+        - "upm-ci~/packages/**/*"
+  triggers:
+    cancel_old_ci: true
diff --git a/.yamato/com.unity.ml-agents-performance.yml b/.yamato/com.unity.ml-agents-performance.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c86372414c88010fb6ab612d1fbc82000f699230
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-performance.yml
@@ -0,0 +1,30 @@
+test_editors:
+  - version: 2022.3
+  - version: 2023.1
+---
+{% for editor in test_editors %}
+Run_Mac_Perfomance_Tests{{ editor.version }}:
+  name: Run Mac Performance Tests {{ editor.version }}
+  agent:
+    type: Unity::VM::osx
+    image: package-ci/mac:stable
+    flavor: b1.small
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+    - unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
+    - curl -s https://artifactory.prd.it.unity3d.com/artifactory/unity-tools-local/utr-standalone/utr --output utr
+    - chmod +x ./utr
+    - ./utr --suite=editor --platform=StandaloneOSX --editor-location=.Editor --testproject=DevProject --artifacts_path=build/test-results --report-performance-data --performance-project-id=com.unity.ml-agents --zero-tests-are-ok=1
+  triggers:
+    cancel_old_ci: true
+    recurring:
+      - branch: develop
+        frequency: daily
+  artifacts:
+    logs:
+      paths:
+        - "build/test-results/**"
+        - "*.log"
+{% endfor %}
diff --git a/.yamato/com.unity.ml-agents-promotion.yml b/.yamato/com.unity.ml-agents-promotion.yml
new file mode 100644
index 0000000000000000000000000000000000000000..dbf35cd812c3bc6aaf8526df8114eab142ed6261
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-promotion.yml
@@ -0,0 +1,67 @@
+test_editors:
+  - version: 2022.3
+test_platforms:
+  - name: win
+    type: Unity::VM
+    image: package-ci/win10:stable
+    flavor: b1.large
+---
+
+{% for editor in test_editors %}
+{% for platform in test_platforms %}
+promotion_test_{{ platform.name }}_{{ editor.version }}:
+  name : Promotion Test {{ editor.version }} on {{ platform.name }}
+  agent:
+    type: {{ platform.type }}
+    image: {{ platform.image }}
+    flavor: {{ platform.flavor}}
+  variables:
+    UPMCI_PROMOTION: 1
+  commands:
+    - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci package test --unity-version {{ editor.version }} --package-path com.unity.ml-agents
+  artifacts:
+    logs:
+      paths:
+        - "upm-ci~/test-results/**/*"
+  dependencies:
+    - .yamato/com.unity.ml-agents-pack.yml#pack
+{% endfor %}
+{% endfor %}
+
+promotion_test_trigger:
+  name: Promotion Tests Trigger
+  dependencies:
+{% for editor in test_editors %}
+{% for platform in test_platforms %}
+    - .yamato/com.unity.ml-agents-promotion.yml#promotion_test_{{platform.name}}_{{editor.version}}
+{% endfor %}
+{% endfor %}
+
+
+promote:
+  name: Promote to Production
+  agent:
+    type: Unity::VM
+    image: package-ci/win10:stable
+    flavor: b1.large
+  variables:
+    UPMCI_PROMOTION: 1
+  commands:
+    - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci package promote --package-path com.unity.ml-agents
+#  triggers:
+#    tags:
+#      only:
+#        - /^(r|R)elease-\d+\.\d+\.\d+(-preview(\.\d+)?)?$/
+  artifacts:
+    artifacts:
+      paths:
+        - "upm-ci~/packages/*.tgz"
+  dependencies:
+    - .yamato/com.unity.ml-agents-pack.yml#pack
+{% for editor in test_editors %}
+{% for platform in test_platforms %}
+    - .yamato/com.unity.ml-agents-promotion.yml#promotion_test_{{ platform.name }}_{{ editor.version }}
+{% endfor %}
+{% endfor %}
diff --git a/.yamato/com.unity.ml-agents-publish.yml b/.yamato/com.unity.ml-agents-publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3f283220070282384526ffdfc45abce6ccef6701
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-publish.yml
@@ -0,0 +1,23 @@
+publish:
+  name: Publish ML-Agents to Internal Registry
+  agent:
+    type: Unity::VM
+    image: package-ci/win10:stable
+    flavor: b1.large
+  variables:
+    UPMCI_ENABLE_PACKAGE_SIGNING: 1
+  commands:
+    - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci package publish --package-path com.unity.ml-agents
+  triggers:
+    cancel_old_ci: true
+#    tags:
+#      only:
+#        - /^(r|R)(c|C)-\d+\.\d+\.\d+(-preview(\.\d+)?)?$/
+  artifacts:
+    artifacts:
+      paths:
+        - "upm-ci~/packages/*.tgz"
+  dependencies:
+    - .yamato/com.unity.ml-agents-pack.yml#pack
+    - .yamato/com.unity.ml-agents-test.yml#all_package_tests
diff --git a/.yamato/com.unity.ml-agents-test.yml b/.yamato/com.unity.ml-agents-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..96bbc1c7fc6a32cde9c0912e7934945ce4d59aff
--- /dev/null
+++ b/.yamato/com.unity.ml-agents-test.yml
@@ -0,0 +1,152 @@
+{% metadata_file .yamato/coverage_tests.metafile %}
+test_editors:
+    - version: 2022.3
+        # We want some scene tests to run in the DevProject, but packages there only support 2020+
+      testProject: Project
+      enableNoDefaultPackages: !!bool true
+    - version: 2023.1
+      testProject: DevProject
+      enableNoDefaultPackages: !!bool true
+
+trunk_editor:
+    - version: trunk
+        # Workaround for MLA-1596 - need to make sure we load the right results.
+      testProject: DevProject
+
+test_platforms:
+    - name: win
+      type: Unity::VM
+      image: package-ci/win10:stable
+      flavor: b1.large
+    - name: mac
+      type: Unity::VM::osx
+      image: package-ci/mac:stable
+      flavor: b1.large
+    - name: linux
+      type: Unity::VM
+      image: ml-agents/ml-agents-ubuntu-18.04:latest
+      flavor: b1.large
+
+packages:
+    - name: com.unity.ml-agents
+      assembly: Unity.ML-Agents
+      minCoveragePct: 72
+    - name: com.unity.ml-agents.extensions
+      assembly: Unity.ML-Agents.Extensions*
+      minCoveragePct: 75
+---
+
+all_package_tests:
+    name: Run All Combinations of Editors/Platforms Tests
+    dependencies:
+        {% for coverage_editor in coverage_test_editors %}
+        {% for coverage_platform in coverage_test_platforms %}
+        {% for coverage_package in coverage_test_packages %}
+        - .yamato/com.unity.ml-agents-coverage.yml#test_coverage_{{ coverage_package.name }}_{{ coverage_platform.name }}_{{ coverage_editor.version }}
+        {% endfor %}
+        {% endfor %}
+        {% endfor %}
+        {% for editor in test_editors %}
+        {% for platform in test_platforms %}
+        - .yamato/com.unity.ml-agents-test.yml#test_com.unity.ml-agents_{{ platform.name }}_{{ editor.version }}
+        {% endfor %}
+        {% endfor %}
+        {% for editor in trunk_editor %}
+        {% for platform in test_platforms %}
+        - .yamato/com.unity.ml-agents-test.yml#test_com.unity.ml-agents_{{ platform.name }}_{{ editor.version }}
+        {% endfor %}
+        {% endfor %}
+    triggers:
+        cancel_old_ci: true
+        recurring:
+            - branch: develop
+              frequency: daily
+
+  {% for package in packages %}
+  {% for editor in test_editors %}
+  {% for platform in test_platforms %}
+
+  {% if editor.enableNoDefaultPackages %}
+  {% assign noDefaultPackagesOptions = "--extra-create-project-arg='-upmNoDefaultPackages'" %}
+  {% else %}
+  {% assign noDefaultPackagesOptions = "" %}
+  {% endif %}
+
+test_{{ package.name }}_{{ platform.name }}_{{ editor.version }}:
+    name : {{ package.name }} test {{ editor.version }} on {{ platform.name }}
+    agent:
+        type: {{ platform.type }}
+        image: {{ platform.image }}
+        flavor: {{ platform.flavor}}
+    commands:
+        - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+        - upm-ci package test -u {{ editor.version }} --package-path {{ package.name }} {{ noDefaultPackagesOptions }} --warnings-as-errors --extra-utr-arg "reruncount=2"
+    artifacts:
+        logs:
+            paths:
+                - "upm-ci~/test-results/**/*"
+    dependencies:
+        - .yamato/com.unity.ml-agents-pack.yml#pack
+        {% for coverage_editor in coverage_test_editors %}
+        {% for coverage_platform in coverage_test_platforms %}
+        {% for coverage_package in coverage_test_packages %}
+        - .yamato/com.unity.ml-agents-coverage.yml#test_coverage_{{ coverage_package.name }}_{{ coverage_platform.name }}_{{ coverage_editor.version }}
+        {% endfor %}
+        {% endfor %}
+        {% endfor %}
+    triggers:
+        cancel_old_ci: true
+      {% if platform.name == "linux" %}
+        expression: |
+            (pull_request.target eq "main" OR
+            pull_request.target eq "develop" OR
+            pull_request.target match "release.+") AND
+            NOT pull_request.draft AND
+            (pull_request.changes.any match "com.unity.ml-agents/**" OR
+             pull_request.changes.any match " {{ editor.testProject }}/**" OR
+            {% if package.name == "com.unity.ml-agents.extensions" %}
+              pull_request.changes.any match "com.unity.ml-agents.extensions/**" OR
+            {% endif %}
+            pull_request.changes.any match ".yamato/com.unity.ml-agents-test.yml")
+      {% endif %}
+      {% endfor %}
+  {% endfor %}
+  {% endfor %}
+
+  {% for package in packages %}
+  {% for editor in trunk_editor %}
+  {% for platform in test_platforms %}
+
+test_{{ package.name }}_{{ platform.name }}_trunk:
+    name : {{ package.name }} test {{ editor.version }} on {{ platform.name }}
+    agent:
+        type: {{ platform.type }}
+        image: {{ platform.image }}
+        flavor: {{ platform.flavor}}
+    commands:
+        - |
+{% if platform.name == "linux" %}
+          conda activate python3.8
+{% endif %}
+          python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+          unity-downloader-cli -u trunk -c editor --wait --fast
+          npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+          upm-ci project test -u {{ editor.version }} --project-path {{ editor.testProject }} --package-filter {{ package.name }} --extra-create-project-arg="-upmNoDefaultPackages" --extra-utr-arg "reruncount=2"
+    artifacts:
+        logs:
+            paths:
+                - "upm-ci~/test-results/**/*"
+    dependencies:
+        - .yamato/com.unity.ml-agents-pack.yml#pack
+        {% for coverage_editor in coverage_test_editors %}
+        {% for coverage_platform in coverage_test_platforms %}
+        {% for coverage_package in coverage_test_packages %}
+        - .yamato/com.unity.ml-agents-coverage.yml#test_coverage_{{ coverage_package.name }}_{{ coverage_platform.name }}_{{ coverage_editor.version }}
+        {% endfor %}
+        {% endfor %}
+        {% endfor %}
+    triggers:
+        cancel_old_ci: true
+      {% endfor %}
+  {% endfor %}
+  {% endfor %}
diff --git a/.yamato/compressed-sensor-test.yml b/.yamato/compressed-sensor-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..23e28a5533fdc5aeb0829d3047d7e823b80949ea
--- /dev/null
+++ b/.yamato/compressed-sensor-test.yml
@@ -0,0 +1,38 @@
+{% metadata_file .yamato/test_versions.metafile %}
+---
+{% for editor in test_editors %}
+test_compressed_obs_{{ editor.version }}:
+  name: Test Compressed Sensor Observation {{ editor.version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.medium
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestGridCompressed
+      python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestTextureCompressed
+  dependencies:
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
+  triggers:
+    cancel_old_ci: true
+    {% if editor.extra_test == "sensor" %}
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "com.unity.ml-agents/**" OR
+      pull_request.changes.any match "com.unity.ml-agents.extensions/**" OR
+      pull_request.changes.any match "Project/**" OR
+      pull_request.changes.any match "ml-agents/tests/yamato/**" OR
+      pull_request.changes.any match "ml-agents-envs/**" OR
+      pull_request.changes.any match ".yamato/compressed-sensor-test.yml") AND
+      NOT pull_request.changes.all match "**/*.md"
+    {% endif %}
+{% endfor %}
diff --git a/.yamato/coverage_tests.metafile b/.yamato/coverage_tests.metafile
new file mode 100644
index 0000000000000000000000000000000000000000..29bcfd73e298b8c93ee40572ca3c9ae905c921e4
--- /dev/null
+++ b/.yamato/coverage_tests.metafile
@@ -0,0 +1,17 @@
+coverage_test_editors:
+    - version: 2022.3
+      testProject: DevProject
+
+coverage_test_platforms:
+    - name: linux
+      type: Unity::VM
+      image: ml-agents/ml-agents-ubuntu-18.04:latest
+      flavor: b1.medium
+
+coverage_test_packages:
+    - name: com.unity.ml-agents
+      assembly: Unity.ML-Agents
+      minCoveragePct: 72
+    - name: com.unity.ml-agents.extensions
+      assembly: Unity.ML-Agents.Extensions*
+      minCoveragePct: 75
diff --git a/.yamato/gym-interface-test.yml b/.yamato/gym-interface-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a90a8ce9ecfa06e5a732d4bbbb67c9393110bc8a
--- /dev/null
+++ b/.yamato/gym-interface-test.yml
@@ -0,0 +1,37 @@
+{% metadata_file .yamato/test_versions.metafile %}
+---
+{% for editor in test_editors %}
+test_gym_interface_{{ editor.version }}:
+  name: Test Linux Gym Interface {{ editor.version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.medium
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python -m pip install wheel --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_gym.py --env=artifacts/testPlayer-Basic
+  dependencies:
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
+  triggers:
+    cancel_old_ci: true
+    {% if editor.extra_test == "gym" %}
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "com.unity.ml-agents/**" OR
+      pull_request.changes.any match "Project/**" OR
+      pull_request.changes.any match "ml-agents/tests/yamato/**" OR
+      pull_request.changes.any match "ml-agents-envs/**" OR
+      pull_request.changes.any match ".yamato/gym-interface-test.yml") AND
+      NOT pull_request.changes.all match "**/*.md"
+    {% endif %}
+{% endfor %}
diff --git a/.yamato/protobuf-generation-test.yml b/.yamato/protobuf-generation-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..be490867e91c8ccc814310cc241fe1195588f38d
--- /dev/null
+++ b/.yamato/protobuf-generation-test.yml
@@ -0,0 +1,44 @@
+test_linux_protobuf_generation:
+  name: Protobuf Generation Tests
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.large
+  variables:
+    GRPC_VERSION: "1.14.1"
+    CS_PROTO_PATH: "com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects"
+  commands:
+    - |
+      sudo apt-get update && sudo apt-get install -y nuget
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      nuget install Grpc.Tools -Version $GRPC_VERSION -OutputDirectory protobuf-definitions/
+      python3 -m pip install --upgrade pip --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python3 -m pip install grpcio==1.28.1 grpcio-tools==1.13.0 protobuf==3.11.3 six==1.14.0 mypy-protobuf==1.16.0  --progress-bar=off --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      pushd protobuf-definitions
+      chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/protoc
+      chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/grpc_csharp_plugin
+      COMPILER=Grpc.Tools.$GRPC_VERSION/tools/linux_x64 ./make.sh
+      popd
+      mkdir -p artifacts
+      touch artifacts/proto.patch
+      git diff --exit-code -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta"                          \
+      || { GIT_ERR=$?; echo "protobufs need to be regenerated, apply the patch uploaded to artifacts."; \
+      echo "Apply the patch with the command 'git apply proto.patch'";                                  \
+      git diff -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta" > ../artifacts/proto.patch; exit $GIT_ERR; }
+  triggers:
+    cancel_old_ci: true
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "protobuf-definitions/**" OR
+      pull_request.changes.any match "com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/**" OR
+      pull_request.changes.any match "ml-agents-envs/mlagents_envs/communicator_objects/**" OR
+      pull_request.changes.any match ".yamato/protobuf-generation-test.yml") AND
+      NOT pull_request.changes.all match "protobuf-definitions/**/*.md"
+  artifacts:
+    patch:
+      paths:
+        - "artifacts/*.*"
diff --git a/.yamato/pytest-gpu.yml b/.yamato/pytest-gpu.yml
new file mode 100644
index 0000000000000000000000000000000000000000..99999ca224ee967bff548ae74362cc5ebbdd46db
--- /dev/null
+++ b/.yamato/pytest-gpu.yml
@@ -0,0 +1,32 @@
+pytest_gpu:
+  name: Pytest GPU
+  agent:
+    type: Unity::VM::GPU
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.large
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python3 -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python3 -u -m ml-agents.tests.yamato.setup_venv
+      python3 -m pip install --progress-bar=off -r test_requirements.txt --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python3 -m pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      if python -c "exec('import torch \nif not torch.cuda.is_available(): raise')" &> /dev/null; then
+        echo 'all good'
+      else
+        exit 1
+      fi
+      python3 -m pytest -m "not slow" --junitxml=junit/test-results.xml -p no:warnings
+  triggers:
+    cancel_old_ci: true
+    expression: |
+      (push.branch eq "main" OR
+      push.branch eq "develop" OR
+      push.branch match "release.+") AND
+      push.changes.any match "ml-agents/**" AND
+      NOT push.changes.all match "**/*.md"
+  artifacts:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
diff --git a/.yamato/python-ll-api-test.yml b/.yamato/python-ll-api-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..cde94d31fb80c2922e9b27f5d7f86b96572a46bf
--- /dev/null
+++ b/.yamato/python-ll-api-test.yml
@@ -0,0 +1,39 @@
+{% metadata_file .yamato/test_versions.metafile %}
+---
+{% for editor in test_editors %}
+test_linux_ll_api_{{ editor.version }}:
+  name: Test Linux LL-API {{ editor.version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.medium
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_llapi.py
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Basic
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-WallJump
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Match3
+  dependencies:
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
+  triggers:
+    cancel_old_ci: true
+    {% if editor.extra_test == "llapi" %}
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "com.unity.ml-agents/**" OR
+      pull_request.changes.any match "Project/**" OR
+      pull_request.changes.any match "ml-agents/tests/yamato/**" OR
+      pull_request.changes.any match "ml-agents-envs/**" OR
+      pull_request.changes.any match ".yamato/python-ll-api-test.yml") AND
+      NOT pull_request.changes.all match "**/*.md"
+    {% endif %}
+{% endfor %}
diff --git a/.yamato/sonar-python-package.yml b/.yamato/sonar-python-package.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a087afe56ab6fe8c9cce289baf8dce992317ca15
--- /dev/null
+++ b/.yamato/sonar-python-package.yml
@@ -0,0 +1,21 @@
+csharp:
+  name: Sonarqube Scan for ml-agents python repo
+  agent:
+    type: Unity::metal::macmini
+    image: package-ci/mac:v1.8.1-822785
+    flavor: m1.mac
+  variables:
+    SONARQUBE_PROJECT_KEY: ai-ml-agents-toolkit
+    TARGET_BRANCH: develop
+  commands:
+    - npm install shellcheck --save-dev
+    - npm install upm-ci-utils@1.27.0 -g --registry https://artifactory.prd.it.unity3d.com/artifactory/api/npm/upm-npm
+    - curl https://binaries.sonarsource.com/Distribution/sonar-scanner-cli/sonar-scanner-cli-4.7.0.2747-macosx.zip -o sonar-scanner-cli-macosx.zip -L
+    - unzip sonar-scanner-cli-macosx.zip -d ~/sonar-scanner-cli
+    - ~/sonar-scanner-cli/sonar-scanner-4.7.0.2747-macosx/bin/sonar-scanner -Dsonar.projectKey=$SONARQUBE_PROJECT_KEY -Dsonar.sources=ml-agents-env -Dsonar.sources=ml-agents -Dsonar.sources=ml-agents-plugin-examples -Dsonar.sources=ml-agents-trainer-plugin -Dsonar.sources=utils -Dsonar.host.url=$SONARQUBE_URL -Dsonar.login=$SONARQUBE_TOKEN -Dsonar.branch.name=$TARGET_BRANCH -Dsonar.scm.provider=git
+  triggers:
+    cancel_old_ci: true
+    expression: |
+      ((pull_request.target eq "main" OR pull_request.target eq "develop" OR pull_request.target match "release.+")
+      AND NOT pull_request.push.changes.all match "**/*.md") OR
+      (push.branch eq "main" OR push.branch eq "develop")
diff --git a/.yamato/standalone-build-test.yml b/.yamato/standalone-build-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..fac93943e9647f2493f430b75d9b628833da7866
--- /dev/null
+++ b/.yamato/standalone-build-test.yml
@@ -0,0 +1,44 @@
+{% metadata_file .yamato/test_versions.metafile %}
+---
+{% for editor in test_editors %}
+test_linux_standalone_{{ editor.version }}:
+  name: Test Linux Standalone {{ editor.version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.large
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python3 -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+      unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedGrid/TestGridCompressed.unity
+      python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity
+  triggers:
+    cancel_old_ci: true
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "com.unity.ml-agents/**" OR
+      pull_request.changes.any match "com.unity.ml-agents.extensions/**" OR
+      pull_request.changes.any match ".yamato/standalone-build-test.yml") AND
+      NOT pull_request.changes.all match "**/*.md"
+  artifacts:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
+    standalonebuild:
+      paths:
+        - "artifacts/testPlayer*/**"
+        - "artifacts/**/UnityPlayer.so"
+{% endfor %}
diff --git a/.yamato/standalone-build-webgl-test.yml b/.yamato/standalone-build-webgl-test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9c7ad0156e56077bcf4b3b497bfee3060590ed13
--- /dev/null
+++ b/.yamato/standalone-build-webgl-test.yml
@@ -0,0 +1,30 @@
+{% capture editor_version %}2022.3{% endcapture %}
+test_webgl_standalone_{{ editor_version }}:
+  name: Test WebGL Standalone {{ editor_version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.large
+  variables:
+    UNITY_VERSION: {{ editor_version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+      unity-downloader-cli -u {{ editor_version }} -c editor -c WebGL --wait --fast
+      python -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=webgl
+  triggers:
+    cancel_old_ci: true
+    recurring:
+      - branch: develop
+        frequency: weekly
+  artifacts:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
+    standalonebuild:
+      paths:
+        - "artifacts/testPlayer*/**"
+        - "artifacts/**/UnityPlayer.so"
diff --git a/.yamato/test_versions.metafile b/.yamato/test_versions.metafile
new file mode 100644
index 0000000000000000000000000000000000000000..853348e5ba8c92ef62cadc9fed452ada05c7a264
--- /dev/null
+++ b/.yamato/test_versions.metafile
@@ -0,0 +1,11 @@
+# List of editor versions for standalone-build-test and its dependencies.
+# We always run training-int-tests for all versions of the editor
+# For each "other" test, we only run it against a single version of the
+# editor to reduce the number of yamato jobs
+test_editors:
+  - version: 2022.3
+    extra_test: gym
+  - version: 2023.1
+    extra_test: sensor
+  - version: trunk
+    extra_test: llapi
diff --git a/.yamato/training-int-tests.yml b/.yamato/training-int-tests.yml
new file mode 100644
index 0000000000000000000000000000000000000000..18f5f4f83dbae2012439ba3d8488f5a41c3b41d6
--- /dev/null
+++ b/.yamato/training-int-tests.yml
@@ -0,0 +1,44 @@
+{% metadata_file .yamato/test_versions.metafile %}
+---
+{% for editor in test_editors %}
+test_linux_training_int_{{ editor.version }}:
+  name: Test Linux Fast Training {{ editor.version }}
+  agent:
+    type: Unity::VM
+    image: ml-agents/ml-agents-ubuntu-18.04:latest
+    flavor: b1.medium
+  variables:
+    UNITY_VERSION: {{ editor.version }}
+  commands:
+    - |
+      eval "$($HOME/anaconda/bin/conda shell.bash hook)"
+      conda activate python3.8
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.training_int_tests
+  dependencies:
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
+  triggers:
+    cancel_old_ci: true
+    expression: |
+      (pull_request.target eq "main" OR
+      pull_request.target eq "develop" OR
+      pull_request.target match "release.+") AND
+      NOT pull_request.draft AND
+      (pull_request.changes.any match "com.unity.ml-agents/**" OR
+      pull_request.changes.any match "com.unity.ml-agents.extensions/**" OR
+      pull_request.changes.any match "Project/**" OR
+      pull_request.changes.any match "ml-agents/**" OR
+      pull_request.changes.any match "ml-agents-envs/**" OR
+      pull_request.changes.any match ".yamato/training-int-tests.yml") AND
+      NOT pull_request.changes.all match "**/*.md"
+  artifacts:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
+        - "artifacts/inference.nn.txt"
+        - "artifacts/inference.onnx.txt"
+    standalonebuild:
+      paths:
+        - "artifacts/testPlayer*/**"
+        - "artifacts/models/**"
+{% endfor %}
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..24853b18dfdfd568debece8faedbe04dcb455c3e
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,74 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, gender identity and expression, level of experience,
+nationality, personal appearance, race, religion, or sexual identity and
+orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at ml-agents@unity3d.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 1.4, available at
+https://www.contributor-covenant.org/version/1/4/code-of-conduct/
+
+[homepage]: https://www.contributor-covenant.org
diff --git a/DevProject/.gitignore b/DevProject/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..589d937759140de790a1ff4111981476783d217b
--- /dev/null
+++ b/DevProject/.gitignore
@@ -0,0 +1,71 @@
+/[Ll]ibrary/
+/Logs/
+/[Tt]emp/
+/[Oo]bj/
+/[Bb]uild/
+/[Bb]uilds/
+/Assets/AssetStoreTools*
+/Assets/Plugins*
+/Assets/Demonstrations*
+/Assets/ML-Agents/Timers*
+/csharp_timers.json
+/CodeCoverage/
+
+# Environemnt logfile
+*Project.log
+
+# Visual Studio 2015 cache directory
+/.vs/
+
+# Autogenerated VS/MD/Consulo solution and project files
+/ProjectExportedObj/
+/Project.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+
+# Unity3D generated meta files
+*.pidb.meta
+
+# Unity3D Generated File On Crash Reports
+/sysinfo.txt
+
+# Builds
+*.apk
+*.unitypackage
+*.app
+*.exe
+*.x86_64
+*.x86
+
+# Plugins
+/Assets/VideoRecorder*
+
+# Mac hidden files
+*.DS_Store
+*/.ipynb_checkpoints
+*/.idea
+*.pyc
+*.idea/misc.xml
+*.idea/modules.xml
+*.idea/
+*.iml
+*.cache
+*/build/
+*/dist/
+*.egg-info*
+*.eggs*
+*.gitignore.swp
+
+# VSCode hidden files
+*.vscode/
+
+.DS_Store
diff --git a/DevProject/Assets/ML-Agents.meta b/DevProject/Assets/ML-Agents.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9aec7896429527ef4334fddb4cf5e6c1a3fec87e
--- /dev/null
+++ b/DevProject/Assets/ML-Agents.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 8498a7556d0b348cbaff70bd8635db8b
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/DevProject/Assets/ML-Agents/Scripts.meta b/DevProject/Assets/ML-Agents/Scripts.meta
new file mode 100644
index 0000000000000000000000000000000000000000..664ce4a064737d03a15c31149ad635e408860d77
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 45f48c01abd5d47048bc3edcc6db1dd0
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests.meta b/DevProject/Assets/ML-Agents/Scripts/Tests.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bfb93955f2dd292b5a3bc75dbed75bb03068494e
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: eb96b88fc6ae14a41949d1bd75e68d38
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5c4107a279443d0c046af59cdad791e3ec004205
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: dc5370caac53244599df51d5c6d39876
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..afb10a33a3c21956b3c8528121c7a726dee69e74
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef
@@ -0,0 +1,28 @@
+{
+    "name": "Unity.ML-Agents.DevTests.Editor",
+    "references": [
+        "Unity.ML-Agents.Editor",
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects",
+        "Unity.PerformanceTesting"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll",
+        "Google.Protobuf.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ]
+}
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5c27e9177e2b67d9a6fe8092a3efb48d005e1ebc
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Editor.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 5b142e67c2d6b4b1e928e4d54f01a596
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings.meta
new file mode 100644
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings/MLAgentsSettingsTests.cs b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings/MLAgentsSettingsTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c2dd8db42c6a7aceb7b67ed09827486f269cdbdc
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings/MLAgentsSettingsTests.cs
@@ -0,0 +1,171 @@
+using System;
+using System.IO;
+using System.Linq;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEditor;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Editor;
+
+
+namespace MLAgentsExamples.Tests.Settings
+{
+    [TestFixture]
+    public class MLAgentsSettingsTests
+    {
+        string EditorBuildSettingsConfigKey = MLAgentsSettingsManager.EditorBuildSettingsConfigKey;
+        string tempSettingsRootPath = "Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings";
+        MLAgentsSettings storedConfigObject;
+        [SetUp]
+        public void SetUp()
+        {
+            if (EditorBuildSettings.TryGetConfigObject(EditorBuildSettingsConfigKey,
+                out MLAgentsSettings settingsAsset))
+            {
+                if (settingsAsset != null)
+                {
+                    storedConfigObject = settingsAsset;
+                    EditorBuildSettings.RemoveConfigObject(EditorBuildSettingsConfigKey);
+                }
+            }
+            MLAgentsSettingsManager.Destroy();
+            ClearSettingsAssets();
+        }
+
+        [TearDown]
+        public void TearDown()
+        {
+            if (storedConfigObject != null)
+            {
+                EditorBuildSettings.AddConfigObject(EditorBuildSettingsConfigKey, storedConfigObject, true);
+                storedConfigObject = null;
+            }
+            MLAgentsSettingsManager.Destroy();
+            ClearSettingsAssets();
+        }
+
+        internal void ClearSettingsAssets()
+        {
+            var assetsGuids = AssetDatabase.FindAssets("t:MLAgentsSettings", new string[] { tempSettingsRootPath });
+            foreach (var guid in assetsGuids)
+            {
+                var path = AssetDatabase.GUIDToAssetPath(guid);
+                AssetDatabase.DeleteAsset(path);
+            }
+        }
+
+        [Test]
+        public void TestMLAgentsSettingsManager()
+        {
+            Assert.AreNotEqual(null, MLAgentsSettingsManager.Settings);
+            Assert.AreEqual(5004, MLAgentsSettingsManager.Settings.EditorPort); // default port
+            MLAgentsSettingsManager.Settings.EditorPort = 6000;
+            Assert.AreEqual(6000, MLAgentsSettingsManager.Settings.EditorPort);
+
+            var settingsObject = ScriptableObject.CreateInstance<MLAgentsSettings>();
+            settingsObject.EditorPort = 7000;
+            var tempSettingsAssetPath = tempSettingsRootPath + "/test.mlagents.settings.asset";
+            AssetDatabase.CreateAsset(settingsObject, tempSettingsAssetPath);
+            EditorBuildSettings.AddConfigObject(EditorBuildSettingsConfigKey, settingsObject, true);
+            // destroy manager instantiated as a side effect by accessing MLAgentsSettings directly without manager
+            MLAgentsSettingsManager.Destroy();
+            Assert.AreEqual(7000, MLAgentsSettingsManager.Settings.EditorPort);
+        }
+
+        // A mock class that can invoke private methods/fields in MLAgentsSettingsProvider
+        internal class MockSettingsProvider
+        {
+            public MLAgentsSettingsProvider Instance
+            {
+                get
+                {
+                    return (MLAgentsSettingsProvider)typeof(MLAgentsSettingsProvider).GetField("s_Instance",
+                        BindingFlags.Static | BindingFlags.NonPublic).GetValue(null);
+                }
+            }
+
+            public MLAgentsSettings Settings
+            {
+                get
+                {
+                    return (MLAgentsSettings)typeof(MLAgentsSettingsProvider).GetField("m_Settings",
+                        BindingFlags.Instance | BindingFlags.NonPublic).GetValue(Instance);
+                }
+            }
+
+            public void CreateMLAgentsSettingsProvider()
+            {
+                MLAgentsSettingsProvider.CreateMLAgentsSettingsProvider();
+            }
+
+            public void Reinitialize()
+            {
+                var method = typeof(MLAgentsSettingsProvider).GetMethod("Reinitialize",
+                    BindingFlags.Instance | BindingFlags.NonPublic);
+                method.Invoke(Instance, null);
+            }
+
+            public string[] FindSettingsInProject()
+            {
+                var method = typeof(MLAgentsSettingsProvider).GetMethod("FindSettingsInProject",
+                    BindingFlags.Static | BindingFlags.NonPublic);
+                return (string[])method.Invoke(null, null);
+            }
+
+            public void CreateNewSettingsAsset(string relativePath)
+            {
+                var method = typeof(MLAgentsSettingsProvider).GetMethod("CreateNewSettingsAsset",
+                    BindingFlags.Static | BindingFlags.NonPublic);
+                method.Invoke(null, new object[] { relativePath });
+            }
+        }
+
+        [Test]
+        public void TestMLAgentsSettingsProviderCreateAsset()
+        {
+            var mockProvider = new MockSettingsProvider();
+            mockProvider.CreateMLAgentsSettingsProvider();
+            Assert.AreNotEqual(null, mockProvider.Instance);
+
+            // mimic MLAgentsSettingsProvider.OnActivate()
+            MLAgentsSettingsManager.OnSettingsChange += mockProvider.Reinitialize;
+
+            mockProvider.Instance.InitializeWithCurrentSettings();
+            Assert.AreEqual(0, mockProvider.FindSettingsInProject().Length);
+
+            var tempSettingsAssetPath1 = tempSettingsRootPath + "/test.mlagents.settings.asset";
+            mockProvider.CreateNewSettingsAsset(tempSettingsAssetPath1);
+            Assert.AreEqual(1, mockProvider.FindSettingsInProject().Length);
+            Assert.AreEqual(5004, mockProvider.Settings.EditorPort);
+            MLAgentsSettingsManager.Settings.EditorPort = 6000; // change to something not default
+            // callback should update the field in provider
+            Assert.AreEqual(6000, mockProvider.Settings.EditorPort);
+
+            var tempSettingsAssetPath2 = tempSettingsRootPath + "/test2.mlagents.settings.asset";
+            mockProvider.CreateNewSettingsAsset(tempSettingsAssetPath2);
+            Assert.AreEqual(2, mockProvider.FindSettingsInProject().Length);
+            // manager should set to the new (default) one, not the previous modified one
+            Assert.AreEqual(5004, MLAgentsSettingsManager.Settings.EditorPort);
+
+            // mimic MLAgentsSettingsProvider.OnDeactivate()
+            MLAgentsSettingsManager.OnSettingsChange -= mockProvider.Reinitialize;
+            mockProvider.Instance.Dispose();
+        }
+
+        [Test]
+        public void TestMLAgentsSettingsProviderLoadAsset()
+        {
+            var mockProvider = new MockSettingsProvider();
+            var tempSettingsAssetPath1 = tempSettingsRootPath + "/test.mlagents.settings.asset";
+            mockProvider.CreateNewSettingsAsset(tempSettingsAssetPath1);
+            MLAgentsSettingsManager.Settings.EditorPort = 8000; // change to something not default
+
+            mockProvider.Instance?.Dispose();
+            MLAgentsSettingsManager.Destroy();
+
+            mockProvider.CreateMLAgentsSettingsProvider();
+            Assert.AreEqual(8000, MLAgentsSettingsManager.Settings.EditorPort);
+        }
+    }
+}
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings/MLAgentsSettingsTests.cs.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/MLAgentsSettings/MLAgentsSettingsTests.cs.meta
new file mode 100644
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance.meta
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance/SensorPerformanceTests.cs b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance/SensorPerformanceTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a1e113a1ad3d1723912b2b288c43bd56ed90061b
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance/SensorPerformanceTests.cs
@@ -0,0 +1,195 @@
+using NUnit.Framework;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+using Unity.PerformanceTesting;
+using UnityEngine;
+
+namespace MLAgentsExamples.Tests.Performance
+{
+    [TestFixture]
+    public class SensorPerformanceTests
+    {
+        string[] s_Markers =
+        {
+            "root.InitializeSensors",
+            "root.AgentSendState.CollectObservations",
+            "root.AgentSendState.RequestDecision"
+        };
+        const int k_NumAgentSteps = 10;
+        const int k_MeasurementCount = 25;
+        const int k_MarkerTestSteps = 10;
+
+        [SetUp]
+        public void SetUp()
+        {
+            // Step a dummy agent here, so that we don't time the Academy initialization connection attempt and
+            // any other static setup costs.
+            RunAgent<DummyAgent>(1, 0, ObservableAttributeOptions.ExamineAll);
+        }
+
+        /// <summary>
+        /// Simple Agent just used for "burning in" the Academy for testing.
+        /// </summary>
+        class DummyAgent : Agent
+        {
+            public override void CollectObservations(VectorSensor sensor)
+            {
+            }
+
+            public override void Heuristic(in ActionBuffers actionsOut)
+            {
+            }
+        }
+
+        /// <summary>
+        /// Agent used for performance testing that uses the CollectObservations interface.
+        /// </summary>
+        class CollectObservationsAgent : Agent
+        {
+            public override void CollectObservations(VectorSensor sensor)
+            {
+                sensor.AddObservation(new Vector3(1, 2, 3));
+                sensor.AddObservation(new Quaternion(1, 2, 3, 4));
+            }
+
+            public override void Heuristic(in ActionBuffers actionsOut)
+            {
+            }
+        }
+
+        /// <summary>
+        /// Agent used for performance testing that uses the ObservableAttributes on fields.
+        /// </summary>
+        class ObservableFieldAgent : Agent
+        {
+            [Observable]
+            public Vector3 Vector3Field = new Vector3(1, 2, 3);
+
+            [Observable]
+            public Quaternion QuaternionField = new Quaternion(1, 2, 3, 4);
+
+            public override void Heuristic(in ActionBuffers actionsOut)
+            {
+            }
+        }
+
+        /// <summary>
+        /// Agent used for performance testing that uses the ObservableAttributes on properties.
+        /// </summary>
+        class ObservablePropertyAgent : Agent
+        {
+            Vector3 m_Vector3Field = new Vector3(1, 2, 3);
+
+            [Observable]
+            Vector3 Vector3Property
+            {
+                get { return m_Vector3Field; }
+            }
+
+            Quaternion m_QuaternionField = new Quaternion(1, 2, 3, 4);
+
+            [Observable]
+            Quaternion QuaternionProperty
+            {
+                get { return m_QuaternionField; }
+            }
+
+            public override void Heuristic(in ActionBuffers actionsOut)
+            {
+            }
+        }
+
+        void RunAgent<T>(int numSteps, int obsSize, ObservableAttributeOptions obsOptions) where T : Agent
+        {
+            var agentGameObj = new GameObject();
+            var agent = agentGameObj.AddComponent<T>();
+
+            var behaviorParams = agent.GetComponent<BehaviorParameters>();
+            behaviorParams.BrainParameters.VectorObservationSize = obsSize;
+            behaviorParams.ObservableAttributeHandling = obsOptions;
+            agent.Awake();
+            agent.LazyInitialize();
+            for (var i = 0; i < numSteps; i++)
+            {
+                agent.RequestDecision();
+                Academy.Instance.EnvironmentStep();
+            }
+            Object.DestroyImmediate(agentGameObj);
+        }
+
+        [Test, Performance]
+        public void TestCollectObservationsAgent()
+        {
+            Measure.Method(() =>
+            {
+                RunAgent<CollectObservationsAgent>(k_NumAgentSteps, 7, ObservableAttributeOptions.Ignore);
+            })
+                .MeasurementCount(k_MeasurementCount)
+                .GC()
+                .Run();
+        }
+
+        [Test, Performance]
+        public void TestObservableFieldAgent()
+        {
+            Measure.Method(() =>
+            {
+                RunAgent<ObservableFieldAgent>(k_NumAgentSteps, 0, ObservableAttributeOptions.ExcludeInherited);
+            })
+                .MeasurementCount(k_MeasurementCount)
+                .GC()
+                .Run();
+        }
+
+        [Test, Performance]
+        public void TestObservablePropertyAgent()
+        {
+            Measure.Method(() =>
+            {
+                RunAgent<ObservablePropertyAgent>(k_NumAgentSteps, 0, ObservableAttributeOptions.ExcludeInherited);
+            })
+                .MeasurementCount(k_MeasurementCount)
+                .GC()
+                .Run();
+        }
+
+        [Test, Performance]
+        public void TestCollectObservationsAgentMarkers()
+        {
+            using (Measure.ProfilerMarkers(s_Markers))
+            {
+                for (var i = 0; i < k_MarkerTestSteps; i++)
+                {
+                    RunAgent<CollectObservationsAgent>(k_NumAgentSteps, 7, ObservableAttributeOptions.Ignore);
+                }
+            }
+        }
+
+        [Test, Performance]
+        public void TestObservableFieldAgentMarkers()
+        {
+            using (Measure.ProfilerMarkers(s_Markers))
+            {
+                for (var i = 0; i < k_MarkerTestSteps; i++)
+                {
+                    RunAgent<ObservableFieldAgent>(k_NumAgentSteps, 0, ObservableAttributeOptions.ExcludeInherited);
+                }
+            }
+        }
+
+        [Test, Performance]
+        public void TestObservablePropertyAgentMarkers()
+        {
+            using (Measure.ProfilerMarkers(s_Markers))
+            {
+                for (var i = 0; i < k_MarkerTestSteps; i++)
+                {
+                    RunAgent<ObservableFieldAgent>(k_NumAgentSteps, 0, ObservableAttributeOptions.ExcludeInherited);
+                }
+            }
+        }
+    }
+}
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance/SensorPerformanceTests.cs.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Editor/Performance/SensorPerformanceTests.cs.meta
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime.meta
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest.meta
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTest.cs b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d69fdef76a924a87001c47091c7a904f5ab17eed
--- /dev/null
+++ b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTest.cs
@@ -0,0 +1,40 @@
+using System.Collections;
+using NUnit.Framework;
+using UnityEngine.TestTools;
+using UnityEngine;
+using UnityEngine.SceneManagement;
+using Unity.MLAgents;
+#if UNITY_EDITOR
+using UnityEditor.SceneManagement;
+#endif
+
+namespace Tests
+{
+    public class AcademyStepperTest
+    {
+        [SetUp]
+        public void Setup()
+        {
+            // We need register the communicator first before accessing the Academy.
+            CommunicatorFactory.Register<ICommunicator>(RpcCommunicator.Create);
+            Academy.Instance.Dispose();
+            SceneManager.LoadScene("ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTestScene");
+            var academy = Academy.Instance;
+        }
+
+        /// <summary>
+        /// Verify in each update, the Academy is only stepped once.
+        /// </summary>
+        [UnityTest]
+        public IEnumerator AcademyStepperCleanupPasses()
+        {
+            var academy = Academy.Instance;
+            int initStepCount = academy.TotalStepCount;
+            for (var i = 0; i < 5; i++)
+            {
+                yield return new WaitForFixedUpdate();
+                Assert.True(academy.TotalStepCount - initStepCount == i + 1);
+            }
+        }
+    }
+}
diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTest.cs.meta b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTest.cs.meta
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diff --git a/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTestScene.unity b/DevProject/Assets/ML-Agents/Scripts/Tests/Runtime/AcademyTest/AcademyStepperTestScene.unity
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+++ b/DevProject/ProjectSettings/QualitySettings.asset
@@ -0,0 +1,236 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!47 &1
+QualitySettings:
+  m_ObjectHideFlags: 0
+  serializedVersion: 5
+  m_CurrentQuality: 5
+  m_QualitySettings:
+  - serializedVersion: 2
+    name: Very Low
+    pixelLightCount: 0
+    shadows: 0
+    shadowResolution: 0
+    shadowProjection: 1
+    shadowCascades: 1
+    shadowDistance: 15
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
+    shadowmaskMode: 0
+    skinWeights: 1
+    textureQuality: 1
+    anisotropicTextures: 0
+    antiAliasing: 0
+    softParticles: 0
+    softVegetation: 0
+    realtimeReflectionProbes: 0
+    billboardsFaceCameraPosition: 0
+    vSyncCount: 0
+    lodBias: 0.3
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 4
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  - serializedVersion: 2
+    name: Low
+    pixelLightCount: 0
+    shadows: 0
+    shadowResolution: 0
+    shadowProjection: 1
+    shadowCascades: 1
+    shadowDistance: 20
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
+    shadowmaskMode: 0
+    skinWeights: 2
+    textureQuality: 0
+    anisotropicTextures: 0
+    antiAliasing: 0
+    softParticles: 0
+    softVegetation: 0
+    realtimeReflectionProbes: 0
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+    lodBias: 0.4
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 16
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  - serializedVersion: 2
+    name: Medium
+    pixelLightCount: 1
+    shadows: 1
+    shadowResolution: 0
+    shadowProjection: 1
+    shadowCascades: 1
+    shadowDistance: 20
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
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+    skinWeights: 2
+    textureQuality: 0
+    anisotropicTextures: 1
+    antiAliasing: 0
+    softParticles: 0
+    softVegetation: 0
+    realtimeReflectionProbes: 0
+    billboardsFaceCameraPosition: 0
+    vSyncCount: 1
+    lodBias: 0.7
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 64
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  - serializedVersion: 2
+    name: High
+    pixelLightCount: 2
+    shadows: 2
+    shadowResolution: 1
+    shadowProjection: 1
+    shadowCascades: 2
+    shadowDistance: 40
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
+    shadowmaskMode: 1
+    skinWeights: 2
+    textureQuality: 0
+    anisotropicTextures: 1
+    antiAliasing: 0
+    softParticles: 0
+    softVegetation: 1
+    realtimeReflectionProbes: 1
+    billboardsFaceCameraPosition: 1
+    vSyncCount: 1
+    lodBias: 1
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 256
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  - serializedVersion: 2
+    name: Very High
+    pixelLightCount: 3
+    shadows: 2
+    shadowResolution: 2
+    shadowProjection: 1
+    shadowCascades: 2
+    shadowDistance: 70
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
+    shadowmaskMode: 1
+    skinWeights: 4
+    textureQuality: 0
+    anisotropicTextures: 2
+    antiAliasing: 2
+    softParticles: 1
+    softVegetation: 1
+    realtimeReflectionProbes: 1
+    billboardsFaceCameraPosition: 1
+    vSyncCount: 1
+    lodBias: 1.5
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 1024
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  - serializedVersion: 2
+    name: Ultra
+    pixelLightCount: 4
+    shadows: 2
+    shadowResolution: 2
+    shadowProjection: 1
+    shadowCascades: 4
+    shadowDistance: 150
+    shadowNearPlaneOffset: 3
+    shadowCascade2Split: 0.33333334
+    shadowCascade4Split: {x: 0.06666667, y: 0.2, z: 0.46666667}
+    shadowmaskMode: 1
+    skinWeights: 255
+    textureQuality: 0
+    anisotropicTextures: 2
+    antiAliasing: 2
+    softParticles: 1
+    softVegetation: 1
+    realtimeReflectionProbes: 1
+    billboardsFaceCameraPosition: 1
+    vSyncCount: 1
+    lodBias: 2
+    maximumLODLevel: 0
+    streamingMipmapsActive: 0
+    streamingMipmapsAddAllCameras: 1
+    streamingMipmapsMemoryBudget: 512
+    streamingMipmapsRenderersPerFrame: 512
+    streamingMipmapsMaxLevelReduction: 2
+    streamingMipmapsMaxFileIORequests: 1024
+    particleRaycastBudget: 4096
+    asyncUploadTimeSlice: 2
+    asyncUploadBufferSize: 16
+    asyncUploadPersistentBuffer: 1
+    resolutionScalingFixedDPIFactor: 1
+    customRenderPipeline: {fileID: 0}
+    excludedTargetPlatforms: []
+  m_PerPlatformDefaultQuality:
+    Android: 2
+    Lumin: 5
+    Nintendo Switch: 5
+    PS4: 5
+    Stadia: 5
+    Standalone: 5
+    WebGL: 3
+    Windows Store Apps: 5
+    XboxOne: 5
+    iPhone: 2
+    tvOS: 2
diff --git a/DevProject/ProjectSettings/TagManager.asset b/DevProject/ProjectSettings/TagManager.asset
new file mode 100644
index 0000000000000000000000000000000000000000..1c92a7840ec11895c76785f65d949a3d20d53355
--- /dev/null
+++ b/DevProject/ProjectSettings/TagManager.asset
@@ -0,0 +1,43 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!78 &1
+TagManager:
+  serializedVersion: 2
+  tags: []
+  layers:
+  - Default
+  - TransparentFX
+  - Ignore Raycast
+  - 
+  - Water
+  - UI
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  - 
+  m_SortingLayers:
+  - name: Default
+    uniqueID: 0
+    locked: 0
diff --git a/DevProject/ProjectSettings/TimeManager.asset b/DevProject/ProjectSettings/TimeManager.asset
new file mode 100644
index 0000000000000000000000000000000000000000..558a017e1f50b2db73414a1abad3c033922774f8
--- /dev/null
+++ b/DevProject/ProjectSettings/TimeManager.asset
@@ -0,0 +1,9 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!5 &1
+TimeManager:
+  m_ObjectHideFlags: 0
+  Fixed Timestep: 0.02
+  Maximum Allowed Timestep: 0.33333334
+  m_TimeScale: 1
+  Maximum Particle Timestep: 0.03
diff --git a/DevProject/ProjectSettings/UnityConnectSettings.asset b/DevProject/ProjectSettings/UnityConnectSettings.asset
new file mode 100644
index 0000000000000000000000000000000000000000..fa0b146579fb787323d29e1d7b7768a0541031af
--- /dev/null
+++ b/DevProject/ProjectSettings/UnityConnectSettings.asset
@@ -0,0 +1,34 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!310 &1
+UnityConnectSettings:
+  m_ObjectHideFlags: 0
+  serializedVersion: 1
+  m_Enabled: 0
+  m_TestMode: 0
+  m_EventOldUrl: https://api.uca.cloud.unity3d.com/v1/events
+  m_EventUrl: https://cdp.cloud.unity3d.com/v1/events
+  m_ConfigUrl: https://config.uca.cloud.unity3d.com
+  m_TestInitMode: 0
+  CrashReportingSettings:
+    m_EventUrl: https://perf-events.cloud.unity3d.com
+    m_Enabled: 0
+    m_LogBufferSize: 10
+    m_CaptureEditorExceptions: 1
+  UnityPurchasingSettings:
+    m_Enabled: 0
+    m_TestMode: 0
+  UnityAnalyticsSettings:
+    m_Enabled: 0
+    m_TestMode: 0
+    m_InitializeOnStartup: 1
+  UnityAdsSettings:
+    m_Enabled: 0
+    m_InitializeOnStartup: 1
+    m_TestMode: 0
+    m_IosGameId: 
+    m_AndroidGameId: 
+    m_GameIds: {}
+    m_GameId: 
+  PerformanceReportingSettings:
+    m_Enabled: 0
diff --git a/DevProject/ProjectSettings/VFXManager.asset b/DevProject/ProjectSettings/VFXManager.asset
new file mode 100644
index 0000000000000000000000000000000000000000..3a95c98bec50cf72538061fd26eee95398f72128
--- /dev/null
+++ b/DevProject/ProjectSettings/VFXManager.asset
@@ -0,0 +1,12 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!937362698 &1
+VFXManager:
+  m_ObjectHideFlags: 0
+  m_IndirectShader: {fileID: 0}
+  m_CopyBufferShader: {fileID: 0}
+  m_SortShader: {fileID: 0}
+  m_StripUpdateShader: {fileID: 0}
+  m_RenderPipeSettingsPath: 
+  m_FixedTimeStep: 0.016666668
+  m_MaxDeltaTime: 0.05
diff --git a/DevProject/ProjectSettings/XRSettings.asset b/DevProject/ProjectSettings/XRSettings.asset
new file mode 100644
index 0000000000000000000000000000000000000000..482590c196f7a82116679f58176b9d1aff21d7f9
--- /dev/null
+++ b/DevProject/ProjectSettings/XRSettings.asset
@@ -0,0 +1,10 @@
+{
+    "m_SettingKeys": [
+        "VR Device Disabled",
+        "VR Device User Alert"
+    ],
+    "m_SettingValues": [
+        "False",
+        "False"
+    ]
+}
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..6c11f5b4594b4f917479d4a1e3f331f5010321e6
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,28 @@
+# From https://gitlab.com/nvidia/container-images/cuda/blob/master/doc/supported-tags.md
+FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+
+RUN yes | unminimize
+
+RUN echo "deb http://packages.cloud.google.com/apt cloud-sdk-xenial main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list
+RUN wget https://packages.cloud.google.com/apt/doc/apt-key.gpg && apt-key add apt-key.gpg
+RUN apt-get update && \
+  apt-get install -y --no-install-recommends wget curl tmux vim git gdebi-core \
+  build-essential python3-pip unzip google-cloud-sdk htop mesa-utils xorg-dev xorg \
+  libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev xvfb && \
+  wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb && \
+  yes | gdebi libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb
+RUN python3 -m pip install --upgrade pip
+RUN pip install setuptools==41.0.0
+
+ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
+
+#checkout ml-agents for SHA
+RUN mkdir /ml-agents
+WORKDIR /ml-agents
+ARG SHA
+RUN git init
+RUN git remote add origin https://github.com/Unity-Technologies/ml-agents.git
+RUN git fetch --depth 1 origin $SHA
+RUN git checkout FETCH_HEAD
+RUN pip install -e /ml-agents/ml-agents-envs
+RUN pip install -e /ml-agents/ml-agents
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..06fa0d94e5fb1f52555e944e33e2cc96e8dba073
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,202 @@
+Copyright 2017-2021 Unity Technologies
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
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+
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+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
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+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
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+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
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+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
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+      Work and such Derivative Works in Source or Object form.
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+
+   4. Redistribution. You may reproduce and distribute copies of the
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+
+      (a) You must give any other recipients of the Work or
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+
+      (b) You must cause any modified files to carry prominent notices
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+
+      (c) You must retain, in the Source form of any Derivative Works
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+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
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+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
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+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
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+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
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+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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diff --git a/Project/.gitignore b/Project/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..2ec312822995887dc8ded3e797c99340d0600960
--- /dev/null
+++ b/Project/.gitignore
@@ -0,0 +1,70 @@
+/[Ll]ibrary/
+/Logs/
+/[Tt]emp/
+/[Oo]bj/
+/[Bb]uild/
+/[Bb]uilds/
+/Assets/AssetStoreTools*
+/Assets/Plugins*
+/Assets/Demonstrations*
+/Assets/ML-Agents/Timers*
+/csharp_timers.json
+
+# Environemnt logfile
+*Project.log
+
+# Visual Studio 2015 cache directory
+/.vs/
+
+# Autogenerated VS/MD/Consulo solution and project files
+/ProjectExportedObj/
+/Project.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+
+# Unity3D generated meta files
+*.pidb.meta
+
+# Unity3D Generated File On Crash Reports
+/sysinfo.txt
+
+# Builds
+*.apk
+*.unitypackage
+*.app
+*.exe
+*.x86_64
+*.x86
+
+# Plugins
+/Assets/VideoRecorder*
+
+# Mac hidden files
+*.DS_Store
+*/.ipynb_checkpoints
+*/.idea
+*.pyc
+*.idea/misc.xml
+*.idea/modules.xml
+*.idea/
+*.iml
+*.cache
+*/build/
+*/dist/
+*.egg-info*
+*.eggs*
+*.gitignore.swp
+
+# VSCode hidden files
+*.vscode/
+
+.DS_Store
diff --git a/Project/Assets/ML-Agents.meta b/Project/Assets/ML-Agents.meta
new file mode 100644
index 0000000000000000000000000000000000000000..59a4dc3f1db33bdd5c8906f07b17b5c13c8652b5
--- /dev/null
+++ b/Project/Assets/ML-Agents.meta
@@ -0,0 +1,9 @@
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diff --git a/Project/Assets/ML-Agents/Editor.meta b/Project/Assets/ML-Agents/Editor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6f8f90082b3e63257cc80575f8018af6772194db
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor.meta
@@ -0,0 +1,8 @@
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+guid: 0464d237504614b8fb9b2d96c707cac2
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs b/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs
new file mode 100644
index 0000000000000000000000000000000000000000..102121de10d5596eb1e18b886d7e4616b81e3740
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs
@@ -0,0 +1,19 @@
+#if UNITY_CLOUD_BUILD
+using UnityEditor;
+
+public class DisableBurstFromMenu
+{
+    /// This method is needed to disable Burst compilation on windows for our cloudbuild tests.
+    /// Barracuda 0.4.0-preview depends on a version of Burst (1.1.1) which does not allow
+    /// users to disable burst compilation on a per platform basis.  The burst version 1.3.0-preview-1
+    /// allows for cross compilation, but is not released yet.
+    ///
+    /// We will be able to remove this when
+    /// 1. Barracuda updates burst 1.3.0-preview-1 or
+    /// 2. We update our edior version for our tests to 2019.1+
+    public static void DisableBurstCompilation()
+    {
+        EditorApplication.ExecuteMenuItem("Jobs/Burst/Enable Compilation");
+    }
+}
+#endif
diff --git a/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs.meta b/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9b7409865c0cda8f4a883b546cbb01e9e0ba28c6
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+++ b/Project/Assets/ML-Agents/Editor/DisableBurstFromMenu.cs.meta
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diff --git a/Project/Assets/ML-Agents/Editor/Tests.meta b/Project/Assets/ML-Agents/Editor/Tests.meta
new file mode 100644
index 0000000000000000000000000000000000000000..62aaceb0fd9b4b435cbf5abe7e6a8e363a4afce3
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor/Tests.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 5c324397e98ed45d28d29fd1056c6df0
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diff --git a/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs b/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ca0604c4b8e8f5fa2b7c337c241648a185682ce4
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs
@@ -0,0 +1,125 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using Newtonsoft.Json;
+using UnityEditor;
+using UnityEngine;
+
+namespace Unity.MLAgents
+{
+    public class SampleExporter
+    {
+        const string k_MLAgentsSampleFile = "mlagents-sample.json";
+        const string k_PackageSampleFile = ".sample.json";
+        const string k_MLAgentsDir = "ML-Agents";
+        const string k_MLAgentsExamplesDir = "Examples";
+        const string k_MLAgentsPackageName = "com.unity.ml-agents";
+        const string k_MLAgentsSamplesDirName = "Samples";
+        const string k_MLAgentsScriptsDirName = "Scripts";
+
+        struct MLAgentsSampleJson
+        {
+#pragma warning disable 649
+            public string displayName;
+            public string description;
+            // ReSharper disable once CollectionNeverUpdated.Local
+            public List<string> scenes;
+#pragma warning restore 649
+        }
+
+        struct PackageSampleJson
+        {
+            public string displayName;
+            public string description;
+        }
+
+        public static void ExportCuratedSamples()
+        {
+            var oldBurst = EditorPrefs.GetBool("BurstCompilation");
+            EditorPrefs.SetBool("BurstCompilation", false);
+            try
+            {
+                // Path to Project/Assets
+                var assetsDir = Application.dataPath;
+                var repoRoot = Directory.GetParent(Directory.GetParent(assetsDir).FullName).FullName;
+
+                // Top level of where to store the samples
+                var samplesDir = Path.Combine(
+                    repoRoot,
+                    k_MLAgentsPackageName,
+                    k_MLAgentsSamplesDirName);
+
+                if (!Directory.Exists(samplesDir))
+                {
+                    Directory.CreateDirectory(samplesDir);
+                }
+
+                // Path to the examples dir in the project
+                var examplesDir = Path.Combine(Application.dataPath, k_MLAgentsDir, k_MLAgentsExamplesDir);
+                foreach (var exampleDirectory in Directory.GetDirectories(examplesDir))
+                {
+                    var mlAgentsSamplePath = Path.Combine(exampleDirectory, k_MLAgentsSampleFile);
+                    if (File.Exists(mlAgentsSamplePath))
+                    {
+                        var sampleJson = JsonConvert.DeserializeObject<MLAgentsSampleJson>(File.ReadAllText(mlAgentsSamplePath));
+                        Debug.Log(JsonConvert.SerializeObject(sampleJson));
+                        foreach (var scene in sampleJson.scenes)
+                        {
+                            var scenePath = Path.Combine(exampleDirectory, scene);
+                            if (File.Exists(scenePath))
+                            {
+                                // Create a Sample Directory
+                                var currentSampleDir = Directory.CreateDirectory(Path.Combine(samplesDir,
+                                    Path.GetFileNameWithoutExtension(scenePath)));
+
+
+                                var scriptsPath = Path.Combine(exampleDirectory, k_MLAgentsScriptsDirName);
+                                Debug.Log($"Scene Path: {scenePath}");
+                                var assets = new List<string> { scenePath.Substring(scenePath.IndexOf("Assets")) };
+                                if (!Directory.Exists(Path.Combine(scriptsPath)))
+                                {
+                                    scriptsPath = exampleDirectory;
+                                }
+
+                                scriptsPath = scriptsPath.Substring(scriptsPath.IndexOf("Assets"));
+                                foreach (var guid in AssetDatabase.FindAssets("t:Script", new[] { scriptsPath }))
+                                {
+                                    var path = AssetDatabase.GUIDToAssetPath(guid);
+                                    assets.Add(path);
+                                    Debug.Log($"Adding Asset: {path}");
+                                }
+
+                                var packageFilePath = Path.GetFileNameWithoutExtension(scenePath) + ".unitypackage";
+                                AssetDatabase.ExportPackage(assets.ToArray(),
+                                    Path.Combine(Application.dataPath, packageFilePath),
+                                    ExportPackageOptions.IncludeDependencies | ExportPackageOptions.Recurse);
+
+                                // Move the .unitypackage into the samples folder.
+                                var packageFileFullPath = Path.Combine(Application.dataPath, packageFilePath);
+
+                                var packageInSamplePath = Path.Combine(currentSampleDir.FullName, packageFilePath);
+                                Debug.Log($"Moving {packageFileFullPath} to {packageInSamplePath}");
+                                File.Move(packageFileFullPath, packageInSamplePath);
+
+                                // write the .sample.json file to the sample directory
+                                File.WriteAllText(Path.Combine(currentSampleDir.FullName, k_PackageSampleFile),
+                                    JsonConvert.SerializeObject(new PackageSampleJson
+                                    {
+                                        description = sampleJson.description,
+                                        displayName = sampleJson.displayName
+                                    }));
+                            }
+                        }
+                    }
+                }
+            }
+            catch (Exception e)
+            {
+                Debug.Log(e);
+                EditorApplication.Exit(1);
+            }
+            EditorPrefs.SetBool("BurstCompilation", oldBurst);
+            EditorApplication.Exit(0);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs.meta b/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..012de8c396619cbe99327a92a96b82f8ec5d8603
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor/Tests/SampleExporter.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 914d5be190bb435eb11383db3aaf70eb
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\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs b/Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..01cfc10573971dc0da0a2c85729044320bc2a643
--- /dev/null
+++ b/Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs
@@ -0,0 +1,73 @@
+using System;
+using UnityEditor;
+using UnityEngine;
+using UnityEditor.Build.Reporting;
+
+namespace Unity.MLAgents
+{
+    public class StandaloneBuildTest
+    {
+        const string k_OutputCommandLineFlag = "--mlagents-build-output-path";
+        const string k_SceneCommandLineFlag = "--mlagents-build-scene-path";
+        private const string k_BuildTargetFlag = "--mlagents-build-target";
+
+        public static void BuildStandalonePlayerOSX()
+        {
+            // Read commandline arguments for options
+            var outputPath = "testPlayer";
+            var scenePath = "Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity";
+            var buildTarget = BuildTarget.StandaloneOSX;
+
+            var args = Environment.GetCommandLineArgs();
+            for (var i = 0; i < args.Length - 1; i++)
+            {
+                if (args[i] == k_OutputCommandLineFlag)
+                {
+                    outputPath = args[i + 1];
+                    Debug.Log($"Overriding output path to {outputPath}");
+                }
+                else if (args[i] == k_SceneCommandLineFlag)
+                {
+                    scenePath = args[i + 1];
+                }
+                else if (args[i] == k_BuildTargetFlag)
+                {
+                    buildTarget = (BuildTarget)Enum.Parse(typeof(BuildTarget), args[i + 1], ignoreCase: true);
+                }
+            }
+
+            string[] scenes = { scenePath };
+            var buildResult = BuildPipeline.BuildPlayer(
+                scenes,
+                outputPath,
+                buildTarget,
+                BuildOptions.Development
+            );
+            var isOk = buildResult.summary.result == BuildResult.Succeeded;
+            var error = "";
+            foreach (var stepInfo in buildResult.steps)
+            {
+                foreach (var msg in stepInfo.messages)
+                {
+                    if (msg.type != LogType.Log && msg.type != LogType.Warning)
+                    {
+                        error += msg.content + "\n";
+                    }
+                }
+            }
+            if (isOk)
+            {
+                EditorApplication.Exit(0);
+            }
+            else
+            {
+                Console.Error.WriteLine(error);
+                EditorApplication.Exit(1);
+
+            }
+            Debug.Log(error);
+
+        }
+
+    }
+}
diff --git a/Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs.meta b/Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples.meta b/Project/Assets/ML-Agents/Examples.meta
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diff --git a/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs b/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8b16087f221441a315a53388a5e9e77122c35974
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs
@@ -0,0 +1,95 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Random = UnityEngine.Random;
+
+public class Ball3DAgent : Agent
+{
+    [Header("Specific to Ball3D")]
+    public GameObject ball;
+    [Tooltip("Whether to use vector observation. This option should be checked " +
+        "in 3DBall scene, and unchecked in Visual3DBall scene. ")]
+    public bool useVecObs;
+    Rigidbody m_BallRb;
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_BallRb = ball.GetComponent<Rigidbody>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+        SetResetParameters();
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        if (useVecObs)
+        {
+            sensor.AddObservation(gameObject.transform.rotation.z);
+            sensor.AddObservation(gameObject.transform.rotation.x);
+            sensor.AddObservation(ball.transform.position - gameObject.transform.position);
+            sensor.AddObservation(m_BallRb.velocity);
+        }
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
+
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
+        }
+        if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
+            Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
+            Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f)
+        {
+            SetReward(-1f);
+            EndEpisode();
+        }
+        else
+        {
+            SetReward(0.1f);
+        }
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        gameObject.transform.rotation = new Quaternion(0f, 0f, 0f, 0f);
+        gameObject.transform.Rotate(new Vector3(1, 0, 0), Random.Range(-10f, 10f));
+        gameObject.transform.Rotate(new Vector3(0, 0, 1), Random.Range(-10f, 10f));
+        m_BallRb.velocity = new Vector3(0f, 0f, 0f);
+        ball.transform.position = new Vector3(Random.Range(-1.5f, 1.5f), 4f, Random.Range(-1.5f, 1.5f))
+            + gameObject.transform.position;
+        //Reset the parameters when the Agent is reset.
+        SetResetParameters();
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var continuousActionsOut = actionsOut.ContinuousActions;
+        continuousActionsOut[0] = -Input.GetAxis("Horizontal");
+        continuousActionsOut[1] = Input.GetAxis("Vertical");
+    }
+
+    public void SetBall()
+    {
+        //Set the attributes of the ball by fetching the information from the academy
+        m_BallRb.mass = m_ResetParams.GetWithDefault("mass", 1.0f);
+        var scale = m_ResetParams.GetWithDefault("scale", 1.0f);
+        ball.transform.localScale = new Vector3(scale, scale, scale);
+    }
+
+    public void SetResetParameters()
+    {
+        SetBall();
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs.meta b/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs b/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ddc335316c2448bc3a545ef2cee7b1e280bfc003
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs
@@ -0,0 +1,91 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors.Reflection;
+
+public class Ball3DHardAgent : Agent
+{
+    [Header("Specific to Ball3DHard")]
+    public GameObject ball;
+    Rigidbody m_BallRb;
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_BallRb = ball.GetComponent<Rigidbody>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+        SetResetParameters();
+    }
+
+    [Observable(numStackedObservations: 9)]
+    Vector2 Rotation
+    {
+        get
+        {
+            return new Vector2(gameObject.transform.rotation.z, gameObject.transform.rotation.x);
+        }
+    }
+
+    [Observable(numStackedObservations: 9)]
+    Vector3 PositionDelta
+    {
+        get
+        {
+            return ball.transform.position - gameObject.transform.position;
+        }
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        var continuousActions = actionBuffers.ContinuousActions;
+        var actionZ = 2f * Mathf.Clamp(continuousActions[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(continuousActions[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
+
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
+        }
+        if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
+            Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
+            Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f)
+        {
+            SetReward(-1f);
+            EndEpisode();
+        }
+        else
+        {
+            SetReward(0.1f);
+        }
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        gameObject.transform.rotation = new Quaternion(0f, 0f, 0f, 0f);
+        gameObject.transform.Rotate(new Vector3(1, 0, 0), Random.Range(-10f, 10f));
+        gameObject.transform.Rotate(new Vector3(0, 0, 1), Random.Range(-10f, 10f));
+        m_BallRb.velocity = new Vector3(0f, 0f, 0f);
+        ball.transform.position = new Vector3(Random.Range(-1.5f, 1.5f), 4f, Random.Range(-1.5f, 1.5f))
+            + gameObject.transform.position;
+    }
+
+    public void SetBall()
+    {
+        //Set the attributes of the ball by fetching the information from the academy
+        m_BallRb.mass = m_ResetParams.GetWithDefault("mass", 1.0f);
+        var scale = m_ResetParams.GetWithDefault("scale", 1.0f);
+        ball.transform.localScale = new Vector3(scale, scale, scale);
+    }
+
+    public void SetResetParameters()
+    {
+        SetBall();
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9a02b6765e7fdab2fbc81de014697d04c84b4a38
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs
@@ -0,0 +1,99 @@
+using System;
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// A simple example of a ActuatorComponent.
+    /// This should be added to the same GameObject as the BasicController
+    /// </summary>
+    public class BasicActuatorComponent : ActuatorComponent
+    {
+        public BasicController basicController;
+        ActionSpec m_ActionSpec = ActionSpec.MakeDiscrete(3);
+
+        /// <summary>
+        /// Creates a BasicActuator.
+        /// </summary>
+        /// <returns></returns>
+        public override IActuator[] CreateActuators()
+        {
+            return new IActuator[] { new BasicActuator(basicController) };
+        }
+
+        public override ActionSpec ActionSpec
+        {
+            get { return m_ActionSpec; }
+        }
+    }
+
+    /// <summary>
+    /// Simple actuator that converts the action into a {-1, 0, 1} direction
+    /// </summary>
+    public class BasicActuator : IActuator
+    {
+        public BasicController basicController;
+        ActionSpec m_ActionSpec;
+
+        public BasicActuator(BasicController controller)
+        {
+            basicController = controller;
+            m_ActionSpec = ActionSpec.MakeDiscrete(3);
+        }
+
+        public ActionSpec ActionSpec
+        {
+            get { return m_ActionSpec; }
+        }
+
+        /// <inheritdoc/>
+        public String Name
+        {
+            get { return "Basic"; }
+        }
+
+        public void ResetData()
+        {
+
+        }
+
+        public void OnActionReceived(ActionBuffers actionBuffers)
+        {
+            var movement = actionBuffers.DiscreteActions[0];
+
+            var direction = 0;
+
+            switch (movement)
+            {
+                case 1:
+                    direction = -1;
+                    break;
+                case 2:
+                    direction = 1;
+                    break;
+            }
+
+            basicController.MoveDirection(direction);
+        }
+
+        public void Heuristic(in ActionBuffers actionBuffersOut)
+        {
+            var direction = Input.GetAxis("Horizontal");
+            var discreteActions = actionBuffersOut.DiscreteActions;
+            if (Mathf.Approximately(direction, 0.0f))
+            {
+                discreteActions[0] = 0;
+                return;
+            }
+            var sign = Math.Sign(direction);
+            discreteActions[0] = sign < 0 ? 1 : 2;
+        }
+
+        public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+        {
+
+        }
+
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs.meta b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicController.cs b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f610f4fb3309a647a1d59da2df83daf0a918aeb7
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicController.cs
@@ -0,0 +1,117 @@
+using System;
+using UnityEngine;
+using UnityEngine.SceneManagement;
+using Unity.MLAgents;
+using UnityEngine.Serialization;
+
+/// <summary>
+/// An example of how to use ML-Agents without inheriting from the Agent class.
+/// Observations are generated by the attached SensorComponent, and the actions
+/// are retrieved from the Agent.
+/// </summary>
+public class BasicController : MonoBehaviour
+{
+    public float timeBetweenDecisionsAtInference;
+    float m_TimeSinceDecision;
+    [FormerlySerializedAs("m_Position")]
+    [HideInInspector]
+    public int position;
+    const int k_SmallGoalPosition = 7;
+    const int k_LargeGoalPosition = 17;
+    public GameObject largeGoal;
+    public GameObject smallGoal;
+    const int k_MinPosition = 0;
+    const int k_MaxPosition = 20;
+    public const int k_Extents = k_MaxPosition - k_MinPosition;
+
+    Agent m_Agent;
+
+    public void Awake()
+    {
+        // Since this example does not inherit from the Agent class, explicit registration
+        // of the RpcCommunicator is required. The RPCCommunicator should only be compiled
+        // for Standalone platforms (i.e. Windows, Linux, or Mac)
+#if UNITY_EDITOR || UNITY_STANDALONE
+        if (!CommunicatorFactory.CommunicatorRegistered)
+        {
+            Debug.Log("Registered Communicator.");
+            CommunicatorFactory.Register<ICommunicator>(RpcCommunicator.Create);
+        }
+#endif
+    }
+
+    public void OnEnable()
+    {
+        m_Agent = GetComponent<Agent>();
+        position = 10;
+        transform.position = new Vector3(position - 10f, 0f, 0f);
+        smallGoal.transform.position = new Vector3(k_SmallGoalPosition - 10f, 0f, 0f);
+        largeGoal.transform.position = new Vector3(k_LargeGoalPosition - 10f, 0f, 0f);
+    }
+
+    /// <summary>
+    /// Controls the movement of the GameObject based on the actions received.
+    /// </summary>
+    /// <param name="direction"></param>
+    public void MoveDirection(int direction)
+    {
+        position += direction;
+        if (position < k_MinPosition) { position = k_MinPosition; }
+        if (position > k_MaxPosition) { position = k_MaxPosition; }
+
+        gameObject.transform.position = new Vector3(position - 10f, 0f, 0f);
+
+        m_Agent.AddReward(-0.01f);
+
+        if (position == k_SmallGoalPosition)
+        {
+            m_Agent.AddReward(0.1f);
+            m_Agent.EndEpisode();
+            ResetAgent();
+        }
+
+        if (position == k_LargeGoalPosition)
+        {
+            m_Agent.AddReward(1f);
+            m_Agent.EndEpisode();
+            ResetAgent();
+        }
+    }
+
+    public void ResetAgent()
+    {
+        // This is a very inefficient way to reset the scene. Used here for testing.
+        SceneManager.LoadScene(SceneManager.GetActiveScene().name);
+        m_Agent = null; // LoadScene only takes effect at the next Update.
+        // We set the Agent to null to avoid using the Agent before the reload
+    }
+
+    public void FixedUpdate()
+    {
+        WaitTimeInference();
+    }
+
+    void WaitTimeInference()
+    {
+        if (m_Agent == null)
+        {
+            return;
+        }
+        if (Academy.Instance.IsCommunicatorOn)
+        {
+            m_Agent?.RequestDecision();
+        }
+        else
+        {
+            if (m_TimeSinceDecision >= timeBetweenDecisionsAtInference)
+            {
+                m_TimeSinceDecision = 0f;
+                m_Agent?.RequestDecision();
+            }
+            else
+            {
+                m_TimeSinceDecision += Time.fixedDeltaTime;
+            }
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicSensorComponent.cs b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f956d747ad33da8755ca8bee5ab56b34e0229c29
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicSensorComponent.cs
@@ -0,0 +1,62 @@
+using System;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// A simple example of a SensorComponent.
+    /// This should be added to the same GameObject as the BasicController
+    /// </summary>
+    public class BasicSensorComponent : SensorComponent
+    {
+        public BasicController basicController;
+
+        /// <summary>
+        /// Creates a BasicSensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            return new ISensor[] { new BasicSensor(basicController) };
+        }
+    }
+
+    /// <summary>
+    /// Simple Sensor implementation that uses a one-hot encoding of the Agent's
+    /// position as the observation.
+    /// </summary>
+    public class BasicSensor : SensorBase
+    {
+        public BasicController basicController;
+
+        public BasicSensor(BasicController controller)
+        {
+            basicController = controller;
+        }
+
+        /// <summary>
+        /// Generate the observations for the sensor.
+        /// In this case, the observations are all 0 except for a 1 at the position of the agent.
+        /// </summary>
+        /// <param name="output"></param>
+        public override void WriteObservation(float[] output)
+        {
+            // One-hot encoding of the position
+            Array.Clear(output, 0, output.Length);
+            output[basicController.position] = 1;
+        }
+
+        /// <inheritdoc/>
+        public override ObservationSpec GetObservationSpec()
+        {
+            return ObservationSpec.Vector(BasicController.k_Extents);
+        }
+
+        /// <inheritdoc/>
+        public override string GetName()
+        {
+            return "Basic";
+        }
+
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs b/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs
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+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
+using Random = UnityEngine.Random;
+
+[RequireComponent(typeof(JointDriveController))] // Required to set joint forces
+public class CrawlerAgent : Agent
+{
+
+    [Header("Walk Speed")]
+    [Range(0.1f, m_maxWalkingSpeed)]
+    [SerializeField]
+    [Tooltip(
+        "The speed the agent will try to match.\n\n" +
+        "TRAINING:\n" +
+        "For VariableSpeed envs, this value will randomize at the start of each training episode.\n" +
+        "Otherwise the agent will try to match the speed set here.\n\n" +
+        "INFERENCE:\n" +
+        "During inference, VariableSpeed agents will modify their behavior based on this value " +
+        "whereas the CrawlerDynamic & CrawlerStatic agents will run at the speed specified during training "
+    )]
+    //The walking speed to try and achieve
+    private float m_TargetWalkingSpeed = m_maxWalkingSpeed;
+
+    const float m_maxWalkingSpeed = 15; //The max walking speed
+
+    //The current target walking speed. Clamped because a value of zero will cause NaNs
+    public float TargetWalkingSpeed
+    {
+        get { return m_TargetWalkingSpeed; }
+        set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
+    }
+
+    //The direction an agent will walk during training.
+    [Header("Target To Walk Towards")]
+    public Transform TargetPrefab; //Target prefab to use in Dynamic envs
+    private Transform m_Target; //Target the agent will walk towards during training.
+
+    [Header("Body Parts")][Space(10)] public Transform body;
+    public Transform leg0Upper;
+    public Transform leg0Lower;
+    public Transform leg1Upper;
+    public Transform leg1Lower;
+    public Transform leg2Upper;
+    public Transform leg2Lower;
+    public Transform leg3Upper;
+    public Transform leg3Lower;
+
+    //This will be used as a stabilized model space reference point for observations
+    //Because ragdolls can move erratically during training, using a stabilized reference transform improves learning
+    OrientationCubeController m_OrientationCube;
+
+    //The indicator graphic gameobject that points towards the target
+    DirectionIndicator m_DirectionIndicator;
+    JointDriveController m_JdController;
+
+    [Header("Foot Grounded Visualization")]
+    [Space(10)]
+    public bool useFootGroundedVisualization;
+
+    public MeshRenderer foot0;
+    public MeshRenderer foot1;
+    public MeshRenderer foot2;
+    public MeshRenderer foot3;
+    public Material groundedMaterial;
+    public Material unGroundedMaterial;
+
+    public override void Initialize()
+    {
+        SpawnTarget(TargetPrefab, transform.position); //spawn target
+
+        m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
+        m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
+        m_JdController = GetComponent<JointDriveController>();
+
+        //Setup each body part
+        m_JdController.SetupBodyPart(body);
+        m_JdController.SetupBodyPart(leg0Upper);
+        m_JdController.SetupBodyPart(leg0Lower);
+        m_JdController.SetupBodyPart(leg1Upper);
+        m_JdController.SetupBodyPart(leg1Lower);
+        m_JdController.SetupBodyPart(leg2Upper);
+        m_JdController.SetupBodyPart(leg2Lower);
+        m_JdController.SetupBodyPart(leg3Upper);
+        m_JdController.SetupBodyPart(leg3Lower);
+    }
+
+    /// <summary>
+    /// Spawns a target prefab at pos
+    /// </summary>
+    /// <param name="prefab"></param>
+    /// <param name="pos"></param>
+    void SpawnTarget(Transform prefab, Vector3 pos)
+    {
+        m_Target = Instantiate(prefab, pos, Quaternion.identity, transform.parent);
+    }
+
+    /// <summary>
+    /// Loop over body parts and reset them to initial conditions.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        foreach (var bodyPart in m_JdController.bodyPartsDict.Values)
+        {
+            bodyPart.Reset(bodyPart);
+        }
+
+        //Random start rotation to help generalize
+        body.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+
+        UpdateOrientationObjects();
+
+        //Set our goal walking speed
+        TargetWalkingSpeed = Random.Range(0.1f, m_maxWalkingSpeed);
+    }
+
+    /// <summary>
+    /// Add relevant information on each body part to observations.
+    /// </summary>
+    public void CollectObservationBodyPart(BodyPart bp, VectorSensor sensor)
+    {
+        //GROUND CHECK
+        sensor.AddObservation(bp.groundContact.touchingGround); // Is this bp touching the ground
+
+        if (bp.rb.transform != body)
+        {
+            sensor.AddObservation(bp.currentStrength / m_JdController.maxJointForceLimit);
+        }
+    }
+
+    /// <summary>
+    /// Loop over body parts to add them to observation.
+    /// </summary>
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        var cubeForward = m_OrientationCube.transform.forward;
+
+        //velocity we want to match
+        var velGoal = cubeForward * TargetWalkingSpeed;
+        //ragdoll's avg vel
+        var avgVel = GetAvgVelocity();
+
+        //current ragdoll velocity. normalized
+        sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
+        //avg body vel relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(avgVel));
+        //vel goal relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
+        //rotation delta
+        sensor.AddObservation(Quaternion.FromToRotation(body.forward, cubeForward));
+
+        //Add pos of target relative to orientation cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(m_Target.transform.position));
+
+        RaycastHit hit;
+        float maxRaycastDist = 10;
+        if (Physics.Raycast(body.position, Vector3.down, out hit, maxRaycastDist))
+        {
+            sensor.AddObservation(hit.distance / maxRaycastDist);
+        }
+        else
+            sensor.AddObservation(1);
+
+        foreach (var bodyPart in m_JdController.bodyPartsList)
+        {
+            CollectObservationBodyPart(bodyPart, sensor);
+        }
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        // The dictionary with all the body parts in it are in the jdController
+        var bpDict = m_JdController.bodyPartsDict;
+
+        var continuousActions = actionBuffers.ContinuousActions;
+        var i = -1;
+        // Pick a new target joint rotation
+        bpDict[leg0Upper].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[leg1Upper].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[leg2Upper].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[leg3Upper].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[leg0Lower].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[leg1Lower].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[leg2Lower].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[leg3Lower].SetJointTargetRotation(continuousActions[++i], 0, 0);
+
+        // Update joint strength
+        bpDict[leg0Upper].SetJointStrength(continuousActions[++i]);
+        bpDict[leg1Upper].SetJointStrength(continuousActions[++i]);
+        bpDict[leg2Upper].SetJointStrength(continuousActions[++i]);
+        bpDict[leg3Upper].SetJointStrength(continuousActions[++i]);
+        bpDict[leg0Lower].SetJointStrength(continuousActions[++i]);
+        bpDict[leg1Lower].SetJointStrength(continuousActions[++i]);
+        bpDict[leg2Lower].SetJointStrength(continuousActions[++i]);
+        bpDict[leg3Lower].SetJointStrength(continuousActions[++i]);
+    }
+
+    void FixedUpdate()
+    {
+        UpdateOrientationObjects();
+
+        // If enabled the feet will light up green when the foot is grounded.
+        // This is just a visualization and isn't necessary for function
+        if (useFootGroundedVisualization)
+        {
+            foot0.material = m_JdController.bodyPartsDict[leg0Lower].groundContact.touchingGround
+                ? groundedMaterial
+                : unGroundedMaterial;
+            foot1.material = m_JdController.bodyPartsDict[leg1Lower].groundContact.touchingGround
+                ? groundedMaterial
+                : unGroundedMaterial;
+            foot2.material = m_JdController.bodyPartsDict[leg2Lower].groundContact.touchingGround
+                ? groundedMaterial
+                : unGroundedMaterial;
+            foot3.material = m_JdController.bodyPartsDict[leg3Lower].groundContact.touchingGround
+                ? groundedMaterial
+                : unGroundedMaterial;
+        }
+
+        var cubeForward = m_OrientationCube.transform.forward;
+
+        // Set reward for this step according to mixture of the following elements.
+        // a. Match target speed
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        var matchSpeedReward = GetMatchingVelocityReward(cubeForward * TargetWalkingSpeed, GetAvgVelocity());
+
+        // b. Rotation alignment with target direction.
+        //This reward will approach 1 if it faces the target direction perfectly and approach zero as it deviates
+        var lookAtTargetReward = (Vector3.Dot(cubeForward, body.forward) + 1) * .5F;
+
+        AddReward(matchSpeedReward * lookAtTargetReward);
+    }
+
+    /// <summary>
+    /// Update OrientationCube and DirectionIndicator
+    /// </summary>
+    void UpdateOrientationObjects()
+    {
+        m_OrientationCube.UpdateOrientation(body, m_Target);
+        if (m_DirectionIndicator)
+        {
+            m_DirectionIndicator.MatchOrientation(m_OrientationCube.transform);
+        }
+    }
+
+    /// <summary>
+    ///Returns the average velocity of all of the body parts
+    ///Using the velocity of the body only has shown to result in more erratic movement from the limbs
+    ///Using the average helps prevent this erratic movement
+    /// </summary>
+    Vector3 GetAvgVelocity()
+    {
+        Vector3 velSum = Vector3.zero;
+        Vector3 avgVel = Vector3.zero;
+
+        //ALL RBS
+        int numOfRb = 0;
+        foreach (var item in m_JdController.bodyPartsList)
+        {
+            numOfRb++;
+            velSum += item.rb.velocity;
+        }
+
+        avgVel = velSum / numOfRb;
+        return avgVel;
+    }
+
+    /// <summary>
+    /// Normalized value of the difference in actual speed vs goal walking speed.
+    /// </summary>
+    public float GetMatchingVelocityReward(Vector3 velocityGoal, Vector3 actualVelocity)
+    {
+        //distance between our actual velocity and goal velocity
+        var velDeltaMagnitude = Mathf.Clamp(Vector3.Distance(actualVelocity, velocityGoal), 0, TargetWalkingSpeed);
+
+        //return the value on a declining sigmoid shaped curve that decays from 1 to 0
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        return Mathf.Pow(1 - Mathf.Pow(velDeltaMagnitude / TargetWalkingSpeed, 2), 2);
+    }
+
+    /// <summary>
+    /// Agent touched the target
+    /// </summary>
+    public void TouchedTarget()
+    {
+        AddReward(1f);
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scenes/DungeonEscape.unity.meta b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scenes/DungeonEscape.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..405987f09e281d2caf672c41689ebb565be60148
--- /dev/null
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diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d9a8d80cd32c8a3fd46e1b8c380d1428ab513e73
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs
@@ -0,0 +1,258 @@
+using System.Collections;
+using System.Collections.Generic;
+using Unity.MLAgents;
+using UnityEngine;
+
+public class DungeonEscapeEnvController : MonoBehaviour
+{
+    [System.Serializable]
+    public class PlayerInfo
+    {
+        public PushAgentEscape Agent;
+        [HideInInspector]
+        public Vector3 StartingPos;
+        [HideInInspector]
+        public Quaternion StartingRot;
+        [HideInInspector]
+        public Rigidbody Rb;
+        [HideInInspector]
+        public Collider Col;
+    }
+
+    [System.Serializable]
+    public class DragonInfo
+    {
+        public SimpleNPC Agent;
+        [HideInInspector]
+        public Vector3 StartingPos;
+        [HideInInspector]
+        public Quaternion StartingRot;
+        [HideInInspector]
+        public Rigidbody Rb;
+        [HideInInspector]
+        public Collider Col;
+        public Transform T;
+        public bool IsDead;
+    }
+
+    /// <summary>
+    /// Max Academy steps before this platform resets
+    /// </summary>
+    /// <returns></returns>
+    [Header("Max Environment Steps")] public int MaxEnvironmentSteps = 25000;
+    private int m_ResetTimer;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+    [HideInInspector]
+    public Bounds areaBounds;
+    /// <summary>
+    /// The ground. The bounds are used to spawn the elements.
+    /// </summary>
+    public GameObject ground;
+
+    Material m_GroundMaterial; //cached on Awake()
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+    Renderer m_GroundRenderer;
+
+    public List<PlayerInfo> AgentsList = new List<PlayerInfo>();
+    public List<DragonInfo> DragonsList = new List<DragonInfo>();
+    private Dictionary<PushAgentEscape, PlayerInfo> m_PlayerDict = new Dictionary<PushAgentEscape, PlayerInfo>();
+    public bool UseRandomAgentRotation = true;
+    public bool UseRandomAgentPosition = true;
+    PushBlockSettings m_PushBlockSettings;
+
+    private int m_NumberOfRemainingPlayers;
+    public GameObject Key;
+    public GameObject Tombstone;
+    private SimpleMultiAgentGroup m_AgentGroup;
+    void Start()
+    {
+
+        // Get the ground's bounds
+        areaBounds = ground.GetComponent<Collider>().bounds;
+        // Get the ground renderer so we can change the material when a goal is scored
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        // Starting material
+        m_GroundMaterial = m_GroundRenderer.material;
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+
+        //Reset Players Remaining
+        m_NumberOfRemainingPlayers = AgentsList.Count;
+
+        //Hide The Key
+        Key.SetActive(false);
+
+        // Initialize TeamManager
+        m_AgentGroup = new SimpleMultiAgentGroup();
+        foreach (var item in AgentsList)
+        {
+            item.StartingPos = item.Agent.transform.position;
+            item.StartingRot = item.Agent.transform.rotation;
+            item.Rb = item.Agent.GetComponent<Rigidbody>();
+            item.Col = item.Agent.GetComponent<Collider>();
+            // Add to team manager
+            m_AgentGroup.RegisterAgent(item.Agent);
+        }
+        foreach (var item in DragonsList)
+        {
+            item.StartingPos = item.Agent.transform.position;
+            item.StartingRot = item.Agent.transform.rotation;
+            item.T = item.Agent.transform;
+            item.Col = item.Agent.GetComponent<Collider>();
+        }
+
+        ResetScene();
+    }
+
+    // Update is called once per frame
+    void FixedUpdate()
+    {
+        m_ResetTimer += 1;
+        if (m_ResetTimer >= MaxEnvironmentSteps && MaxEnvironmentSteps > 0)
+        {
+            m_AgentGroup.GroupEpisodeInterrupted();
+            ResetScene();
+        }
+    }
+
+    public void TouchedHazard(PushAgentEscape agent)
+    {
+        m_NumberOfRemainingPlayers--;
+        if (m_NumberOfRemainingPlayers == 0 || agent.IHaveAKey)
+        {
+            m_AgentGroup.EndGroupEpisode();
+            ResetScene();
+        }
+        else
+        {
+            agent.gameObject.SetActive(false);
+        }
+    }
+
+    public void UnlockDoor()
+    {
+        m_AgentGroup.AddGroupReward(1f);
+        StartCoroutine(GoalScoredSwapGroundMaterial(m_PushBlockSettings.goalScoredMaterial, 0.5f));
+
+        print("Unlocked Door");
+        m_AgentGroup.EndGroupEpisode();
+
+        ResetScene();
+    }
+
+    public void KilledByBaddie(PushAgentEscape agent, Collision baddieCol)
+    {
+        baddieCol.gameObject.SetActive(false);
+        m_NumberOfRemainingPlayers--;
+        agent.gameObject.SetActive(false);
+        print($"{baddieCol.gameObject.name} ate {agent.transform.name}");
+
+        //Spawn Tombstone
+        Tombstone.transform.SetPositionAndRotation(agent.transform.position, agent.transform.rotation);
+        Tombstone.SetActive(true);
+
+        //Spawn the Key Pickup
+        Key.transform.SetPositionAndRotation(baddieCol.collider.transform.position, baddieCol.collider.transform.rotation);
+        Key.SetActive(true);
+    }
+
+    /// <summary>
+    /// Use the ground's bounds to pick a random spawn position.
+    /// </summary>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var foundNewSpawnLocation = false;
+        var randomSpawnPos = Vector3.zero;
+        while (foundNewSpawnLocation == false)
+        {
+            var randomPosX = Random.Range(-areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier);
+
+            var randomPosZ = Random.Range(-areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier);
+            randomSpawnPos = ground.transform.position + new Vector3(randomPosX, 1f, randomPosZ);
+            if (Physics.CheckBox(randomSpawnPos, new Vector3(2.5f, 0.01f, 2.5f)) == false)
+            {
+                foundNewSpawnLocation = true;
+            }
+        }
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Swap ground material, wait time seconds, then swap back to the regular material.
+    /// </summary>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time); // Wait for 2 sec
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    public void BaddieTouchedBlock()
+    {
+        m_AgentGroup.EndGroupEpisode();
+
+        // Swap ground material for a bit to indicate we scored.
+        StartCoroutine(GoalScoredSwapGroundMaterial(m_PushBlockSettings.failMaterial, 0.5f));
+        ResetScene();
+    }
+
+    Quaternion GetRandomRot()
+    {
+        return Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+    }
+
+    void ResetScene()
+    {
+
+        //Reset counter
+        m_ResetTimer = 0;
+
+        //Reset Players Remaining
+        m_NumberOfRemainingPlayers = AgentsList.Count;
+
+        //Random platform rot
+        var rotation = Random.Range(0, 4);
+        var rotationAngle = rotation * 90f;
+        transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+
+        //Reset Agents
+        foreach (var item in AgentsList)
+        {
+            var pos = UseRandomAgentPosition ? GetRandomSpawnPos() : item.StartingPos;
+            var rot = UseRandomAgentRotation ? GetRandomRot() : item.StartingRot;
+
+            item.Agent.transform.SetPositionAndRotation(pos, rot);
+            item.Rb.velocity = Vector3.zero;
+            item.Rb.angularVelocity = Vector3.zero;
+            item.Agent.MyKey.SetActive(false);
+            item.Agent.IHaveAKey = false;
+            item.Agent.gameObject.SetActive(true);
+            m_AgentGroup.RegisterAgent(item.Agent);
+        }
+
+        //Reset Key
+        Key.SetActive(false);
+
+        //Reset Tombstone
+        Tombstone.SetActive(false);
+
+        //End Episode
+        foreach (var item in DragonsList)
+        {
+            if (!item.Agent)
+            {
+                return;
+            }
+            item.Agent.transform.SetPositionAndRotation(item.StartingPos, item.StartingRot);
+            item.Agent.SetRandomWalkSpeed();
+            item.Agent.gameObject.SetActive(true);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs.meta b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..012517d2f06769c5fb83314bd0ee0b5138fb7b64
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+++ b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/DungeonEscapeEnvController.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/PushAgentEscape.cs b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/PushAgentEscape.cs
new file mode 100644
index 0000000000000000000000000000000000000000..52c8e57b1087e5b6dbf5091eb510130925cf9e3b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/PushAgentEscape.cs
@@ -0,0 +1,135 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Actuators;
+
+public class PushAgentEscape : Agent
+{
+
+    public GameObject MyKey; //my key gameobject. will be enabled when key picked up.
+    public bool IHaveAKey; //have i picked up a key
+    private PushBlockSettings m_PushBlockSettings;
+    private Rigidbody m_AgentRb;
+    private DungeonEscapeEnvController m_GameController;
+
+    public override void Initialize()
+    {
+        m_GameController = GetComponentInParent<DungeonEscapeEnvController>();
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+        MyKey.SetActive(false);
+        IHaveAKey = false;
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        MyKey.SetActive(false);
+        IHaveAKey = false;
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        sensor.AddObservation(IHaveAKey);
+    }
+
+    /// <summary>
+    /// Moves the agent according to the selected action.
+    /// </summary>
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                rotateDir = transform.up * -1f;
+                break;
+            case 5:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 6:
+                dirToGo = transform.right * 0.75f;
+                break;
+        }
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        // Move the agent using the action.
+        MoveAgent(actionBuffers.DiscreteActions);
+    }
+
+    void OnCollisionEnter(Collision col)
+    {
+        if (col.transform.CompareTag("lock"))
+        {
+            if (IHaveAKey)
+            {
+                MyKey.SetActive(false);
+                IHaveAKey = false;
+                m_GameController.UnlockDoor();
+            }
+        }
+        if (col.transform.CompareTag("dragon"))
+        {
+            m_GameController.KilledByBaddie(this, col);
+            MyKey.SetActive(false);
+            IHaveAKey = false;
+        }
+        if (col.transform.CompareTag("portal"))
+        {
+            m_GameController.TouchedHazard(this);
+        }
+    }
+
+    void OnTriggerEnter(Collider col)
+    {
+        //if we find a key and it's parent is the main platform we can pick it up
+        if (col.transform.CompareTag("key") && col.transform.parent == transform.parent && gameObject.activeInHierarchy)
+        {
+            print("Picked up key");
+            MyKey.SetActive(true);
+            IHaveAKey = true;
+            col.gameObject.SetActive(false);
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/PushAgentEscape.cs.meta b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/PushAgentEscape.cs.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/SimpleNPC.cs b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/SimpleNPC.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e498fbf4255c5cac91deec231859eb58496f1b55
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/DungeonEscape/Scripts/SimpleNPC.cs
@@ -0,0 +1,42 @@
+using UnityEngine;
+
+public class SimpleNPC : MonoBehaviour
+{
+
+    public Transform target;
+
+    private Rigidbody rb;
+
+    public float walkSpeed = 1;
+    // public ForceMode walkForceMode;
+    private Vector3 dirToGo;
+
+    // private Vector3 m_StartingPos;
+    // Start is called before the first frame update
+    void Awake()
+    {
+        rb = GetComponent<Rigidbody>();
+        // m_StartingPos = transform.position;
+    }
+
+    // Update is called once per frame
+    void Update()
+    {
+    }
+
+    void FixedUpdate()
+    {
+        dirToGo = target.position - transform.position;
+        dirToGo.y = 0;
+        rb.rotation = Quaternion.LookRotation(dirToGo);
+        // rb.AddForce(dirToGo.normalized * walkSpeed * Time.fixedDeltaTime, walkForceMode);
+        // rb.MovePosition(rb.transform.TransformDirection(Vector3.forward * walkSpeed * Time.deltaTime));
+        // rb.MovePosition(rb.transform.TransformVector() (Vector3.forward * walkSpeed * Time.deltaTime));
+        rb.MovePosition(transform.position + transform.forward * walkSpeed * Time.deltaTime);
+    }
+
+    public void SetRandomWalkSpeed()
+    {
+        walkSpeed = Random.Range(1f, 7f);
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scenes/VisualFoodCollector.unity.meta b/Project/Assets/ML-Agents/Examples/FoodCollector/Scenes/VisualFoodCollector.unity.meta
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index 0000000000000000000000000000000000000000..16d66877209fde915c9cc8a0314a13b25e4b861b
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts.meta b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3bb6a0749292862adcd9112ad54666b9494a582e
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs
@@ -0,0 +1,272 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Random = UnityEngine.Random;
+
+public class FoodCollectorAgent : Agent
+{
+    FoodCollectorSettings m_FoodCollecterSettings;
+    public GameObject area;
+    FoodCollectorArea m_MyArea;
+    bool m_Frozen;
+    bool m_Poisoned;
+    bool m_Satiated;
+    bool m_Shoot;
+    float m_FrozenTime;
+    float m_EffectTime;
+    Rigidbody m_AgentRb;
+    float m_LaserLength;
+    // Speed of agent rotation.
+    public float turnSpeed = 300;
+
+    // Speed of agent movement.
+    public float moveSpeed = 2;
+    public Material normalMaterial;
+    public Material badMaterial;
+    public Material goodMaterial;
+    public Material frozenMaterial;
+    public GameObject myLaser;
+    public bool contribute;
+    public bool useVectorObs;
+    [Tooltip("Use only the frozen flag in vector observations. If \"Use Vector Obs\" " +
+             "is checked, this option has no effect. This option is necessary for the " +
+             "VisualFoodCollector scene.")]
+    public bool useVectorFrozenFlag;
+
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_MyArea = area.GetComponent<FoodCollectorArea>();
+        m_FoodCollecterSettings = FindObjectOfType<FoodCollectorSettings>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+        SetResetParameters();
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        if (useVectorObs)
+        {
+            var localVelocity = transform.InverseTransformDirection(m_AgentRb.velocity);
+            sensor.AddObservation(localVelocity.x);
+            sensor.AddObservation(localVelocity.z);
+            sensor.AddObservation(m_Frozen);
+            sensor.AddObservation(m_Shoot);
+        }
+        else if (useVectorFrozenFlag)
+        {
+            sensor.AddObservation(m_Frozen);
+        }
+    }
+
+    public Color32 ToColor(int hexVal)
+    {
+        var r = (byte)((hexVal >> 16) & 0xFF);
+        var g = (byte)((hexVal >> 8) & 0xFF);
+        var b = (byte)(hexVal & 0xFF);
+        return new Color32(r, g, b, 255);
+    }
+
+    public void MoveAgent(ActionBuffers actionBuffers)
+    {
+        m_Shoot = false;
+
+        if (Time.time > m_FrozenTime + 4f && m_Frozen)
+        {
+            Unfreeze();
+        }
+        if (Time.time > m_EffectTime + 0.5f)
+        {
+            if (m_Poisoned)
+            {
+                Unpoison();
+            }
+            if (m_Satiated)
+            {
+                Unsatiate();
+            }
+        }
+
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var continuousActions = actionBuffers.ContinuousActions;
+        var discreteActions = actionBuffers.DiscreteActions;
+
+        if (!m_Frozen)
+        {
+            var forward = Mathf.Clamp(continuousActions[0], -1f, 1f);
+            var right = Mathf.Clamp(continuousActions[1], -1f, 1f);
+            var rotate = Mathf.Clamp(continuousActions[2], -1f, 1f);
+
+            dirToGo = transform.forward * forward;
+            dirToGo += transform.right * right;
+            rotateDir = -transform.up * rotate;
+
+            var shootCommand = discreteActions[0] > 0;
+            if (shootCommand)
+            {
+                m_Shoot = true;
+                dirToGo *= 0.5f;
+                m_AgentRb.velocity *= 0.75f;
+            }
+            m_AgentRb.AddForce(dirToGo * moveSpeed, ForceMode.VelocityChange);
+            transform.Rotate(rotateDir, Time.fixedDeltaTime * turnSpeed);
+        }
+
+        if (m_AgentRb.velocity.sqrMagnitude > 25f) // slow it down
+        {
+            m_AgentRb.velocity *= 0.95f;
+        }
+
+        if (m_Shoot)
+        {
+            var myTransform = transform;
+            myLaser.transform.localScale = new Vector3(1f, 1f, m_LaserLength);
+            var rayDir = 25.0f * myTransform.forward;
+            Debug.DrawRay(myTransform.position, rayDir, Color.red, 0f, true);
+            RaycastHit hit;
+            if (Physics.SphereCast(transform.position, 2f, rayDir, out hit, 25f))
+            {
+                if (hit.collider.gameObject.CompareTag("agent"))
+                {
+                    hit.collider.gameObject.GetComponent<FoodCollectorAgent>().Freeze();
+                }
+            }
+        }
+        else
+        {
+            myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
+        }
+    }
+
+    void Freeze()
+    {
+        gameObject.tag = "frozenAgent";
+        m_Frozen = true;
+        m_FrozenTime = Time.time;
+        gameObject.GetComponentInChildren<Renderer>().material = frozenMaterial;
+    }
+
+    void Unfreeze()
+    {
+        m_Frozen = false;
+        gameObject.tag = "agent";
+        gameObject.GetComponentInChildren<Renderer>().material = normalMaterial;
+    }
+
+    void Poison()
+    {
+        m_Poisoned = true;
+        m_EffectTime = Time.time;
+        gameObject.GetComponentInChildren<Renderer>().material = badMaterial;
+    }
+
+    void Unpoison()
+    {
+        m_Poisoned = false;
+        gameObject.GetComponentInChildren<Renderer>().material = normalMaterial;
+    }
+
+    void Satiate()
+    {
+        m_Satiated = true;
+        m_EffectTime = Time.time;
+        gameObject.GetComponentInChildren<Renderer>().material = goodMaterial;
+    }
+
+    void Unsatiate()
+    {
+        m_Satiated = false;
+        gameObject.GetComponentInChildren<Renderer>().material = normalMaterial;
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        MoveAgent(actionBuffers);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var continuousActionsOut = actionsOut.ContinuousActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            continuousActionsOut[2] = 1;
+        }
+        if (Input.GetKey(KeyCode.W))
+        {
+            continuousActionsOut[0] = 1;
+        }
+        if (Input.GetKey(KeyCode.A))
+        {
+            continuousActionsOut[2] = -1;
+        }
+        if (Input.GetKey(KeyCode.S))
+        {
+            continuousActionsOut[0] = -1;
+        }
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        discreteActionsOut[0] = Input.GetKey(KeyCode.Space) ? 1 : 0;
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        Unfreeze();
+        Unpoison();
+        Unsatiate();
+        m_Shoot = false;
+        m_AgentRb.velocity = Vector3.zero;
+        myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
+        transform.position = new Vector3(Random.Range(-m_MyArea.range, m_MyArea.range),
+            2f, Random.Range(-m_MyArea.range, m_MyArea.range))
+            + area.transform.position;
+        transform.rotation = Quaternion.Euler(new Vector3(0f, Random.Range(0, 360)));
+
+        SetResetParameters();
+    }
+
+    void OnCollisionEnter(Collision collision)
+    {
+        if (collision.gameObject.CompareTag("food"))
+        {
+            Satiate();
+            collision.gameObject.GetComponent<FoodLogic>().OnEaten();
+            AddReward(1f);
+            if (contribute)
+            {
+                m_FoodCollecterSettings.totalScore += 1;
+            }
+        }
+        if (collision.gameObject.CompareTag("badFood"))
+        {
+            Poison();
+            collision.gameObject.GetComponent<FoodLogic>().OnEaten();
+
+            AddReward(-1f);
+            if (contribute)
+            {
+                m_FoodCollecterSettings.totalScore -= 1;
+            }
+        }
+    }
+
+    public void SetLaserLengths()
+    {
+        m_LaserLength = m_ResetParams.GetWithDefault("laser_length", 1.0f);
+    }
+
+    public void SetAgentScale()
+    {
+        float agentScale = m_ResetParams.GetWithDefault("agent_scale", 1.0f);
+        gameObject.transform.localScale = new Vector3(agentScale, agentScale, agentScale);
+    }
+
+    public void SetResetParameters()
+    {
+        SetLaserLengths();
+        SetAgentScale();
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs.meta b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f44011ff63ed3313d4772f92374e32bdf8e241c6
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+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs.meta
@@ -0,0 +1,12 @@
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7fa3c911443d58545fb2741134a1f84cc19aec24
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs
@@ -0,0 +1,45 @@
+using UnityEngine;
+using Unity.MLAgentsExamples;
+
+public class FoodCollectorArea : Area
+{
+    public GameObject food;
+    public GameObject badFood;
+    public int numFood;
+    public int numBadFood;
+    public bool respawnFood;
+    public float range;
+
+    void CreateFood(int num, GameObject type)
+    {
+        for (int i = 0; i < num; i++)
+        {
+            GameObject f = Instantiate(type, new Vector3(Random.Range(-range, range), 1f,
+                Random.Range(-range, range)) + transform.position,
+                Quaternion.Euler(new Vector3(0f, Random.Range(0f, 360f), 90f)));
+            f.GetComponent<FoodLogic>().respawn = respawnFood;
+            f.GetComponent<FoodLogic>().myArea = this;
+        }
+    }
+
+    public void ResetFoodArea(GameObject[] agents)
+    {
+        foreach (GameObject agent in agents)
+        {
+            if (agent.transform.parent == gameObject.transform)
+            {
+                agent.transform.position = new Vector3(Random.Range(-range, range), 2f,
+                    Random.Range(-range, range))
+                    + transform.position;
+                agent.transform.rotation = Quaternion.Euler(new Vector3(0f, Random.Range(0, 360)));
+            }
+        }
+
+        CreateFood(numFood, food);
+        CreateFood(numBadFood, badFood);
+    }
+
+    public override void ResetArea()
+    {
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs.meta b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..46c51778537f660594e660309488227ad0cd02b2
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+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorArea.cs.meta
@@ -0,0 +1,13 @@
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs
new file mode 100644
index 0000000000000000000000000000000000000000..640911332f16ec7f9d6e8ea68ef5a2d23aad8f5c
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs
@@ -0,0 +1,58 @@
+using UnityEngine;
+using UnityEngine.UI;
+using Unity.MLAgents;
+
+public class FoodCollectorSettings : MonoBehaviour
+{
+    [HideInInspector]
+    public GameObject[] agents;
+    [HideInInspector]
+    public FoodCollectorArea[] listArea;
+
+    public int totalScore;
+    public Text scoreText;
+
+    StatsRecorder m_Recorder;
+
+    public void Awake()
+    {
+        Academy.Instance.OnEnvironmentReset += EnvironmentReset;
+        m_Recorder = Academy.Instance.StatsRecorder;
+    }
+
+    void EnvironmentReset()
+    {
+        ClearObjects(GameObject.FindGameObjectsWithTag("food"));
+        ClearObjects(GameObject.FindGameObjectsWithTag("badFood"));
+
+        agents = GameObject.FindGameObjectsWithTag("agent");
+        listArea = FindObjectsOfType<FoodCollectorArea>();
+        foreach (var fa in listArea)
+        {
+            fa.ResetFoodArea(agents);
+        }
+
+        totalScore = 0;
+    }
+
+    void ClearObjects(GameObject[] objects)
+    {
+        foreach (var food in objects)
+        {
+            Destroy(food);
+        }
+    }
+
+    public void Update()
+    {
+        scoreText.text = $"Score: {totalScore}";
+
+        // Send stats via SideChannel so that they'll appear in TensorBoard.
+        // These values get averaged every summary_frequency steps, so we don't
+        // need to send every Update() call.
+        if ((Time.frameCount % 100) == 0)
+        {
+            m_Recorder.Add("TotalScore", totalScore);
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodLogic.cs b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodLogic.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4e961d99d20f30e730d916addb43fe5624df986d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodLogic.cs
@@ -0,0 +1,21 @@
+using UnityEngine;
+
+public class FoodLogic : MonoBehaviour
+{
+    public bool respawn;
+    public FoodCollectorArea myArea;
+
+    public void OnEaten()
+    {
+        if (respawn)
+        {
+            transform.position = new Vector3(Random.Range(-myArea.range, myArea.range),
+                3f,
+                Random.Range(-myArea.range, myArea.range)) + myArea.transform.position;
+        }
+        else
+        {
+            Destroy(gameObject);
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorldColab.unity.meta b/Project/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorldColab.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts.meta b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..98325a8f5296b336964dc68ab36e8eeca55e028c
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs
@@ -0,0 +1,246 @@
+using System;
+using UnityEngine;
+using System.Linq;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Actuators;
+using UnityEngine.Rendering;
+using UnityEngine.Serialization;
+
+public class GridAgent : Agent
+{
+    [FormerlySerializedAs("m_Area")]
+    [Header("Specific to GridWorld")]
+    public GridArea area;
+    public float timeBetweenDecisionsAtInference;
+    float m_TimeSinceDecision;
+
+    [Tooltip("Because we want an observation right before making a decision, we can force " +
+        "a camera to render before making a decision. Place the agentCam here if using " +
+        "RenderTexture as observations.")]
+    public Camera renderCamera;
+
+    VectorSensorComponent m_GoalSensor;
+
+    public enum GridGoal
+    {
+        GreenPlus,
+        RedEx,
+    }
+
+    // Visual representations of the agent. Both are blue on top, but different colors on the bottom - this
+    // allows the user to see which corresponds to the current goal, but it's not visible to the camera.
+    // Only one is active at a time.
+    public GameObject GreenBottom;
+    public GameObject RedBottom;
+
+    GridGoal m_CurrentGoal;
+
+    public GridGoal CurrentGoal
+    {
+        get { return m_CurrentGoal; }
+        set
+        {
+            switch (value)
+            {
+                case GridGoal.GreenPlus:
+                    GreenBottom.SetActive(true);
+                    RedBottom.SetActive(false);
+                    break;
+                case GridGoal.RedEx:
+                    GreenBottom.SetActive(false);
+                    RedBottom.SetActive(true);
+                    break;
+            }
+            m_CurrentGoal = value;
+        }
+    }
+
+    [Tooltip("Selecting will turn on action masking. Note that a model trained with action " +
+        "masking turned on may not behave optimally when action masking is turned off.")]
+    public bool maskActions = true;
+
+    const int k_NoAction = 0;  // do nothing!
+    const int k_Up = 1;
+    const int k_Down = 2;
+    const int k_Left = 3;
+    const int k_Right = 4;
+
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_GoalSensor = this.GetComponent<VectorSensorComponent>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        Array values = Enum.GetValues(typeof(GridGoal));
+
+        if (m_GoalSensor is object)
+        {
+            int goalNum = (int)CurrentGoal;
+            m_GoalSensor.GetSensor().AddOneHotObservation(goalNum, values.Length);
+        }
+    }
+
+    public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+    {
+        // Mask the necessary actions if selected by the user.
+        if (maskActions)
+        {
+            // Prevents the agent from picking an action that would make it collide with a wall
+            var positionX = (int)transform.localPosition.x;
+            var positionZ = (int)transform.localPosition.z;
+            var maxPosition = (int)m_ResetParams.GetWithDefault("gridSize", 5f) - 1;
+
+            if (positionX == 0)
+            {
+                actionMask.SetActionEnabled(0, k_Left, false);
+            }
+
+            if (positionX == maxPosition)
+            {
+                actionMask.SetActionEnabled(0, k_Right, false);
+            }
+
+            if (positionZ == 0)
+            {
+                actionMask.SetActionEnabled(0, k_Down, false);
+            }
+
+            if (positionZ == maxPosition)
+            {
+                actionMask.SetActionEnabled(0, k_Up, false);
+            }
+        }
+    }
+
+    // to be implemented by the developer
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        AddReward(-0.01f);
+        var action = actionBuffers.DiscreteActions[0];
+
+        var targetPos = transform.position;
+        switch (action)
+        {
+            case k_NoAction:
+                // do nothing
+                break;
+            case k_Right:
+                targetPos = transform.position + new Vector3(1f, 0, 0f);
+                break;
+            case k_Left:
+                targetPos = transform.position + new Vector3(-1f, 0, 0f);
+                break;
+            case k_Up:
+                targetPos = transform.position + new Vector3(0f, 0, 1f);
+                break;
+            case k_Down:
+                targetPos = transform.position + new Vector3(0f, 0, -1f);
+                break;
+            default:
+                throw new ArgumentException("Invalid action value");
+        }
+
+        var hit = Physics.OverlapBox(
+            targetPos, new Vector3(0.3f, 0.3f, 0.3f));
+        if (hit.Where(col => col.gameObject.CompareTag("wall")).ToArray().Length == 0)
+        {
+            transform.position = targetPos;
+
+            if (hit.Where(col => col.gameObject.CompareTag("plus")).ToArray().Length == 1)
+            {
+                ProvideReward(GridGoal.GreenPlus);
+                EndEpisode();
+            }
+            else if (hit.Where(col => col.gameObject.CompareTag("ex")).ToArray().Length == 1)
+            {
+                ProvideReward(GridGoal.RedEx);
+                EndEpisode();
+            }
+        }
+    }
+
+    private void ProvideReward(GridGoal hitObject)
+    {
+        if (CurrentGoal == hitObject)
+        {
+            SetReward(1f);
+        }
+        else
+        {
+            SetReward(-1f);
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        discreteActionsOut[0] = k_NoAction;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = k_Right;
+        }
+        if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = k_Up;
+        }
+        if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = k_Left;
+        }
+        if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = k_Down;
+        }
+    }
+
+    // to be implemented by the developer
+    public override void OnEpisodeBegin()
+    {
+        area.AreaReset();
+        Array values = Enum.GetValues(typeof(GridGoal));
+        if (m_GoalSensor is object)
+        {
+            CurrentGoal = (GridGoal)values.GetValue(UnityEngine.Random.Range(0, values.Length));
+        }
+        else
+        {
+            CurrentGoal = GridGoal.GreenPlus;
+        }
+    }
+
+    public void FixedUpdate()
+    {
+        WaitTimeInference();
+    }
+
+    void WaitTimeInference()
+    {
+        if (renderCamera != null && SystemInfo.graphicsDeviceType != GraphicsDeviceType.Null)
+        {
+            renderCamera.Render();
+        }
+
+        if (Academy.Instance.IsCommunicatorOn)
+        {
+            RequestDecision();
+        }
+        else
+        {
+            if (m_TimeSinceDecision >= timeBetweenDecisionsAtInference)
+            {
+                m_TimeSinceDecision = 0f;
+                RequestDecision();
+            }
+            else
+            {
+                m_TimeSinceDecision += Time.fixedDeltaTime;
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs.meta b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f2158d9fb6159814c8473a143a265b9fe01d297f
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs.meta
@@ -0,0 +1,12 @@
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diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7d3c85ae74e78e3bef4310a51d39688666d07106
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs
@@ -0,0 +1,118 @@
+using System.Collections.Generic;
+using UnityEngine;
+using System.Linq;
+using Unity.MLAgents;
+using UnityEngine.Serialization;
+
+
+public class GridArea : MonoBehaviour
+{
+    [HideInInspector]
+    public List<GameObject> actorObjs;
+    [HideInInspector]
+    public int[] players;
+
+    public GameObject trueAgent;
+
+    Camera m_AgentCam;
+
+    [FormerlySerializedAs("PlusPref")] public GameObject GreenPlusPrefab;
+    [FormerlySerializedAs("ExPref")] public GameObject RedExPrefab;
+    GameObject[] m_Objects;
+    public int numberOfPlus = 1;
+    public int numberOfEx = 1;
+
+    GameObject m_Plane;
+    GameObject m_Sn;
+    GameObject m_Ss;
+    GameObject m_Se;
+    GameObject m_Sw;
+
+    Vector3 m_InitialPosition;
+
+    EnvironmentParameters m_ResetParams;
+
+    public void Start()
+    {
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+
+        m_Objects = new[] { GreenPlusPrefab, RedExPrefab };
+
+        m_AgentCam = transform.Find("agentCam").GetComponent<Camera>();
+
+        actorObjs = new List<GameObject>();
+
+        var sceneTransform = transform.Find("scene");
+
+        m_Plane = sceneTransform.Find("Plane").gameObject;
+        m_Sn = sceneTransform.Find("sN").gameObject;
+        m_Ss = sceneTransform.Find("sS").gameObject;
+        m_Sw = sceneTransform.Find("sW").gameObject;
+        m_Se = sceneTransform.Find("sE").gameObject;
+        m_InitialPosition = transform.position;
+    }
+
+    void SetEnvironment()
+    {
+        transform.position = m_InitialPosition * (m_ResetParams.GetWithDefault("gridSize", 5f) + 1);
+        var playersList = new List<int>();
+
+        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numPlusGoals", numberOfPlus); i++)
+        {
+            playersList.Add(0);
+        }
+
+        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numExGoals", numberOfEx); i++)
+        {
+            playersList.Add(1);
+        }
+        players = playersList.ToArray();
+
+        var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
+        m_Plane.transform.localScale = new Vector3(gridSize / 10.0f, 1f, gridSize / 10.0f);
+        m_Plane.transform.localPosition = new Vector3((gridSize - 1) / 2f, -0.5f, (gridSize - 1) / 2f);
+        m_Sn.transform.localScale = new Vector3(1, 1, gridSize + 2);
+        m_Ss.transform.localScale = new Vector3(1, 1, gridSize + 2);
+        m_Sn.transform.localPosition = new Vector3((gridSize - 1) / 2f, 0.0f, gridSize);
+        m_Ss.transform.localPosition = new Vector3((gridSize - 1) / 2f, 0.0f, -1);
+        m_Se.transform.localScale = new Vector3(1, 1, gridSize + 2);
+        m_Sw.transform.localScale = new Vector3(1, 1, gridSize + 2);
+        m_Se.transform.localPosition = new Vector3(gridSize, 0.0f, (gridSize - 1) / 2f);
+        m_Sw.transform.localPosition = new Vector3(-1, 0.0f, (gridSize - 1) / 2f);
+
+        m_AgentCam.orthographicSize = (gridSize) / 2f;
+        m_AgentCam.transform.localPosition = new Vector3((gridSize - 1) / 2f, gridSize + 1f, (gridSize - 1) / 2f);
+    }
+
+    public void AreaReset()
+    {
+        var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
+        foreach (var actor in actorObjs)
+        {
+            DestroyImmediate(actor);
+        }
+        SetEnvironment();
+
+        actorObjs.Clear();
+
+        var numbers = new HashSet<int>();
+        while (numbers.Count < players.Length + 1)
+        {
+            numbers.Add(Random.Range(0, gridSize * gridSize));
+        }
+        var numbersA = numbers.ToArray();
+
+        for (var i = 0; i < players.Length; i++)
+        {
+            var x = (numbersA[i]) / gridSize;
+            var y = (numbersA[i]) % gridSize;
+            var actorObj = Instantiate(m_Objects[players[i]], transform);
+            actorObj.transform.localPosition = new Vector3(x, -0.25f, y);
+            actorObjs.Add(actorObj);
+        }
+
+        var xA = (numbersA[players.Length]) / gridSize;
+        var yA = (numbersA[players.Length]) % gridSize;
+        trueAgent.transform.localPosition = new Vector3(xA, -0.25f, yA);
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs.meta b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs.meta
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index 0000000000000000000000000000000000000000..cfac9e9f5bac2b957ebfcef5d926295fd763aea3
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridSettings.cs b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridSettings.cs
new file mode 100644
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridSettings.cs
@@ -0,0 +1,17 @@
+using UnityEngine;
+using Unity.MLAgents;
+
+public class GridSettings : MonoBehaviour
+{
+    public Camera MainCamera;
+
+    public void Awake()
+    {
+        Academy.Instance.EnvironmentParameters.RegisterCallback("gridSize", f =>
+        {
+            MainCamera.transform.position = new Vector3(-(f - 1) / 2f, f * 1.25f, -(f - 1) / 2f);
+            MainCamera.orthographicSize = (f + 5f) / 2f;
+        });
+
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Hallway/Scenes/Hallway.unity.meta b/Project/Assets/ML-Agents/Examples/Hallway/Scenes/Hallway.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs b/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..86ed43c53321928155a1fcd51a193d2723592974
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs
@@ -0,0 +1,165 @@
+using System.Collections;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+public class HallwayAgent : Agent
+{
+    public GameObject ground;
+    public GameObject area;
+    public GameObject symbolOGoal;
+    public GameObject symbolXGoal;
+    public GameObject symbolO;
+    public GameObject symbolX;
+    public bool useVectorObs;
+    Rigidbody m_AgentRb;
+    Material m_GroundMaterial;
+    Renderer m_GroundRenderer;
+    HallwaySettings m_HallwaySettings;
+    int m_Selection;
+    StatsRecorder m_statsRecorder;
+
+    public override void Initialize()
+    {
+        m_HallwaySettings = FindObjectOfType<HallwaySettings>();
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        m_GroundMaterial = m_GroundRenderer.material;
+        m_statsRecorder = Academy.Instance.StatsRecorder;
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        if (useVectorObs)
+        {
+            sensor.AddObservation(StepCount / (float)MaxStep);
+        }
+    }
+
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time);
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                rotateDir = transform.up * -1f;
+                break;
+        }
+        transform.Rotate(rotateDir, Time.deltaTime * 150f);
+        m_AgentRb.AddForce(dirToGo * m_HallwaySettings.agentRunSpeed, ForceMode.VelocityChange);
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        AddReward(-1f / MaxStep);
+        MoveAgent(actionBuffers.DiscreteActions);
+    }
+
+    void OnCollisionEnter(Collision col)
+    {
+        if (col.gameObject.CompareTag("symbol_O_Goal") || col.gameObject.CompareTag("symbol_X_Goal"))
+        {
+            if ((m_Selection == 0 && col.gameObject.CompareTag("symbol_O_Goal")) ||
+                (m_Selection == 1 && col.gameObject.CompareTag("symbol_X_Goal")))
+            {
+                SetReward(1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(m_HallwaySettings.goalScoredMaterial, 0.5f));
+                m_statsRecorder.Add("Goal/Correct", 1, StatAggregationMethod.Sum);
+            }
+            else
+            {
+                SetReward(-0.1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(m_HallwaySettings.failMaterial, 0.5f));
+                m_statsRecorder.Add("Goal/Wrong", 1, StatAggregationMethod.Sum);
+            }
+            EndEpisode();
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        var agentOffset = -15f;
+        var blockOffset = 0f;
+        m_Selection = Random.Range(0, 2);
+        if (m_Selection == 0)
+        {
+            symbolO.transform.position =
+                new Vector3(0f + Random.Range(-3f, 3f), 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+            symbolX.transform.position =
+                new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+        }
+        else
+        {
+            symbolO.transform.position =
+                new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+            symbolX.transform.position =
+                new Vector3(0f, 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+        }
+
+        transform.position = new Vector3(0f + Random.Range(-3f, 3f),
+            1f, agentOffset + Random.Range(-5f, 5f))
+            + ground.transform.position;
+        transform.rotation = Quaternion.Euler(0f, Random.Range(0f, 360f), 0f);
+        m_AgentRb.velocity *= 0f;
+
+        var goalPos = Random.Range(0, 2);
+        if (goalPos == 0)
+        {
+            symbolOGoal.transform.position = new Vector3(7f, 0.5f, 22.29f) + area.transform.position;
+            symbolXGoal.transform.position = new Vector3(-7f, 0.5f, 22.29f) + area.transform.position;
+        }
+        else
+        {
+            symbolXGoal.transform.position = new Vector3(7f, 0.5f, 22.29f) + area.transform.position;
+            symbolOGoal.transform.position = new Vector3(-7f, 0.5f, 22.29f) + area.transform.position;
+        }
+        m_statsRecorder.Add("Goal/Correct", 0, StatAggregationMethod.Sum);
+        m_statsRecorder.Add("Goal/Wrong", 0, StatAggregationMethod.Sum);
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs.meta b/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwaySettings.cs b/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwaySettings.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4dfff28e3e555d7a657a46b0c6a34a5771b0a21b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwaySettings.cs
@@ -0,0 +1,10 @@
+using UnityEngine;
+
+public class HallwaySettings : MonoBehaviour
+{
+    public float agentRunSpeed;
+    public float agentRotationSpeed;
+    public Material goalScoredMaterial; // when a goal is scored the ground will use this material for a few seconds.
+    public Material failMaterial; // when fail, the ground will use this material for a few seconds.
+
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity.meta b/Project/Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..000573c5b87f5fda62dadbdd6f3e155c9d447306
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diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f86675a57eb43ef4eb5c31eda68edf9448cbb081
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diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..998440dac203df382be1d7b11f8cd71f25e09314
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs
@@ -0,0 +1,211 @@
+using System;
+using UnityEngine;
+using Unity.MLAgents;
+
+namespace Unity.MLAgentsExamples
+{
+
+    /// <summary>
+    /// State of the "game" when showing all steps of the simulation. This is only used outside of training.
+    /// The state diagram is
+    ///
+    ///      | <--------------------------------------- ^
+    ///      |                                          |
+    ///      v                                          |
+    ///  +--------+      +-------+      +-----+      +------+
+    ///  |Find    | ---> |Clear  | ---> |Drop | ---> |Fill  |
+    ///  |Matches |      |Matched|      |     |      |Empty |
+    ///  +--------+      +-------+      +-----+      +------+
+    ///
+    ///    |     ^
+    ///    |     |
+    ///    v     |
+    ///
+    ///  +--------+
+    ///  |Wait for|
+    ///  |Move    |
+    ///  +--------+
+    ///
+    /// The stats advances each "MoveTime" seconds.
+    /// </summary>
+    enum State
+    {
+        /// <summary>
+        /// Guard value, should never happen.
+        /// </summary>
+        Invalid = -1,
+
+        /// <summary>
+        /// Look for matches. If there are matches, the next state is ClearMatched, otherwise WaitForMove.
+        /// </summary>
+        FindMatches = 0,
+
+        /// <summary>
+        /// Remove matched cells and replace them with a placeholder value.
+        /// </summary>
+        ClearMatched = 1,
+
+        /// <summary>
+        /// Move cells "down" to fill empty space.
+        /// </summary>
+        Drop = 2,
+
+        /// <summary>
+        /// Replace empty cells with new random values.
+        /// </summary>
+        FillEmpty = 3,
+
+        /// <summary>
+        /// Request a move from the Agent.
+        /// </summary>
+        WaitForMove = 4,
+    }
+
+    public class Match3Agent : Agent
+    {
+        [HideInInspector]
+        public Match3Board Board;
+
+        public float MoveTime = 1.0f;
+        public int MaxMoves = 500;
+
+
+        State m_CurrentState = State.WaitForMove;
+        float m_TimeUntilMove;
+        private int m_MovesMade;
+        private ModelOverrider m_ModelOverrider;
+
+        private const float k_RewardMultiplier = 0.01f;
+        protected override void Awake()
+        {
+            base.Awake();
+            Board = GetComponent<Match3Board>();
+            m_ModelOverrider = GetComponent<ModelOverrider>();
+        }
+
+        public override void OnEpisodeBegin()
+        {
+            base.OnEpisodeBegin();
+
+            Board.UpdateCurrentBoardSize();
+            Board.InitSettled();
+            m_CurrentState = State.FindMatches;
+            m_TimeUntilMove = MoveTime;
+            m_MovesMade = 0;
+        }
+
+        private void FixedUpdate()
+        {
+            // Make a move every step if we're training, or we're overriding models in CI.
+            var useFast = Academy.Instance.IsCommunicatorOn || (m_ModelOverrider != null && m_ModelOverrider.HasOverrides);
+            if (useFast)
+            {
+                FastUpdate();
+            }
+            else
+            {
+                AnimatedUpdate();
+            }
+
+            // We can't use the normal MaxSteps system to decide when to end an episode,
+            // since different agents will make moves at different frequencies (depending on the number of
+            // chained moves). So track a number of moves per Agent and manually interrupt the episode.
+            if (m_MovesMade >= MaxMoves)
+            {
+                EpisodeInterrupted();
+            }
+        }
+
+        void FastUpdate()
+        {
+            while (true)
+            {
+                var hasMatched = Board.MarkMatchedCells();
+                if (!hasMatched)
+                {
+                    break;
+                }
+                var pointsEarned = Board.ClearMatchedCells();
+                AddReward(k_RewardMultiplier * pointsEarned);
+                Board.DropCells();
+                Board.FillFromAbove();
+            }
+
+            while (!HasValidMoves())
+            {
+                // Shuffle the board until we have a valid move.
+                Board.InitSettled();
+            }
+            RequestDecision();
+            m_MovesMade++;
+        }
+
+        void AnimatedUpdate()
+        {
+            m_TimeUntilMove -= Time.deltaTime;
+            if (m_TimeUntilMove > 0.0f)
+            {
+                return;
+            }
+
+            m_TimeUntilMove = MoveTime;
+
+            State nextState;
+            switch (m_CurrentState)
+            {
+                case State.FindMatches:
+                    var hasMatched = Board.MarkMatchedCells();
+                    nextState = hasMatched ? State.ClearMatched : State.WaitForMove;
+                    if (nextState == State.WaitForMove)
+                    {
+                        m_MovesMade++;
+                    }
+                    break;
+                case State.ClearMatched:
+                    var pointsEarned = Board.ClearMatchedCells();
+                    AddReward(k_RewardMultiplier * pointsEarned);
+                    nextState = State.Drop;
+                    break;
+                case State.Drop:
+                    Board.DropCells();
+                    nextState = State.FillEmpty;
+                    break;
+                case State.FillEmpty:
+                    Board.FillFromAbove();
+                    nextState = State.FindMatches;
+                    break;
+                case State.WaitForMove:
+                    while (true)
+                    {
+                        // Shuffle the board until we have a valid move.
+                        bool hasMoves = HasValidMoves();
+                        if (hasMoves)
+                        {
+                            break;
+                        }
+                        Board.InitSettled();
+                    }
+                    RequestDecision();
+
+                    nextState = State.FindMatches;
+                    break;
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+
+            m_CurrentState = nextState;
+        }
+
+        bool HasValidMoves()
+        {
+            foreach (var unused in Board.ValidMoves())
+            {
+                return true;
+            }
+
+            return false;
+        }
+
+    }
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..680a91543efdf3bb7ad76b1fc055eb6b6589679d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d982f0cd92214bd2b689be838fa40c44
+timeCreated: 1598221207
\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs
new file mode 100644
index 0000000000000000000000000000000000000000..df6c3a56a45d603caa841cfea09076a1228b487a
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs
@@ -0,0 +1,338 @@
+using System;
+using Unity.MLAgents.Integrations.Match3;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgentsExamples
+{
+
+
+    public class Match3Board : AbstractBoard
+    {
+        public int MinRows;
+        [FormerlySerializedAs("Rows")]
+        public int MaxRows;
+
+        public int MinColumns;
+        [FormerlySerializedAs("Columns")]
+        public int MaxColumns;
+
+        public int NumCellTypes;
+        public int NumSpecialTypes;
+
+        public const int k_EmptyCell = -1;
+        [Tooltip("Points earned for clearing a basic cell (cube)")]
+        public int BasicCellPoints = 1;
+
+        [Tooltip("Points earned for clearing a special cell (sphere)")]
+        public int SpecialCell1Points = 2;
+
+        [Tooltip("Points earned for clearing an extra special cell (plus)")]
+        public int SpecialCell2Points = 3;
+
+        /// <summary>
+        /// Seed to initialize the <see cref="System.Random"/> object.
+        /// </summary>
+        public int RandomSeed;
+
+        (int CellType, int SpecialType)[,] m_Cells;
+        bool[,] m_Matched;
+
+        private BoardSize m_CurrentBoardSize;
+
+        System.Random m_Random;
+
+        void Awake()
+        {
+            m_Cells = new (int, int)[MaxColumns, MaxRows];
+            m_Matched = new bool[MaxColumns, MaxRows];
+
+            // Start using the max rows and columns, but we'll update the current size at the start of each episode.
+            m_CurrentBoardSize = new BoardSize
+            {
+                Rows = MaxRows,
+                Columns = MaxColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        void Start()
+        {
+            m_Random = new System.Random(RandomSeed == -1 ? gameObject.GetInstanceID() : RandomSeed);
+            InitRandom();
+        }
+
+        public override BoardSize GetMaxBoardSize()
+        {
+            return new BoardSize
+            {
+                Rows = MaxRows,
+                Columns = MaxColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        public override BoardSize GetCurrentBoardSize()
+        {
+            return m_CurrentBoardSize;
+        }
+
+        /// <summary>
+        /// Change the board size to a random size between the min and max rows and columns. This is
+        /// cached so that the size is consistent until it is updated.
+        /// This is just for an example; you can change your board size however you want.
+        /// </summary>
+        public void UpdateCurrentBoardSize()
+        {
+            var newRows = m_Random.Next(MinRows, MaxRows + 1);
+            var newCols = m_Random.Next(MinColumns, MaxColumns + 1);
+            m_CurrentBoardSize.Rows = newRows;
+            m_CurrentBoardSize.Columns = newCols;
+        }
+
+        public override bool MakeMove(Move move)
+        {
+            if (!IsMoveValid(move))
+            {
+                return false;
+            }
+            var originalValue = m_Cells[move.Column, move.Row];
+            var (otherRow, otherCol) = move.OtherCell();
+            var destinationValue = m_Cells[otherCol, otherRow];
+
+            m_Cells[move.Column, move.Row] = destinationValue;
+            m_Cells[otherCol, otherRow] = originalValue;
+            return true;
+        }
+
+        public override int GetCellType(int row, int col)
+        {
+            if (row >= m_CurrentBoardSize.Rows || col >= m_CurrentBoardSize.Columns)
+            {
+                throw new IndexOutOfRangeException();
+            }
+            return m_Cells[col, row].CellType;
+        }
+
+        public override int GetSpecialType(int row, int col)
+        {
+            if (row >= m_CurrentBoardSize.Rows || col >= m_CurrentBoardSize.Columns)
+            {
+                throw new IndexOutOfRangeException();
+            }
+            return m_Cells[col, row].SpecialType;
+        }
+
+        public override bool IsMoveValid(Move m)
+        {
+            if (m_Cells == null)
+            {
+                return false;
+            }
+
+            return SimpleIsMoveValid(m);
+        }
+
+        public bool MarkMatchedCells(int[,] cells = null)
+        {
+            ClearMarked();
+            bool madeMatch = false;
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
+            {
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
+                {
+                    // Check vertically
+                    var matchedRows = 0;
+                    for (var iOffset = i; iOffset < m_CurrentBoardSize.Rows; iOffset++)
+                    {
+                        if (m_Cells[j, i].CellType != m_Cells[j, iOffset].CellType)
+                        {
+                            break;
+                        }
+
+                        matchedRows++;
+                    }
+
+                    if (matchedRows >= 3)
+                    {
+                        madeMatch = true;
+                        for (var k = 0; k < matchedRows; k++)
+                        {
+                            m_Matched[j, i + k] = true;
+                        }
+                    }
+
+                    // Check vertically
+                    var matchedCols = 0;
+                    for (var jOffset = j; jOffset < m_CurrentBoardSize.Columns; jOffset++)
+                    {
+                        if (m_Cells[j, i].CellType != m_Cells[jOffset, i].CellType)
+                        {
+                            break;
+                        }
+
+                        matchedCols++;
+                    }
+
+                    if (matchedCols >= 3)
+                    {
+                        madeMatch = true;
+                        for (var k = 0; k < matchedCols; k++)
+                        {
+                            m_Matched[j + k, i] = true;
+                        }
+                    }
+                }
+            }
+
+            return madeMatch;
+        }
+
+        /// <summary>
+        /// Sets cells that are matched to the empty cell, and returns the score earned.
+        /// </summary>
+        /// <returns></returns>
+        public int ClearMatchedCells()
+        {
+            var pointsByType = new[] { BasicCellPoints, SpecialCell1Points, SpecialCell2Points };
+            int pointsEarned = 0;
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
+            {
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
+                {
+                    if (m_Matched[j, i])
+                    {
+                        var speciaType = GetSpecialType(i, j);
+                        pointsEarned += pointsByType[speciaType];
+                        m_Cells[j, i] = (k_EmptyCell, 0);
+                    }
+                }
+            }
+
+            ClearMarked(); // TODO clear here or at start of matching?
+            return pointsEarned;
+        }
+
+        public bool DropCells()
+        {
+            var madeChanges = false;
+            // Gravity is applied in the negative row direction
+            for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
+            {
+                var writeIndex = 0;
+                for (var readIndex = 0; readIndex < m_CurrentBoardSize.Rows; readIndex++)
+                {
+                    m_Cells[j, writeIndex] = m_Cells[j, readIndex];
+                    if (m_Cells[j, readIndex].CellType != k_EmptyCell)
+                    {
+                        writeIndex++;
+                    }
+                }
+
+                // Fill in empties at the end
+                for (; writeIndex < m_CurrentBoardSize.Rows; writeIndex++)
+                {
+                    madeChanges = true;
+                    m_Cells[j, writeIndex] = (k_EmptyCell, 0);
+                }
+            }
+
+            return madeChanges;
+        }
+
+        public bool FillFromAbove()
+        {
+            bool madeChanges = false;
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
+            {
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
+                {
+                    if (m_Cells[j, i].CellType == k_EmptyCell)
+                    {
+                        madeChanges = true;
+                        m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
+                    }
+                }
+            }
+
+            return madeChanges;
+        }
+
+        public (int, int)[,] Cells
+        {
+            get { return m_Cells; }
+        }
+
+        public bool[,] Matched
+        {
+            get { return m_Matched; }
+        }
+
+        // Initialize the board to random values.
+        public void InitRandom()
+        {
+            for (var i = 0; i < MaxRows; i++)
+            {
+                for (var j = 0; j < MaxColumns; j++)
+                {
+                    m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
+                }
+            }
+        }
+
+        public void InitSettled()
+        {
+            InitRandom();
+            while (true)
+            {
+                var anyMatched = MarkMatchedCells();
+                if (!anyMatched)
+                {
+                    return;
+                }
+                ClearMatchedCells();
+                DropCells();
+                FillFromAbove();
+            }
+        }
+
+        void ClearMarked()
+        {
+            for (var i = 0; i < MaxRows; i++)
+            {
+                for (var j = 0; j < MaxColumns; j++)
+                {
+                    m_Matched[j, i] = false;
+                }
+            }
+        }
+
+        int GetRandomCellType()
+        {
+            return m_Random.Next(0, NumCellTypes);
+        }
+
+        int GetRandomSpecialType()
+        {
+            // 1 in N chance to get a type-2 special
+            // 2 in N chance to get a type-1 special
+            // otherwise 0 (boring)
+            var N = 10;
+            var val = m_Random.Next(0, N);
+            if (val == 0)
+            {
+                return 2;
+            }
+
+            if (val <= 2)
+            {
+                return 1;
+            }
+
+            return 0;
+        }
+
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b76250f9a6d7b1f0a52dc63a5c9aefde2d076d4a
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 6d852a063770348b68caa91b8e7642a5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4442926724d321aa2bf18ae0793e0ec174d3a197
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs
@@ -0,0 +1,187 @@
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Profiling;
+
+namespace Unity.MLAgentsExamples
+{
+    public class Match3Drawer : MonoBehaviour
+    {
+        public int DebugMoveIndex = -1;
+
+        static Color[] s_Colors = new[]
+        {
+            Color.red,
+            Color.green,
+            Color.blue,
+            Color.cyan,
+            Color.magenta,
+            Color.yellow,
+            Color.gray,
+            Color.black,
+        };
+
+        private static Color s_EmptyColor = new Color(0.5f, 0.5f, 0.5f, .25f);
+
+        public Dictionary<(int, int), Match3TileSelector> tilesDict = new Dictionary<(int, int), Match3TileSelector>();
+        public float CubeSpacing = 1.25f;
+        public GameObject TilePrefab;
+
+        private bool m_Initialized;
+        private Match3Board m_Board;
+
+        void Awake()
+        {
+            if (!m_Initialized)
+            {
+                InitializeDict();
+            }
+        }
+
+        void InitializeDict()
+        {
+            m_Board = GetComponent<Match3Board>();
+            foreach (var item in tilesDict)
+            {
+                if (item.Value)
+                {
+                    DestroyImmediate(item.Value.gameObject);
+                }
+            }
+
+            tilesDict.Clear();
+
+            for (var i = 0; i < m_Board.MaxRows; i++)
+            {
+                for (var j = 0; j < m_Board.MaxColumns; j++)
+                {
+                    var go = Instantiate(TilePrefab, transform.position, Quaternion.identity, transform);
+                    go.name = $"r{i}_c{j}";
+                    tilesDict.Add((i, j), go.GetComponent<Match3TileSelector>());
+                }
+            }
+
+            m_Initialized = true;
+        }
+
+        void Update()
+        {
+            if (!m_Board)
+            {
+                m_Board = GetComponent<Match3Board>();
+            }
+
+            if (!m_Initialized)
+            {
+                InitializeDict();
+            }
+
+            var currentSize = m_Board.GetCurrentBoardSize();
+            for (var i = 0; i < m_Board.MaxRows; i++)
+            {
+                for (var j = 0; j < m_Board.MaxColumns; j++)
+                {
+                    int value = Match3Board.k_EmptyCell;
+                    int specialType = 0;
+                    if (m_Board.Cells != null && i < currentSize.Rows && j < currentSize.Columns)
+                    {
+                        value = m_Board.GetCellType(i, j);
+                        specialType = m_Board.GetSpecialType(i, j);
+                    }
+                    var pos = new Vector3(j, i, 0);
+                    pos *= CubeSpacing;
+
+                    tilesDict[(i, j)].transform.position = transform.TransformPoint(pos);
+                    tilesDict[(i, j)].SetActiveTile(specialType, value);
+                }
+            }
+        }
+
+        void OnDrawGizmos()
+        {
+            Profiler.BeginSample("Match3.OnDrawGizmos");
+            var cubeSize = .5f;
+            var matchedWireframeSize = .5f * (cubeSize + CubeSpacing);
+
+            if (!m_Board)
+            {
+                m_Board = GetComponent<Match3Board>();
+                if (m_Board == null)
+                {
+                    return;
+                }
+            }
+
+            var currentSize = m_Board.GetCurrentBoardSize();
+            for (var i = 0; i < m_Board.MaxRows; i++)
+            {
+                for (var j = 0; j < m_Board.MaxColumns; j++)
+                {
+                    int value = Match3Board.k_EmptyCell;
+                    int specialType = 0;
+                    if (m_Board.Cells != null && i < currentSize.Rows && j < currentSize.Columns)
+                    {
+                        value = m_Board.GetCellType(i, j);
+                        specialType = m_Board.GetSpecialType(i, j);
+                    }
+
+                    if (value >= 0 && value < s_Colors.Length)
+                    {
+                        Gizmos.color = s_Colors[value];
+                    }
+                    else
+                    {
+                        Gizmos.color = s_EmptyColor;
+                    }
+
+                    var pos = new Vector3(j, i, 0);
+                    pos *= CubeSpacing;
+
+                    if (specialType == 2)
+                    {
+                        Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(1f, .5f, .5f));
+                        Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(.5f, 1f, .5f));
+                        Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(.5f, .5f, 1f));
+                    }
+                    else if (specialType == 1)
+                    {
+                        Gizmos.DrawSphere(transform.TransformPoint(pos), .5f * cubeSize);
+                    }
+                    else
+                    {
+                        Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * Vector3.one);
+                    }
+
+                    Gizmos.color = Color.yellow;
+                    if (m_Board.Matched != null && m_Board.Matched[j, i])
+                    {
+                        Gizmos.DrawWireCube(transform.TransformPoint(pos), matchedWireframeSize * Vector3.one);
+                    }
+                }
+            }
+
+            // Draw valid moves
+            foreach (var move in m_Board.AllMoves())
+            {
+                if (DebugMoveIndex >= 0 && move.MoveIndex != DebugMoveIndex)
+                {
+                    continue;
+                }
+
+                if (!m_Board.IsMoveValid(move))
+                {
+                    continue;
+                }
+
+                var (otherRow, otherCol) = move.OtherCell();
+                var pos = new Vector3(move.Column, move.Row, 0) * CubeSpacing;
+                var otherPos = new Vector3(otherCol, otherRow, 0) * CubeSpacing;
+
+                var oneQuarter = Vector3.Lerp(pos, otherPos, .25f);
+                var threeQuarters = Vector3.Lerp(pos, otherPos, .75f);
+                Gizmos.DrawLine(transform.TransformPoint(oneQuarter), transform.TransformPoint(threeQuarters));
+            }
+
+            Profiler.EndSample();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..24aa1ade8815d29442a398d89e49669aa305a467
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: abebb7ad4a5547d7a3b04373784ff195
+timeCreated: 1598221188
\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d54e824df069438791e705f7698bcf7b38935ceb
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs
@@ -0,0 +1,124 @@
+using Unity.MLAgents.Integrations.Match3;
+
+namespace Unity.MLAgentsExamples
+{
+    public class Match3ExampleActuator : Match3Actuator
+    {
+        private Match3Board m_Board;
+        Match3Board Board => m_Board;
+
+        public Match3ExampleActuator(Match3Board board,
+            bool forceHeuristic,
+            string name,
+            int seed
+        )
+            : base(board, forceHeuristic, seed, name)
+        {
+            m_Board = board;
+        }
+
+
+        protected override int EvalMovePoints(Move move)
+        {
+            var pointsByType = new[] { Board.BasicCellPoints, Board.SpecialCell1Points, Board.SpecialCell2Points };
+            // Counts the expected points for making the move.
+            var moveVal = Board.GetCellType(move.Row, move.Column);
+            var moveSpecial = Board.GetSpecialType(move.Row, move.Column);
+            var (otherRow, otherCol) = move.OtherCell();
+            var oppositeVal = Board.GetCellType(otherRow, otherCol);
+            var oppositeSpecial = Board.GetSpecialType(otherRow, otherCol);
+
+
+            int movePoints = EvalHalfMove(
+                otherRow, otherCol, moveVal, moveSpecial, move.Direction, pointsByType
+            );
+            int otherPoints = EvalHalfMove(
+                move.Row, move.Column, oppositeVal, oppositeSpecial, move.OtherDirection(), pointsByType
+            );
+            return movePoints + otherPoints;
+        }
+
+        int EvalHalfMove(int newRow, int newCol, int newValue, int newSpecial, Direction incomingDirection, int[] pointsByType)
+        {
+            // This is a essentially a duplicate of AbstractBoard.CheckHalfMove but also counts the points for the move.
+            var currentBoardSize = Board.GetCurrentBoardSize();
+            int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
+            int scoreLeft = 0, scoreRight = 0, scoreUp = 0, scoreDown = 0;
+
+            if (incomingDirection != Direction.Right)
+            {
+                for (var c = newCol - 1; c >= 0; c--)
+                {
+                    if (Board.GetCellType(newRow, c) == newValue)
+                    {
+                        matchedLeft++;
+                        scoreLeft += pointsByType[Board.GetSpecialType(newRow, c)];
+                    }
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Left)
+            {
+                for (var c = newCol + 1; c < currentBoardSize.Columns; c++)
+                {
+                    if (Board.GetCellType(newRow, c) == newValue)
+                    {
+                        matchedRight++;
+                        scoreRight += pointsByType[Board.GetSpecialType(newRow, c)];
+                    }
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Down)
+            {
+                for (var r = newRow + 1; r < currentBoardSize.Rows; r++)
+                {
+                    if (Board.GetCellType(r, newCol) == newValue)
+                    {
+                        matchedUp++;
+                        scoreUp += pointsByType[Board.GetSpecialType(r, newCol)];
+                    }
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Up)
+            {
+                for (var r = newRow - 1; r >= 0; r--)
+                {
+                    if (Board.GetCellType(r, newCol) == newValue)
+                    {
+                        matchedDown++;
+                        scoreDown += pointsByType[Board.GetSpecialType(r, newCol)];
+                    }
+                    else
+                        break;
+                }
+            }
+
+            if ((matchedUp + matchedDown >= 2) || (matchedLeft + matchedRight >= 2))
+            {
+                // It's a match. Start from counting the piece being moved
+                var totalScore = pointsByType[newSpecial];
+                if (matchedUp + matchedDown >= 2)
+                {
+                    totalScore += scoreUp + scoreDown;
+                }
+
+                if (matchedLeft + matchedRight >= 2)
+                {
+                    totalScore += scoreLeft + scoreRight;
+                }
+                return totalScore;
+            }
+
+            return 0;
+        }
+    }
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..acaf52892cde1d0ac40360bee9d0d64cfc64e83a
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 9e6fe1a020a04421ab828be4543a655c
+timeCreated: 1610665874
\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ca70181be0261f00bf58085695ce441b3610ce29
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs
@@ -0,0 +1,17 @@
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Integrations.Match3;
+
+namespace Unity.MLAgentsExamples
+{
+    public class Match3ExampleActuatorComponent : Match3ActuatorComponent
+    {
+        /// <inheritdoc/>
+        public override IActuator[] CreateActuators()
+        {
+            var board = GetComponent<Match3Board>();
+            var seed = RandomSeed == -1 ? gameObject.GetInstanceID() : RandomSeed + 1;
+            return new IActuator[] { new Match3ExampleActuator(board, ForceHeuristic, ActuatorName, seed) };
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs.meta b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs.meta
new file mode 100644
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs.meta
@@ -0,0 +1,3 @@
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+guid: b17adcc6c9b241da903aa134f2dac930
+timeCreated: 1610665885
\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3TileSelector.cs b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3TileSelector.cs
new file mode 100644
index 0000000000000000000000000000000000000000..685cf5866af15b3950f648302f92cb89f3c72098
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3TileSelector.cs
@@ -0,0 +1,55 @@
+using System.Collections.Generic;
+using UnityEngine;
+
+public class Match3TileSelector : MonoBehaviour
+{
+    public GameObject emptyTile;
+    public GameObject[] tileTypes = new GameObject[0];
+    public Material[] materialTypes = new Material[0];
+
+    private Dictionary<int, MeshRenderer> tileDict = new Dictionary<int, MeshRenderer>();
+
+    // Start is called before the first frame update
+    void Awake()
+    {
+        for (int i = 0; i < tileTypes.Length; i++)
+        {
+            tileDict.Add(i, tileTypes[i].GetComponent<MeshRenderer>());
+        }
+
+        SetActiveTile(0, 0);
+    }
+
+    public void AllTilesOff()
+    {
+        foreach (var item in tileTypes)
+        {
+            item.SetActive(false);
+        }
+    }
+
+    public void SetActiveTile(int typeIndex, int matIndex)
+    {
+        if (matIndex == -1)
+        {
+            AllTilesOff();
+            emptyTile.SetActive(true);
+        }
+        else
+        {
+            emptyTile.SetActive(false);
+            for (int i = 0; i < tileTypes.Length; i++)
+            {
+                if (i == typeIndex)
+                {
+                    tileTypes[i].SetActive(true);
+                    tileDict[i].sharedMaterial = materialTypes[matIndex];
+                }
+                else
+                {
+                    tileTypes[i].SetActive(false);
+                }
+            }
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Prefabs/PushBlockCollabAreaGrid.prefab b/Project/Assets/ML-Agents/Examples/PushBlock/Prefabs/PushBlockCollabAreaGrid.prefab
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockCollab.unity.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockCollab.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2d48a30f1cb0188e15000be4105fe041b2365f22
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+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockCollab.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts.meta
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+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts.meta
@@ -0,0 +1,10 @@
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs
new file mode 100644
index 0000000000000000000000000000000000000000..77193fefee50032251023e27da3f118073fb6d9e
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs
@@ -0,0 +1,26 @@
+//Detect when the orange block has touched the goal.
+//Detect when the orange block has touched an obstacle.
+//Put this script onto the orange block. There's nothing you need to set in the editor.
+//Make sure the goal is tagged with "goal" in the editor.
+
+using UnityEngine;
+
+public class GoalDetect : MonoBehaviour
+{
+    /// <summary>
+    /// The associated agent.
+    /// This will be set by the agent script on Initialization.
+    /// Don't need to manually set.
+    /// </summary>
+    [HideInInspector]
+    public PushAgentBasic agent;  //
+
+    void OnCollisionEnter(Collision col)
+    {
+        // Touched goal.
+        if (col.gameObject.CompareTag("goal"))
+        {
+            agent.ScoredAGoal();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..45ae4f861c5afe580e5abc35db1761811e0ed2a3
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetect.cs.meta
@@ -0,0 +1,13 @@
+fileFormatVersion: 2
+guid: 7d079d09ceed84ff49cf6841c66cf7ec
+timeCreated: 1513645763
+licenseType: Free
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs
new file mode 100644
index 0000000000000000000000000000000000000000..baa5efe75a2c87b9f398e142cf5d35b487788b91
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs
@@ -0,0 +1,58 @@
+using UnityEngine;
+using UnityEngine.Events;
+
+public class GoalDetectTrigger : MonoBehaviour
+{
+
+    [Header("Trigger Collider Tag To Detect")]
+    public string tagToDetect = "goal"; //collider tag to detect
+
+    [Header("Goal Value")]
+    public float GoalValue = 1;
+
+    private Collider m_col;
+    [System.Serializable]
+    public class TriggerEvent : UnityEvent<Collider, float>
+    {
+    }
+
+    [Header("Trigger Callbacks")]
+    public TriggerEvent onTriggerEnterEvent = new TriggerEvent();
+    public TriggerEvent onTriggerStayEvent = new TriggerEvent();
+    public TriggerEvent onTriggerExitEvent = new TriggerEvent();
+
+    private void OnTriggerEnter(Collider col)
+    {
+        if (col.CompareTag(tagToDetect))
+        {
+            onTriggerEnterEvent.Invoke(m_col, GoalValue);
+        }
+    }
+
+    private void OnTriggerStay(Collider col)
+    {
+        if (col.CompareTag(tagToDetect))
+        {
+            onTriggerStayEvent.Invoke(m_col, GoalValue);
+        }
+    }
+
+    private void OnTriggerExit(Collider col)
+    {
+        if (col.CompareTag(tagToDetect))
+        {
+            onTriggerExitEvent.Invoke(m_col, GoalValue);
+        }
+    }
+    // Start is called before the first frame update
+    void Awake()
+    {
+        m_col = GetComponent<Collider>();
+    }
+
+    // Update is called once per frame
+    void Update()
+    {
+
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..11568fc0e6e60be8e8f66aebec2d3fb997d1b555
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/GoalDetectTrigger.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 9dcea692df32b46b4a884cfcc150aef7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8613c3eabde45b23d579641fd20b4ec06289607d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs
@@ -0,0 +1,255 @@
+//Put this script on your blue cube.
+
+using System.Collections;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+
+public class PushAgentBasic : Agent
+{
+    /// <summary>
+    /// The ground. The bounds are used to spawn the elements.
+    /// </summary>
+    public GameObject ground;
+
+    public GameObject area;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+    [HideInInspector]
+    public Bounds areaBounds;
+
+    PushBlockSettings m_PushBlockSettings;
+
+    /// <summary>
+    /// The goal to push the block to.
+    /// </summary>
+    public GameObject goal;
+
+    /// <summary>
+    /// The block to be pushed to the goal.
+    /// </summary>
+    public GameObject block;
+
+    /// <summary>
+    /// Detects when the block touches the goal.
+    /// </summary>
+    [HideInInspector]
+    public GoalDetect goalDetect;
+
+    public bool useVectorObs;
+
+    Rigidbody m_BlockRb;  //cached on initialization
+    Rigidbody m_AgentRb;  //cached on initialization
+    Material m_GroundMaterial; //cached on Awake()
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+    Renderer m_GroundRenderer;
+
+    EnvironmentParameters m_ResetParams;
+
+    protected override void Awake()
+    {
+        base.Awake();
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+    }
+
+    public override void Initialize()
+    {
+        goalDetect = block.GetComponent<GoalDetect>();
+        goalDetect.agent = this;
+
+        // Cache the agent rigidbody
+        m_AgentRb = GetComponent<Rigidbody>();
+        // Cache the block rigidbody
+        m_BlockRb = block.GetComponent<Rigidbody>();
+        // Get the ground's bounds
+        areaBounds = ground.GetComponent<Collider>().bounds;
+        // Get the ground renderer so we can change the material when a goal is scored
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        // Starting material
+        m_GroundMaterial = m_GroundRenderer.material;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+
+        SetResetParameters();
+    }
+
+    /// <summary>
+    /// Use the ground's bounds to pick a random spawn position.
+    /// </summary>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var foundNewSpawnLocation = false;
+        var randomSpawnPos = Vector3.zero;
+        while (foundNewSpawnLocation == false)
+        {
+            var randomPosX = Random.Range(-areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier);
+
+            var randomPosZ = Random.Range(-areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier);
+            randomSpawnPos = ground.transform.position + new Vector3(randomPosX, 1f, randomPosZ);
+            if (Physics.CheckBox(randomSpawnPos, new Vector3(2.5f, 0.01f, 2.5f)) == false)
+            {
+                foundNewSpawnLocation = true;
+            }
+        }
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Called when the agent moves the block into the goal.
+    /// </summary>
+    public void ScoredAGoal()
+    {
+        // We use a reward of 5.
+        AddReward(5f);
+
+        // By marking an agent as done AgentReset() will be called automatically.
+        EndEpisode();
+
+        // Swap ground material for a bit to indicate we scored.
+        StartCoroutine(GoalScoredSwapGroundMaterial(m_PushBlockSettings.goalScoredMaterial, 0.5f));
+    }
+
+    /// <summary>
+    /// Swap ground material, wait time seconds, then swap back to the regular material.
+    /// </summary>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time); // Wait for 2 sec
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    /// <summary>
+    /// Moves the agent according to the selected action.
+    /// </summary>
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                rotateDir = transform.up * -1f;
+                break;
+            case 5:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 6:
+                dirToGo = transform.right * 0.75f;
+                break;
+        }
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        // Move the agent using the action.
+        MoveAgent(actionBuffers.DiscreteActions);
+
+        // Penalty given each step to encourage agent to finish task quickly.
+        AddReward(-1f / MaxStep);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+
+    /// <summary>
+    /// Resets the block position and velocities.
+    /// </summary>
+    void ResetBlock()
+    {
+        // Get a random position for the block.
+        block.transform.position = GetRandomSpawnPos();
+
+        // Reset block velocity back to zero.
+        m_BlockRb.velocity = Vector3.zero;
+
+        // Reset block angularVelocity back to zero.
+        m_BlockRb.angularVelocity = Vector3.zero;
+    }
+
+    /// <summary>
+    /// In the editor, if "Reset On Done" is checked then AgentReset() will be
+    /// called automatically anytime we mark done = true in an agent script.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        var rotation = Random.Range(0, 4);
+        var rotationAngle = rotation * 90f;
+        area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+
+        ResetBlock();
+        transform.position = GetRandomSpawnPos();
+        m_AgentRb.velocity = Vector3.zero;
+        m_AgentRb.angularVelocity = Vector3.zero;
+
+        SetResetParameters();
+    }
+
+    public void SetGroundMaterialFriction()
+    {
+        var groundCollider = ground.GetComponent<Collider>();
+
+        groundCollider.material.dynamicFriction = m_ResetParams.GetWithDefault("dynamic_friction", 0);
+        groundCollider.material.staticFriction = m_ResetParams.GetWithDefault("static_friction", 0);
+    }
+
+    public void SetBlockProperties()
+    {
+        var scale = m_ResetParams.GetWithDefault("block_scale", 2);
+        //Set the scale of the block
+        m_BlockRb.transform.localScale = new Vector3(scale, 0.75f, scale);
+
+        // Set the drag of the block
+        m_BlockRb.drag = m_ResetParams.GetWithDefault("block_drag", 0.5f);
+    }
+
+    void SetResetParameters()
+    {
+        SetGroundMaterialFriction();
+        SetBlockProperties();
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cfdc094962b7ec971778347f36607b573acd7963
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs.meta
@@ -0,0 +1,12 @@
+fileFormatVersion: 2
+guid: dea8c4f2604b947e6b7b97750dde87ca
+timeCreated: 1506829537
+licenseType: Pro
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2d4eec1c1de9150b0d1538cf29e1cf5b720c06d4
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs
@@ -0,0 +1,90 @@
+//Put this script on your blue cube.
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+
+public class PushAgentCollab : Agent
+{
+
+    private PushBlockSettings m_PushBlockSettings;
+    private Rigidbody m_AgentRb;  //cached on initialization
+
+    protected override void Awake()
+    {
+        base.Awake();
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+    }
+
+    public override void Initialize()
+    {
+        // Cache the agent rb
+        m_AgentRb = GetComponent<Rigidbody>();
+    }
+
+    /// <summary>
+    /// Moves the agent according to the selected action.
+    /// </summary>
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                rotateDir = transform.up * -1f;
+                break;
+            case 5:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 6:
+                dirToGo = transform.right * 0.75f;
+                break;
+        }
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        // Move the agent using the action.
+        MoveAgent(actionBuffers.DiscreteActions);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..921951249f0446ad3604e135696f0fb460855994
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentCollab.cs.meta
@@ -0,0 +1,12 @@
+fileFormatVersion: 2
+guid: d94a85eca2e074578943301959c555ba
+timeCreated: 1506829537
+licenseType: Pro
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockEnvController.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockEnvController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1b4634820ff559c1f3a1a0086821c708b4eb6d9b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockEnvController.cs
@@ -0,0 +1,234 @@
+using System.Collections;
+using System.Collections.Generic;
+using Unity.MLAgents;
+using UnityEngine;
+
+public class PushBlockEnvController : MonoBehaviour
+{
+    [System.Serializable]
+    public class PlayerInfo
+    {
+        public PushAgentCollab Agent;
+        [HideInInspector]
+        public Vector3 StartingPos;
+        [HideInInspector]
+        public Quaternion StartingRot;
+        [HideInInspector]
+        public Rigidbody Rb;
+    }
+
+    [System.Serializable]
+    public class BlockInfo
+    {
+        public Transform T;
+        [HideInInspector]
+        public Vector3 StartingPos;
+        [HideInInspector]
+        public Quaternion StartingRot;
+        [HideInInspector]
+        public Rigidbody Rb;
+    }
+
+    /// <summary>
+    /// Max Academy steps before this platform resets
+    /// </summary>
+    /// <returns></returns>
+    [Header("Max Environment Steps")] public int MaxEnvironmentSteps = 25000;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+    [HideInInspector]
+    public Bounds areaBounds;
+    /// <summary>
+    /// The ground. The bounds are used to spawn the elements.
+    /// </summary>
+    public GameObject ground;
+
+    public GameObject area;
+
+    Material m_GroundMaterial; //cached on Awake()
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+    Renderer m_GroundRenderer;
+
+    //List of Agents On Platform
+    public List<PlayerInfo> AgentsList = new List<PlayerInfo>();
+    //List of Blocks On Platform
+    public List<BlockInfo> BlocksList = new List<BlockInfo>();
+
+    public bool UseRandomAgentRotation = true;
+    public bool UseRandomAgentPosition = true;
+    public bool UseRandomBlockRotation = true;
+    public bool UseRandomBlockPosition = true;
+    private PushBlockSettings m_PushBlockSettings;
+
+    private int m_NumberOfRemainingBlocks;
+
+    private SimpleMultiAgentGroup m_AgentGroup;
+
+    private int m_ResetTimer;
+
+    void Start()
+    {
+
+        // Get the ground's bounds
+        areaBounds = ground.GetComponent<Collider>().bounds;
+        // Get the ground renderer so we can change the material when a goal is scored
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        // Starting material
+        m_GroundMaterial = m_GroundRenderer.material;
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+        // Initialize Blocks
+        foreach (var item in BlocksList)
+        {
+            item.StartingPos = item.T.transform.position;
+            item.StartingRot = item.T.transform.rotation;
+            item.Rb = item.T.GetComponent<Rigidbody>();
+        }
+        // Initialize TeamManager
+        m_AgentGroup = new SimpleMultiAgentGroup();
+        foreach (var item in AgentsList)
+        {
+            item.StartingPos = item.Agent.transform.position;
+            item.StartingRot = item.Agent.transform.rotation;
+            item.Rb = item.Agent.GetComponent<Rigidbody>();
+            m_AgentGroup.RegisterAgent(item.Agent);
+        }
+        ResetScene();
+    }
+
+    void FixedUpdate()
+    {
+        m_ResetTimer += 1;
+        if (m_ResetTimer >= MaxEnvironmentSteps && MaxEnvironmentSteps > 0)
+        {
+            m_AgentGroup.GroupEpisodeInterrupted();
+            ResetScene();
+        }
+
+        //Hurry Up Penalty
+        m_AgentGroup.AddGroupReward(-0.5f / MaxEnvironmentSteps);
+    }
+
+    /// <summary>
+    /// Use the ground's bounds to pick a random spawn position.
+    /// </summary>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var foundNewSpawnLocation = false;
+        var randomSpawnPos = Vector3.zero;
+        while (foundNewSpawnLocation == false)
+        {
+            var randomPosX = Random.Range(-areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier);
+
+            var randomPosZ = Random.Range(-areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier);
+            randomSpawnPos = ground.transform.position + new Vector3(randomPosX, 1f, randomPosZ);
+            if (Physics.CheckBox(randomSpawnPos, new Vector3(1.5f, 0.01f, 1.5f)) == false)
+            {
+                foundNewSpawnLocation = true;
+            }
+        }
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Resets the block position and velocities.
+    /// </summary>
+    void ResetBlock(BlockInfo block)
+    {
+        // Get a random position for the block.
+        block.T.position = GetRandomSpawnPos();
+
+        // Reset block velocity back to zero.
+        block.Rb.velocity = Vector3.zero;
+
+        // Reset block angularVelocity back to zero.
+        block.Rb.angularVelocity = Vector3.zero;
+    }
+
+    /// <summary>
+    /// Swap ground material, wait time seconds, then swap back to the regular material.
+    /// </summary>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time); // Wait for 2 sec
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    /// <summary>
+    /// Called when the agent moves the block into the goal.
+    /// </summary>
+    public void ScoredAGoal(Collider col, float score)
+    {
+        print($"Scored {score} on {gameObject.name}");
+
+        //Decrement the counter
+        m_NumberOfRemainingBlocks--;
+
+        //Are we done?
+        bool done = m_NumberOfRemainingBlocks == 0;
+
+        //Disable the block
+        col.gameObject.SetActive(false);
+
+        //Give Agent Rewards
+        m_AgentGroup.AddGroupReward(score);
+
+        // Swap ground material for a bit to indicate we scored.
+        StartCoroutine(GoalScoredSwapGroundMaterial(m_PushBlockSettings.goalScoredMaterial, 0.5f));
+
+        if (done)
+        {
+            //Reset assets
+            m_AgentGroup.EndGroupEpisode();
+            ResetScene();
+        }
+    }
+
+    Quaternion GetRandomRot()
+    {
+        return Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+    }
+
+    public void ResetScene()
+    {
+        m_ResetTimer = 0;
+
+        //Random platform rotation
+        var rotation = Random.Range(0, 4);
+        var rotationAngle = rotation * 90f;
+        area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+
+        //Reset Agents
+        foreach (var item in AgentsList)
+        {
+            var pos = UseRandomAgentPosition ? GetRandomSpawnPos() : item.StartingPos;
+            var rot = UseRandomAgentRotation ? GetRandomRot() : item.StartingRot;
+
+            item.Agent.transform.SetPositionAndRotation(pos, rot);
+            item.Rb.velocity = Vector3.zero;
+            item.Rb.angularVelocity = Vector3.zero;
+        }
+
+        //Reset Blocks
+        foreach (var item in BlocksList)
+        {
+            var pos = UseRandomBlockPosition ? GetRandomSpawnPos() : item.StartingPos;
+            var rot = UseRandomBlockRotation ? GetRandomRot() : item.StartingRot;
+
+            item.T.transform.SetPositionAndRotation(pos, rot);
+            item.Rb.velocity = Vector3.zero;
+            item.Rb.angularVelocity = Vector3.zero;
+            item.T.gameObject.SetActive(true);
+        }
+
+        //Reset counter
+        m_NumberOfRemainingBlocks = BlocksList.Count;
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockEnvController.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockEnvController.cs.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockSettings.cs b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockSettings.cs
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockSettings.cs
@@ -0,0 +1,35 @@
+using UnityEngine;
+
+public class PushBlockSettings : MonoBehaviour
+{
+    /// <summary>
+    /// The "walking speed" of the agents in the scene.
+    /// </summary>
+    public float agentRunSpeed;
+
+    /// <summary>
+    /// The agent rotation speed.
+    /// Every agent will use this setting.
+    /// </summary>
+    public float agentRotationSpeed;
+
+    /// <summary>
+    /// The spawn area margin multiplier.
+    /// ex: .9 means 90% of spawn area will be used.
+    /// .1 margin will be left (so players don't spawn off of the edge).
+    /// The higher this value, the longer training time required.
+    /// </summary>
+    public float spawnAreaMarginMultiplier;
+
+    /// <summary>
+    /// When a goal is scored the ground will switch to this
+    /// material for a few seconds.
+    /// </summary>
+    public Material goalScoredMaterial;
+
+    /// <summary>
+    /// When an agent fails, the ground will turn this material for a few seconds.
+    /// </summary>
+    public Material failMaterial;
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockSettings.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushBlockSettings.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlock/TFModels.meta b/Project/Assets/ML-Agents/Examples/PushBlock/TFModels.meta
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/PushBlockActions.inputactions b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/PushBlockActions.inputactions
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+}
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scenes/PushBlockWithInput.unity.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scenes/PushBlockWithInput.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..97811eea0895c4704afc57d4a0795cd9cc8a8901
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+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scenes/PushBlockWithInput.unity.meta
@@ -0,0 +1,9 @@
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c2ee2dd41f5cd4e7fe2b5cc65e377cc6e751bd3e
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts.meta
@@ -0,0 +1,10 @@
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9306d7ff0f05f4716f4c67ef09b0f51dd986990b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs
@@ -0,0 +1,26 @@
+//Detect when the orange block has touched the goal.
+//Detect when the orange block has touched an obstacle.
+//Put this script onto the orange block. There's nothing you need to set in the editor.
+//Make sure the goal is tagged with "goal" in the editor.
+
+using UnityEngine;
+
+public class GoalDetectWithInput : MonoBehaviour
+{
+    /// <summary>
+    /// The associated agent.
+    /// This will be set by the agent script on Initialization.
+    /// Don't need to manually set.
+    /// </summary>
+    [HideInInspector]
+    public PushBlockWithInputAgentBasic agent;  //
+
+    void OnCollisionEnter(Collision col)
+    {
+        // Touched goal.
+        if (col.gameObject.CompareTag("goal"))
+        {
+            agent.ScoredAGoal();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs.meta
new file mode 100644
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+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/GoalDetectWithInput.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c05997bbb968ee752feeb02bdf7c0cf5bb07b5d1
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs
@@ -0,0 +1,333 @@
+//------------------------------------------------------------------------------
+// <auto-generated>
+//     This code was auto-generated by com.unity.inputsystem:InputActionCodeGenerator
+//     version 1.6.1
+//     from Assets/ML-Agents/Examples/PushBlockWithInput/PushBlockActions.inputactions
+//
+//     Changes to this file may cause incorrect behavior and will be lost if
+//     the code is regenerated.
+// </auto-generated>
+//------------------------------------------------------------------------------
+
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Utilities;
+
+public partial class @PushBlockActions: IInputActionCollection2, IDisposable
+{
+    public InputActionAsset asset { get; }
+    public @PushBlockActions()
+    {
+        asset = InputActionAsset.FromJson(@"{
+    ""name"": ""PushBlockActions"",
+    ""maps"": [
+        {
+            ""name"": ""Movement"",
+            ""id"": ""03a2e5d4-ae81-47f1-a575-0779fb7da538"",
+            ""actions"": [
+                {
+                    ""name"": ""movement"",
+                    ""type"": ""Value"",
+                    ""id"": ""5f47cbc6-de46-4d33-93e2-2b1af4f5996d"",
+                    ""expectedControlType"": ""Vector2"",
+                    ""processors"": """",
+                    ""interactions"": """",
+                    ""initialStateCheck"": true
+                },
+                {
+                    ""name"": ""jump"",
+                    ""type"": ""Button"",
+                    ""id"": ""ca5eb833-5dfb-4b7c-880d-6118bd5d1378"",
+                    ""expectedControlType"": ""Button"",
+                    ""processors"": """",
+                    ""interactions"": """",
+                    ""initialStateCheck"": false
+                }
+            ],
+            ""bindings"": [
+                {
+                    ""name"": ""gamepad_move"",
+                    ""id"": ""477500ef-6d32-4b84-b9f8-158f18bcb906"",
+                    ""path"": ""2DVector"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": """",
+                    ""action"": ""movement"",
+                    ""isComposite"": true,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": ""up"",
+                    ""id"": ""6d2537b8-2266-4a50-8575-fb0fe310daa5"",
+                    ""path"": ""<Gamepad>/dpad/up"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""down"",
+                    ""id"": ""50584c83-beb6-4e90-a453-a635c03a761e"",
+                    ""path"": ""<Gamepad>/dpad/down"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""left"",
+                    ""id"": ""44408b8f-27e7-4c6d-b078-7536ba020d1a"",
+                    ""path"": ""<Gamepad>/dpad/left"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""right"",
+                    ""id"": ""f5681423-d3e3-41a5-b85e-0a7642c774aa"",
+                    ""path"": ""<Gamepad>/dpad/right"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""keyboard_move"",
+                    ""id"": ""6bcba4bf-5ce0-4005-9e6a-0de2487211b0"",
+                    ""path"": ""2DVector"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": """",
+                    ""action"": ""movement"",
+                    ""isComposite"": true,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": ""up"",
+                    ""id"": ""63da699e-b354-4e63-b0f8-26fb92abea41"",
+                    ""path"": ""<Keyboard>/w"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""down"",
+                    ""id"": ""39409748-9002-4aff-9a09-cdc05b9708ad"",
+                    ""path"": ""<Keyboard>/s"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""left"",
+                    ""id"": ""0afe45fc-dc45-4310-9c73-7dc3c503addf"",
+                    ""path"": ""<Keyboard>/a"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""right"",
+                    ""id"": ""69fe0335-9e0c-495d-a90d-4b0fcbfd2b34"",
+                    ""path"": ""<Keyboard>/d"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": """",
+                    ""id"": ""ab696218-63cd-4eb8-9fe1-48a68e32e92f"",
+                    ""path"": ""<Keyboard>/space"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""jump"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": """",
+                    ""id"": ""7adcb138-5175-4cc4-addc-d2b02cb5f0de"",
+                    ""path"": ""<Gamepad>/buttonSouth"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""jump"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": false
+                }
+            ]
+        }
+    ],
+    ""controlSchemes"": [
+        {
+            ""name"": ""Keyboard"",
+            ""bindingGroup"": ""Keyboard"",
+            ""devices"": [
+                {
+                    ""devicePath"": ""<Keyboard>"",
+                    ""isOptional"": true,
+                    ""isOR"": false
+                },
+                {
+                    ""devicePath"": ""<Gamepad>"",
+                    ""isOptional"": true,
+                    ""isOR"": false
+                }
+            ]
+        }
+    ]
+}");
+        // Movement
+        m_Movement = asset.FindActionMap("Movement", throwIfNotFound: true);
+        m_Movement_movement = m_Movement.FindAction("movement", throwIfNotFound: true);
+        m_Movement_jump = m_Movement.FindAction("jump", throwIfNotFound: true);
+    }
+
+    public void Dispose()
+    {
+        UnityEngine.Object.Destroy(asset);
+    }
+
+    public InputBinding? bindingMask
+    {
+        get => asset.bindingMask;
+        set => asset.bindingMask = value;
+    }
+
+    public ReadOnlyArray<InputDevice>? devices
+    {
+        get => asset.devices;
+        set => asset.devices = value;
+    }
+
+    public ReadOnlyArray<InputControlScheme> controlSchemes => asset.controlSchemes;
+
+    public bool Contains(InputAction action)
+    {
+        return asset.Contains(action);
+    }
+
+    public IEnumerator<InputAction> GetEnumerator()
+    {
+        return asset.GetEnumerator();
+    }
+
+    IEnumerator IEnumerable.GetEnumerator()
+    {
+        return GetEnumerator();
+    }
+
+    public void Enable()
+    {
+        asset.Enable();
+    }
+
+    public void Disable()
+    {
+        asset.Disable();
+    }
+
+    public IEnumerable<InputBinding> bindings => asset.bindings;
+
+    public InputAction FindAction(string actionNameOrId, bool throwIfNotFound = false)
+    {
+        return asset.FindAction(actionNameOrId, throwIfNotFound);
+    }
+
+    public int FindBinding(InputBinding bindingMask, out InputAction action)
+    {
+        return asset.FindBinding(bindingMask, out action);
+    }
+
+    // Movement
+    private readonly InputActionMap m_Movement;
+    private List<IMovementActions> m_MovementActionsCallbackInterfaces = new List<IMovementActions>();
+    private readonly InputAction m_Movement_movement;
+    private readonly InputAction m_Movement_jump;
+    public struct MovementActions
+    {
+        private @PushBlockActions m_Wrapper;
+        public MovementActions(@PushBlockActions wrapper) { m_Wrapper = wrapper; }
+        public InputAction @movement => m_Wrapper.m_Movement_movement;
+        public InputAction @jump => m_Wrapper.m_Movement_jump;
+        public InputActionMap Get() { return m_Wrapper.m_Movement; }
+        public void Enable() { Get().Enable(); }
+        public void Disable() { Get().Disable(); }
+        public bool enabled => Get().enabled;
+        public static implicit operator InputActionMap(MovementActions set) { return set.Get(); }
+        public void AddCallbacks(IMovementActions instance)
+        {
+            if (instance == null || m_Wrapper.m_MovementActionsCallbackInterfaces.Contains(instance)) return;
+            m_Wrapper.m_MovementActionsCallbackInterfaces.Add(instance);
+            @movement.started += instance.OnMovement;
+            @movement.performed += instance.OnMovement;
+            @movement.canceled += instance.OnMovement;
+            @jump.started += instance.OnJump;
+            @jump.performed += instance.OnJump;
+            @jump.canceled += instance.OnJump;
+        }
+
+        private void UnregisterCallbacks(IMovementActions instance)
+        {
+            @movement.started -= instance.OnMovement;
+            @movement.performed -= instance.OnMovement;
+            @movement.canceled -= instance.OnMovement;
+            @jump.started -= instance.OnJump;
+            @jump.performed -= instance.OnJump;
+            @jump.canceled -= instance.OnJump;
+        }
+
+        public void RemoveCallbacks(IMovementActions instance)
+        {
+            if (m_Wrapper.m_MovementActionsCallbackInterfaces.Remove(instance))
+                UnregisterCallbacks(instance);
+        }
+
+        public void SetCallbacks(IMovementActions instance)
+        {
+            foreach (var item in m_Wrapper.m_MovementActionsCallbackInterfaces)
+                UnregisterCallbacks(item);
+            m_Wrapper.m_MovementActionsCallbackInterfaces.Clear();
+            AddCallbacks(instance);
+        }
+    }
+    public MovementActions @Movement => new MovementActions(this);
+    private int m_KeyboardSchemeIndex = -1;
+    public InputControlScheme KeyboardScheme
+    {
+        get
+        {
+            if (m_KeyboardSchemeIndex == -1) m_KeyboardSchemeIndex = asset.FindControlSchemeIndex("Keyboard");
+            return asset.controlSchemes[m_KeyboardSchemeIndex];
+        }
+    }
+    public interface IMovementActions
+    {
+        void OnMovement(InputAction.CallbackContext context);
+        void OnJump(InputAction.CallbackContext context);
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2ae70f840ceeb927d11826ea1d02c0faea40dcc5
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockActions.cs.meta
@@ -0,0 +1,11 @@
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+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7e54ad7a20e8a962372dfeb3d2bfb678e2dcb12f
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs
@@ -0,0 +1,198 @@
+//Put this script on your blue cube.
+using System.Collections;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Random = UnityEngine.Random;
+
+public class PushBlockWithInputAgentBasic : Agent
+{
+    /// <summary>
+    /// The ground. The bounds are used to spawn the elements.
+    /// </summary>
+    public GameObject ground;
+
+    public GameObject area;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+    [HideInInspector]
+    public Bounds areaBounds;
+
+    PushBlockWithInputSettings m_PushBlockSettings;
+
+    /// <summary>
+    /// The block to be pushed to the goal.
+    /// </summary>
+    public GameObject block;
+
+    /// <summary>
+    /// Detects when the block touches the goal.
+    /// </summary>
+    [HideInInspector]
+    public GoalDetectWithInput goalDetect;
+
+    Rigidbody m_BlockRb; //cached on initialization
+    Rigidbody m_AgentRb; //cached on initialization
+    Material m_GroundMaterial; //cached on Awake()
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+    Renderer m_GroundRenderer;
+
+    EnvironmentParameters m_ResetParams;
+
+    protected override void Awake()
+    {
+        base.Awake();
+        m_PushBlockSettings = FindObjectOfType<PushBlockWithInputSettings>();
+
+        goalDetect = block.GetComponent<GoalDetectWithInput>();
+        goalDetect.agent = this;
+
+        // Cache the agent rigidbody
+        m_AgentRb = GetComponent<Rigidbody>();
+
+        // Cache the block rigidbody
+        m_BlockRb = block.GetComponent<Rigidbody>();
+
+        // Get the ground's bounds
+        areaBounds = ground.GetComponent<Collider>().bounds;
+
+        // Get the ground renderer so we can change the material when a goal is scored
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+
+        // Starting material
+        m_GroundMaterial = m_GroundRenderer.material;
+    }
+
+    public override void Initialize()
+    {
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+
+        SetResetParameters();
+    }
+
+    /// <summary>
+    /// Use the ground's bounds to pick a random spawn position.
+    /// </summary>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var foundNewSpawnLocation = false;
+        var randomSpawnPos = Vector3.zero;
+        var tries = 0;
+        while (foundNewSpawnLocation == false && tries < 50)
+        {
+            var randomPosX = Random.Range(-areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier);
+
+            var randomPosZ = Random.Range(-areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier);
+            randomSpawnPos = ground.transform.position + new Vector3(randomPosX, 1f, randomPosZ);
+            if (Physics.CheckBox(randomSpawnPos, new Vector3(2.5f, 0.01f, 2.5f)) == false)
+            {
+                foundNewSpawnLocation = true;
+            }
+
+            tries++;
+        }
+
+        return randomSpawnPos;
+    }
+
+
+    /// <summary>
+    /// Called when the agent moves the block into the goal.
+    /// </summary>
+    public void ScoredAGoal()
+    {
+        // We use a reward of 5.
+        AddReward(5f);
+
+        // By marking an agent as done AgentReset() will be called automatically.
+        EndEpisode();
+
+        // Swap ground material for a bit to indicate we scored.
+        StartCoroutine(GoalScoredSwapGroundMaterial(m_PushBlockSettings.goalScoredMaterial, 0.5f));
+    }
+
+    /// <summary>
+    /// Swap ground material, wait time seconds, then swap back to the regular material.
+    /// </summary>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time); // Wait for 2 sec
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        // Penalty given each step to encourage agent to finish task quickly.
+        AddReward(-1f / MaxStep);
+    }
+
+    /// <summary>
+    /// Resets the block position and velocities.
+    /// </summary>
+    void ResetBlock()
+    {
+        // Get a random position for the block.
+        block.transform.position = GetRandomSpawnPos();
+
+        // Reset block velocity back to zero.
+        m_BlockRb.velocity = Vector3.zero;
+
+        // Reset block angularVelocity back to zero.
+        m_BlockRb.angularVelocity = Vector3.zero;
+    }
+
+    /// <summary>
+    /// In the editor, if "Reset On Done" is checked then AgentReset() will be
+    /// called automatically anytime we mark done = true in an agent script.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        var rotation = Random.Range(0, 4);
+        var rotationAngle = rotation * 90f;
+        area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+
+        ResetBlock();
+        transform.position = GetRandomSpawnPos();
+        m_AgentRb.velocity = Vector3.zero;
+        m_AgentRb.angularVelocity = Vector3.zero;
+
+        SetResetParameters();
+    }
+
+    public void SetGroundMaterialFriction()
+    {
+        var groundCollider = ground.GetComponent<Collider>();
+
+        groundCollider.material.dynamicFriction = m_ResetParams.GetWithDefault("dynamic_friction", 0);
+        groundCollider.material.staticFriction = m_ResetParams.GetWithDefault("static_friction", 0);
+    }
+
+    public void SetBlockProperties()
+    {
+        var scale = m_ResetParams.GetWithDefault("block_scale", 2);
+
+        //Set the scale of the block
+        m_BlockRb.transform.localScale = new Vector3(scale, 0.75f, scale);
+
+        // Set the drag of the block
+        m_BlockRb.drag = m_ResetParams.GetWithDefault("block_drag", 0.5f);
+    }
+
+    void SetResetParameters()
+    {
+        SetGroundMaterialFriction();
+        SetBlockProperties();
+    }
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3f5e3d05b2945737a3b454a200b0ee1dcc7541ee
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputAgentBasic.cs.meta
@@ -0,0 +1,11 @@
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+  serializedVersion: 2
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9e9922429db22b5fbcc6a0e6802a518fb5f23f59
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs
@@ -0,0 +1,114 @@
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+/// <summary>
+/// This class handles the input for the PushBlock Cube character in the PushBlock scene.
+/// Note that the only ML-Agents code here is the implementation of the <see cref="IInputActionAssetProvider"/>.
+/// The <see cref="InputActuatorComponent"/> looks for a component that implements that interface in order to
+/// rebind actions to virtual controllers when training agents or running inference.  This means that you can
+/// keep your input handling code separate from ML-Agents, and have your agent's action space defined by the
+/// actions defined in your project's <see cref="GetInputActionAsset"/>.
+///
+/// If you don't implement <see cref="IInputActionAssetProvider"/> the <see cref="InputActuatorComponent"/> will
+/// look for a <see cref="PlayerInput"/> component on the GameObject it live on.  It will rebind the actions of that
+/// instance of the asset.
+///
+/// It is important to note that if you have multiple components on the same GameObject handling input, you will
+/// need to share the instance of the generated C# <see cref="IInputActionCollection2"/> (named <see cref="m_PushBlockActions"/>
+/// here) in order to ensure that all of your actions are bound correctly for ml-agents training and inference.
+/// </summary>
+public class PushBlockWithInputPlayerController : MonoBehaviour, IInputActionAssetProvider
+{
+
+    PushBlockWithInputSettings m_PushBlockSettings;
+    public float JumpTime = 0.5f;
+    float m_JumpTimeRemaining;
+    Rigidbody m_PlayerRb; //cached on initialization
+    PushBlockActions m_PushBlockActions;
+    float m_JumpCoolDownStart;
+
+    void Awake()
+    {
+        m_PushBlockSettings = FindObjectOfType<PushBlockWithInputSettings>();
+        LazyInitializeActions();
+
+        // Cache the agent rigidbody
+        m_PlayerRb = GetComponent<Rigidbody>();
+    }
+
+    void LazyInitializeActions()
+    {
+        if (m_PushBlockActions != null)
+        {
+            return;
+        }
+
+        m_PushBlockActions = new PushBlockActions();
+        m_PushBlockActions.Enable();
+
+        // You can listen to C# events.
+        m_PushBlockActions.Movement.jump.performed += JumpOnperformed;
+    }
+
+    void JumpOnperformed(InputAction.CallbackContext callbackContext)
+    {
+        InnerJump(gameObject.transform);
+    }
+
+    void FixedUpdate()
+    {
+        // Or you can poll the action itself like we do here.
+        InnerMove(gameObject.transform, m_PushBlockActions.Movement.movement.ReadValue<Vector2>());
+        if (m_JumpTimeRemaining < 0)
+        {
+            m_PlayerRb.AddForce(-transform.up * (m_PushBlockSettings.agentJumpForce * 3), ForceMode.Acceleration);
+        }
+
+        m_JumpTimeRemaining -= Time.fixedDeltaTime;
+    }
+
+    void InnerJump(Transform t)
+    {
+        if (Time.realtimeSinceStartup - m_JumpCoolDownStart > m_PushBlockSettings.agentJumpCoolDown)
+        {
+            m_JumpTimeRemaining = JumpTime;
+            m_PlayerRb.AddForce(t.up * m_PushBlockSettings.agentJumpForce, ForceMode.VelocityChange);
+            m_JumpCoolDownStart = Time.realtimeSinceStartup;
+        }
+    }
+
+    void InnerMove(Transform t, Vector2 v)
+    {
+        var forward = CreateForwardVector(v);
+        var up = CreateUpVector(v);
+        var dirToGo = t.forward * forward;
+        var rotateDir = t.up * up;
+        t.Rotate(rotateDir, Time.deltaTime * 200f);
+        m_PlayerRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    static float CreateUpVector(Vector2 move)
+    {
+        return Mathf.Abs(move.x) > Mathf.Abs(move.y) ? move.x : 0f;
+    }
+
+    static float CreateForwardVector(Vector2 move)
+    {
+        return Mathf.Abs(move.y) > Mathf.Abs(move.x) ? move.y : 0f;
+    }
+
+    /// <summary>
+    /// This is the implementation of the <see cref="IInputActionAssetProvider"/> for this class.  We need
+    /// both the <see cref="GetInputActionAsset"/> and the <see cref="IInputActionCollection2"/> if you are
+    /// listening to C# events, Unity Events, or receiving Messages from the Input System Package as those callbacks
+    /// are set up through the generated <see cref="IInputActionCollection2"/>.
+    /// </summary>
+    /// <returns></returns>
+    public (InputActionAsset, IInputActionCollection2) GetInputActionAsset()
+    {
+        LazyInitializeActions();
+        return (m_PushBlockActions.asset, m_PushBlockActions);
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs.meta b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..78e7862777484544feffe0db7ca0384bab2f60a0
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputPlayerController.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputSettings.cs b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputSettings.cs
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/PushBlockWithInput/Scripts/PushBlockWithInputSettings.cs
@@ -0,0 +1,39 @@
+using UnityEngine;
+
+public class PushBlockWithInputSettings : MonoBehaviour
+{
+    /// <summary>
+    /// The "walking speed" of the agents in the scene.
+    /// </summary>
+    public float agentRunSpeed;
+
+    /// <summary>
+    /// The agent rotation speed.
+    /// Every agent will use this setting.
+    /// </summary>
+    public float agentRotationSpeed;
+
+    public float agentJumpForce;
+
+    public float agentJumpCoolDown;
+
+    /// <summary>
+    /// The spawn area margin multiplier.
+    /// ex: .9 means 90% of spawn area will be used.
+    /// .1 margin will be left (so players don't spawn off of the edge).
+    /// The higher this value, the longer training time required.
+    /// </summary>
+    public float spawnAreaMarginMultiplier;
+
+    /// <summary>
+    /// When a goal is scored the ground will switch to this
+    /// material for a few seconds.
+    /// </summary>
+    public Material goalScoredMaterial;
+
+    /// <summary>
+    /// When an agent fails, the ground will turn this material for a few seconds.
+    /// </summary>
+    public Material failMaterial;
+
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Prefabs/StonePyramid.prefab.meta b/Project/Assets/ML-Agents/Examples/Pyramids/Prefabs/StonePyramid.prefab.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Scenes/Pyramids.unity.meta b/Project/Assets/ML-Agents/Examples/Pyramids/Scenes/Pyramids.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5371a33d8412ee01e26159eb76a35f8d54244f98
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs
@@ -0,0 +1,115 @@
+using System;
+using System.Linq;
+using UnityEngine;
+using Random = UnityEngine.Random;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+public class PyramidAgent : Agent
+{
+    public GameObject area;
+    PyramidArea m_MyArea;
+    Rigidbody m_AgentRb;
+    PyramidSwitch m_SwitchLogic;
+    public GameObject areaSwitch;
+    public bool useVectorObs;
+
+    public override void Initialize()
+    {
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_MyArea = area.GetComponent<PyramidArea>();
+        m_SwitchLogic = areaSwitch.GetComponent<PyramidSwitch>();
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        if (useVectorObs)
+        {
+            sensor.AddObservation(m_SwitchLogic.GetState());
+            sensor.AddObservation(transform.InverseTransformDirection(m_AgentRb.velocity));
+        }
+    }
+
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                rotateDir = transform.up * -1f;
+                break;
+        }
+        transform.Rotate(rotateDir, Time.deltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * 2f, ForceMode.VelocityChange);
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        AddReward(-1f / MaxStep);
+        MoveAgent(actionBuffers.DiscreteActions);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        var enumerable = Enumerable.Range(0, 9).OrderBy(x => Guid.NewGuid()).Take(9);
+        var items = enumerable.ToArray();
+
+        m_MyArea.CleanPyramidArea();
+
+        m_AgentRb.velocity = Vector3.zero;
+        m_MyArea.PlaceObject(gameObject, items[0]);
+        transform.rotation = Quaternion.Euler(new Vector3(0f, Random.Range(0, 360)));
+
+        m_SwitchLogic.ResetSwitch(items[1], items[2]);
+        m_MyArea.CreateStonePyramid(1, items[3]);
+        m_MyArea.CreateStonePyramid(1, items[4]);
+        m_MyArea.CreateStonePyramid(1, items[5]);
+        m_MyArea.CreateStonePyramid(1, items[6]);
+        m_MyArea.CreateStonePyramid(1, items[7]);
+        m_MyArea.CreateStonePyramid(1, items[8]);
+    }
+
+    void OnCollisionEnter(Collision collision)
+    {
+        if (collision.gameObject.CompareTag("goal"))
+        {
+            SetReward(2f);
+            EndEpisode();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs.meta b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidArea.cs b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidArea.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2116e172a2e4c4f897a56eb3eab4ebfc392a9f6d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidArea.cs
@@ -0,0 +1,54 @@
+using UnityEngine;
+using Unity.MLAgentsExamples;
+
+public class PyramidArea : Area
+{
+    public GameObject pyramid;
+    public GameObject stonePyramid;
+    public GameObject[] spawnAreas;
+    public int numPyra;
+    public float range;
+
+    public void CreatePyramid(int numObjects, int spawnAreaIndex)
+    {
+        CreateObject(numObjects, pyramid, spawnAreaIndex);
+    }
+
+    public void CreateStonePyramid(int numObjects, int spawnAreaIndex)
+    {
+        CreateObject(numObjects, stonePyramid, spawnAreaIndex);
+    }
+
+    void CreateObject(int numObjects, GameObject desiredObject, int spawnAreaIndex)
+    {
+        for (var i = 0; i < numObjects; i++)
+        {
+            var newObject = Instantiate(desiredObject, Vector3.zero,
+                Quaternion.Euler(0f, 0f, 0f), transform);
+            PlaceObject(newObject, spawnAreaIndex);
+        }
+    }
+
+    public void PlaceObject(GameObject objectToPlace, int spawnAreaIndex)
+    {
+        var spawnTransform = spawnAreas[spawnAreaIndex].transform;
+        var xRange = spawnTransform.localScale.x / 2.1f;
+        var zRange = spawnTransform.localScale.z / 2.1f;
+
+        objectToPlace.transform.position = new Vector3(Random.Range(-xRange, xRange), 2f, Random.Range(-zRange, zRange))
+            + spawnTransform.position;
+    }
+
+    public void CleanPyramidArea()
+    {
+        foreach (Transform child in transform)
+            if (child.CompareTag("pyramid"))
+            {
+                Destroy(child.gameObject);
+            }
+    }
+
+    public override void ResetArea()
+    {
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidSwitch.cs b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidSwitch.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6f2b62739836b1d518c73081441e8b4d90123267
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidSwitch.cs
@@ -0,0 +1,44 @@
+using UnityEngine;
+
+public class PyramidSwitch : MonoBehaviour
+{
+    public Material onMaterial;
+    public Material offMaterial;
+    public GameObject myButton;
+    bool m_State;
+    GameObject m_Area;
+    PyramidArea m_AreaComponent;
+    int m_PyramidIndex;
+
+    public bool GetState()
+    {
+        return m_State;
+    }
+
+    void Start()
+    {
+        m_Area = gameObject.transform.parent.gameObject;
+        m_AreaComponent = m_Area.GetComponent<PyramidArea>();
+    }
+
+    public void ResetSwitch(int spawnAreaIndex, int pyramidSpawnIndex)
+    {
+        m_AreaComponent.PlaceObject(gameObject, spawnAreaIndex);
+        m_State = false;
+        m_PyramidIndex = pyramidSpawnIndex;
+        tag = "switchOff";
+        transform.rotation = Quaternion.Euler(0f, 0f, 0f);
+        myButton.GetComponent<Renderer>().material = offMaterial;
+    }
+
+    void OnCollisionEnter(Collision other)
+    {
+        if (other.gameObject.CompareTag("agent") && m_State == false)
+        {
+            myButton.GetComponent<Renderer>().material = onMaterial;
+            m_State = true;
+            m_AreaComponent.CreatePyramid(1, m_PyramidIndex);
+            tag = "switchOn";
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/GridPatternShader.shader b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/GridPatternShader.shader
new file mode 100644
index 0000000000000000000000000000000000000000..9979743aea1c7fad27c1370d0c2f044f489f45cf
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/GridPatternShader.shader
@@ -0,0 +1,162 @@
+// Upgrade NOTE: upgraded instancing buffer 'Props' to new syntax.
+
+Shader "ML-Agents/GridPattern" {
+	Properties {
+		_LineColor ("Line Color", Color) = (1,1,1,1)
+		_CellColor ("Cell Color", Color) = (0,0,0,0)
+		// _SelectedColor ("Selected Color", Color) = (1,0,0,1)
+		[PerRendererData] _MainTex ("Albedo (RGB)", 2D) = "white" {}
+		[IntRange] _GridSize("Grid Size", Range(1,100)) = 10
+		_LineSize("Line Size", Range(0,1)) = 0.15
+		// [FloatRange] _LineOffset("Line Offset", Range(0,1)) = 0
+		[IntRange] _DrawU("Draw U Toggle ( 0 = False , 1 = True )", Range(0,1)) = 1
+		[IntRange] _DrawV("Draw V Toggle ( 0 = False , 1 = True )", Range(0,1)) = 1
+		// [IntRange] _SelectCell("Select Cell Toggle ( 0 = False , 1 = True )", Range(0,1)) = 0.0
+		// [IntRange] _SelectedCellX("Selected Cell X", Range(0,100)) = 0.0
+		// [IntRange] _SelectedCellY("Selected Cell Y", Range(0,100)) = 0.0
+	}
+	SubShader {
+		Tags { "Queue"="AlphaTest" "RenderType"="TransparentCutout" }
+		LOD 200
+	
+
+		CGPROGRAM
+		// Physically based Standard lighting model, and enable shadows on all light types
+		#pragma surface surf Standard fullforwardshadows
+
+		// Use shader model 3.0 target, to get nicer looking lighting
+		#pragma target 3.0
+
+		sampler2D _MainTex;
+
+		struct Input {
+			float2 uv_MainTex;
+		};
+
+		half _Glossiness = 0.0;
+		half _Metallic = 0.0;
+		float4 _LineColor;
+		float4 _CellColor;
+		// float4 _SelectedColor;
+
+		float _GridSize;
+		// float _LineOffset;
+		float _LineSize;
+
+		float _DrawU;
+		float _DrawV;
+		// float _SelectCell;
+		// float _SelectedCellX;
+		// float _SelectedCellY;
+
+		// Add instancing support for this shader. You need to check 'Enable Instancing' on materials that use the shader.
+		// See https://docs.unity3d.com/Manual/GPUInstancing.html for more information about instancing.
+		// #pragma instancing_options assumeuniformscaling
+		UNITY_INSTANCING_BUFFER_START(Props)
+			// put more per-instance properties here
+		UNITY_INSTANCING_BUFFER_END(Props)
+
+		void surf (Input IN, inout SurfaceOutputStandard o) {
+			// Albedo comes from a texture tinted by color
+
+			float2 uv = IN.uv_MainTex;
+
+			// _SelectedCellX = floor(_SelectedCellX);
+			// _SelectedCellY = floor(_SelectedCellY);
+
+			fixed4 c = float4(0.0,0.0,0.0,0.0);
+
+			float brightness = 1.0;
+
+			float gsize = floor(_GridSize);
+
+
+
+			gsize += _LineSize;
+
+			float2 id;
+
+			id.x = floor(uv.x/(1.0/gsize));
+			// id.y = floor(uv.y/(1.0/gsize));
+			id.y = floor(uv.y/(1.0/gsize));
+
+			float4 color = _CellColor;
+			brightness = _CellColor.w;
+
+			// //This checks that the cell is currently selected if the Select Cell slider is set to 1 ( True )
+			// if (round(_SelectCell) == 1.0 && id.x == _SelectedCellX && id.y == _SelectedCellY)
+			// {
+			// 	brightness = _SelectedColor.w;
+			// 	color = _SelectedColor;
+			// }
+
+			// if (frac(uv.x*gsize) <= _LineSize || frac(uv.y*gsize) <= _LineSize)
+			// {
+			// 	brightness = _LineColor.w;
+			// 	color = _LineColor;
+			// }
+			if(round(_DrawU) == 1.0)
+			{
+				if (frac(uv.x*gsize) <= _LineSize)
+				{
+					// if (frac(uv.x*gsize) <= _LineSize)
+					// {
+					brightness = _LineColor.w;
+					color = _LineColor;
+				}
+			}
+			if(round(_DrawV) == 1.0)
+			{
+				if (frac(uv.y*gsize) <= _LineSize)
+				{
+					brightness = _LineColor.w;
+					color = _LineColor;
+				}
+			}
+			// // if (round(uv.x*gsize) == .8)
+			// // if (uv.x >= _LineOffset)
+			// if (uv.x >= .2 && uv.x <= .3)
+			// {
+			// 	brightness = _LineColor.w;
+			// 	color = _LineColor;
+			// 	// color = _CellColor;
+			// 	// brightness = _CellColor.w;
+			// }
+
+			// if (frac(uv.x*gsize) <= _LineSize && frac(uv.y*gsize) <= _LineSize)
+			// {
+			// 	// brightness = _LineColor.w;
+			// 	// color = _LineColor;
+			// 	color = _CellColor;
+			// 	brightness = _CellColor.w;
+			// }
+
+
+			// if (frac(uv.x*gsize/_LineOffset) <= _LineSize)
+			// {
+			// 	brightness = _LineColor.w;
+			// 	color = _LineColor;
+			// }
+			// if (frac(uv.x*gsize/.5) <= _LineSize || fradc(uv.y*gsize) <= _LineSize)
+			// {
+			// 	brightness = _LineColor.w;
+			// 	color = _LineColor;
+			// }
+			
+
+			//Clip transparent spots using alpha cutout
+			if (brightness == 0.0) {
+				clip(c.a - 1.0);
+			}
+			
+
+			o.Albedo = float4( color.x*brightness,color.y*brightness,color.z*brightness,brightness);
+			// Metallic and smoothness come from slider variables
+			o.Metallic = 0.0;
+			o.Smoothness = 0.0;
+			o.Alpha = 0.0;
+		}
+		ENDCG
+	}
+	FallBack "Diffuse"
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/GridPatternShader.shader.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/GridPatternShader.shader.meta
new file mode 100644
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/Headband.mat b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/Headband.mat
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+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderOutline.shader
@@ -0,0 +1,88 @@
+// Upgrade NOTE: replaced 'mul(UNITY_MATRIX_MVP,*)' with 'UnityObjectToClipPos(*)'
+
+Shader "Custom/Outline and ScreenSpace texture"
+{
+	Properties
+	{
+		[Header(Outline)]
+		_OutlineVal ("Outline value", Range(0., 2.)) = 1.
+		_OutlineCol ("Outline color", color) = (1., 1., 1., 1.)
+		[Header(Texture)]
+		_MainTex ("Texture", 2D) = "white" {}
+		_Zoom ("Zoom", Range(0.5, 20)) = 1
+		_SpeedX ("Speed along X", Range(-1, 1)) = 0
+		_SpeedY ("Speed along Y", Range(-1, 1)) = 0
+	}
+	SubShader
+	{
+		Tags { "Queue"="Geometry" "RenderType"="Opaque" }
+		
+		Pass
+		{
+			Cull Front
+
+			CGPROGRAM
+			#pragma vertex vert
+			#pragma fragment frag
+			#include "UnityCG.cginc"
+
+			struct v2f {
+				float4 pos : SV_POSITION;
+			};
+
+			float _OutlineVal;
+
+			v2f vert(appdata_base v) {
+				v2f o;
+
+				// Convert vertex to clip space
+				o.pos = UnityObjectToClipPos(v.vertex);
+
+				// Convert normal to view space (camera space)
+				float3 normal = mul((float3x3) UNITY_MATRIX_IT_MV, v.normal);
+
+				// Compute normal value in clip space
+				normal.x *= UNITY_MATRIX_P[0][0];
+				normal.y *= UNITY_MATRIX_P[1][1];
+
+				// Scale the model depending the previous computed normal and outline value
+				o.pos.xy += _OutlineVal * normal.xy;
+				return o;
+			}
+
+			fixed4 _OutlineCol;
+
+			fixed4 frag(v2f i) : SV_Target {
+				return _OutlineCol;
+			}
+
+			ENDCG
+		}
+
+		Pass
+		{
+			CGPROGRAM
+			#pragma vertex vert
+			#pragma fragment frag
+			#include "UnityCG.cginc"
+
+			float4 vert(appdata_base v) : SV_POSITION
+			{
+				return UnityObjectToClipPos(v.vertex);
+			}
+
+			sampler2D _MainTex;
+			float _Zoom;
+			float _SpeedX;
+			float _SpeedY;
+
+			fixed4 frag(float4 i : VPOS) : SV_Target
+			{
+				// Screen space texture
+				return tex2D(_MainTex, ((i.xy / _ScreenParams.xy) + float2(_Time.y * _SpeedX, _Time.y * _SpeedY)) / _Zoom);
+			}
+
+			ENDCG
+		}
+	}
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderOutline.shader.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderOutline.shader.meta
new file mode 100644
index 0000000000000000000000000000000000000000..dcbd0f9d60e69f78dc910f43ca9a62765bda365e
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderOutline.shader.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 5dfe02aa25125453eaaaa295936dc8cd
+ShaderImporter:
+  externalObjects: {}
+  defaultTextures: []
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderRadialGrid.shader b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderRadialGrid.shader
new file mode 100644
index 0000000000000000000000000000000000000000..3de3352442ff603dfd07f638c5cb5c9258b49fff
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Materials/ShaderRadialGrid.shader
@@ -0,0 +1,88 @@
+Shader "Custom/WireFrame" {
+	Properties {
+		_LineColor ("LineColor", Color) = (1, 1, 1, 1)
+		_MainColor ("_MainColor", Color) = (1, 1, 1, 1)
+		_LineWidth ("Line width", Range(0, 1)) = 0.1
+		_ParcelSize ("ParcelSize", Range(0, 100)) = 1
+	}
+	SubShader {
+		Tags { "Queue"="Transparent" "RenderType"="Transparent" }
+		
+		CGPROGRAM
+		#pragma surface surf Lambert alpha
+
+		sampler2D _MainTex;
+		float4 _LineColor;
+		float4 _MainColor;
+		fixed _LineWidth;
+		float _ParcelSize;
+
+		struct Input {
+			float2 uv_MainTex;
+			float3 worldPos;
+		};
+
+		void surf (Input IN, inout SurfaceOutput o) {
+			half val1 = step(_LineWidth * 2, frac(IN.worldPos.x / _ParcelSize) + _LineWidth);
+			half val2 = step(_LineWidth * 2, frac(IN.worldPos.z / _ParcelSize) + _LineWidth);
+			fixed val = 1 - (val1 * val2);
+			o.Albedo = lerp(_MainColor, _LineColor, val);
+			o.Alpha = 1;
+		}
+		ENDCG
+	} 
+	FallBack "Diffuse"
+}
+
+// //Shader uses screen-space partial derivatives, works the best with terrain meshes.
+
+// Shader "Wireframe"
+// {
+// 	Properties
+// 	{
+// 		[Header(Settings)] [Toggle] _transparency ("Transparency", Float) = 1					
+// 	}
+// 	Subshader
+// 	{
+// 		Pass
+// 		{
+// 			Cull Off
+// 			CGPROGRAM
+// 			#pragma vertex vertex_shader
+// 			#pragma fragment pixel_shader
+// 			#pragma target 3.0
+			
+// 			struct structure
+// 			{
+// 				float4 gl_Position : SV_POSITION;
+// 				float3 vertex : TEXCOORD0;
+// 			};
+
+// 			float _transparency;
+			
+// 			structure vertex_shader (float4 vertex:POSITION) 
+// 			{
+// 				structure vs;
+// 				vs.gl_Position = UnityObjectToClipPos (vertex);
+// 				vs.vertex = vertex;
+// 				return vs;
+// 			}
+
+// 			float4 pixel_shader (structure ps) : COLOR
+// 			{
+// 				float2 p = ps.vertex.xz;
+// 				float2 g = abs(frac(p - 0.5) - 0.5) / fwidth(p);
+// 				float s = min(g.x, g.y);
+// 				float4 c =  float4(s,s,s, 1.0);	
+// 				if (c.r<1.0)
+// 					return 1.0-c;
+// 				else
+// 				{
+// 					if (_transparency==1) discard;
+// 					return 0;
+// 				}
+// 			}
+// 			ENDCG
+// 		}
+// 	}
+// }
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Prefabs/Logo-PlaneMesh-BLACK.prefab b/Project/Assets/ML-Agents/Examples/SharedAssets/Prefabs/Logo-PlaneMesh-BLACK.prefab
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e111353c99d36a69fc90c1b6a1eb96a2ee8fd0c5
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs
@@ -0,0 +1,54 @@
+//This script lets you change time scale during training. It is not a required script for this demo to function
+
+using UnityEngine;
+
+namespace MLAgentsExamples
+{
+    public class AdjustTrainingTimescale : MonoBehaviour
+    {
+        // Update is called once per frame
+        void Update()
+        {
+            if (Input.GetKeyDown(KeyCode.Alpha1))
+            {
+                Time.timeScale = 1f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha2))
+            {
+                Time.timeScale = 2f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha3))
+            {
+                Time.timeScale = 3f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha4))
+            {
+                Time.timeScale = 4f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha5))
+            {
+                Time.timeScale = 5f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha6))
+            {
+                Time.timeScale = 6f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha7))
+            {
+                Time.timeScale = 7f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha8))
+            {
+                Time.timeScale = 8f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha9))
+            {
+                Time.timeScale = 9f;
+            }
+            if (Input.GetKeyDown(KeyCode.Alpha0))
+            {
+                Time.timeScale *= 2f;
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs.meta
new file mode 100644
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+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/AdjustTrainingTimescale.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs
new file mode 100644
index 0000000000000000000000000000000000000000..65bc08acdac9f67b05921a9718943913e168d324
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs
@@ -0,0 +1,11 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    public class Area : MonoBehaviour
+    {
+        public virtual void ResetArea()
+        {
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d387223c67801df97d2d96a19e13f71310117fa1
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs
@@ -0,0 +1,31 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    public class CameraFollow : MonoBehaviour
+    {
+        [Tooltip("The target to follow")] public Transform target;
+
+        [Tooltip("The time it takes to move to the new position")]
+        public float smoothingTime; //The time it takes to move to the new position
+
+        private Vector3 m_Offset;
+        private Vector3 m_CamVelocity; //Camera's velocity (used by SmoothDamp)
+
+        // Use this for initialization
+        void Start()
+        {
+            m_Offset = gameObject.transform.position - target.position;
+        }
+
+        void FixedUpdate()
+        {
+            var newPosition = new Vector3(target.position.x + m_Offset.x, transform.position.y,
+                target.position.z + m_Offset.z);
+
+            gameObject.transform.position =
+                Vector3.SmoothDamp(transform.position, newPosition, ref m_CamVelocity, smoothingTime, Mathf.Infinity,
+                    Time.fixedDeltaTime);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cd6bf5139e4aaff290779f5da641a00d0759622f
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs.meta
@@ -0,0 +1,12 @@
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CollisionCallbacks.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CollisionCallbacks.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c9a26bfc8cdcbf62f7ef80fa72fcda5da94b7fd9
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CollisionCallbacks.cs
@@ -0,0 +1,141 @@
+using UnityEngine;
+using UnityEngine.Events;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// Utility class to allow target placement and collision detection with an agent
+    /// Add this script to the target you want the agent to touch.
+    /// Callbacks will be triggered any time the target is touched with a collider tagged as 'tagToDetect'
+    /// </summary>
+    public class CollisionCallbacks : MonoBehaviour
+    {
+        //        [System.Serializable] public class BoolEvent : UnityEvent<bool> { }
+        //        [SerializeField] BoolEvent boolEvent = new BoolEvent();
+        //        public void OnBoolEvent(bool value)
+        //        {
+        //            Debug.Log($"OnBoolEvent {value}");
+        //        }
+
+
+        [Header("Collider Tag To Detect")]
+        public string tagToDetect = "agent"; //collider tag to detect
+
+        //        [Header("Target Placement")]
+        //        public float spawnRadius; //The radius in which a target can be randomly spawned.
+        //        public bool respawnIfTouched; //Should the target respawn to a different position when touched
+        //
+        //        [Header("Target Fell Protection")]
+        //        public bool respawnIfFallsOffPlatform = true; //If the target falls off the platform, reset the position.
+        //        public float fallDistance = 5; //distance below the starting height that will trigger a respawn
+        //
+        //
+        //        private Vector3 m_startingPos; //the starting position of the target
+        //        private Agent m_agentTouching; //the agent currently touching the target
+
+        [System.Serializable]
+        //        public class TriggerEvent : UnityEvent<string>
+        public class TriggerEvent : UnityEvent<Collider>
+        {
+        }
+
+        [Header("Trigger Callbacks")]
+        public TriggerEvent onTriggerEnterEvent = new TriggerEvent();
+        public TriggerEvent onTriggerStayEvent = new TriggerEvent();
+        public TriggerEvent onTriggerExitEvent = new TriggerEvent();
+
+        [System.Serializable]
+        public class CollisionEvent : UnityEvent<Collision, Transform>
+        {
+        }
+
+        [Header("Collision Callbacks")]
+        public CollisionEvent onCollisionEnterEvent = new CollisionEvent();
+        public CollisionEvent onCollisionStayEvent = new CollisionEvent();
+        public CollisionEvent onCollisionExitEvent = new CollisionEvent();
+
+        //        // Start is called before the first frame update
+        //        void OnEnable()
+        //        {
+        //            m_startingPos = transform.position;
+        //            if (respawnIfTouched)
+        //            {
+        //                MoveTargetToRandomPosition();
+        //            }
+        //        }
+
+        //        void Update()
+        //        {
+        //            if (respawnIfFallsOffPlatform)
+        //            {
+        //                if (transform.position.y < m_startingPos.y - fallDistance)
+        //                {
+        //                    Debug.Log($"{transform.name} Fell Off Platform");
+        //                    MoveTargetToRandomPosition();
+        //                }
+        //            }
+        //        }
+
+        //        /// <summary>
+        //        /// Moves target to a random position within specified radius.
+        //        /// </summary>
+        //        public void MoveTargetToRandomPosition()
+        //        {
+        //            var newTargetPos = m_startingPos + (Random.insideUnitSphere * spawnRadius);
+        //            newTargetPos.y = m_startingPos.y;
+        //            transform.position = newTargetPos;
+        //        }
+
+        private void OnCollisionEnter(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionEnterEvent.Invoke(col, transform);
+                //                if (respawnIfTouched)
+                //                {
+                //                    MoveTargetToRandomPosition();
+                //                }
+            }
+        }
+
+        private void OnCollisionStay(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionStayEvent.Invoke(col, transform);
+            }
+        }
+
+        private void OnCollisionExit(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionExitEvent.Invoke(col, transform);
+            }
+        }
+
+        private void OnTriggerEnter(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerEnterEvent.Invoke(col);
+            }
+        }
+
+        private void OnTriggerStay(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerStayEvent.Invoke(col);
+            }
+        }
+
+        private void OnTriggerExit(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerExitEvent.Invoke(col);
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CollisionCallbacks.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CollisionCallbacks.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fe6a07f0c799b43108b4b84597cf0eb93b177ff1
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs
@@ -0,0 +1,37 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    public class DirectionIndicator : MonoBehaviour
+    {
+
+        public bool updatedByAgent; //should this be updated by the agent? If not, it will use local settings
+        public Transform transformToFollow; //ex: hips or body
+        public Transform targetToLookAt; //target in the scene the indicator will point to
+        public float heightOffset;
+        private float m_StartingYPos;
+
+        void OnEnable()
+        {
+            m_StartingYPos = transform.position.y;
+        }
+
+        void Update()
+        {
+            if (updatedByAgent)
+                return;
+            transform.position = new Vector3(transformToFollow.position.x, m_StartingYPos + heightOffset,
+                transformToFollow.position.z);
+            Vector3 walkDir = targetToLookAt.position - transform.position;
+            walkDir.y = 0; //flatten dir on the y
+            transform.rotation = Quaternion.LookRotation(walkDir);
+        }
+
+        //Public method to allow an agent to directly update this component
+        public void MatchOrientation(Transform t)
+        {
+            transform.position = new Vector3(t.position.x, m_StartingYPos + heightOffset, t.position.z);
+            transform.rotation = t.rotation;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7d3b7674caacfc9187a87cc9be8cc755f00bba0d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 1513f8a85fedd47efba089213b7c5bde
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8fe3fd0cdd3a52c20e6438209f596248b064e2e5
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs
@@ -0,0 +1,116 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    public class FlyCamera : MonoBehaviour
+    {
+        /*
+        wasd : basic movement
+        shift : Makes camera accelerate
+        space : Moves camera on X and Z axis only.  So camera doesn't gain any height*/
+
+
+        public float mainSpeed = 100.0f; // regular speed
+        public float shiftAdd = 250.0f; // multiplied by how long shift is held.  Basically running
+        public float maxShift = 1000.0f; // Maximum speed when holdin gshift
+        public float camSens = 0.25f; // How sensitive it with mouse
+        public bool rotateOnlyIfMousedown = true;
+        public bool movementStaysFlat = true;
+
+        Vector3
+            m_LastMouse =
+            new Vector3(255, 255,
+                255);     // kind of in the middle of the screen, rather than at the top (play)
+
+        float m_TotalRun = 1.0f;
+
+        void Awake()
+        {
+            Debug.Log("FlyCamera Awake() - RESETTING CAMERA POSITION"); // nop?
+            // nop:
+            // transform.position.Set(0,8,-32);
+            // transform.rotation.Set(15,0,0,1);
+            transform.position = new Vector3(0, 8, -32);
+            transform.rotation = Quaternion.Euler(25, 0, 0);
+        }
+
+        void Update()
+        {
+            if (Input.GetMouseButtonDown(1))
+            {
+                m_LastMouse = Input.mousePosition; // $CTK reset when we begin
+            }
+
+            if (!rotateOnlyIfMousedown ||
+                (rotateOnlyIfMousedown && Input.GetMouseButton(1)))
+            {
+                m_LastMouse = Input.mousePosition - m_LastMouse;
+                m_LastMouse = new Vector3(-m_LastMouse.y * camSens, m_LastMouse.x * camSens, 0);
+                m_LastMouse = new Vector3(transform.eulerAngles.x + m_LastMouse.x,
+                    transform.eulerAngles.y + m_LastMouse.y, 0);
+                transform.eulerAngles = m_LastMouse;
+                m_LastMouse = Input.mousePosition;
+                // Mouse  camera angle done.
+            }
+
+            // Keyboard commands
+            var p = GetBaseInput();
+            if (Input.GetKey(KeyCode.LeftShift))
+            {
+                m_TotalRun += Time.deltaTime;
+                p = shiftAdd * m_TotalRun * p;
+                p.x = Mathf.Clamp(p.x, -maxShift, maxShift);
+                p.y = Mathf.Clamp(p.y, -maxShift, maxShift);
+                p.z = Mathf.Clamp(p.z, -maxShift, maxShift);
+            }
+            else
+            {
+                m_TotalRun = Mathf.Clamp(m_TotalRun * 0.5f, 1f, 1000f);
+                p = p * mainSpeed;
+            }
+
+            p = p * Time.deltaTime;
+            var newPosition = transform.position;
+            if (Input.GetKey(KeyCode.Space)
+                || (movementStaysFlat && !(rotateOnlyIfMousedown && Input.GetMouseButton(1))))
+            {
+                // If player wants to move on X and Z axis only
+                transform.Translate(p);
+                newPosition.x = transform.position.x;
+                newPosition.z = transform.position.z;
+                transform.position = newPosition;
+            }
+            else
+            {
+                transform.Translate(p);
+            }
+        }
+
+        Vector3 GetBaseInput()
+        {
+            // returns the basic values, if it's 0 than it's not active.
+            var pVelocity = new Vector3();
+            if (Input.GetKey(KeyCode.W))
+            {
+                pVelocity += new Vector3(0, 0, 1);
+            }
+
+            if (Input.GetKey(KeyCode.S))
+            {
+                pVelocity += new Vector3(0, 0, -1);
+            }
+
+            if (Input.GetKey(KeyCode.A))
+            {
+                pVelocity += new Vector3(-1, 0, 0);
+            }
+
+            if (Input.GetKey(KeyCode.D))
+            {
+                pVelocity += new Vector3(1, 0, 0);
+            }
+
+            return pVelocity;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..31dd4804738369984615a2817da1c9060d8222be
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs.meta
@@ -0,0 +1,12 @@
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+guid: 83d7b886385fb48b999bc3fbc41bc181
+timeCreated: 1508627099
+licenseType: Pro
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs
new file mode 100644
index 0000000000000000000000000000000000000000..785d3579814f70c1866c403b375d56fbd74f56aa
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs
@@ -0,0 +1,53 @@
+using UnityEngine;
+using Unity.MLAgents;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// This class contains logic for locomotion agents with joints which might make contact with the ground.
+    /// By attaching this as a component to those joints, their contact with the ground can be used as either
+    /// an observation for that agent, and/or a means of punishing the agent for making undesirable contact.
+    /// </summary>
+    [DisallowMultipleComponent]
+    public class GroundContact : MonoBehaviour
+    {
+        [HideInInspector] public Agent agent;
+
+        [Header("Ground Check")] public bool agentDoneOnGroundContact; // Whether to reset agent on ground contact.
+        public bool penalizeGroundContact; // Whether to penalize on contact.
+        public float groundContactPenalty; // Penalty amount (ex: -1).
+        public bool touchingGround;
+        const string k_Ground = "ground"; // Tag of ground object.
+
+        /// <summary>
+        /// Check for collision with ground, and optionally penalize agent.
+        /// </summary>
+        void OnCollisionEnter(Collision col)
+        {
+            if (col.transform.CompareTag(k_Ground))
+            {
+                touchingGround = true;
+                if (penalizeGroundContact)
+                {
+                    agent.SetReward(groundContactPenalty);
+                }
+
+                if (agentDoneOnGroundContact)
+                {
+                    agent.EndEpisode();
+                }
+            }
+        }
+
+        /// <summary>
+        /// Check for end of ground collision and reset flag appropriately.
+        /// </summary>
+        void OnCollisionExit(Collision other)
+        {
+            if (other.transform.CompareTag(k_Ground))
+            {
+                touchingGround = false;
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d0857933f277ead547fdf382b938b899b06cf15d
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs.meta
@@ -0,0 +1,11 @@
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+  defaultReferences: []
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3a443978b5097a7c26207d406ec0fc9f59c32956
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs
@@ -0,0 +1,188 @@
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Serialization;
+using Unity.MLAgents;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// Used to store relevant information for acting and learning for each body part in agent.
+    /// </summary>
+    [System.Serializable]
+    public class BodyPart
+    {
+        [Header("Body Part Info")][Space(10)] public ConfigurableJoint joint;
+        public Rigidbody rb;
+        [HideInInspector] public Vector3 startingPos;
+        [HideInInspector] public Quaternion startingRot;
+
+        [Header("Ground & Target Contact")]
+        [Space(10)]
+        public GroundContact groundContact;
+
+        public TargetContact targetContact;
+
+        [FormerlySerializedAs("thisJDController")]
+        [HideInInspector] public JointDriveController thisJdController;
+
+        [Header("Current Joint Settings")]
+        [Space(10)]
+        public Vector3 currentEularJointRotation;
+
+        [HideInInspector] public float currentStrength;
+        public float currentXNormalizedRot;
+        public float currentYNormalizedRot;
+        public float currentZNormalizedRot;
+
+        [Header("Other Debug Info")]
+        [Space(10)]
+        public Vector3 currentJointForce;
+
+        public float currentJointForceSqrMag;
+        public Vector3 currentJointTorque;
+        public float currentJointTorqueSqrMag;
+        public AnimationCurve jointForceCurve = new AnimationCurve();
+        public AnimationCurve jointTorqueCurve = new AnimationCurve();
+
+        /// <summary>
+        /// Reset body part to initial configuration.
+        /// </summary>
+        public void Reset(BodyPart bp)
+        {
+            bp.rb.transform.position = bp.startingPos;
+            bp.rb.transform.rotation = bp.startingRot;
+            bp.rb.velocity = Vector3.zero;
+            bp.rb.angularVelocity = Vector3.zero;
+            if (bp.groundContact)
+            {
+                bp.groundContact.touchingGround = false;
+            }
+
+            if (bp.targetContact)
+            {
+                bp.targetContact.touchingTarget = false;
+            }
+        }
+
+        /// <summary>
+        /// Apply torque according to defined goal `x, y, z` angle and force `strength`.
+        /// </summary>
+        public void SetJointTargetRotation(float x, float y, float z)
+        {
+            x = (x + 1f) * 0.5f;
+            y = (y + 1f) * 0.5f;
+            z = (z + 1f) * 0.5f;
+
+            var xRot = Mathf.Lerp(joint.lowAngularXLimit.limit, joint.highAngularXLimit.limit, x);
+            var yRot = Mathf.Lerp(-joint.angularYLimit.limit, joint.angularYLimit.limit, y);
+            var zRot = Mathf.Lerp(-joint.angularZLimit.limit, joint.angularZLimit.limit, z);
+
+            currentXNormalizedRot =
+                Mathf.InverseLerp(joint.lowAngularXLimit.limit, joint.highAngularXLimit.limit, xRot);
+            currentYNormalizedRot = Mathf.InverseLerp(-joint.angularYLimit.limit, joint.angularYLimit.limit, yRot);
+            currentZNormalizedRot = Mathf.InverseLerp(-joint.angularZLimit.limit, joint.angularZLimit.limit, zRot);
+
+            joint.targetRotation = Quaternion.Euler(xRot, yRot, zRot);
+            currentEularJointRotation = new Vector3(xRot, yRot, zRot);
+        }
+
+        public void SetJointStrength(float strength)
+        {
+            var rawVal = (strength + 1f) * 0.5f * thisJdController.maxJointForceLimit;
+            var jd = new JointDrive
+            {
+                positionSpring = thisJdController.maxJointSpring,
+                positionDamper = thisJdController.jointDampen,
+                maximumForce = rawVal
+            };
+            joint.slerpDrive = jd;
+            currentStrength = jd.maximumForce;
+        }
+    }
+
+    public class JointDriveController : MonoBehaviour
+    {
+        [Header("Joint Drive Settings")]
+        [Space(10)]
+        public float maxJointSpring;
+
+        public float jointDampen;
+        public float maxJointForceLimit;
+
+        [HideInInspector] public Dictionary<Transform, BodyPart> bodyPartsDict = new Dictionary<Transform, BodyPart>();
+
+        [HideInInspector] public List<BodyPart> bodyPartsList = new List<BodyPart>();
+        const float k_MaxAngularVelocity = 50.0f;
+
+        /// <summary>
+        /// Create BodyPart object and add it to dictionary.
+        /// </summary>
+        public void SetupBodyPart(Transform t)
+        {
+            var bp = new BodyPart
+            {
+                rb = t.GetComponent<Rigidbody>(),
+                joint = t.GetComponent<ConfigurableJoint>(),
+                startingPos = t.position,
+                startingRot = t.rotation
+            };
+            bp.rb.maxAngularVelocity = k_MaxAngularVelocity;
+
+            // Add & setup the ground contact script
+            bp.groundContact = t.GetComponent<GroundContact>();
+            if (!bp.groundContact)
+            {
+                bp.groundContact = t.gameObject.AddComponent<GroundContact>();
+                bp.groundContact.agent = gameObject.GetComponent<Agent>();
+            }
+            else
+            {
+                bp.groundContact.agent = gameObject.GetComponent<Agent>();
+            }
+
+            if (bp.joint)
+            {
+                var jd = new JointDrive
+                {
+                    positionSpring = maxJointSpring,
+                    positionDamper = jointDampen,
+                    maximumForce = maxJointForceLimit
+                };
+                bp.joint.slerpDrive = jd;
+            }
+
+            bp.thisJdController = this;
+            bodyPartsDict.Add(t, bp);
+            bodyPartsList.Add(bp);
+        }
+
+        public void GetCurrentJointForces()
+        {
+            foreach (var bodyPart in bodyPartsDict.Values)
+            {
+                if (bodyPart.joint)
+                {
+                    bodyPart.currentJointForce = bodyPart.joint.currentForce;
+                    bodyPart.currentJointForceSqrMag = bodyPart.joint.currentForce.magnitude;
+                    bodyPart.currentJointTorque = bodyPart.joint.currentTorque;
+                    bodyPart.currentJointTorqueSqrMag = bodyPart.joint.currentTorque.magnitude;
+                    if (Application.isEditor)
+                    {
+                        if (bodyPart.jointForceCurve.length > 1000)
+                        {
+                            bodyPart.jointForceCurve = new AnimationCurve();
+                        }
+
+                        if (bodyPart.jointTorqueCurve.length > 1000)
+                        {
+                            bodyPart.jointTorqueCurve = new AnimationCurve();
+                        }
+
+                        bodyPart.jointForceCurve.AddKey(Time.time, bodyPart.currentJointForceSqrMag);
+                        bodyPart.jointTorqueCurve.AddKey(Time.time, bodyPart.currentJointTorqueSqrMag);
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..03c214aea118d6cbd72b40d0693fefab5d6ca22b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs.meta
@@ -0,0 +1,11 @@
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+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..891fc4bb0b1d1a32977480121f8aa4bdfd2736e1
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs
@@ -0,0 +1,203 @@
+using System;
+using System.Collections.Generic;
+using System.Text.RegularExpressions;
+using TMPro;
+using Unity.Barracuda;
+using Unity.MLAgents;
+using Unity.MLAgents.Policies;
+using UnityEditor;
+#if UNITY_EDITOR
+using UnityEditor.Recorder;
+#endif
+using UnityEngine;
+
+/**
+ *  Usage Notes:
+ *
+ *  Add onnx models to the list and they will be played sequentially.
+ *  Create a recorder and a video of the sequence will be captured automatically if "Auto Record" is selected.
+ *  Recording only works in the Editor (not in standalone build)
+ *  Create a TextMeshPro Text GameObject and attach it to have the number of training steps of the current model shown.
+ *  To manually control transition between models choose a very large time, or "Pause" the system,
+ *  then use "Force Next" to advance.
+ *  "Reset" will start the sequence from the beginning again, use "Start" to proceed after resetting.
+ *  "Time Scale Override" can be set, "Seconds Between Switches" will decrease proportionally if you increase this
+ *  (i.e it represents simulated seconds between switches, not real time)
+ */
+
+
+public class ModelCarousel : MonoBehaviour
+{
+    public bool m_Start = true;
+    public bool m_Reset = false;
+    public bool m_Pause = true;
+    public bool m_ForceNext = false;
+    public bool m_Loop = false;
+    public bool m_AutoRecord = true;
+    public bool m_ResetAgentOnModelChange = false;
+    public int m_SecondsBetweenSwitches = 10;
+    public float m_TimeScaleOverride = 0.0f;
+    public List<NNModel> m_Models = new List<NNModel>();
+    public bool m_ShowStepNumber = true;
+    public int m_StepNumberRounding = 10000;
+
+    private int m_StepsSinceLastSwitch = 0;
+    private int m_CurrentModelIndex = 0;
+    private int m_CurrentlySetModelIndex = -1;
+
+    private NNModel m_OriginalModel = null;
+
+    private int k_FixedUpdatePerSecond;
+
+    // The attached Agent
+    Agent m_Agent;
+
+    public TextMeshProUGUI textMeshComponent;
+
+#if UNITY_EDITOR
+    private RecorderWindow GetRecorderWindow()
+    {
+        return (RecorderWindow)EditorWindow.GetWindow(typeof(RecorderWindow));
+    }
+#endif
+
+    private void Reset()
+    {
+        m_Reset = false;
+        m_StepsSinceLastSwitch = 0;
+        m_CurrentModelIndex = 0;
+        m_Agent.SetModel(m_OriginalModel.name, m_OriginalModel);
+        textMeshComponent?.SetText("Ready to Start");
+    }
+
+    private void OnEnable()
+    {
+        m_Agent = GetComponent<Agent>();
+        m_OriginalModel = m_Agent.GetComponent<BehaviorParameters>().Model;
+
+        Reset();
+
+        k_FixedUpdatePerSecond = (int)(1.0f / Time.fixedDeltaTime);
+
+        if (m_TimeScaleOverride > 0.0f)
+        {
+            Time.timeScale = m_TimeScaleOverride;
+        }
+    }
+
+    void StartRecording()
+    {
+#if UNITY_EDITOR
+        if (!m_AutoRecord)
+            return;
+
+        Debug.Log("Starting Recording");
+        RecorderWindow recorderWindow = GetRecorderWindow();
+        if (!recorderWindow.IsRecording())
+            recorderWindow.StartRecording();
+#endif
+    }
+
+    void StopRecording()
+    {
+#if UNITY_EDITOR
+        if (!m_AutoRecord)
+            return;
+
+        Debug.Log("Stopping Recording");
+        RecorderWindow recorderWindow = GetRecorderWindow();
+        if (recorderWindow.IsRecording())
+            recorderWindow.StopRecording();
+#endif
+    }
+
+    void UpdateStepNumberText()
+    {
+        if (!m_ShowStepNumber)
+            return;
+
+        var result = Regex.Match(m_Models[m_CurrentModelIndex].name, @".*-(\d+)$");
+
+        string newText = "";
+        if (result.Success && result.Groups.Count > 0)
+        {
+            var steps = Int32.Parse(result.Groups[1].Captures[0].Value);
+
+            int round = m_StepNumberRounding;
+            steps += round / 2;
+            steps /= round;
+            steps *= round;
+
+            newText = $"After {steps:n0} steps";
+        }
+
+        textMeshComponent?.SetText(newText);
+    }
+
+    void SetModel()
+    {
+        if (m_CurrentModelIndex < 0 || m_CurrentModelIndex >= m_Models.Count)
+            return;
+
+        m_Agent.SetModel(m_Models[m_CurrentModelIndex].name, m_Models[m_CurrentModelIndex]);
+        m_CurrentlySetModelIndex = m_CurrentModelIndex;
+
+        UpdateStepNumberText();
+
+        if (m_ResetAgentOnModelChange)
+            m_Agent.EndEpisode();
+    }
+
+    void FixedUpdate()
+    {
+        if (m_Start)
+        {
+            m_Start = false;
+            m_Pause = false;
+            StartRecording();
+        }
+
+        if (m_Reset)
+        {
+            StopRecording();
+            Reset();
+            m_Pause = true;
+            m_Start = false;
+        }
+
+        if (m_Pause && !m_ForceNext)
+            return;
+
+        if (m_CurrentlySetModelIndex != m_CurrentModelIndex)
+        {
+            SetModel();
+        }
+
+        m_StepsSinceLastSwitch++;
+
+        if (m_StepsSinceLastSwitch >= m_SecondsBetweenSwitches * k_FixedUpdatePerSecond || m_ForceNext)
+        {
+            m_ForceNext = false;
+            m_StepsSinceLastSwitch = 0;
+            m_CurrentModelIndex++;
+
+            if (m_CurrentModelIndex == m_Models.Count)
+            {
+                if (m_Loop)
+                {
+                    m_CurrentModelIndex = 0;
+                }
+                else
+                {
+                    Application.Quit(0);
+#if UNITY_EDITOR
+                    EditorApplication.isPlaying = false;
+#endif
+                    return;
+                }
+            }
+
+            SetModel();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..affb030bc91dfa120d895c34838a1c1dc52c6e74
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelCarousel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: b941e667fa5163242bb52545d80e4059
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9390598a03125e10a7637dcf3f3192615af1f55f
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs
@@ -0,0 +1,378 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.Barracuda;
+using System.IO;
+using Unity.Barracuda.ONNX;
+using Unity.MLAgents;
+using Unity.MLAgents.Policies;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// Utility class to allow the NNModel file for an agent to be overriden during inference.
+    /// This is used internally to validate the file after training is done.
+    /// The behavior name to override and file path are specified on the commandline, e.g.
+    /// player.exe --mlagents-override-model-directory /path/to/models
+    ///
+    /// Additionally, a number of episodes to run can be specified; after this, the application will quit.
+    /// Note this will only work with example scenes that have 1:1 Agent:Behaviors. More complicated scenes like WallJump
+    /// probably won't override correctly.
+    /// </summary>
+    public class ModelOverrider : MonoBehaviour
+    {
+        HashSet<string> k_SupportedExtensions = new HashSet<string> { "nn", "onnx" };
+        const string k_CommandLineModelOverrideDirectoryFlag = "--mlagents-override-model-directory";
+        const string k_CommandLineModelOverrideExtensionFlag = "--mlagents-override-model-extension";
+        const string k_CommandLineQuitAfterEpisodesFlag = "--mlagents-quit-after-episodes";
+        const string k_CommandLineQuitAfterSeconds = "--mlagents-quit-after-seconds";
+        const string k_CommandLineQuitOnLoadFailure = "--mlagents-quit-on-load-failure";
+
+        // The attached Agent
+        Agent m_Agent;
+
+        // Whether or not the commandline args have already been processed.
+        // Used to make sure that HasOverrides doesn't spam the logs if it's called multiple times.
+        private bool m_HaveProcessedCommandLine;
+
+        string m_BehaviorNameOverrideDirectory;
+
+        private string m_OriginalBehaviorName;
+
+        private List<string> m_OverrideExtensions = new List<string>();
+
+        // Cached loaded NNModels, with the behavior name as the key.
+        Dictionary<string, NNModel> m_CachedModels = new Dictionary<string, NNModel>();
+
+
+        // Max episodes to run. Only used if > 0
+        // Will default to 1 if override models are specified, otherwise 0.
+        int m_MaxEpisodes;
+
+        // Deadline - exit if the time exceeds this
+        DateTime m_Deadline = DateTime.MaxValue;
+
+        int m_NumSteps;
+        int m_PreviousNumSteps;
+        int m_PreviousAgentCompletedEpisodes;
+
+        bool m_QuitOnLoadFailure;
+        [Tooltip("Debug values to be used in place of the command line for overriding models.")]
+        public string debugCommandLineOverride;
+
+        // Static values to keep track of completed episodes and steps across resets
+        // These are updated in OnDisable.
+        static int s_PreviousAgentCompletedEpisodes;
+        static int s_PreviousNumSteps;
+
+        int TotalCompletedEpisodes
+        {
+            get { return m_PreviousAgentCompletedEpisodes + (m_Agent == null ? 0 : m_Agent.CompletedEpisodes); }
+        }
+
+        int TotalNumSteps
+        {
+            get { return m_PreviousNumSteps + m_NumSteps; }
+        }
+
+        public bool HasOverrides
+        {
+            get
+            {
+                GetAssetPathFromCommandLine();
+                return !string.IsNullOrEmpty(m_BehaviorNameOverrideDirectory);
+            }
+        }
+
+        /// <summary>
+        /// The original behavior name of the agent. The actual behavior name will change when it is overridden.
+        /// </summary>
+        public string OriginalBehaviorName
+        {
+            get
+            {
+                if (string.IsNullOrEmpty(m_OriginalBehaviorName))
+                {
+                    var bp = m_Agent.GetComponent<BehaviorParameters>();
+                    m_OriginalBehaviorName = bp.BehaviorName;
+                }
+
+                return m_OriginalBehaviorName;
+            }
+        }
+
+        public static string GetOverrideBehaviorName(string originalBehaviorName)
+        {
+            return $"Override_{originalBehaviorName}";
+        }
+
+        /// <summary>
+        /// Get the asset path to use from the commandline arguments.
+        /// Can be called multiple times - if m_HaveProcessedCommandLine is set, will have no effect.
+        /// </summary>
+        /// <returns></returns>
+        void GetAssetPathFromCommandLine()
+        {
+            if (m_HaveProcessedCommandLine)
+            {
+                return;
+            }
+            var maxEpisodes = 0;
+            var timeoutSeconds = 0;
+
+            string[] commandLineArgsOverride = null;
+            if (!string.IsNullOrEmpty(debugCommandLineOverride) && Application.isEditor)
+            {
+                commandLineArgsOverride = debugCommandLineOverride.Split(' ');
+            }
+
+            var args = commandLineArgsOverride ?? Environment.GetCommandLineArgs();
+            for (var i = 0; i < args.Length; i++)
+            {
+                if (args[i] == k_CommandLineModelOverrideDirectoryFlag && i < args.Length - 1)
+                {
+                    m_BehaviorNameOverrideDirectory = args[i + 1].Trim();
+                }
+                else if (args[i] == k_CommandLineModelOverrideExtensionFlag && i < args.Length - 1)
+                {
+                    var overrideExtension = args[i + 1].Trim().ToLower();
+                    var isKnownExtension = k_SupportedExtensions.Contains(overrideExtension);
+                    if (!isKnownExtension)
+                    {
+                        Debug.LogError($"loading unsupported format: {overrideExtension}");
+                        Application.Quit(1);
+#if UNITY_EDITOR
+                        EditorApplication.isPlaying = false;
+#endif
+                    }
+                    m_OverrideExtensions.Add(overrideExtension);
+                }
+                else if (args[i] == k_CommandLineQuitAfterEpisodesFlag && i < args.Length - 1)
+                {
+                    Int32.TryParse(args[i + 1], out maxEpisodes);
+                }
+                else if (args[i] == k_CommandLineQuitAfterSeconds && i < args.Length - 1)
+                {
+                    Int32.TryParse(args[i + 1], out timeoutSeconds);
+                }
+                else if (args[i] == k_CommandLineQuitOnLoadFailure)
+                {
+                    m_QuitOnLoadFailure = true;
+                }
+            }
+
+            if (!string.IsNullOrEmpty(m_BehaviorNameOverrideDirectory))
+            {
+                // If overriding models, set maxEpisodes to 1 or the command line value
+                m_MaxEpisodes = maxEpisodes > 0 ? maxEpisodes : 1;
+                Debug.Log($"setting m_MaxEpisodes to {maxEpisodes}");
+            }
+
+            if (timeoutSeconds > 0)
+            {
+                m_Deadline = DateTime.Now + TimeSpan.FromSeconds(timeoutSeconds);
+                Debug.Log($"setting deadline to {timeoutSeconds} from now.");
+            }
+
+            m_HaveProcessedCommandLine = true;
+        }
+
+        void OnEnable()
+        {
+            // Start with these initialized to previous values in the case where we're resetting scenes.
+            m_PreviousNumSteps = s_PreviousNumSteps;
+            m_PreviousAgentCompletedEpisodes = s_PreviousAgentCompletedEpisodes;
+
+            m_Agent = GetComponent<Agent>();
+
+            GetAssetPathFromCommandLine();
+            if (HasOverrides)
+            {
+                OverrideModel();
+            }
+        }
+
+        void OnDisable()
+        {
+            // Update the static episode and step counts.
+            // For a single agent in the scene, this will be a straightforward increment.
+            // If there are multiple agents, we'll increment the count by the Agent that completed the most episodes.
+            s_PreviousAgentCompletedEpisodes = Mathf.Max(s_PreviousAgentCompletedEpisodes, TotalCompletedEpisodes);
+            s_PreviousNumSteps = Mathf.Max(s_PreviousNumSteps, TotalNumSteps);
+        }
+
+        void FixedUpdate()
+        {
+            if (m_MaxEpisodes > 0)
+            {
+                // For Agents without maxSteps, exit as soon as we've hit the target number of episodes.
+                // For Agents that specify MaxStep, also make sure we've gone at least that many steps.
+                // Since we exit as soon as *any* Agent hits its target, the maxSteps condition keeps us running
+                // a bit longer in case there's an early failure.
+                if (TotalCompletedEpisodes >= m_MaxEpisodes && TotalNumSteps > m_MaxEpisodes * m_Agent.MaxStep)
+                {
+                    Debug.Log($"ModelOverride reached {TotalCompletedEpisodes} episodes and {TotalNumSteps} steps. Exiting.");
+                    Application.Quit(0);
+#if UNITY_EDITOR
+                    EditorApplication.isPlaying = false;
+#endif
+                }
+                else if (DateTime.Now >= m_Deadline)
+                {
+                    Debug.Log(
+                        $"Deadline exceeded. " +
+                        $"{TotalCompletedEpisodes}/{m_MaxEpisodes} episodes and " +
+                        $"{TotalNumSteps}/{m_MaxEpisodes * m_Agent.MaxStep} steps completed. Exiting.");
+                    Application.Quit(0);
+#if UNITY_EDITOR
+                    EditorApplication.isPlaying = false;
+#endif
+                }
+            }
+
+            m_NumSteps++;
+        }
+
+        public NNModel GetModelForBehaviorName(string behaviorName)
+        {
+            if (m_CachedModels.ContainsKey(behaviorName))
+            {
+                return m_CachedModels[behaviorName];
+            }
+
+            if (string.IsNullOrEmpty(m_BehaviorNameOverrideDirectory))
+            {
+                Debug.Log($"No override directory set.");
+                return null;
+            }
+
+            // Try the override extensions in order. If they weren't set, try .nn first, then .onnx.
+            var overrideExtensions = (m_OverrideExtensions.Count > 0)
+                ? m_OverrideExtensions.ToArray()
+                : new[] { "nn", "onnx" };
+
+            byte[] rawModel = null;
+            bool isOnnx = false;
+            string assetName = null;
+            foreach (var overrideExtension in overrideExtensions)
+            {
+                var assetPath = Path.Combine(m_BehaviorNameOverrideDirectory, $"{behaviorName}.{overrideExtension}");
+                try
+                {
+                    rawModel = File.ReadAllBytes(assetPath);
+                    isOnnx = overrideExtension.Equals("onnx");
+                    assetName = "Override - " + Path.GetFileName(assetPath);
+                    break;
+                }
+                catch (IOException)
+                {
+                    // Do nothing - try the next extension, or we'll exit if nothing loaded.
+                }
+            }
+
+            if (rawModel == null)
+            {
+                Debug.Log($"Couldn't load model file(s) for {behaviorName} in {m_BehaviorNameOverrideDirectory} (full path: {Path.GetFullPath(m_BehaviorNameOverrideDirectory)}");
+                // Cache the null so we don't repeatedly try to load a missing file
+                m_CachedModels[behaviorName] = null;
+                return null;
+            }
+
+            var asset = isOnnx ? LoadOnnxModel(rawModel) : LoadBarracudaModel(rawModel);
+            asset.name = assetName;
+            m_CachedModels[behaviorName] = asset;
+            return asset;
+        }
+
+        NNModel LoadBarracudaModel(byte[] rawModel)
+        {
+            var asset = ScriptableObject.CreateInstance<NNModel>();
+            asset.modelData = ScriptableObject.CreateInstance<NNModelData>();
+            asset.modelData.Value = rawModel;
+            return asset;
+        }
+
+        NNModel LoadOnnxModel(byte[] rawModel)
+        {
+            var converter = new ONNXModelConverter(true);
+            var onnxModel = converter.Convert(rawModel);
+
+            NNModelData assetData = ScriptableObject.CreateInstance<NNModelData>();
+            using (var memoryStream = new MemoryStream())
+            using (var writer = new BinaryWriter(memoryStream))
+            {
+                ModelWriter.Save(writer, onnxModel);
+                assetData.Value = memoryStream.ToArray();
+            }
+            assetData.name = "Data";
+            assetData.hideFlags = HideFlags.HideInHierarchy;
+
+            var asset = ScriptableObject.CreateInstance<NNModel>();
+            asset.modelData = assetData;
+            return asset;
+        }
+
+
+        /// <summary>
+        /// Load the NNModel file from the specified path, and give it to the attached agent.
+        /// </summary>
+        void OverrideModel()
+        {
+            bool overrideOk = false;
+            string overrideError = null;
+
+            m_Agent.LazyInitialize();
+
+            NNModel nnModel = null;
+            try
+            {
+                nnModel = GetModelForBehaviorName(OriginalBehaviorName);
+            }
+            catch (Exception e)
+            {
+                overrideError = $"Exception calling GetModelForBehaviorName: {e}";
+            }
+
+            if (nnModel == null)
+            {
+                if (string.IsNullOrEmpty(overrideError))
+                {
+                    overrideError =
+                        $"Didn't find a model for behaviorName {OriginalBehaviorName}. Make " +
+                        "sure the behaviorName is set correctly in the commandline " +
+                        "and that the model file exists";
+                }
+            }
+            else
+            {
+                var modelName = nnModel != null ? nnModel.name : "<null>";
+                Debug.Log($"Overriding behavior {OriginalBehaviorName} for agent with model {modelName}");
+                try
+                {
+                    m_Agent.SetModel(GetOverrideBehaviorName(OriginalBehaviorName), nnModel);
+                    overrideOk = true;
+                }
+                catch (Exception e)
+                {
+                    overrideError = $"Exception calling Agent.SetModel: {e}";
+                }
+            }
+
+            if (!overrideOk && m_QuitOnLoadFailure)
+            {
+                if (!string.IsNullOrEmpty(overrideError))
+                {
+                    Debug.LogWarning(overrideError);
+                }
+                Application.Quit(1);
+#if UNITY_EDITOR
+                EditorApplication.isPlaying = false;
+#endif
+            }
+
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f16a462ebf1790a0da1edbad5ef22b50accce553
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 3a6da8f78a394c6ab027688eab81e04d
+timeCreated: 1579651041
\ No newline at end of file
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..73e7c34d23bf2046da654205ffd3f20a79c152cf
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs
@@ -0,0 +1,563 @@
+using System.Collections.Generic;
+using System.Linq;
+using UnityEngine;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Monitor is used to display information about the Agent within the Unity
+    /// scene. Use the log function to add information to your monitor.
+    /// </summary>
+    public class Monitor : MonoBehaviour
+    {
+        /// <summary>
+        /// The type of monitor the information must be displayed in.
+        /// <slider> corresponds to a single rectangle whose width is given
+        /// by a float between -1 and 1. (green is positive, red is negative)
+        /// </slider>
+        /// <hist> corresponds to n vertical sliders. </hist>
+        /// <text> is a text field. </text>
+        /// <bar> is a rectangle of fixed length to represent the proportions </bar>
+        /// of a list of floats.
+        /// </summary>
+        public enum DisplayType
+        {
+            Independent,
+            Proportion
+        }
+
+        /// <summary>
+        /// Represents how high above the target the monitors will be.
+        /// </summary>
+        public static float verticalOffset = 3f;
+
+        static bool s_IsInstantiated;
+        static GameObject s_Canvas;
+        static Dictionary<Transform, Dictionary<string, DisplayValue>> s_DisplayTransformValues;
+
+        /// <summary>
+        /// Camera used to calculate GUI screen position relative to the target
+        /// transform.
+        /// </summary>
+        static Dictionary<Transform, Camera> s_TransformCamera;
+
+        static Color[] s_BarColors;
+
+        struct DisplayValue
+        {
+            public float time;
+            public string stringValue;
+            public float floatValue;
+            public float[] floatArrayValues;
+
+            public enum ValueType
+            {
+                Float,
+                FloatarrayIndependent,
+                FloatarrayProportion,
+                String
+            }
+
+            public ValueType valueType;
+        }
+
+        static GUIStyle s_KeyStyle;
+        static GUIStyle s_ValueStyle;
+        static GUIStyle s_GreenStyle;
+        static GUIStyle s_RedStyle;
+        static GUIStyle[] s_ColorStyle;
+        static bool s_Initialized;
+
+        /// <summary>
+        /// Use the Monitor.Log static function to attach information to a transform.
+        /// </summary>
+        /// <returns>The log.</returns>
+        /// <param name="key">The name of the information you wish to Log.</param>
+        /// <param name="value">The string value you want to display.</param>
+        /// <param name="target">The transform you want to attach the information to.
+        /// </param>
+        /// <param name="camera">Camera used to calculate GUI position relative to
+        /// the target. If null, `Camera.main` will be used.</param>
+        public static void Log(
+            string key,
+            string value,
+            Transform target = null,
+            Camera camera = null)
+        {
+            if (!s_IsInstantiated)
+            {
+                InstantiateCanvas();
+                s_IsInstantiated = true;
+            }
+            if (s_Canvas == null)
+            {
+                return;
+            }
+
+            if (target == null)
+            {
+                target = s_Canvas.transform;
+            }
+
+            s_TransformCamera[target] = camera;
+
+            if (!s_DisplayTransformValues.Keys.Contains(target))
+            {
+                s_DisplayTransformValues[target] =
+                    new Dictionary<string, DisplayValue>();
+            }
+
+            var displayValues =
+                s_DisplayTransformValues[target];
+
+            if (value == null)
+            {
+                RemoveValue(target, key);
+                return;
+            }
+
+            if (!displayValues.ContainsKey(key))
+            {
+                var dv = new DisplayValue();
+                dv.time = Time.timeSinceLevelLoad;
+                dv.stringValue = value;
+                dv.valueType = DisplayValue.ValueType.String;
+                displayValues[key] = dv;
+                while (displayValues.Count > 20)
+                {
+                    var max = (
+                        displayValues
+                            .Aggregate((l, r) => l.Value.time < r.Value.time ? l : r)
+                            .Key
+                    );
+                    RemoveValue(target, max);
+                }
+            }
+            else
+            {
+                var dv = displayValues[key];
+                dv.stringValue = value;
+                dv.valueType = DisplayValue.ValueType.String;
+                displayValues[key] = dv;
+            }
+        }
+
+        /// <summary>
+        /// Use the Monitor.Log static function to attach information to a transform.
+        /// </summary>
+        /// <returns>The log.</returns>
+        /// <param name="key">The name of the information you wish to Log.</param>
+        /// <param name="value">The float value you want to display.</param>
+        /// <param name="target">The transform you want to attach the information to.
+        /// </param>
+        /// <param name="camera">Camera used to calculate GUI position relative to
+        /// the target. If null, `Camera.main` will be used.</param>
+        public static void Log(
+            string key,
+            float value,
+            Transform target = null,
+            Camera camera = null)
+        {
+            if (!s_IsInstantiated)
+            {
+                InstantiateCanvas();
+                s_IsInstantiated = true;
+            }
+
+            if (target == null)
+            {
+                target = s_Canvas.transform;
+            }
+
+            s_TransformCamera[target] = camera;
+
+            if (!s_DisplayTransformValues.Keys.Contains(target))
+            {
+                s_DisplayTransformValues[target] = new Dictionary<string, DisplayValue>();
+            }
+
+            var displayValues = s_DisplayTransformValues[target];
+
+            if (!displayValues.ContainsKey(key))
+            {
+                var dv = new DisplayValue();
+                dv.time = Time.timeSinceLevelLoad;
+                dv.floatValue = value;
+                dv.valueType = DisplayValue.ValueType.Float;
+                displayValues[key] = dv;
+                while (displayValues.Count > 20)
+                {
+                    var max = (
+                        displayValues.Aggregate((l, r) => l.Value.time < r.Value.time ? l : r).Key);
+                    RemoveValue(target, max);
+                }
+            }
+            else
+            {
+                var dv = displayValues[key];
+                dv.floatValue = value;
+                dv.valueType = DisplayValue.ValueType.Float;
+                displayValues[key] = dv;
+            }
+        }
+
+        /// <summary>
+        /// Use the Monitor.Log static function to attach information to a transform.
+        /// </summary>
+        /// <returns>The log.</returns>
+        /// <param name="key">The name of the information you wish to Log.</param>
+        /// <param name="value">The array of float you want to display.</param>
+        /// <param name="displayType">The type of display.</param>
+        /// <param name="target">The transform you want to attach the information to.
+        /// </param>
+        /// <param name="camera">Camera used to calculate GUI position relative to
+        /// the target. If null, `Camera.main` will be used.</param>
+        public static void Log(
+            string key,
+            float[] value,
+            Transform target = null,
+            DisplayType displayType = DisplayType.Independent,
+            Camera camera = null
+        )
+        {
+            if (!s_IsInstantiated)
+            {
+                InstantiateCanvas();
+                s_IsInstantiated = true;
+            }
+
+            if (target == null)
+            {
+                target = s_Canvas.transform;
+            }
+
+            s_TransformCamera[target] = camera;
+
+            if (!s_DisplayTransformValues.Keys.Contains(target))
+            {
+                s_DisplayTransformValues[target] = new Dictionary<string, DisplayValue>();
+            }
+
+            var displayValues = s_DisplayTransformValues[target];
+
+            if (!displayValues.ContainsKey(key))
+            {
+                var dv = new DisplayValue();
+                dv.time = Time.timeSinceLevelLoad;
+                dv.floatArrayValues = value;
+                if (displayType == DisplayType.Independent)
+                {
+                    dv.valueType = DisplayValue.ValueType.FloatarrayIndependent;
+                }
+                else
+                {
+                    dv.valueType = DisplayValue.ValueType.FloatarrayProportion;
+                }
+
+                displayValues[key] = dv;
+                while (displayValues.Count > 20)
+                {
+                    var max = (
+                        displayValues.Aggregate((l, r) => l.Value.time < r.Value.time ? l : r).Key);
+                    RemoveValue(target, max);
+                }
+            }
+            else
+            {
+                var dv = displayValues[key];
+                dv.floatArrayValues = value;
+                if (displayType == DisplayType.Independent)
+                {
+                    dv.valueType = DisplayValue.ValueType.FloatarrayIndependent;
+                }
+                else
+                {
+                    dv.valueType = DisplayValue.ValueType.FloatarrayProportion;
+                }
+
+                displayValues[key] = dv;
+            }
+        }
+
+        /// <summary>
+        /// Remove a value from a monitor.
+        /// </summary>
+        /// <param name="target">
+        /// The transform to which the information is attached.
+        /// </param>
+        /// <param name="key">The key of the information you want to remove.</param>
+        public static void RemoveValue(Transform target, string key)
+        {
+            if (target == null)
+            {
+                target = s_Canvas.transform;
+            }
+
+            if (s_DisplayTransformValues.Keys.Contains(target))
+            {
+                if (s_DisplayTransformValues[target].ContainsKey(key))
+                {
+                    s_DisplayTransformValues[target].Remove(key);
+                    if (s_DisplayTransformValues[target].Keys.Count == 0)
+                    {
+                        s_DisplayTransformValues.Remove(target);
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// Remove all information from a monitor.
+        /// </summary>
+        /// <param name="target">
+        /// The transform to which the information is attached.
+        /// </param>
+        public static void RemoveAllValues(Transform target)
+        {
+            if (target == null)
+            {
+                target = s_Canvas.transform;
+            }
+
+            if (s_DisplayTransformValues.Keys.Contains(target))
+            {
+                s_DisplayTransformValues.Remove(target);
+            }
+        }
+
+        /// <summary>
+        /// Use SetActive to enable or disable the Monitor via script
+        /// </summary>
+        /// <param name="active">Value to set the Monitor's status to.</param>
+        public static void SetActive(bool active)
+        {
+            if (!s_IsInstantiated)
+            {
+                InstantiateCanvas();
+                s_IsInstantiated = true;
+            }
+
+            if (s_Canvas != null)
+            {
+                s_Canvas.SetActive(active);
+            }
+        }
+
+        /// Initializes the canvas.
+        static void InstantiateCanvas()
+        {
+            s_Canvas = GameObject.Find("AgentMonitorCanvas");
+            if (s_Canvas == null)
+            {
+                s_Canvas = new GameObject();
+                s_Canvas.name = "AgentMonitorCanvas";
+                s_Canvas.AddComponent<Monitor>();
+            }
+
+            s_DisplayTransformValues = new Dictionary<Transform,
+                                                      Dictionary<string, DisplayValue>>();
+
+            s_TransformCamera = new Dictionary<Transform, Camera>();
+        }
+
+        void OnGUI()
+        {
+            if (!s_Initialized)
+            {
+                Initialize();
+                s_Initialized = true;
+            }
+
+            var toIterate = s_DisplayTransformValues.Keys.ToList();
+            foreach (var target in toIterate)
+            {
+                if (target == null)
+                {
+                    s_DisplayTransformValues.Remove(target);
+                    continue;
+                }
+
+                // get camera
+                var cam = s_TransformCamera[target];
+                if (cam == null)
+                {
+                    cam = Camera.main;
+                }
+
+                var widthScaler = (Screen.width / 1000f);
+                var keyPixelWidth = 100 * widthScaler;
+                var keyPixelHeight = 20 * widthScaler;
+                var paddingWidth = 10 * widthScaler;
+
+                var scale = 1f;
+                var origin = new Vector3(
+                    Screen.width / 2.0f - keyPixelWidth, Screen.height);
+                if (!(target == s_Canvas.transform))
+                {
+                    var camTransform = cam.transform;
+                    var position = target.position;
+                    var cam2Obj = position - camTransform.position;
+                    scale = Mathf.Min(
+                        1,
+                        20f / (Vector3.Dot(cam2Obj, camTransform.forward)));
+                    var worldPosition = cam.WorldToScreenPoint(
+                        position + new Vector3(0, verticalOffset, 0));
+                    origin = new Vector3(
+                        worldPosition.x - keyPixelWidth * scale, Screen.height - worldPosition.y);
+                }
+
+                keyPixelWidth *= scale;
+                keyPixelHeight *= scale;
+                paddingWidth *= scale;
+                s_KeyStyle.fontSize = (int)(keyPixelHeight * 0.8f);
+                if (s_KeyStyle.fontSize < 2)
+                {
+                    continue;
+                }
+
+
+                var displayValues = s_DisplayTransformValues[target];
+
+                var index = 0;
+                var orderedKeys = displayValues.Keys.OrderBy(x => -displayValues[x].time);
+                foreach (var key in orderedKeys)
+                {
+                    s_KeyStyle.alignment = TextAnchor.MiddleRight;
+                    GUI.Label(
+                        new Rect(
+                            origin.x, origin.y - (index + 1) * keyPixelHeight,
+                            keyPixelWidth, keyPixelHeight),
+                        key,
+                        s_KeyStyle);
+                    float[] vals;
+                    GUIStyle s;
+                    switch (displayValues[key].valueType)
+                    {
+                        case DisplayValue.ValueType.String:
+                            s_ValueStyle.alignment = TextAnchor.MiddleLeft;
+                            GUI.Label(
+                                new Rect(
+                                    origin.x + paddingWidth + keyPixelWidth,
+                                    origin.y - (index + 1) * keyPixelHeight,
+                                    keyPixelWidth, keyPixelHeight),
+                                displayValues[key].stringValue,
+                                s_ValueStyle);
+                            break;
+                        case DisplayValue.ValueType.Float:
+                            var sliderValue = displayValues[key].floatValue;
+                            sliderValue = Mathf.Min(1f, sliderValue);
+                            s = s_GreenStyle;
+                            if (sliderValue < 0)
+                            {
+                                sliderValue = Mathf.Min(1f, -sliderValue);
+                                s = s_RedStyle;
+                            }
+
+                            GUI.Box(
+                                new Rect(
+                                    origin.x + paddingWidth + keyPixelWidth,
+                                    origin.y - (index + 0.9f) * keyPixelHeight,
+                                    keyPixelWidth * sliderValue, keyPixelHeight * 0.8f),
+                                GUIContent.none,
+                                s);
+                            break;
+
+                        case DisplayValue.ValueType.FloatarrayIndependent:
+                            const float histWidth = 0.15f;
+                            vals = displayValues[key].floatArrayValues;
+                            for (var i = 0; i < vals.Length; i++)
+                            {
+                                var value = Mathf.Min(vals[i], 1);
+                                s = s_GreenStyle;
+                                if (value < 0)
+                                {
+                                    value = Mathf.Min(1f, -value);
+                                    s = s_RedStyle;
+                                }
+
+                                GUI.Box(
+                                    new Rect(
+                                        origin.x + paddingWidth + keyPixelWidth +
+                                        (keyPixelWidth * histWidth + paddingWidth / 2) * i,
+                                        origin.y - (index + 0.1f) * keyPixelHeight,
+                                        keyPixelWidth * histWidth, -keyPixelHeight * value),
+                                    GUIContent.none,
+                                    s);
+                            }
+
+                            break;
+
+                        case DisplayValue.ValueType.FloatarrayProportion:
+                            var valsSum = 0f;
+                            var valsCum = 0f;
+                            vals = displayValues[key].floatArrayValues;
+                            foreach (var f in vals)
+                            {
+                                valsSum += Mathf.Max(f, 0);
+                            }
+
+                            if (valsSum < float.Epsilon)
+                            {
+                                Debug.LogError(
+                                    $"The Monitor value for key {key} " +
+                                    "must be a list or array of " +
+                                    "positive values and cannot " +
+                                    "be empty.");
+                            }
+                            else
+                            {
+                                for (var i = 0; i < vals.Length; i++)
+                                {
+                                    var value = Mathf.Max(vals[i], 0) / valsSum;
+                                    GUI.Box(
+                                        new Rect(
+                                            origin.x + paddingWidth +
+                                            keyPixelWidth + keyPixelWidth * valsCum,
+                                            origin.y - (index + 0.9f) * keyPixelHeight,
+                                            keyPixelWidth * value, keyPixelHeight * 0.8f),
+                                        GUIContent.none,
+                                        s_ColorStyle[i % s_ColorStyle.Length]);
+                                    valsCum += value;
+                                }
+                            }
+
+                            break;
+                    }
+
+                    index++;
+                }
+            }
+        }
+
+        /// Helper method used to initialize the GUI. Called once.
+        void Initialize()
+        {
+            s_KeyStyle = GUI.skin.label;
+            s_ValueStyle = GUI.skin.label;
+            s_ValueStyle.clipping = TextClipping.Overflow;
+            s_ValueStyle.wordWrap = false;
+            s_BarColors = new[]
+            {
+                Color.magenta,
+                Color.blue,
+                Color.cyan,
+                Color.green,
+                Color.yellow,
+                Color.red
+            };
+            s_ColorStyle = new GUIStyle[s_BarColors.Length];
+            for (var i = 0; i < s_BarColors.Length; i++)
+            {
+                var texture = new Texture2D(1, 1, TextureFormat.ARGB32, false);
+                texture.SetPixel(0, 0, s_BarColors[i]);
+                texture.Apply();
+                var staticRectStyle = new GUIStyle();
+                staticRectStyle.normal.background = texture;
+                s_ColorStyle[i] = staticRectStyle;
+            }
+
+            s_GreenStyle = s_ColorStyle[3];
+            s_RedStyle = s_ColorStyle[5];
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e10bd36d988600b2f33bcdf164e1d448a75f1fca
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: eca114d0a5b45439384024291ba7d40a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b257b3021590d05c2cd9200e9a93f6b67b5a7462
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs
@@ -0,0 +1,24 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// Utility class to allow a stable observation platform.
+    /// </summary>
+    public class OrientationCubeController : MonoBehaviour
+    {
+        //Update position and Rotation
+        public void UpdateOrientation(Transform rootBP, Transform target)
+        {
+            var dirVector = target.position - transform.position;
+            dirVector.y = 0; //flatten dir on the y. this will only work on level, uneven surfaces
+            var lookRot =
+                dirVector == Vector3.zero
+                    ? Quaternion.identity
+                    : Quaternion.LookRotation(dirVector); //get our look rot to the target
+
+            //UPDATE ORIENTATION CUBE POS & ROT
+            transform.SetPositionAndRotation(rootBP.position, lookRot);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9fb2a07facb9b4241deba897f178cf6ce4422532
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs.meta
@@ -0,0 +1,11 @@
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+guid: 771e78c5e980e440e8cd19716b55075f
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9b77251e6500d831728ae8a52ad3664927bd6844
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs
@@ -0,0 +1,69 @@
+using UnityEngine;
+using Unity.MLAgents;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// A helper class for the ML-Agents example scenes to override various
+    /// global settings, and restore them afterwards.
+    /// This can modify some Physics and time-stepping properties, so you
+    /// shouldn't copy it into your project unless you know what you're doing.
+    /// </summary>
+    public class ProjectSettingsOverrides : MonoBehaviour
+    {
+        // Original values
+        Vector3 m_OriginalGravity;
+        float m_OriginalFixedDeltaTime;
+        float m_OriginalMaximumDeltaTime;
+        int m_OriginalSolverIterations;
+        int m_OriginalSolverVelocityIterations;
+        bool m_OriginalReuseCollisionCallbacks;
+
+        [Tooltip("Increase or decrease the scene gravity. Use ~3x to make things less floaty")]
+        public float gravityMultiplier = 1.0f;
+
+        [Header("Advanced physics settings")]
+        [Tooltip("The interval in seconds at which physics and other fixed frame rate updates (like MonoBehaviour's FixedUpdate) are performed.")]
+        public float fixedDeltaTime = .02f;
+        [Tooltip("The maximum time a frame can take. Physics and other fixed frame rate updates (like MonoBehaviour's FixedUpdate) will be performed only for this duration of time per frame.")]
+        public float maximumDeltaTime = 1.0f / 3.0f;
+        [Tooltip("Determines how accurately Rigidbody joints and collision contacts are resolved. (default 6). Must be positive.")]
+        public int solverIterations = 6;
+        [Tooltip("Affects how accurately the Rigidbody joints and collision contacts are resolved. (default 1). Must be positive.")]
+        public int solverVelocityIterations = 1;
+        [Tooltip("Determines whether the garbage collector should reuse only a single instance of a Collision type for all collision callbacks. Reduces Garbage.")]
+        public bool reuseCollisionCallbacks = true;
+
+        public void Awake()
+        {
+            // Save the original values
+            m_OriginalGravity = Physics.gravity;
+            m_OriginalFixedDeltaTime = Time.fixedDeltaTime;
+            m_OriginalMaximumDeltaTime = Time.maximumDeltaTime;
+            m_OriginalSolverIterations = Physics.defaultSolverIterations;
+            m_OriginalSolverVelocityIterations = Physics.defaultSolverVelocityIterations;
+            m_OriginalReuseCollisionCallbacks = Physics.reuseCollisionCallbacks;
+
+            // Override
+            Physics.gravity *= gravityMultiplier;
+            Time.fixedDeltaTime = fixedDeltaTime;
+            Time.maximumDeltaTime = maximumDeltaTime;
+            Physics.defaultSolverIterations = solverIterations;
+            Physics.defaultSolverVelocityIterations = solverVelocityIterations;
+            Physics.reuseCollisionCallbacks = reuseCollisionCallbacks;
+
+            // Make sure the Academy singleton is initialized first, since it will create the SideChannels.
+            Academy.Instance.EnvironmentParameters.RegisterCallback("gravity", f => { Physics.gravity = new Vector3(0, -f, 0); });
+        }
+
+        public void OnDestroy()
+        {
+            Physics.gravity = m_OriginalGravity;
+            Time.fixedDeltaTime = m_OriginalFixedDeltaTime;
+            Time.maximumDeltaTime = m_OriginalMaximumDeltaTime;
+            Physics.defaultSolverIterations = m_OriginalSolverIterations;
+            Physics.defaultSolverVelocityIterations = m_OriginalSolverVelocityIterations;
+            Physics.reuseCollisionCallbacks = m_OriginalReuseCollisionCallbacks;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9dbadc5b82f09ea3b77643e265d4b13a77acd3f1
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs.meta
@@ -0,0 +1,11 @@
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+guid: ec51f47c5ed0478080c449c74fd9c154
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+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 100
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs
new file mode 100644
index 0000000000000000000000000000000000000000..31bc7c8556328408cd9b48f6a963fc7c93d21f84
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs
@@ -0,0 +1,59 @@
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// A simple sensor that provides a number default implementations.
+    /// </summary>
+    public abstract class SensorBase : ISensor
+    {
+        /// <summary>
+        /// Write the observations to the output buffer. This size of the buffer will be product
+        /// of the Shape array values returned by <see cref="ObservationSpec"/>.
+        /// </summary>
+        /// <param name="output"></param>
+        public abstract void WriteObservation(float[] output);
+
+        /// <inheritdoc/>
+        public abstract ObservationSpec GetObservationSpec();
+
+        /// <inheritdoc/>
+        public abstract string GetName();
+
+        /// <summary>
+        /// Default implementation of Write interface. This creates a temporary array,
+        /// calls WriteObservation, and then writes the results to the ObservationWriter.
+        /// </summary>
+        /// <param name="writer"></param>
+        /// <returns>The number of elements written.</returns>
+        public virtual int Write(ObservationWriter writer)
+        {
+            // TODO reuse buffer for similar agents
+            var numFloats = this.ObservationSize();
+            float[] buffer = new float[numFloats];
+            WriteObservation(buffer);
+
+            writer.AddList(buffer);
+
+            return numFloats;
+        }
+
+        /// <inheritdoc/>
+        public void Update() { }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public virtual byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public virtual CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..af6783dbf6e1a490f85b2e40ebeed9e412dc4847
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs.meta
@@ -0,0 +1,11 @@
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+guid: cf19888920fa24df7ad75a52ede51cf3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fa4580f40bb56031b0ec3c9281482e1f81f41487
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs
@@ -0,0 +1,38 @@
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// This class contains logic for locomotion agents with joints which might make contact with a target.
+    /// By attaching this as a component to those joints, their contact with the ground can be used as
+    /// an observation for that agent.
+    /// </summary>
+    [DisallowMultipleComponent]
+    public class TargetContact : MonoBehaviour
+    {
+        [Header("Detect Targets")] public bool touchingTarget;
+        const string k_Target = "target"; // Tag on target object.
+
+        /// <summary>
+        /// Check for collision with a target.
+        /// </summary>
+        void OnCollisionEnter(Collision col)
+        {
+            if (col.transform.CompareTag(k_Target))
+            {
+                touchingTarget = true;
+            }
+        }
+
+        /// <summary>
+        /// Check for end of ground collision and reset flag appropriately.
+        /// </summary>
+        void OnCollisionExit(Collision other)
+        {
+            if (other.transform.CompareTag(k_Target))
+            {
+                touchingTarget = false;
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..02b1fe71b177324145a64f5ae33f6a5f65de6c3b
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs.meta
@@ -0,0 +1,11 @@
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+  serializedVersion: 2
+  defaultReferences: []
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diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetController.cs b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e2b2c627788e94092f20c1463ad27f22695b59da
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetController.cs
@@ -0,0 +1,133 @@
+using UnityEngine;
+using Random = UnityEngine.Random;
+using Unity.MLAgents;
+using UnityEngine.Events;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// Utility class to allow target placement and collision detection with an agent
+    /// Add this script to the target you want the agent to touch.
+    /// Callbacks will be triggered any time the target is touched with a collider tagged as 'tagToDetect'
+    /// </summary>
+    public class TargetController : MonoBehaviour
+    {
+
+        [Header("Collider Tag To Detect")]
+        public string tagToDetect = "agent"; //collider tag to detect
+
+        [Header("Target Placement")]
+        public float spawnRadius; //The radius in which a target can be randomly spawned.
+        public bool respawnIfTouched; //Should the target respawn to a different position when touched
+
+        [Header("Target Fell Protection")]
+        public bool respawnIfFallsOffPlatform = true; //If the target falls off the platform, reset the position.
+        public float fallDistance = 5; //distance below the starting height that will trigger a respawn
+
+        private Vector3 m_startingPos; //the starting position of the target
+
+        [System.Serializable]
+        public class TriggerEvent : UnityEvent<Collider>
+        {
+        }
+
+        [Header("Trigger Callbacks")]
+        public TriggerEvent onTriggerEnterEvent = new TriggerEvent();
+        public TriggerEvent onTriggerStayEvent = new TriggerEvent();
+        public TriggerEvent onTriggerExitEvent = new TriggerEvent();
+
+        [System.Serializable]
+        public class CollisionEvent : UnityEvent<Collision>
+        {
+        }
+
+        [Header("Collision Callbacks")]
+        public CollisionEvent onCollisionEnterEvent = new CollisionEvent();
+        public CollisionEvent onCollisionStayEvent = new CollisionEvent();
+        public CollisionEvent onCollisionExitEvent = new CollisionEvent();
+
+        // Start is called before the first frame update
+        void OnEnable()
+        {
+            m_startingPos = transform.position;
+            if (respawnIfTouched)
+            {
+                MoveTargetToRandomPosition();
+            }
+        }
+
+        void Update()
+        {
+            if (respawnIfFallsOffPlatform)
+            {
+                if (transform.position.y < m_startingPos.y - fallDistance)
+                {
+                    Debug.Log($"{transform.name} Fell Off Platform");
+                    MoveTargetToRandomPosition();
+                }
+            }
+        }
+
+        /// <summary>
+        /// Moves target to a random position within specified radius.
+        /// </summary>
+        public void MoveTargetToRandomPosition()
+        {
+            var newTargetPos = m_startingPos + (Random.insideUnitSphere * spawnRadius);
+            newTargetPos.y = m_startingPos.y;
+            transform.position = newTargetPos;
+        }
+
+        private void OnCollisionEnter(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionEnterEvent.Invoke(col);
+                if (respawnIfTouched)
+                {
+                    MoveTargetToRandomPosition();
+                }
+            }
+        }
+
+        private void OnCollisionStay(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionStayEvent.Invoke(col);
+            }
+        }
+
+        private void OnCollisionExit(Collision col)
+        {
+            if (col.transform.CompareTag(tagToDetect))
+            {
+                onCollisionExitEvent.Invoke(col);
+            }
+        }
+
+        private void OnTriggerEnter(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerEnterEvent.Invoke(col);
+            }
+        }
+
+        private void OnTriggerStay(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerStayEvent.Invoke(col);
+            }
+        }
+
+        private void OnTriggerExit(Collider col)
+        {
+            if (col.CompareTag(tagToDetect))
+            {
+                onTriggerExitEvent.Invoke(col);
+            }
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetController.cs.meta b/Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetController.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..439d4c10ea09f2711db1ac8bb23fcde7ac1f8050
--- /dev/null
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Prefabs/StrikersVsGoalieField.prefab b/Project/Assets/ML-Agents/Examples/Soccer/Prefabs/StrikersVsGoalieField.prefab
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scenes/StrikersVsGoalie.unity.meta b/Project/Assets/ML-Agents/Examples/Soccer/Scenes/StrikersVsGoalie.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts.meta b/Project/Assets/ML-Agents/Examples/Soccer/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs
new file mode 100644
index 0000000000000000000000000000000000000000..163826ae9fee32f5d44a10175cf98aab1f560fd9
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs
@@ -0,0 +1,217 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+
+public enum Team
+{
+    Blue = 0,
+    Purple = 1
+}
+
+public class AgentSoccer : Agent
+{
+    // Note that that the detectable tags are different for the blue and purple teams. The order is
+    // * ball
+    // * own goal
+    // * opposing goal
+    // * wall
+    // * own teammate
+    // * opposing player
+
+    public enum Position
+    {
+        Striker,
+        Goalie,
+        Generic
+    }
+
+    [HideInInspector]
+    public Team team;
+    float m_KickPower;
+    // The coefficient for the reward for colliding with a ball. Set using curriculum.
+    float m_BallTouch;
+    public Position position;
+
+    const float k_Power = 2000f;
+    float m_Existential;
+    float m_LateralSpeed;
+    float m_ForwardSpeed;
+
+
+    [HideInInspector]
+    public Rigidbody agentRb;
+    SoccerSettings m_SoccerSettings;
+    BehaviorParameters m_BehaviorParameters;
+    public Vector3 initialPos;
+    public float rotSign;
+
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        SoccerEnvController envController = GetComponentInParent<SoccerEnvController>();
+        if (envController != null)
+        {
+            m_Existential = 1f / envController.MaxEnvironmentSteps;
+        }
+        else
+        {
+            m_Existential = 1f / MaxStep;
+        }
+
+        m_BehaviorParameters = gameObject.GetComponent<BehaviorParameters>();
+        if (m_BehaviorParameters.TeamId == (int)Team.Blue)
+        {
+            team = Team.Blue;
+            initialPos = new Vector3(transform.position.x - 5f, .5f, transform.position.z);
+            rotSign = 1f;
+        }
+        else
+        {
+            team = Team.Purple;
+            initialPos = new Vector3(transform.position.x + 5f, .5f, transform.position.z);
+            rotSign = -1f;
+        }
+        if (position == Position.Goalie)
+        {
+            m_LateralSpeed = 1.0f;
+            m_ForwardSpeed = 1.0f;
+        }
+        else if (position == Position.Striker)
+        {
+            m_LateralSpeed = 0.3f;
+            m_ForwardSpeed = 1.3f;
+        }
+        else
+        {
+            m_LateralSpeed = 0.3f;
+            m_ForwardSpeed = 1.0f;
+        }
+        m_SoccerSettings = FindObjectOfType<SoccerSettings>();
+        agentRb = GetComponent<Rigidbody>();
+        agentRb.maxAngularVelocity = 500;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+    }
+
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        m_KickPower = 0f;
+
+        var forwardAxis = act[0];
+        var rightAxis = act[1];
+        var rotateAxis = act[2];
+
+        switch (forwardAxis)
+        {
+            case 1:
+                dirToGo = transform.forward * m_ForwardSpeed;
+                m_KickPower = 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -m_ForwardSpeed;
+                break;
+        }
+
+        switch (rightAxis)
+        {
+            case 1:
+                dirToGo = transform.right * m_LateralSpeed;
+                break;
+            case 2:
+                dirToGo = transform.right * -m_LateralSpeed;
+                break;
+        }
+
+        switch (rotateAxis)
+        {
+            case 1:
+                rotateDir = transform.up * -1f;
+                break;
+            case 2:
+                rotateDir = transform.up * 1f;
+                break;
+        }
+
+        transform.Rotate(rotateDir, Time.deltaTime * 100f);
+        agentRb.AddForce(dirToGo * m_SoccerSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+
+        if (position == Position.Goalie)
+        {
+            // Existential bonus for Goalies.
+            AddReward(m_Existential);
+        }
+        else if (position == Position.Striker)
+        {
+            // Existential penalty for Strikers
+            AddReward(-m_Existential);
+        }
+        MoveAgent(actionBuffers.DiscreteActions);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        //forward
+        if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+        //rotate
+        if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[2] = 1;
+        }
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[2] = 2;
+        }
+        //right
+        if (Input.GetKey(KeyCode.E))
+        {
+            discreteActionsOut[1] = 1;
+        }
+        if (Input.GetKey(KeyCode.Q))
+        {
+            discreteActionsOut[1] = 2;
+        }
+    }
+    /// <summary>
+    /// Used to provide a "kick" to the ball.
+    /// </summary>
+    void OnCollisionEnter(Collision c)
+    {
+        var force = k_Power * m_KickPower;
+        if (position == Position.Goalie)
+        {
+            force = k_Power;
+        }
+        if (c.gameObject.CompareTag("ball"))
+        {
+            AddReward(.2f * m_BallTouch);
+            var dir = c.contacts[0].point - transform.position;
+            dir = dir.normalized;
+            c.gameObject.GetComponent<Rigidbody>().AddForce(dir * force);
+        }
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        m_BallTouch = m_ResetParams.GetWithDefault("ball_touch", 0);
+    }
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs.meta b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs.meta
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index 0000000000000000000000000000000000000000..d61849e8cf238c48fa682b0ba00f146fc4aaa418
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+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs.meta
@@ -0,0 +1,12 @@
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a1847ba27f55291d29101c2dd3f8929d7e244c58
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs
@@ -0,0 +1,27 @@
+using UnityEngine;
+
+public class SoccerBallController : MonoBehaviour
+{
+    public GameObject area;
+    [HideInInspector]
+    public SoccerEnvController envController;
+    public string purpleGoalTag; //will be used to check if collided with purple goal
+    public string blueGoalTag; //will be used to check if collided with blue goal
+
+    void Start()
+    {
+        envController = area.GetComponent<SoccerEnvController>();
+    }
+
+    void OnCollisionEnter(Collision col)
+    {
+        if (col.gameObject.CompareTag(purpleGoalTag)) //ball touched purple goal
+        {
+            envController.GoalTouched(Team.Blue);
+        }
+        if (col.gameObject.CompareTag(blueGoalTag)) //ball touched blue goal
+        {
+            envController.GoalTouched(Team.Purple);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs.meta b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs.meta
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index 0000000000000000000000000000000000000000..69d56951f693b720ec2a539cf4512fb83db2601f
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+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs.meta
@@ -0,0 +1,13 @@
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerEnvController.cs b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerEnvController.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d76d43ef92bb8342a69472e0c182eea6473afc0f
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerEnvController.cs
@@ -0,0 +1,138 @@
+using System.Collections.Generic;
+using Unity.MLAgents;
+using UnityEngine;
+
+public class SoccerEnvController : MonoBehaviour
+{
+    [System.Serializable]
+    public class PlayerInfo
+    {
+        public AgentSoccer Agent;
+        [HideInInspector]
+        public Vector3 StartingPos;
+        [HideInInspector]
+        public Quaternion StartingRot;
+        [HideInInspector]
+        public Rigidbody Rb;
+    }
+
+
+    /// <summary>
+    /// Max Academy steps before this platform resets
+    /// </summary>
+    /// <returns></returns>
+    [Tooltip("Max Environment Steps")] public int MaxEnvironmentSteps = 25000;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+
+    public GameObject ball;
+    [HideInInspector]
+    public Rigidbody ballRb;
+    Vector3 m_BallStartingPos;
+
+    //List of Agents On Platform
+    public List<PlayerInfo> AgentsList = new List<PlayerInfo>();
+
+    private SoccerSettings m_SoccerSettings;
+
+
+    private SimpleMultiAgentGroup m_BlueAgentGroup;
+    private SimpleMultiAgentGroup m_PurpleAgentGroup;
+
+    private int m_ResetTimer;
+
+    void Start()
+    {
+
+        m_SoccerSettings = FindObjectOfType<SoccerSettings>();
+        // Initialize TeamManager
+        m_BlueAgentGroup = new SimpleMultiAgentGroup();
+        m_PurpleAgentGroup = new SimpleMultiAgentGroup();
+        ballRb = ball.GetComponent<Rigidbody>();
+        m_BallStartingPos = new Vector3(ball.transform.position.x, ball.transform.position.y, ball.transform.position.z);
+        foreach (var item in AgentsList)
+        {
+            item.StartingPos = item.Agent.transform.position;
+            item.StartingRot = item.Agent.transform.rotation;
+            item.Rb = item.Agent.GetComponent<Rigidbody>();
+            if (item.Agent.team == Team.Blue)
+            {
+                m_BlueAgentGroup.RegisterAgent(item.Agent);
+            }
+            else
+            {
+                m_PurpleAgentGroup.RegisterAgent(item.Agent);
+            }
+        }
+        ResetScene();
+    }
+
+    void FixedUpdate()
+    {
+        m_ResetTimer += 1;
+        if (m_ResetTimer >= MaxEnvironmentSteps && MaxEnvironmentSteps > 0)
+        {
+            m_BlueAgentGroup.GroupEpisodeInterrupted();
+            m_PurpleAgentGroup.GroupEpisodeInterrupted();
+            ResetScene();
+        }
+    }
+
+
+    public void ResetBall()
+    {
+        var randomPosX = Random.Range(-2.5f, 2.5f);
+        var randomPosZ = Random.Range(-2.5f, 2.5f);
+
+        ball.transform.position = m_BallStartingPos + new Vector3(randomPosX, 0f, randomPosZ);
+        ballRb.velocity = Vector3.zero;
+        ballRb.angularVelocity = Vector3.zero;
+
+    }
+
+    public void GoalTouched(Team scoredTeam)
+    {
+        if (scoredTeam == Team.Blue)
+        {
+            m_BlueAgentGroup.AddGroupReward(1 - (float)m_ResetTimer / MaxEnvironmentSteps);
+            m_PurpleAgentGroup.AddGroupReward(-1);
+        }
+        else
+        {
+            m_PurpleAgentGroup.AddGroupReward(1 - (float)m_ResetTimer / MaxEnvironmentSteps);
+            m_BlueAgentGroup.AddGroupReward(-1);
+        }
+        m_PurpleAgentGroup.EndGroupEpisode();
+        m_BlueAgentGroup.EndGroupEpisode();
+        ResetScene();
+
+    }
+
+
+    public void ResetScene()
+    {
+        m_ResetTimer = 0;
+
+        //Reset Agents
+        foreach (var item in AgentsList)
+        {
+            var randomPosX = Random.Range(-5f, 5f);
+            var newStartPos = item.Agent.initialPos + new Vector3(randomPosX, 0f, 0f);
+            var rot = item.Agent.rotSign * Random.Range(80.0f, 100.0f);
+            var newRot = Quaternion.Euler(0, rot, 0);
+            item.Agent.transform.SetPositionAndRotation(newStartPos, newRot);
+
+            item.Rb.velocity = Vector3.zero;
+            item.Rb.angularVelocity = Vector3.zero;
+        }
+
+        //Reset Ball
+        ResetBall();
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerEnvController.cs.meta b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerEnvController.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerSettings.cs b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerSettings.cs
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerSettings.cs
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+using UnityEngine;
+
+public class SoccerSettings : MonoBehaviour
+{
+    public Material purpleMaterial;
+    public Material blueMaterial;
+    public bool randomizePlayersTeamForTraining = true;
+    public float agentRunSpeed;
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scenes/Sorter.unity.meta b/Project/Assets/ML-Agents/Examples/Sorter/Scenes/Sorter.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scripts.meta b/Project/Assets/ML-Agents/Examples/Sorter/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scripts/NumberTile.cs b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/NumberTile.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f1f80a8f4a588ac00d9a964fc216533dab34499e
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/NumberTile.cs
@@ -0,0 +1,32 @@
+using UnityEngine;
+
+public class NumberTile : MonoBehaviour
+{
+    public int NumberValue;
+    public Material DefaultMaterial;
+    public Material SuccessMaterial;
+
+    private bool m_Visited;
+    private MeshRenderer m_Renderer;
+
+    public bool IsVisited
+    {
+        get { return m_Visited; }
+    }
+
+    public void VisitTile()
+    {
+        m_Renderer.sharedMaterial = SuccessMaterial;
+        m_Visited = true;
+    }
+
+    public void ResetTile()
+    {
+        if (m_Renderer is null)
+        {
+            m_Renderer = GetComponentInChildren<MeshRenderer>();
+        }
+        m_Renderer.sharedMaterial = DefaultMaterial;
+        m_Visited = false;
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scripts/NumberTile.cs.meta b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/NumberTile.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..99fe6f91620a96dfa7fea9b8423f31b741e77134
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diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a2cdb51612d1b55f80ae1a5da35ae0c9692b8bb8
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs
@@ -0,0 +1,269 @@
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Random = UnityEngine.Random;
+
+
+public class SorterAgent : Agent
+{
+    [Range(1, 20)]
+    public int DefaultMaxNumTiles;
+    private const int k_HighestTileValue = 20;
+
+    int m_NumberOfTilesToSpawn;
+    int m_MaxNumberOfTiles;
+    Rigidbody m_AgentRb;
+
+    // The BufferSensorComponent is the Sensor that allows the Agent to observe
+    // a variable number of items (here, numbered tiles)
+    BufferSensorComponent m_BufferSensor;
+
+    public List<NumberTile> NumberTilesList = new List<NumberTile>();
+
+    private List<NumberTile> CurrentlyVisibleTilesList = new List<NumberTile>();
+    private List<Transform> AlreadyTouchedList = new List<Transform>();
+
+    private List<int> m_UsedPositionsList = new List<int>();
+    private Vector3 m_StartingPos;
+
+    GameObject m_Area;
+    EnvironmentParameters m_ResetParams;
+
+    private int m_NextExpectedTileIndex;
+
+
+    public override void Initialize()
+    {
+        m_Area = transform.parent.gameObject;
+        m_MaxNumberOfTiles = k_HighestTileValue;
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+        m_BufferSensor = GetComponent<BufferSensorComponent>();
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_StartingPos = transform.position;
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        m_MaxNumberOfTiles = (int)m_ResetParams.GetWithDefault("num_tiles", DefaultMaxNumTiles);
+
+        m_NumberOfTilesToSpawn = Random.Range(1, m_MaxNumberOfTiles + 1);
+        SelectTilesToShow();
+        SetTilePositions();
+
+        transform.position = m_StartingPos;
+        m_AgentRb.velocity = Vector3.zero;
+        m_AgentRb.angularVelocity = Vector3.zero;
+    }
+
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        sensor.AddObservation((transform.position.x - m_Area.transform.position.x) / 20f);
+        sensor.AddObservation((transform.position.z - m_Area.transform.position.z) / 20f);
+
+        sensor.AddObservation(transform.forward.x);
+        sensor.AddObservation(transform.forward.z);
+
+        foreach (var item in CurrentlyVisibleTilesList)
+        {
+            // Each observation / tile in the BufferSensor will have 22 values
+            // The first 20 are one hot encoding of the value of the tile
+            // The 21st and 22nd are the position of the tile relative to the agent
+            // The 23rd is a boolean : 1 if the tile was visited already and 0 otherwise
+            float[] listObservation = new float[k_HighestTileValue + 3];
+            listObservation[item.NumberValue] = 1.0f;
+            var tileTransform = item.transform.GetChild(1);
+            listObservation[k_HighestTileValue] = (tileTransform.position.x - transform.position.x) / 20f;
+            listObservation[k_HighestTileValue + 1] = (tileTransform.position.z - transform.position.z) / 20f;
+            listObservation[k_HighestTileValue + 2] = item.IsVisited ? 1.0f : 0.0f;
+            // Here, the observation for the tile is added to the BufferSensor
+            m_BufferSensor.AppendObservation(listObservation);
+
+        }
+
+    }
+
+    private void OnCollisionEnter(Collision col)
+    {
+        if (!col.gameObject.CompareTag("tile"))
+        {
+            return;
+        }
+        if (AlreadyTouchedList.Contains(col.transform))
+        {
+            return;
+        }
+        if (col.transform.parent != CurrentlyVisibleTilesList[m_NextExpectedTileIndex].transform)
+        {
+            // The Agent Failed
+            AddReward(-1);
+            EndEpisode();
+        }
+        else
+        {
+            // The Agent Succeeded
+            AddReward(1);
+            var tile = col.gameObject.GetComponentInParent<NumberTile>();
+            tile.VisitTile();
+            m_NextExpectedTileIndex++;
+
+            AlreadyTouchedList.Add(col.transform);
+
+            //We got all of them. Can reset now.
+            if (m_NextExpectedTileIndex == m_NumberOfTilesToSpawn)
+            {
+                EndEpisode();
+            }
+        }
+    }
+
+    void SetTilePositions()
+    {
+
+        m_UsedPositionsList.Clear();
+
+        //Disable all. We will enable the ones selected
+        foreach (var item in NumberTilesList)
+        {
+            item.ResetTile();
+            item.gameObject.SetActive(false);
+        }
+
+
+        foreach (var item in CurrentlyVisibleTilesList)
+        {
+            //Select a rnd spawnAngle
+            bool posChosen = false;
+            int rndPosIndx = 0;
+            while (!posChosen)
+            {
+                rndPosIndx = Random.Range(0, k_HighestTileValue);
+                if (!m_UsedPositionsList.Contains(rndPosIndx))
+                {
+                    m_UsedPositionsList.Add(rndPosIndx);
+                    posChosen = true;
+                }
+            }
+            item.transform.localRotation = Quaternion.Euler(0, rndPosIndx * (360f / k_HighestTileValue), 0);
+            item.gameObject.SetActive(true);
+        }
+    }
+
+    void SelectTilesToShow()
+    {
+
+        CurrentlyVisibleTilesList.Clear();
+        AlreadyTouchedList.Clear();
+
+        int numLeft = m_NumberOfTilesToSpawn;
+        while (numLeft > 0)
+        {
+            int rndInt = Random.Range(0, k_HighestTileValue);
+            var tmp = NumberTilesList[rndInt];
+            if (!CurrentlyVisibleTilesList.Contains(tmp))
+            {
+                CurrentlyVisibleTilesList.Add(tmp);
+                numLeft--;
+            }
+        }
+
+        //Sort Ascending
+        CurrentlyVisibleTilesList.Sort((x, y) => x.NumberValue.CompareTo(y.NumberValue));
+        m_NextExpectedTileIndex = 0;
+    }
+
+
+    /// <summary>
+    /// Moves the agent according to the selected action.
+    /// </summary>
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var forwardAxis = act[0];
+        var rightAxis = act[1];
+        var rotateAxis = act[2];
+
+        switch (forwardAxis)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+        }
+
+        switch (rightAxis)
+        {
+            case 1:
+                dirToGo = transform.right * 1f;
+                break;
+            case 2:
+                dirToGo = transform.right * -1f;
+                break;
+        }
+
+        switch (rotateAxis)
+        {
+            case 1:
+                rotateDir = transform.up * -1f;
+                break;
+            case 2:
+                rotateDir = transform.up * 1f;
+                break;
+        }
+
+        transform.Rotate(rotateDir, Time.deltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * 2, ForceMode.VelocityChange);
+
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        // Move the agent using the action.
+        MoveAgent(actionBuffers.DiscreteActions);
+
+        // Penalty given each step to encourage agent to finish task quickly.
+        AddReward(-1f / MaxStep);
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        //forward
+        if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+        //rotate
+        if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[2] = 1;
+        }
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[2] = 2;
+        }
+        //right
+        if (Input.GetKey(KeyCode.E))
+        {
+            discreteActionsOut[1] = 1;
+        }
+        if (Input.GetKey(KeyCode.Q))
+        {
+            discreteActionsOut[1] = 2;
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs.meta b/Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Startup/Scripts/Startup.cs b/Project/Assets/ML-Agents/Examples/Startup/Scripts/Startup.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3eb7324f093a4903406bc77eff8d8056d66b6508
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Startup/Scripts/Startup.cs
@@ -0,0 +1,58 @@
+using System;
+using UnityEngine;
+using UnityEngine.SceneManagement;
+
+namespace Unity.MLAgentsExamples
+{
+    internal class Startup : MonoBehaviour
+    {
+        const string k_SceneVariableName = "SCENE_NAME";
+        const string k_SceneCommandLineFlag = "--mlagents-scene-name";
+
+        void Awake()
+        {
+            var sceneName = "";
+
+            // Check for the CLI '--scene-name' flag.  This will be used if
+            // no scene environment variable is found.
+            var args = Environment.GetCommandLineArgs();
+            Console.WriteLine("Command line arguments passed: " + String.Join(" ", args));
+            for (int i = 0; i < args.Length; i++)
+            {
+                if (args[i] == k_SceneCommandLineFlag && i < args.Length - 1)
+                {
+                    sceneName = args[i + 1];
+                }
+            }
+
+            var sceneEnvironmentVariable = Environment.GetEnvironmentVariable(k_SceneVariableName);
+            if (!string.IsNullOrEmpty(sceneEnvironmentVariable))
+            {
+                sceneName = sceneEnvironmentVariable;
+            }
+
+            SwitchScene(sceneName);
+        }
+
+        static void SwitchScene(string sceneName)
+        {
+            if (sceneName == null)
+            {
+                Console.WriteLine(
+                    $"You didn't specify the {k_SceneVariableName} environment variable or the {k_SceneCommandLineFlag} command line argument."
+                );
+                Application.Quit(22);
+                return;
+            }
+            if (SceneUtility.GetBuildIndexByScenePath(sceneName) < 0)
+            {
+                Console.WriteLine(
+                    $"The scene {sceneName} doesn't exist within your build."
+                );
+                Application.Quit(22);
+                return;
+            }
+            SceneManager.LoadSceneAsync(sceneName);
+        }
+    }
+}
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diff --git a/Project/Assets/ML-Agents/Examples/Walker/Scenes/Walker.unity.meta b/Project/Assets/ML-Agents/Examples/Walker/Scenes/Walker.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Walker/Scripts.meta b/Project/Assets/ML-Agents/Examples/Walker/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs b/Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1ecd88cc7fb0c697b718573f8f918b9bc6de39fd
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs
@@ -0,0 +1,294 @@
+using System;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
+using BodyPart = Unity.MLAgentsExamples.BodyPart;
+using Random = UnityEngine.Random;
+
+public class WalkerAgent : Agent
+{
+    [Header("Walk Speed")]
+    [Range(0.1f, 10)]
+    [SerializeField]
+    //The walking speed to try and achieve
+    private float m_TargetWalkingSpeed = 10;
+
+    public float MTargetWalkingSpeed // property
+    {
+        get { return m_TargetWalkingSpeed; }
+        set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
+    }
+
+    const float m_maxWalkingSpeed = 10; //The max walking speed
+
+    //Should the agent sample a new goal velocity each episode?
+    //If true, walkSpeed will be randomly set between zero and m_maxWalkingSpeed in OnEpisodeBegin()
+    //If false, the goal velocity will be walkingSpeed
+    public bool randomizeWalkSpeedEachEpisode;
+
+    //The direction an agent will walk during training.
+    private Vector3 m_WorldDirToWalk = Vector3.right;
+
+    [Header("Target To Walk Towards")] public Transform target; //Target the agent will walk towards during training.
+
+    [Header("Body Parts")] public Transform hips;
+    public Transform chest;
+    public Transform spine;
+    public Transform head;
+    public Transform thighL;
+    public Transform shinL;
+    public Transform footL;
+    public Transform thighR;
+    public Transform shinR;
+    public Transform footR;
+    public Transform armL;
+    public Transform forearmL;
+    public Transform handL;
+    public Transform armR;
+    public Transform forearmR;
+    public Transform handR;
+
+    //This will be used as a stabilized model space reference point for observations
+    //Because ragdolls can move erratically during training, using a stabilized reference transform improves learning
+    OrientationCubeController m_OrientationCube;
+
+    //The indicator graphic gameobject that points towards the target
+    DirectionIndicator m_DirectionIndicator;
+    JointDriveController m_JdController;
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
+        m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
+
+        //Setup each body part
+        m_JdController = GetComponent<JointDriveController>();
+        m_JdController.SetupBodyPart(hips);
+        m_JdController.SetupBodyPart(chest);
+        m_JdController.SetupBodyPart(spine);
+        m_JdController.SetupBodyPart(head);
+        m_JdController.SetupBodyPart(thighL);
+        m_JdController.SetupBodyPart(shinL);
+        m_JdController.SetupBodyPart(footL);
+        m_JdController.SetupBodyPart(thighR);
+        m_JdController.SetupBodyPart(shinR);
+        m_JdController.SetupBodyPart(footR);
+        m_JdController.SetupBodyPart(armL);
+        m_JdController.SetupBodyPart(forearmL);
+        m_JdController.SetupBodyPart(handL);
+        m_JdController.SetupBodyPart(armR);
+        m_JdController.SetupBodyPart(forearmR);
+        m_JdController.SetupBodyPart(handR);
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+    }
+
+    /// <summary>
+    /// Loop over body parts and reset them to initial conditions.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        //Reset all of the body parts
+        foreach (var bodyPart in m_JdController.bodyPartsDict.Values)
+        {
+            bodyPart.Reset(bodyPart);
+        }
+
+        //Random start rotation to help generalize
+        hips.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+
+        UpdateOrientationObjects();
+
+        //Set our goal walking speed
+        MTargetWalkingSpeed =
+            randomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : MTargetWalkingSpeed;
+    }
+
+    /// <summary>
+    /// Add relevant information on each body part to observations.
+    /// </summary>
+    public void CollectObservationBodyPart(BodyPart bp, VectorSensor sensor)
+    {
+        //GROUND CHECK
+        sensor.AddObservation(bp.groundContact.touchingGround); // Is this bp touching the ground
+
+        //Get velocities in the context of our orientation cube's space
+        //Note: You can get these velocities in world space as well but it may not train as well.
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.velocity));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.angularVelocity));
+
+        //Get position relative to hips in the context of our orientation cube's space
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.position - hips.position));
+
+        if (bp.rb.transform != hips && bp.rb.transform != handL && bp.rb.transform != handR)
+        {
+            sensor.AddObservation(bp.rb.transform.localRotation);
+            sensor.AddObservation(bp.currentStrength / m_JdController.maxJointForceLimit);
+        }
+    }
+
+    /// <summary>
+    /// Loop over body parts to add them to observation.
+    /// </summary>
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        var cubeForward = m_OrientationCube.transform.forward;
+
+        //velocity we want to match
+        var velGoal = cubeForward * MTargetWalkingSpeed;
+        //ragdoll's avg vel
+        var avgVel = GetAvgVelocity();
+
+        //current ragdoll velocity. normalized
+        sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
+        //avg body vel relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(avgVel));
+        //vel goal relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
+
+        //rotation deltas
+        sensor.AddObservation(Quaternion.FromToRotation(hips.forward, cubeForward));
+        sensor.AddObservation(Quaternion.FromToRotation(head.forward, cubeForward));
+
+        //Position of target position relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(target.transform.position));
+
+        foreach (var bodyPart in m_JdController.bodyPartsList)
+        {
+            CollectObservationBodyPart(bodyPart, sensor);
+        }
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        var bpDict = m_JdController.bodyPartsDict;
+        var i = -1;
+
+        var continuousActions = actionBuffers.ContinuousActions;
+        bpDict[chest].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], continuousActions[++i]);
+        bpDict[spine].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], continuousActions[++i]);
+
+        bpDict[thighL].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[thighR].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[shinL].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[shinR].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[footR].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], continuousActions[++i]);
+        bpDict[footL].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], continuousActions[++i]);
+
+        bpDict[armL].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[armR].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[forearmL].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[forearmR].SetJointTargetRotation(continuousActions[++i], 0, 0);
+        bpDict[head].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+
+        //update joint strength settings
+        bpDict[chest].SetJointStrength(continuousActions[++i]);
+        bpDict[spine].SetJointStrength(continuousActions[++i]);
+        bpDict[head].SetJointStrength(continuousActions[++i]);
+        bpDict[thighL].SetJointStrength(continuousActions[++i]);
+        bpDict[shinL].SetJointStrength(continuousActions[++i]);
+        bpDict[footL].SetJointStrength(continuousActions[++i]);
+        bpDict[thighR].SetJointStrength(continuousActions[++i]);
+        bpDict[shinR].SetJointStrength(continuousActions[++i]);
+        bpDict[footR].SetJointStrength(continuousActions[++i]);
+        bpDict[armL].SetJointStrength(continuousActions[++i]);
+        bpDict[forearmL].SetJointStrength(continuousActions[++i]);
+        bpDict[armR].SetJointStrength(continuousActions[++i]);
+        bpDict[forearmR].SetJointStrength(continuousActions[++i]);
+    }
+
+    //Update OrientationCube and DirectionIndicator
+    void UpdateOrientationObjects()
+    {
+        m_WorldDirToWalk = target.position - hips.position;
+        m_OrientationCube.UpdateOrientation(hips, target);
+        if (m_DirectionIndicator)
+        {
+            m_DirectionIndicator.MatchOrientation(m_OrientationCube.transform);
+        }
+    }
+
+    void FixedUpdate()
+    {
+        UpdateOrientationObjects();
+
+        var cubeForward = m_OrientationCube.transform.forward;
+
+        // Set reward for this step according to mixture of the following elements.
+        // a. Match target speed
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        var matchSpeedReward = GetMatchingVelocityReward(cubeForward * MTargetWalkingSpeed, GetAvgVelocity());
+
+        //Check for NaNs
+        if (float.IsNaN(matchSpeedReward))
+        {
+            throw new ArgumentException(
+                "NaN in moveTowardsTargetReward.\n" +
+                $" cubeForward: {cubeForward}\n" +
+                $" hips.velocity: {m_JdController.bodyPartsDict[hips].rb.velocity}\n" +
+                $" maximumWalkingSpeed: {m_maxWalkingSpeed}"
+            );
+        }
+
+        // b. Rotation alignment with target direction.
+        //This reward will approach 1 if it faces the target direction perfectly and approach zero as it deviates
+        var headForward = head.forward;
+        headForward.y = 0;
+        // var lookAtTargetReward = (Vector3.Dot(cubeForward, head.forward) + 1) * .5F;
+        var lookAtTargetReward = (Vector3.Dot(cubeForward, headForward) + 1) * .5F;
+
+        //Check for NaNs
+        if (float.IsNaN(lookAtTargetReward))
+        {
+            throw new ArgumentException(
+                "NaN in lookAtTargetReward.\n" +
+                $" cubeForward: {cubeForward}\n" +
+                $" head.forward: {head.forward}"
+            );
+        }
+
+        AddReward(matchSpeedReward * lookAtTargetReward);
+    }
+
+    //Returns the average velocity of all of the body parts
+    //Using the velocity of the hips only has shown to result in more erratic movement from the limbs, so...
+    //...using the average helps prevent this erratic movement
+    Vector3 GetAvgVelocity()
+    {
+        Vector3 velSum = Vector3.zero;
+
+        //ALL RBS
+        int numOfRb = 0;
+        foreach (var item in m_JdController.bodyPartsList)
+        {
+            numOfRb++;
+            velSum += item.rb.velocity;
+        }
+
+        var avgVel = velSum / numOfRb;
+        return avgVel;
+    }
+
+    //normalized value of the difference in avg speed vs goal walking speed.
+    public float GetMatchingVelocityReward(Vector3 velocityGoal, Vector3 actualVelocity)
+    {
+        //distance between our actual velocity and goal velocity
+        var velDeltaMagnitude = Mathf.Clamp(Vector3.Distance(actualVelocity, velocityGoal), 0, MTargetWalkingSpeed);
+
+        //return the value on a declining sigmoid shaped curve that decays from 1 to 0
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        return Mathf.Pow(1 - Mathf.Pow(velDeltaMagnitude / MTargetWalkingSpeed, 2), 2);
+    }
+
+    /// <summary>
+    /// Agent touched the target
+    /// </summary>
+    public void TouchedTarget()
+    {
+        AddReward(1f);
+    }
+}
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+;------------------------------------------------------------------
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+	
+	;Material::Black, Model::pupil_1
+	C: "OO",140631868308000,140633766180352
+	
+	;Geometry::Scene, Model::pupil_1
+	C: "OO",140631868341152,140633766180352
+	
+	;Material::Headband, Model::headband
+	C: "OO",140631868354304,140633766183936
+	
+	;Geometry::Scene, Model::headband
+	C: "OO",140631868352768,140633766183936
+	
+	;Model::Arrow, Model::DirectionIndicator
+	C: "OO",140633769324032,140633769320448
+	
+	;Model::symbol_circle, Model::DirectionIndicator
+	C: "OO",140633769334784,140633769320448
+	
+	;Material::spawnVolumeMaterial, Model::DirectionIndicator
+	C: "OO",140631875984624,140633769320448
+	
+	;Geometry::Scene, Model::DirectionIndicator
+	C: "OO",140631868352768,140633769320448
+	
+	;Model::symbol_tri, Model::Arrow
+	C: "OO",140633769327616,140633769324032
+	
+	;Model::Symbol_Triangle, Model::Arrow
+	C: "OO",140633769331200,140633769324032
+	
+	;Material::Green, Model::symbol_tri
+	C: "OO",140631874146400,140633769327616
+	
+	;Geometry::Scene, Model::symbol_tri
+	C: "OO",140631874080352,140633769327616
+	
+	;Material::Green, Model::Symbol_Triangle
+	C: "OO",140631874146400,140633769331200
+	
+	;Geometry::Scene, Model::Symbol_Triangle
+	C: "OO",140631868305376,140633769331200
+	
+	;Material::AgentBlue, Model::symbol_circle
+	C: "OO",140631868244576,140633769334784
+	
+	;Geometry::Scene, Model::symbol_circle
+	C: "OO",140631874151072,140633769334784
+	
+	;Model::Cube, Model::OrientationCube
+	C: "OO",140633769341952,140633769338368
+	
+	;Model::Arrow_1, Model::Cube
+	C: "OO",140633769345536,140633769341952
+	
+	;Material::Orange, Model::Cube
+	C: "OO",140631874157856,140633769341952
+	
+	;Geometry::Scene, Model::Cube
+	C: "OO",140631868305376,140633769341952
+	
+	;Model::symbol_tri_1, Model::Arrow_1
+	C: "OO",140633769350656,140633769345536
+	
+	;Model::Symbol_Triangle_1, Model::Arrow_1
+	C: "OO",140633769354240,140633769345536
+	
+	;Material::Black, Model::symbol_tri_1
+	C: "OO",140631868308000,140633769350656
+	
+	;Geometry::Scene, Model::symbol_tri_1
+	C: "OO",140631874080352,140633769350656
+	
+	;Material::Black, Model::Symbol_Triangle_1
+	C: "OO",140631868308000,140633769354240
+	
+	;Geometry::Scene, Model::Symbol_Triangle_1
+	C: "OO",140631868305376,140633769354240
+	
+	;Texture::DiffuseColor_Texture, Material::LogoSymbol
+	C: "OO",140631868325984,140631868321024
+	
+	;Texture::DiffuseColor_Texture, Material::LogoSymbol
+	C: "OP",140631868325984,140631868321024, "DiffuseColor"
+	
+	;Video::DiffuseColor_Texture, Texture::DiffuseColor_Texture
+	C: "OO",140631874187376,140631868325984
+}
+;Takes section
+;----------------------------------------------------
+
+Takes:  {
+	Current: ""
+}
diff --git a/Project/Assets/ML-Agents/Examples/Walker/WalkerRagdoll.fbx.meta b/Project/Assets/ML-Agents/Examples/Walker/WalkerRagdoll.fbx.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a4a11a2e586dc47c2e93e1b4312ab97e343d0b8a
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Walker/WalkerRagdoll.fbx.meta
@@ -0,0 +1,493 @@
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diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity.meta b/Project/Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scripts.meta b/Project/Assets/ML-Agents/Examples/WallJump/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs
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index 0000000000000000000000000000000000000000..d9379c6629ab41b43dacf2e74220ea60c61b3e90
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs
@@ -0,0 +1,365 @@
+//Put this script on your blue cube.
+
+using System.Collections;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgentsExamples;
+
+public class WallJumpAgent : Agent
+{
+    // Depending on this value, the wall will have different height
+    int m_Configuration;
+    // Brain to use when no wall is present
+    public NNModel noWallBrain;
+    // Brain to use when a jumpable wall is present
+    public NNModel smallWallBrain;
+    // Brain to use when a wall requiring a block to jump over is present
+    public NNModel bigWallBrain;
+
+    public GameObject ground;
+    public GameObject spawnArea;
+    Bounds m_SpawnAreaBounds;
+
+
+    public GameObject goal;
+    public GameObject shortBlock;
+    public GameObject wall;
+    Rigidbody m_ShortBlockRb;
+    Rigidbody m_AgentRb;
+    Material m_GroundMaterial;
+    Renderer m_GroundRenderer;
+    WallJumpSettings m_WallJumpSettings;
+
+    public float jumpingTime;
+    public float jumpTime;
+    // This is a downward force applied when falling to make jumps look
+    // less floaty
+    public float fallingForce;
+    // Use to check the coliding objects
+    public Collider[] hitGroundColliders = new Collider[3];
+    Vector3 m_JumpTargetPos;
+    Vector3 m_JumpStartingPos;
+
+    string m_NoWallBehaviorName = "SmallWallJump";
+    string m_SmallWallBehaviorName = "SmallWallJump";
+    string m_BigWallBehaviorName = "BigWallJump";
+
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_WallJumpSettings = FindObjectOfType<WallJumpSettings>();
+        m_Configuration = Random.Range(0, 5);
+
+        m_AgentRb = GetComponent<Rigidbody>();
+        m_ShortBlockRb = shortBlock.GetComponent<Rigidbody>();
+        m_SpawnAreaBounds = spawnArea.GetComponent<Collider>().bounds;
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        m_GroundMaterial = m_GroundRenderer.material;
+
+        spawnArea.SetActive(false);
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+
+        // Update model references if we're overriding
+        var modelOverrider = GetComponent<ModelOverrider>();
+        if (modelOverrider.HasOverrides)
+        {
+            noWallBrain = modelOverrider.GetModelForBehaviorName(m_NoWallBehaviorName);
+            m_NoWallBehaviorName = ModelOverrider.GetOverrideBehaviorName(m_NoWallBehaviorName);
+
+            smallWallBrain = modelOverrider.GetModelForBehaviorName(m_SmallWallBehaviorName);
+            m_SmallWallBehaviorName = ModelOverrider.GetOverrideBehaviorName(m_SmallWallBehaviorName);
+
+            bigWallBrain = modelOverrider.GetModelForBehaviorName(m_BigWallBehaviorName);
+            m_BigWallBehaviorName = ModelOverrider.GetOverrideBehaviorName(m_BigWallBehaviorName);
+        }
+    }
+
+    // Begin the jump sequence
+    public void Jump()
+    {
+        jumpingTime = 0.2f;
+        m_JumpStartingPos = m_AgentRb.position;
+    }
+
+    /// <summary>
+    /// Does the ground check.
+    /// </summary>
+    /// <returns><c>true</c>, if the agent is on the ground,
+    /// <c>false</c> otherwise.</returns>
+    /// <param name="smallCheck"></param>
+    public bool DoGroundCheck(bool smallCheck)
+    {
+        if (!smallCheck)
+        {
+            hitGroundColliders = new Collider[3];
+            var o = gameObject;
+            Physics.OverlapBoxNonAlloc(
+                o.transform.position + new Vector3(0, -0.05f, 0),
+                new Vector3(0.95f / 2f, 0.5f, 0.95f / 2f),
+                hitGroundColliders,
+                o.transform.rotation);
+            var grounded = false;
+            foreach (var col in hitGroundColliders)
+            {
+                if (col != null && col.transform != transform &&
+                    (col.CompareTag("walkableSurface") ||
+                     col.CompareTag("block") ||
+                     col.CompareTag("wall")))
+                {
+                    grounded = true; //then we're grounded
+                    break;
+                }
+            }
+            return grounded;
+        }
+        else
+        {
+            RaycastHit hit;
+            Physics.Raycast(transform.position + new Vector3(0, -0.05f, 0), -Vector3.up, out hit,
+                1f);
+
+            if (hit.collider != null &&
+                (hit.collider.CompareTag("walkableSurface") ||
+                 hit.collider.CompareTag("block") ||
+                 hit.collider.CompareTag("wall"))
+                && hit.normal.y > 0.95f)
+            {
+                return true;
+            }
+
+            return false;
+        }
+    }
+
+    /// <summary>
+    /// Moves  a rigidbody towards a position smoothly.
+    /// </summary>
+    /// <param name="targetPos">Target position.</param>
+    /// <param name="rb">The rigidbody to be moved.</param>
+    /// <param name="targetVel">The velocity to target during the
+    ///  motion.</param>
+    /// <param name="maxVel">The maximum velocity posible.</param>
+    void MoveTowards(
+        Vector3 targetPos, Rigidbody rb, float targetVel, float maxVel)
+    {
+        var moveToPos = targetPos - rb.worldCenterOfMass;
+        var velocityTarget = Time.fixedDeltaTime * targetVel * moveToPos;
+        if (float.IsNaN(velocityTarget.x) == false)
+        {
+            rb.velocity = Vector3.MoveTowards(
+                rb.velocity, velocityTarget, maxVel);
+        }
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        var agentPos = m_AgentRb.position - ground.transform.position;
+
+        sensor.AddObservation(agentPos / 20f);
+        sensor.AddObservation(DoGroundCheck(true) ? 1 : 0);
+    }
+
+    /// <summary>
+    /// Gets a random spawn position in the spawningArea.
+    /// </summary>
+    /// <returns>The random spawn position.</returns>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var randomPosX = Random.Range(-m_SpawnAreaBounds.extents.x,
+            m_SpawnAreaBounds.extents.x);
+        var randomPosZ = Random.Range(-m_SpawnAreaBounds.extents.z,
+            m_SpawnAreaBounds.extents.z);
+
+        var randomSpawnPos = spawnArea.transform.position +
+            new Vector3(randomPosX, 0.45f, randomPosZ);
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Changes the color of the ground for a moment.
+    /// </summary>
+    /// <returns>The Enumerator to be used in a Coroutine.</returns>
+    /// <param name="mat">The material to be swapped.</param>
+    /// <param name="time">The time the material will remain.</param>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        m_GroundRenderer.material = mat;
+        yield return new WaitForSeconds(time); //wait for 2 sec
+        m_GroundRenderer.material = m_GroundMaterial;
+    }
+
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        AddReward(-0.0005f);
+        var smallGrounded = DoGroundCheck(true);
+        var largeGrounded = DoGroundCheck(false);
+
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+        var dirToGoForwardAction = act[0];
+        var rotateDirAction = act[1];
+        var dirToGoSideAction = act[2];
+        var jumpAction = act[3];
+
+        if (dirToGoForwardAction == 1)
+            dirToGo = (largeGrounded ? 1f : 0.5f) * 1f * transform.forward;
+        else if (dirToGoForwardAction == 2)
+            dirToGo = (largeGrounded ? 1f : 0.5f) * -1f * transform.forward;
+        if (rotateDirAction == 1)
+            rotateDir = transform.up * -1f;
+        else if (rotateDirAction == 2)
+            rotateDir = transform.up * 1f;
+        if (dirToGoSideAction == 1)
+            dirToGo = (largeGrounded ? 1f : 0.5f) * -0.6f * transform.right;
+        else if (dirToGoSideAction == 2)
+            dirToGo = (largeGrounded ? 1f : 0.5f) * 0.6f * transform.right;
+        if (jumpAction == 1)
+            if ((jumpingTime <= 0f) && smallGrounded)
+            {
+                Jump();
+            }
+
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 300f);
+        m_AgentRb.AddForce(dirToGo * m_WallJumpSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+
+        if (jumpingTime > 0f)
+        {
+            m_JumpTargetPos =
+                new Vector3(m_AgentRb.position.x,
+                    m_JumpStartingPos.y + m_WallJumpSettings.agentJumpHeight,
+                    m_AgentRb.position.z) + dirToGo;
+            MoveTowards(m_JumpTargetPos, m_AgentRb, m_WallJumpSettings.agentJumpVelocity,
+                m_WallJumpSettings.agentJumpVelocityMaxChange);
+        }
+
+        if (!(jumpingTime > 0f) && !largeGrounded)
+        {
+            m_AgentRb.AddForce(
+                Vector3.down * fallingForce, ForceMode.Acceleration);
+        }
+        jumpingTime -= Time.fixedDeltaTime;
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        MoveAgent(actionBuffers.DiscreteActions);
+        if ((!Physics.Raycast(m_AgentRb.position, Vector3.down, 20))
+            || (!Physics.Raycast(m_ShortBlockRb.position, Vector3.down, 20)))
+        {
+            SetReward(-1f);
+            EndEpisode();
+            ResetBlock(m_ShortBlockRb);
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(m_WallJumpSettings.failMaterial, .5f));
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[1] = 2;
+        }
+        if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[1] = 1;
+        }
+        if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+        discreteActionsOut[3] = Input.GetKey(KeyCode.Space) ? 1 : 0;
+    }
+
+    // Detect when the agent hits the goal
+    void OnTriggerStay(Collider col)
+    {
+        if (col.gameObject.CompareTag("goal") && DoGroundCheck(true))
+        {
+            SetReward(1f);
+            EndEpisode();
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(m_WallJumpSettings.goalScoredMaterial, 2));
+        }
+    }
+
+    //Reset the orange block position
+    void ResetBlock(Rigidbody blockRb)
+    {
+        blockRb.transform.position = GetRandomSpawnPos();
+        blockRb.velocity = Vector3.zero;
+        blockRb.angularVelocity = Vector3.zero;
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        ResetBlock(m_ShortBlockRb);
+        transform.localPosition = new Vector3(
+            18 * (Random.value - 0.5f), 1, -12);
+        m_Configuration = Random.Range(0, 5);
+        m_AgentRb.velocity = default(Vector3);
+    }
+
+    void FixedUpdate()
+    {
+        if (m_Configuration != -1)
+        {
+            ConfigureAgent(m_Configuration);
+            m_Configuration = -1;
+        }
+    }
+
+    /// <summary>
+    /// Configures the agent. Given an integer config, the wall will have
+    /// different height and a different brain will be assigned to the agent.
+    /// </summary>
+    /// <param name="config">Config.
+    /// If 0 : No wall and noWallBrain.
+    /// If 1:  Small wall and smallWallBrain.
+    /// Other : Tall wall and BigWallBrain.
+    /// </param>
+    void ConfigureAgent(int config)
+    {
+        var localScale = wall.transform.localScale;
+        if (config == 0)
+        {
+            localScale = new Vector3(
+                localScale.x,
+                m_ResetParams.GetWithDefault("no_wall_height", 0),
+                localScale.z);
+            wall.transform.localScale = localScale;
+            SetModel(m_NoWallBehaviorName, noWallBrain);
+        }
+        else if (config == 1)
+        {
+            localScale = new Vector3(
+                localScale.x,
+                m_ResetParams.GetWithDefault("small_wall_height", 4),
+                localScale.z);
+            wall.transform.localScale = localScale;
+            SetModel(m_SmallWallBehaviorName, smallWallBrain);
+        }
+        else
+        {
+            var height = m_ResetParams.GetWithDefault("big_wall_height", 8);
+            localScale = new Vector3(
+                localScale.x,
+                height,
+                localScale.z);
+            wall.transform.localScale = localScale;
+            SetModel(m_BigWallBehaviorName, bigWallBrain);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs.meta b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0be641c8333c4a05ad418752f11b00676f021941
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+++ b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs.meta
@@ -0,0 +1,13 @@
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+timeCreated: 1517445814
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diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpSettings.cs b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpSettings.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1c36ea996531e71c7530408a7b6691b6525557ff
--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpSettings.cs
@@ -0,0 +1,18 @@
+using UnityEngine;
+
+public class WallJumpSettings : MonoBehaviour
+{
+    [Header("Specific to WallJump")]
+    public float agentRunSpeed;
+    public float agentJumpHeight;
+    //when a goal is scored the ground will use this material for a few seconds.
+    public Material goalScoredMaterial;
+    //when fail, the ground will use this material for a few seconds.
+    public Material failMaterial;
+
+    [HideInInspector]
+    public float agentJumpVelocity = 777;
+    [HideInInspector]
+    public float agentJumpVelocityMaxChange = 10;
+
+}
diff --git a/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpSettings.cs.meta b/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpSettings.cs.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Worm/Scenes/Worm.unity.meta b/Project/Assets/ML-Agents/Examples/Worm/Scenes/Worm.unity.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Worm/Scripts.meta b/Project/Assets/ML-Agents/Examples/Worm/Scripts.meta
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diff --git a/Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs b/Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs
new file mode 100644
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--- /dev/null
+++ b/Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs
@@ -0,0 +1,211 @@
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
+
+[RequireComponent(typeof(JointDriveController))] // Required to set joint forces
+public class WormAgent : Agent
+{
+    const float m_MaxWalkingSpeed = 10; //The max walking speed
+
+    [Header("Target Prefabs")] public Transform TargetPrefab; //Target prefab to use in Dynamic envs
+    private Transform m_Target; //Target the agent will walk towards during training.
+
+    [Header("Body Parts")] public Transform bodySegment0;
+    public Transform bodySegment1;
+    public Transform bodySegment2;
+    public Transform bodySegment3;
+
+    //This will be used as a stabilized model space reference point for observations
+    //Because ragdolls can move erratically during training, using a stabilized reference transform improves learning
+    OrientationCubeController m_OrientationCube;
+
+    //The indicator graphic gameobject that points towards the target
+    DirectionIndicator m_DirectionIndicator;
+    JointDriveController m_JdController;
+
+    private Vector3 m_StartingPos; //starting position of the agent
+
+    public override void Initialize()
+    {
+        SpawnTarget(TargetPrefab, transform.position); //spawn target
+
+        m_StartingPos = bodySegment0.position;
+        m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
+        m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
+        m_JdController = GetComponent<JointDriveController>();
+
+        UpdateOrientationObjects();
+
+        //Setup each body part
+        m_JdController.SetupBodyPart(bodySegment0);
+        m_JdController.SetupBodyPart(bodySegment1);
+        m_JdController.SetupBodyPart(bodySegment2);
+        m_JdController.SetupBodyPart(bodySegment3);
+    }
+
+
+    /// <summary>
+    /// Spawns a target prefab at pos
+    /// </summary>
+    /// <param name="prefab"></param>
+    /// <param name="pos"></param>
+    void SpawnTarget(Transform prefab, Vector3 pos)
+    {
+        m_Target = Instantiate(prefab, pos, Quaternion.identity, transform.parent);
+    }
+
+    /// <summary>
+    /// Loop over body parts and reset them to initial conditions.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        foreach (var bodyPart in m_JdController.bodyPartsList)
+        {
+            bodyPart.Reset(bodyPart);
+        }
+
+        //Random start rotation to help generalize
+        bodySegment0.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+
+        UpdateOrientationObjects();
+    }
+
+    /// <summary>
+    /// Add relevant information on each body part to observations.
+    /// </summary>
+    public void CollectObservationBodyPart(BodyPart bp, VectorSensor sensor)
+    {
+        //GROUND CHECK
+        sensor.AddObservation(bp.groundContact.touchingGround ? 1 : 0); // Whether the bp touching the ground
+
+        //Get velocities in the context of our orientation cube's space
+        //Note: You can get these velocities in world space as well but it may not train as well.
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.velocity));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.angularVelocity));
+
+
+        if (bp.rb.transform != bodySegment0)
+        {
+            //Get position relative to hips in the context of our orientation cube's space
+            sensor.AddObservation(
+                m_OrientationCube.transform.InverseTransformDirection(bp.rb.position - bodySegment0.position));
+            sensor.AddObservation(bp.rb.transform.localRotation);
+        }
+
+        if (bp.joint)
+            sensor.AddObservation(bp.currentStrength / m_JdController.maxJointForceLimit);
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        RaycastHit hit;
+        float maxDist = 10;
+        if (Physics.Raycast(bodySegment0.position, Vector3.down, out hit, maxDist))
+        {
+            sensor.AddObservation(hit.distance / maxDist);
+        }
+        else
+            sensor.AddObservation(1);
+
+        var cubeForward = m_OrientationCube.transform.forward;
+        var velGoal = cubeForward * m_MaxWalkingSpeed;
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
+        sensor.AddObservation(Quaternion.Angle(m_OrientationCube.transform.rotation,
+                                  m_JdController.bodyPartsDict[bodySegment0].rb.rotation) / 180);
+        sensor.AddObservation(Quaternion.FromToRotation(bodySegment0.forward, cubeForward));
+
+        //Add pos of target relative to orientation cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(m_Target.transform.position));
+
+        foreach (var bodyPart in m_JdController.bodyPartsList)
+        {
+            CollectObservationBodyPart(bodyPart, sensor);
+        }
+    }
+
+    /// <summary>
+    /// Agent touched the target
+    /// </summary>
+    public void TouchedTarget()
+    {
+        AddReward(1f);
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        // The dictionary with all the body parts in it are in the jdController
+        var bpDict = m_JdController.bodyPartsDict;
+
+        var i = -1;
+        var continuousActions = actionBuffers.ContinuousActions;
+        // Pick a new target joint rotation
+        bpDict[bodySegment0].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[bodySegment1].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+        bpDict[bodySegment2].SetJointTargetRotation(continuousActions[++i], continuousActions[++i], 0);
+
+        // Update joint strength
+        bpDict[bodySegment0].SetJointStrength(continuousActions[++i]);
+        bpDict[bodySegment1].SetJointStrength(continuousActions[++i]);
+        bpDict[bodySegment2].SetJointStrength(continuousActions[++i]);
+
+        //Reset if Worm fell through floor;
+        if (bodySegment0.position.y < m_StartingPos.y - 2)
+        {
+            EndEpisode();
+        }
+    }
+
+    void FixedUpdate()
+    {
+        UpdateOrientationObjects();
+
+        var velReward =
+            GetMatchingVelocityReward(m_OrientationCube.transform.forward * m_MaxWalkingSpeed,
+                m_JdController.bodyPartsDict[bodySegment0].rb.velocity);
+
+        //Angle of the rotation delta between cube and body.
+        //This will range from (0, 180)
+        var rotAngle = Quaternion.Angle(m_OrientationCube.transform.rotation,
+            m_JdController.bodyPartsDict[bodySegment0].rb.rotation);
+
+        //The reward for facing the target
+        var facingRew = 0f;
+        //If we are within 30 degrees of facing the target
+        if (rotAngle < 30)
+        {
+            //Set normalized facingReward
+            //Facing the target perfectly yields a reward of 1
+            facingRew = 1 - (rotAngle / 180);
+        }
+
+        //Add the product of these two rewards
+        AddReward(velReward * facingRew);
+    }
+
+    /// <summary>
+    /// Normalized value of the difference in actual speed vs goal walking speed.
+    /// </summary>
+    public float GetMatchingVelocityReward(Vector3 velocityGoal, Vector3 actualVelocity)
+    {
+        //distance between our actual velocity and goal velocity
+        var velDeltaMagnitude = Mathf.Clamp(Vector3.Distance(actualVelocity, velocityGoal), 0, m_MaxWalkingSpeed);
+
+        //return the value on a declining sigmoid shaped curve that decays from 1 to 0
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        return Mathf.Pow(1 - Mathf.Pow(velDeltaMagnitude / m_MaxWalkingSpeed, 2), 2);
+    }
+
+    /// <summary>
+    /// Update OrientationCube and DirectionIndicator
+    /// </summary>
+    void UpdateOrientationObjects()
+    {
+        m_OrientationCube.UpdateOrientation(bodySegment0, m_Target);
+        if (m_DirectionIndicator)
+        {
+            m_DirectionIndicator.MatchOrientation(m_OrientationCube.transform);
+        }
+    }
+}
diff --git a/Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs.meta b/Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs.meta
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diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity.meta b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..950b984dc6c7940139ae45f0eca2ac4e2b1698d5
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: bf09dea4d49cd471bb220b4dd4e0711a
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b4702b15d16e96c349e16e1162026c3baa5da2b2
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs
@@ -0,0 +1,70 @@
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+public class TestTextureSensor : ISensor
+{
+    Texture2D m_Texture;
+    string m_Name;
+    private ObservationSpec m_ObservationSpec;
+    SensorCompressionType m_CompressionType;
+
+    /// <summary>
+    /// The compression type used by the sensor.
+    /// </summary>
+    public SensorCompressionType CompressionType
+    {
+        get { return m_CompressionType; }
+        set { m_CompressionType = value; }
+    }
+
+
+    public TestTextureSensor(
+        Texture2D texture, string name, SensorCompressionType compressionType)
+    {
+        m_Texture = texture;
+        var width = texture.width;
+        var height = texture.height;
+        m_Name = name;
+        m_ObservationSpec = ObservationSpec.Visual(height, width, 3);
+        m_CompressionType = compressionType;
+    }
+
+    /// <inheritdoc/>
+    public string GetName()
+    {
+        return m_Name;
+    }
+
+    /// <inheritdoc/>
+    public ObservationSpec GetObservationSpec()
+    {
+        return m_ObservationSpec;
+    }
+
+    /// <inheritdoc/>
+    public byte[] GetCompressedObservation()
+    {
+        var compressed = m_Texture.EncodeToPNG();
+        return compressed;
+    }
+
+    /// <inheritdoc/>
+    public int Write(ObservationWriter writer)
+    {
+        var numWritten = writer.WriteTexture(m_Texture, false);
+        return numWritten;
+    }
+
+    /// <inheritdoc/>
+    public void Update() { }
+
+    /// <inheritdoc/>
+    public void Reset() { }
+
+    /// <inheritdoc/>
+    public CompressionSpec GetCompressionSpec()
+    {
+        return CompressionSpec.Default();
+    }
+}
+
diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs.meta b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4791f24e210e598254a9d83974e2fbbe05a4b242
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: f521fc880d878456081dfe81a741be8d
+MonoImporter:
+  externalObjects: {}
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+  assetBundleVariant: 
diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4a126575050e2668c39ed25ac48398aad22bf934
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs
@@ -0,0 +1,36 @@
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+
+public class TestTextureSensorComponent : SensorComponent
+{
+    TestTextureSensor m_Sensor;
+
+    public Texture2D TestTexture;
+
+    string m_SensorName = "TextureSensor";
+
+    public string SensorName
+    {
+        get { return m_SensorName; }
+        set { m_SensorName = value; }
+    }
+
+
+    public int ObservationStacks = 4;
+
+    public SensorCompressionType CompressionType = SensorCompressionType.PNG;
+
+
+    /// <inheritdoc/>
+    public override ISensor[] CreateSensors()
+    {
+        m_Sensor = new TestTextureSensor(TestTexture, SensorName, CompressionType);
+        if (ObservationStacks != 1)
+        {
+            return new ISensor[] { new StackingSensor(m_Sensor, ObservationStacks) };
+        }
+        return new ISensor[] { m_Sensor };
+    }
+}
+
diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs.meta b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..417d8dad5838cfb0437f3bb4f835ff10c02ecce2
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs.meta
@@ -0,0 +1,11 @@
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+guid: 8eda65155b8ff4d14a3b048dae0fb21c
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diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png
new file mode 100644
index 0000000000000000000000000000000000000000..6805a8db08cafee8fab846b04eb8155363d2d010
Binary files /dev/null and b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png differ
diff --git a/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png.meta b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1998b0aa95500c5a06009b71368b31756092dadf
--- /dev/null
+++ b/Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/Texture.png.meta
@@ -0,0 +1,99 @@
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+guid: eeba1f6a35c72450894cf4f4cb516004
+TextureImporter:
+  fileIDToRecycleName: {}
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+    maxTextureSize: 2048
+    resizeAlgorithm: 0
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+    overridden: 0
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+  spriteSheet:
+    serializedVersion: 2
+    sprites: []
+    outline: []
+    physicsShape: []
+    bones: []
+    spriteID: 
+    vertices: []
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diff --git a/Project/Assets/csc.rsp b/Project/Assets/csc.rsp
new file mode 100644
index 0000000000000000000000000000000000000000..ddc521a6358b6a0a557220b377180d0768776280
--- /dev/null
+++ b/Project/Assets/csc.rsp
@@ -0,0 +1,2 @@
+-warnaserror+
+-warnaserror-:618
diff --git a/Project/Assets/csc.rsp.meta b/Project/Assets/csc.rsp.meta
new file mode 100644
index 0000000000000000000000000000000000000000..48393cd818b5e4c7e11f93ff18342867372b2640
--- /dev/null
+++ b/Project/Assets/csc.rsp.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: c919ab2efb86f4001a0176ec5a1a3329
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/Project/Packages/manifest.json b/Project/Packages/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c2bdc9be6bb5c211802f228c45033c78d3907095
--- /dev/null
+++ b/Project/Packages/manifest.json
@@ -0,0 +1,27 @@
+{
+  "dependencies": {
+    "com.unity.ai.navigation": "1.1.4",
+    "com.unity.formats.fbx": "4.2.1",
+    "com.unity.ide.rider": "3.0.24",
+    "com.unity.inputsystem": "1.6.1",
+    "com.unity.ml-agents": "file:../../com.unity.ml-agents",
+    "com.unity.ml-agents.extensions": "file:../../com.unity.ml-agents.extensions",
+    "com.unity.nuget.newtonsoft-json": "3.2.1",
+    "com.unity.recorder": "4.0.1",
+    "com.unity.test-framework": "1.1.33",
+    "com.unity.textmeshpro": "3.0.6",
+    "com.unity.toolchain.macos-x86_64-linux-x86_64": "2.0.4",
+    "com.unity.ugui": "1.0.0",
+    "com.unity.modules.imageconversion": "1.0.0",
+    "com.unity.modules.jsonserialize": "1.0.0",
+    "com.unity.modules.physics": "1.0.0",
+    "com.unity.modules.physics2d": "1.0.0",
+    "com.unity.modules.uielements": "1.0.0",
+    "com.unity.modules.unityanalytics": "1.0.0"
+  },
+  "testables": [
+    "com.unity.ml-agents",
+    "com.unity.ml-agents.extensions",
+    "com.unity.inputsystem"
+  ]
+}
diff --git a/Project/Packages/packages-lock.json b/Project/Packages/packages-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..ebeac26fb5bb0545493bfe749273a493ec67fd7c
--- /dev/null
+++ b/Project/Packages/packages-lock.json
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+{
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+      "url": "https://packages.unity.com"
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+      "depth": 1,
+      "source": "registry",
+      "dependencies": {
+        "com.unity.sysroot": "2.0.5"
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+      "url": "https://packages.unity.com"
+    },
+    "com.unity.test-framework": {
+      "version": "1.1.33",
+      "depth": 0,
+      "source": "registry",
+      "dependencies": {
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+        "com.unity.modules.imgui": "1.0.0",
+        "com.unity.modules.jsonserialize": "1.0.0"
+      },
+      "url": "https://packages.unity.com"
+    },
+    "com.unity.textmeshpro": {
+      "version": "3.0.6",
+      "depth": 0,
+      "source": "registry",
+      "dependencies": {
+        "com.unity.ugui": "1.0.0"
+      },
+      "url": "https://packages.unity.com"
+    },
+    "com.unity.timeline": {
+      "version": "1.7.4",
+      "depth": 1,
+      "source": "registry",
+      "dependencies": {
+        "com.unity.modules.director": "1.0.0",
+        "com.unity.modules.animation": "1.0.0",
+        "com.unity.modules.audio": "1.0.0",
+        "com.unity.modules.particlesystem": "1.0.0"
+      },
+      "url": "https://packages.unity.com"
+    },
+    "com.unity.toolchain.macos-x86_64-linux-x86_64": {
+      "version": "2.0.4",
+      "depth": 0,
+      "source": "registry",
+      "dependencies": {
+        "com.unity.sysroot": "2.0.5",
+        "com.unity.sysroot.linux-x86_64": "2.0.4"
+      },
+      "url": "https://packages.unity.com"
+    },
+    "com.unity.ugui": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {
+        "com.unity.modules.ui": "1.0.0",
+        "com.unity.modules.imgui": "1.0.0"
+      }
+    },
+    "com.unity.modules.ai": {
+      "version": "1.0.0",
+      "depth": 1,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.animation": {
+      "version": "1.0.0",
+      "depth": 2,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.audio": {
+      "version": "1.0.0",
+      "depth": 2,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.director": {
+      "version": "1.0.0",
+      "depth": 2,
+      "source": "builtin",
+      "dependencies": {
+        "com.unity.modules.audio": "1.0.0",
+        "com.unity.modules.animation": "1.0.0"
+      }
+    },
+    "com.unity.modules.imageconversion": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.imgui": {
+      "version": "1.0.0",
+      "depth": 1,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.jsonserialize": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.particlesystem": {
+      "version": "1.0.0",
+      "depth": 2,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.physics": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.physics2d": {
+      "version": "1.0.0",
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+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.ui": {
+      "version": "1.0.0",
+      "depth": 1,
+      "source": "builtin",
+      "dependencies": {}
+    },
+    "com.unity.modules.uielements": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {
+        "com.unity.modules.ui": "1.0.0",
+        "com.unity.modules.imgui": "1.0.0",
+        "com.unity.modules.jsonserialize": "1.0.0"
+      }
+    },
+    "com.unity.modules.unityanalytics": {
+      "version": "1.0.0",
+      "depth": 0,
+      "source": "builtin",
+      "dependencies": {
+        "com.unity.modules.unitywebrequest": "1.0.0",
+        "com.unity.modules.jsonserialize": "1.0.0"
+      }
+    },
+    "com.unity.modules.unitywebrequest": {
+      "version": "1.0.0",
+      "depth": 1,
+      "source": "builtin",
+      "dependencies": {}
+    }
+  }
+}
diff --git a/Project/Project.sln.DotSettings b/Project/Project.sln.DotSettings
new file mode 100644
index 0000000000000000000000000000000000000000..43dea280cb174a2a201c67d957eaf4eeae30faa5
--- /dev/null
+++ b/Project/Project.sln.DotSettings
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+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=ML/@EntryIndexedValue">ML</s:String>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Dont/@EntryIndexedValue">True</s:Boolean></wpf:ResourceDictionary>
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+++ b/Project/ProjectSettings/AudioManager.asset
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diff --git a/Project/ProjectSettings/ClusterInputManager.asset b/Project/ProjectSettings/ClusterInputManager.asset
new file mode 100644
index 0000000000000000000000000000000000000000..e7886b266a005f4d9d80f2fef8d1649dcfd3ed2b
--- /dev/null
+++ b/Project/ProjectSettings/ClusterInputManager.asset
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+++ b/Project/ProjectSettings/DynamicsManager.asset
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+++ b/Project/ProjectSettings/EditorBuildSettings.asset
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+%YAML 1.1
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diff --git a/Project/ProjectSettings/EditorSettings.asset b/Project/ProjectSettings/EditorSettings.asset
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+++ b/Project/ProjectSettings/EditorSettings.asset
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index 0000000000000000000000000000000000000000..17c8f538e2152c0a0310b4870979eeecece2153c
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diff --git a/README.md b/README.md
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+---
+library_name: ml-agents
+tags:
+- Pyramids
+- deep-reinforcement-learning
+- reinforcement-learning
+- ML-Agents-Pyramids
+---
+
+  # **ppo** Agent playing **Pyramids**
+  This is a trained model of a **ppo** agent playing **Pyramids**
+  using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
+
+  ## Usage (with ML-Agents)
+  The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
+
+  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
+  - A *short tutorial* where you teach Huggy the Dog � to fetch the stick and then play with him directly in your
+  browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
+  - A *longer tutorial* to understand how works ML-Agents:
+  https://huggingface.co/learn/deep-rl-course/unit5/introduction
+
+  ### Resume the training
+  ```bash
+  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
+  ```
+
+  ### Watch your Agent play
+  You can watch your agent **playing directly in your browser**
+
+  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
+  2. Step 1: Find your model_id: AnnaMats/ppo-Pyramids-Training
+  3. Step 2: Select your *.nn /*.onnx file
+  4. Click on Watch the agent play 👀
+  
\ No newline at end of file
diff --git a/SURVEY.md b/SURVEY.md
new file mode 100644
index 0000000000000000000000000000000000000000..1eb6bb1b7b2994fdfd5b0696632655c61cf5e0ab
--- /dev/null
+++ b/SURVEY.md
@@ -0,0 +1,7 @@
+# Unity ML-Agents Toolkit Survey
+
+Your opinion matters a great deal to us. Only by hearing your thoughts on the
+Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few
+minutes to let us know about it. Please email us at [ml-agents@unity3d.com](mailto:ml-agents@unity3d.com).
+
+<!-- [Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1) -->
diff --git a/colab/Colab_UnityEnvironment_1_Run.ipynb b/colab/Colab_UnityEnvironment_1_Run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a1104c3587389a00f00b3b6cce57bdd634a9a40a
--- /dev/null
+++ b/colab/Colab_UnityEnvironment_1_Run.ipynb
@@ -0,0 +1,503 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Colab-UnityEnvironment-1-Run.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pbVXrmEsLXDt"
+   },
+   "source": [
+    "# ML-Agents Open a UnityEnvironment\n",
+    "<img src=\"https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/images/image-banner.png?raw=true\" align=\"middle\" width=\"435\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WNKTwHU3d2-l"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "htb-p1hSNX7D"
+   },
+   "source": [
+    "#@title Install Rendering Dependencies { display-mode: \"form\" }\n",
+    "#@markdown (You only need to run this code when using Colab's hosted runtime)\n",
+    "\n",
+    "import os\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def progress(value, max=100):\n",
+    "    return HTML(\"\"\"\n",
+    "        <progress\n",
+    "            value='{value}'\n",
+    "            max='{max}',\n",
+    "            style='width: 100%'\n",
+    "        >\n",
+    "            {value}\n",
+    "        </progress>\n",
+    "    \"\"\".format(value=value, max=max))\n",
+    "\n",
+    "pro_bar = display(progress(0, 100), display_id=True)\n",
+    "\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  INSTALL_XVFB = True\n",
+    "except ImportError:\n",
+    "  INSTALL_XVFB = 'COLAB_ALWAYS_INSTALL_XVFB' in os.environ\n",
+    "\n",
+    "if INSTALL_XVFB:\n",
+    "  with open('frame-buffer', 'w') as writefile:\n",
+    "    writefile.write(\"\"\"#taken from https://gist.github.com/jterrace/2911875\n",
+    "XVFB=/usr/bin/Xvfb\n",
+    "XVFBARGS=\":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\"\n",
+    "PIDFILE=./frame-buffer.pid\n",
+    "case \"$1\" in\n",
+    "  start)\n",
+    "    echo -n \"Starting virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  stop)\n",
+    "    echo -n \"Stopping virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --stop --quiet --pidfile $PIDFILE\n",
+    "    rm $PIDFILE\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  restart)\n",
+    "    $0 stop\n",
+    "    $0 start\n",
+    "    ;;\n",
+    "  *)\n",
+    "        echo \"Usage: /etc/init.d/xvfb {start|stop|restart}\"\n",
+    "        exit 1\n",
+    "esac\n",
+    "exit 0\n",
+    "    \"\"\")\n",
+    "  !sudo apt-get update\n",
+    "  pro_bar.update(progress(10, 100))\n",
+    "  !sudo DEBIAN_FRONTEND=noninteractive apt install -y daemon wget gdebi-core build-essential libfontenc1 libfreetype6 xorg-dev xorg\n",
+    "  pro_bar.update(progress(20, 100))\n",
+    "  !wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(30, 100))\n",
+    "  !wget --output-document xvfb.deb http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.12_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(40, 100))\n",
+    "  !sudo dpkg -i libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(50, 100))\n",
+    "  !sudo dpkg -i xvfb.deb 2>&1\n",
+    "  pro_bar.update(progress(70, 100))\n",
+    "  !rm libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb\n",
+    "  pro_bar.update(progress(80, 100))\n",
+    "  !rm xvfb.deb\n",
+    "  pro_bar.update(progress(90, 100))\n",
+    "  !bash frame-buffer start\n",
+    "  os.environ[\"DISPLAY\"] = \":1\"\n",
+    "pro_bar.update(progress(100, 100))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pzj7wgapAcDs"
+   },
+   "source": [
+    "### Installing ml-agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "N8yfQqkbebQ5"
+   },
+   "source": [
+    "try:\n",
+    "  import mlagents\n",
+    "  print(\"ml-agents already installed\")\n",
+    "except ImportError:\n",
+    "  !python -m pip install -q mlagents==0.30.0\n",
+    "  print(\"Installed ml-agents\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_u74YhSmW6gD"
+   },
+   "source": [
+    "## Run the Environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "DpZPbRvRuLZv"
+   },
+   "source": [
+    "#@title Select Environment { display-mode: \"form\" }\n",
+    "env_id = \"GridWorld\" #@param ['Basic', '3DBall', '3DBallHard', 'GridWorld', 'Hallway', 'VisualHallway', 'CrawlerDynamicTarget', 'CrawlerStaticTarget', 'Bouncer', 'SoccerTwos', 'PushBlock', 'VisualPushBlock', 'WallJump', 'Tennis', 'Reacher', 'Pyramids', 'VisualPyramids', 'Walker', 'FoodCollector', 'VisualFoodCollector', 'StrikersVsGoalie', 'WormStaticTarget', 'WormDynamicTarget']\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P-r_cB2rqp5x"
+   },
+   "source": [
+    "### Start Environment from the registry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "YSf-WhxbqtLw"
+   },
+   "source": [
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "from mlagents_envs.registry import default_registry\n",
+    "\n",
+    "env = default_registry[env_id].make()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h1lIx3_l24OP"
+   },
+   "source": [
+    "### Reset the environment\n",
+    "To reset the environment, simply call `env.reset()`. This method takes no argument and returns nothing but will send a signal to the simulation to reset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dhtl0mpeqxYi"
+   },
+   "source": [
+    "env.reset()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k1rwnVq2qyoO"
+   },
+   "source": [
+    "### Behavior Specs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TrD0rSv92T8A"
+   },
+   "source": [
+    "#### Get the Behavior Specs from the Environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a7KatdThq7OV"
+   },
+   "source": [
+    "# We will only consider the first Behavior\n",
+    "behavior_name = list(env.behavior_specs)[0]\n",
+    "print(f\"Name of the behavior : {behavior_name}\")\n",
+    "spec = env.behavior_specs[behavior_name]"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m1L8DHADrAbe"
+   },
+   "source": [
+    "#### Get the Observation Space from the Behavior Specs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "PqDTV5mSrJF5"
+   },
+   "source": [
+    "# Examine the number of observations per Agent\n",
+    "print(\"Number of observations : \", len(spec.observation_specs))\n",
+    "\n",
+    "# Is there a visual observation ?\n",
+    "# Visual observation have 3 dimensions: Height, Width and number of channels\n",
+    "vis_obs = any(len(spec.shape) == 3 for spec in spec.observation_specs)\n",
+    "print(\"Is there a visual observation ?\", vis_obs)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yVLN_wbG1G5-"
+   },
+   "source": [
+    "#### Get the Action Space from the Behavior Specs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "M9zk1-az1L-G"
+   },
+   "source": [
+    "# Is the Action continuous or multi-discrete ?\n",
+    "if spec.action_spec.continuous_size > 0:\n",
+    "  print(f\"There are {spec.action_spec.continuous_size} continuous actions\")\n",
+    "if spec.action_spec.is_discrete():\n",
+    "  print(f\"There are {spec.action_spec.discrete_size} discrete actions\")\n",
+    "\n",
+    "\n",
+    "# How many actions are possible ?\n",
+    "#print(f\"There are {spec.action_size} action(s)\")\n",
+    "\n",
+    "# For discrete actions only : How many different options does each action has ?\n",
+    "if spec.action_spec.discrete_size > 0:\n",
+    "  for action, branch_size in enumerate(spec.action_spec.discrete_branches):\n",
+    "    print(f\"Action number {action} has {branch_size} different options\")\n",
+    "\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3cX07SGw22Lm"
+   },
+   "source": [
+    "### Stepping the environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xO5p0s0prfsQ"
+   },
+   "source": [
+    "#### Get the steps from the Environment\n",
+    "You can do this with the `env.get_steps(behavior_name)` method. If there are multiple behaviors in the Environment, you can call this method with each of the behavior's names.\n",
+    "_Note_ This will not move the simulation forward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ePZtcHXUrjyf"
+   },
+   "source": [
+    "decision_steps, terminal_steps = env.get_steps(behavior_name)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f-Oj3ix530mx"
+   },
+   "source": [
+    "#### Set actions for each behavior\n",
+    "You can set the actions for the Agents of a Behavior by calling `env.set_actions()` you will need to specify the behavior name and pass a tensor of dimension 2. The first dimension of the action must be equal to the number of Agents that requested a decision during the step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "KB-nxfbw337g"
+   },
+   "source": [
+    "env.set_actions(behavior_name, spec.action_spec.empty_action(len(decision_steps)))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MQCybRs84cmq"
+   },
+   "source": [
+    "#### Move the simulation forward\n",
+    "Call `env.step()` to move the simulation forward. The simulation will progress until an Agent requestes a decision or terminates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "nl3K40ZR4bh2"
+   },
+   "source": [
+    "env.step()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i9gdextn2vJy"
+   },
+   "source": [
+    "### Observations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iAMqnnddr8Xo"
+   },
+   "source": [
+    "#### Show the observations for one of the Agents\n",
+    "`DecisionSteps.obs` is a tuple containing all of the observations for all of the Agents with the provided Behavior name.\n",
+    "Each value in the tuple is an observation tensor containing the observation data for all of the agents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "OJpta61TsBiO"
+   },
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "for index, obs_spec in enumerate(spec.observation_specs):\n",
+    "  if len(obs_spec.shape) == 3:\n",
+    "    print(\"Here is the first visual observation\")\n",
+    "    plt.imshow(decision_steps.obs[index][0,:,:,:])\n",
+    "    plt.show()\n",
+    "\n",
+    "for index, obs_spec in enumerate(spec.observation_specs):\n",
+    "  if len(obs_spec.shape) == 1:\n",
+    "    print(\"First vector observations : \", decision_steps.obs[index][0,:])"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y60u21sys8kA"
+   },
+   "source": [
+    "### Run the Environment for a few episodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a2uQUsoMtIUK"
+   },
+   "source": [
+    "for episode in range(3):\n",
+    "  env.reset()\n",
+    "  decision_steps, terminal_steps = env.get_steps(behavior_name)\n",
+    "  tracked_agent = -1 # -1 indicates not yet tracking\n",
+    "  done = False # For the tracked_agent\n",
+    "  episode_rewards = 0 # For the tracked_agent\n",
+    "  while not done:\n",
+    "    # Track the first agent we see if not tracking\n",
+    "    # Note : len(decision_steps) = [number of agents that requested a decision]\n",
+    "    if tracked_agent == -1 and len(decision_steps) >= 1:\n",
+    "      tracked_agent = decision_steps.agent_id[0]\n",
+    "\n",
+    "    # Generate an action for all agents\n",
+    "    action = spec.action_spec.random_action(len(decision_steps))\n",
+    "\n",
+    "    # Set the actions\n",
+    "    env.set_actions(behavior_name, action)\n",
+    "\n",
+    "    # Move the simulation forward\n",
+    "    env.step()\n",
+    "\n",
+    "    # Get the new simulation results\n",
+    "    decision_steps, terminal_steps = env.get_steps(behavior_name)\n",
+    "    if tracked_agent in decision_steps: # The agent requested a decision\n",
+    "      episode_rewards += decision_steps[tracked_agent].reward\n",
+    "    if tracked_agent in terminal_steps: # The agent terminated its episode\n",
+    "      episode_rewards += terminal_steps[tracked_agent].reward\n",
+    "      done = True\n",
+    "  print(f\"Total rewards for episode {episode} is {episode_rewards}\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R-3grXNEtJPa"
+   },
+   "source": [
+    "### Close the Environment to free the port it is using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vdWG6_SqtNtv"
+   },
+   "source": [
+    "env.close()\n",
+    "print(\"Closed environment\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  }
+ ]
+}
diff --git a/colab/Colab_UnityEnvironment_2_Train.ipynb b/colab/Colab_UnityEnvironment_2_Train.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5ad5b2b7445189a18dc1cf84158b0dc83bf7f8ff
--- /dev/null
+++ b/colab/Colab_UnityEnvironment_2_Train.ipynb
@@ -0,0 +1,689 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Colab-UnityEnvironment-2-Train.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pbVXrmEsLXDt"
+   },
+   "source": [
+    "# ML-Agents Q-Learning with GridWorld\n",
+    "<img src=\"https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/images/gridworld.png?raw=true\" align=\"middle\" width=\"435\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WNKTwHU3d2-l"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "htb-p1hSNX7D"
+   },
+   "source": [
+    "#@title Install Rendering Dependencies { display-mode: \"form\" }\n",
+    "#@markdown (You only need to run this code when using Colab's hosted runtime)\n",
+    "\n",
+    "import os\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def progress(value, max=100):\n",
+    "    return HTML(\"\"\"\n",
+    "        <progress\n",
+    "            value='{value}'\n",
+    "            max='{max}',\n",
+    "            style='width: 100%'\n",
+    "        >\n",
+    "            {value}\n",
+    "        </progress>\n",
+    "    \"\"\".format(value=value, max=max))\n",
+    "\n",
+    "pro_bar = display(progress(0, 100), display_id=True)\n",
+    "\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  INSTALL_XVFB = True\n",
+    "except ImportError:\n",
+    "  INSTALL_XVFB = 'COLAB_ALWAYS_INSTALL_XVFB' in os.environ\n",
+    "\n",
+    "if INSTALL_XVFB:\n",
+    "  with open('frame-buffer', 'w') as writefile:\n",
+    "    writefile.write(\"\"\"#taken from https://gist.github.com/jterrace/2911875\n",
+    "XVFB=/usr/bin/Xvfb\n",
+    "XVFBARGS=\":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\"\n",
+    "PIDFILE=./frame-buffer.pid\n",
+    "case \"$1\" in\n",
+    "  start)\n",
+    "    echo -n \"Starting virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  stop)\n",
+    "    echo -n \"Stopping virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --stop --quiet --pidfile $PIDFILE\n",
+    "    rm $PIDFILE\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  restart)\n",
+    "    $0 stop\n",
+    "    $0 start\n",
+    "    ;;\n",
+    "  *)\n",
+    "        echo \"Usage: /etc/init.d/xvfb {start|stop|restart}\"\n",
+    "        exit 1\n",
+    "esac\n",
+    "exit 0\n",
+    "    \"\"\")\n",
+    "  !sudo apt-get update\n",
+    "  pro_bar.update(progress(10, 100))\n",
+    "  !sudo DEBIAN_FRONTEND=noninteractive apt install -y daemon wget gdebi-core build-essential libfontenc1 libfreetype6 xorg-dev xorg\n",
+    "  pro_bar.update(progress(20, 100))\n",
+    "  !wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(30, 100))\n",
+    "  !wget --output-document xvfb.deb http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.12_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(40, 100))\n",
+    "  !sudo dpkg -i libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(50, 100))\n",
+    "  !sudo dpkg -i xvfb.deb 2>&1\n",
+    "  pro_bar.update(progress(70, 100))\n",
+    "  !rm libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb\n",
+    "  pro_bar.update(progress(80, 100))\n",
+    "  !rm xvfb.deb\n",
+    "  pro_bar.update(progress(90, 100))\n",
+    "  !bash frame-buffer start\n",
+    "  os.environ[\"DISPLAY\"] = \":1\"\n",
+    "pro_bar.update(progress(100, 100))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pzj7wgapAcDs"
+   },
+   "source": [
+    "### Installing ml-agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "N8yfQqkbebQ5"
+   },
+   "source": [
+    "try:\n",
+    "  import mlagents\n",
+    "  print(\"ml-agents already installed\")\n",
+    "except ImportError:\n",
+    "  !python -m pip install -q mlagents==0.30.0\n",
+    "  print(\"Installed ml-agents\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jz81TWAkbuFY"
+   },
+   "source": [
+    "## Train the GridWorld Environment with Q-Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "29n3dt1Zx5ty"
+   },
+   "source": [
+    "### What is the GridWorld Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pZhVRfdoyPmv"
+   },
+   "source": [
+    "The [GridWorld](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Examples.md#gridworld) Environment is a simple Unity visual environment. The Agent is a blue square in a 3x3 grid that is trying to reach a green __`+`__ while avoiding a red __`x`__.\n",
+    "\n",
+    "The observation is an image obtained by a camera on top of the grid.\n",
+    "\n",
+    "The Action can be one of 5 :\n",
+    " - Do not move\n",
+    " - Move up\n",
+    " - Move down\n",
+    " - Move right\n",
+    " - Move left\n",
+    "\n",
+    "The Agent receives a reward of _1.0_ if it reaches the green __`+`__, a penalty of _-1.0_ if it touches the red __`x`__ and a penalty of `-0.01` at every step (to force the Agent to solve the task as fast as possible)\n",
+    "\n",
+    "__Note__ There are 9 Agents, each in their own grid, at once in the environment. This alows for faster data collection.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4Gt-ZydJyJWD"
+   },
+   "source": [
+    "### The Q-Learning Algorithm\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KA1qOgfq0Xdv"
+   },
+   "source": [
+    "In this Notebook, we will implement a very simple Q-Learning algorithm. We will use [pytorch](https://pytorch.org/) to do so.\n",
+    "\n",
+    "Below is the code to create the neural network we will use in the Notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "q79rUp_Sx6A_"
+   },
+   "source": [
+    "import torch\n",
+    "from typing import Tuple\n",
+    "from math import floor\n",
+    "from torch.nn import Parameter\n",
+    "\n",
+    "\n",
+    "class VisualQNetwork(torch.nn.Module):\n",
+    "  def __init__(\n",
+    "    self,\n",
+    "    input_shape: Tuple[int, int, int],\n",
+    "    encoding_size: int,\n",
+    "    output_size: int\n",
+    "  ):\n",
+    "    \"\"\"\n",
+    "    Creates a neural network that takes as input a batch of images (3\n",
+    "    dimensional tensors) and outputs a batch of outputs (1 dimensional\n",
+    "    tensors)\n",
+    "    \"\"\"\n",
+    "    super(VisualQNetwork, self).__init__()\n",
+    "    height = input_shape[1]\n",
+    "    width = input_shape[2]\n",
+    "    initial_channels = input_shape[0]\n",
+    "    conv_1_hw = self.conv_output_shape((height, width), 8, 4)\n",
+    "    conv_2_hw = self.conv_output_shape(conv_1_hw, 4, 2)\n",
+    "    self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 32\n",
+    "    self.conv1 = torch.nn.Conv2d(initial_channels, 16, [8, 8], [4, 4])\n",
+    "    self.conv2 = torch.nn.Conv2d(16, 32, [4, 4], [2, 2])\n",
+    "    self.dense1 = torch.nn.Linear(self.final_flat, encoding_size)\n",
+    "    self.dense2 = torch.nn.Linear(encoding_size, output_size)\n",
+    "\n",
+    "  def forward(self, visual_obs: torch.tensor):\n",
+    "    conv_1 = torch.relu(self.conv1(visual_obs))\n",
+    "    conv_2 = torch.relu(self.conv2(conv_1))\n",
+    "    hidden = self.dense1(conv_2.reshape([-1, self.final_flat]))\n",
+    "    hidden = torch.relu(hidden)\n",
+    "    hidden = self.dense2(hidden)\n",
+    "    return hidden\n",
+    "\n",
+    "  @staticmethod\n",
+    "  def conv_output_shape(\n",
+    "    h_w: Tuple[int, int],\n",
+    "    kernel_size: int = 1,\n",
+    "    stride: int = 1,\n",
+    "    pad: int = 0,\n",
+    "    dilation: int = 1,\n",
+    "  ):\n",
+    "    \"\"\"\n",
+    "    Computes the height and width of the output of a convolution layer.\n",
+    "    \"\"\"\n",
+    "    h = floor(\n",
+    "      ((h_w[0] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\n",
+    "    )\n",
+    "    w = floor(\n",
+    "      ((h_w[1] + (2 * pad) - (dilation * (kernel_size - 1)) - 1) / stride) + 1\n",
+    "    )\n",
+    "    return h, w\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EZoaEBAo2L0F"
+   },
+   "source": [
+    "We will now create a few classes to help us store the data we will use to train the Q-Learning algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "L772fe2q39DO"
+   },
+   "source": [
+    "import numpy as np\n",
+    "from typing import NamedTuple, List\n",
+    "\n",
+    "\n",
+    "class Experience(NamedTuple):\n",
+    "  \"\"\"\n",
+    "  An experience contains the data of one Agent transition.\n",
+    "  - Observation\n",
+    "  - Action\n",
+    "  - Reward\n",
+    "  - Done flag\n",
+    "  - Next Observation\n",
+    "  \"\"\"\n",
+    "\n",
+    "  obs: np.ndarray\n",
+    "  action: np.ndarray\n",
+    "  reward: float\n",
+    "  done: bool\n",
+    "  next_obs: np.ndarray\n",
+    "\n",
+    "# A Trajectory is an ordered sequence of Experiences\n",
+    "Trajectory = List[Experience]\n",
+    "\n",
+    "# A Buffer is an unordered list of Experiences from multiple Trajectories\n",
+    "Buffer = List[Experience]"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6HsM1d5I3_Tj"
+   },
+   "source": [
+    "Now, we can create our trainer class. The role of this trainer is to collect data from the Environment according to a Policy, and then train the Q-Network with that data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "KkzBoRJCb18t"
+   },
+   "source": [
+    "from mlagents_envs.environment import ActionTuple, BaseEnv\n",
+    "from typing import Dict\n",
+    "import random\n",
+    "\n",
+    "\n",
+    "class Trainer:\n",
+    "  @staticmethod\n",
+    "  def generate_trajectories(\n",
+    "    env: BaseEnv, q_net: VisualQNetwork, buffer_size: int, epsilon: float\n",
+    "  ):\n",
+    "    \"\"\"\n",
+    "    Given a Unity Environment and a Q-Network, this method will generate a\n",
+    "    buffer of Experiences obtained by running the Environment with the Policy\n",
+    "    derived from the Q-Network.\n",
+    "    :param BaseEnv: The UnityEnvironment used.\n",
+    "    :param q_net: The Q-Network used to collect the data.\n",
+    "    :param buffer_size: The minimum size of the buffer this method will return.\n",
+    "    :param epsilon: Will add a random normal variable with standard deviation.\n",
+    "    epsilon to the value heads of the Q-Network to encourage exploration.\n",
+    "    :returns: a Tuple containing the created buffer and the average cumulative\n",
+    "    the Agents obtained.\n",
+    "    \"\"\"\n",
+    "    # Create an empty Buffer\n",
+    "    buffer: Buffer = []\n",
+    "\n",
+    "    # Reset the environment\n",
+    "    env.reset()\n",
+    "    # Read and store the Behavior Name of the Environment\n",
+    "    behavior_name = list(env.behavior_specs)[0]\n",
+    "    # Read and store the Behavior Specs of the Environment\n",
+    "    spec = env.behavior_specs[behavior_name]\n",
+    "\n",
+    "    # Create a Mapping from AgentId to Trajectories. This will help us create\n",
+    "    # trajectories for each Agents\n",
+    "    dict_trajectories_from_agent: Dict[int, Trajectory] = {}\n",
+    "    # Create a Mapping from AgentId to the last observation of the Agent\n",
+    "    dict_last_obs_from_agent: Dict[int, np.ndarray] = {}\n",
+    "    # Create a Mapping from AgentId to the last observation of the Agent\n",
+    "    dict_last_action_from_agent: Dict[int, np.ndarray] = {}\n",
+    "    # Create a Mapping from AgentId to cumulative reward (Only for reporting)\n",
+    "    dict_cumulative_reward_from_agent: Dict[int, float] = {}\n",
+    "    # Create a list to store the cumulative rewards obtained so far\n",
+    "    cumulative_rewards: List[float] = []\n",
+    "\n",
+    "    while len(buffer) < buffer_size:  # While not enough data in the buffer\n",
+    "      # Get the Decision Steps and Terminal Steps of the Agents\n",
+    "      decision_steps, terminal_steps = env.get_steps(behavior_name)\n",
+    "\n",
+    "      # permute the tensor to go from NHWC to NCHW\n",
+    "      order = (0, 3, 1, 2)\n",
+    "      decision_steps.obs = [np.transpose(obs, order) for obs in decision_steps.obs]\n",
+    "      terminal_steps.obs = [np.transpose(obs, order) for obs in terminal_steps.obs]\n",
+    "\n",
+    "      # For all Agents with a Terminal Step:\n",
+    "      for agent_id_terminated in terminal_steps:\n",
+    "        # Create its last experience (is last because the Agent terminated)\n",
+    "        last_experience = Experience(\n",
+    "          obs=dict_last_obs_from_agent[agent_id_terminated].copy(),\n",
+    "          reward=terminal_steps[agent_id_terminated].reward,\n",
+    "          done=not terminal_steps[agent_id_terminated].interrupted,\n",
+    "          action=dict_last_action_from_agent[agent_id_terminated].copy(),\n",
+    "          next_obs=terminal_steps[agent_id_terminated].obs[0],\n",
+    "        )\n",
+    "        # Clear its last observation and action (Since the trajectory is over)\n",
+    "        dict_last_obs_from_agent.pop(agent_id_terminated)\n",
+    "        dict_last_action_from_agent.pop(agent_id_terminated)\n",
+    "        # Report the cumulative reward\n",
+    "        cumulative_reward = (\n",
+    "          dict_cumulative_reward_from_agent.pop(agent_id_terminated)\n",
+    "          + terminal_steps[agent_id_terminated].reward\n",
+    "        )\n",
+    "        cumulative_rewards.append(cumulative_reward)\n",
+    "        # Add the Trajectory and the last experience to the buffer\n",
+    "        buffer.extend(dict_trajectories_from_agent.pop(agent_id_terminated))\n",
+    "        buffer.append(last_experience)\n",
+    "\n",
+    "      # For all Agents with a Decision Step:\n",
+    "      for agent_id_decisions in decision_steps:\n",
+    "        # If the Agent does not have a Trajectory, create an empty one\n",
+    "        if agent_id_decisions not in dict_trajectories_from_agent:\n",
+    "          dict_trajectories_from_agent[agent_id_decisions] = []\n",
+    "          dict_cumulative_reward_from_agent[agent_id_decisions] = 0\n",
+    "\n",
+    "        # If the Agent requesting a decision has a \"last observation\"\n",
+    "        if agent_id_decisions in dict_last_obs_from_agent:\n",
+    "          # Create an Experience from the last observation and the Decision Step\n",
+    "          exp = Experience(\n",
+    "            obs=dict_last_obs_from_agent[agent_id_decisions].copy(),\n",
+    "            reward=decision_steps[agent_id_decisions].reward,\n",
+    "            done=False,\n",
+    "            action=dict_last_action_from_agent[agent_id_decisions].copy(),\n",
+    "            next_obs=decision_steps[agent_id_decisions].obs[0],\n",
+    "          )\n",
+    "          # Update the Trajectory of the Agent and its cumulative reward\n",
+    "          dict_trajectories_from_agent[agent_id_decisions].append(exp)\n",
+    "          dict_cumulative_reward_from_agent[agent_id_decisions] += (\n",
+    "            decision_steps[agent_id_decisions].reward\n",
+    "          )\n",
+    "        # Store the observation as the new \"last observation\"\n",
+    "        dict_last_obs_from_agent[agent_id_decisions] = (\n",
+    "          decision_steps[agent_id_decisions].obs[0]\n",
+    "        )\n",
+    "\n",
+    "      # Generate an action for all the Agents that requested a decision\n",
+    "      # Compute the values for each action given the observation\n",
+    "      actions_values = (\n",
+    "        q_net(torch.from_numpy(decision_steps.obs[0])).detach().numpy()\n",
+    "      )\n",
+    "      # Add some noise with epsilon to the values\n",
+    "      actions_values += epsilon * (\n",
+    "        np.random.randn(actions_values.shape[0], actions_values.shape[1])\n",
+    "      ).astype(np.float32)\n",
+    "      # Pick the best action using argmax\n",
+    "      actions = np.argmax(actions_values, axis=1)\n",
+    "      actions.resize((len(decision_steps), 1))\n",
+    "      # Store the action that was picked, it will be put in the trajectory later\n",
+    "      for agent_index, agent_id in enumerate(decision_steps.agent_id):\n",
+    "        dict_last_action_from_agent[agent_id] = actions[agent_index]\n",
+    "\n",
+    "      # Set the actions in the environment\n",
+    "      # Unity Environments expect ActionTuple instances.\n",
+    "      action_tuple = ActionTuple()\n",
+    "      action_tuple.add_discrete(actions)\n",
+    "      env.set_actions(behavior_name, action_tuple)\n",
+    "      # Perform a step in the simulation\n",
+    "      env.step()\n",
+    "    return buffer, np.mean(cumulative_rewards)\n",
+    "\n",
+    "  @staticmethod\n",
+    "  def update_q_net(\n",
+    "    q_net: VisualQNetwork,\n",
+    "    optimizer: torch.optim,\n",
+    "    buffer: Buffer,\n",
+    "    action_size: int\n",
+    "  ):\n",
+    "    \"\"\"\n",
+    "    Performs an update of the Q-Network using the provided optimizer and buffer\n",
+    "    \"\"\"\n",
+    "    BATCH_SIZE = 1000\n",
+    "    NUM_EPOCH = 3\n",
+    "    GAMMA = 0.9\n",
+    "    batch_size = min(len(buffer), BATCH_SIZE)\n",
+    "    random.shuffle(buffer)\n",
+    "    # Split the buffer into batches\n",
+    "    batches = [\n",
+    "      buffer[batch_size * start : batch_size * (start + 1)]\n",
+    "      for start in range(int(len(buffer) / batch_size))\n",
+    "    ]\n",
+    "    for _ in range(NUM_EPOCH):\n",
+    "      for batch in batches:\n",
+    "        # Create the Tensors that will be fed in the network\n",
+    "        obs = torch.from_numpy(np.stack([ex.obs for ex in batch]))\n",
+    "        reward = torch.from_numpy(\n",
+    "          np.array([ex.reward for ex in batch], dtype=np.float32).reshape(-1, 1)\n",
+    "        )\n",
+    "        done = torch.from_numpy(\n",
+    "          np.array([ex.done for ex in batch], dtype=np.float32).reshape(-1, 1)\n",
+    "        )\n",
+    "        action = torch.from_numpy(np.stack([ex.action for ex in batch]))\n",
+    "        next_obs = torch.from_numpy(np.stack([ex.next_obs for ex in batch]))\n",
+    "\n",
+    "        # Use the Bellman equation to update the Q-Network\n",
+    "        target = (\n",
+    "          reward\n",
+    "          + (1.0 - done)\n",
+    "          * GAMMA\n",
+    "          * torch.max(q_net(next_obs).detach(), dim=1, keepdim=True).values\n",
+    "        )\n",
+    "        mask = torch.zeros((len(batch), action_size))\n",
+    "        mask.scatter_(1, action, 1)\n",
+    "        prediction = torch.sum(q_net(obs) * mask, dim=1, keepdim=True)\n",
+    "        criterion = torch.nn.MSELoss()\n",
+    "        loss = criterion(prediction, target)\n",
+    "\n",
+    "        # Perform the backpropagation\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vcU4ZMAEWCvX"
+   },
+   "source": [
+    "### Run Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "_lIHijQfbYjh"
+   },
+   "source": [
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "from mlagents_envs.registry import default_registry\n",
+    "from mlagents_envs.environment import UnityEnvironment\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Create the GridWorld Environment from the registry\n",
+    "env = default_registry[\"GridWorld\"].make()\n",
+    "print(\"GridWorld environment created.\")\n",
+    "\n",
+    "num_actions = 5\n",
+    "\n",
+    "try:\n",
+    "  # Create a new Q-Network.\n",
+    "  qnet = VisualQNetwork((3, 64, 84), 126, num_actions)\n",
+    "\n",
+    "  experiences: Buffer = []\n",
+    "  optim = torch.optim.Adam(qnet.parameters(), lr= 0.001)\n",
+    "\n",
+    "  cumulative_rewards: List[float] = []\n",
+    "\n",
+    "  # The number of training steps that will be performed\n",
+    "  NUM_TRAINING_STEPS = int(os.getenv('QLEARNING_NUM_TRAINING_STEPS', 70))\n",
+    "  # The number of experiences to collect per training step\n",
+    "  NUM_NEW_EXP = int(os.getenv('QLEARNING_NUM_NEW_EXP', 1000))\n",
+    "  # The maximum size of the Buffer\n",
+    "  BUFFER_SIZE = int(os.getenv('QLEARNING_BUFFER_SIZE', 10000))\n",
+    "\n",
+    "  for n in range(NUM_TRAINING_STEPS):\n",
+    "    new_exp,_ = Trainer.generate_trajectories(env, qnet, NUM_NEW_EXP, epsilon=0.1)\n",
+    "    random.shuffle(experiences)\n",
+    "    if len(experiences) > BUFFER_SIZE:\n",
+    "      experiences = experiences[:BUFFER_SIZE]\n",
+    "    experiences.extend(new_exp)\n",
+    "    Trainer.update_q_net(qnet, optim, experiences, num_actions)\n",
+    "    _, rewards = Trainer.generate_trajectories(env, qnet, 100, epsilon=0)\n",
+    "    cumulative_rewards.append(rewards)\n",
+    "    print(\"Training step \", n+1, \"\\treward \", rewards)\n",
+    "except KeyboardInterrupt:\n",
+    "  print(\"\\nTraining interrupted, continue to next cell to save to save the model.\")\n",
+    "finally:\n",
+    "  env.close()\n",
+    "\n",
+    "# Show the training graph\n",
+    "try:\n",
+    "  plt.plot(range(NUM_TRAINING_STEPS), cumulative_rewards)\n",
+    "except ValueError:\n",
+    "  print(\"\\nPlot failed on interrupted training.\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Export PyTorch Model to ONNX"
+   ],
+   "metadata": {
+    "id": "pFEP9JT5KuO-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The following cell provides an example of some of the extra tensors a model needs to work for ML-Agents inference with Barracuda. The GridWorldColab scene is configured to work with this ONNX file.\n",
+    "Only policy models need to be exported for inference and they need the following additional tensors:\n",
+    "\n",
+    "*   All models need version_number\n",
+    "*   All models need memory_size\n",
+    "*   Models with continuous outputs need continuous_action_output_shape\n",
+    "*   Models with discrete outputs need discrete_action_output_shape and an additional mask input that matches the shape of the discrete outputs\n",
+    "*   The mask input must be connected to the outputs or it will be pruned on export, if mask values aren't being set they will be 1, so multiplying the discrete outputs by the mask will have no effect"
+   ],
+   "metadata": {
+    "id": "V0V7bH4mOnUF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "class WrapperNet(torch.nn.Module):\n",
+    "    def __init__(\n",
+    "            self,\n",
+    "            qnet: VisualQNetwork,\n",
+    "            discrete_output_sizes: List[int],\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Wraps the VisualQNetwork adding extra constants and dummy mask inputs\n",
+    "        required by runtime inference with Barracuda.\n",
+    "\n",
+    "        For environment continuous actions outputs would need to add them\n",
+    "        similarly to how discrete action outputs work, both in the wrapper\n",
+    "        and in the ONNX output_names / dynamic_axes.\n",
+    "        \"\"\"\n",
+    "        super(WrapperNet, self).__init__()\n",
+    "        self.qnet = qnet\n",
+    "\n",
+    "        # version_number\n",
+    "        #   MLAgents1_0 = 2   (not covered by this example)\n",
+    "        #   MLAgents2_0 = 3\n",
+    "        version_number = torch.Tensor([3])\n",
+    "        self.version_number = Parameter(version_number, requires_grad=False)\n",
+    "\n",
+    "        # memory_size\n",
+    "        # TODO: document case where memory is not zero.\n",
+    "        memory_size = torch.Tensor([0])\n",
+    "        self.memory_size = Parameter(memory_size, requires_grad=False)\n",
+    "\n",
+    "        # discrete_action_output_shape\n",
+    "        output_shape=torch.Tensor([discrete_output_sizes])\n",
+    "        self.discrete_shape = Parameter(output_shape, requires_grad=False)\n",
+    "\n",
+    "\n",
+    "    # if you have discrete actions ML-agents expects corresponding a mask\n",
+    "    # tensor with the same shape to exist as input\n",
+    "    def forward(self, visual_obs: torch.tensor, mask: torch.tensor):\n",
+    "        qnet_result = self.qnet(visual_obs)\n",
+    "        # Connect mask to keep it from getting pruned\n",
+    "        # Mask values will be 1 if you never call SetActionMask() in\n",
+    "        # WriteDiscreteActionMask()\n",
+    "        qnet_result = torch.mul(qnet_result, mask)\n",
+    "        action = torch.argmax(qnet_result, dim=1, keepdim=True)\n",
+    "        return [action], self.discrete_shape, self.version_number, self.memory_size\n",
+    "\n",
+    "\n",
+    "torch.onnx.export(\n",
+    "    WrapperNet(qnet, [num_actions]),\n",
+    "    # A tuple with an example of the input tensors\n",
+    "    (torch.tensor([experiences[0].obs]), torch.ones(1, num_actions)),\n",
+    "    'GridWorldColab.onnx',\n",
+    "    opset_version=9,\n",
+    "    # input_names must correspond to the WrapperNet forward parameters\n",
+    "    # obs will be obs_0, obs_1, etc.\n",
+    "    input_names=[\"obs_0\", \"action_masks\"],\n",
+    "    # output_names must correspond to the return tuple of the WrapperNet\n",
+    "    # forward function.\n",
+    "    output_names=[\"discrete_actions\", \"discrete_action_output_shape\",\n",
+    "                  \"version_number\", \"memory_size\"],\n",
+    "    # All inputs and outputs should have their 0th dimension be designated\n",
+    "    # as 'batch'\n",
+    "    dynamic_axes={'obs_0': {0: 'batch'},\n",
+    "                  'action_masks': {0: 'batch'},\n",
+    "                  'discrete_actions': {0: 'batch'},\n",
+    "                  'discrete_action_output_shape': {0: 'batch'}\n",
+    "                 }\n",
+    "    )"
+   ],
+   "metadata": {
+    "id": "T0zzg8fWpTYO"
+   },
+   "execution_count": null,
+   "outputs": []
+  }
+ ]
+}
diff --git a/colab/Colab_UnityEnvironment_3_SideChannel.ipynb b/colab/Colab_UnityEnvironment_3_SideChannel.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..81711d3c84af5b6a0b8604e331cf5449e5954ac4
--- /dev/null
+++ b/colab/Colab_UnityEnvironment_3_SideChannel.ipynb
@@ -0,0 +1,293 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Colab-UnityEnvironment-3-SideChannel.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pbVXrmEsLXDt"
+   },
+   "source": [
+    "# ML-Agents Use SideChannels\n",
+    "<img src=\"https://raw.githubusercontent.com/Unity-Technologies/ml-agents/release_20_docs/docs/images/3dball_big.png\" align=\"middle\" width=\"435\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WNKTwHU3d2-l"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "htb-p1hSNX7D"
+   },
+   "source": [
+    "#@title Install Rendering Dependencies { display-mode: \"form\" }\n",
+    "#@markdown (You only need to run this code when using Colab's hosted runtime)\n",
+    "\n",
+    "import os\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def progress(value, max=100):\n",
+    "    return HTML(\"\"\"\n",
+    "        <progress\n",
+    "            value='{value}'\n",
+    "            max='{max}',\n",
+    "            style='width: 100%'\n",
+    "        >\n",
+    "            {value}\n",
+    "        </progress>\n",
+    "    \"\"\".format(value=value, max=max))\n",
+    "\n",
+    "pro_bar = display(progress(0, 100), display_id=True)\n",
+    "\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  INSTALL_XVFB = True\n",
+    "except ImportError:\n",
+    "  INSTALL_XVFB = 'COLAB_ALWAYS_INSTALL_XVFB' in os.environ\n",
+    "\n",
+    "if INSTALL_XVFB:\n",
+    "  with open('frame-buffer', 'w') as writefile:\n",
+    "    writefile.write(\"\"\"#taken from https://gist.github.com/jterrace/2911875\n",
+    "XVFB=/usr/bin/Xvfb\n",
+    "XVFBARGS=\":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\"\n",
+    "PIDFILE=./frame-buffer.pid\n",
+    "case \"$1\" in\n",
+    "  start)\n",
+    "    echo -n \"Starting virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  stop)\n",
+    "    echo -n \"Stopping virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --stop --quiet --pidfile $PIDFILE\n",
+    "    rm $PIDFILE\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  restart)\n",
+    "    $0 stop\n",
+    "    $0 start\n",
+    "    ;;\n",
+    "  *)\n",
+    "        echo \"Usage: /etc/init.d/xvfb {start|stop|restart}\"\n",
+    "        exit 1\n",
+    "esac\n",
+    "exit 0\n",
+    "    \"\"\")\n",
+    "  !sudo apt-get update\n",
+    "  pro_bar.update(progress(10, 100))\n",
+    "  !sudo DEBIAN_FRONTEND=noninteractive apt install -y daemon wget gdebi-core build-essential libfontenc1 libfreetype6 xorg-dev xorg\n",
+    "  pro_bar.update(progress(20, 100))\n",
+    "  !wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(30, 100))\n",
+    "  !wget --output-document xvfb.deb http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.12_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(40, 100))\n",
+    "  !sudo dpkg -i libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(50, 100))\n",
+    "  !sudo dpkg -i xvfb.deb 2>&1\n",
+    "  pro_bar.update(progress(70, 100))\n",
+    "  !rm libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb\n",
+    "  pro_bar.update(progress(80, 100))\n",
+    "  !rm xvfb.deb\n",
+    "  pro_bar.update(progress(90, 100))\n",
+    "  !bash frame-buffer start\n",
+    "  os.environ[\"DISPLAY\"] = \":1\"\n",
+    "pro_bar.update(progress(100, 100))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pzj7wgapAcDs"
+   },
+   "source": [
+    "### Installing ml-agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "N8yfQqkbebQ5"
+   },
+   "source": [
+    "try:\n",
+    "  import mlagents\n",
+    "  print(\"ml-agents already installed\")\n",
+    "except ImportError:\n",
+    "  !python -m pip install -q mlagents==0.30.0\n",
+    "  print(\"Installed ml-agents\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_u74YhSmW6gD"
+   },
+   "source": [
+    "## Side Channel\n",
+    "\n",
+    "SideChannels are objects that can be passed to the constructor of a UnityEnvironment or the `make()` method of a registry entry to send non Reinforcement Learning related data.\n",
+    "More information available [here](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Python-API.md#communicating-additional-information-with-the-environment)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U4RXnhLRk7Uc"
+   },
+   "source": [
+    "### Engine Configuration SideChannel\n",
+    "The [Engine Configuration Side Channel](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Python-API.md#engineconfigurationchannel) is used to configure how the Unity Engine should run.\n",
+    "We will use the GridWorld environment to demonstrate how to use the EngineConfigurationChannel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "YSf-WhxbqtLw"
+   },
+   "source": [
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "from mlagents_envs.registry import default_registry\n",
+    "env_id = \"GridWorld\"\n",
+    "\n",
+    "# Import the EngineConfigurationChannel class\n",
+    "from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel\n",
+    "\n",
+    "# Create the side channel\n",
+    "engine_config_channel = EngineConfigurationChannel()\n",
+    "\n",
+    "# Pass the side channel to the make method\n",
+    "# Note, the make method takes a LIST of SideChannel as input\n",
+    "env = default_registry[env_id].make(side_channels = [engine_config_channel])\n",
+    "\n",
+    "# Configure the Unity Engine\n",
+    "engine_config_channel.set_configuration_parameters(target_frame_rate = 30)\n",
+    "\n",
+    "env.reset()\n",
+    "\n",
+    "# ...\n",
+    "# Perform experiment on environment\n",
+    "# ...\n",
+    "\n",
+    "env.close()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h1lIx3_l24OP"
+   },
+   "source": [
+    "### Environment Parameters Channel\n",
+    "The [Environment Parameters Side Channel](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Python-API.md#environmentparameters) is used to modify environment parameters during the simulation.\n",
+    "We will use the GridWorld environment to demonstrate how to use the EngineConfigurationChannel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dhtl0mpeqxYi"
+   },
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "from mlagents_envs.registry import default_registry\n",
+    "env_id = \"GridWorld\"\n",
+    "\n",
+    "# Import the EngineConfigurationChannel class\n",
+    "from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel\n",
+    "\n",
+    "# Create the side channel\n",
+    "env_parameters = EnvironmentParametersChannel()\n",
+    "\n",
+    "# Pass the side channel to the make method\n",
+    "# Note, the make method takes a LIST of SideChannel as input\n",
+    "env = default_registry[env_id].make(side_channels = [env_parameters])\n",
+    "\n",
+    "env.reset()\n",
+    "behavior_name = list(env.behavior_specs)[0]\n",
+    "\n",
+    "print(\"Observation without changing the environment parameters\")\n",
+    "decision_steps, terminal_steps = env.get_steps(behavior_name)\n",
+    "plt.imshow(decision_steps.obs[0][0,:,:,:])\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Increasing the dimensions of the grid from 5 to 7\")\n",
+    "env_parameters.set_float_parameter(\"gridSize\", 7)\n",
+    "print(\"Increasing the number of X from 1 to 5\")\n",
+    "env_parameters.set_float_parameter(\"numObstacles\", 5)\n",
+    "\n",
+    "# Any change to a SideChannel will only be effective after a step or reset\n",
+    "# In the GridWorld Environment, the grid's dimensions can only change at reset\n",
+    "env.reset()\n",
+    "\n",
+    "\n",
+    "decision_steps, terminal_steps = env.get_steps(behavior_name)\n",
+    "plt.imshow(decision_steps.obs[0][0,:,:,:])\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "env.close()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k1rwnVq2qyoO"
+   },
+   "source": [
+    "### Creating your own Side Channels\n",
+    "You can send various kinds of data between a Unity Environment and Python but you will need to [create your own implementation of a Side Channel](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Custom-SideChannels.md#custom-side-channels) for advanced use cases.\n"
+   ]
+  }
+ ]
+}
diff --git a/colab/Colab_UnityEnvironment_4_SB3VectorEnv.ipynb b/colab/Colab_UnityEnvironment_4_SB3VectorEnv.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfd80f019708a50fb37857593a4fda20aecf5b10
--- /dev/null
+++ b/colab/Colab_UnityEnvironment_4_SB3VectorEnv.ipynb
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pbVXrmEsLXDt"
+   },
+   "source": [
+    "# ML-Agents run with Stable Baselines 3\n",
+    "<img src=\"https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/images/image-banner.png?raw=true\" align=\"middle\" width=\"435\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WNKTwHU3d2-l"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#@title Install Rendering Dependencies { display-mode: \"form\" }\n",
+    "#@markdown (You only need to run this code when using Colab's hosted runtime)\n",
+    "\n",
+    "import os\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def progress(value, max=100):\n",
+    "    return HTML(\"\"\"\n",
+    "        <progress\n",
+    "            value='{value}'\n",
+    "            max='{max}',\n",
+    "            style='width: 100%'\n",
+    "        >\n",
+    "            {value}\n",
+    "        </progress>\n",
+    "    \"\"\".format(value=value, max=max))\n",
+    "\n",
+    "pro_bar = display(progress(0, 100), display_id=True)\n",
+    "\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  INSTALL_XVFB = True\n",
+    "except ImportError:\n",
+    "  INSTALL_XVFB = 'COLAB_ALWAYS_INSTALL_XVFB' in os.environ\n",
+    "\n",
+    "if INSTALL_XVFB:\n",
+    "  with open('frame-buffer', 'w') as writefile:\n",
+    "    writefile.write(\"\"\"#taken from https://gist.github.com/jterrace/2911875\n",
+    "XVFB=/usr/bin/Xvfb\n",
+    "XVFBARGS=\":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\"\n",
+    "PIDFILE=./frame-buffer.pid\n",
+    "case \"$1\" in\n",
+    "  start)\n",
+    "    echo -n \"Starting virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  stop)\n",
+    "    echo -n \"Stopping virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --stop --quiet --pidfile $PIDFILE\n",
+    "    rm $PIDFILE\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  restart)\n",
+    "    $0 stop\n",
+    "    $0 start\n",
+    "    ;;\n",
+    "  *)\n",
+    "        echo \"Usage: /etc/init.d/xvfb {start|stop|restart}\"\n",
+    "        exit 1\n",
+    "esac\n",
+    "exit 0\n",
+    "    \"\"\")\n",
+    "  !sudo apt-get update\n",
+    "  pro_bar.update(progress(10, 100))\n",
+    "  !sudo DEBIAN_FRONTEND=noninteractive apt install -y daemon wget gdebi-core build-essential libfontenc1 libfreetype6 xorg-dev xorg\n",
+    "  pro_bar.update(progress(20, 100))\n",
+    "  !wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(30, 100))\n",
+    "  !wget --output-document xvfb.deb http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.12_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(40, 100))\n",
+    "  !sudo dpkg -i libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb 2>&1\n",
+    "  pro_bar.update(progress(50, 100))\n",
+    "  !sudo dpkg -i xvfb.deb 2>&1\n",
+    "  pro_bar.update(progress(70, 100))\n",
+    "  !rm libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb\n",
+    "  pro_bar.update(progress(80, 100))\n",
+    "  !rm xvfb.deb\n",
+    "  pro_bar.update(progress(90, 100))\n",
+    "  !bash frame-buffer start\n",
+    "  os.environ[\"DISPLAY\"] = \":1\"\n",
+    "pro_bar.update(progress(100, 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pzj7wgapAcDs"
+   },
+   "source": [
+    "### Installing ml-agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "N8yfQqkbebQ5",
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "  import mlagents\n",
+    "  print(\"ml-agents already installed\")\n",
+    "except ImportError:\n",
+    "  !python -m pip install -q mlagents==0.30.0\n",
+    "  print(\"Installed ml-agents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_u74YhSmW6gD"
+   },
+   "source": [
+    "## Run the Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P-r_cB2rqp5x"
+   },
+   "source": [
+    "### Import dependencies and set some high level parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YSf-WhxbqtLw"
+   },
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from pathlib import Path\n",
+    "from typing import Callable, Any\n",
+    "\n",
+    "import gym\n",
+    "from gym import Env\n",
+    "\n",
+    "from stable_baselines3 import PPO\n",
+    "from stable_baselines3.common.vec_env import VecMonitor, VecEnv, SubprocVecEnv\n",
+    "from supersuit import observation_lambda_v0\n",
+    "\n",
+    "\n",
+    "from mlagents_envs.environment import UnityEnvironment\n",
+    "from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper\n",
+    "from mlagents_envs.registry import UnityEnvRegistry, default_registry\n",
+    "from mlagents_envs.side_channel.engine_configuration_channel import (\n",
+    "    EngineConfig,\n",
+    "    EngineConfigurationChannel,\n",
+    ")\n",
+    "\n",
+    "NUM_ENVS = 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Environment  and Engine Configurations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Default values from CLI (See cli_utils.py)\n",
+    "DEFAULT_ENGINE_CONFIG = EngineConfig(\n",
+    "    width=84,\n",
+    "    height=84,\n",
+    "    quality_level=4,\n",
+    "    time_scale=20,\n",
+    "    target_frame_rate=-1,\n",
+    "    capture_frame_rate=60,\n",
+    ")\n",
+    "\n",
+    "# Some config subset of an actual config.yaml file for MLA.\n",
+    "@dataclass\n",
+    "class LimitedConfig:\n",
+    "    # The local path to a Unity executable or the name of an entry in the registry.\n",
+    "    env_path_or_name: str\n",
+    "    base_port: int\n",
+    "    base_seed: int = 0\n",
+    "    num_env: int = 1\n",
+    "    engine_config: EngineConfig = DEFAULT_ENGINE_CONFIG\n",
+    "    visual_obs: bool = False\n",
+    "    # TODO: Decide if we should just tell users to always use MultiInputPolicy so we can simplify the user workflow.\n",
+    "    # WARNING: Make sure to use MultiInputPolicy if you turn this on.\n",
+    "    allow_multiple_obs: bool = False\n",
+    "    env_registry: UnityEnvRegistry = default_registry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unity Environment SB3 Factory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _unity_env_from_path_or_registry(\n",
+    "    env: str, registry: UnityEnvRegistry, **kwargs: Any\n",
+    ") -> UnityEnvironment:\n",
+    "    if Path(env).expanduser().absolute().exists():\n",
+    "        return UnityEnvironment(file_name=env, **kwargs)\n",
+    "    elif env in registry:\n",
+    "        return registry.get(env).make(**kwargs)\n",
+    "    else:\n",
+    "        raise ValueError(f\"Environment '{env}' wasn't a local path or registry entry\")\n",
+    "        \n",
+    "def make_mla_sb3_env(config: LimitedConfig, **kwargs: Any) -> VecEnv:\n",
+    "    def handle_obs(obs, space):\n",
+    "        if isinstance(space, gym.spaces.Tuple):\n",
+    "            if len(space) == 1:\n",
+    "                return obs[0]\n",
+    "            # Turn the tuple into a dict (stable baselines can handle spaces.Dict but not spaces.Tuple).\n",
+    "            return {str(i): v for i, v in enumerate(obs)}\n",
+    "        return obs\n",
+    "\n",
+    "    def handle_obs_space(space):\n",
+    "        if isinstance(space, gym.spaces.Tuple):\n",
+    "            if len(space) == 1:\n",
+    "                return space[0]\n",
+    "            # Turn the tuple into a dict (stable baselines can handle spaces.Dict but not spaces.Tuple).\n",
+    "            return gym.spaces.Dict({str(i): v for i, v in enumerate(space)})\n",
+    "        return space\n",
+    "\n",
+    "    def create_env(env: str, worker_id: int) -> Callable[[], Env]:\n",
+    "        def _f() -> Env:\n",
+    "            engine_configuration_channel = EngineConfigurationChannel()\n",
+    "            engine_configuration_channel.set_configuration(config.engine_config)\n",
+    "            kwargs[\"side_channels\"] = kwargs.get(\"side_channels\", []) + [\n",
+    "                engine_configuration_channel\n",
+    "            ]\n",
+    "            unity_env = _unity_env_from_path_or_registry(\n",
+    "                env=env,\n",
+    "                registry=config.env_registry,\n",
+    "                worker_id=worker_id,\n",
+    "                base_port=config.base_port,\n",
+    "                seed=config.base_seed + worker_id,\n",
+    "                **kwargs,\n",
+    "            )\n",
+    "            new_env = UnityToGymWrapper(\n",
+    "                unity_env=unity_env,\n",
+    "                uint8_visual=config.visual_obs,\n",
+    "                allow_multiple_obs=config.allow_multiple_obs,\n",
+    "            )\n",
+    "            new_env = observation_lambda_v0(new_env, handle_obs, handle_obs_space)\n",
+    "            return new_env\n",
+    "\n",
+    "        return _f\n",
+    "\n",
+    "    env_facts = [\n",
+    "        create_env(config.env_path_or_name, worker_id=x) for x in range(config.num_env)\n",
+    "    ]\n",
+    "    return SubprocVecEnv(env_facts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start Environment from the registry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "env = make_mla_sb3_env(\n",
+    "    config=LimitedConfig(\n",
+    "        env_path_or_name='Basic',  # Can use any name from a registry or a path to your own unity build.\n",
+    "        base_port=6006,\n",
+    "        base_seed=42,\n",
+    "        num_env=NUM_ENVS,\n",
+    "        allow_multiple_obs=True,\n",
+    "    ),\n",
+    "    no_graphics=True,  # Set to false if you are running locally and want to watch the environments move around as they train.\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 250K should train to a reward ~= 0.90 for the \"Basic\" environment.\n",
+    "# We set the value lower here to demonstrate just a small amount of trianing.\n",
+    "BATCH_SIZE = 32\n",
+    "BUFFER_SIZE = 256\n",
+    "UPDATES = 50\n",
+    "TOTAL_TAINING_STEPS_GOAL = BUFFER_SIZE * UPDATES\n",
+    "BETA = 0.0005\n",
+    "N_EPOCHS = 3 \n",
+    "STEPS_PER_UPDATE = BUFFER_SIZE / NUM_ENVS\n",
+    "\n",
+    "# Helps gather stats for our eval() calls later so we can see reward stats.\n",
+    "env = VecMonitor(env)\n",
+    "\n",
+    "#Policy and Value function with 2 layers of 128 units each and no shared layers.\n",
+    "policy_kwargs = {\"net_arch\" : [{\"pi\": [32,32], \"vf\": [32,32]}]}\n",
+    "\n",
+    "model = PPO(\n",
+    "    \"MlpPolicy\",\n",
+    "    env,\n",
+    "    verbose=1,\n",
+    "    learning_rate=lambda progress: 0.0003 * (1.0 - progress),\n",
+    "    clip_range=lambda progress: 0.2 * (1.0 - progress),\n",
+    "    clip_range_vf=lambda progress: 0.2 * (1.0 - progress),\n",
+    "    # Uncomment this if you want to log tensorboard results when running this notebook locally.\n",
+    "    # tensorboard_log=\"results\",\n",
+    "    policy_kwargs=policy_kwargs,\n",
+    "    n_steps=int(STEPS_PER_UPDATE),\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    n_epochs=N_EPOCHS,\n",
+    "    ent_coef=BETA,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 0.93 is considered solved for the Basic environment\n",
+    "for i in range(UPDATES):\n",
+    "    print(f\"Training round {i + 1}/{UPDATES}\")\n",
+    "    # NOTE: rest_num_timesteps should only happen the first time so that tensorboard logs are consistent.\n",
+    "    model.learn(total_timesteps=BUFFER_SIZE, reset_num_timesteps=(i == 0))\n",
+    "    model.policy.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h1lIx3_l24OP"
+   },
+   "source": [
+    "### Close the environment\n",
+    "Frees up the ports being used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "vdWG6_SqtNtv",
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "env.close()\n",
+    "print(\"Closed environment\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Colab-UnityEnvironment-1-Run.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/colab_requirements.txt b/colab_requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad064135a124b904aaec3b07556908a30326d388
--- /dev/null
+++ b/colab_requirements.txt
@@ -0,0 +1,2 @@
+matplotlib
+jupyter
diff --git a/com.unity.ml-agents.extensions/.gitignore b/com.unity.ml-agents.extensions/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..9f297351a83c2547c4b8857805fa54d1ef3cef26
--- /dev/null
+++ b/com.unity.ml-agents.extensions/.gitignore
@@ -0,0 +1,16 @@
+artifacts/**
+build/**
+.build_script/**
+node_modules/**
+.DS_Store
+.npmrc
+!Documentation~
+!.Documentation
+npm-debug.log
+build.sh.meta
+build.bat.meta
+.idea/
+
+# API Scraper output
+*.api
+*.api.meta
diff --git a/com.unity.ml-agents.extensions/.npmignore b/com.unity.ml-agents.extensions/.npmignore
new file mode 100644
index 0000000000000000000000000000000000000000..696d110ce9e1091e96f4c89c27e58b72bdfdca4b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/.npmignore
@@ -0,0 +1,19 @@
+artifacts/**
+build/**
+.build_script/**
+node_modules/**
+Documentation/ApiDocs/**
+Documentation~/ApiDocs/**
+.DS_Store
+.npmrc
+.npmignore
+.gitignore
+CONTRIBUTING.md
+CONTRIBUTING.md.meta
+QAReport.md
+QAReport.md.meta
+.gitlab-ci.yml
+build.sh
+build.sh.meta
+build.bat
+build.bat.meta
diff --git a/com.unity.ml-agents.extensions/CHANGELOG.md b/com.unity.ml-agents.extensions/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..cdff153bbee09c0a1570d621b28a21bf1f7ca335
--- /dev/null
+++ b/com.unity.ml-agents.extensions/CHANGELOG.md
@@ -0,0 +1,6 @@
+# Changelog
+This changelog isn't used; please add to the `com.unity.ml-agents` changelog instead.
+
+## [Unreleased]
+## [0.6.1-preview] - 2022-11-21
+ * Initial version
diff --git a/com.unity.ml-agents.extensions/CHANGELOG.md.meta b/com.unity.ml-agents.extensions/CHANGELOG.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..222c3d68692598c5250d3376a0cf4c3c6971946c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/CHANGELOG.md.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: cb3bf154cb17b41fca51ce7b73ef4d1b
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Documentation~/CustomGridSensors.md b/com.unity.ml-agents.extensions/Documentation~/CustomGridSensors.md
new file mode 100644
index 0000000000000000000000000000000000000000..c0244dc18691f41f65dfd10f88495c1469c6d69d
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Documentation~/CustomGridSensors.md
@@ -0,0 +1,76 @@
+# Extending Grid Sensors
+
+## Overview
+Grid Sensor provides a 2D observation that detects objects around an agent from a top-down view. Compared to RayCasts, it receives a full observation in a grid area without gaps, and the detection is not blocked by objects around the agents. This gives a more granular view while requiring a higher usage of compute resources.
+
+One extra feature with Grid Sensors is that you can derive from the Grid Sensor base class to collect custom data besides the object tags, to include custom attributes as observations. This allows more flexibility for the use of GridSensor. This doc will elaborate how to create custom grid-based sensor class, and the sensors implementations provided in ml-agents package.
+
+## Customized Grid Sensor
+To create a custom grid sensor, you'll need to derive from two classes: `GridSensorBase` and `GridSensorComponent`.
+
+### Deriving from `GridSensorBase`
+This is the implementation of your sensor. This defines how your sensor process detected colliders,
+what the data looks like, and how the observations are constructed from the detected objects.
+Consider overriding the following methods depending on your use case:
+* `protected virtual int GetCellObservationSize()`: Return the observation size per cell. Default to `1`.
+* `protected virtual void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)`: Constructs observations from the detected object. The input provides the detected GameObject and the index of its tag (0-indexed). The observations should be written to the given `dataBuffer` and the buffer size is defined in `GetCellObservationSize()`. This data will be gathered from each cell and sent to the trainer as observation.
+* `protected virtual bool IsDataNormalized()`: Return whether the observation is normalized to 0~1. This affects whether you're able to use compressed observations as compressed data only supports normalized data. Return `true` if all the values written in `GetObjectData` are within the range of (0, 1), otherwise return `false`. Default to `false`.
+
+    There might be cases when your data is not in the range of (0, 1) but you still wish to use compressed data to speed up training. If your data is naturally bounded within a range, normalize your data first to the possible range and fill the buffer with normalized data. For example, since the angle of rotation is bounded within `0 ~ 360`, record an angle `x` as `x/360` instead of `x`. If your data value is not bounded (position, velocity, etc.), consider setting a reasonable min/max value and use that to normalize your data.
+* `protected internal virtual ProcessCollidersMethod GetProcessCollidersMethod()`: Return the method to process colliders detected in a cell. This defines the sensor behavior when multiple objects with detectable tags are detected within a cell.
+Currently two methods are provided:
+  * `ProcessCollidersMethod.ProcessClosestColliders` (Default): Process the closest collider to the agent. In this case each cell's data is represented by one object.
+  * `ProcessCollidersMethod.ProcessAllColliders`: Process all detected colliders. This is useful when the data from each cell is additive, for instance, the count of detected objects in a cell. When using this option, the input `dataBuffer` in `GetObjectData()` will contain processed data from other colliders detected in the cell. You'll more likely want to add/subtract values from the buffer instead of overwrite it completely.
+
+
+### Deriving from `GridSensorComponent`
+To create your sensor, you need to override the sensor component and add your sensor to the creation.
+Specifically, you need to override `GetGridSensors()` and return an array of grid sensors you want to use in the component.
+It can be used to create multiple different customized grid sensors, or you can also include the ones provided in our package (listed in the next section).
+
+Example:
+```
+public class CustomGridSensorComponent : GridSensorComponent
+{
+    protected override GridSensorBase[] GetGridSensors()
+    {
+        return new GridSensorBase[] { new CustomGridSensor(...)};
+    }
+}
+```
+
+## Grid Sensor Types
+Here we list out two types of grid sensor we provide in our package: `OneHotGridSensor` and `CountingGridSensor`.
+Their implementations are also a good reference for making you own ones.
+
+### OneHotGridSensor (provided in `com.unity.ml-agents`)
+This is the default sensor used by `GridSensorComponent`. It detects objects with detectable tags and the observation is the one-hot representation of the detected tag index.
+
+The implementation of the sensor is defined as following:
+* `GetCellObservationSize()`: `detectableTags.Length`
+* `IsDataNormalized()`: `true`
+* `ProcessCollidersMethod()`: `ProcessCollidersMethod.ProcessClosestColliders`
+* `GetObjectData()`:
+
+```
+protected override void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+{
+    dataBuffer[tagIndex] = 1;
+}
+```
+
+### CountingGridSensor (provided in `com.unity.ml-agents.extensions`)
+This is an example of using all colliders detected in a cell. It counts the number of objects detected for each detectable tag. The sensor cannot be used with data compression.
+
+The implementation of the sensor is defined as following:
+* `GetCellObservationSize()`: `detectableTags.Length`
+* `IsDataNormalized()`: `false`
+* `ProcessCollidersMethod()`: `ProcessCollidersMethod.ProcessAllColliders`
+* `GetObjectData()`:
+
+```
+protected override void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+{
+    dataBuffer[tagIndex] += 1;
+}
+```
diff --git a/com.unity.ml-agents.extensions/Documentation~/InputActuatorComponent.md b/com.unity.ml-agents.extensions/Documentation~/InputActuatorComponent.md
new file mode 100644
index 0000000000000000000000000000000000000000..b72fb3fd8c7f4b1e1021c61cacf9707fc2f03589
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Documentation~/InputActuatorComponent.md
@@ -0,0 +1,57 @@
+# Integration of the Input System Package with ML-Agents
+
+## Overview
+One area we are always trying to improve is getting developers up and running with ML-Agents.  With this in mind,
+we have implemented an `InputActuatorComponent`.  This component integrates with the
+[Input System Package](https://docs.unity3d.com/Packages/com.unity.inputsystem@1.1/manual/QuickStartGuide.html)
+to set up an action space for your `Agent` based on an `InputActionAsset` that is referenced by the
+`IInputActionAssetProvider` interface, or the `PlayerInput` component that may be living on your player controlled
+`Agent`.  This means that if you have code outside of your agent that handles input, you will not need to implement
+the Heuristic function in agent as well.  The `InputActuatorComponent` will handle this for you.  You can now train and
+run inference on `Agents` with an action space defined by an `InputActionAsset`.
+
+This implementation includes:
+
+* C# `InputActuatorComponent` you can attach to your Agent.
+* Implement the `IInputActionAssetProvider` in the `Componenet` where you handle player input.
+* An example environment where the input handling code is not in the Heuristic function of the Agent subclass.
+
+### Feedback
+We have only implemented a small subset of `InputControl` types that we thought would cover a large portion of what
+most developers would use.  Please let us know if you want more control types implemented by posting in the [ML-Agents
+forum.](https://forum.unity.com/forums/ml-agents.453/)
+
+We would also like your feedback on the workflow of integrating this into your games.  If you run
+into workflow issues please let us know in the ML-Agents forums, or if you've discovered a bug,
+please file a bug on our GitHub page.
+
+## Getting started
+The C# code for the `InputActuatorComponent` exists inside of the extensions package (com.unity.ml-agents.extensions).  A good first step would be to familiarize with the extensions package by reading the document [here](com.unity.ml-agents.extensions.md).  The second step would be to take a look at how we have implemented the C# code in the example Input Integration scene (located under  ML-Agents-Input-Example/Assets/ML-Agents/Examples/PushBlock/).  Once you have some familiarity, then the next step would be to add the InputActuatorComponent to your player Agent.  The example we have implemented uses C# Events to send information from the Input System.
+
+Additionally, see below for additional technical specifications on the C# code for the InputActuatorComponent.
+
+## Technical specifications for the InputActuatorComponent
+
+### `IInputActionsAssetProvider` Interface
+The `InputActuatorComponent` searches for a `Component` that implements
+`IInputActionAssetProvider` on the `GameObject` they both are attached to.  It is important to note
+that if multiple `Components` on your `GameObject` need to access an `InputActionAsset` to handle events,
+they will need to share the same instance of the `InputActionAsset` that is returned from the
+`IInputActionAssetProvider`.
+
+### `InputActuatorComponent` class
+The `InputActuatorComponent` is the bridge between ML-Agents and the Input System.. It allows ML-Agents to
+* create an `ActionSpec` for your Agent based on an `InputActionAsset` that comes from an
+`IInputActionAssetProvider`.
+* send simulated input from a training process or a neural network
+* let developers keep their input handling code in one place
+
+This is accomplished by adding the `InputActuatorComponenet` to an Agent which already has the PlayerInput component attached.
+
+### Setting up a scene using the `InputActuatorComponent`
+1. Add the `com.unity.inputsystem` version 1.1.0-preview.3 or later to your project via the Package Manager window.
+2. If you have already setup an InputActionAsset skip to Step 3, otherwise follow these sub steps:
+    1. Create an InputActionAsset to allow your Agent to be controlled by the Input System.
+    2. Handle the events from the Input System where you normally would (i.e. a script external to your Agent class).
+3. Add the InputSystemActuatorComponent to the GameObject that has the `PlayerInput` and `Agent` components attached.
+
diff --git a/com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md b/com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md
new file mode 100644
index 0000000000000000000000000000000000000000..fb2dc99a519312d057bb06f429c7914176b65743
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md
@@ -0,0 +1,70 @@
+# About ML-Agents Extensions package (`com.unity.ml-agents.extensions`)
+
+The Unity ML-Agents Extensions package contains optional add-ons to the C# SDK for the
+[Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
+
+These extensions are all considered experimental, and their API or behavior
+may change between versions.
+
+
+## Package contents
+
+The following table describes the package folder structure:
+
+| **Location**     | **Description**                                                        |
+| ---------------- | ---------------------------------------------------------------------- |
+| _Documentation~_ | Contains the documentation for the Unity package.                      |
+| _Editor_         | Contains utilities for Editor windows and drawers.                     |
+| _Runtime_        | Contains core C# APIs for integrating ML-Agents into your Unity scene. |
+| _Tests_          | Contains the unit tests for the package.                               |
+
+The Runtime directory currently contains these features:
+ * Physics-based sensors
+ * [Input System Package Integration](InputActuatorComponent.md)
+ * [Custom Grid-based Sensors](CustomGridSensors.md)
+
+## Installation
+The ML-Agents Extensions package is not currently available in the Package Manager. There are two
+recommended ways to install the package:
+
+### Local Installation
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
+[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Installation.md#advanced-local-installation-for-development-1)
+directions (substituting `com.unity.ml-agents.extensions` for the package name).
+
+### Github via Package Manager
+In Unity 2019.4 or later, open the Package Manager, hit the "+" button, and select "Add package from git URL".
+
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/images/unity_package_manager_git_url.png)
+
+In the dialog that appears, enter
+ ```
+git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20
+```
+
+You can also edit your project's `manifest.json` directly and add the following line to the `dependencies`
+section:
+```
+"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20",
+```
+See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
+may take several minutes to resolve the packages the first time that you add it.
+
+
+## Requirements
+
+This version of the Unity ML-Agents package is compatible with the following
+versions of the Unity Editor:
+
+- 2019.4 and later
+
+If using the `InputActuatorComponent`
+- install the `com.unity.inputsystem` package version `1.1.0-preview.3` or later.
+
+## Known Limitations
+- For the `InputActuatorComponent`
+    - Limited implementation of `InputControls`
+    - No way to customize the action space of the `InputActuatorComponent`
+
+## Need Help?
+The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/README.md) contains links for contacting the team or getting support.
diff --git a/com.unity.ml-agents.extensions/Documentation~/images/match3-moves.png b/com.unity.ml-agents.extensions/Documentation~/images/match3-moves.png
new file mode 100644
index 0000000000000000000000000000000000000000..7a0c9d4a344e725d098d34038f9216fd104ec1d6
Binary files /dev/null and b/com.unity.ml-agents.extensions/Documentation~/images/match3-moves.png differ
diff --git a/com.unity.ml-agents.extensions/Documentation~/images/match3.png b/com.unity.ml-agents.extensions/Documentation~/images/match3.png
new file mode 100644
index 0000000000000000000000000000000000000000..0091d798b7c3f6e993960dbe211c58fc660db970
Binary files /dev/null and b/com.unity.ml-agents.extensions/Documentation~/images/match3.png differ
diff --git a/com.unity.ml-agents.extensions/Documentation~/images/persp_ortho_proj.png b/com.unity.ml-agents.extensions/Documentation~/images/persp_ortho_proj.png
new file mode 100644
index 0000000000000000000000000000000000000000..e5ea5e30df56f15a3b3fbf49dcd7e202378c9dc6
Binary files /dev/null and b/com.unity.ml-agents.extensions/Documentation~/images/persp_ortho_proj.png differ
diff --git a/com.unity.ml-agents.extensions/Editor.meta b/com.unity.ml-agents.extensions/Editor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..67487200060d20ad640bf5ef362ddbcffac6b3e4
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 40d78a1c3c0ef4584a1816f66812de87
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Editor/EditorExample.cs b/com.unity.ml-agents.extensions/Editor/EditorExample.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e5aaef17242e1d6511370b9c48a5998aa60c67d8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/EditorExample.cs
@@ -0,0 +1,3 @@
+namespace Unity.MLAgents.Extensions.Editor
+{
+}
diff --git a/com.unity.ml-agents.extensions/Editor/EditorExample.cs.meta b/com.unity.ml-agents.extensions/Editor/EditorExample.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5b9a890fa96658b73a3b7d1588ecd753e7eba157
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/EditorExample.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 4d278d118acf74b1294c16f6a065b4ab
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Editor/Input.meta b/com.unity.ml-agents.extensions/Editor/Input.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2b437a39f89dfa0708fd51877c05e2c7008581a8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Input.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 1f773a20e85042999e87f5d3c7b55281
+timeCreated: 1613637190
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs b/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f48f6481f5a2ad2c6c22353b2dfb73d7bd7f530e
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs
@@ -0,0 +1,26 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Extensions.Input;
+using UnityEditor;
+
+namespace Unity.MLAgents.Extensions.Editor.Input
+{
+    [CustomEditor(typeof(InputActuatorComponent))]
+    internal class InputActuatorComponentEditor : UnityEditor.Editor
+    {
+        const string k_ActionSpecName = "m_ActionSpec";
+
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+            InputActuatorComponent o = so.targetObject as InputActuatorComponent;
+            _ = o.ActionSpec;
+            EditorGUI.indentLevel++;
+            EditorGUI.BeginDisabledGroup(true);
+            EditorGUILayout.PropertyField(so.FindProperty(k_ActionSpecName));
+            EditorGUI.EndDisabledGroup();
+            EditorGUI.indentLevel--;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs.meta b/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7fe63a5420814b63f5109bd65209dd2aa54b469b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Input/InputActuatorComponentEditor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 3435eeef4d1645be8c0f770b68f2ba19
+timeCreated: 1613637202
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef b/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..162bac225273a05130f52a19eb1bc7c5196a87a0
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef
@@ -0,0 +1,26 @@
+{
+    "name": "Unity.ML-Agents.Extensions.Editor.Input",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.ML-Agents.Extensions.Input",
+        "Unity.ML-Agents.Editor",
+        "Unity.InputSystem"
+    ],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": false,
+    "precompiledReferences": [],
+    "autoReferenced": true,
+    "defineConstraints": [],
+    "versionDefines": [
+        {
+            "name": "com.unity.inputsystem",
+            "expression": "1.1.0-preview",
+            "define": "MLA_INPUT_SYSTEM"
+        }
+    ],
+    "noEngineReferences": false
+}
diff --git a/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef.meta b/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0d252ad1754fecc18b033f80b0ee3a84e0cad99a
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Input/Unity.ML-Agents.Extensions.Editor.Input.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 4851f2d02f9f1423a8593f60b1a9cd7e
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs b/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..632a2e99e9b0562ae4e77647bf195c49ef634e68
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs
@@ -0,0 +1,73 @@
+using UnityEditor;
+using Unity.MLAgents.Editor;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Editor
+{
+    [CustomEditor(typeof(RigidBodySensorComponent))]
+    [CanEditMultipleObjects]
+    internal class RigidBodySensorComponentEditor : UnityEditor.Editor
+    {
+        bool ShowHierarchy = true;
+
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            var rbSensorComp = so.targetObject as RigidBodySensorComponent;
+            if (rbSensorComp.IsTrivial())
+            {
+                EditorGUILayout.HelpBox(
+                    "The Root Body has no Joints, and the Virtual Root is null or the same as the " +
+                    "Root Body's GameObject. This will not generate any useful observations; they will always " +
+                    "be the identity values. Consider removing this component since it won't help the Agent.",
+                    MessageType.Warning
+                );
+            }
+
+            bool requireExtractorUpdate;
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // All the fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUI.BeginChangeCheck();
+                EditorGUILayout.PropertyField(so.FindProperty("RootBody"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("VirtualRoot"), true);
+
+                // Changing the root body or virtual root changes the hierarchy, so we need to reset later.
+                requireExtractorUpdate = EditorGUI.EndChangeCheck();
+
+                EditorGUILayout.PropertyField(so.FindProperty("Settings"), true);
+
+                // Collapsible tree for the body hierarchy
+                ShowHierarchy = EditorGUILayout.Foldout(ShowHierarchy, "Hierarchy", true);
+                if (ShowHierarchy)
+                {
+                    var treeNodes = rbSensorComp.GetDisplayNodes();
+                    var originalIndent = EditorGUI.indentLevel;
+                    foreach (var node in treeNodes)
+                    {
+                        var obj = node.NodeObject;
+                        var objContents = EditorGUIUtility.ObjectContent(obj, obj.GetType());
+                        EditorGUI.indentLevel = originalIndent + node.Depth;
+                        var enabled = EditorGUILayout.Toggle(objContents, node.Enabled);
+                        rbSensorComp.SetPoseEnabled(node.OriginalIndex, enabled);
+                    }
+
+                    EditorGUI.indentLevel = originalIndent;
+                }
+
+                EditorGUILayout.PropertyField(so.FindProperty("sensorName"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            so.ApplyModifiedProperties();
+            if (requireExtractorUpdate)
+            {
+                rbSensorComp.ResetPoseExtractor();
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs.meta b/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..51c90805bb2891a8df550d0c51ec7e92fd13214b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8c3481f5312564501b381742673d3100
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef b/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..300a1530e4e4a1cfb9ef71be3f2b829e10977f09
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef
@@ -0,0 +1,12 @@
+{
+    "name": "Unity.ML-Agents.Extensions.Editor",
+    "references": [
+      "Unity.ML-Agents.Extensions",
+      "Unity.ML-Agents",
+      "Unity.ML-Agents.Editor"
+    ],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": []
+}
diff --git a/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef.meta b/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bd9ebd4b70a0350960345c0a4804df332550ea35
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Editor/Unity.ML-Agents.Extensions.Editor.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: eec81f5d1363e46538604927f0b9199c
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/LICENSE.md b/com.unity.ml-agents.extensions/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..5653ad84f21a169e2c8d76ea7223f44f590f62b1
--- /dev/null
+++ b/com.unity.ml-agents.extensions/LICENSE.md
@@ -0,0 +1,202 @@
+Copyright 2020-2021 Unity Technologies
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      "Licensor" shall mean the copyright owner or entity authorized by
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+      "Legal Entity" shall mean the union of the acting entity and all
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+      "You" (or "Your") shall mean an individual or Legal Entity
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+      To apply the Apache License to your work, attach the following
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diff --git a/com.unity.ml-agents.extensions/LICENSE.md.meta b/com.unity.ml-agents.extensions/LICENSE.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a909dc934ed6e19bdb626db1874ab884901ca920
--- /dev/null
+++ b/com.unity.ml-agents.extensions/LICENSE.md.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 1f518b76bbd7b4806b0653aac1143b42
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/README.md b/com.unity.ml-agents.extensions/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ec1b5448ced7b700e4ccf00128f485644349e9df
--- /dev/null
+++ b/com.unity.ml-agents.extensions/README.md
@@ -0,0 +1,3 @@
+# ML-Agents Extensions
+
+See the [package documentation](Documentation~/com.unity.ml-agents.extensions.md) for more information
diff --git a/com.unity.ml-agents.extensions/README.md.meta b/com.unity.ml-agents.extensions/README.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..62684ffce0ecf0296e5919874243e60c2f912366
--- /dev/null
+++ b/com.unity.ml-agents.extensions/README.md.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 86620336dab3b4d3783b0b3114496654
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime.meta b/com.unity.ml-agents.extensions/Runtime.meta
new file mode 100644
index 0000000000000000000000000000000000000000..58017d2891e7d6af2ed286ddd7fff66413198166
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 35f52f986759c491cb377b1f3841c566
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs b/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c1bdcc8d5f6c24c956ab11b52a9837a99938d972
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs
@@ -0,0 +1,5 @@
+using System.Runtime.CompilerServices;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.EditorTests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.Editor")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.Tests")]
diff --git a/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs.meta b/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..21cec76829e88b206c903b3c4a45a2729a4e2b9f
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/AssemblyInfo.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 48c8790647c3345e19c57d6c21065112
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Input.meta b/com.unity.ml-agents.extensions/Runtime/Input.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6d9bda962b0b913539c6a27b1b27f47b4856f565
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 1694e881b9ec420ba1c201f0612392d6
+timeCreated: 1610754907
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f75266d5b71ec4708c923ff44854743f204e6269
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: de3fc3f4fd664e3ab579a102f7fabc88
+timeCreated: 1612204931
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b37582422a585e0aa646aab66ae4ca3bbc88837c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs
@@ -0,0 +1,45 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Class that translates data between the a <see cref="UnityEngine.InputSystem.Controls.ButtonControl"/> and
+    /// the ML-Agents <see cref="ActionBuffers"/> object.
+    /// </summary>
+    public class ButtonInputActionAdaptor : IRLActionInputAdaptor
+    {
+        /// <summary>
+        /// TODO this method needs to be more nuanced depending the types of controls that can back it.  i.e. TriggerControls
+        /// are continuous buttons, etc.
+        /// Currently returns an <see cref="ActionSpec"/> with 1 branch of size 2.  One value for not pressed, and one
+        /// for pressed.
+        /// </summary>
+        /// <param name="action">The action associated with this adaptor to help determine the action space.</param>
+        /// <returns></returns>
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            return ActionSpec.MakeDiscrete(2);
+        }
+
+        /// TODO again this might need to be more nuanced for things like continuous buttons.
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        {
+            var val = actionBuffers.DiscreteActions[0];
+            ((ButtonControl)control).WriteValueIntoEvent((float)val, eventPtr);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>>
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            var discreteActions = actionBuffers.DiscreteActions;
+            var val = action.ReadValue<float>();
+            discreteActions[0] = (int)val;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..68f92bc212e4c214c3ca06d7fd6fb64782505907
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f3a0fe3f0bd446958c729d6f71e8d00b
+timeCreated: 1612373241
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3d42226eaa8861ac2e3e5adba85396952cb5f66e
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs
@@ -0,0 +1,36 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Translates data from a <see cref="UnityEngine.InputSystem.Controls.DoubleControl"/>.
+    /// </summary>
+    public class DoubleInputActionAdaptor : IRLActionInputAdaptor
+    {
+        /// <inheritdoc cref="IRLActionInputAdaptor.GetActionSpecForInputAction"/>
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            return ActionSpec.MakeContinuous(1);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        {
+            var val = actionBuffers.ContinuousActions[0];
+            ((DoubleControl)control).WriteValueIntoEvent((double)val, eventPtr);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            var actions = actionBuffers.ContinuousActions;
+            var val = (float)action.ReadValue<double>();
+            actions[0] = val;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ced0139369e5b806179b9e5c79cac50cf7260863
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 14ec2823be0a4b3bbee5490ed4840e9c
+timeCreated: 1612574422
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5de783d812f874cd27b74c329924ec7e984f45e2
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs
@@ -0,0 +1,35 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Translates data from any control that extends from <see cref="InputControl{Single}"/>.
+    /// </summary>
+    public class FloatInputActionAdaptor : IRLActionInputAdaptor
+    {
+        /// <inheritdoc cref="IRLActionInputAdaptor.GetActionSpecForInputAction"/>
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            return ActionSpec.MakeContinuous(1);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        {
+            var val = actionBuffers.ContinuousActions[0];
+            control.WriteValueIntoEvent(val, eventPtr);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            var actions = actionBuffers.ContinuousActions;
+            var val = action.ReadValue<float>();
+            actions[0] = val;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9e12bca95fd8453dd7d9690dfd1fb1201f88c532
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6c98cc3fdaec4664aae128a05cfe6560
+timeCreated: 1612573580
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3bb9f98d57c67905ed148a8709bcc28f87b88e94
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs
@@ -0,0 +1,36 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Translates data from a <see cref="UnityEngine.InputSystem.Controls.IntegerControl"/>.
+    /// </summary>
+    public class IntegerInputActionAdaptor : IRLActionInputAdaptor
+    {
+        // TODO need to figure out how we can infer the branch size from here.
+        /// <inheritdoc cref="IRLActionInputAdaptor.GetActionSpecForInputAction"/>
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            return ActionSpec.MakeDiscrete(2);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        {
+            var val = actionBuffers.DiscreteActions[0];
+            control.WriteValueIntoEvent(val, eventPtr);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            var actions = actionBuffers.DiscreteActions;
+            var val = action.ReadValue<int>();
+            actions[0] = val;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3db5316f100844fc3200568d8c3eeb22ffb03fae
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 592012f9a30847a29c618f0cf8addfdf
+timeCreated: 1612572952
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e46c5a9ef2ef1fdb6c11c89b398f29645e00d480
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs
@@ -0,0 +1,42 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Translates data from any control that extends from <see cref="InputControl{Vector2}"/>.
+    /// </summary>
+    public class Vector2InputActionAdaptor : IRLActionInputAdaptor
+    {
+        /// <inheritdoc cref="IRLActionInputAdaptor.GetActionSpecForInputAction"/>
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            // TODO create the action spec based on what controls back the action
+            return ActionSpec.MakeContinuous(2);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action,
+            InputControl control,
+            ActionSpec actionSpec,
+            in ActionBuffers actionBuffers)
+        {
+            var x = actionBuffers.ContinuousActions[0];
+            var y = actionBuffers.ContinuousActions[1];
+            control.WriteValueIntoEvent(new Vector2(x, y), eventPtr);
+        }
+
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            var value = action.ReadValue<Vector2>();
+            var continuousActions = actionBuffers.ContinuousActions;
+            continuousActions[0] = value.x;
+            continuousActions[1] = value.y;
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7f87c8fefd78603106daec6b65f34a720bab4519
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: aa311fde3dac44f3b7998bb7fee77225
+timeCreated: 1611356491
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs b/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ffd66a0ad7ce7a06071d7967682aab6ad39419a2
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs
@@ -0,0 +1,3 @@
+using System.Runtime.CompilerServices;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.Input.Tests.Runtime")]
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..93ccdcd83334f0d2173446fb083997c5bc4f4400
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/AssemblyInfo.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 989e62db4b694586bf2c832ce13e2d50
+timeCreated: 1612916938
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs b/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fd56c3503fe9804d1bb5acffc23d0ca066f347c0
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs
@@ -0,0 +1,27 @@
+#if MLA_INPUT_SYSTEM
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Implement this interface if you are listening to C# events from the generated C# class from the
+    /// <see cref="InputActionAsset"/>. This interface works with the <see cref="InputActuatorComponent"/> in order
+    /// to allow ML-Agents to simulate input actions based on the instance of the <see cref="InputActionAsset"/>
+    /// used to listen to events.  If you implement this interface the <see cref="InputActuatorComponent"/> will use
+    /// what is returned from <see cref="GetInputActionAsset"/> as the asset to base it's simulated input for.
+    /// Otherwise, the <see cref="InputActuatorComponent"/> will look for the <see cref="PlayerInput"/> component
+    /// and use the asset from there.  If you have multiple components handling PlayerInput on the same GameObject
+    /// they will need to share the same instance of the <see cref="InputActionAsset"/> in order to get the simulated
+    /// input.
+    /// </summary>
+    public interface IInputActionAssetProvider
+    {
+        /// <summary>
+        /// Returns the <see cref="InputActionAsset"/> instance being from the generated C# class of the
+        /// <see cref="InputActionAsset"/> in order to correctly fire events when simulating input from ML-Agents.
+        /// </summary>
+        /// <returns>The instance of the <see cref="InputActionAsset"/> you are listening for events on.</returns>
+        (InputActionAsset, IInputActionCollection2) GetInputActionAsset();
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8b441de4dcfb95b92f28afca014445850c4923fa
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/IInputActionAssetProvider.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3ce1e3218e46e48a4ab64f6f631553a4
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs b/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c0f1ade2fb12577baf4865c9b2812b27a6c6c736
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs
@@ -0,0 +1,40 @@
+#if MLA_INPUT_SYSTEM
+using Unity.MLAgents.Actuators;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Implement this interface in order to customize how information is translated <see cref="InputControl"/>s
+    /// and <see cref="ActionBuffers"/>.
+    /// </summary>
+    public interface IRLActionInputAdaptor
+    {
+        /// <summary>
+        /// Generate an <see cref="ActionSpec"/> for a given action which determines how data is translated between
+        /// the <see cref="InputSystem"/> and ML-Agents.
+        /// </summary>
+        /// <param name="action">The <see cref="InputAction"/> to based the <see cref="ActionSpec"/> from.</param>
+        /// <returns>An <see cref="ActionSpec"/> instance based off the information in the <see cref="InputAction"/>.</returns>
+        ActionSpec GetActionSpecForInputAction(InputAction action);
+
+        /// <summary>
+        /// Translates data from the <see cref="ActionBuffers"/> object to the <see cref="InputSystem"/>.
+        /// </summary>
+        /// <param name="eventPtr">The Event pointer to write to.</param>
+        /// <param name="action">The action associated with this adaptor.</param>
+        /// <param name="control">The control which will write the event to the <see cref="InputSystem"/>.</param>
+        /// <param name="actionSpec">The <see cref="ActionSpec"/> associated with this action and adaptor pair.</param>
+        /// <param name="actionBuffers">The <see cref="ActionBuffers"/> object to read from.</param>
+        void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
+
+        /// <summary>
+        /// Writes data from the <paramref name="action"/> to the <paramref name="actionBuffers"/>.
+        /// </summary>
+        /// <param name="action">The <paramref name="action"/> to read data from.</param>
+        /// <param name="actionBuffers">The <paramref name="actionBuffers"/> object to write data to.</param>
+        void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers);
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f59800ce366ba1b860f5088311a406747240733d
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 010af7dd365b48849a9d498b84a4be94
+timeCreated: 1611350962
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs b/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8f7d4f530f0a89a273898b6d094c70ebe8ccb529
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs
@@ -0,0 +1,101 @@
+#if MLA_INPUT_SYSTEM
+
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using UnityEngine.InputSystem;
+using UnityEngine.Profiling;
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// This implementation of <see cref="IActuator"/> will send events from the ML-Agents training process, or from
+    /// neural networks to the <see cref="InputSystem"/> via the <see cref="IRLActionInputAdaptor"/> interface.  If an
+    /// <see cref="Agent"/>'s <see cref="BehaviorParameters"/> indicate that the Agent is running in Heuristic Mode,
+    /// this Actuator will write actions from the <see cref="InputSystem"/> to the <see cref="ActionBuffers"/> object.
+    /// </summary>
+    public class InputActionActuator : IActuator, IBuiltInActuator
+    {
+        readonly BehaviorParameters m_BehaviorParameters;
+        readonly InputAction m_Action;
+        readonly IRLActionInputAdaptor m_InputAdaptor;
+        InputActuatorEventContext m_InputActuatorEventContext;
+        InputDevice m_Device;
+        InputControl m_Control;
+
+        /// <summary>
+        /// Construct an <see cref="InputActionActuator"/> with the <see cref="BehaviorParameters"/> of the
+        /// <see cref="Agent"/> component, the relevant <see cref="InputAction"/>, and the relevant
+        /// <see cref="IRLActionInputAdaptor"/> to convert between ml-agents &lt;--&gt; <see cref="InputSystem"/>.
+        /// </summary>
+        /// <param name="inputDevice">The input device this action is bound to.</param>
+        /// <param name="behaviorParameters">Used to determine if the <see cref="Agent"/> is running in
+        ///     heuristic mode.</param>
+        /// <param name="action">The <see cref="InputAction"/> this <see cref="IActuator"/> we read/write data to/from
+        ///     via the <see cref="IRLActionInputAdaptor"/>.</param>
+        /// <param name="adaptor">The <see cref="IRLActionInputAdaptor"/> that will convert data between ML-Agents
+        ///     and the <see cref="InputSystem"/>.</param>
+        /// <param name="inputActuatorEventContext">The object that will provide the event ptr to write to.</param>
+        public InputActionActuator(InputDevice inputDevice, BehaviorParameters behaviorParameters,
+                                   InputAction action,
+                                   IRLActionInputAdaptor adaptor,
+                                   InputActuatorEventContext inputActuatorEventContext)
+        {
+            m_BehaviorParameters = behaviorParameters;
+            Name = $"InputActionActuator-{action.name}";
+            m_Action = action;
+            m_InputAdaptor = adaptor;
+            m_InputActuatorEventContext = inputActuatorEventContext;
+            ActionSpec = adaptor.GetActionSpecForInputAction(m_Action);
+            m_Device = inputDevice;
+            m_Control = m_Device?.GetChildControl(m_Action.name);
+        }
+
+        /// <inheritdoc cref="IActionReceiver.OnActionReceived"/>
+        public void OnActionReceived(ActionBuffers actionBuffers)
+        {
+            Profiler.BeginSample("InputActionActuator.OnActionReceived");
+            if (!m_BehaviorParameters.IsInHeuristicMode())
+            {
+                using (m_InputActuatorEventContext.GetEventForFrame(out var eventPtr))
+                {
+                    m_InputAdaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, ActionSpec, actionBuffers);
+                }
+            }
+            Profiler.EndSample();
+        }
+
+        /// <inheritdoc cref="IActionReceiver.WriteDiscreteActionMask"/>
+        public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+        {
+            // TODO configure mask from editor UI?
+        }
+
+        /// <inheritdoc cref="IActuator.ActionSpec"/>
+        public ActionSpec ActionSpec { get; }
+
+        /// <inheritdoc cref="IActuator.Name"/>
+        public string Name { get; }
+
+        /// <inheritdoc cref="IActuator.ResetData"/>
+        public void ResetData()
+        {
+            // do nothing for now
+        }
+
+        /// <inheritdoc cref="IHeuristicProvider.Heuristic"/>
+        public void Heuristic(in ActionBuffers actionBuffersOut)
+        {
+            Profiler.BeginSample("InputActionActuator.Heuristic");
+            m_InputAdaptor.WriteToHeuristic(m_Action, actionBuffersOut);
+            Profiler.EndSample();
+        }
+
+        /// <inheritdoc/>
+        public BuiltInActuatorType GetBuiltInActuatorType()
+        {
+            return BuiltInActuatorType.InputActionActuator;
+        }
+    }
+}
+
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..158711cbab72c0c893d70e331309728b5e48fe77
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d4dfb5125cc2461088f18e27b6c21842
+timeCreated: 1612305510
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ac8ae75236ca1fdcde80b2a3d0780847c63fe4f9
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs
@@ -0,0 +1,376 @@
+#if MLA_INPUT_SYSTEM
+using System;
+using System.Collections.Generic;
+using Unity.Collections;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using UnityEngine;
+using UnityEngine.Assertions;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
+using UnityEngine.InputSystem.LowLevel;
+using UnityEngine.InputSystem.Layouts;
+using UnityEngine.InputSystem.Utilities;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// Component class that handles the parsing of the <see cref="InputActionAsset"/> and translates that into
+    /// <see cref="InputActionActuator"/>s.
+    /// </summary>
+    [RequireComponent(typeof(PlayerInput), typeof(IInputActionAssetProvider))]
+    [AddComponentMenu("ML Agents/Input Actuator", (int)MenuGroup.Actuators)]
+    public class InputActuatorComponent : ActuatorComponent
+    {
+        InputActionAsset m_InputAsset;
+        IInputActionCollection2 m_AssetCollection;
+        PlayerInput m_PlayerInput;
+        BehaviorParameters m_BehaviorParameters;
+        IActuator[] m_Actuators;
+        InputDevice m_Device;
+
+        /// <summary>
+        /// Mapping of <see cref="InputControl"/> types to types of <see cref="IRLActionInputAdaptor"/> concrete classes.
+        /// </summary>
+        public static Dictionary<Type, Type> controlTypeToAdaptorType = new Dictionary<Type, Type>
+        {
+            { typeof(Vector2Control), typeof(Vector2InputActionAdaptor) },
+            { typeof(ButtonControl), typeof(ButtonInputActionAdaptor) },
+            { typeof(IntegerControl), typeof(IntegerInputActionAdaptor) },
+            { typeof(AxisControl), typeof(FloatInputActionAdaptor) },
+            { typeof(DoubleControl), typeof(DoubleInputActionAdaptor) }
+        };
+
+        string m_LayoutName;
+        [SerializeField]
+        ActionSpec m_ActionSpec;
+        InputControlScheme m_ControlScheme;
+
+        public const string mlAgentsLayoutFormat = "MLAT";
+        public const string mlAgentsLayoutName = "MLAgentsLayout";
+        public const string mlAgentsControlSchemeName = "ml-agents";
+
+        /// <inheritdoc cref="IActuator.ActionSpec"/>
+        public override ActionSpec ActionSpec
+        {
+            get
+            {
+#if UNITY_EDITOR
+                if (!EditorApplication.isPlaying && m_ActionSpec.NumContinuousActions == 0
+                    && m_ActionSpec.BranchSizes == null
+                    || m_ActionSpec.BranchSizes.Length == 0)
+                {
+                    FindNeededComponents();
+                    var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap),
+                        m_BehaviorParameters,
+                        null,
+                        InputActuatorEventContext.s_EditorContext);
+                    m_ActionSpec = CombineActuatorActionSpecs(actuators);
+                }
+#endif
+                return m_ActionSpec;
+            }
+        }
+
+        void OnDisable()
+        {
+            CleanupActionAsset();
+        }
+
+        /// <summary>
+        /// This method is where the <see cref="InputActionAsset"/> gets parsed and translated into
+        /// <see cref="InputActionActuator"/>s that communicate with the <see cref="InputSystem"/> via a
+        /// virtual <see cref="InputDevice"/>.
+        /// <remarks>
+        /// The flow of this method is as follows:
+        /// <list type="number">
+        ///     <item>
+        ///     <description>Ensure that our custom <see cref="InputBindingComposite{TValue}"/>s are registered with
+        ///     the InputSystem.</description>
+        ///     </item>
+        ///     <item>
+        ///     <description>Look for the components that are needed by this class in order to retrieve the
+        ///     <see cref="InputActionAsset"/>.  It first looks for <see cref="IInputActionAssetProvider"/>, if that
+        ///     is not found, it will get the asset from the <see cref="PlayerInput"/> component.</description>
+        ///     </item>
+        ///     <item>
+        ///     <description>Create the list <see cref="InputActionActuator"/>s, one for each action in the default
+        ///     <see cref="InputActionMap"/> as set by the <see cref="PlayerInput"/> component.  Within the method
+        ///     where the actuators are being created, an <see cref="InputControlLayout"/> is also being built based
+        ///     on the number and types of <see cref="InputAction"/>s.  This will be used to create a virtual
+        ///     <see cref="InputDevice"/> with a <see cref="InputControlLayout"/> that is specific to the
+        ///     <see cref="InputActionMap"/> specified by <see cref="PlayerInput"/></description>
+        ///     </item>
+        ///     <item>
+        ///     <description>Create our device based on the layout that was generated and registered during
+        ///     actuator creation.</description>
+        ///     </item>
+        ///     <item>
+        ///     <description>Create an ml-agents control scheme and add it to the <see cref="InputActionAsset"/> so
+        ///     our virtual devices can be used.</description>
+        ///     </item>
+        ///     <item>
+        ///     <description>Add our virtual <see cref="InputDevice"/> to the input system.</description>
+        ///     </item>
+        /// </list>
+        /// </remarks>
+        /// </summary>
+        /// <returns>A list of </returns>
+        public override IActuator[] CreateActuators()
+        {
+            FindNeededComponents();
+            var collection = m_AssetCollection ?? m_InputAsset;
+            collection.Disable();
+            var inputActionMap = m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap);
+
+            RegisterLayoutBuilder(inputActionMap, m_LayoutName);
+            m_Device = InputSystem.AddDevice(m_LayoutName);
+
+            var context = new InputActuatorEventContext(inputActionMap.actions.Count, m_Device);
+            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device, context);
+
+            UpdateDeviceBinding(m_BehaviorParameters.IsInHeuristicMode());
+            inputActionMap.Enable();
+
+            m_ActionSpec = CombineActuatorActionSpecs(m_Actuators);
+            collection.Enable();
+            return m_Actuators;
+        }
+
+        static ActionSpec CombineActuatorActionSpecs(IActuator[] actuators)
+        {
+            var specs = new ActionSpec[actuators.Length];
+            for (var i = 0; i < actuators.Length; i++)
+            {
+                specs[i] = actuators[i].ActionSpec;
+            }
+            return ActionSpec.Combine(specs);
+        }
+
+        internal static IActuator[] CreateActuatorsFromMap(InputActionMap inputActionMap,
+            BehaviorParameters behaviorParameters,
+            InputDevice inputDevice,
+            InputActuatorEventContext context)
+        {
+            var actuators = new IActuator[inputActionMap.actions.Count];
+            for (var i = 0; i < inputActionMap.actions.Count; i++)
+            {
+                var action = inputActionMap.actions[i];
+                var actionLayout = InputSystem.LoadLayout(action.expectedControlType);
+                var adaptor = (IRLActionInputAdaptor)Activator.CreateInstance(controlTypeToAdaptorType[actionLayout.type]);
+                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor, context);
+
+                // Reasonably, the input system starts adding numbers after the first none numbered name
+                // is added.  So for device ID of 0, we use the empty string in the path.
+                var path = $"{inputDevice?.path}{InputControlPath.Separator}{action.name}";
+                action.AddBinding(path,
+                    action.interactions,
+                    action.processors,
+                    mlAgentsControlSchemeName);
+                action.bindingMask = InputBinding.MaskByGroup(mlAgentsControlSchemeName);
+            }
+            return actuators;
+        }
+
+        /// <summary>
+        /// Set up bindings based on whether or not the BehaviorParameters are working in Heuristic mode or not.
+        /// If we are working in Heuristic mode, we want the input system to handle everything.  If not, we
+        /// want the neural network to send input from virtual devices.
+        /// </summary>
+        /// <param name="isInHeuristicMode">true if the Agent connected to this GameObject is working in
+        /// Heuristic mode.</param>
+        /// <seealso cref="BehaviorParameters.IsInHeuristicMode"/>
+        internal void UpdateDeviceBinding(bool isInHeuristicMode)
+        {
+            if (ReferenceEquals(m_Device, null))
+            {
+                return;
+            }
+            var collection = m_AssetCollection ?? m_InputAsset;
+            m_ControlScheme = CreateControlScheme(m_Device, isInHeuristicMode, m_InputAsset);
+            if (m_InputAsset.FindControlSchemeIndex(m_ControlScheme.name) != -1)
+            {
+                m_InputAsset.RemoveControlScheme(m_ControlScheme.name);
+            }
+
+            if (!isInHeuristicMode)
+            {
+                var inputActionMap = m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap);
+                m_InputAsset.AddControlScheme(m_ControlScheme);
+                collection.bindingMask = InputBinding.MaskByGroup(m_ControlScheme.bindingGroup);
+                collection.devices = new ReadOnlyArray<InputDevice>(new[] { m_Device });
+                inputActionMap.bindingMask = collection.bindingMask;
+                inputActionMap.devices = collection.devices;
+            }
+            else
+            {
+                var inputActionMap = m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap);
+                collection.bindingMask = null;
+                collection.devices = InputSystem.devices;
+                inputActionMap.devices = InputSystem.devices;
+                inputActionMap.bindingMask = null;
+            }
+            collection.Enable();
+        }
+
+        /// <summary>
+        /// This method creates a control scheme and adds it to the <see cref="InputActionAsset"/> passed in so
+        /// we can add our device to in order for it to be discovered by the <see cref="InputSystem"/>.
+        /// </summary>
+        /// <param name="device">The virtual device to add to our custom control scheme.</param>
+        /// <param name="isInHeuristicMode">if we are in heuristic mode, we need to add other other device requirements.</param>
+        /// <param name="asset">The InputActionAsset to get the device requirements from</param>
+        internal static InputControlScheme CreateControlScheme(InputControl device,
+            bool isInHeuristicMode,
+            InputActionAsset asset)
+        {
+            var deviceRequirements = new List<InputControlScheme.DeviceRequirement>
+            {
+                new InputControlScheme.DeviceRequirement
+                {
+                    controlPath = InputBinding.Separator + mlAgentsLayoutName
+                }
+            };
+
+            if (isInHeuristicMode)
+            {
+                for (var i = 0; i < asset.controlSchemes.Count; i++)
+                {
+                    var scheme = asset.controlSchemes[i];
+                    for (var ii = 0; ii < scheme.deviceRequirements.Count; ii++)
+                    {
+                        deviceRequirements.Add(scheme.deviceRequirements[ii]);
+                    }
+                }
+            }
+
+            var inputControlScheme = new InputControlScheme(
+                mlAgentsControlSchemeName,
+                deviceRequirements);
+
+            return inputControlScheme;
+        }
+
+        /// <summary>
+        ///
+        /// </summary>
+        /// <param name="defaultMap"></param>
+        /// <param name="layoutName"></param>
+        /// <returns></returns>
+        internal static void RegisterLayoutBuilder(InputActionMap defaultMap, string layoutName)
+        {
+            if (InputSystem.LoadLayout(layoutName) == null)
+            {
+                InputSystem.RegisterLayoutBuilder(() =>
+                {
+                    // TODO does this need to change based on the action map we use?
+                    var builder = new InputControlLayout.Builder()
+                        .WithName(layoutName)
+                        .WithFormat(mlAgentsLayoutFormat);
+                    for (var i = 0; i < defaultMap.actions.Count; i++)
+                    {
+                        var action = defaultMap.actions[i];
+                        builder.AddControl(action.name)
+                            .WithLayout(action.expectedControlType);
+                    }
+                    return builder.Build();
+                }, layoutName);
+            }
+        }
+
+        internal void FindNeededComponents()
+        {
+            if (m_InputAsset == null)
+            {
+                var assetProvider = GetComponent<IInputActionAssetProvider>();
+                Assert.IsNotNull(assetProvider);
+                (m_InputAsset, m_AssetCollection) = assetProvider.GetInputActionAsset();
+                Assert.IsNotNull(m_InputAsset, "An InputActionAsset could not be found on IInputActionAssetProvider or PlayerInput.");
+            }
+            if (m_PlayerInput == null)
+            {
+                m_PlayerInput = GetComponent<PlayerInput>();
+                Assert.IsNotNull(m_PlayerInput, "PlayerInput component could not be found on this GameObject.");
+            }
+
+            if (m_BehaviorParameters == null)
+            {
+                m_BehaviorParameters = GetComponent<BehaviorParameters>();
+                Assert.IsNotNull(m_BehaviorParameters, "BehaviorParameters were not on the current GameObject.");
+                m_BehaviorParameters.OnPolicyUpdated += UpdateDeviceBinding;
+                m_LayoutName = mlAgentsLayoutName + m_BehaviorParameters.BehaviorName;
+            }
+        }
+
+        internal void CleanupActionAsset()
+        {
+            InputSystem.RemoveLayout(mlAgentsLayoutName);
+            if (!ReferenceEquals(m_Device, null))
+            {
+                InputSystem.RemoveDevice(m_Device);
+            }
+
+            if (!ReferenceEquals(m_InputAsset, null)
+                && m_InputAsset.FindControlSchemeIndex(mlAgentsControlSchemeName) != -1)
+            {
+                m_InputAsset.RemoveControlScheme(mlAgentsControlSchemeName);
+            }
+
+            if (m_Actuators != null)
+            {
+                Array.Clear(m_Actuators, 0, m_Actuators.Length);
+            }
+
+            if (!ReferenceEquals(m_BehaviorParameters, null))
+            {
+                m_BehaviorParameters.OnPolicyUpdated -= UpdateDeviceBinding;
+            }
+
+            m_InputAsset = null;
+            m_PlayerInput = null;
+            m_BehaviorParameters = null;
+            m_Device = null;
+        }
+
+        int m_ActuatorsWrittenToEvent;
+        NativeArray<byte> m_InputBufferForFrame;
+        InputEventPtr m_InputEventPtrForFrame;
+        public InputEventPtr GetEventForFrame()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return new InputEventPtr();
+            }
+#endif
+            if (m_ActuatorsWrittenToEvent % m_Actuators.Length == 0 || !m_InputEventPtrForFrame.valid)
+            {
+                m_ActuatorsWrittenToEvent = 0;
+                m_InputEventPtrForFrame = new InputEventPtr();
+                m_InputBufferForFrame = StateEvent.From(m_Device, out m_InputEventPtrForFrame);
+            }
+
+            return m_InputEventPtrForFrame;
+        }
+
+        public void EventProcessedInFrame()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return;
+            }
+#endif
+            m_ActuatorsWrittenToEvent++;
+            if (m_ActuatorsWrittenToEvent == m_Actuators.Length && m_InputEventPtrForFrame.valid)
+            {
+                InputSystem.QueueEvent(m_InputEventPtrForFrame);
+                m_InputBufferForFrame.Dispose();
+            }
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ee3aa3d02407538ea2239492b20740c29e66e0b3
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 33005b124d7f841a191249baf2bacb2a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs
new file mode 100644
index 0000000000000000000000000000000000000000..816c55dbba67051188cf810a93365f239deed0d3
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs
@@ -0,0 +1,79 @@
+#if MLA_INPUT_SYSTEM
+using System;
+using Unity.Collections;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// This interface is passed to InputActionActuators to allow them to write to InputEvents.
+    /// The way this interface should be used is to request the <see cref="InputEventPtr"/> by calling
+    /// <see cref="GetEventForFrame"/> then call <see cref="EventProcessedInFrame"/> before returning from
+    /// </summary>
+    public class InputActuatorEventContext : IDisposable
+    {
+        /// <summary>
+        /// The number of times to allow the use of an event before queuing it in the InputSystem.
+        /// </summary>
+        public readonly int NumTimesToProcess;
+        public readonly InputDevice InputDevice;
+        NativeArray<byte> m_EventBuffer;
+        InputEventPtr m_Ptr;
+        int m_Count;
+
+#if UNITY_EDITOR
+        public static InputActuatorEventContext s_EditorContext = new InputActuatorEventContext();
+#endif
+
+        public InputActuatorEventContext(int numTimesToProcess = 1, InputDevice device = null)
+        {
+            NumTimesToProcess = numTimesToProcess;
+            InputDevice = device;
+            m_Count = 0;
+            m_Ptr = new InputEventPtr();
+            m_EventBuffer = new NativeArray<byte>();
+        }
+
+        /// <summary>
+        /// Returns the <see cref="InputEventPtr"/> to write to for the current frame.
+        /// </summary>
+        /// <returns>The <see cref="InputEventPtr"/> to write to for the current frame.</returns>
+        public IDisposable GetEventForFrame(out InputEventPtr eventPtr)
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                eventPtr = new InputEventPtr();
+            }
+#endif
+            if (m_Count % NumTimesToProcess == 0)
+            {
+                m_Count = 0;
+                m_EventBuffer = StateEvent.From(InputDevice, out m_Ptr);
+            }
+            eventPtr = m_Ptr;
+            return this;
+        }
+
+        public void Dispose()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return;
+            }
+#endif
+            m_Count++;
+            if (m_Count == NumTimesToProcess && m_Ptr.valid)
+            {
+                InputSystem.QueueEvent(m_Ptr);
+                m_EventBuffer.Dispose();
+            }
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs.meta b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bc7c18aeab9cda3c61c37d519a86b7fcbc033e15
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 76c31df314114ec6bf104e8db4e5568b
+timeCreated: 1614892215
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef b/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..650ad842b1f2241cbd094f6e99023757ab0d6d8c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef
@@ -0,0 +1,23 @@
+{
+    "name": "Unity.ML-Agents.Extensions.Input",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.InputSystem"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": false,
+    "precompiledReferences": [],
+    "autoReferenced": true,
+    "defineConstraints": [],
+    "versionDefines": [
+        {
+            "name": "com.unity.inputsystem",
+            "expression": "1.3.0",
+            "define": "MLA_INPUT_SYSTEM"
+        }
+    ],
+    "noEngineReferences": false
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef.meta b/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5ad61670a73fce2dda191e9cc1a42766f04f0725
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: a7b0d999fb2a7493a85c4c7017412530
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors.meta b/com.unity.ml-agents.extensions/Runtime/Sensors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8a56d015934126688e7e629266ae8674ae41a3b3
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 2a66e31170bb04777b9ade862995a624
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fca56a479552b2f78d7fc1925d8a73a719c3ea78
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs
@@ -0,0 +1,147 @@
+#if UNITY_2020_1_OR_NEWER
+
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    public class ArticulationBodyJointExtractor : IJointExtractor
+    {
+        ArticulationBody m_Body;
+
+        public ArticulationBodyJointExtractor(ArticulationBody body)
+        {
+            m_Body = body;
+        }
+
+        public int NumObservations(PhysicsSensorSettings settings)
+        {
+            return NumObservations(m_Body, settings);
+        }
+
+        public static int NumObservations(ArticulationBody body, PhysicsSensorSettings settings)
+        {
+            if (body == null || body.isRoot)
+            {
+                return 0;
+            }
+
+            var totalCount = 0;
+            if (settings.UseJointPositionsAndAngles)
+            {
+                switch (body.jointType)
+                {
+                    case ArticulationJointType.RevoluteJoint:
+                    case ArticulationJointType.SphericalJoint:
+                        // Both RevoluteJoint and SphericalJoint have all angular components.
+                        // We use sine and cosine of the angles for the observations.
+                        totalCount += 2 * body.dofCount;
+                        break;
+                    case ArticulationJointType.FixedJoint:
+                        // Since FixedJoint can't moved, there aren't any interesting observations for it.
+                        break;
+                    case ArticulationJointType.PrismaticJoint:
+                        // One linear component
+                        totalCount += body.dofCount;
+                        break;
+                }
+            }
+
+            if (settings.UseJointForces)
+            {
+                totalCount += body.dofCount;
+            }
+
+            return totalCount;
+        }
+
+        public int Write(PhysicsSensorSettings settings, ObservationWriter writer, int offset)
+        {
+            if (m_Body == null || m_Body.isRoot)
+            {
+                return 0;
+            }
+
+            var currentOffset = offset;
+
+            // Write joint positions
+            if (settings.UseJointPositionsAndAngles)
+            {
+                switch (m_Body.jointType)
+                {
+                    case ArticulationJointType.RevoluteJoint:
+                    case ArticulationJointType.SphericalJoint:
+                        // All joint positions are angular
+                        for (var dofIndex = 0; dofIndex < m_Body.dofCount; dofIndex++)
+                        {
+                            var jointRotationRads = m_Body.jointPosition[dofIndex];
+                            writer[currentOffset++] = Mathf.Sin(jointRotationRads);
+                            writer[currentOffset++] = Mathf.Cos(jointRotationRads);
+                        }
+                        break;
+                    case ArticulationJointType.FixedJoint:
+                        // No observations
+                        break;
+                    case ArticulationJointType.PrismaticJoint:
+                        writer[currentOffset++] = GetPrismaticValue();
+                        break;
+                }
+            }
+
+            if (settings.UseJointForces)
+            {
+                for (var dofIndex = 0; dofIndex < m_Body.dofCount; dofIndex++)
+                {
+                    // take tanh to keep in [-1, 1]
+                    writer[currentOffset++] = (float)System.Math.Tanh(m_Body.jointForce[dofIndex]);
+                }
+            }
+
+            return currentOffset - offset;
+        }
+
+        float GetPrismaticValue()
+        {
+            // Prismatic joints should have at most one free axis.
+            bool limited = false;
+            var drive = m_Body.xDrive;
+            if (m_Body.linearLockX == ArticulationDofLock.LimitedMotion)
+            {
+                drive = m_Body.xDrive;
+                limited = true;
+            }
+            else if (m_Body.linearLockY == ArticulationDofLock.LimitedMotion)
+            {
+                drive = m_Body.yDrive;
+                limited = true;
+            }
+            else if (m_Body.linearLockZ == ArticulationDofLock.LimitedMotion)
+            {
+                drive = m_Body.zDrive;
+                limited = true;
+            }
+
+            var jointPos = m_Body.jointPosition[0];
+            if (limited)
+            {
+                // If locked, interpolate between the limits.
+                var upperLimit = drive.upperLimit;
+                var lowerLimit = drive.lowerLimit;
+                if (upperLimit <= lowerLimit)
+                {
+                    // Invalid limits (probably equal), so don't try to lerp
+                    return 0;
+                }
+                var invLerped = Mathf.InverseLerp(lowerLimit, upperLimit, jointPos);
+
+                // Convert [0, 1] -> [-1, 1]
+                var normalized = 2.0f * invLerped - 1.0f;
+                return normalized;
+            }
+            // take tanh() to keep in [-1, 1]
+            return (float)System.Math.Tanh(jointPos);
+        }
+    }
+}
+#endif
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8b5c4d67292348ad12ac70f08e211b99afccd64e
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyJointExtractor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 238d15f867b9c4ced9cef331b7420b27
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c307da0811e2322600a80c39eb8b5b04ea6bb696
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs
@@ -0,0 +1,107 @@
+#if UNITY_2020_1_OR_NEWER
+
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Utility class to track a hierarchy of ArticulationBodies.
+    /// </summary>
+    public class ArticulationBodyPoseExtractor : PoseExtractor
+    {
+        ArticulationBody[] m_Bodies;
+
+        public ArticulationBodyPoseExtractor(ArticulationBody rootBody)
+        {
+            if (rootBody == null)
+            {
+                return;
+            }
+
+            if (!rootBody.isRoot)
+            {
+                Debug.Log("Must pass ArticulationBody.isRoot");
+                return;
+            }
+
+            var bodies = rootBody.GetComponentsInChildren<ArticulationBody>();
+            if (bodies[0] != rootBody)
+            {
+                Debug.Log("Expected root body at index 0");
+                return;
+            }
+
+            var numBodies = bodies.Length;
+            m_Bodies = bodies;
+            int[] parentIndices = new int[numBodies];
+            parentIndices[0] = -1;
+
+            var bodyToIndex = new Dictionary<ArticulationBody, int>();
+            for (var i = 0; i < numBodies; i++)
+            {
+                bodyToIndex[m_Bodies[i]] = i;
+            }
+
+            for (var i = 1; i < numBodies; i++)
+            {
+                var currentArticBody = m_Bodies[i];
+                // Component.GetComponentInParent will consider the provided object as well.
+                // So start looking from the parent.
+                var currentGameObject = currentArticBody.gameObject;
+                var parentGameObject = currentGameObject.transform.parent;
+                var parentArticBody = parentGameObject.GetComponentInParent<ArticulationBody>();
+                parentIndices[i] = bodyToIndex[parentArticBody];
+            }
+
+            Setup(parentIndices);
+        }
+
+        /// <inheritdoc/>
+        protected internal override Vector3 GetLinearVelocityAt(int index)
+        {
+            return m_Bodies[index].velocity;
+        }
+
+        /// <inheritdoc/>
+        protected internal override Pose GetPoseAt(int index)
+        {
+            var body = m_Bodies[index];
+            var go = body.gameObject;
+            var t = go.transform;
+            return new Pose { rotation = t.rotation, position = t.position };
+        }
+
+        /// <inheritdoc/>
+        protected internal override Object GetObjectAt(int index)
+        {
+            return m_Bodies[index];
+        }
+
+        internal ArticulationBody[] Bodies => m_Bodies;
+
+        internal IEnumerable<ArticulationBody> GetEnabledArticulationBodies()
+        {
+            if (m_Bodies == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_Bodies.Length; i++)
+            {
+                var articBody = m_Bodies[i];
+                if (articBody == null)
+                {
+                    // Ignore a virtual root.
+                    continue;
+                }
+
+                if (IsPoseEnabled(i))
+                {
+                    yield return articBody;
+                }
+            }
+        }
+    }
+}
+#endif // UNITY_2020_1_OR_NEWER
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..41f7baad442098a16937ca2df86a169580eba9bf
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs.meta
@@ -0,0 +1,11 @@
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+guid: 11fe037a02b4a483cb9342c3454232cd
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+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..41e8acde56cf4a1248e29a2f04abde304b667e02
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs
@@ -0,0 +1,25 @@
+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    public class ArticulationBodySensorComponent : SensorComponent
+    {
+        public ArticulationBody RootBody;
+
+        [SerializeField]
+        public PhysicsSensorSettings Settings = PhysicsSensorSettings.Default();
+        public string sensorName;
+
+        /// <summary>
+        /// Creates a PhysicsBodySensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            return new ISensor[] {new PhysicsBodySensor(RootBody, Settings, sensorName)};
+        }
+    }
+}
+#endif // UNITY_2020_1_OR_NEWER
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3cdd83ac522d890f5525e39da17649c3c453adac
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs.meta
@@ -0,0 +1,11 @@
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+guid: e57a788acd5e049c6aa9642b450ca318
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f7a212be1eade3cd687d0b398462d671bc6c5470
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs
@@ -0,0 +1,61 @@
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Grid-based sensor that counts the number of detctable objects.
+    /// </summary>
+    public class CountingGridSensor : GridSensorBase
+    {
+        /// <summary>
+        /// Create a CountingGridSensor with the specified configuration.
+        /// </summary>
+        /// <param name="name">The sensor name</param>
+        /// <param name="cellScale">The scale of each cell in the grid</param>
+        /// <param name="gridSize">Number of cells on each side of the grid</param>
+        /// <param name="detectableTags">Tags to be detected by the sensor</param>
+        /// <param name="compression">Compression type</param>
+        public CountingGridSensor(
+            string name,
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            string[] detectableTags,
+            SensorCompressionType compression
+        ) : base(name, cellScale, gridSize, detectableTags, compression)
+        {
+            CompressionType = SensorCompressionType.None;
+        }
+
+        /// <inheritdoc/>
+        protected override int GetCellObservationSize()
+        {
+            return DetectableTags == null ? 0 : DetectableTags.Length;
+        }
+
+        /// <inheritdoc/>
+        protected override bool IsDataNormalized()
+        {
+            return false;
+        }
+
+        /// <inheritdoc/>
+        protected internal override ProcessCollidersMethod GetProcessCollidersMethod()
+        {
+            return ProcessCollidersMethod.ProcessAllColliders;
+        }
+
+        /// <summary>
+        /// Get object counts for each detectable tags detected in a cell.
+        /// </summary>
+        /// <param name="detectedObject">The game object that was detected within a certain cell</param>
+        /// <param name="tagIndex">The index of the detectedObject's tag in the DetectableObjects list</param>
+        /// <param name="dataBuffer">The buffer to write the observation values.
+        ///         The buffer size is configured by <seealso cref="GetCellObservationSize"/>.
+        /// </param>
+        protected override void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+        {
+            dataBuffer[tagIndex] += 1;
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..853e1b61452397a8b64a1a0bd48efd0c1ce91652
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs.meta
@@ -0,0 +1,11 @@
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+guid: a99e8702817b641c2a3ccb6520403e9b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..401e3abf5009d703e760f2a61ea606010c98bd76
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs
@@ -0,0 +1,27 @@
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Interface for generating observations from a physical joint or constraint.
+    /// </summary>
+    public interface IJointExtractor
+    {
+        /// <summary>
+        /// Determine the number of observations that would be generated for the particular joint
+        /// using the provided PhysicsSensorSettings.
+        /// </summary>
+        /// <param name="settings"></param>
+        /// <returns>Number of floats that will be written.</returns>
+        int NumObservations(PhysicsSensorSettings settings);
+
+        /// <summary>
+        /// Write the observations to the ObservationWriter, starting at the specified offset.
+        /// </summary>
+        /// <param name="settings"></param>
+        /// <param name="writer"></param>
+        /// <param name="offset"></param>
+        /// <returns>Number of floats that were written.</returns>
+        int Write(PhysicsSensorSettings settings, ObservationWriter writer, int offset);
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a1ef9c2f7b3f7f04c7567315f35a03aae8347d86
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/IJointExtractor.cs.meta
@@ -0,0 +1,11 @@
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+guid: 2d2a01ea194334a4682d5c8cad4a956b
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+  defaultReferences: []
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..edcfd169664eb939ee6e25851f42a2eaf6ae00dc
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs
@@ -0,0 +1,131 @@
+using System.Collections.Generic;
+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+#endif
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// ISensor implementation that generates observations for a group of Rigidbodies or ArticulationBodies.
+    /// </summary>
+    public class PhysicsBodySensor : ISensor, IBuiltInSensor
+    {
+        ObservationSpec m_ObservationSpec;
+        string m_SensorName;
+
+        PoseExtractor m_PoseExtractor;
+        List<IJointExtractor> m_JointExtractors;
+        PhysicsSensorSettings m_Settings;
+
+        /// <summary>
+        /// Construct a new PhysicsBodySensor
+        /// </summary>
+        /// <param name="poseExtractor"></param>
+        /// <param name="settings"></param>
+        /// <param name="sensorName"></param>
+        public PhysicsBodySensor(
+            RigidBodyPoseExtractor poseExtractor,
+            PhysicsSensorSettings settings,
+            string sensorName
+        )
+        {
+            m_PoseExtractor = poseExtractor;
+            m_SensorName = sensorName;
+            m_Settings = settings;
+
+            var numJointExtractorObservations = 0;
+            m_JointExtractors = new List<IJointExtractor>(poseExtractor.NumEnabledPoses);
+            foreach (var rb in poseExtractor.GetEnabledRigidbodies())
+            {
+                var jointExtractor = new RigidBodyJointExtractor(rb);
+                numJointExtractorObservations += jointExtractor.NumObservations(settings);
+                m_JointExtractors.Add(jointExtractor);
+            }
+
+            var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
+            m_ObservationSpec = ObservationSpec.Vector(numTransformObservations + numJointExtractorObservations);
+        }
+
+#if UNITY_2020_1_OR_NEWER
+        public PhysicsBodySensor(ArticulationBody rootBody, PhysicsSensorSettings settings, string sensorName = null)
+        {
+            var poseExtractor = new ArticulationBodyPoseExtractor(rootBody);
+            m_PoseExtractor = poseExtractor;
+            m_SensorName = string.IsNullOrEmpty(sensorName) ? $"ArticulationBodySensor:{rootBody?.name}" : sensorName;
+            m_Settings = settings;
+
+            var numJointExtractorObservations = 0;
+            m_JointExtractors = new List<IJointExtractor>(poseExtractor.NumEnabledPoses);
+            foreach (var articBody in poseExtractor.GetEnabledArticulationBodies())
+            {
+                var jointExtractor = new ArticulationBodyJointExtractor(articBody);
+                numJointExtractorObservations += jointExtractor.NumObservations(settings);
+                m_JointExtractors.Add(jointExtractor);
+            }
+
+            var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
+            m_ObservationSpec = ObservationSpec.Vector(numTransformObservations + numJointExtractorObservations);
+        }
+
+#endif
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            var numWritten = writer.WritePoses(m_Settings, m_PoseExtractor);
+            foreach (var jointExtractor in m_JointExtractors)
+            {
+                numWritten += jointExtractor.Write(m_Settings, writer, numWritten);
+            }
+            return numWritten;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            if (m_Settings.UseModelSpace)
+            {
+                m_PoseExtractor.UpdateModelSpacePoses();
+            }
+
+            if (m_Settings.UseLocalSpace)
+            {
+                m_PoseExtractor.UpdateLocalSpacePoses();
+            }
+        }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_SensorName;
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.PhysicsBodySensor;
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2fce9c0200134a6e5980c92237a86b3a9af4e7f3
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d9f9c0d4418c6ce9c6dbc9c483514d9f79809c6c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs
@@ -0,0 +1,152 @@
+using System;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Settings that define the observations generated for physics-based sensors.
+    /// </summary>
+    [Serializable]
+    public struct PhysicsSensorSettings
+    {
+        /// <summary>
+        /// Whether to use model space (relative to the root body) translations as observations.
+        /// </summary>
+        public bool UseModelSpaceTranslations;
+
+        /// <summary>
+        /// Whether to use model space (relative to the root body) rotations as observations.
+        /// </summary>
+        public bool UseModelSpaceRotations;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) translations as observations.
+        /// </summary>
+        public bool UseLocalSpaceTranslations;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) translations as observations.
+        /// </summary>
+        public bool UseLocalSpaceRotations;
+
+        /// <summary>
+        /// Whether to use model space (relative to the root body) linear velocities as observations.
+        /// </summary>
+        public bool UseModelSpaceLinearVelocity;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) linear velocities as observations.
+        /// </summary>
+        public bool UseLocalSpaceLinearVelocity;
+
+        /// <summary>
+        /// Whether to use joint-specific positions and angles as observations.
+        /// </summary>
+        public bool UseJointPositionsAndAngles;
+
+        /// <summary>
+        /// Whether to use the joint forces and torques that are applied by the solver as observations.
+        /// </summary>
+        public bool UseJointForces;
+
+        /// <summary>
+        /// Creates a PhysicsSensorSettings with reasonable default values.
+        /// </summary>
+        /// <returns></returns>
+        public static PhysicsSensorSettings Default()
+        {
+            return new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseModelSpaceRotations = true,
+            };
+        }
+
+        /// <summary>
+        /// Whether any model space observations are being used.
+        /// </summary>
+        public bool UseModelSpace
+        {
+            get { return UseModelSpaceTranslations || UseModelSpaceRotations || UseModelSpaceLinearVelocity; }
+        }
+
+        /// <summary>
+        /// Whether any local space observations are being used.
+        /// </summary>
+        public bool UseLocalSpace
+        {
+            get { return UseLocalSpaceTranslations || UseLocalSpaceRotations || UseLocalSpaceLinearVelocity; }
+        }
+    }
+
+    internal static class ObservationWriterPhysicsExtensions
+    {
+        /// <summary>
+        /// Utility method for writing a PoseExtractor to an ObservationWriter.
+        /// </summary>
+        /// <param name="writer"></param>
+        /// <param name="settings"></param>
+        /// <param name="poseExtractor"></param>
+        /// <param name="baseOffset">The offset into the ObservationWriter to start writing at.</param>
+        /// <returns>The number of observations written.</returns>
+        public static int WritePoses(this ObservationWriter writer, PhysicsSensorSettings settings, PoseExtractor poseExtractor, int baseOffset = 0)
+        {
+            var offset = baseOffset;
+            if (settings.UseModelSpace)
+            {
+                foreach (var pose in poseExtractor.GetEnabledModelSpacePoses())
+                {
+                    if (settings.UseModelSpaceTranslations)
+                    {
+                        writer.Add(pose.position, offset);
+                        offset += 3;
+                    }
+
+                    if (settings.UseModelSpaceRotations)
+                    {
+                        writer.Add(pose.rotation, offset);
+                        offset += 4;
+                    }
+                }
+
+                foreach (var vel in poseExtractor.GetEnabledModelSpaceVelocities())
+                {
+                    if (settings.UseModelSpaceLinearVelocity)
+                    {
+                        writer.Add(vel, offset);
+                        offset += 3;
+                    }
+                }
+            }
+
+            if (settings.UseLocalSpace)
+            {
+                foreach (var pose in poseExtractor.GetEnabledLocalSpacePoses())
+                {
+                    if (settings.UseLocalSpaceTranslations)
+                    {
+                        writer.Add(pose.position, offset);
+                        offset += 3;
+                    }
+
+                    if (settings.UseLocalSpaceRotations)
+                    {
+                        writer.Add(pose.rotation, offset);
+                        offset += 4;
+                    }
+                }
+
+                foreach (var vel in poseExtractor.GetEnabledLocalSpaceVelocities())
+                {
+                    if (settings.UseLocalSpaceLinearVelocity)
+                    {
+                        writer.Add(vel, offset);
+                        offset += 3;
+                    }
+                }
+            }
+
+            return offset - baseOffset;
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..04f26d1a8a06b8e86d9ef80fec16ac5d12c9b5f2
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: fcb7a51f0d5f8404db7b85bd35ecc1fb
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9777473a95e0a34f7bdd1e34d12042d2c4b32c9d
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs
@@ -0,0 +1,471 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using Object = UnityEngine.Object;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Abstract class for managing the transforms of a hierarchy of objects.
+    /// This could be GameObjects or Monobehaviours in the scene graph, but this is
+    /// not a requirement; for example, the objects could be rigid bodies whose hierarchy
+    /// is defined by Joint configurations.
+    ///
+    /// Poses are either considered in model space, which is relative to a root body,
+    /// or in local space, which is relative to their parent.
+    /// </summary>
+    public abstract class PoseExtractor
+    {
+        int[] m_ParentIndices;
+        Pose[] m_ModelSpacePoses;
+        Pose[] m_LocalSpacePoses;
+
+        Vector3[] m_ModelSpaceLinearVelocities;
+        Vector3[] m_LocalSpaceLinearVelocities;
+
+        bool[] m_PoseEnabled;
+
+
+        /// <summary>
+        /// Read iterator for the enabled model space transforms.
+        /// </summary>
+        public IEnumerable<Pose> GetEnabledModelSpacePoses()
+        {
+            if (m_ModelSpacePoses == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_ModelSpacePoses.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_ModelSpacePoses[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled local space transforms.
+        /// </summary>
+        public IEnumerable<Pose> GetEnabledLocalSpacePoses()
+        {
+            if (m_LocalSpacePoses == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_LocalSpacePoses.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_LocalSpacePoses[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled model space linear velocities.
+        /// </summary>
+        public IEnumerable<Vector3> GetEnabledModelSpaceVelocities()
+        {
+            if (m_ModelSpaceLinearVelocities == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_ModelSpaceLinearVelocities.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_ModelSpaceLinearVelocities[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled local space linear velocities.
+        /// </summary>
+        public IEnumerable<Vector3> GetEnabledLocalSpaceVelocities()
+        {
+            if (m_LocalSpaceLinearVelocities == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_LocalSpaceLinearVelocities.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_LocalSpaceLinearVelocities[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Number of enabled poses in the hierarchy (read-only).
+        /// </summary>
+        public int NumEnabledPoses
+        {
+            get
+            {
+                if (m_PoseEnabled == null)
+                {
+                    return 0;
+                }
+
+                var numEnabled = 0;
+                for (var i = 0; i < m_PoseEnabled.Length; i++)
+                {
+                    numEnabled += m_PoseEnabled[i] ? 1 : 0;
+                }
+
+                return numEnabled;
+            }
+        }
+
+        /// <summary>
+        /// Number of total poses in the hierarchy (read-only).
+        /// </summary>
+        public int NumPoses
+        {
+            get { return m_ModelSpacePoses?.Length ?? 0; }
+        }
+
+        /// <summary>
+        /// Get the parent index of the body at the specified index.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        public int GetParentIndex(int index)
+        {
+            if (m_ParentIndices == null)
+            {
+                throw new NullReferenceException("No parent indices set");
+            }
+
+            return m_ParentIndices[index];
+        }
+
+        /// <summary>
+        /// Set whether the pose at the given index is enabled or disabled for observations.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <param name="val"></param>
+        public void SetPoseEnabled(int index, bool val)
+        {
+            m_PoseEnabled[index] = val;
+        }
+
+        public bool IsPoseEnabled(int index)
+        {
+            return m_PoseEnabled[index];
+        }
+
+        /// <summary>
+        /// Initialize with the mapping of parent indices.
+        /// The 0th element is assumed to be -1, indicating that it's the root.
+        /// </summary>
+        /// <param name="parentIndices"></param>
+        protected void Setup(int[] parentIndices)
+        {
+#if DEBUG
+            if (parentIndices[0] != -1)
+            {
+                throw new UnityAgentsException($"Expected parentIndices[0] to be -1, got {parentIndices[0]}");
+            }
+#endif
+            m_ParentIndices = parentIndices;
+            var numPoses = parentIndices.Length;
+            m_ModelSpacePoses = new Pose[numPoses];
+            m_LocalSpacePoses = new Pose[numPoses];
+
+            m_ModelSpaceLinearVelocities = new Vector3[numPoses];
+            m_LocalSpaceLinearVelocities = new Vector3[numPoses];
+
+            m_PoseEnabled = new bool[numPoses];
+            // All poses are enabled by default. Generally we'll want to disable the root though.
+            for (var i = 0; i < numPoses; i++)
+            {
+                m_PoseEnabled[i] = true;
+            }
+        }
+
+        /// <summary>
+        /// Return the world space Pose of the i'th object.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        protected internal abstract Pose GetPoseAt(int index);
+
+        /// <summary>
+        /// Return the world space linear velocity of the i'th object.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        protected internal abstract Vector3 GetLinearVelocityAt(int index);
+
+        /// <summary>
+        /// Return the underlying object at the given index. This is only
+        /// used for display in the inspector.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        protected internal virtual Object GetObjectAt(int index)
+        {
+            return null;
+        }
+
+        /// <summary>
+        /// Update the internal model space transform storage based on the underlying system.
+        /// </summary>
+        public void UpdateModelSpacePoses()
+        {
+            using (TimerStack.Instance.Scoped("UpdateModelSpacePoses"))
+            {
+                if (m_ModelSpacePoses == null)
+                {
+                    return;
+                }
+
+                var rootWorldTransform = GetPoseAt(0);
+                var worldToModel = rootWorldTransform.Inverse();
+                var rootLinearVel = GetLinearVelocityAt(0);
+
+                for (var i = 0; i < m_ModelSpacePoses.Length; i++)
+                {
+                    var currentWorldSpacePose = GetPoseAt(i);
+                    var currentModelSpacePose = worldToModel.Multiply(currentWorldSpacePose);
+                    m_ModelSpacePoses[i] = currentModelSpacePose;
+
+                    var currentBodyLinearVel = GetLinearVelocityAt(i);
+                    var relativeVelocity = currentBodyLinearVel - rootLinearVel;
+                    m_ModelSpaceLinearVelocities[i] = worldToModel.rotation * relativeVelocity;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Update the internal model space transform storage based on the underlying system.
+        /// </summary>
+        public void UpdateLocalSpacePoses()
+        {
+            using (TimerStack.Instance.Scoped("UpdateLocalSpacePoses"))
+            {
+                if (m_LocalSpacePoses == null)
+                {
+                    return;
+                }
+
+                for (var i = 0; i < m_LocalSpacePoses.Length; i++)
+                {
+                    if (m_ParentIndices[i] != -1)
+                    {
+                        var parentTransform = GetPoseAt(m_ParentIndices[i]);
+                        // This is slightly inefficient, since for a body with multiple children, we'll end up inverting
+                        // the transform multiple times. Might be able to trade space for perf here.
+                        var invParent = parentTransform.Inverse();
+                        var currentTransform = GetPoseAt(i);
+                        m_LocalSpacePoses[i] = invParent.Multiply(currentTransform);
+
+                        var parentLinearVel = GetLinearVelocityAt(m_ParentIndices[i]);
+                        var currentLinearVel = GetLinearVelocityAt(i);
+                        m_LocalSpaceLinearVelocities[i] = invParent.rotation * (currentLinearVel - parentLinearVel);
+                    }
+                    else
+                    {
+                        m_LocalSpacePoses[i] = Pose.identity;
+                        m_LocalSpaceLinearVelocities[i] = Vector3.zero;
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// Compute the number of floats needed to represent the poses for the given PhysicsSensorSettings.
+        /// </summary>
+        /// <param name="settings"></param>
+        /// <returns></returns>
+        public int GetNumPoseObservations(PhysicsSensorSettings settings)
+        {
+            int obsPerPose = 0;
+            obsPerPose += settings.UseModelSpaceTranslations ? 3 : 0;
+            obsPerPose += settings.UseModelSpaceRotations ? 4 : 0;
+            obsPerPose += settings.UseLocalSpaceTranslations ? 3 : 0;
+            obsPerPose += settings.UseLocalSpaceRotations ? 4 : 0;
+
+            obsPerPose += settings.UseModelSpaceLinearVelocity ? 3 : 0;
+            obsPerPose += settings.UseLocalSpaceLinearVelocity ? 3 : 0;
+
+            return NumEnabledPoses * obsPerPose;
+        }
+
+        internal void DrawModelSpace(Vector3 offset)
+        {
+            UpdateLocalSpacePoses();
+            UpdateModelSpacePoses();
+
+            var pose = m_ModelSpacePoses;
+            var localPose = m_LocalSpacePoses;
+            for (var i = 0; i < pose.Length; i++)
+            {
+                var current = pose[i];
+                if (m_ParentIndices[i] == -1)
+                {
+                    continue;
+                }
+
+                var parent = pose[m_ParentIndices[i]];
+                Debug.DrawLine(current.position + offset, parent.position + offset, Color.cyan);
+                var localUp = localPose[i].rotation * Vector3.up;
+                var localFwd = localPose[i].rotation * Vector3.forward;
+                var localRight = localPose[i].rotation * Vector3.right;
+                Debug.DrawLine(current.position + offset, current.position + offset + .1f * localUp, Color.red);
+                Debug.DrawLine(current.position + offset, current.position + offset + .1f * localFwd, Color.green);
+                Debug.DrawLine(current.position + offset, current.position + offset + .1f * localRight, Color.blue);
+            }
+        }
+
+        /// <summary>
+        /// Simplified representation of the a node in the hierarchy for display.
+        /// </summary>
+        internal struct DisplayNode
+        {
+            /// <summary>
+            /// Underlying object in the hierarchy. Pass to EditorGUIUtility.ObjectContent() for display.
+            /// </summary>
+            public Object NodeObject;
+
+            /// <summary>
+            /// Whether the poses for the object are enabled.
+            /// </summary>
+            public bool Enabled;
+
+            /// <summary>
+            /// Depth in the hierarchy, used for adjusting the indent level.
+            /// </summary>
+            public int Depth;
+
+            /// <summary>
+            /// The index of the corresponding object in the PoseExtractor.
+            /// </summary>
+            public int OriginalIndex;
+        }
+
+        /// <summary>
+        /// Get a list of display nodes in depth-first order.
+        /// </summary>
+        /// <returns></returns>
+        internal IList<DisplayNode> GetDisplayNodes()
+        {
+            if (NumPoses == 0)
+            {
+                return Array.Empty<DisplayNode>();
+            }
+            var nodesOut = new List<DisplayNode>(NumPoses);
+
+            // List of children for each node
+            var tree = new Dictionary<int, List<int>>();
+            for (var i = 0; i < NumPoses; i++)
+            {
+                var parent = GetParentIndex(i);
+                if (i == -1)
+                {
+                    continue;
+                }
+
+                if (!tree.ContainsKey(parent))
+                {
+                    tree[parent] = new List<int>();
+                }
+                tree[parent].Add(i);
+            }
+
+            // Store (index, depth) in the stack
+            var stack = new Stack<(int, int)>();
+            stack.Push((0, 0));
+
+            while (stack.Count != 0)
+            {
+                var (current, depth) = stack.Pop();
+                var obj = GetObjectAt(current);
+
+                var node = new DisplayNode
+                {
+                    NodeObject = obj,
+                    Enabled = IsPoseEnabled(current),
+                    OriginalIndex = current,
+                    Depth = depth
+                };
+                nodesOut.Add(node);
+
+                // Add children
+                if (tree.ContainsKey(current))
+                {
+                    // Push to the stack in reverse order
+                    var children = tree[current];
+                    for (var childIdx = children.Count - 1; childIdx >= 0; childIdx--)
+                    {
+                        stack.Push((children[childIdx], depth + 1));
+                    }
+                }
+
+                // Safety check
+                // This shouldn't even happen, but in case we have a cycle in the graph
+                // exit instead of looping forever and eating up all the memory.
+                if (nodesOut.Count > NumPoses)
+                {
+                    return nodesOut;
+                }
+            }
+
+            return nodesOut;
+        }
+    }
+
+    /// <summary>
+    /// Extension methods for the Pose struct, in order to improve the readability of some math.
+    /// </summary>
+    public static class PoseExtensions
+    {
+        /// <summary>
+        /// Compute the inverse of a Pose. For any Pose P,
+        ///   P.Inverse() * P
+        /// will equal the identity pose (within tolerance).
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <returns></returns>
+        public static Pose Inverse(this Pose pose)
+        {
+            var rotationInverse = Quaternion.Inverse(pose.rotation);
+            var translationInverse = -(rotationInverse * pose.position);
+            return new Pose { rotation = rotationInverse, position = translationInverse };
+        }
+
+        /// <summary>
+        /// This is equivalent to Pose.GetTransformedBy(), but keeps the order more intuitive.
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <param name="rhs"></param>
+        /// <returns></returns>
+        public static Pose Multiply(this Pose pose, Pose rhs)
+        {
+            return rhs.GetTransformedBy(pose);
+        }
+
+        /// <summary>
+        /// Transform the vector by the pose. Conceptually this is equivalent to treating the Pose
+        /// as a 4x4 matrix and multiplying the augmented vector.
+        /// See https://en.wikipedia.org/wiki/Affine_transformation#Augmented_matrix for more details.
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <param name="rhs"></param>
+        /// <returns></returns>
+        public static Vector3 Multiply(this Pose pose, Vector3 rhs)
+        {
+            return pose.rotation * rhs + pose.position;
+        }
+
+        // TODO optimize inv(A)*B?
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5a16a709bef3f1384cd6d848e1873b123007a99c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e0f1e5147a394f428f9a6447c4a8a1f4
+timeCreated: 1591919825
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1a5795521ff7ecb8601753ec700590a28053929d
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs
@@ -0,0 +1,61 @@
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    public class RigidBodyJointExtractor : IJointExtractor
+    {
+        Rigidbody m_Body;
+        Joint m_Joint;
+
+        public RigidBodyJointExtractor(Rigidbody body)
+        {
+            m_Body = body;
+            m_Joint = m_Body?.GetComponent<Joint>();
+        }
+
+        public int NumObservations(PhysicsSensorSettings settings)
+        {
+            return NumObservations(m_Body, m_Joint, settings);
+        }
+
+        public static int NumObservations(Rigidbody body, Joint joint, PhysicsSensorSettings settings)
+        {
+            if (body == null || joint == null)
+            {
+                return 0;
+            }
+
+            var numObservations = 0;
+            if (settings.UseJointForces)
+            {
+                // 3 force and 3 torque values
+                numObservations += 6;
+            }
+
+            return numObservations;
+        }
+
+        public int Write(PhysicsSensorSettings settings, ObservationWriter writer, int offset)
+        {
+            if (m_Body == null || m_Joint == null)
+            {
+                return 0;
+            }
+
+            var currentOffset = offset;
+            if (settings.UseJointForces)
+            {
+                // Take tanh of the forces and torques to ensure they're in [-1, 1]
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentForce.x);
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentForce.y);
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentForce.z);
+
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentTorque.x);
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentTorque.y);
+                writer[currentOffset++] = (float)System.Math.Tanh(m_Joint.currentTorque.z);
+            }
+            return currentOffset - offset;
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9d3dc91df92163188f3ffc139b2d581cec5563de
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5014d7ab95c6a44469f447b8a7019746
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..38fad10fdeca7cd57e67fa12fa8956057d7b86f5
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs
@@ -0,0 +1,207 @@
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Utility class to track a hierarchy of RigidBodies. These are assumed to have a root node,
+    /// and child nodes are connect to their parents via Joints.
+    /// </summary>
+    public class RigidBodyPoseExtractor : PoseExtractor
+    {
+        Rigidbody[] m_Bodies;
+
+        /// <summary>
+        /// Optional game object used to determine the root of the poses, separate from the actual Rigidbodies
+        /// in the hierarchy. For locomotion
+        /// </summary>
+        GameObject m_VirtualRoot;
+
+        /// <summary>
+        /// Initialize given a root RigidBody.
+        /// </summary>
+        /// <param name="rootBody">The root Rigidbody. This has no Joints on it (but other Joints may connect to it).</param>
+        /// <param name="rootGameObject">Optional GameObject used to find Rigidbodies in the hierarchy.</param>
+        /// <param name="virtualRoot">Optional GameObject used to determine the root of the poses,
+        /// separate from the actual Rigidbodies in the hierarchy. For locomotion tasks, with ragdolls, this provides
+        /// a stabilized reference frame, which can improve learning.</param>
+        /// <param name="enableBodyPoses">Optional mapping of whether a body's psoe should be enabled or not.</param>
+        public RigidBodyPoseExtractor(Rigidbody rootBody, GameObject rootGameObject = null,
+                                      GameObject virtualRoot = null, Dictionary<Rigidbody, bool> enableBodyPoses = null)
+        {
+            if (rootBody == null)
+            {
+                return;
+            }
+
+            Rigidbody[] rbs;
+            Joint[] joints;
+            if (rootGameObject == null)
+            {
+                rbs = rootBody.GetComponentsInChildren<Rigidbody>();
+                joints = rootBody.GetComponentsInChildren<Joint>();
+            }
+            else
+            {
+                rbs = rootGameObject.GetComponentsInChildren<Rigidbody>();
+                joints = rootGameObject.GetComponentsInChildren<Joint>();
+            }
+
+            if (rbs == null || rbs.Length == 0)
+            {
+                Debug.Log("No rigid bodies found!");
+                return;
+            }
+
+            if (rbs[0] != rootBody)
+            {
+                Debug.Log("Expected root body at index 0");
+                return;
+            }
+
+            // Adjust the array if we have a virtual root.
+            // This will be at index 0, and the "real" root will be parented to it.
+            if (virtualRoot != null)
+            {
+                var extendedRbs = new Rigidbody[rbs.Length + 1];
+                for (var i = 0; i < rbs.Length; i++)
+                {
+                    extendedRbs[i + 1] = rbs[i];
+                }
+
+                rbs = extendedRbs;
+            }
+
+            var bodyToIndex = new Dictionary<Rigidbody, int>(rbs.Length);
+            var parentIndices = new int[rbs.Length];
+            parentIndices[0] = -1;
+
+            for (var i = 0; i < rbs.Length; i++)
+            {
+                if (rbs[i] != null)
+                {
+                    bodyToIndex[rbs[i]] = i;
+                }
+            }
+
+            foreach (var j in joints)
+            {
+                var parent = j.connectedBody;
+                var child = j.GetComponent<Rigidbody>();
+
+                var parentIndex = bodyToIndex[parent];
+                var childIndex = bodyToIndex[child];
+                parentIndices[childIndex] = parentIndex;
+            }
+
+            if (virtualRoot != null)
+            {
+                // Make sure the original root treats the virtual root as its parent.
+                parentIndices[1] = 0;
+                m_VirtualRoot = virtualRoot;
+            }
+
+            m_Bodies = rbs;
+            Setup(parentIndices);
+
+            // By default, ignore the root
+            SetPoseEnabled(0, false);
+
+            if (enableBodyPoses != null)
+            {
+                foreach (var pair in enableBodyPoses)
+                {
+                    var rb = pair.Key;
+                    if (bodyToIndex.TryGetValue(rb, out var index))
+                    {
+                        SetPoseEnabled(index, pair.Value);
+                    }
+                }
+            }
+        }
+
+        /// <inheritdoc/>
+        protected internal override Vector3 GetLinearVelocityAt(int index)
+        {
+            if (index == 0 && m_VirtualRoot != null)
+            {
+                // No velocity on the virtual root
+                return Vector3.zero;
+            }
+            return m_Bodies[index].velocity;
+        }
+
+        /// <inheritdoc/>
+        protected internal override Pose GetPoseAt(int index)
+        {
+            if (index == 0 && m_VirtualRoot != null)
+            {
+                // Use the GameObject's world transform
+                return new Pose
+                {
+                    rotation = m_VirtualRoot.transform.rotation,
+                    position = m_VirtualRoot.transform.position
+                };
+            }
+
+            var body = m_Bodies[index];
+            return new Pose { rotation = body.rotation, position = body.position };
+        }
+
+        /// <inheritdoc/>
+        protected internal override Object GetObjectAt(int index)
+        {
+            if (index == 0 && m_VirtualRoot != null)
+            {
+                return m_VirtualRoot;
+            }
+            return m_Bodies[index];
+        }
+
+        internal Rigidbody[] Bodies => m_Bodies;
+
+        /// <summary>
+        /// Get a dictionary indicating which Rigidbodies' poses are enabled or disabled.
+        /// </summary>
+        /// <returns></returns>
+        internal Dictionary<Rigidbody, bool> GetBodyPosesEnabled()
+        {
+            var bodyPosesEnabled = new Dictionary<Rigidbody, bool>(m_Bodies.Length);
+            for (var i = 0; i < m_Bodies.Length; i++)
+            {
+                var rb = m_Bodies[i];
+                if (rb == null)
+                {
+                    continue; // skip virtual root
+                }
+
+                bodyPosesEnabled[rb] = IsPoseEnabled(i);
+            }
+
+            return bodyPosesEnabled;
+        }
+
+        internal IEnumerable<Rigidbody> GetEnabledRigidbodies()
+        {
+            if (m_Bodies == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_Bodies.Length; i++)
+            {
+                var rb = m_Bodies[i];
+                if (rb == null)
+                {
+                    // Ignore a virtual root.
+                    continue;
+                }
+
+                if (IsPoseEnabled(i))
+                {
+                    yield return rb;
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8418e2f14696d6937529a7a4e298fa39ec48999e
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 867cab4a07f244518bae3e6fdda14416
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..283a37b3a560d4505083421cd37f6b31d0cc509f
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs
@@ -0,0 +1,113 @@
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Editor component that creates a PhysicsBodySensor for the Agent.
+    /// </summary>
+    public class RigidBodySensorComponent : SensorComponent
+    {
+        /// <summary>
+        /// The root Rigidbody of the system.
+        /// </summary>
+        public Rigidbody RootBody;
+
+        /// <summary>
+        /// Optional GameObject used to determine the root of the poses.
+        /// </summary>
+        public GameObject VirtualRoot;
+
+        /// <summary>
+        /// Settings defining what types of observations will be generated.
+        /// </summary>
+        [SerializeField]
+        public PhysicsSensorSettings Settings = PhysicsSensorSettings.Default();
+
+        /// <summary>
+        /// Optional sensor name. This must be unique for each Agent.
+        /// </summary>
+        [SerializeField]
+        public string sensorName;
+
+        [SerializeField]
+        [HideInInspector]
+        RigidBodyPoseExtractor m_PoseExtractor;
+
+        /// <summary>
+        /// Creates a PhysicsBodySensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            var _sensorName = string.IsNullOrEmpty(sensorName) ? $"PhysicsBodySensor:{RootBody?.name}" : sensorName;
+            return new ISensor[] { new PhysicsBodySensor(GetPoseExtractor(), Settings, _sensorName) };
+        }
+
+        /// <summary>
+        /// Get the DisplayNodes of the hierarchy.
+        /// </summary>
+        /// <returns></returns>
+        internal IList<PoseExtractor.DisplayNode> GetDisplayNodes()
+        {
+            return GetPoseExtractor().GetDisplayNodes();
+        }
+
+        /// <summary>
+        /// Lazy construction of the PoseExtractor.
+        /// </summary>
+        /// <returns></returns>
+        RigidBodyPoseExtractor GetPoseExtractor()
+        {
+            if (m_PoseExtractor == null)
+            {
+                ResetPoseExtractor();
+            }
+
+            return m_PoseExtractor;
+        }
+
+        /// <summary>
+        /// Reset the pose extractor, trying to keep the enabled state of the corresponding poses the same.
+        /// </summary>
+        internal void ResetPoseExtractor()
+        {
+            // Get the current enabled state of each body, so that we can reinitialize with them.
+            Dictionary<Rigidbody, bool> bodyPosesEnabled = null;
+            if (m_PoseExtractor != null)
+            {
+                bodyPosesEnabled = m_PoseExtractor.GetBodyPosesEnabled();
+            }
+            m_PoseExtractor = new RigidBodyPoseExtractor(RootBody, gameObject, VirtualRoot, bodyPosesEnabled);
+        }
+
+        /// <summary>
+        /// Toggle the pose at the given index.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <param name="enabled"></param>
+        internal void SetPoseEnabled(int index, bool enabled)
+        {
+            GetPoseExtractor().SetPoseEnabled(index, enabled);
+        }
+
+        internal bool IsTrivial()
+        {
+            if (ReferenceEquals(RootBody, null))
+            {
+                // It *is* trivial, but this will happen when the sensor is being set up, so don't warn then.
+                return false;
+            }
+            var joints = RootBody.GetComponentsInChildren<Joint>();
+            if (joints.Length == 0)
+            {
+                if (ReferenceEquals(VirtualRoot, null) || ReferenceEquals(VirtualRoot, RootBody.gameObject))
+                {
+                    return true;
+                }
+            }
+            return false;
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs.meta b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..59ba148382c057db2ec0d4be26963d08ae80290c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: df0f8be9a37d6486498061e2cbc4cd94
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef b/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..416d9e8b7f5a9885850e93e0de2752f4ffba5da9
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef
@@ -0,0 +1,8 @@
+{
+    "name": "Unity.ML-Agents.Extensions",
+    "references": [
+        "Unity.Barracuda",
+        "Unity.ML-Agents",
+        "Unity.ML-Agents.Extensions.Input"
+    ]
+}
diff --git a/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef.meta b/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..71b01c55fc74a952246f777e51e3bc1285c1dbf8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Runtime/Unity.ML-Agents.Extensions.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 0234213bbb9574f66bb4d93920cc6bb7
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests.meta b/com.unity.ml-agents.extensions/Tests.meta
new file mode 100644
index 0000000000000000000000000000000000000000..983c35226f31a46bcafde3a737642e6382185681
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 8b9992bc4a2804deda293d0d28bbf5e0
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/.tests.json b/com.unity.ml-agents.extensions/Tests/.tests.json
new file mode 100644
index 0000000000000000000000000000000000000000..327abb29e523ac0bf5338190d4ca6c9a765838bc
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/.tests.json
@@ -0,0 +1,3 @@
+{
+	"createSeparatePackage": false
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime.meta b/com.unity.ml-agents.extensions/Tests/Runtime.meta
new file mode 100644
index 0000000000000000000000000000000000000000..39b875009d1a97d8f77ae95bf40813de9f6e85ef
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 302e80f9aed2d40a3899643525e5b706
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ad97c88194d045613ca1d3b365ed676c589e0e38
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 27f8e1ce37d7485f814ce50a37101203
+timeCreated: 1612908869
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..13aedd73ff5c9feaaa2aed8ec2cec35a8be6d6a5
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 9b59c16f3e30469ca26156b96351164a
+timeCreated: 1613081382
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7187a7e331897f1403f09f1e16f5c2005e6d2519
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs
@@ -0,0 +1,80 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    public class ButtonInputActionAdaptorTests : InputTestFixture
+    {
+        ButtonInputActionAdaptor m_Adaptor;
+        InputDevice m_Device;
+        InputControl<float> m_Control;
+        InputAction m_Action;
+
+        public override void Setup()
+        {
+            base.Setup();
+            const string kLayout = @"
+                {
+                    ""name"" : ""TestDevice"",
+                    ""extend"" : ""HID"",
+                    ""controls"" : [
+                        { ""name"" : ""button"", ""layout"" : ""Button"" }
+                    ]
+                }";
+            InputSystem.RegisterLayout(kLayout);
+            m_Device = InputSystem.AddDevice("TestDevice");
+            m_Control = (InputControl<float>)m_Device["button"];
+            m_Action = new InputAction("action", InputActionType.Button, "/TestDevice/button", null, null, "Button");
+            m_Action.Enable();
+            m_Adaptor = new ButtonInputActionAdaptor();
+        }
+
+        public override void TearDown()
+        {
+            base.TearDown();
+            m_Adaptor = null;
+        }
+
+        [Test]
+        public void TestGenerateActionSpec()
+        {
+            var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
+            Assert.IsTrue(actionSpec.NumDiscreteActions == 1);
+            Assert.IsTrue(actionSpec.BranchSizes[0] == 2);
+        }
+
+        [Test]
+        public void TestQueueEvent()
+        {
+            var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var val = m_Action.ReadValue<float>();
+            Assert.IsTrue(Mathf.Approximately(1f, val));
+        }
+
+        [Test]
+        public void TestWriteToHeuristic()
+        {
+            var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
+            m_Adaptor.WriteToHeuristic(m_Action, buffer);
+            Assert.IsTrue(buffer.DiscreteActions[0] == 1);
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b8047c76fb30c88b995452b8e9a140d1aa1fb94c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f53bd7ea7b154c9a992b48c9fbc34e47
+timeCreated: 1613081399
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7033b383fe3f828ec2f9e5e8b3924b279bc3c1ce
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs
@@ -0,0 +1,78 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    public class DoubleInputActionAdaptorTests : InputTestFixture
+    {
+        DoubleInputActionAdaptor m_Adaptor;
+        InputDevice m_Device;
+        InputControl<double> m_Control;
+        InputAction m_Action;
+
+        public override void Setup()
+        {
+            base.Setup();
+            const string kLayout = @"
+                {
+                    ""name"" : ""TestDevice"",
+                    ""extend"" : ""HID"",
+                    ""controls"" : [
+                        { ""name"" : ""button"", ""layout"" : ""Double"" }
+                    ]
+                }";
+            InputSystem.RegisterLayout(kLayout);
+            m_Device = InputSystem.AddDevice("TestDevice");
+            m_Control = (InputControl<double>)m_Device["button"];
+            m_Action = new InputAction("action", InputActionType.Value, "/TestDevice/button", null, null, "double");
+            m_Action.Enable();
+            m_Adaptor = new DoubleInputActionAdaptor();
+        }
+
+        public override void TearDown()
+        {
+            base.TearDown();
+            m_Adaptor = null;
+        }
+
+        [Test]
+        public void TestGenerateActionSpec()
+        {
+            var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
+            Assert.IsTrue(actionSpec.NumContinuousActions == 1);
+        }
+
+        [Test]
+        public void TestQueueEvent()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            Assert.IsTrue(Mathf.Approximately(1f, (float)m_Action.ReadValue<double>()));
+        }
+
+        [Test]
+        public void TestWriteToHeuristic()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            m_Adaptor.WriteToHeuristic(m_Action, buffer);
+            Assert.IsTrue(Mathf.Approximately(buffer.ContinuousActions[0], 1f));
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b5779ccaaa689b07cb2ce7f353ec6428a80f4041
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ae2012c098aa4b57ab09dbf72bcd4efe
+timeCreated: 1613074622
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..300dabec0cf771174cbfba045d370c6cbbbbe2d9
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs
@@ -0,0 +1,79 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    public class FloatInputActionAdaptorTests : InputTestFixture
+    {
+        FloatInputActionAdaptor m_Adaptor;
+        InputDevice m_Device;
+        InputControl<float> m_Control;
+        InputAction m_Action;
+
+        public override void Setup()
+        {
+            base.Setup();
+            const string kLayout = @"
+                {
+                    ""name"" : ""TestDevice"",
+                    ""extend"" : ""HID"",
+                    ""controls"" : [
+                        { ""name"" : ""button"", ""layout"" : ""Axis"" }
+                    ]
+                }";
+            InputSystem.RegisterLayout(kLayout);
+            m_Device = InputSystem.AddDevice("TestDevice");
+            m_Control = (InputControl<float>)m_Device["button"];
+            m_Action = new InputAction("action", InputActionType.Value, "/TestDevice/button", null, null, "Axis");
+            m_Action.Enable();
+            m_Adaptor = new FloatInputActionAdaptor();
+        }
+
+        public override void TearDown()
+        {
+            base.TearDown();
+            m_Adaptor = null;
+        }
+
+        [Test]
+        public void TestGenerateActionSpec()
+        {
+            var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
+            Assert.IsTrue(actionSpec.NumContinuousActions == 1);
+        }
+
+        [Test]
+        public void TestQueueEvent()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var val = m_Action.ReadValue<float>();
+            Assert.IsTrue(Mathf.Approximately(1f, val));
+        }
+
+        [Test]
+        public void TestWriteToHeuristic()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
+            m_Adaptor.WriteToHeuristic(m_Action, buffer);
+            Assert.IsTrue(Mathf.Approximately(1f, buffer.ContinuousActions[0]));
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f8abad1d70227425c131acdf8cdf02a5168fb3a8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: a85fbb8b3e154eccadbdab826c3c5cae
+timeCreated: 1613083958
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a6d92e7af2739259f79cac3940bf7a4cf0843547
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs
@@ -0,0 +1,79 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    public class IntegerInputActionAdaptorTests : InputTestFixture
+    {
+        IntegerInputActionAdaptor m_Adaptor;
+        InputDevice m_Device;
+        InputControl<int> m_Control;
+        InputAction m_Action;
+
+        public override void Setup()
+        {
+            base.Setup();
+            const string kLayout = @"
+                {
+                    ""name"" : ""TestDevice"",
+                    ""extend"" : ""HID"",
+                    ""controls"" : [
+                        { ""name"" : ""button"", ""layout"" : ""integer"" }
+                    ]
+                }";
+            InputSystem.RegisterLayout(kLayout);
+            m_Device = InputSystem.AddDevice("TestDevice");
+            m_Control = (InputControl<int>)m_Device["button"];
+            m_Action = new InputAction("action", InputActionType.Value, "/TestDevice/button", null, null, "int");
+            m_Action.Enable();
+            m_Adaptor = new IntegerInputActionAdaptor();
+        }
+
+        public override void TearDown()
+        {
+            base.TearDown();
+            m_Adaptor = null;
+        }
+
+        [Test]
+        public void TestGenerateActionSpec()
+        {
+            var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
+            Assert.IsTrue(actionSpec.NumDiscreteActions == 1);
+            Assert.IsTrue(actionSpec.SumOfDiscreteBranchSizes == 2);
+        }
+
+        [Test]
+        public void TestQueueEvent()
+        {
+            var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var val = m_Action.ReadValue<int>();
+            Assert.IsTrue(val == 1);
+        }
+
+        [Test]
+        public void TestWriteToHeuristic()
+        {
+            var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new int[1]));
+            m_Adaptor.WriteToHeuristic(m_Action, buffer);
+            Assert.IsTrue(buffer.DiscreteActions[0] == 1);
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4af1d870816d3110bdc2a82a48f2cd1d217702f8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: a8631ede70c049a7836284217ddfdc94
+timeCreated: 1613082414
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..70f29d9a884461b8d0b32013cb9b8b0044681e16
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs
@@ -0,0 +1,81 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    public class Vector2InputActionAdaptorTests : InputTestFixture
+    {
+        Vector2InputActionAdaptor m_Adaptor;
+        InputDevice m_Device;
+        InputControl<Vector2> m_Control;
+        InputAction m_Action;
+
+        public override void Setup()
+        {
+            base.Setup();
+            const string kLayout = @"
+                {
+                    ""name"" : ""TestDevice"",
+                    ""extend"" : ""HID"",
+                    ""controls"" : [
+                        { ""name"" : ""button"", ""layout"" : ""Vector2"" }
+                    ]
+                }";
+            InputSystem.RegisterLayout(kLayout);
+            m_Device = InputSystem.AddDevice("TestDevice");
+            m_Control = (InputControl<Vector2>)m_Device["button"];
+            m_Action = new InputAction("action", InputActionType.Value, "/TestDevice/button", null, null, "Vector2");
+            m_Action.Enable();
+            m_Adaptor = new Vector2InputActionAdaptor();
+        }
+
+        public override void TearDown()
+        {
+            base.TearDown();
+            m_Adaptor = null;
+        }
+
+        [Test]
+        public void TestGenerateActionSpec()
+        {
+            var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
+            Assert.IsTrue(actionSpec.NumContinuousActions == 2);
+        }
+
+        [Test]
+        public void TestQueueEvent()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var val = m_Action.ReadValue<Vector2>();
+            Assert.IsTrue(Mathf.Approximately(0f, val.x));
+            Assert.IsTrue(Mathf.Approximately(1f, val.y));
+        }
+
+        [Test]
+        public void TestWriteToHeuristic()
+        {
+            var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
+            InputSystem.Update();
+            var buffer = new ActionBuffers(new ActionSegment<float>(new float[2]), ActionSegment<int>.Empty);
+            m_Adaptor.WriteToHeuristic(m_Action, buffer);
+            Assert.IsTrue(Mathf.Approximately(buffer.ContinuousActions[0], 0f));
+            Assert.IsTrue(Mathf.Approximately(buffer.ContinuousActions[1], 1f));
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cfadda331400a4c0ef14446c20efc45f6e8bf5c5
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 135c4a0f33174ea090da18b0d67bd169
+timeCreated: 1613082080
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..dc3f26835941e63b5f05549e0b4b10a36fb5e67a
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs
@@ -0,0 +1,83 @@
+#if MLA_INPUT_TESTS
+using NUnit.Framework;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using Unity.MLAgents.Policies;
+using UnityEngine;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    class TestAdaptor : IRLActionInputAdaptor
+    {
+        public bool eventWritten;
+        public bool writtenToHeuristic;
+
+        public ActionSpec GetActionSpecForInputAction(InputAction action)
+        {
+            return ActionSpec.MakeContinuous(1);
+        }
+
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        {
+            eventWritten = true;
+        }
+
+        public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
+        {
+            writtenToHeuristic = true;
+        }
+
+        public void Reset()
+        {
+            eventWritten = false;
+            writtenToHeuristic = false;
+        }
+    }
+
+    [TestFixture]
+    public class InputActionActuatorTests
+    {
+        BehaviorParameters m_BehaviorParameters;
+        InputActionActuator m_Actuator;
+        TestAdaptor m_Adaptor;
+
+        [SetUp]
+        public void Setup()
+        {
+            var go = new GameObject();
+            m_BehaviorParameters = go.AddComponent<BehaviorParameters>();
+            var action = new InputAction("action");
+            m_Adaptor = new TestAdaptor();
+            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor, new InputActuatorEventContext(1, InputSystem.AddDevice<Gamepad>()));
+        }
+
+        [Test]
+        public void TestOnActionReceived()
+        {
+            m_BehaviorParameters.BehaviorType = BehaviorType.HeuristicOnly;
+            m_Actuator.OnActionReceived(new ActionBuffers());
+            m_Actuator.Heuristic(new ActionBuffers());
+            Assert.IsFalse(m_Adaptor.eventWritten);
+            Assert.IsTrue(m_Adaptor.writtenToHeuristic);
+            m_Adaptor.Reset();
+
+            m_BehaviorParameters.BehaviorType = BehaviorType.Default;
+            m_Actuator.OnActionReceived(new ActionBuffers());
+            Assert.IsFalse(m_Adaptor.eventWritten);
+            m_Adaptor.Reset();
+
+            m_BehaviorParameters.Model = ScriptableObject.CreateInstance<NNModel>();
+            m_Actuator.OnActionReceived(new ActionBuffers());
+            Assert.IsTrue(m_Adaptor.eventWritten);
+            m_Adaptor.Reset();
+
+            Assert.AreEqual(m_Actuator.Name, "InputActionActuator-action");
+            m_Actuator.ResetData();
+            m_Actuator.WriteDiscreteActionMask(null);
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..decea5110f60f5d8986af2bbd4c342c41f8940db
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c5adebe0f22e48938e7730469f034c70
+timeCreated: 1613070186
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..74849278dc58a65b34751ff4c34f2570a5307d23
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs
@@ -0,0 +1,117 @@
+#if MLA_INPUT_TESTS
+using System;
+using System.Linq;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Extensions.Input;
+using Unity.MLAgents.Policies;
+using UnityEngine;
+using UnityEngine.InputSystem;
+
+namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
+{
+    class TestProvider : MonoBehaviour, IInputActionAssetProvider
+    {
+        public InputActionAsset asset;
+        public IInputActionCollection2 collection;
+
+        public (InputActionAsset, IInputActionCollection2) GetInputActionAsset()
+        {
+            return (asset, collection);
+        }
+    }
+    public class InputActuatorComponentTests : InputTestFixture
+    {
+        InputActionAsset m_Asset;
+        GameObject m_GameObject;
+        PlayerInput m_PlayerInput;
+        BehaviorParameters m_BehaviorParameters;
+        InputActuatorComponent m_ActuatorComponent;
+        TestPushBlockActions m_Actions;
+        TestProvider m_Provider;
+
+        public override void Setup()
+        {
+            base.Setup();
+            m_Actions = new TestPushBlockActions();
+            m_Asset = m_Actions.asset;
+            m_GameObject = new GameObject();
+            m_PlayerInput = m_GameObject.AddComponent<PlayerInput>();
+            m_Provider = m_GameObject.AddComponent<TestProvider>();
+            m_Provider.asset = m_Asset;
+            m_Provider.collection = m_Actions;
+            m_ActuatorComponent = m_GameObject.AddComponent<InputActuatorComponent>();
+            m_BehaviorParameters = m_GameObject.AddComponent<BehaviorParameters>();
+            m_BehaviorParameters.BehaviorName = "InputTest";
+            m_BehaviorParameters.BehaviorType = BehaviorType.Default;
+        }
+
+        public override void TearDown()
+        {
+            m_ActuatorComponent.CleanupActionAsset();
+            var objects = GameObject.FindObjectsOfType<GameObject>();
+            foreach (var o in objects)
+            {
+                UnityEngine.Object.DestroyImmediate(o);
+            }
+            base.TearDown();
+        }
+
+        [Test]
+        public void InputActuatorComponentTestCreateActuators()
+        {
+            // Use the Assert class to test conditions.
+            m_PlayerInput.actions = m_Asset;
+            m_PlayerInput.defaultActionMap = m_Asset.actionMaps[0].name;
+            var actuators = m_ActuatorComponent.CreateActuators();
+            Assert.IsTrue(actuators.Length == 2);
+            Assert.IsTrue(actuators[0].ActionSpec.Equals(ActionSpec.MakeContinuous(2)));
+            Assert.IsTrue(actuators[1].ActionSpec.NumDiscreteActions == 1);
+
+            var actuatorComponentActionSpec = m_ActuatorComponent.ActionSpec;
+            Assert.IsTrue(actuatorComponentActionSpec.BranchSizes.SequenceEqual(new[] {2}));
+            Assert.IsTrue(actuatorComponentActionSpec.NumContinuousActions == 2);
+        }
+
+        [Test]
+        public void InputActuatorComponentTestGenerateActuatorsFromAsset()
+        {
+            // Use the Assert class to test conditions.
+            m_PlayerInput.actions = m_Asset;
+            m_PlayerInput.defaultActionMap = m_Asset.actionMaps[0].name;
+            var inputActionMap = m_Asset.FindActionMap(m_PlayerInput.defaultActionMap);
+            InputActuatorComponent.RegisterLayoutBuilder(
+                inputActionMap,
+                "TestLayout");
+
+            var device = InputSystem.AddDevice("TestLayout");
+
+            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device, new InputActuatorEventContext());
+            Assert.IsTrue(actuators.Length == 2);
+            Assert.IsTrue(actuators[0].ActionSpec.Equals(ActionSpec.MakeContinuous(2)));
+            Assert.IsTrue(actuators[1].ActionSpec.NumDiscreteActions == 1);
+        }
+
+        [Test]
+        public void InputActuatorComponentTestCreateDevice()
+        {
+            // Use the Assert class to test conditions.
+            m_PlayerInput.actions = m_Asset;
+            m_PlayerInput.defaultActionMap = m_Asset.actionMaps[0].name;
+
+            // need to call this to load the layout in the input system
+            InputActuatorComponent.RegisterLayoutBuilder(
+                m_Asset.FindActionMap(m_PlayerInput.defaultActionMap),
+                "TestLayout");
+
+            InputSystem.LoadLayout("TestLayout");
+            var device = InputSystem.AddDevice("TestLayout");
+            Assert.AreEqual("TestLayout", device.layout);
+            Assert.IsTrue(device.children.Count == 2);
+            Assert.AreEqual(device.children[0].path, $"{device.path}{InputControlPath.Separator}movement");
+            Assert.AreEqual(device.children[1].path, $"{device.path}{InputControlPath.Separator}jump");
+            Assert.NotNull(InputSystem.LoadLayout("TestLayout"));
+        }
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a506e544f5a1ca8908b5a51a40e1de77534568bd
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 1894abc7110a4ab39719618db7eb55a9
+timeCreated: 1612910162
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d7dff9102d215026b118318958108dfd02c7289a
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs
@@ -0,0 +1,318 @@
+#if MLA_INPUT_TESTS
+//------------------------------------------------------------------------------
+// <auto-generated>
+//     This code was auto-generated by com.unity.inputsystem:InputActionCodeGenerator
+//     version 1.1.0
+//     from Assets/ML-Agents/Examples/PushBlock/TestPushBlockActions.inputactions
+//
+//     Changes to this file may cause incorrect behavior and will be lost if
+//     the code is regenerated.
+// </auto-generated>
+//------------------------------------------------------------------------------
+
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Utilities;
+
+public partial class TestPushBlockActions : IInputActionCollection2, IDisposable
+{
+    public InputActionAsset asset { get; }
+    public TestPushBlockActions()
+    {
+        asset = InputActionAsset.FromJson(@"{
+    ""name"": ""TestPushBlockActions"",
+    ""maps"": [
+        {
+            ""name"": ""Movement"",
+            ""id"": ""03a2e5d4-ae81-47f1-a575-0779fb7da538"",
+            ""actions"": [
+                {
+                    ""name"": ""movement"",
+                    ""type"": ""Value"",
+                    ""id"": ""5f47cbc6-de46-4d33-93e2-2b1af4f5996d"",
+                    ""expectedControlType"": ""Vector2"",
+                    ""processors"": """",
+                    ""interactions"": """"
+                },
+                {
+                    ""name"": ""jump"",
+                    ""type"": ""Value"",
+                    ""id"": ""ca5eb833-5dfb-4b7c-880d-6118bd5d1378"",
+                    ""expectedControlType"": ""Integer"",
+                    ""processors"": """",
+                    ""interactions"": """"
+                }
+            ],
+            ""bindings"": [
+                {
+                    ""name"": ""gamepad_move"",
+                    ""id"": ""477500ef-6d32-4b84-b9f8-158f18bcb906"",
+                    ""path"": ""2DVector"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": """",
+                    ""action"": ""movement"",
+                    ""isComposite"": true,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": ""up"",
+                    ""id"": ""6d2537b8-2266-4a50-8575-fb0fe310daa5"",
+                    ""path"": ""<Gamepad>/dpad/up"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""down"",
+                    ""id"": ""50584c83-beb6-4e90-a453-a635c03a761e"",
+                    ""path"": ""<Gamepad>/dpad/down"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""left"",
+                    ""id"": ""44408b8f-27e7-4c6d-b078-7536ba020d1a"",
+                    ""path"": ""<Gamepad>/dpad/left"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""right"",
+                    ""id"": ""f5681423-d3e3-41a5-b85e-0a7642c774aa"",
+                    ""path"": ""<Gamepad>/dpad/right"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""keyboard_move"",
+                    ""id"": ""6bcba4bf-5ce0-4005-9e6a-0de2487211b0"",
+                    ""path"": ""2DVector"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": """",
+                    ""action"": ""movement"",
+                    ""isComposite"": true,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": ""up"",
+                    ""id"": ""63da699e-b354-4e63-b0f8-26fb92abea41"",
+                    ""path"": ""<Keyboard>/w"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""down"",
+                    ""id"": ""39409748-9002-4aff-9a09-cdc05b9708ad"",
+                    ""path"": ""<Keyboard>/s"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""left"",
+                    ""id"": ""0afe45fc-dc45-4310-9c73-7dc3c503addf"",
+                    ""path"": ""<Keyboard>/a"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": ""right"",
+                    ""id"": ""69fe0335-9e0c-495d-a90d-4b0fcbfd2b34"",
+                    ""path"": ""<Keyboard>/d"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""movement"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": true
+                },
+                {
+                    ""name"": """",
+                    ""id"": ""ab696218-63cd-4eb8-9fe1-48a68e32e92f"",
+                    ""path"": ""<Keyboard>/space"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""jump"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": false
+                },
+                {
+                    ""name"": """",
+                    ""id"": ""7adcb138-5175-4cc4-addc-d2b02cb5f0de"",
+                    ""path"": ""<Gamepad>/buttonSouth"",
+                    ""interactions"": """",
+                    ""processors"": """",
+                    ""groups"": ""Keyboard"",
+                    ""action"": ""jump"",
+                    ""isComposite"": false,
+                    ""isPartOfComposite"": false
+                }
+            ]
+        }
+    ],
+    ""controlSchemes"": [
+        {
+            ""name"": ""Keyboard"",
+            ""bindingGroup"": ""Keyboard"",
+            ""devices"": [
+                {
+                    ""devicePath"": ""<Keyboard>"",
+                    ""isOptional"": true,
+                    ""isOR"": false
+                },
+                {
+                    ""devicePath"": ""<Gamepad>"",
+                    ""isOptional"": true,
+                    ""isOR"": false
+                }
+            ]
+        }
+    ]
+}");
+        // Movement
+        m_Movement = asset.FindActionMap("Movement", throwIfNotFound: true);
+        m_Movement_movement = m_Movement.FindAction("movement", throwIfNotFound: true);
+        m_Movement_jump = m_Movement.FindAction("jump", throwIfNotFound: true);
+    }
+
+    public void Dispose()
+    {
+        UnityEngine.Object.Destroy(asset);
+    }
+
+    public InputBinding? bindingMask
+    {
+        get => asset.bindingMask;
+        set => asset.bindingMask = value;
+    }
+
+    public ReadOnlyArray<InputDevice>? devices
+    {
+        get => asset.devices;
+        set => asset.devices = value;
+    }
+
+    public ReadOnlyArray<InputControlScheme> controlSchemes => asset.controlSchemes;
+
+    public bool Contains(InputAction action)
+    {
+        return asset.Contains(action);
+    }
+
+    public IEnumerator<InputAction> GetEnumerator()
+    {
+        return asset.GetEnumerator();
+    }
+
+    IEnumerator IEnumerable.GetEnumerator()
+    {
+        return GetEnumerator();
+    }
+
+    public void Enable()
+    {
+        asset.Enable();
+    }
+
+    public void Disable()
+    {
+        asset.Disable();
+    }
+    public IEnumerable<InputBinding> bindings => asset.bindings;
+
+    public InputAction FindAction(string actionNameOrId, bool throwIfNotFound = false)
+    {
+        return asset.FindAction(actionNameOrId, throwIfNotFound);
+    }
+    public int FindBinding(InputBinding bindingMask, out InputAction action)
+    {
+        return asset.FindBinding(bindingMask, out action);
+    }
+
+    // Movement
+    private readonly InputActionMap m_Movement;
+    private IMovementActions m_MovementActionsCallbackInterface;
+    private readonly InputAction m_Movement_movement;
+    private readonly InputAction m_Movement_jump;
+    public struct MovementActions
+    {
+        private TestPushBlockActions m_Wrapper;
+        public MovementActions(TestPushBlockActions wrapper) { m_Wrapper = wrapper; }
+        public InputAction @movement => m_Wrapper.m_Movement_movement;
+        public InputAction @jump => m_Wrapper.m_Movement_jump;
+        public InputActionMap Get() { return m_Wrapper.m_Movement; }
+        public void Enable() { Get().Enable(); }
+        public void Disable() { Get().Disable(); }
+        public bool enabled => Get().enabled;
+        public static implicit operator InputActionMap(MovementActions set) { return set.Get(); }
+        public void SetCallbacks(IMovementActions instance)
+        {
+            if (m_Wrapper.m_MovementActionsCallbackInterface != null)
+            {
+                @movement.started -= m_Wrapper.m_MovementActionsCallbackInterface.OnMovement;
+                @movement.performed -= m_Wrapper.m_MovementActionsCallbackInterface.OnMovement;
+                @movement.canceled -= m_Wrapper.m_MovementActionsCallbackInterface.OnMovement;
+                @jump.started -= m_Wrapper.m_MovementActionsCallbackInterface.OnJump;
+                @jump.performed -= m_Wrapper.m_MovementActionsCallbackInterface.OnJump;
+                @jump.canceled -= m_Wrapper.m_MovementActionsCallbackInterface.OnJump;
+            }
+            m_Wrapper.m_MovementActionsCallbackInterface = instance;
+            if (instance != null)
+            {
+                @movement.started += instance.OnMovement;
+                @movement.performed += instance.OnMovement;
+                @movement.canceled += instance.OnMovement;
+                @jump.started += instance.OnJump;
+                @jump.performed += instance.OnJump;
+                @jump.canceled += instance.OnJump;
+            }
+        }
+    }
+    public MovementActions @Movement => new MovementActions(this);
+    private int m_KeyboardSchemeIndex = -1;
+    public InputControlScheme KeyboardScheme
+    {
+        get
+        {
+            if (m_KeyboardSchemeIndex == -1) m_KeyboardSchemeIndex = asset.FindControlSchemeIndex("Keyboard");
+            return asset.controlSchemes[m_KeyboardSchemeIndex];
+        }
+    }
+    public interface IMovementActions
+    {
+        void OnMovement(InputAction.CallbackContext context);
+        void OnJump(InputAction.CallbackContext context);
+    }
+}
+#endif // MLA_INPUT_TESTS
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..18f5eb644ad3221fe30691923dec7407c21eb7a9
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/TestPushBlockActions.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 87c358a50faa48c1bba3e5f48a5cb75d
+timeCreated: 1613172830
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..7c74185ac98fe1cac20e28a5ea098b3e48e0e40d
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef
@@ -0,0 +1,31 @@
+{
+    "name": "Unity.ML-Agents.Extensions.Input.Tests.Runtime",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.ML-Agents.Extensions.Input",
+        "Unity.InputSystem.TestFramework",
+        "UnityEngine.TestRunner",
+        "UnityEditor.TestRunner",
+        "Unity.InputSystem",
+        "Unity.Barracuda"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "nunit.framework.dll"
+    ],
+    "autoReferenced": true,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ],
+    "versionDefines": [
+        {
+            "name": "com.unity.inputsystem",
+            "expression": "1.1.0-preview.3",
+            "define": "MLA_INPUT_TESTS"
+        }
+    ],
+    "noEngineReferences": false
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..874e8ded0ea92f1b62393f064cca92a8acb4711c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: ca257bdcba9544f71baf0c291c36b05a
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs b/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4065dce1dc7b2d136dba5c15e33785811aff4f00
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs
@@ -0,0 +1,13 @@
+using NUnit.Framework;
+
+namespace Unity.MLAgents.Extensions.Tests
+{
+    internal class RuntimeExampleTest
+    {
+        [Test]
+        public void RuntimeTestMath()
+        {
+            Assert.AreEqual(2, 1 + 1);
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0d17785136ee54f32f33e049de71512d071179b7
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/RuntimeExampleTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: be1c02c37007345aa8ad2e44603852b2
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b3ae6c4f70092e54ead01455e92e78a699636691
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 88803e617d2d429aa4838bdaaeb64bac
+timeCreated: 1616140923
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d7dada005e841a965f5da2a8bd945373533ac90b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs
@@ -0,0 +1,63 @@
+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class ArticulationBodyPoseExtractorTests
+    {
+        [TearDown]
+        public void RemoveGameObjects()
+        {
+            var objects = GameObject.FindObjectsOfType<GameObject>();
+            foreach (var o in objects)
+            {
+                UnityEngine.Object.DestroyImmediate(o);
+            }
+        }
+
+        [Test]
+        public void TestNullRoot()
+        {
+            var poseExtractor = new ArticulationBodyPoseExtractor(null);
+            // These should be no-ops
+            poseExtractor.UpdateLocalSpacePoses();
+            poseExtractor.UpdateModelSpacePoses();
+
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestSingleBody()
+        {
+            var go = new GameObject();
+            var rootArticBody = go.AddComponent<ArticulationBody>();
+            var poseExtractor = new ArticulationBodyPoseExtractor(rootArticBody);
+            Assert.AreEqual(1, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestTwoBodies()
+        {
+            // * rootObj
+            //   - rootArticBody
+            //   * leafGameObj
+            //     - leafArticBody
+            var rootObj = new GameObject();
+            var rootArticBody = rootObj.AddComponent<ArticulationBody>();
+
+            var leafGameObj = new GameObject();
+            var leafArticBody = leafGameObj.AddComponent<ArticulationBody>();
+            leafGameObj.transform.SetParent(rootObj.transform);
+
+            leafArticBody.jointType = ArticulationJointType.RevoluteJoint;
+
+            var poseExtractor = new ArticulationBodyPoseExtractor(rootArticBody);
+            Assert.AreEqual(2, poseExtractor.NumPoses);
+            Assert.AreEqual(-1, poseExtractor.GetParentIndex(0));
+            Assert.AreEqual(0, poseExtractor.GetParentIndex(1));
+        }
+    }
+}
+#endif
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a23b4edb891795d6d14d5c221d763bca22f529ec
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodyPoseExtractorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e8d41ed0d5b594ef0813fa0935e20b7e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..322f934f74d2e13e937e662a1954896fda42d985
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs
@@ -0,0 +1,139 @@
+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class ArticulationBodySensorTests
+    {
+        [Test]
+        public void TestNullRootBody()
+        {
+            var gameObj = new GameObject();
+
+            var sensorComponent = gameObj.AddComponent<ArticulationBodySensorComponent>();
+            var sensor = sensorComponent.CreateSensors()[0];
+            SensorTestHelper.CompareObservation(sensor, new float[0]);
+        }
+
+        [Test]
+        public void TestSingleBody()
+        {
+            var gameObj = new GameObject();
+            var articulationBody = gameObj.AddComponent<ArticulationBody>();
+            var sensorComponent = gameObj.AddComponent<ArticulationBodySensorComponent>();
+            sensorComponent.RootBody = articulationBody;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceLinearVelocity = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceRotations = true
+            };
+
+            var sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+            var expected = new[]
+            {
+                0f, 0f, 0f, // ModelSpaceLinearVelocity
+                0f, 0f, 0f, // LocalSpaceTranslations
+                0f, 0f, 0f, 1f // LocalSpaceRotations
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+        }
+
+        [Test]
+        public void TestBodiesWithJoint()
+        {
+            var rootObj = new GameObject();
+            var rootArticBody = rootObj.AddComponent<ArticulationBody>();
+
+            var middleGamObj = new GameObject();
+            var middleArticBody = middleGamObj.AddComponent<ArticulationBody>();
+            middleArticBody.AddForce(new Vector3(0f, 1f, 0f));
+            middleGamObj.transform.SetParent(rootObj.transform);
+            middleGamObj.transform.localPosition = new Vector3(13.37f, 0f, 0f);
+            middleArticBody.jointType = ArticulationJointType.RevoluteJoint;
+
+            var leafGameObj = new GameObject();
+            var leafArticBody = leafGameObj.AddComponent<ArticulationBody>();
+            leafGameObj.transform.SetParent(middleGamObj.transform);
+            leafGameObj.transform.localPosition = new Vector3(4.2f, 0f, 0f);
+            leafArticBody.jointType = ArticulationJointType.PrismaticJoint;
+            leafArticBody.linearLockZ = ArticulationDofLock.LimitedMotion;
+            leafArticBody.zDrive = new ArticulationDrive
+            {
+                lowerLimit = -3,
+                upperLimit = 1
+            };
+
+
+#if UNITY_2020_2_OR_NEWER
+            // ArticulationBody.velocity is read-only in 2020.1
+            rootArticBody.velocity = new Vector3(1f, 0f, 0f);
+            middleArticBody.velocity = new Vector3(0f, 1f, 0f);
+            leafArticBody.velocity = new Vector3(0f, 0f, 1f);
+#endif
+
+            var sensorComponent = rootObj.AddComponent<ArticulationBodySensorComponent>();
+            sensorComponent.RootBody = rootArticBody;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseLocalSpaceTranslations = true,
+#if UNITY_2020_2_OR_NEWER
+                UseLocalSpaceLinearVelocity = true
+#endif
+            };
+
+            var sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+            var expected = new[]
+            {
+                // Model space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Middle pos
+                leafGameObj.transform.position.x, 0f, 0f, // Leaf pos
+
+                // Local space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Attached pos
+                4.2f, 0f, 0f, // Leaf pos
+
+#if UNITY_2020_2_OR_NEWER
+                0f, 0f, 0f, // Root vel
+                -1f, 1f, 0f, // Attached vel
+                0f, -1f, 1f // Leaf vel
+#endif
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+
+            // Update the settings to only process joint observations
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseJointForces = true,
+                UseJointPositionsAndAngles = true,
+            };
+
+            sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+
+            expected = new[]
+            {
+                // revolute
+                0f, 1f, // joint1.position (sin and cos)
+                0f, // joint1.force
+
+                // prismatic
+                0.5f, // joint2.position (interpolate between limits)
+                0f, // joint2.force
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+        }
+    }
+}
+#endif // #if UNITY_2020_1_OR_NEWER
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8f5dfaeb12a7ed92637edc6dc48674c8eb6e0ad8
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/ArticulationBodySensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: bef5eb8df11b549c6a25d698a47ff428
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0cd831e21fe104aa363900304ffe83b48f46ad63
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs
@@ -0,0 +1,3 @@
+using System.Runtime.CompilerServices;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.EditorTests")]
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5eb7568b6e6dd6461e65234de64d3627490f9c2f
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/AssemblyInfo.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 60e8a1df689a49cc9f7dca42c93a8100
+timeCreated: 1616141554
\ No newline at end of file
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4e2054358ba74c25cddd1429650fe483dada3baf
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs
@@ -0,0 +1,145 @@
+using System;
+using System.Collections;
+using System.Linq;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEngine.TestTools;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Extensions.Sensors;
+using Object = UnityEngine.Object;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class CountingGridSensorTests
+    {
+        GameObject testGo;
+        GameObject boxGo;
+        TestCountingGridSensorComponent gridSensorComponent;
+
+        // Use built-in tags
+        const string k_Tag1 = "Player";
+        const string k_Tag2 = "Respawn";
+
+        [UnitySetUp]
+        public IEnumerator SetupScene()
+        {
+            testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            gridSensorComponent = testGo.AddComponent<TestCountingGridSensorComponent>();
+
+            boxGo = new GameObject("block");
+            boxGo.tag = k_Tag1;
+            boxGo.transform.position = new Vector3(3f, 0f, 3f);
+            boxGo.AddComponent<BoxCollider>();
+
+            yield return null;
+        }
+
+        [TearDown]
+        public void ClearScene()
+        {
+            Object.DestroyImmediate(boxGo);
+            Object.DestroyImmediate(testGo);
+        }
+
+        public class TestCountingGridSensorComponent : GridSensorComponent
+        {
+            public void SetParameters(string[] detectableTags)
+            {
+                DetectableTags = detectableTags;
+                CellScale = new Vector3(1, 0.01f, 1);
+                GridSize = new Vector3Int(10, 1, 10);
+                ColliderMask = LayerMask.GetMask("Default");
+                RotateWithAgent = false;
+                CompressionType = SensorCompressionType.None;
+            }
+
+            protected override GridSensorBase[] GetGridSensors()
+            {
+                return new GridSensorBase[]
+                {
+                    new CountingGridSensor(
+                        "TestSensor",
+                        CellScale,
+                        GridSize,
+                        DetectableTags,
+                        CompressionType)
+                };
+            }
+        }
+
+        // Copied from GridSensorTests in main package
+        public static float[][] DuplicateArray(float[] array, int numCopies)
+        {
+            float[][] duplicated = new float[numCopies][];
+            for (int i = 0; i < numCopies; i++)
+            {
+                duplicated[i] = array;
+            }
+            return duplicated;
+        }
+
+        // Copied from GridSensorTests in main package
+        public static void AssertSubarraysAtIndex(float[] total, int[] indicies, float[][] expectedArrays, float[] expectedDefaultArray)
+        {
+            int totalIndex = 0;
+            int subIndex = 0;
+            int subarrayIndex = 0;
+            int lenOfData = expectedDefaultArray.Length;
+            int numArrays = total.Length / lenOfData;
+            for (int i = 0; i < numArrays; i++)
+            {
+                totalIndex = i * lenOfData;
+
+                if (indicies.Contains(i))
+                {
+                    subarrayIndex = Array.IndexOf(indicies, i);
+                    for (subIndex = 0; subIndex < lenOfData; subIndex++)
+                    {
+                        Assert.AreEqual(expectedArrays[subarrayIndex][subIndex], total[totalIndex],
+                            "Expected " + expectedArrays[subarrayIndex][subIndex] + " at subarray index " + totalIndex + ", index = " + subIndex + " but was " + total[totalIndex]);
+                        totalIndex++;
+                    }
+                }
+                else
+                {
+                    for (subIndex = 0; subIndex < lenOfData; subIndex++)
+                    {
+                        Assert.AreEqual(expectedDefaultArray[subIndex], total[totalIndex],
+                            "Expected default value " + expectedDefaultArray[subIndex] + " at subarray index " + totalIndex + ", index = " + subIndex + " but was " + total[totalIndex]);
+                        totalIndex++;
+                    }
+                }
+            }
+        }
+
+        [Test]
+        public void TestCountingSensor()
+        {
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetParameters(tags);
+            var gridSensor = (CountingGridSensor)gridSensorComponent.CreateSensors()[0];
+            Assert.AreEqual(gridSensor.PerceptionBuffer.Length, 10 * 10 * 2);
+
+            gridSensor.Update();
+
+            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
+            float[][] expectedSubarrays = DuplicateArray(new float[] { 1, 0 }, 4);
+            float[] expectedDefault = new float[] { 0, 0 };
+            AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+
+            var boxGo2 = new GameObject("block");
+            boxGo2.tag = k_Tag1;
+            boxGo2.transform.position = new Vector3(3.1f, 0f, 3f);
+            boxGo2.AddComponent<BoxCollider>();
+
+            gridSensor.Update();
+
+            subarrayIndicies = new[] { 77, 78, 87, 88 };
+            expectedSubarrays = DuplicateArray(new float[] { 2, 0 }, 4);
+            expectedDefault = new float[] { 0, 0 };
+            AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+            Object.DestroyImmediate(boxGo2);
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f1a711faf830327dfe3a13ec20188659db7e098f
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/CountingGridSensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2a1d17f91519347e0a8692e2816b7c8b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7aa8528a972078ed821fb321f7a8beaae17be68b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs
@@ -0,0 +1,243 @@
+using System;
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class PoseExtractorTests
+    {
+        class BasicPoseExtractor : PoseExtractor
+        {
+            protected internal override Pose GetPoseAt(int index)
+            {
+                return Pose.identity;
+            }
+
+            protected internal override Vector3 GetLinearVelocityAt(int index)
+            {
+                return Vector3.zero;
+            }
+        }
+
+        class UselessPoseExtractor : BasicPoseExtractor
+        {
+            public void Init(int[] parentIndices)
+            {
+                Setup(parentIndices);
+            }
+        }
+
+        [Test]
+        public void TestEmptyExtractor()
+        {
+            var poseExtractor = new UselessPoseExtractor();
+
+            // These should be no-ops
+            poseExtractor.UpdateLocalSpacePoses();
+            poseExtractor.UpdateModelSpacePoses();
+
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+
+            // Iterating through poses and velocities should be an empty loop
+            foreach (var pose in poseExtractor.GetEnabledModelSpacePoses())
+            {
+                throw new UnityAgentsException("This shouldn't happen");
+            }
+
+            foreach (var pose in poseExtractor.GetEnabledLocalSpacePoses())
+            {
+                throw new UnityAgentsException("This shouldn't happen");
+            }
+
+            foreach (var vel in poseExtractor.GetEnabledModelSpaceVelocities())
+            {
+                throw new UnityAgentsException("This shouldn't happen");
+            }
+
+            foreach (var vel in poseExtractor.GetEnabledLocalSpaceVelocities())
+            {
+                throw new UnityAgentsException("This shouldn't happen");
+            }
+
+            // Getting a parent index should throw an index exception
+            Assert.Throws<NullReferenceException>(
+                () => poseExtractor.GetParentIndex(0)
+            );
+
+            // DisplayNodes should be empty
+            var displayNodes = poseExtractor.GetDisplayNodes();
+            Assert.AreEqual(0, displayNodes.Count);
+        }
+
+        [Test]
+        public void TestSimpleExtractor()
+        {
+            var poseExtractor = new UselessPoseExtractor();
+            var parentIndices = new[] { -1, 0 };
+            poseExtractor.Init(parentIndices);
+            Assert.AreEqual(2, poseExtractor.NumPoses);
+        }
+
+        /// <summary>
+        /// A simple "chain" hierarchy, where each object is parented to the one before it.
+        ///   0 <- 1 <- 2 <- ...
+        /// </summary>
+        class ChainPoseExtractor : PoseExtractor
+        {
+            public Vector3 offset;
+            public ChainPoseExtractor(int size)
+            {
+                var parents = new int[size];
+                for (var i = 0; i < size; i++)
+                {
+                    parents[i] = i - 1;
+                }
+                Setup(parents);
+            }
+
+            protected internal override Pose GetPoseAt(int index)
+            {
+                var rotation = Quaternion.identity;
+                var translation = offset + new Vector3(index, index, index);
+                return new Pose
+                {
+                    rotation = rotation,
+                    position = translation
+                };
+            }
+
+            protected internal override Vector3 GetLinearVelocityAt(int index)
+            {
+                return Vector3.zero;
+            }
+        }
+
+        [Test]
+        public void TestChain()
+        {
+            var size = 4;
+            var chain = new ChainPoseExtractor(size);
+            chain.offset = new Vector3(.5f, .75f, .333f);
+
+            chain.UpdateModelSpacePoses();
+            chain.UpdateLocalSpacePoses();
+
+
+            var modelPoseIndex = 0;
+            foreach (var modelSpace in chain.GetEnabledModelSpacePoses())
+            {
+                if (modelPoseIndex == 0)
+                {
+                    // Root transforms are currently always the identity.
+                    Assert.IsTrue(modelSpace == Pose.identity);
+                }
+                else
+                {
+                    var expectedModelTranslation = new Vector3(modelPoseIndex, modelPoseIndex, modelPoseIndex);
+                    Assert.IsTrue(expectedModelTranslation == modelSpace.position);
+                }
+                modelPoseIndex++;
+            }
+            Assert.AreEqual(size, modelPoseIndex);
+
+            var localPoseIndex = 0;
+            foreach (var localSpace in chain.GetEnabledLocalSpacePoses())
+            {
+                if (localPoseIndex == 0)
+                {
+                    // Root transforms are currently always the identity.
+                    Assert.IsTrue(localSpace == Pose.identity);
+                }
+                else
+                {
+                    var expectedLocalTranslation = new Vector3(1, 1, 1);
+                    Assert.IsTrue(expectedLocalTranslation == localSpace.position, $"{expectedLocalTranslation} != {localSpace.position}");
+                }
+
+                localPoseIndex++;
+            }
+            Assert.AreEqual(size, localPoseIndex);
+        }
+
+        [Test]
+        public void TestChainDisplayNodes()
+        {
+            var size = 4;
+            var chain = new ChainPoseExtractor(size);
+
+            var displayNodes = chain.GetDisplayNodes();
+            Assert.AreEqual(size, displayNodes.Count);
+
+            for (var i = 0; i < size; i++)
+            {
+                var displayNode = displayNodes[i];
+                Assert.AreEqual(i, displayNode.OriginalIndex);
+                Assert.AreEqual(null, displayNode.NodeObject);
+                Assert.AreEqual(i, displayNode.Depth);
+                Assert.AreEqual(true, displayNode.Enabled);
+            }
+        }
+
+        [Test]
+        public void TestDisplayNodesLoop()
+        {
+            // Degenerate case with a loop
+            var poseExtractor = new UselessPoseExtractor();
+            poseExtractor.Init(new[] { -1, 2, 1 });
+
+            // This just shouldn't blow up
+            poseExtractor.GetDisplayNodes();
+
+            // Self-loop
+            poseExtractor.Init(new[] { -1, 1 });
+
+            // This just shouldn't blow up
+            poseExtractor.GetDisplayNodes();
+        }
+
+        class BadPoseExtractor : BasicPoseExtractor
+        {
+            public BadPoseExtractor()
+            {
+                var size = 2;
+                var parents = new int[size];
+                // Parents are intentionally invalid - expect -1 at root
+                for (var i = 0; i < size; i++)
+                {
+                    parents[i] = i;
+                }
+                Setup(parents);
+            }
+        }
+
+        [Test]
+        public void TestExpectedRoot()
+        {
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                var unused = new BadPoseExtractor();
+            });
+        }
+    }
+
+    public class PoseExtensionTests
+    {
+        [Test]
+        public void TestInverse()
+        {
+            Pose t = new Pose
+            {
+                rotation = Quaternion.AngleAxis(23.0f, new Vector3(1, 1, 1).normalized),
+                position = new Vector3(-1.0f, 2.0f, 3.0f)
+            };
+
+            var inverseT = t.Inverse();
+            var product = inverseT.Multiply(t);
+            Assert.IsTrue(Vector3.zero == product.position);
+            Assert.IsTrue(Quaternion.identity == product.rotation);
+
+            Assert.IsTrue(Pose.identity == product);
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ecf5b74dae9b9a892327271855e217a7ecf8b859
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/PoseExtractorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e228666ae564440a3afb6fb5afddd921
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a8a378beb00b23e2f6dc79699c4a387eddd4416c
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs
@@ -0,0 +1,183 @@
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class RigidBodyPoseExtractorTests
+    {
+        [TearDown]
+        public void RemoveGameObjects()
+        {
+            var objects = GameObject.FindObjectsOfType<GameObject>();
+            foreach (var o in objects)
+            {
+                UnityEngine.Object.DestroyImmediate(o);
+            }
+        }
+
+        [Test]
+        public void TestNullRoot()
+        {
+            var poseExtractor = new RigidBodyPoseExtractor(null);
+            // These should be no-ops
+            poseExtractor.UpdateLocalSpacePoses();
+            poseExtractor.UpdateModelSpacePoses();
+
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestSingleBody()
+        {
+            var go = new GameObject();
+            var rootRb = go.AddComponent<Rigidbody>();
+            var poseExtractor = new RigidBodyPoseExtractor(rootRb);
+            Assert.AreEqual(1, poseExtractor.NumPoses);
+
+            // Also pass the GameObject
+            poseExtractor = new RigidBodyPoseExtractor(rootRb, go);
+            Assert.AreEqual(1, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestNoBodiesFound()
+        {
+            // Check that if we can't find any bodies under the game object, we get an empty extractor
+            var gameObj = new GameObject();
+            var rootRb = gameObj.AddComponent<Rigidbody>();
+            var otherGameObj = new GameObject();
+            var poseExtractor = new RigidBodyPoseExtractor(rootRb, otherGameObj);
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+
+            // Add an RB under the other GameObject. Constructor will find a rigid body, but not the root.
+            otherGameObj.AddComponent<Rigidbody>();
+            poseExtractor = new RigidBodyPoseExtractor(rootRb, otherGameObj);
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestTwoBodies()
+        {
+            // * rootObj
+            //   - rb1
+            //   * go2
+            //     - rb2
+            //     - joint
+            var rootObj = new GameObject();
+            var rb1 = rootObj.AddComponent<Rigidbody>();
+
+            var go2 = new GameObject();
+            var rb2 = go2.AddComponent<Rigidbody>();
+            go2.transform.SetParent(rootObj.transform);
+
+            var joint = go2.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rb1;
+
+            var poseExtractor = new RigidBodyPoseExtractor(rb1);
+            Assert.AreEqual(2, poseExtractor.NumPoses);
+
+            rb1.position = new Vector3(1, 0, 0);
+            rb1.rotation = Quaternion.Euler(0, 13.37f, 0);
+            rb1.velocity = new Vector3(2, 0, 0);
+
+            Assert.AreEqual(rb1.position, poseExtractor.GetPoseAt(0).position);
+            Assert.IsTrue(rb1.rotation == poseExtractor.GetPoseAt(0).rotation);
+            Assert.AreEqual(rb1.velocity, poseExtractor.GetLinearVelocityAt(0));
+
+            // Check DisplayNodes gives expected results
+            var displayNodes = poseExtractor.GetDisplayNodes();
+            Assert.AreEqual(2, displayNodes.Count);
+            Assert.AreEqual(rb1, displayNodes[0].NodeObject);
+            Assert.AreEqual(false, displayNodes[0].Enabled);
+
+            Assert.AreEqual(rb2, displayNodes[1].NodeObject);
+            Assert.AreEqual(true, displayNodes[1].Enabled);
+        }
+
+        [Test]
+        public void TestTwoBodiesVirtualRoot()
+        {
+            // * virtualRoot
+            // * rootObj
+            //   - rb1
+            //   * go2
+            //     - rb2
+            //     - joint
+            var virtualRoot = new GameObject("I am vroot");
+
+            var rootObj = new GameObject();
+            var rb1 = rootObj.AddComponent<Rigidbody>();
+
+            var go2 = new GameObject();
+            go2.AddComponent<Rigidbody>();
+            go2.transform.SetParent(rootObj.transform);
+
+            var joint = go2.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rb1;
+
+            var poseExtractor = new RigidBodyPoseExtractor(rb1, null, virtualRoot);
+            Assert.AreEqual(3, poseExtractor.NumPoses);
+
+            // "body" 0 has no parent
+            Assert.AreEqual(-1, poseExtractor.GetParentIndex(0));
+
+            // body 1 has parent 0
+            Assert.AreEqual(0, poseExtractor.GetParentIndex(1));
+
+            var virtualRootPos = new Vector3(0, 2, 0);
+            var virtualRootRot = Quaternion.Euler(0, 42, 0);
+            virtualRoot.transform.position = virtualRootPos;
+            virtualRoot.transform.rotation = virtualRootRot;
+
+            Assert.AreEqual(virtualRootPos, poseExtractor.GetPoseAt(0).position);
+            Assert.IsTrue(virtualRootRot == poseExtractor.GetPoseAt(0).rotation);
+            Assert.AreEqual(Vector3.zero, poseExtractor.GetLinearVelocityAt(0));
+
+            // Same as above test, but using index 1
+            rb1.position = new Vector3(1, 0, 0);
+            rb1.rotation = Quaternion.Euler(0, 13.37f, 0);
+            rb1.velocity = new Vector3(2, 0, 0);
+
+            Assert.AreEqual(rb1.position, poseExtractor.GetPoseAt(1).position);
+            Assert.IsTrue(rb1.rotation == poseExtractor.GetPoseAt(1).rotation);
+            Assert.AreEqual(rb1.velocity, poseExtractor.GetLinearVelocityAt(1));
+        }
+
+        [Test]
+        public void TestBodyPosesEnabledDictionary()
+        {
+            // * rootObj
+            //   - rb1
+            //   * go2
+            //     - rb2
+            //     - joint
+            var rootObj = new GameObject();
+            var rb1 = rootObj.AddComponent<Rigidbody>();
+
+            var go2 = new GameObject();
+            var rb2 = go2.AddComponent<Rigidbody>();
+            go2.transform.SetParent(rootObj.transform);
+
+            var joint = go2.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rb1;
+
+            var poseExtractor = new RigidBodyPoseExtractor(rb1);
+
+            // Expect the root body disabled and the attached one enabled.
+            Assert.IsFalse(poseExtractor.IsPoseEnabled(0));
+            Assert.IsTrue(poseExtractor.IsPoseEnabled(1));
+            var bodyPosesEnabled = poseExtractor.GetBodyPosesEnabled();
+            Assert.IsFalse(bodyPosesEnabled[rb1]);
+            Assert.IsTrue(bodyPosesEnabled[rb2]);
+
+            // Swap the values
+            bodyPosesEnabled[rb1] = true;
+            bodyPosesEnabled[rb2] = false;
+
+            var poseExtractor2 = new RigidBodyPoseExtractor(rb1, null, null, bodyPosesEnabled);
+            Assert.IsTrue(poseExtractor2.IsPoseEnabled(0));
+            Assert.IsFalse(poseExtractor2.IsPoseEnabled(1));
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8d26a9ce5d6d84227464cf02c5e5b88a33f4545a
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodyPoseExtractorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: f83546d14c62641b9a2d88f066d120c7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f5b86e99dedd3db6fb755bd9a532ab3adce12af0
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs
@@ -0,0 +1,143 @@
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public static class SensorTestHelper
+    {
+        public static void CompareObservation(ISensor sensor, float[] expected)
+        {
+            string errorMessage;
+            bool isOk = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
+            Assert.IsTrue(isOk, errorMessage);
+        }
+
+        public static void CompareObservation(ISensor sensor, float[,,] expected)
+        {
+            string errorMessage;
+            bool isOk = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
+            Assert.IsTrue(isOk, errorMessage);
+        }
+    }
+
+    public class RigidBodySensorTests
+    {
+        [Test]
+        public void TestNullRootBody()
+        {
+            var gameObj = new GameObject();
+
+            var sensorComponent = gameObj.AddComponent<RigidBodySensorComponent>();
+            Assert.IsFalse(sensorComponent.IsTrivial());
+            var sensor = sensorComponent.CreateSensors()[0];
+            SensorTestHelper.CompareObservation(sensor, new float[0]);
+        }
+
+        [Test]
+        public void TestSingleRigidbody()
+        {
+            var gameObj = new GameObject();
+            var rootRb = gameObj.AddComponent<Rigidbody>();
+            var sensorComponent = gameObj.AddComponent<RigidBodySensorComponent>();
+            sensorComponent.RootBody = rootRb;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceLinearVelocity = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceRotations = true
+            };
+            Assert.IsTrue(sensorComponent.IsTrivial());
+
+            var sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+
+            // The root body is ignored since it always generates identity values
+            // and there are no other bodies to generate observations.
+            var expected = new float[0];
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+            SensorTestHelper.CompareObservation(sensor, expected);
+        }
+
+        [Test]
+        public void TestBodiesWithJoint()
+        {
+            var rootObj = new GameObject();
+            var rootRb = rootObj.AddComponent<Rigidbody>();
+            rootRb.velocity = new Vector3(1f, 0f, 0f);
+
+            var middleGamObj = new GameObject();
+            var middleRb = middleGamObj.AddComponent<Rigidbody>();
+            middleRb.velocity = new Vector3(0f, 1f, 0f);
+            middleGamObj.transform.SetParent(rootObj.transform);
+            middleGamObj.transform.localPosition = new Vector3(13.37f, 0f, 0f);
+            var joint = middleGamObj.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rootRb;
+
+            var leafGameObj = new GameObject();
+            var leafRb = leafGameObj.AddComponent<Rigidbody>();
+            leafRb.velocity = new Vector3(0f, 0f, 1f);
+            leafGameObj.transform.SetParent(middleGamObj.transform);
+            leafGameObj.transform.localPosition = new Vector3(4.2f, 0f, 0f);
+            var joint2 = leafGameObj.AddComponent<ConfigurableJoint>();
+            joint2.connectedBody = middleRb;
+
+            var virtualRoot = new GameObject();
+
+            var sensorComponent = rootObj.AddComponent<RigidBodySensorComponent>();
+            sensorComponent.RootBody = rootRb;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceLinearVelocity = true
+            };
+            sensorComponent.VirtualRoot = virtualRoot;
+            Assert.IsFalse(sensorComponent.IsTrivial());
+
+            var sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+
+            // Note that the VirtualRoot is ignored from the observations
+            var expected = new[]
+            {
+                // Model space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Middle pos
+                leafGameObj.transform.position.x, 0f, 0f, // Leaf pos
+
+                // Local space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Attached pos
+                4.2f, 0f, 0f, // Leaf pos
+
+                1f, 0f, 0f, // Root vel (relative to virtual root)
+                -1f, 1f, 0f, // Attached vel
+                0f, -1f, 1f // Leaf vel
+            };
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+            SensorTestHelper.CompareObservation(sensor, expected);
+
+            // Update the settings to only process joint observations
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseJointPositionsAndAngles = true,
+                UseJointForces = true,
+            };
+
+            sensor = sensorComponent.CreateSensors()[0];
+            sensor.Update();
+
+            expected = new[]
+            {
+                0f, 0f, 0f, // joint1.force
+                0f, 0f, 0f, // joint1.torque
+                0f, 0f, 0f, // joint2.force
+                0f, 0f, 0f, // joint2.torque
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensor.GetObservationSpec().Shape[0]);
+        }
+    }
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6837d9c5492533d8adee43f5b068ac2ed3c9029b
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Sensors/RigidBodySensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8b8b560acc80a4ddab0239d0535d5324
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef b/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..14da330bec5abce4818001e0adcd723eed79cc99
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef
@@ -0,0 +1,12 @@
+{
+    "name": "Unity.ML-Agents.Extensions.Tests",
+    "references": [
+        "Unity.ML-Agents.Extensions",
+        "Unity.ML-Agents"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": []
+}
diff --git a/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef.meta b/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..05ab872e43836d315f3df186294dd47afdbfb9e0
--- /dev/null
+++ b/com.unity.ml-agents.extensions/Tests/Runtime/Unity.ML-Agents.Extensions.Tests.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 30cbc899aa9234b7c93bfcba45275b9c
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents.extensions/package.json b/com.unity.ml-agents.extensions/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..26bea420d3090e8a414799815d27ddabe46a6e61
--- /dev/null
+++ b/com.unity.ml-agents.extensions/package.json
@@ -0,0 +1,11 @@
+{
+  "name": "com.unity.ml-agents.extensions",
+  "displayName": "ML Agents Extensions",
+  "version": "0.6.1-preview",
+  "unity": "2022.3",
+  "description": "A source-only package for new features based on ML-Agents",
+  "dependencies": {
+    "com.unity.ml-agents": "2.3.0-exp.4",
+    "com.unity.modules.physics": "1.0.0"
+  }
+}
diff --git a/com.unity.ml-agents.extensions/package.json.meta b/com.unity.ml-agents.extensions/package.json.meta
new file mode 100644
index 0000000000000000000000000000000000000000..375a7c7db086dda302fd07bff466a1d9a1e8ccda
--- /dev/null
+++ b/com.unity.ml-agents.extensions/package.json.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: a12a065df9b6f44d0b9a8e85d08d1cef
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/.gitignore b/com.unity.ml-agents/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b40e78e61a6a1c835a4116528a96a615f72fd0f2
--- /dev/null
+++ b/com.unity.ml-agents/.gitignore
@@ -0,0 +1,30 @@
+artifacts/**
+build/**
+.build_script/**
+node_modules/**
+.DS_Store
+.npmrc
+!Documentation~
+!.Documentation
+npm-debug.log
+build.sh.meta
+build.bat.meta
+.idea/
+!Samples/*/*.unitypackage
+
+/[Ll]ibrary/
+/Logs/
+/[Tt]emp/
+/[Oo]bj/
+/[Bb]uild/
+/[Bb]uilds/
+/Assets/AssetStoreTools*
+/Assets/Plugins*
+/Assets/Demonstrations*
+/csharp_timers.json
+
+# Visual Studio 2015 cache directory
+/.vs/
+
+*.api
+*.api.meta
diff --git a/com.unity.ml-agents/.npmignore b/com.unity.ml-agents/.npmignore
new file mode 100644
index 0000000000000000000000000000000000000000..0f2d8d322c2f837b95765e290e6d2cea98e848d5
--- /dev/null
+++ b/com.unity.ml-agents/.npmignore
@@ -0,0 +1,20 @@
+artifacts/**
+build/**
+.build_script/**
+node_modules/**
+Documentation/ApiDocs/**
+Documentation~/ApiDocs/**
+.DS_Store
+.npmrc
+.npmignore
+.gitignore
+CONTRIBUTING.md
+CONTRIBUTING.md.meta
+QAReport.md
+QAReport.md.meta
+.gitlab-ci.yml
+build.sh
+build.sh.meta
+build.bat
+build.bat.meta
+upm-ci.log
diff --git a/com.unity.ml-agents/CHANGELOG.md b/com.unity.ml-agents/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..ca528f4d1200b7e057206edff9feb5b1f2072e07
--- /dev/null
+++ b/com.unity.ml-agents/CHANGELOG.md
@@ -0,0 +1,924 @@
+# Changelog
+
+All notable changes to this package will be documented in this file.
+
+The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
+and this project adheres to
+[Semantic Versioning](http://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+### Major Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The minimum supported Unity version was updated to 2022.3. (#)
+- Added batched raycast sensor option. (#)
+
+#### ml-agents / ml-agents-envs
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Added DecisionStep parameter to DecisionRequester (#)
+  - This will allow the staggering of execution timing when using multi-agents, leading to more stable performance.
+
+#### ml-agents / ml-agents-envs
+- Added training config feature to evenly distribute checkpoints throughout training. (#5842)
+- Updated training area replicator to add a condition to only replicate training areas when running a build. (#5842)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### ml-agents / ml-agents-envs
+
+
+## [2.3.0-exp.3] - 2022-11-21
+### Major Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The minimum supported Unity version was updated to 2021.3. (#)
+
+#### ml-agents / ml-agents-envs
+- Add your trainers to the package using Ml-Agents Custom Trainers plugin. (#)
+  - ML-Agents Custom Trainers plugin is an extensible plugin system to define new trainers based on the
+  High level trainer API, read more [here](../docs/Python-Custom-Trainer-Plugin.md).
+- Refactored core modules to make ML-Agents internal classes more generalizable to various RL algorithms. (#)
+- The minimum supported Python version for ML-agents has changed to 3.8.13. (#)
+- The minimum supported version of PyTorch was changed to 1.8.0. (#)
+- Add shared critic configurability for PPO. (#)
+- We moved `UnityToGymWrapper` and `PettingZoo` API to `ml-agents-envs` package. All these environments will be
+versioned under `ml-agents-envs` package in the future (#)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Added switch to RayPerceptionSensor to allow rays to be ordered left to right. (#26)
+	- Current alternating order is still the default but will be deprecated.
+- Added support for enabling/disabling camera object attached to camera sensor in order to improve performance. (#31)
+
+#### ml-agents / ml-agents-envs
+- Renaming the path that shadows torch with "mlagents/trainers/torch_entities" and update respective imports (#)
+
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### ml-agents / ml-agents-envs
+
+
+## [2.3.0-exp.2] - 2022-03-28
+### Major Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### ml-agents / ml-agents-envs
+- Refactored to support the new ML-Agents Pro package.
+- The minimum supported Python version for ML-Agents-envs is changed to 3.7.2 (#)
+- Added support for the PettingZoo multi-agent API (#)
+- Refactored `gym-unity` into the `ml-agents-envs` package (#)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Upgrade barracuda dependency to 3.0.0 (#)
+#### ml-agents / ml-agents-envs
+- Added the new unity_vec_env file to the ml-agents-envs module
+- Extended support to python 3.9.10
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### ml-agents / ml-agents-envs
+
+## [2.2.1-exp.1] - 2022-01-14
+### Major Changes
+
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The minimum supported Unity version was updated to 2020.3. (#5673)
+- Added a new feature to replicate training areas dynamically during runtime. (#5568)
+- Update Barracuda to 2.3.1-preview (#5591)
+- Update Input System to 1.3.0 (#5661)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+### Minor Changes
+
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Added the capacity to initialize behaviors from any checkpoint and not just the latest one (#5525)
+- Added the ability to get a read-only view of the stacked observations (#5523)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Set gym version in gym-unity to gym release 0.20.0 (#5540)
+- Added support for having `beta`, `epsilon`, and `learning rate` on separate schedules (affects only PPO and POCA). (#5538)
+- Changed default behavior to restart crashed Unity environments rather than exiting. (#5553)
+  - Rate & lifetime limits on this are configurable via 3 new yaml options
+    1. env_params.max_lifetime_restarts (--max-lifetime-restarts) [default=10]
+    2. env_params.restarts_rate_limit_n (--restarts-rate-limit-n) [default=1]
+    3. env_params.restarts_rate_limit_period_s (--restarts-rate-limit-period-s) [default=60]
+- Deterministic action selection is now supported during training and inference(#5619)
+    - Added a new `--deterministic` cli flag to deterministically select the most probable actions in policy. The same thing can
+      be achieved by adding `deterministic: true` under `network_settings` of the run options configuration.(#5597)
+    - Extra tensors are now serialized to support deterministic action selection in onnx. (#5593)
+    - Support inference with deterministic action selection in editor (#5599)
+- Added minimal analytics collection to LL-API (#5511)
+- Update Colab notebooks for GridWorld example with DQN illustrating the use of the Python API and how to export to ONNX (#5643)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Update gRPC native lib to universal for arm64 and x86_64. This change should enable ml-agents usage on mac M1 (#5283, #5519)
+- Fixed a bug where ml-agents code wouldn't compile on platforms that didn't support analytics (PS4/5, XBoxOne) (#5628)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed a bug where the critics were not being normalized during training. (#5595)
+- Fixed the bug where curriculum learning would crash because of the incorrect run_options parsing. (#5586)
+- Fixed a bug in multi-agent cooperative training where agents might not receive all of the states of
+terminated teammates. (#5441)
+- Fixed wrong attribute name in argparser for torch device option (#5433)(#5467)
+- Fixed conflicting CLI and yaml options regarding resume & initialize_from (#5495)
+- Fixed failing tests for gym-unity due to gym 0.20.0 release (#5540)
+- Fixed a bug in VAIL where the variational bottleneck was not properly passing gradients (#5546)
+- Harden user PII protection logic and extend TrainingAnalytics to expose detailed configuration parameters. (#5512)
+
+## [2.1.0-exp.1] - 2021-06-09
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- update Barracuda to 2.0.0-pre.3. (#5385)
+- Fixed NullReferenceException when adding Behavior Parameters with no Agent. (#5382)
+- Add stacking option in Editor for `VectorSensorComponent`. (#5376)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Lock cattrs dependency version to 1.6. (#5397)
+- Added a fully connected visual encoder for environments with very small image inputs. (#5351)
+- Colab notebooks illustrating the use of the Python API are now part of the repository. (#5399)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- RigidBodySensorComponent now displays a warning if it's used in a way that won't generate useful observations. (#5387)
+- Update the documentation with a note saying that `GridSensor` does not work in 2D environments. (#5396)
+- Fixed an error where sensors would not reset properly before collecting the last observation at the end of an
+episode. (#5375)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The calculation of the target entropy of SAC with continuous actions was incorrect and has been fixed. (#5372)
+- Fixed an issue where the histogram stats would not be reported correctly in TensorBoard. (#5410)
+- Fixed error when importing models which use the ResNet encoder. (#5358)
+
+
+## [2.0.0-exp.1] - 2021-04-22
+### Major Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The minimum supported Unity version was updated to 2019.4. (#5166)
+- Several breaking interface changes were made. See the
+[Migration Guide](https://github.com/Unity-Technologies/ml-agents/blob/release_17_docs/docs/Migrating.md) for more
+details.
+- Some methods previously marked as `Obsolete` have been removed. If you were using these methods, you need to replace them with their supported counterpart.
+- The interface for disabling discrete actions in `IDiscreteActionMask` has changed.
+`WriteMask(int branch, IEnumerable<int> actionIndices)` was replaced with
+`SetActionEnabled(int branch, int actionIndex, bool isEnabled)`. (#5060)
+- IActuator now implements IHeuristicProvider. (#5110)
+- `ISensor.GetObservationShape()` was removed, and `GetObservationSpec()` was added. The `ITypedSensor`
+and `IDimensionPropertiesSensor` interfaces were removed. (#5127)
+- `ISensor.GetCompressionType()` was removed, and `GetCompressionSpec()` was added. The `ISparseChannelSensor`
+interface was removed. (#5164)
+- The abstract method `SensorComponent.GetObservationShape()` was no longer being called, so it has been removed. (#5172)
+- `SensorComponent.CreateSensor()` was replaced with `SensorComponent.CreateSensors()`, which returns an `ISensor[]`. (#5181)
+- `Match3Sensor` was refactored to produce cell and special type observations separately, and `Match3SensorComponent` now
+produces two `Match3Sensor`s (unless there are no special types). Previously trained models will have different observation
+sizes and will need to be retrained. (#5181)
+- The `AbstractBoard` class for integration with Match-3 games was changed to make it easier to support boards with
+different sizes using the same model. For a summary of the interface changes, please see the Migration Guide. (##5189)
+- Updated the Barracuda package to version `1.4.0-preview`(#5236)
+- `GridSensor` has been refactored and moved to main package, with changes to both sensor interfaces and behaviors.
+Exsisting GridSensor created by extension package will not work in newer version. Previously trained models will
+need to be retrained. Please see the Migration Guide for more details. (#5256)
+- Models trained with 1.x versions of ML-Agents will no longer work at inference if they were trained using recurrent neural networks (#5254)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The `.onnx` models input names have changed. All input placeholders will now use the prefix `obs_` removing the distinction between visual and vector observations. In addition, the inputs and outputs of LSTM changed. Models created with this version will not be usable with previous versions of the package (#5080, #5236)
+- The `.onnx` models discrete action output now contains the discrete actions values and not the logits. Models created with this version will not be usable with previous versions of the package (#5080)
+- Added ML-Agents package settings. (#5027)
+- Make com.unity.modules.unityanalytics an optional dependency. (#5109)
+- Make com.unity.modules.physics and com.unity.modules.physics2d optional dependencies. (#5112)
+- The default `InferenceDevice` is now `InferenceDevice.Default`, which is equivalent to `InferenceDevice.Burst`. If you
+depend on the previous behavior, you can explicitly set the Agent's `InferenceDevice` to `InferenceDevice.CPU`. (#5175)
+- Added support for `Goal Signal` as a type of observation. Trainers can now use HyperNetworks to process `Goal Signal`. Trainers with HyperNetworks are more effective at solving multiple tasks. (#5142, #5159, #5149)
+- Modified the [GridWorld environment](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md#gridworld) to use the new `Goal Signal` feature. (#5193)
+- `DecisionRequester.ShouldRequestDecision()` and `ShouldRequestAction()`methods were added. These are used to
+determine whether `Agent.RequestDecision()` and `Agent.RequestAction()` are called (respectively). (#5223)
+- `RaycastPerceptionSensor` now caches its raycast results; they can be accessed via `RayPerceptionSensor.RayPerceptionOutput`. (#5222)
+- `ActionBuffers` are now reset to zero before being passed to `Agent.Heuristic()` and
+`IHeuristicProvider.Heuristic()`. (#5227)
+- `Agent` will now call `IDisposable.Dispose()` on all `ISensor`s that implement the `IDisposable` interface. (#5233)
+- `CameraSensor`, `RenderTextureSensor`, and `Match3Sensor` will now reuse their `Texture2D`s, reducing the
+amount of memory that needs to be allocated during runtime. (#5233)
+- Optimzed `ObservationWriter.WriteTexture()` so that it doesn't call `Texture2D.GetPixels32()` for `RGB24` textures.
+This results in much less memory being allocated during inference with `CameraSensor` and `RenderTextureSensor`. (#5233)
+- The Match-3 integration utilities were moved from `com.unity.ml-agents.extensions` to `com.unity.ml-agents`. (#5259)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Some console output have been moved from `info` to `debug` and will not be printed by default. If you want all messages to be printed, you can run `mlagents-learn` with the `--debug` option or add the line `debug: true` at the top of the yaml config file. (#5211)
+- When using a configuration YAML, it is required to define all behaviors found in a Unity
+executable in the trainer configuration YAML, or specify `default_settings`. (#5210)
+- The embedding size of attention layers used when a BufferSensor is in the scene has been changed. It is now fixed to 128 units. It might be impossible to resume training from a checkpoint of a previous version. (#5272)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Fixed a bug where sensors and actuators could get sorted inconsistently on different systems to different Culture
+settings. Unfortunately, this may require retraining models if it changes the resulting order of the sensors
+or actuators on your system. (#5194)
+- Removed additional memory allocations that were occurring due to assert messages and iterating of DemonstrationRecorders. (#5246)
+- Fixed a bug where agent trying to access unintialized fields when creating a new RayPerceptionSensorComponent on an agent. (#5261)
+- Fixed a bug where the DemonstrationRecorder would throw a null reference exception if Num Steps To Record was > 0 and Record was turned off. (#5274)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed a bug where --results-dir has no effect. (#5269)
+- Fixed a bug where old `.pt` checkpoints were not deleted during training. (#5271)
+- The `UnityToGymWrapper` initializer now accepts an optional `action_space_seed` seed. If this is specified, it will
+be used to set the random seed on the resulting action space. (#5303)
+
+
+## [1.9.1-preview] - 2021-04-13
+### Major Changes
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The `--resume` flag now supports resuming experiments with additional reward providers or
+ loading partial models if the network architecture has changed. See
+ [here](https://github.com/Unity-Technologies/ml-agents/blob/release_16_docs/docs/Training-ML-Agents.md#loading-an-existing-model)
+ for more details. (#5213)
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Fixed erroneous warnings when using the Demonstration Recorder. (#5216)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed an issue which was causing increased variance when using LSTMs. Also fixed an issue with LSTM when used with POCA and `sequence_length` < `time_horizon`. (#5206)
+- Fixed a bug where the SAC replay buffer would not be saved out at the end of a run, even if `save_replay_buffer` was enabled. (#5205)
+- ELO now correctly resumes when loading from a checkpoint. (#5202)
+- In the Python API, fixed `validate_action` to expect the right dimensions when `set_action_single_agent` is called. (#5208)
+- In the `GymToUnityWrapper`, raise an appropriate warning if `step()` is called after an environment is done. (#5204)
+- Fixed an issue where using one of the `gym` wrappers would override user-set log levels. (#5201)
+## [1.9.0-preview] - 2021-03-17
+### Major Changes
+#### com.unity.ml-agents (C#)
+- The `BufferSensor` and `BufferSensorComponent` have been added. They allow the Agent to observe variable number of entities. For an example, see the [Sorter environment](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#sorter). (#4909)
+- The `SimpleMultiAgentGroup` class and `IMultiAgentGroup` interface have been added. These allow Agents to be given rewards and
+  end episodes in groups. For examples, see the [Cooperative Push Block](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#cooperative-push-block), [Dungeon Escape](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#dungeon-escape) and [Soccer](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#soccer-twos) environments. (#4923)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The MA-POCA trainer has been added. This is a new trainer that enables Agents to learn how to work together in groups. Configure
+  `poca` as the trainer in the configuration YAML after instantiating a `SimpleMultiAgentGroup` to use this feature. (#5005)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Updated com.unity.barracuda to 1.3.2-preview. (#5084)
+- Added 3D Ball to the `com.unity.ml-agents` samples. (#5077)
+- Make com.unity.modules.unityanalytics an optional dependency. (#5109)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The `encoding_size` setting for RewardSignals has been deprecated. Please use `network_settings` instead. (#4982)
+- Sensor names are now passed through to `ObservationSpec.name`. (#5036)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- An issue that caused `GAIL` to fail for environments where agents can terminate episodes by self-sacrifice has been fixed. (#4971)
+- Made the error message when observations of different shapes are sent to the trainer clearer. (#5030)
+- An issue that prevented curriculums from incrementing with self-play has been fixed. (#5098)
+
+## [1.8.1-preview] - 2021-03-08
+### Minor Changes
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The `cattrs` version dependency was updated to allow `>=1.1.0` on Python 3.8 or higher. (#4821)
+
+### Bug Fixes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Fix an issue where queuing InputEvents overwrote data from previous events in the same frame. (#5034)
+
+## [1.8.0-preview] - 2021-02-17
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- TensorFlow trainers have been removed, please use the Torch trainers instead. (#4707)
+- A plugin system for `mlagents-learn` has been added. You can now define custom
+  `StatsWriter` implementations and register them to be called during training.
+  More types of plugins will be added in the future. (#4788)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The `ActionSpec` constructor is now public. Previously, it was not possible to create an
+  ActionSpec with both continuous and discrete actions from code. (#4896)
+- `StatAggregationMethod.Sum` can now be passed to `StatsRecorder.Add()`. This
+  will result in the values being summed (instead of averaged) when written to
+  TensorBoard. Thanks to @brccabral for the contribution! (#4816)
+- The upper limit for the time scale (by setting the `--time-scale` paramater in mlagents-learn) was
+  removed when training with a player. The Editor still requires it to be clamped to 100. (#4867)
+- Added the IHeuristicProvider interface to allow IActuators as well as Agent implement the Heuristic function to generate actions.
+  Updated the Basic example and the Match3 Example to use Actuators.
+  Changed the namespace and file names of classes in com.unity.ml-agents.extensions. (#4849)
+- Added `VectorSensor.AddObservation(IList<float>)`. `VectorSensor.AddObservation(IEnumerable<float>)`
+  is deprecated. The `IList` version is recommended, as it does not generate any
+  additional memory allocations. (#4887)
+- Added `ObservationWriter.AddList()` and deprecated `ObservationWriter.AddRange()`.
+  `AddList()` is recommended, as it does not generate any additional memory allocations. (#4887)
+- The Barracuda dependency was upgraded to 1.3.0. (#4898)
+- Added `ActuatorComponent.CreateActuators`, and deprecate `ActuatorComponent.CreateActuator`.  The
+  default implementation will wrap `ActuatorComponent.CreateActuator` in an array and return that. (#4899)
+- `InferenceDevice.Burst` was added, indicating that Agent's model will be run using Barracuda's Burst backend.
+  This is the default for new Agents, but existing ones that use `InferenceDevice.CPU` should update to
+  `InferenceDevice.Burst`. (#4925)
+- Add an InputActuatorComponent to allow the generation of Agent action spaces from an InputActionAsset.
+  Projects wanting to use this feature will need to add the
+  [Input System Package](https://docs.unity3d.com/Packages/com.unity.inputsystem@1.1/manual/index.html)
+  at version 1.1.0-preview.3 or later. (#4881)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Tensorboard now logs the Environment Reward as both a scalar and a histogram. (#4878)
+- Added a `--torch-device` commandline option to `mlagents-learn`, which sets the default
+  [`torch.device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device) used for training. (#4888)
+- The `--cpu` commandline option had no effect and was removed. Use `--torch-device=cpu` to force CPU training. (#4888)
+- The `mlagents_env` API has changed, `BehaviorSpec` now has a `observation_specs` property containing a list of `ObservationSpec`. For more information on `ObservationSpec` see [here](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Python-API.md#behaviorspec). (#4763, #4825)
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Fix a compile warning about using an obsolete enum in `GrpcExtensions.cs`. (#4812)
+- CameraSensor now logs an error if the GraphicsDevice is null. (#4880)
+- Removed unnecessary memory allocations in `ActuatorManager.UpdateActionArray()` (#4877)
+- Removed unnecessary memory allocations in `SensorShapeValidator.ValidateSensors()` (#4879)
+- Removed unnecessary memory allocations in `SideChannelManager.GetSideChannelMessage()` (#4886)
+- Removed several memory allocations that happened during inference. On a test scene, this
+  reduced the amount of memory allocated by approximately 25%. (#4887)
+- Removed several memory allocations that happened during inference with discrete actions. (#4922)
+- Properly catch permission errors when writing timer files. (#4921)
+- Unexpected exceptions during training initialization and shutdown are now logged. If you see
+  "noisy" logs, please let us know! (#4930, #4935)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed a bug that would cause an exception when `RunOptions` was deserialized via `pickle`. (#4842)
+- Fixed a bug that can cause a crash if a behavior can appear during training in multi-environment training. (#4872)
+- Fixed the computation of entropy for continuous actions. (#4869)
+- Fixed a bug that would cause `UnityEnvironment` to wait the full timeout
+  period and report a misleading error message if the executable crashed
+  without closing the connection. It now periodically checks the process status
+  while waiting for a connection, and raises a better error message if it crashes. (#4880)
+- Passing a `-logfile` option in the `--env-args` option to `mlagents-learn` is
+  no longer overwritten. (#4880)
+- The `load_weights` function was being called unnecessarily often in the Ghost Trainer leading to training slowdowns. (#4934)
+
+
+## [1.7.2-preview] - 2020-12-22
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Add analytics package dependency to the package manifest. (#4794)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed the docker build process. (#4791)
+
+
+## [1.7.0-preview] - 2020-12-21
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- PyTorch trainers now support training agents with both continuous and discrete action spaces. (#4702)
+The `.onnx` models generated by the trainers of this release are incompatible with versions of Barracuda before `1.2.1-preview`. If you upgrade the trainers, you must upgrade the version of the Barracuda package as well (which can be done by upgrading the `com.unity.ml-agents` package).
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Agents with both continuous and discrete actions are now supported. You can specify
+both continuous and discrete action sizes in Behavior Parameters. (#4702, #4718)
+- In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics.
+Please refer to "Information that is passively collected by Unity" in the
+[Unity Privacy Policy](https://unity3d.com/legal/privacy-policy). (#4677)
+- The FoodCollector example environment now uses continuous actions for moving and
+discrete actions for shooting. (#4746)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- `ActionSpec.validate_action()` now enforces that `UnityEnvironment.set_action_for_agent()` receives a 1D `np.array`. (#4691)
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Removed noisy warnings about API minor version mismatches in both the C# and python code. (#4688)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+
+## [1.6.0-preview] - 2020-11-18
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+ - PyTorch trainers are now the default. See the
+ [installation docs](https://github.com/Unity-Technologies/ml-agents/blob/release_10_docs/docs/Installation.md) for
+ more information on installing PyTorch. For the time being, TensorFlow is still available;
+ you can use the TensorFlow backend by adding `--tensorflow` to the CLI, or
+ adding `framework: tensorflow` in the configuration YAML. (#4517)
+
+### Minor Changes
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- The Barracuda dependency was upgraded to 1.1.2 (#4571)
+- Utilities were added to `com.unity.ml-agents.extensions` to make it easier to
+integrate with match-3 games. See the [readme](https://github.com/Unity-Technologies/ml-agents/blob/release_10_docs/com.unity.ml-agents.extensions/Documentation~/Match3.md)
+for more details. (#4515)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The `action_probs` node is no longer listed as an output in TensorFlow models (#4613).
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- `Agent.CollectObservations()` and `Agent.EndEpisode()` will now throw an exception
+if they are called recursively (for example, if they call `Agent.EndEpisode()`).
+Previously, this would result in an infinite loop and cause the editor to hang. (#4573)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed an issue where runs could not be resumed when using TensorFlow and Ghost Training. (#4593)
+- Change the tensor type of step count from int32 to int64 to address the overflow issue when step
+goes larger than 2^31. Previous Tensorflow checkpoints will become incompatible and cannot be loaded. (#4607)
+- Remove extra period after "Training" in console log. (#4674)
+
+
+## [1.5.0-preview] - 2020-10-14
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+ - Added the Random Network Distillation (RND) intrinsic reward signal to the Pytorch
+ trainers. To use RND, add a `rnd` section to the `reward_signals` section of your
+ yaml configuration file. [More information here](https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Training-Configuration-File.md#rnd-intrinsic-reward) (#4473)
+### Minor Changes
+#### com.unity.ml-agents (C#)
+ - Stacking for compressed observations is now supported. An additional setting
+ option `Observation Stacks` is added in editor to sensor components that support
+ compressed observations. A new class `ISparseChannelSensor` with an
+ additional method `GetCompressedChannelMapping()`is added to generate a mapping
+ of the channels in compressed data to the actual channel after decompression,
+ for the python side to decompress correctly. (#4476)
+ - Added a new visual 3DBall environment. (#4513)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+ - The Communication API was changed to 1.2.0 to indicate support for stacked
+ compressed observation. A new entry `compressed_channel_mapping` is added to the
+ proto to handle decompression correctly. Newer versions of the package that wish to
+ make use of this will also need a compatible version of the Python trainers. (#4476)
+ - In the `VisualFoodCollector` scene, a vector flag representing the frozen state of
+ the agent is added to the input observations in addition to the original first-person
+ camera frame. The scene is able to train with the provided default config file. (#4511)
+ - Added conversion to string for sampler classes to increase the verbosity of
+ the curriculum lesson changes. The lesson updates would now output the sampler
+ stats in addition to the lesson and parameter name to the console.  (#4484)
+ - Localized documentation in Russian is added. Thanks to @SergeyMatrosov for
+ the contribution. (#4529)
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+ - Fixed a bug where accessing the Academy outside of play mode would cause the
+ Academy to get stepped multiple times when in play mode. (#4532)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+
+## [1.4.0-preview] - 2020-09-16
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+### Minor Changes
+#### com.unity.ml-agents (C#)
+- The `IActuator` interface and `ActuatorComponent` abstract class were added.
+These are analogous to `ISensor` and `SensorComponent`, but for applying actions
+for an Agent. They allow you to control the action space more programmatically
+than defining the actions in the Agent's Behavior Parameters. See
+[BasicActuatorComponent.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs)
+ for an example of how to use them. (#4297, #4315)
+- Update Barracuda to 1.1.1-preview (#4482)
+- Enabled C# formatting using `dotnet-format`. (#4362)
+- GridSensor was added to the `com.unity.ml-agents.extensions` package. Thank you
+to Jaden Travnik from Eidos Montreal for the contribution! (#4399)
+- Added `Agent.EpisodeInterrupted()`, which can be used to reset the agent when
+it has reached a user-determined maximum number of steps. This behaves similarly
+to `Agent.EndEpsiode()` but has a slightly different effect on training (#4453).
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Experimental PyTorch support has been added. Use `--torch` when running `mlagents-learn`, or add
+`framework: pytorch` to your trainer configuration (under the behavior name) to enable it.
+Note that PyTorch 1.6.0 or greater should be installed to use this feature; see
+[the PyTorch website](https://pytorch.org/) for installation instructions and
+[the relevant ML-Agents docs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Training-ML-Agents.md#using-pytorch-experimental) for usage. (#4335)
+- The minimum supported version of TensorFlow was increased to 1.14.0. (#4411)
+- Compressed visual observations with >3 channels are now supported. In
+`ISensor.GetCompressedObservation()`, this can be done by writing 3 channels at a
+time to a PNG and concatenating the resulting bytes. (#4399)
+- The Communication API was changed to 1.1.0 to indicate support for concatenated PNGs
+(see above). Newer versions of the package that wish to make use of this will also need
+a compatible version of the trainer. (#4462)
+- A CNN (`vis_encode_type: match3`) for smaller grids, e.g. board games, has been added.
+(#4434)
+- You can now again specify a default configuration for your behaviors. Specify `default_settings` in
+your trainer configuration to do so. (#4448)
+- Improved the executable detection logic for environments on Windows. (#4485)
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Previously, `com.unity.ml-agents` was not declaring built-in packages as
+dependencies in its package.json. The relevant dependencies are now listed. (#4384)
+- Agents no longer try to send observations when they become disabled if the
+Academy has been shut down. (#4489)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed the sample code in the custom SideChannel example. (#4466)
+- A bug in the observation normalizer that would cause rewards to decrease
+when using `--resume` was fixed. (#4463)
+- Fixed a bug in exporting Pytorch models when using multiple discrete actions. (#4491)
+
+## [1.3.0-preview] - 2020-08-12
+
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The minimum supported Python version for ml-agents-envs was changed to 3.6.1. (#4244)
+- The interaction between EnvManager and TrainerController was changed; EnvManager.advance() was split into to stages,
+and TrainerController now uses the results from the first stage to handle new behavior names. This change speeds up
+Python training by approximately 5-10%. (#4259)
+
+### Minor Changes
+#### com.unity.ml-agents (C#)
+- StatsSideChannel now stores multiple values per key. This means that multiple
+calls to `StatsRecorder.Add()` with the same key in the same step will no
+longer overwrite each other. (#4236)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The versions of `numpy` supported by ml-agents-envs were changed to disallow 1.19.0 or later. This was done to reflect
+a similar change in TensorFlow's requirements. (#4274)
+- Model checkpoints are now also saved as .nn files during training. (#4127)
+- Model checkpoint info is saved in TrainingStatus.json after training is concluded (#4127)
+- CSV statistics writer was removed (#4300).
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Academy.EnvironmentStep() will now throw an exception if it is called
+recursively (for example, by an Agent's CollectObservations method).
+Previously, this would result in an infinite loop and cause the editor to hang.
+(#4226)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The algorithm used to normalize observations was introducing NaNs if the initial observations were too large
+due to incorrect initialization. The initialization was fixed and is now the observation means from the
+first trajectory processed. (#4299)
+
+## [1.2.0-preview] - 2020-07-15
+
+### Major Changes
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- The Parameter Randomization feature has been refactored to enable sampling of new parameters per episode to improve robustness. The
+  `resampling-interval` parameter has been removed and the config structure updated. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/release_5_docs/docs/Training-ML-Agents.md). (#4065)
+- The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler
+in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the
+behavior. More information
+[here](https://github.com/Unity-Technologies/ml-agents/blob/release_5_docs/docs/Training-ML-Agents.md).(#4160)
+
+### Minor Changes
+#### com.unity.ml-agents (C#)
+- `SideChannelsManager` was renamed to `SideChannelManager`. The old name is still supported, but deprecated. (#4137)
+- `RayPerceptionSensor.Perceive()` now additionally store the GameObject that was hit by the ray. (#4111)
+- The Barracuda dependency was upgraded to 1.0.1 (#4188)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Added new Google Colab notebooks to show how to use `UnityEnvironment'. (#4117)
+
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Fixed an issue where RayPerceptionSensor would raise an exception when the
+list of tags was empty, or a tag in the list was invalid (unknown, null, or
+empty string). (#4155)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Fixed an error when setting `initialize_from` in the trainer confiiguration YAML to
+`null`. (#4175)
+- Fixed issue with FoodCollector, Soccer, and WallJump when playing with keyboard. (#4147, #4174)
+- Fixed a crash in StatsReporter when using threaded trainers with very frequent summary writes
+(#4201)
+- `mlagents-learn` will now raise an error immediately if `--num-envs` is greater than 1 without setting the `--env`
+argument. (#4203)
+
+## [1.1.0-preview] - 2020-06-10
+### Major Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Added new Walker environments. Improved ragdoll stability/performance. (#4037)
+- `max_step` in the `TerminalStep` and `TerminalSteps` objects was renamed `interrupted`.
+- `beta` and `epsilon` in `PPO` are no longer decayed by default but follow the same schedule as learning rate. (#3940)
+- `get_behavior_names()` and `get_behavior_spec()` on UnityEnvironment were replaced by the `behavior_specs` property. (#3946)
+- The first version of the Unity Environment Registry (Experimental) has been released. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/release_5_docs/docs/Unity-Environment-Registry.md)(#3967)
+- `use_visual` and `allow_multiple_visual_obs` in the `UnityToGymWrapper` constructor
+were replaced by `allow_multiple_obs` which allows one or more visual observations and
+vector observations to be used simultaneously. (#3981) Thank you @shakenes !
+- Curriculum and Parameter Randomization configurations have been merged
+  into the main training configuration file. Note that this means training
+  configuration files are now environment-specific. (#3791)
+- The format for trainer configuration has changed, and the "default" behavior has been deprecated.
+  See the [Migration Guide](https://github.com/Unity-Technologies/ml-agents/blob/release_5_docs/docs/Migrating.md) for more details. (#3936)
+- Training artifacts (trained models, summaries) are now found in the `results/`
+  directory. (#3829)
+- When using Curriculum, the current lesson will resume if training is quit and resumed. As such,
+  the `--lesson` CLI option has been removed. (#4025)
+### Minor Changes
+#### com.unity.ml-agents (C#)
+- `ObservableAttribute` was added. Adding the attribute to fields or properties on an Agent will allow it to generate
+  observations via reflection. (#3925, #4006)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- Unity Player logs are now written out to the results directory. (#3877)
+- Run configuration YAML files are written out to the results directory at the end of the run. (#3815)
+- The `--save-freq` CLI option has been removed, and replaced by a `checkpoint_interval` option in the trainer configuration YAML. (#4034)
+- When trying to load/resume from a checkpoint created with an earlier verison of ML-Agents,
+  a warning will be thrown. (#4035)
+### Bug Fixes
+- Fixed an issue where SAC would perform too many model updates when resuming from a
+  checkpoint, and too few when using `buffer_init_steps`. (#4038)
+- Fixed a bug in the onnx export that would cause constants needed for inference to not be visible to some versions of
+  the Barracuda importer. (#4073)
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+
+## [1.0.2-preview] - 2020-05-20
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- Fix missing .meta file
+
+
+## [1.0.1-preview] - 2020-05-19
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+- A bug that would cause the editor to go into a loop when a prefab was selected was fixed. (#3949)
+- BrainParameters.ToProto() no longer throws an exception if none of the fields have been set. (#3930)
+- The Barracuda dependency was upgraded to 0.7.1-preview. (#3977)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+- An issue was fixed where using `--initialize-from` would resume from the past step count. (#3962)
+- The gym wrapper error for the wrong number of agents now fires more consistently, and more details
+  were added to the error message when the input dimension is wrong. (#3963)
+
+
+## [1.0.0-preview] - 2020-04-30
+### Major Changes
+#### com.unity.ml-agents (C#)
+
+- The `MLAgents` C# namespace was renamed to `Unity.MLAgents`, and other nested
+  namespaces were similarly renamed. (#3843)
+- The offset logic was removed from DecisionRequester. (#3716)
+- The signature of `Agent.Heuristic()` was changed to take a float array as a
+  parameter, instead of returning the array. This was done to prevent a common
+  source of error where users would return arrays of the wrong size. (#3765)
+- The communication API version has been bumped up to 1.0.0 and will use
+  [Semantic Versioning](https://semver.org/) to do compatibility checks for
+  communication between Unity and the Python process. (#3760)
+- The obsolete `Agent` methods `GiveModel`, `Done`, `InitializeAgent`,
+  `AgentAction` and `AgentReset` have been removed. (#3770)
+- The SideChannel API has changed:
+  - Introduced the `SideChannelManager` to register, unregister and access side
+    channels. (#3807)
+  - `Academy.FloatProperties` was replaced by `Academy.EnvironmentParameters`.
+    See the [Migration Guide](https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Migrating.md)
+    for more details on upgrading. (#3807)
+  - `SideChannel.OnMessageReceived` is now a protected method (was public)
+  - SideChannel IncomingMessages methods now take an optional default argument,
+    which is used when trying to read more data than the message contains. (#3751)
+  - Added a feature to allow sending stats from C# environments to TensorBoard
+    (and other python StatsWriters). To do this from your code, use
+    `Academy.Instance.StatsRecorder.Add(key, value)`. (#3660)
+- `CameraSensorComponent.m_Grayscale` and
+  `RenderTextureSensorComponent.m_Grayscale` were changed from `public` to
+  `private`. These can still be accessed via their corresponding properties.
+  (#3808)
+- Public fields and properties on several classes were renamed to follow Unity's
+  C# style conventions. All public fields and properties now use "PascalCase"
+  instead of "camelCase"; for example, `Agent.maxStep` was renamed to
+  `Agent.MaxStep`. For a full list of changes, see the pull request. (#3828)
+- `WriteAdapter` was renamed to `ObservationWriter`. If you have a custom
+  `ISensor` implementation, you will need to change the signature of its
+  `Write()` method. (#3834)
+- The Barracuda dependency was upgraded to 0.7.0-preview (which has breaking
+  namespace and assembly name changes). (#3875)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+- The `--load` and `--train` command-line flags have been deprecated. Training
+  now happens by default, and use `--resume` to resume training instead of
+  `--load`. (#3705)
+- The Jupyter notebooks have been removed from the repository. (#3704)
+- The multi-agent gym option was removed from the gym wrapper. For multi-agent
+  scenarios, use the [Low Level Python API](https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Python-API.md). (#3681)
+- The low level Python API has changed. You can look at the document
+  [Low Level Python API](https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Python-API.md)
+  documentation for more information. If you use `mlagents-learn` for training, this should be a
+  transparent change. (#3681)
+- Added ability to start training (initialize model weights) from a previous run
+  ID. (#3710)
+- The GhostTrainer has been extended to support asymmetric games and the
+  asymmetric example environment Strikers Vs. Goalie has been added. (#3653)
+- The `UnityEnv` class from the `gym-unity` package was renamed
+  `UnityToGymWrapper` and no longer creates the `UnityEnvironment`. Instead, the
+  `UnityEnvironment` must be passed as input to the constructor of
+  `UnityToGymWrapper` (#3812)
+
+### Minor Changes
+
+#### com.unity.ml-agents (C#)
+
+- Added new 3-joint Worm ragdoll environment. (#3798)
+- `StackingSensor` was changed from `internal` visibility to `public`. (#3701)
+- The internal event `Academy.AgentSetStatus` was renamed to
+  `Academy.AgentPreStep` and made public. (#3716)
+- Academy.InferenceSeed property was added. This is used to initialize the
+  random number generator in ModelRunner, and is incremented for each
+  ModelRunner. (#3823)
+- `Agent.GetObservations()` was added, which returns a read-only view of the
+  observations added in `CollectObservations()`. (#3825)
+- `UnityRLCapabilities` was added to help inform users when RL features are
+  mismatched between C# and Python packages. (#3831)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+- Format of console output has changed slightly and now matches the name of the
+  model/summary directory. (#3630, #3616)
+- Renamed 'Generalization' feature to 'Environment Parameter Randomization'.
+  (#3646)
+- Timer files now contain a dictionary of metadata, including things like the
+  package version numbers. (#3758)
+- The way that UnityEnvironment decides the port was changed. If no port is
+  specified, the behavior will depend on the `file_name` parameter. If it is
+  `None`, 5004 (the editor port) will be used; otherwise 5005 (the base
+  environment port) will be used. (#3673)
+- Running `mlagents-learn` with the same `--run-id` twice will no longer
+  overwrite the existing files. (#3705)
+- Model updates can now happen asynchronously with environment steps for better
+  performance. (#3690)
+- `num_updates` and `train_interval` for SAC were replaced with
+  `steps_per_update`. (#3690)
+- The maximum compatible version of tensorflow was changed to allow tensorflow
+  2.1 and 2.2. This will allow use with python 3.8 using tensorflow 2.2.0rc3.
+  (#3830)
+- `mlagents-learn` will no longer set the width and height of the executable
+  window to 84x84 when no width nor height arguments are given. (#3867)
+
+### Bug Fixes
+
+#### com.unity.ml-agents (C#)
+
+- Fixed a display bug when viewing Demonstration files in the inspector. The
+  shapes of the observations in the file now display correctly. (#3771)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+- Fixed an issue where exceptions from environments provided a return code of 0.
+  (#3680)
+- Self-Play team changes will now trigger a full environment reset. This
+  prevents trajectories in progress during a team change from getting into the
+  buffer. (#3870)
+
+## [0.15.1-preview] - 2020-03-30
+
+### Bug Fixes
+
+- Raise the wall in CrawlerStatic scene to prevent Agent from falling off.
+  (#3650)
+- Fixed an issue where specifying `vis_encode_type` was required only for SAC.
+  (#3677)
+- Fixed the reported entropy values for continuous actions (#3684)
+- Fixed an issue where switching models using `SetModel()` during training would
+  use an excessive amount of memory. (#3664)
+- Environment subprocesses now close immediately on timeout or wrong API
+  version. (#3679)
+- Fixed an issue in the gym wrapper that would raise an exception if an Agent
+  called EndEpisode multiple times in the same step. (#3700)
+- Fixed an issue where logging output was not visible; logging levels are now
+  set consistently. (#3703)
+
+## [0.15.0-preview] - 2020-03-18
+
+### Major Changes
+
+- `Agent.CollectObservations` now takes a VectorSensor argument. (#3352, #3389)
+- Added `Agent.CollectDiscreteActionMasks` virtual method with a
+  `DiscreteActionMasker` argument to specify which discrete actions are
+  unavailable to the Agent. (#3525)
+- Beta support for ONNX export was added. If the `tf2onnx` python package is
+  installed, models will be saved to `.onnx` as well as `.nn` format. Note that
+  Barracuda 0.6.0 or later is required to import the `.onnx` files properly
+- Multi-GPU training and the `--multi-gpu` option has been removed temporarily.
+  (#3345)
+- All Sensor related code has been moved to the namespace `MLAgents.Sensors`.
+- All SideChannel related code has been moved to the namespace
+  `MLAgents.SideChannels`.
+- `BrainParameters` and `SpaceType` have been removed from the public API
+- `BehaviorParameters` have been removed from the public API.
+- The following methods in the `Agent` class have been deprecated and will be
+  removed in a later release:
+  - `InitializeAgent()` was renamed to `Initialize()`
+  - `AgentAction()` was renamed to `OnActionReceived()`
+  - `AgentReset()` was renamed to `OnEpisodeBegin()`
+  - `Done()` was renamed to `EndEpisode()`
+  - `GiveModel()` was renamed to `SetModel()`
+
+### Minor Changes
+
+- Monitor.cs was moved to Examples. (#3372)
+- Automatic stepping for Academy is now controlled from the
+  AutomaticSteppingEnabled property. (#3376)
+- The GetEpisodeCount, GetStepCount, GetTotalStepCount and methods of Academy
+  were changed to EpisodeCount, StepCount, TotalStepCount properties
+  respectively. (#3376)
+- Several classes were changed from public to internal visibility. (#3390)
+- Academy.RegisterSideChannel and UnregisterSideChannel methods were added.
+  (#3391)
+- A tutorial on adding custom SideChannels was added (#3391)
+- The stepping logic for the Agent and the Academy has been simplified (#3448)
+- Update Barracuda to 0.6.1-preview
+
+* The interface for `RayPerceptionSensor.PerceiveStatic()` was changed to take
+  an input class and write to an output class, and the method was renamed to
+  `Perceive()`.
+
+- The checkpoint file suffix was changed from `.cptk` to `.ckpt` (#3470)
+- The command-line argument used to determine the port that an environment will
+  listen on was changed from `--port` to `--mlagents-port`.
+- `DemonstrationRecorder` can now record observations outside of the editor.
+- `DemonstrationRecorder` now has an optional path for the demonstrations. This
+  will default to `Application.dataPath` if not set.
+- `DemonstrationStore` was changed to accept a `Stream` for its constructor, and
+  was renamed to `DemonstrationWriter`
+- The method `GetStepCount()` on the Agent class has been replaced with the
+  property getter `StepCount`
+- `RayPerceptionSensorComponent` and related classes now display the debug
+  gizmos whenever the Agent is selected (not just Play mode).
+- Most fields on `RayPerceptionSensorComponent` can now be changed while the
+  editor is in Play mode. The exceptions to this are fields that affect the
+  number of observations.
+- Most fields on `CameraSensorComponent` and `RenderTextureSensorComponent` were
+  changed to private and replaced by properties with the same name.
+- Unused static methods from the `Utilities` class (ShiftLeft, ReplaceRange,
+  AddRangeNoAlloc, and GetSensorFloatObservationSize) were removed.
+- The `Agent` class is no longer abstract.
+- SensorBase was moved out of the package and into the Examples directory.
+- `AgentInfo.actionMasks` has been renamed to `AgentInfo.discreteActionMasks`.
+- `DecisionRequester` has been made internal (you can still use the
+  DecisionRequesterComponent from the inspector). `RepeatAction` was renamed
+  `TakeActionsBetweenDecisions` for clarity. (#3555)
+- The `IFloatProperties` interface has been removed.
+- Fix #3579.
+- Improved inference performance for models with multiple action branches.
+  (#3598)
+- Fixed an issue when using GAIL with less than `batch_size` number of
+  demonstrations. (#3591)
+- The interfaces to the `SideChannel` classes (on C# and python) have changed to
+  use new `IncomingMessage` and `OutgoingMessage` classes. These should make
+  reading and writing data to the channel easier. (#3596)
+- Updated the ExpertPyramid.demo example demonstration file (#3613)
+- Updated project version for example environments to 2018.4.18f1. (#3618)
+- Changed the Product Name in the example environments to remove spaces, so that
+  the default build executable file doesn't contain spaces. (#3612)
+
+## [0.14.1-preview] - 2020-02-25
+
+### Bug Fixes
+
+- Fixed an issue which caused self-play training sessions to consume a lot of
+  memory. (#3451)
+- Fixed an IndexError when using GAIL or behavioral cloning with demonstrations
+  recorded with 0.14.0 or later (#3464)
+- Updated the `gail_config.yaml` to work with per-Agent steps (#3475)
+- Fixed demonstration recording of experiences when the Agent is done. (#3463)
+- Fixed a bug with the rewards of multiple Agents in the gym interface (#3471,
+  #3496)
+
+## [0.14.0-preview] - 2020-02-13
+
+### Major Changes
+
+- A new self-play mechanism for training agents in adversarial scenarios was
+  added (#3194)
+- Tennis and Soccer environments were refactored to enable training with
+  self-play (#3194, #3331)
+- UnitySDK folder was split into a Unity Package (com.unity.ml-agents) and our
+  examples were moved to the Project folder (#3267)
+- Academy is now a singleton and is no longer abstract (#3210, #3184)
+- In order to reduce the size of the API, several classes and methods were
+  marked as internal or private. Some public fields on the Agent were trimmed
+  (#3342, #3353, #3269)
+- Decision Period and on-demand decision checkboxes were removed from the Agent.
+  on-demand decision is now the default (#3243)
+- Calling Done() on the Agent will reset it immediately and call the AgentReset
+  virtual method (#3291, #3242)
+- The "Reset on Done" setting in AgentParameters was removed; this is now always
+  true. AgentOnDone virtual method on the Agent was removed (#3311, #3222)
+- Trainer steps are now counted per-Agent, not per-environment as in previous
+  versions. For instance, if you have 10 Agents in the scene, 20 environment
+  steps now correspond to 200 steps as printed in the terminal and in
+  Tensorboard (#3113)
+
+### Minor Changes
+
+- Barracuda was updated to 0.5.0-preview (#3329)
+- --num-runs option was removed from mlagents-learn (#3155)
+- Curriculum config files are now YAML formatted and all curricula for a
+  training run are combined into a single file (#3186)
+- ML-Agents components, such as BehaviorParameters and various Sensor
+  implementations, now appear in the Components menu (#3231)
+- Exceptions are now raised in Unity (in debug mode only) if NaN observations or
+  rewards are passed (#3221)
+- RayPerception MonoBehavior, which was previously deprecated, was removed
+  (#3304)
+- Uncompressed visual (i.e. 3d float arrays) observations are now supported.
+  CameraSensorComponent and RenderTextureSensor now have an option to write
+  uncompressed observations (#3148)
+- Agent’s handling of observations during training was improved so that an extra
+  copy of the observations is no longer maintained (#3229)
+- Error message for missing trainer config files was improved to include the
+  absolute path (#3230)
+- Support for 2017.4 LTS was dropped (#3121, #3168)
+- Some documentation improvements were made (#3296, #3292, #3295, #3281)
+
+### Bug Fixes
+
+- Numpy warning when stats don’t exist (#3251)
+- A bug that caused RayPerceptionSensor to behave inconsistently with transforms
+  that have non-1 scale was fixed (#3321)
+- Some small bugfixes to tensorflow_to_barracuda.py were backported from the
+  barracuda release (#3341)
+- Base port in the jupyter notebook example was updated to use the same port
+  that the editor uses (#3283)
+
+## [0.13.0-preview] - 2020-01-24
+
+### This is the first release of _Unity Package ML-Agents_.
+
+_Short description of this release_
diff --git a/com.unity.ml-agents/CHANGELOG.md.meta b/com.unity.ml-agents/CHANGELOG.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6331df01c1f4837e97f59ff0950dc409d32ca7f9
--- /dev/null
+++ b/com.unity.ml-agents/CHANGELOG.md.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: e19737407870a49abaaa1a90dae1a334
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/CODEOWNERS b/com.unity.ml-agents/CODEOWNERS
new file mode 100644
index 0000000000000000000000000000000000000000..949fd41395c340b9ffdf7df932291b10f8dae6a2
--- /dev/null
+++ b/com.unity.ml-agents/CODEOWNERS
@@ -0,0 +1,2 @@
+# see https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners for more information
+* @unity/behavior-authoring
diff --git a/com.unity.ml-agents/CODEOWNERS.meta b/com.unity.ml-agents/CODEOWNERS.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f288a2353710343a8f9075e552610d957508a8f8
--- /dev/null
+++ b/com.unity.ml-agents/CODEOWNERS.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 5de323c2110f44676ba99dc49409363c
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/CONTRIBUTING.md b/com.unity.ml-agents/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..65b01e775b96889d6d108ba400ac596bb626b05e
--- /dev/null
+++ b/com.unity.ml-agents/CONTRIBUTING.md
@@ -0,0 +1,97 @@
+# Contribution Guidelines
+
+Thank you for your interest in contributing to the ML-Agents Toolkit! We are
+incredibly excited to see how members of our community will use and extend the
+ML-Agents Toolkit. To facilitate your contributions, we've outlined a brief set
+of guidelines to ensure that your extensions can be easily integrated.
+
+## Communication
+
+First, please read through our
+[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/main/CODE_OF_CONDUCT.md),
+as we expect all our contributors to follow it.
+
+Second, before starting on a project that you intend to contribute to the
+ML-Agents Toolkit (whether environments or modifications to the codebase), we
+**strongly** recommend posting on our
+[Issues page](https://github.com/Unity-Technologies/ml-agents/issues) and
+briefly outlining the changes you plan to make. This will enable us to provide
+some context that may be helpful for you. This could range from advice and
+feedback on how to optimally perform your changes or reasons for not doing it.
+
+Lastly, if you're looking for input on what to contribute, feel free to reach
+out to us directly at ml-agents@unity3d.com and/or browse the GitHub issues with
+the `Requests` or `Bug` label.
+
+## Git Branches
+
+The main branch corresponds to the most recent version of the project. Note
+that this may be newer that the
+[latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release).
+
+When contributing to the project, please make sure that your Pull Request (PR)
+contains the following:
+
+- Detailed description of the changes performed
+- Corresponding changes to documentation, unit tests and sample environments (if
+  applicable)
+- Summary of the tests performed to validate your changes
+- Issue numbers that the PR resolves (if any)
+
+## Environments
+
+We are currently not accepting environment contributions directly into ML-Agents.
+However, we believe community created enviornments have a lot of value to the
+community. If you have an interesting enviornment and are willing to share,
+feel free to showcase it and share any relevant files in the
+[ML-Agents forum](https://forum.unity.com/forums/ml-agents.453/).
+
+## Continuous Integration (CI)
+
+We run continuous integration on all PRs; all tests must be passing before the PR is merged.
+
+Several static checks are run on the codebase using the
+[pre-commit framework](https://pre-commit.com/) during CI. To execute the same
+checks locally, run:
+```bash
+pip install pre-commit>=2.8.0
+pip install identify>==2.1.3
+pre-commit run --all-files
+```
+
+Some hooks (for example, `black`) will output the corrected version of the code;
+others (like `mypy`) may require more effort to fix. You can optionally run
+`pre-commit install` to install it as a git hook; after this it will run on all
+commits that you make.
+
+### Code style
+
+All python code should be formatted with
+[`black`](https://github.com/psf/black).
+
+C# code is formatted using [`dotnet-format`](https://github.com/dotnet/format).
+You must have [dotnet](https://dotnet.microsoft.com/download) installed first
+(but don't need to install `dotnet-format` - `pre-commit` will do that for you).
+
+### Python type annotations
+
+We use [`mypy`](http://mypy-lang.org/) to perform static type checking on python
+code. Currently not all code is annotated but we will increase coverage over
+time. If you are adding or refactoring code, please
+
+1. Add type annotations to the new or refactored code.
+2. Make sure that code calling or called by the modified code also has type
+   annotations.
+
+The
+[type hint cheat sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)
+provides a good introduction to adding type hints.
+
+## Contributor License Agreements
+
+When you open a pull request, you will be asked to acknolwedge our Contributor
+License Agreement. We allow both individual contributions and contributions made
+on behalf of companies. We use an open source tool called CLA assistant. If you
+have any questions on our CLA, please
+[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) or
+email us at ml-agents@unity3d.com.
diff --git a/com.unity.ml-agents/CONTRIBUTING.md.meta b/com.unity.ml-agents/CONTRIBUTING.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..acf109e975d1145943eec914a8f0ea464e0caa84
--- /dev/null
+++ b/com.unity.ml-agents/CONTRIBUTING.md.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 60b8c21afae8d449ebcdd512e85e97ac
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Documentation~/com.unity.ml-agents.md b/com.unity.ml-agents/Documentation~/com.unity.ml-agents.md
new file mode 100644
index 0000000000000000000000000000000000000000..461b854d6f75f23f25af78e1f59b2cf608f46635
--- /dev/null
+++ b/com.unity.ml-agents/Documentation~/com.unity.ml-agents.md
@@ -0,0 +1,163 @@
+# About ML-Agents package (`com.unity.ml-agents`)
+
+The _ML-Agents_ package contains the primary C# SDK for the [Unity ML-Agents
+Toolkit].
+
+The package allows you to convert any Unity scene into a learning environment
+and train character behaviors using a variety of machine learning algorithms.
+Additionally, it allows you to embed these trained behaviors back into Unity
+scenes to control your characters. More specifically, the package provides the
+following core functionalities:
+
+- Define Agents: entities, or characters, whose behavior will be learned. Agents
+  are entities that generate observations (through sensors), take actions, and
+  receive rewards from the environment.
+- Define Behaviors: entities that specify how an agent should act. Multiple
+  agents can share the same Behavior and a scene may have multiple Behaviors.
+- Record demonstrations of an agent within the Editor. You can use
+  demonstrations to help train a behavior for that agent.
+- Embedding a trained behavior into the scene via the [Unity Inference Engine].
+  Embedded behaviors allow you to switch an Agent between learning and
+  inference.
+
+Note that the _ML-Agents_ package does not contain the machine learning
+algorithms for training behaviors. The _ML-Agents_ package only supports
+instrumenting a Unity scene, setting it up for training, and then embedding the
+trained model back into your Unity scene. The machine learning algorithms that
+orchestrate training are part of the companion [Python package].
+
+Note that we also provide an _ML-Agents Extensions_ package
+(`com.unity.ml-agents.extensions`) that contains early/experimental features
+that you may find useful. This package is only available from the [ML-Agents
+GitHub repo].
+
+## Package contents
+
+The following table describes the package folder structure:
+
+| **Location**           | **Description**                                                         |
+| ---------------------- | ----------------------------------------------------------------------- |
+| _Documentation~_       | Contains the documentation for the Unity package.                       |
+| _Editor_               | Contains utilities for Editor windows and drawers.                      |
+| _Plugins_              | Contains third-party DLLs.                                              |
+| _Runtime_              | Contains core C# APIs for integrating ML-Agents into your Unity scene.  |
+| _Runtime/Integrations_ | Contains utilities for integrating ML-Agents into specific game genres. |
+| _Tests_                | Contains the unit tests for the package.                                |
+
+<a name="Installation"></a>
+
+## Installation
+
+To install this _ML-Agents_ package, follow the instructions in the [Package
+Manager documentation].
+
+To install the companion Python package to enable training behaviors, follow the
+[installation instructions] on our [GitHub repository].
+
+### Advanced Installation
+With the changes to Unity Package Manager in 2021, experimental packages will not show up in the package list and have to be installed manually. There are two recommended ways to install the package manually:
+
+#### Github via Package Manager
+
+In Unity 2019.4 or later, open the Package Manager, hit the "+" button, and select "Add package from git URL".
+
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/images/unity_package_manager_git_url.png)
+
+In the dialog that appears, enter
+
+```
+git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20
+```
+
+You can also edit your project's `manifest.json` directly and add the following line to the `dependencies`
+section:
+
+```
+"com.unity.ml-agents": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20",
+```
+
+See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
+may take several minutes to resolve the packages the first time that you add it.
+
+#### Local Installation for Development
+
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
+[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Installation.md#advanced-local-installation-for-development-1) directions.
+
+## Requirements
+
+This version of the Unity ML-Agents package is compatible with the following
+versions of the Unity Editor:
+
+- 2019.4 and later
+
+## Known Limitations
+
+### Training
+
+Training is limited to the Unity Editor and Standalone builds on Windows, MacOS,
+and Linux with the Mono scripting backend. Currently, training does not work
+with the IL2CPP scripting backend. Your environment will default to inference
+mode if training is not supported or is not currently running.
+
+### Inference
+
+Inference is executed via the
+[Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html).
+
+**CPU**
+
+All platforms supported.
+
+**GPU**
+
+All platforms supported except:
+
+- WebGL and GLES 3/2 on Android / iPhone
+
+**NOTE:** Mobile platform support includes:
+
+- Vulkan for Android
+- Metal for iOS.
+
+### Headless Mode
+
+If you enable Headless mode, you will not be able to collect visual observations
+from your agents.
+
+### Rendering Speed and Synchronization
+
+Currently the speed of the game physics can only be increased to 100x real-time.
+The Academy also moves in time with FixedUpdate() rather than Update(), so game
+behavior implemented in Update() may be out of sync with the agent decision
+making. See [Execution Order of Event Functions] for more information.
+
+You can control the frequency of Academy stepping by calling
+`Academy.Instance.DisableAutomaticStepping()`, and then calling
+`Academy.Instance.EnvironmentStep()`
+
+### Unity Inference Engine Models
+
+Currently, only models created with our trainers are supported for running
+ML-Agents with a neural network behavior.
+
+## Helpful links
+
+If you are new to the Unity ML-Agents package, or have a question after reading
+the documentation, you can checkout our [GitHub Repository], which also includes
+a number of ways to [connect with us] including our [ML-Agents Forum].
+
+In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics.
+Please refer to "Information that is passively collected by Unity" in the
+[Unity Privacy Policy](https://unity3d.com/legal/privacy-policy).
+
+[unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
+[unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
+[package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Installation.md
+[github repository]: https://github.com/Unity-Technologies/ml-agents
+[python package]: https://github.com/Unity-Technologies/ml-agents
+[execution order of event functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html
+[connect with us]: https://github.com/Unity-Technologies/ml-agents#community-and-feedback
+[ml-agents forum]: https://forum.unity.com/forums/ml-agents.453/
+[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/com.unity.ml-agents.extensions
diff --git a/com.unity.ml-agents/Documentation~/filter.yml b/com.unity.ml-agents/Documentation~/filter.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ce144daf6111a750b20c003bcb99b4212ae81be7
--- /dev/null
+++ b/com.unity.ml-agents/Documentation~/filter.yml
@@ -0,0 +1,14 @@
+apiRules:
+- exclude:
+    uidRegex: .*Test.*
+    type: Namespace
+- exclude:
+    uidRegex: ^Unity.MLAgents\.CommunicatorObjects$
+    type: Namespace
+- exclude:
+    uidRegex: ^Unity.MLAgents\.Editor$
+    type: Namespace
+- exclude:
+    uidRegex: ^Unity.MLAgentsExamples$
+    type: Namespace
+
diff --git a/com.unity.ml-agents/Editor.meta b/com.unity.ml-agents/Editor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..89d980b08810f90e843d77e5e1b082b32719e3ce
--- /dev/null
+++ b/com.unity.ml-agents/Editor.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: e9de88a64ac5c4d2eb8955836199d61b
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/AgentEditor.cs b/com.unity.ml-agents/Editor/AgentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fdebf1cb15b60371a0405dc37897aa916698e580
--- /dev/null
+++ b/com.unity.ml-agents/Editor/AgentEditor.cs
@@ -0,0 +1,31 @@
+using UnityEngine;
+using UnityEditor;
+
+namespace Unity.MLAgents.Editor
+{
+    /*
+     This code is meant to modify the behavior of the inspector on Agent Components.
+    */
+    [CustomEditor(typeof(Agent), true)]
+    [CanEditMultipleObjects]
+    internal class AgentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var serializedAgent = serializedObject;
+            serializedAgent.Update();
+
+            var maxSteps = serializedAgent.FindProperty("MaxStep");
+
+            EditorGUILayout.PropertyField(
+                maxSteps,
+                new GUIContent("Max Step", "The per-agent maximum number of steps.")
+            );
+
+            serializedAgent.ApplyModifiedProperties();
+
+            EditorGUILayout.LabelField("", GUI.skin.horizontalSlider);
+            base.OnInspectorGUI();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/AgentEditor.cs.meta b/com.unity.ml-agents/Editor/AgentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..66bc325f8b1246764946727079303a939fd99a77
--- /dev/null
+++ b/com.unity.ml-agents/Editor/AgentEditor.cs.meta
@@ -0,0 +1,12 @@
+fileFormatVersion: 2
+guid: c3b291e1cd0c64781861652b579d0ac1
+timeCreated: 1503270350
+licenseType: Free
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs b/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a95b2846f3cf52a08fe134abc94505ed0e153025
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs
@@ -0,0 +1,192 @@
+using System.Collections.Generic;
+using UnityEditor;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+using CheckTypeEnum = Unity.MLAgents.Inference.BarracudaModelParamLoader.FailedCheck.CheckTypeEnum;
+
+namespace Unity.MLAgents.Editor
+{
+    /*
+     This code is meant to modify the behavior of the inspector on Agent Components.
+    */
+    [CustomEditor(typeof(BehaviorParameters))]
+    [CanEditMultipleObjects]
+    internal class BehaviorParametersEditor : UnityEditor.Editor
+    {
+        const float k_TimeBetweenModelReloads = 2f;
+        // Time since the last reload of the model
+        float m_TimeSinceModelReload;
+        // Whether or not the model needs to be reloaded
+        bool m_RequireReload;
+        const string k_BehaviorName = "m_BehaviorName";
+        const string k_BrainParametersName = "m_BrainParameters";
+        const string k_ModelName = "m_Model";
+        const string k_InferenceDeviceName = "m_InferenceDevice";
+        const string k_DeterministicInference = "m_DeterministicInference";
+        const string k_BehaviorTypeName = "m_BehaviorType";
+        const string k_TeamIdName = "TeamId";
+        const string k_UseChildSensorsName = "m_UseChildSensors";
+        const string k_ObservableAttributeHandlingName = "m_ObservableAttributeHandling";
+
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+            bool needPolicyUpdate; // Whether the name, model, inference device, or BehaviorType changed.
+
+            var behaviorParameters = (BehaviorParameters)target;
+            var agent = behaviorParameters.gameObject.GetComponent<Agent>();
+            if (agent == null)
+            {
+                EditorGUILayout.HelpBox(
+                    "No Agent is associated with this Behavior Parameters. Attach an Agent to " +
+                    "this GameObject to configure your Agent with these behavior parameters.",
+                    MessageType.Warning);
+            }
+
+            // Drawing the Behavior Parameters
+            EditorGUI.indentLevel++;
+            EditorGUI.BeginChangeCheck(); // global
+
+            EditorGUI.BeginChangeCheck();
+            {
+                EditorGUILayout.PropertyField(so.FindProperty(k_BehaviorName));
+            }
+            needPolicyUpdate = EditorGUI.EndChangeCheck();
+
+            EditorGUI.BeginChangeCheck();
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty(k_BrainParametersName), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            EditorGUI.BeginChangeCheck();
+            {
+                EditorGUILayout.PropertyField(so.FindProperty(k_ModelName), true);
+                EditorGUI.indentLevel++;
+                EditorGUILayout.PropertyField(so.FindProperty(k_InferenceDeviceName), true);
+                EditorGUILayout.PropertyField(so.FindProperty(k_DeterministicInference), true);
+                EditorGUI.indentLevel--;
+            }
+            needPolicyUpdate = needPolicyUpdate || EditorGUI.EndChangeCheck();
+
+            EditorGUI.BeginChangeCheck();
+            {
+                EditorGUILayout.PropertyField(so.FindProperty(k_BehaviorTypeName));
+            }
+            needPolicyUpdate = needPolicyUpdate || EditorGUI.EndChangeCheck();
+
+            EditorGUILayout.PropertyField(so.FindProperty(k_TeamIdName));
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty(k_UseChildSensorsName), true);
+                EditorGUILayout.PropertyField(so.FindProperty(k_ObservableAttributeHandlingName), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            EditorGUI.indentLevel--;
+            m_RequireReload = EditorGUI.EndChangeCheck();
+            DisplayFailedModelChecks();
+            so.ApplyModifiedProperties();
+
+            if (needPolicyUpdate)
+            {
+                UpdateAgentPolicy();
+            }
+        }
+
+        /// <summary>
+        /// Must be called within OnEditorGUI()
+        /// </summary>
+        void DisplayFailedModelChecks()
+        {
+            if (m_RequireReload && m_TimeSinceModelReload > k_TimeBetweenModelReloads)
+            {
+                m_RequireReload = false;
+                m_TimeSinceModelReload = 0;
+            }
+            // Display all failed checks
+            D.logEnabled = false;
+            Model barracudaModel = null;
+            var model = (NNModel)serializedObject.FindProperty(k_ModelName).objectReferenceValue;
+            var behaviorParameters = (BehaviorParameters)target;
+
+            // Grab the sensor components, since we need them to determine the observation sizes.
+            // TODO make these methods of BehaviorParameters
+            var agent = behaviorParameters.gameObject.GetComponent<Agent>();
+            if (agent == null)
+            {
+                return;
+            }
+            agent.sensors = new List<ISensor>();
+            agent.InitializeSensors();
+            var sensors = agent.sensors.ToArray();
+
+            ActuatorComponent[] actuatorComponents;
+            if (behaviorParameters.UseChildActuators)
+            {
+                actuatorComponents = behaviorParameters.GetComponentsInChildren<ActuatorComponent>();
+            }
+            else
+            {
+                actuatorComponents = behaviorParameters.GetComponents<ActuatorComponent>();
+            }
+
+            // Get the total size of the sensors generated by ObservableAttributes.
+            // If there are any errors (e.g. unsupported type, write-only properties), display them too.
+            int observableAttributeSensorTotalSize = 0;
+            if (agent != null && behaviorParameters.ObservableAttributeHandling != ObservableAttributeOptions.Ignore)
+            {
+                List<string> observableErrors = new List<string>();
+                observableAttributeSensorTotalSize = ObservableAttribute.GetTotalObservationSize(agent, false, observableErrors);
+                foreach (var check in observableErrors)
+                {
+                    EditorGUILayout.HelpBox(check, MessageType.Warning);
+                }
+            }
+
+            var brainParameters = behaviorParameters.BrainParameters;
+            if (model != null)
+            {
+                barracudaModel = ModelLoader.Load(model);
+            }
+            if (brainParameters != null)
+            {
+                var failedChecks = Inference.BarracudaModelParamLoader.CheckModel(
+                    barracudaModel, brainParameters, sensors, actuatorComponents,
+                    observableAttributeSensorTotalSize, behaviorParameters.BehaviorType, behaviorParameters.DeterministicInference
+                );
+                foreach (var check in failedChecks)
+                {
+                    if (check != null)
+                    {
+                        switch (check.CheckType)
+                        {
+                            case CheckTypeEnum.Info:
+                                EditorGUILayout.HelpBox(check.Message, MessageType.Info);
+                                break;
+                            case CheckTypeEnum.Warning:
+                                EditorGUILayout.HelpBox(check.Message, MessageType.Warning);
+                                break;
+                            case CheckTypeEnum.Error:
+                                EditorGUILayout.HelpBox(check.Message, MessageType.Error);
+                                break;
+                            default:
+                                break;
+                        }
+                    }
+                }
+            }
+        }
+
+        void UpdateAgentPolicy()
+        {
+            var behaviorParameters = (BehaviorParameters)target;
+            behaviorParameters.UpdateAgentPolicy();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs.meta b/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6eb612f3e33b967fbb9de97ad472f93c2557faba
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BehaviorParametersEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 72b0b21a2d4ee4bc2be0530fd134720d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/BrainParametersDrawer.cs b/com.unity.ml-agents/Editor/BrainParametersDrawer.cs
new file mode 100644
index 0000000000000000000000000000000000000000..52f40e20d35b51aa2e7a3d27a14ff69327291131
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BrainParametersDrawer.cs
@@ -0,0 +1,172 @@
+using UnityEngine;
+using UnityEditor;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Editor
+{
+    /// <summary>
+    /// PropertyDrawer for BrainParameters. Defines how BrainParameters are displayed in the
+    /// Inspector.
+    /// </summary>
+    [CustomPropertyDrawer(typeof(BrainParameters))]
+    internal class BrainParametersDrawer : PropertyDrawer
+    {
+        // The height of a line in the Unity Inspectors
+        const float k_LineHeight = 17f;
+        const int k_VecObsNumLine = 3;
+        const string k_ActionSpecName = "m_ActionSpec";
+        const string k_ContinuousActionSizeName = "m_NumContinuousActions";
+        const string k_DiscreteBranchSizeName = "BranchSizes";
+        const string k_ActionDescriptionPropName = "VectorActionDescriptions";
+        const string k_VecObsPropName = "VectorObservationSize";
+        const string k_NumVecObsPropName = "NumStackedVectorObservations";
+
+        /// <inheritdoc />
+        public override float GetPropertyHeight(SerializedProperty property, GUIContent label)
+        {
+            return GetHeightDrawVectorObservation() +
+                GetHeightDrawVectorAction(property);
+        }
+
+        /// <inheritdoc />
+        public override void OnGUI(Rect position, SerializedProperty property, GUIContent label)
+        {
+            var indent = EditorGUI.indentLevel;
+            EditorGUI.indentLevel = 0;
+            position.height = k_LineHeight;
+            EditorGUI.BeginProperty(position, label, property);
+            EditorGUI.indentLevel++;
+
+            // Vector Observations
+            DrawVectorObservation(position, property);
+            position.y += GetHeightDrawVectorObservation();
+
+            // Vector Action
+            DrawVectorAction(position, property);
+            position.y += GetHeightDrawVectorAction(property);
+
+            EditorGUI.EndProperty();
+            EditorGUI.indentLevel = indent;
+        }
+
+        /// <summary>
+        /// Draws the Vector Observations for the Brain Parameters
+        /// </summary>
+        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
+        /// <param name="property">The SerializedProperty of the BrainParameters
+        /// to make the custom GUI for.</param>
+        static void DrawVectorObservation(Rect position, SerializedProperty property)
+        {
+            EditorGUI.LabelField(position, "Vector Observation");
+            position.y += k_LineHeight;
+
+            EditorGUI.indentLevel++;
+            EditorGUI.PropertyField(position,
+                property.FindPropertyRelative(k_VecObsPropName),
+                new GUIContent("Space Size",
+                    "Length of state " +
+                    "vector for brain (In Continuous state space)." +
+                    "Or number of possible values (in Discrete state space)."));
+            position.y += k_LineHeight;
+
+            EditorGUI.PropertyField(position,
+                property.FindPropertyRelative(k_NumVecObsPropName),
+                new GUIContent("Stacked Vectors",
+                    "Number of states that will be stacked before " +
+                    "being fed to the neural network."));
+            position.y += k_LineHeight;
+            EditorGUI.indentLevel--;
+        }
+
+        /// <summary>
+        /// The Height required to draw the Vector Observations paramaters
+        /// </summary>
+        /// <returns>The height of the drawer of the Vector Observations </returns>
+        static float GetHeightDrawVectorObservation()
+        {
+            return k_VecObsNumLine * k_LineHeight;
+        }
+
+        /// <summary>
+        /// Draws the Vector Actions parameters for the Brain Parameters
+        /// </summary>
+        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
+        /// <param name="property">The SerializedProperty of the BrainParameters
+        /// to make the custom GUI for.</param>
+        static void DrawVectorAction(Rect position, SerializedProperty property)
+        {
+            EditorGUI.LabelField(position, "Actions");
+            position.y += k_LineHeight;
+            EditorGUI.indentLevel++;
+            var actionSpecProperty = property.FindPropertyRelative(k_ActionSpecName);
+            DrawContinuousVectorAction(position, actionSpecProperty);
+            position.y += k_LineHeight;
+            DrawDiscreteVectorAction(position, actionSpecProperty);
+        }
+
+        /// <summary>
+        /// Draws the Continuous Vector Actions parameters for the Brain Parameters
+        /// </summary>
+        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
+        /// <param name="property">The SerializedProperty of the BrainParameters
+        /// to make the custom GUI for.</param>
+        static void DrawContinuousVectorAction(Rect position, SerializedProperty property)
+        {
+            var continuousActionSize = property.FindPropertyRelative(k_ContinuousActionSizeName);
+            EditorGUI.PropertyField(
+                position,
+                continuousActionSize,
+                new GUIContent("Continuous Actions", "Number of continuous actions."));
+        }
+
+        /// <summary>
+        /// Draws the Discrete Vector Actions parameters for the Brain Parameters
+        /// </summary>
+        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
+        /// <param name="property">The SerializedProperty of the BrainParameters
+        /// to make the custom GUI for.</param>
+        static void DrawDiscreteVectorAction(Rect position, SerializedProperty property)
+        {
+            var branchSizes = property.FindPropertyRelative(k_DiscreteBranchSizeName);
+            var newSize = EditorGUI.IntField(
+                position, "Discrete Branches", branchSizes.arraySize);
+
+            // This check is here due to:
+            // https://fogbugz.unity3d.com/f/cases/1246524/
+            // If this case has been resolved, please remove this if condition.
+            if (newSize != branchSizes.arraySize)
+            {
+                branchSizes.arraySize = newSize;
+            }
+
+            position.y += k_LineHeight;
+            position.x += 20;
+            position.width -= 20;
+            for (var branchIndex = 0;
+                 branchIndex < branchSizes.arraySize;
+                 branchIndex++)
+            {
+                var branchActionSize =
+                    branchSizes.GetArrayElementAtIndex(branchIndex);
+
+                EditorGUI.PropertyField(
+                    position,
+                    branchActionSize,
+                    new GUIContent("Branch " + branchIndex + " Size",
+                        "Number of possible actions for the branch number " + branchIndex + "."));
+                position.y += k_LineHeight;
+            }
+        }
+
+        /// <summary>
+        /// The Height required to draw the Vector Action parameters.
+        /// </summary>
+        /// <returns>The height of the drawer of the Vector Action.</returns>
+        static float GetHeightDrawVectorAction(SerializedProperty property)
+        {
+            var actionSpecProperty = property.FindPropertyRelative(k_ActionSpecName);
+            var numActionLines = 3 + actionSpecProperty.FindPropertyRelative(k_DiscreteBranchSizeName).arraySize;
+            return numActionLines * k_LineHeight;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/BrainParametersDrawer.cs.meta b/com.unity.ml-agents/Editor/BrainParametersDrawer.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9379a5f0eb218302e162136554766b4e6d7e5a5d
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BrainParametersDrawer.cs.meta
@@ -0,0 +1,12 @@
+fileFormatVersion: 2
+guid: b060ae8e687cf49bcae88b24db17bfa6
+timeCreated: 1517291065
+licenseType: Free
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs b/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b4b478a35b93e0d3479a7614761422d1dad9c9dd
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs
@@ -0,0 +1,30 @@
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(BufferSensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class BufferSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the BufferSensorComponent
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservableSize"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_MaxNumObservables"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            so.ApplyModifiedProperties();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..62de961c7e294704f7bc64ee73001fa465f45eb0
--- /dev/null
+++ b/com.unity.ml-agents/Editor/BufferSensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: b042fe65027f94c1eb38a2ee1362d38d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs b/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1df66ee3c90a83399dfcb873a70b08764b26dd1d
--- /dev/null
+++ b/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs
@@ -0,0 +1,49 @@
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(CameraSensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class CameraSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the CameraSensorComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUILayout.PropertyField(so.FindProperty("m_Camera"), true);
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_Width"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_Height"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_Grayscale"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+            EditorGUILayout.PropertyField(so.FindProperty("m_RuntimeCameraEnable"), true);
+            EditorGUILayout.PropertyField(so.FindProperty("m_Compression"), true);
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateSensor();
+            }
+        }
+
+        void UpdateSensor()
+        {
+            var sensorComponent = serializedObject.targetObject as CameraSensorComponent;
+            sensorComponent?.UpdateSensor();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..70b1e314324fef09b16ee1d1eeb7bcf26a4e16e2
--- /dev/null
+++ b/com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: fdda773c024894cf0ae47d1b1396c38d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/DemonstrationDrawer.cs b/com.unity.ml-agents/Editor/DemonstrationDrawer.cs
new file mode 100644
index 0000000000000000000000000000000000000000..484e6b6a9a372dc45e8ead647764500c0b8c9ec8
--- /dev/null
+++ b/com.unity.ml-agents/Editor/DemonstrationDrawer.cs
@@ -0,0 +1,136 @@
+using System.Collections.Generic;
+using System.Text;
+using UnityEditor;
+using Unity.MLAgents.Demonstrations;
+
+namespace Unity.MLAgents.Editor
+{
+    /// <summary>
+    /// Renders a custom UI for DemonstrationSummary ScriptableObject.
+    /// </summary>
+    [CustomEditor(typeof(DemonstrationSummary))]
+    [CanEditMultipleObjects]
+    internal class DemonstrationEditor : UnityEditor.Editor
+    {
+        SerializedProperty m_BrainParameters;
+        SerializedProperty m_DemoMetaData;
+        SerializedProperty m_ObservationShapes;
+        const string k_BrainParametersName = "brainParameters";
+        const string k_MetaDataName = "metaData";
+        const string k_ObservationSummariesName = "observationSummaries";
+        const string k_DemonstrationName = "demonstrationName";
+        const string k_NumberStepsName = "numberSteps";
+        const string k_NumberEpisodesName = "numberEpisodes";
+        const string k_MeanRewardName = "meanReward";
+        const string k_ActionSpecName = "m_ActionSpec";
+        const string k_NumContinuousActionsName = "m_NumContinuousActions";
+        const string k_NumDiscreteActionsName = "BranchSizes";
+        const string k_ShapeName = "shape";
+
+
+        void OnEnable()
+        {
+            m_BrainParameters = serializedObject.FindProperty(k_BrainParametersName);
+            m_DemoMetaData = serializedObject.FindProperty(k_MetaDataName);
+            m_ObservationShapes = serializedObject.FindProperty(k_ObservationSummariesName);
+        }
+
+        /// <summary>
+        /// Renders Inspector UI for Demonstration metadata.
+        /// </summary>
+        void MakeMetaDataProperty(SerializedProperty property)
+        {
+            var nameProp = property.FindPropertyRelative(k_DemonstrationName);
+            var experiencesProp = property.FindPropertyRelative(k_NumberStepsName);
+            var episodesProp = property.FindPropertyRelative(k_NumberEpisodesName);
+            var rewardsProp = property.FindPropertyRelative(k_MeanRewardName);
+
+            var nameLabel = nameProp.displayName + ": " + nameProp.stringValue;
+            var experiencesLabel = experiencesProp.displayName + ": " + experiencesProp.intValue;
+            var episodesLabel = episodesProp.displayName + ": " + episodesProp.intValue;
+            var rewardsLabel = rewardsProp.displayName + ": " + rewardsProp.floatValue;
+
+            EditorGUILayout.LabelField(nameLabel);
+            EditorGUILayout.LabelField(experiencesLabel);
+            EditorGUILayout.LabelField(episodesLabel);
+            EditorGUILayout.LabelField(rewardsLabel);
+        }
+
+        /// <summary>
+        /// Constructs label for a serialized integer array.
+        /// </summary>
+        static string BuildIntArrayLabel(SerializedProperty actionSizeProperty)
+        {
+            var actionSize = actionSizeProperty.arraySize;
+            var actionLabel = new StringBuilder("[ ");
+            for (var i = 0; i < actionSize; i++)
+            {
+                actionLabel.Append(actionSizeProperty.GetArrayElementAtIndex(i).intValue);
+                if (i < actionSize - 1)
+                {
+                    actionLabel.Append(", ");
+                }
+            }
+
+            actionLabel.Append(" ]");
+            return actionLabel.ToString();
+        }
+
+        /// <summary>
+        /// Renders Inspector UI for BrainParameters of a DemonstrationSummary.
+        /// Only the Action size and type are used from the BrainParameters.
+        /// </summary>
+        void MakeActionsProperty(SerializedProperty property)
+        {
+            var actSpecProperty = property.FindPropertyRelative(k_ActionSpecName);
+            var continuousSizeProperty = actSpecProperty.FindPropertyRelative(k_NumContinuousActionsName);
+            var discreteSizeProperty = actSpecProperty.FindPropertyRelative(k_NumDiscreteActionsName);
+            var continuousSizeLabel = "Continuous Actions: " + continuousSizeProperty.intValue;
+            var discreteSizeLabel = "Discrete Action Branches: ";
+            discreteSizeLabel += discreteSizeProperty == null ? "[]" : BuildIntArrayLabel(discreteSizeProperty);
+            EditorGUILayout.LabelField(continuousSizeLabel);
+            EditorGUILayout.LabelField(discreteSizeLabel);
+        }
+
+        /// <summary>
+        /// Render the observation shapes of a DemonstrationSummary.
+        /// </summary>
+        /// <param name="obsSummariesProperty"></param>
+        void MakeObservationsProperty(SerializedProperty obsSummariesProperty)
+        {
+            var shapesLabels = new List<string>();
+            var numObservations = obsSummariesProperty.arraySize;
+            for (var i = 0; i < numObservations; i++)
+            {
+                var summary = obsSummariesProperty.GetArrayElementAtIndex(i);
+                var shapeProperty = summary.FindPropertyRelative(k_ShapeName);
+                shapesLabels.Add(BuildIntArrayLabel(shapeProperty));
+            }
+
+            var shapeLabel = $"Shapes: {string.Join(",  ", shapesLabels)}";
+            EditorGUILayout.LabelField(shapeLabel);
+        }
+
+        public override void OnInspectorGUI()
+        {
+            serializedObject.Update();
+
+            EditorGUILayout.LabelField("Meta Data", EditorStyles.boldLabel);
+            EditorGUI.indentLevel++;
+            MakeMetaDataProperty(m_DemoMetaData);
+            EditorGUI.indentLevel--;
+
+            EditorGUILayout.LabelField("Observations", EditorStyles.boldLabel);
+            EditorGUI.indentLevel++;
+            MakeObservationsProperty(m_ObservationShapes);
+            EditorGUI.indentLevel--;
+
+            EditorGUILayout.LabelField("Actions", EditorStyles.boldLabel);
+            EditorGUI.indentLevel++;
+            MakeActionsProperty(m_BrainParameters);
+            EditorGUI.indentLevel--;
+
+            serializedObject.ApplyModifiedProperties();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/DemonstrationDrawer.cs.meta b/com.unity.ml-agents/Editor/DemonstrationDrawer.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..57c06813027105b62752b929b18b68c9dd1e0d1b
--- /dev/null
+++ b/com.unity.ml-agents/Editor/DemonstrationDrawer.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 84f9cd83f56c74790a51444a6cfe4945
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/DemonstrationImporter.cs b/com.unity.ml-agents/Editor/DemonstrationImporter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..767f5556f9613296115f002103fd3e42295c71de
--- /dev/null
+++ b/com.unity.ml-agents/Editor/DemonstrationImporter.cs
@@ -0,0 +1,75 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using Unity.MLAgents.CommunicatorObjects;
+using UnityEditor;
+using UnityEngine;
+#if UNITY_2020_2_OR_NEWER
+using UnityEditor.AssetImporters;
+#else
+using UnityEditor.Experimental.AssetImporters;
+#endif
+using Unity.MLAgents.Demonstrations;
+
+namespace Unity.MLAgents.Editor
+{
+    /// <summary>
+    /// Asset Importer used to parse demonstration files.
+    /// </summary>
+    [ScriptedImporter(1, new[] { "demo" })]
+    internal class DemonstrationImporter : ScriptedImporter
+    {
+        const string k_IconPath = "Packages/com.unity.ml-agents/Editor/Icons/DemoIcon.png";
+
+        public override void OnImportAsset(AssetImportContext ctx)
+        {
+            var inputType = Path.GetExtension(ctx.assetPath);
+            if (inputType == null)
+            {
+                throw new Exception("Demonstration import error.");
+            }
+
+            try
+            {
+                // Read first three proto objects containing metadata, brain parameters, and observations.
+                Stream reader = File.OpenRead(ctx.assetPath);
+
+                var metaDataProto = DemonstrationMetaProto.Parser.ParseDelimitedFrom(reader);
+                var metaData = metaDataProto.ToDemonstrationMetaData();
+
+                reader.Seek(DemonstrationWriter.MetaDataBytes + 1, 0);
+                var brainParamsProto = BrainParametersProto.Parser.ParseDelimitedFrom(reader);
+                var brainParameters = brainParamsProto.ToBrainParameters();
+
+                // Read the first AgentInfoActionPair so that we can get the observation sizes.
+                List<ObservationSummary> observationSummaries;
+                try
+                {
+                    var agentInfoActionPairProto = AgentInfoActionPairProto.Parser.ParseDelimitedFrom(reader);
+                    observationSummaries = agentInfoActionPairProto.GetObservationSummaries();
+                }
+                catch
+                {
+                    // Just in case there weren't any AgentInfoActionPair or they couldn't be read.
+                    observationSummaries = new List<ObservationSummary>();
+                }
+
+                reader.Close();
+
+                var demonstrationSummary = ScriptableObject.CreateInstance<DemonstrationSummary>();
+                demonstrationSummary.Initialize(brainParameters, metaData, observationSummaries);
+                userData = demonstrationSummary.ToString();
+
+                var texture = (Texture2D)
+                    AssetDatabase.LoadAssetAtPath(k_IconPath, typeof(Texture2D));
+
+                ctx.AddObjectToAsset(ctx.assetPath, demonstrationSummary, texture);
+                ctx.SetMainObject(demonstrationSummary);
+            }
+            catch
+            {
+                // ignored
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/DemonstrationImporter.cs.meta b/com.unity.ml-agents/Editor/DemonstrationImporter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bbdca977a3db4009852d960cd0aa5ea9e8f04d2d
--- /dev/null
+++ b/com.unity.ml-agents/Editor/DemonstrationImporter.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7bd65ce151aaa4a41a45312543c56be1
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/EditorUtilities.cs b/com.unity.ml-agents/Editor/EditorUtilities.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8ef266f25986d288c186757b6d11378c7010bd99
--- /dev/null
+++ b/com.unity.ml-agents/Editor/EditorUtilities.cs
@@ -0,0 +1,19 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Editor
+{
+    /// <summary>
+    /// A static helper class for the Editor components of the ML-Agents SDK.
+    /// </summary>
+    public static class EditorUtilities
+    {
+        /// <summary>
+        /// Whether or not properties that affect the model can be updated at the current time.
+        /// </summary>
+        /// <returns></returns>
+        public static bool CanUpdateModelProperties()
+        {
+            return !Application.isPlaying;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/EditorUtilities.cs.meta b/com.unity.ml-agents/Editor/EditorUtilities.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2e58d7e8a3720805792d0ba8507086166a8bbc89
--- /dev/null
+++ b/com.unity.ml-agents/Editor/EditorUtilities.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 840f5a76642c24b789ee312f0aa8e33b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs b/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9541d869b5b56fd82d19c5032fc228d497f678fc
--- /dev/null
+++ b/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs
@@ -0,0 +1,109 @@
+using UnityEditor;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(GridSensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class GridSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+#if !MLA_UNITY_PHYSICS_MODULE
+            EditorGUILayout.HelpBox("The Physics Module is not currently present.  " +
+                "Please add it to your project in order to use the GridSensor APIs in the " +
+                $"{nameof(GridSensorComponent)}", MessageType.Warning);
+#endif
+
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the GridSensorComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_SensorName)), true);
+
+                EditorGUILayout.LabelField("Grid Settings", EditorStyles.boldLabel);
+                EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_CellScale)), true);
+                // We only supports 2D GridSensor now so lock gridSize.y to 1
+                var gridSize = so.FindProperty(nameof(GridSensorComponent.m_GridSize));
+                var gridSize2d = new Vector3Int(gridSize.vector3IntValue.x, 1, gridSize.vector3IntValue.z);
+                var newGridSize = EditorGUILayout.Vector3IntField("Grid Size", gridSize2d);
+                gridSize.vector3IntValue = new Vector3Int(newGridSize.x, 1, newGridSize.z);
+            }
+            EditorGUI.EndDisabledGroup();
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_AgentGameObject)), true);
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_RotateWithAgent)), true);
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // detectable tags
+                var detectableTags = so.FindProperty(nameof(GridSensorComponent.m_DetectableTags));
+                var newSize = EditorGUILayout.IntField("Detectable Tags", detectableTags.arraySize);
+                if (newSize != detectableTags.arraySize)
+                {
+                    detectableTags.arraySize = newSize;
+                }
+                EditorGUI.indentLevel++;
+                for (var i = 0; i < detectableTags.arraySize; i++)
+                {
+                    var objectTag = detectableTags.GetArrayElementAtIndex(i);
+                    EditorGUILayout.PropertyField(objectTag, new GUIContent("Tag " + i), true);
+                }
+                EditorGUI.indentLevel--;
+            }
+            EditorGUI.EndDisabledGroup();
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_ColliderMask)), true);
+            EditorGUILayout.LabelField("Sensor Settings", EditorStyles.boldLabel);
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_ObservationStacks)), true);
+            EditorGUI.EndDisabledGroup();
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_CompressionType)), true);
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.LabelField("Collider and Buffer", EditorStyles.boldLabel);
+                EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_InitialColliderBufferSize)), true);
+                EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_MaxColliderBufferSize)), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            EditorGUILayout.LabelField("Debug Gizmo", EditorStyles.boldLabel);
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_ShowGizmos)), true);
+            EditorGUILayout.PropertyField(so.FindProperty(nameof(GridSensorComponent.m_GizmoYOffset)), true);
+
+            // detectable objects
+            var debugColors = so.FindProperty(nameof(GridSensorComponent.m_DebugColors));
+            var detectableObjectSize = so.FindProperty(nameof(GridSensorComponent.m_DetectableTags)).arraySize;
+            if (detectableObjectSize != debugColors.arraySize)
+            {
+                debugColors.arraySize = detectableObjectSize;
+            }
+            EditorGUILayout.LabelField("Debug Colors");
+            EditorGUI.indentLevel++;
+            for (var i = 0; i < debugColors.arraySize; i++)
+            {
+                var debugColor = debugColors.GetArrayElementAtIndex(i);
+                EditorGUILayout.PropertyField(debugColor, new GUIContent("Tag " + i + " Color"), true);
+            }
+            EditorGUI.indentLevel--;
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateSensor();
+            }
+        }
+
+        void UpdateSensor()
+        {
+            var sensorComponent = serializedObject.targetObject as GridSensorComponent;
+            sensorComponent?.UpdateSensor();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c27459abceac8410e7b829e65fcf5f355cd0db8e
--- /dev/null
+++ b/com.unity.ml-agents/Editor/GridSensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 584686b36fcb2435c8be47d70c332ed0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/Icons.meta b/com.unity.ml-agents/Editor/Icons.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6071205cd3514cc29e49774591630ad987a287fd
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Icons.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e6f6d464e3884bf883137660dee8aebf
+timeCreated: 1581721596
\ No newline at end of file
diff --git a/com.unity.ml-agents/Editor/Icons/DemoIcon.png b/com.unity.ml-agents/Editor/Icons/DemoIcon.png
new file mode 100644
index 0000000000000000000000000000000000000000..ddc91181bf3b559fb8af0a08b2da22b7fbb1126e
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diff --git a/com.unity.ml-agents/Editor/Icons/DemoIcon.png.meta b/com.unity.ml-agents/Editor/Icons/DemoIcon.png.meta
new file mode 100644
index 0000000000000000000000000000000000000000..37831fb256c58b22f1207e19b43dc1cd71992789
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Icons/DemoIcon.png.meta
@@ -0,0 +1,86 @@
+fileFormatVersion: 2
+guid: 3352a0e8d253b4a4ea3782a6d7e09d9b
+TextureImporter:
+  fileIDToRecycleName: {}
+  externalObjects: {}
+  serializedVersion: 4
+  mipmaps:
+    mipMapMode: 0
+    enableMipMap: 1
+    sRGBTexture: 1
+    linearTexture: 0
+    fadeOut: 0
+    borderMipMap: 0
+    mipMapsPreserveCoverage: 0
+    alphaTestReferenceValue: 0.5
+    mipMapFadeDistanceStart: 1
+    mipMapFadeDistanceEnd: 3
+  bumpmap:
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+    heightScale: 0.25
+    normalMapFilter: 0
+  isReadable: 0
+  grayScaleToAlpha: 0
+  generateCubemap: 6
+  cubemapConvolution: 0
+  seamlessCubemap: 0
+  textureFormat: 1
+  maxTextureSize: 2048
+  textureSettings:
+    serializedVersion: 2
+    filterMode: -1
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+    mipBias: -1
+    wrapU: -1
+    wrapV: -1
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+  nPOTScale: 1
+  lightmap: 0
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+  spriteExtrude: 1
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+  spritePivot: {x: 0.5, y: 0.5}
+  spritePixelsToUnits: 100
+  spriteBorder: {x: 0, y: 0, z: 0, w: 0}
+  spriteGenerateFallbackPhysicsShape: 1
+  alphaUsage: 1
+  alphaIsTransparency: 1
+  spriteTessellationDetail: -1
+  textureType: 0
+  textureShape: 1
+  maxTextureSizeSet: 0
+  compressionQualitySet: 0
+  textureFormatSet: 0
+  platformSettings:
+  - buildTarget: DefaultTexturePlatform
+    maxTextureSize: 2048
+    resizeAlgorithm: 0
+    textureFormat: -1
+    textureCompression: 1
+    compressionQuality: 50
+    crunchedCompression: 0
+    allowsAlphaSplitting: 0
+    overridden: 0
+    androidETC2FallbackOverride: 0
+  - buildTarget: Standalone
+    maxTextureSize: 2048
+    resizeAlgorithm: 0
+    textureFormat: -1
+    textureCompression: 1
+    compressionQuality: 50
+    crunchedCompression: 0
+    allowsAlphaSplitting: 0
+    overridden: 0
+    androidETC2FallbackOverride: 0
+  spriteSheet:
+    serializedVersion: 2
+    sprites: []
+    outline: []
+    physicsShape: []
+  spritePackingTag: 
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs b/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs
new file mode 100644
index 0000000000000000000000000000000000000000..223b67c4b27e243e41f8b257fbe75f8a581e6d5e
--- /dev/null
+++ b/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs
@@ -0,0 +1,37 @@
+using System.Linq;
+using UnityEngine;
+using UnityEditor;
+using UnityEditor.Build;
+using UnityEditor.Build.Reporting;
+
+
+namespace Unity.MLAgents.Editor
+{
+    internal class MLAgentsSettingsBuildProvider : IPreprocessBuildWithReport, IPostprocessBuildWithReport
+    {
+        public int callbackOrder => 0;
+
+        public void OnPreprocessBuild(BuildReport report)
+        {
+            if (!EditorUtility.IsPersistent(MLAgentsSettingsManager.Settings))
+                return;
+
+            var preloadedAssets = PlayerSettings.GetPreloadedAssets().ToList();
+            if (!preloadedAssets.Contains(MLAgentsSettingsManager.Settings))
+            {
+                preloadedAssets.Add(MLAgentsSettingsManager.Settings);
+                PlayerSettings.SetPreloadedAssets(preloadedAssets.ToArray());
+            }
+        }
+
+        public void OnPostprocessBuild(BuildReport report)
+        {
+            var preloadedAssets = PlayerSettings.GetPreloadedAssets().ToList();
+            if (preloadedAssets.Contains(MLAgentsSettingsManager.Settings))
+            {
+                preloadedAssets.Remove(MLAgentsSettingsManager.Settings);
+                PlayerSettings.SetPreloadedAssets(preloadedAssets.ToArray());
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs.meta b/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..214ea9863fb489001a7955e7f1d19909ab2aaf99
--- /dev/null
+++ b/com.unity.ml-agents/Editor/MLAgentsSettingsBuildProvider.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: bd59ff34305fa4259a2735e08afdb424
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs b/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4077927a4e462bf523dc7e4ddbf4820ad1147b7a
--- /dev/null
+++ b/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs
@@ -0,0 +1,194 @@
+using System;
+using System.Linq;
+using System.IO;
+using System.Runtime.CompilerServices;
+using UnityEngine;
+using UnityEditor;
+using UnityEngine.UIElements;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.DevTests.Editor")]
+namespace Unity.MLAgents.Editor
+{
+    internal class MLAgentsSettingsProvider : SettingsProvider, IDisposable
+    {
+        const string k_SettingsPath = "Project/ML-Agents";
+        private static MLAgentsSettingsProvider s_Instance;
+        private string[] m_AvailableSettingsAssets;
+        private int m_CurrentSelectedSettingsAsset;
+        private SerializedObject m_SettingsObject;
+        [SerializeField]
+        private MLAgentsSettings m_Settings;
+
+
+        private MLAgentsSettingsProvider(string path, SettingsScope scope = SettingsScope.Project)
+            : base(path, scope)
+        {
+            s_Instance = this;
+        }
+
+        [SettingsProvider]
+        public static SettingsProvider CreateMLAgentsSettingsProvider()
+        {
+            return new MLAgentsSettingsProvider(k_SettingsPath, SettingsScope.Project);
+        }
+
+        public override void OnActivate(string searchContext, VisualElement rootElement)
+        {
+            base.OnActivate(searchContext, rootElement);
+            MLAgentsSettingsManager.OnSettingsChange += Reinitialize;
+        }
+
+        public override void OnDeactivate()
+        {
+            base.OnDeactivate();
+            MLAgentsSettingsManager.OnSettingsChange -= Reinitialize;
+        }
+
+        public void Dispose()
+        {
+            m_SettingsObject?.Dispose();
+        }
+
+        public override void OnTitleBarGUI()
+        {
+            if (EditorGUILayout.DropdownButton(EditorGUIUtility.IconContent("_Popup"), FocusType.Passive, EditorStyles.label))
+            {
+                var menu = new GenericMenu();
+                for (var i = 0; i < m_AvailableSettingsAssets.Length; i++)
+                {
+                    menu.AddItem(ExtractDisplayName(m_AvailableSettingsAssets[i]), m_CurrentSelectedSettingsAsset == i, (path) =>
+                    {
+                        MLAgentsSettingsManager.Settings = AssetDatabase.LoadAssetAtPath<MLAgentsSettings>((string)path);
+                    }, m_AvailableSettingsAssets[i]);
+                }
+                menu.AddSeparator("");
+                menu.AddItem(new GUIContent("New Settings Asset…"), false, CreateNewSettingsAsset);
+                menu.ShowAsContext();
+                Event.current.Use();
+            }
+        }
+
+        private GUIContent ExtractDisplayName(string name)
+        {
+            if (name.StartsWith("Assets/"))
+                name = name.Substring("Assets/".Length);
+            if (name.EndsWith(".asset"))
+                name = name.Substring(0, name.Length - ".asset".Length);
+            if (name.EndsWith(".mlagents.settings"))
+                name = name.Substring(0, name.Length - ".mlagents.settings".Length);
+
+            // Ugly hack: GenericMenu interprets "/" as a submenu path. But luckily, "/" is not the only slash we have in Unicode.
+            return new GUIContent(name.Replace("/", "\u29f8"));
+        }
+
+        private void CreateNewSettingsAsset()
+        {
+            // Asset database always use forward slashes. Use forward slashes for all the paths.
+            var projectName = PlayerSettings.productName;
+            var path = EditorUtility.SaveFilePanel("Create ML-Agents Settings File", "Assets",
+                projectName + ".mlagents.settings", "asset");
+            if (string.IsNullOrEmpty(path))
+            {
+                return;
+            }
+
+            path = path.Replace("\\", "/"); // Make sure we only get '/' separators.
+            var assetPath = Application.dataPath + "/";
+            if (!path.StartsWith(assetPath, StringComparison.CurrentCultureIgnoreCase))
+            {
+                Debug.LogError(string.Format(
+                    "Settings must be stored in Assets folder of the project (got: '{0}')", path));
+                return;
+            }
+
+            var extension = Path.GetExtension(path);
+            if (string.Compare(extension, ".asset", StringComparison.InvariantCultureIgnoreCase) != 0)
+            {
+                path += ".asset";
+            }
+            var relativePath = "Assets/" + path.Substring(assetPath.Length);
+            CreateNewSettingsAsset(relativePath);
+        }
+
+        private static void CreateNewSettingsAsset(string relativePath)
+        {
+            var settings = ScriptableObject.CreateInstance<MLAgentsSettings>();
+            AssetDatabase.CreateAsset(settings, relativePath);
+            EditorGUIUtility.PingObject(settings);
+            // Install the settings. This will lead to an MLAgentsManager.OnSettingsChange event
+            // which in turn will cause this Provider to reinitialize
+            MLAgentsSettingsManager.Settings = settings;
+        }
+
+        public override void OnGUI(string searchContext)
+        {
+            if (m_Settings == null)
+            {
+                InitializeWithCurrentSettings();
+            }
+
+            if (m_AvailableSettingsAssets.Length == 0)
+            {
+                EditorGUILayout.HelpBox(
+                    "Click the button below to create a settings asset you can edit.",
+                    MessageType.Info);
+                if (GUILayout.Button("Create settings asset", GUILayout.Height(30)))
+                    CreateNewSettingsAsset();
+                GUILayout.Space(20);
+            }
+
+            using (new EditorGUI.DisabledScope(m_AvailableSettingsAssets.Length == 0))
+            {
+                EditorGUI.BeginChangeCheck();
+                EditorGUILayout.LabelField("Trainer Settings", EditorStyles.boldLabel);
+                EditorGUI.indentLevel++;
+                EditorGUILayout.PropertyField(m_SettingsObject.FindProperty("m_ConnectTrainer"), new GUIContent("Connect to Trainer"));
+                EditorGUILayout.PropertyField(m_SettingsObject.FindProperty("m_EditorPort"), new GUIContent("Editor Training Port"));
+                EditorGUI.indentLevel--;
+                if (EditorGUI.EndChangeCheck())
+                    m_SettingsObject.ApplyModifiedProperties();
+            }
+        }
+
+        internal void InitializeWithCurrentSettings()
+        {
+            m_AvailableSettingsAssets = FindSettingsInProject();
+
+            m_Settings = MLAgentsSettingsManager.Settings;
+            var currentSettingsPath = AssetDatabase.GetAssetPath(m_Settings);
+            if (string.IsNullOrEmpty(currentSettingsPath))
+            {
+                if (m_AvailableSettingsAssets.Length > 0)
+                {
+                    m_CurrentSelectedSettingsAsset = 0;
+                    m_Settings = AssetDatabase.LoadAssetAtPath<MLAgentsSettings>(m_AvailableSettingsAssets[0]);
+                    MLAgentsSettingsManager.Settings = m_Settings;
+                }
+            }
+            else
+            {
+                var settingsList = m_AvailableSettingsAssets.ToList();
+                m_CurrentSelectedSettingsAsset = settingsList.IndexOf(currentSettingsPath);
+
+                EditorBuildSettings.AddConfigObject(MLAgentsSettingsManager.EditorBuildSettingsConfigKey, m_Settings, true);
+            }
+
+            m_SettingsObject = new SerializedObject(m_Settings);
+        }
+
+        private static string[] FindSettingsInProject()
+        {
+            var guids = AssetDatabase.FindAssets("t:MLAgentsSettings");
+            return guids.Select(guid => AssetDatabase.GUIDToAssetPath(guid)).ToArray();
+        }
+
+        private void Reinitialize()
+        {
+            if (m_Settings != null && MLAgentsSettingsManager.Settings != m_Settings)
+            {
+                InitializeWithCurrentSettings();
+            }
+            Repaint();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs.meta b/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..09eaa72b4e0e9444918b87c00633b34129448414
--- /dev/null
+++ b/com.unity.ml-agents/Editor/MLAgentsSettingsProvider.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 162489862d7f64a40990a0c06bb73bd0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs b/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0b072c5b52ea5eb6c44fcddccdfdd2f6e678057c
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs
@@ -0,0 +1,46 @@
+using UnityEditor;
+using Unity.MLAgents.Integrations.Match3;
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(Match3ActuatorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class Match3ActuatorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            var component = (Match3ActuatorComponent)target;
+            var board = component.GetComponent<AbstractBoard>();
+            if (board == null)
+            {
+                EditorGUILayout.HelpBox("You must provide an implementation of an AbstractBoard.", MessageType.Warning);
+                return;
+            }
+
+            // Drawing the RenderTextureComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_ActuatorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_RandomSeed"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+            EditorGUILayout.PropertyField(so.FindProperty("m_ForceHeuristic"), true);
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateActuator();
+            }
+        }
+
+        void UpdateActuator()
+        {
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ce515a12342062e53559cf867870a779f716a45e
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Match3ActuatorComponentEditor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: b545474cca77481bbc3c6c161dd6bbc3
+timeCreated: 1618441761
\ No newline at end of file
diff --git a/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs b/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..857bedeee85d2ad0103118624ea94aefbea5fb68
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs
@@ -0,0 +1,45 @@
+using UnityEditor;
+using Unity.MLAgents.Integrations.Match3;
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(Match3SensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class Match3SensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            var component = (Match3SensorComponent)target;
+            var board = component.GetComponent<AbstractBoard>();
+            if (board == null)
+            {
+                EditorGUILayout.HelpBox("You must provide an implementation of an AbstractBoard.", MessageType.Warning);
+                return;
+            }
+
+            // Drawing the RenderTextureComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateSensor();
+            }
+        }
+
+        void UpdateSensor()
+        {
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..82a80140edcfb13a622b33519bc11311e323f1c1
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Match3SensorComponentEditor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ab55bf118d03479bb797c0037989c308
+timeCreated: 1618440499
\ No newline at end of file
diff --git a/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs b/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7fdfbc7c521bcbbf9756696dcdb1d7cd58e823a3
--- /dev/null
+++ b/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs
@@ -0,0 +1,115 @@
+using UnityEngine;
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    internal class RayPerceptionSensorComponentBaseEditor : UnityEditor.Editor
+    {
+        bool m_RequireSensorUpdate;
+
+        protected void OnRayPerceptionInspectorGUI(bool is3d)
+        {
+#if !MLA_UNITY_PHYSICS_MODULE
+            if (is3d)
+            {
+                EditorGUILayout.HelpBox("The Physics Module is not currently present.  " +
+                    "Please add it to your project in order to use the Ray Perception APIs in the " +
+                    $"{nameof(RayPerceptionSensorComponent3D)}", MessageType.Warning);
+            }
+#endif
+#if !MLA_UNITY_PHYSICS2D_MODULE
+            if (!is3d)
+            {
+                EditorGUILayout.HelpBox("The Physics2D Module is not currently present.  " +
+                    "Please add it to your project in order to use the Ray Perception APIs in the " +
+                    $"{nameof(RayPerceptionSensorComponent3D)}", MessageType.Warning);
+            }
+#endif
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the RayPerceptionSensorComponent
+            EditorGUI.BeginChangeCheck();
+            EditorGUI.indentLevel++;
+
+            // Don't allow certain fields to be modified during play mode.
+            // * SensorName affects the ordering of the Agent's observations
+            // * The number of tags and rays affects the size of the observations.
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_DetectableTags"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_RaysPerDirection"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            EditorGUILayout.PropertyField(so.FindProperty("m_MaxRayDegrees"), true);
+            EditorGUILayout.PropertyField(so.FindProperty("m_SphereCastRadius"), true);
+            EditorGUILayout.PropertyField(so.FindProperty("m_RayLength"), true);
+            EditorGUILayout.PropertyField(so.FindProperty("m_RayLayerMask"), true);
+
+            // Because the number of observation stacks affects the observation shape,
+            // it is not editable during play mode.
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), new GUIContent("Stacked Raycasts"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            if (is3d)
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_StartVerticalOffset"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_EndVerticalOffset"), true);
+            }
+
+            EditorGUILayout.PropertyField(so.FindProperty("m_AlternatingRayOrder"), true);
+            if (is3d)
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_UseBatchedRaycasts"), true);
+            }
+
+            EditorGUILayout.PropertyField(so.FindProperty("rayHitColor"), true);
+            EditorGUILayout.PropertyField(so.FindProperty("rayMissColor"), true);
+
+            EditorGUI.indentLevel--;
+            if (EditorGUI.EndChangeCheck())
+            {
+                m_RequireSensorUpdate = true;
+            }
+
+            so.ApplyModifiedProperties();
+            UpdateSensorIfDirty();
+        }
+
+        void UpdateSensorIfDirty()
+        {
+            if (m_RequireSensorUpdate)
+            {
+                var sensorComponent = serializedObject.targetObject as RayPerceptionSensorComponentBase;
+                sensorComponent?.UpdateSensor();
+                m_RequireSensorUpdate = false;
+            }
+        }
+    }
+
+    [CustomEditor(typeof(RayPerceptionSensorComponent2D), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class RayPerceptionSensorComponent2DEditor : RayPerceptionSensorComponentBaseEditor
+    {
+        public override void OnInspectorGUI()
+        {
+            OnRayPerceptionInspectorGUI(false);
+        }
+    }
+
+    [CustomEditor(typeof(RayPerceptionSensorComponent3D), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class RayPerceptionSensorComponent3DEditor : RayPerceptionSensorComponentBaseEditor
+    {
+        public override void OnInspectorGUI()
+        {
+            OnRayPerceptionInspectorGUI(true);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs.meta b/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c02a8ca2a9e7823121fbb5ed001e8226bf539115
--- /dev/null
+++ b/com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: c0182483e53c24d0e9f264f711ed89a9
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs b/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8e5fd892d3aab86452a57ee77939b236be0984fa
--- /dev/null
+++ b/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs
@@ -0,0 +1,43 @@
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(RenderTextureSensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class RenderTextureSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the RenderTextureComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                EditorGUILayout.PropertyField(so.FindProperty("m_RenderTexture"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_Grayscale"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            EditorGUILayout.PropertyField(so.FindProperty("m_Compression"), true);
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateSensor();
+            }
+        }
+
+        void UpdateSensor()
+        {
+            var sensorComponent = serializedObject.targetObject as RenderTextureSensorComponent;
+            sensorComponent?.UpdateSensor();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fd7a57d05eded9deecc65943a399fedc14a7fac8
--- /dev/null
+++ b/com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: dab309e01d2964f0792de3ef914ca6b9
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef b/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..27f67d0d1cb6d25747b60b115515e7fa153aedb7
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef
@@ -0,0 +1,30 @@
+{
+    "name": "Unity.ML-Agents.Editor",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects"
+    ],
+    "optionalUnityReferences": [],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": false,
+    "precompiledReferences": [],
+    "autoReferenced": true,
+    "defineConstraints": [],
+    "versionDefines": [
+        {
+            "name": "com.unity.modules.physics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics2d",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS2D_MODULE"
+        }
+    ]
+}
diff --git a/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef.meta b/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0c031cb370114f361d57a9ec8ea8ae19316d4b80
--- /dev/null
+++ b/com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 42675ddec8c314cf08d17ee0f6f5e5a5
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/UnityColors.colors b/com.unity.ml-agents/Editor/UnityColors.colors
new file mode 100644
index 0000000000000000000000000000000000000000..af773637c7d60b3fda238f4ec59ff4e870098f7a
--- /dev/null
+++ b/com.unity.ml-agents/Editor/UnityColors.colors
@@ -0,0 +1,64 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!114 &1
+MonoBehaviour:
+  m_ObjectHideFlags: 52
+  m_PrefabParentObject: {fileID: 0}
+  m_PrefabInternal: {fileID: 0}
+  m_GameObject: {fileID: 0}
+  m_Enabled: 1
+  m_EditorHideFlags: 1
+  m_Script: {fileID: 12323, guid: 0000000000000000e000000000000000, type: 0}
+  m_Name: UnityColors
+  m_EditorClassIdentifier: 
+  m_Presets:
+  - m_Name: 
+    m_Color: {r: 0.12941177, g: 0.5882353, b: 0.9529412, a: 1}
+  - m_Name: 
+    m_Color: {r: 0, g: 0.34117648, b: 0.6039216, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.2627451, g: 0.7019608, b: 0.9019608, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.92156863, g: 0.25490198, b: 0.47843137, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.92941177, g: 0.3254902, b: 0.31764707, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.3647059, g: 0.41568628, b: 0.69411767, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.46666667, g: 0.5647059, b: 0.60784316, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.74509805, g: 0.7372549, b: 0.7411765, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.9254902, g: 0.9372549, b: 0.9411765, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.6039216, g: 0.31764707, b: 0.627451, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.2901961, g: 0.1764706, b: 0.5254902, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.4627451, g: 0.35686275, b: 0.654902, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.6039216, g: 0.31764707, b: 0.627451, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.20392157, g: 0.75686276, b: 0.8392157, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.1254902, g: 0.6509804, b: 0.60784316, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.39609292, g: 0.49962592, b: 0.6509434, a: 0}
+  - m_Name: 
+    m_Color: {r: 0.40392157, g: 0.7372549, b: 0.41960785, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.60784316, g: 0.8039216, b: 0.39607844, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.8235294, g: 0.8784314, b: 0.34901962, a: 1}
+  - m_Name: 
+    m_Color: {r: 1, g: 0.79607844, b: 0.15294118, a: 1}
+  - m_Name: 
+    m_Color: {r: 1, g: 0.93333334, b: 0.34509805, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.98039216, g: 0.6509804, b: 0.16078432, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.9529412, g: 0.4392157, b: 0.27450982, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.74509805, g: 0.22745098, b: 0.15294118, a: 1}
+  - m_Name: 
+    m_Color: {r: 0.9529412, g: 0.4392157, b: 0.27450982, a: 1}
diff --git a/com.unity.ml-agents/Editor/UnityColors.colors.meta b/com.unity.ml-agents/Editor/UnityColors.colors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..34519c21cefbefbe352ddc1b2b7ca516114bd016
--- /dev/null
+++ b/com.unity.ml-agents/Editor/UnityColors.colors.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: b20b0226063034686a6cf92ade284285
+NativeFormatImporter:
+  externalObjects: {}
+  mainObjectFileID: 0
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs b/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..aae6fd796f2746450f7eca5d30289b039738c3c9
--- /dev/null
+++ b/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs
@@ -0,0 +1,31 @@
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(VectorSensorComponent), editorForChildClasses: true)]
+    [CanEditMultipleObjects]
+    internal class VectorSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the VectorSensorComponent
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationSize"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            so.ApplyModifiedProperties();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs.meta b/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9862a23944e0a904bd2c8091c33390457ba3b707
--- /dev/null
+++ b/com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: aa0230c3402f04921acdbbdb61f6ff00
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/LICENSE.md b/com.unity.ml-agents/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..42863a2c9855393f093a6a8023f159f7de2c1c66
--- /dev/null
+++ b/com.unity.ml-agents/LICENSE.md
@@ -0,0 +1,202 @@
+com.unity.ml-agents copyright © 2017 Unity Technologies
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
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+# com.unity.ml-agents
+
+ML-Agents is a Unity package that allows users to use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).
+
+## Installation
+
+Please refer to the [ML-Agents github repo] for installation instructions.
+
+## Usage
+
+Please refer to the [ML-Agents documentation] page for usage guides.
+
+
+[ML-Agents github repo]: https://github.com/Unity-Technologies/ml-agents
+[ML-Agents documentation]: https://unity-technologies.github.io/ml-agents/
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@@ -0,0 +1,8 @@
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diff --git a/com.unity.ml-agents/Runtime/Academy.cs b/com.unity.ml-agents/Runtime/Academy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f2aeaa57a8700b730414a3096bcdb620716d4b28
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Academy.cs
@@ -0,0 +1,695 @@
+using System;
+using UnityEngine;
+using System.Collections.Generic;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.SideChannels;
+using Unity.Barracuda;
+
+/**
+ * Welcome to Unity Machine Learning Agents (ML-Agents).
+ *
+ * The ML-Agents toolkit contains four entities: Academy, Agent, Communicator and
+ * Python API. The academy and connected agents live within
+ * a learning environment (herein called Environment), while the communicator
+ * manages the communication between the learning environment and the Python
+ * API. For more information on each of these entities, in addition to how to
+ * set-up a learning environment and train the behavior of characters in a
+ * Unity scene, please browse our documentation pages on GitHub:
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/
+ */
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Helper class to step the Academy during FixedUpdate phase.
+    /// </summary>
+    internal class AcademyFixedUpdateStepper : MonoBehaviour
+    {
+        void FixedUpdate()
+        {
+            // Check if the stepper belongs to the current Academy and destroy it if it's not.
+            // This is to prevent from having leaked stepper from previous runs.
+            if (!Academy.IsInitialized || !Academy.Instance.IsStepperOwner(this))
+            {
+                Destroy(this.gameObject);
+            }
+            else
+            {
+                Academy.Instance.EnvironmentStep();
+            }
+        }
+    }
+
+    /// <summary>
+    /// The Academy singleton manages agent training and decision making.
+    /// </summary>
+    /// <remarks>
+    /// Access the Academy singleton through the <see cref="Instance"/>
+    /// property. The Academy instance is initialized the first time it is accessed (which will
+    /// typically be by the first <see cref="Agent"/> initialized in a scene).
+    ///
+    /// At initialization, the Academy attempts to connect to the Python training process through
+    /// the external communicator. If successful, the training process can train <see cref="Agent"/>
+    /// instances. When you set an agent's <see cref="BehaviorParameters.BehaviorType"/> setting
+    /// to <see cref="BehaviorType.Default"/>, the agent exchanges data with the training process
+    /// to make decisions. If no training process is available, agents with the default behavior
+    /// fall back to inference or heuristic decisions. (You can also set agents to always use
+    /// inference or heuristics.)
+    /// </remarks>
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/" +
+        "docs/Learning-Environment-Design.md")]
+    public class Academy : IDisposable
+    {
+        /// <summary>
+        /// Communication protocol version.
+        /// When connecting to python, this must be compatible with UnityEnvironment.API_VERSION.
+        /// We follow semantic versioning on the communication version, so existing
+        /// functionality will work as long the major versions match.
+        /// This should be changed whenever a change is made to the communication protocol.
+        /// </summary>
+        /// <remarks>
+        /// History:
+        /// <list type="bullet">
+        ///     <item>
+        ///         <term>1.0.0</term>
+        ///         <description>Initial version</description>
+        ///     </item>
+        ///     <item>
+        ///         <term>1.1.0</term>
+        ///         <description>Support concatenated PNGs for compressed observations.</description>
+        ///     </item>
+        ///     <item>
+        ///         <term>1.2.0</term>
+        ///         <description>Support compression mapping for stacked compressed observations.</description>
+        ///     </item>
+        ///     <item>
+        ///         <term>1.3.0</term>
+        ///         <description>Support both continuous and discrete actions.</description>
+        ///     </item>
+        ///     <item>
+        ///         <term>1.4.0</term>
+        ///         <description>Support training analytics sent from python trainer to the editor.</description>
+        ///     </item>
+        ///     <item>
+        ///         <term>1.5.0</term>
+        ///         <description>Support variable length observation training and multi-agent groups.</description>
+        ///     </item>
+        /// </list>
+        /// </remarks>
+        const string k_ApiVersion = "1.5.0";
+
+        /// <summary>
+        /// Unity package version of com.unity.ml-agents.
+        /// This must match the version string in package.json and is checked in a unit test.
+        /// </summary>
+        internal const string k_PackageVersion = "2.3.0-exp.4";
+
+        const int k_EditorTrainingPort = 5004;
+
+        const string k_PortCommandLineFlag = "--mlagents-port";
+
+        // Lazy initializer pattern, see https://csharpindepth.com/articles/singleton#lazy
+        static Lazy<Academy> s_Lazy = new Lazy<Academy>(() => new Academy());
+
+        /// <summary>
+        ///Reports whether the Academy has been initialized yet.
+        /// </summary>
+        /// <value><c>True</c> if the Academy is initialized, <c>false</c> otherwise.</value>
+        public static bool IsInitialized
+        {
+            get { return s_Lazy.IsValueCreated; }
+        }
+
+        /// <summary>
+        /// The singleton Academy object.
+        /// </summary>
+        /// <value>Getting the instance initializes the Academy, if necessary.</value>
+        public static Academy Instance { get { return s_Lazy.Value; } }
+
+        // Fields not provided in the Inspector.
+
+        /// <summary>
+        /// Reports whether or not the communicator is on.
+        /// </summary>
+        /// <seealso cref="ICommunicator"/>
+        /// <value>
+        /// <c>True</c>, if communicator is on, <c>false</c> otherwise.
+        /// </value>
+        public bool IsCommunicatorOn
+        {
+            get { return Communicator != null; }
+        }
+
+        /// The number of episodes completed by the environment. Incremented
+        /// each time the environment is reset.
+        int m_EpisodeCount;
+
+        /// The number of steps completed within the current episode. Incremented
+        /// each time a step is taken in the environment. Is reset to 0 during
+        /// <see cref="EnvironmentReset"/>.
+        int m_StepCount;
+
+        /// The number of total number of steps completed during the whole simulation. Incremented
+        /// each time a step is taken in the environment.
+        int m_TotalStepCount;
+
+        /// Pointer to the communicator currently in use by the Academy.
+        internal ICommunicator Communicator;
+
+        bool m_Initialized;
+        List<ModelRunner> m_ModelRunners = new List<ModelRunner>();
+
+        // Flag used to keep track of the first time the Academy is reset.
+        bool m_HadFirstReset;
+
+        // Detect an Academy step called by user code that is also called by the Academy.
+        private RecursionChecker m_StepRecursionChecker = new RecursionChecker("EnvironmentStep");
+
+        // Random seed used for inference.
+        int m_InferenceSeed;
+
+        /// <summary>
+        /// Set the random seed used for inference. This should be set before any Agents are added
+        /// to the scene. The seed is passed to the ModelRunner constructor, and incremented each
+        /// time a new ModelRunner is created.
+        /// </summary>
+        public int InferenceSeed
+        {
+            set { m_InferenceSeed = value; }
+        }
+
+        int m_NumAreas;
+
+        /// <summary>
+        /// Number of training areas to instantiate.
+        /// </summary>
+        public int NumAreas => m_NumAreas;
+
+        /// <summary>
+        /// Returns the RLCapabilities of the python client that the unity process is connected to.
+        /// </summary>
+        internal UnityRLCapabilities TrainerCapabilities { get; set; }
+
+
+        // The Academy uses a series of events to communicate with agents
+        // to facilitate synchronization. More specifically, it ensures
+        // that all the agents perform their steps in a consistent order (i.e. no
+        // agent can act based on a decision before another agent has had a chance
+        // to request a decision).
+
+        // Signals to all the Agents at each environment step so they can use
+        // their Policy to decide on their next action.
+        internal event Action DecideAction;
+
+        // Signals to all the listeners that the academy is being destroyed
+        internal event Action DestroyAction;
+
+        // Signals to the Agent that a new step is about to start.
+        // This will mark the Agent as Done if it has reached its maxSteps.
+        internal event Action AgentIncrementStep;
+
+
+        /// <summary>
+        /// Signals to all of the <see cref="Agent"/>s that their step is about to begin.
+        /// This is a good time for an <see cref="Agent"/> to decide if it would like to
+        /// call <see cref="Agent.RequestDecision"/> or <see cref="Agent.RequestAction"/>
+        /// for this step.  Any other pre-step setup could be done during this event as well.
+        /// </summary>
+        public event Action<int> AgentPreStep;
+
+        // Signals to all the agents at each environment step so they can send
+        // their state to their Policy if they have requested a decision.
+        internal event Action AgentSendState;
+
+        // Signals to all the agents at each environment step so they can act if
+        // they have requested a decision.
+        internal event Action AgentAct;
+
+        // Signals to all the agents each time the Academy force resets.
+        internal event Action AgentForceReset;
+
+        /// <summary>
+        /// Signals that the Academy has been reset by the training process.
+        /// </summary>
+        public event Action OnEnvironmentReset;
+
+        AcademyFixedUpdateStepper m_FixedUpdateStepper;
+        GameObject m_StepperObject;
+
+
+        /// <summary>
+        /// Private constructor called the first time the Academy is used.
+        /// Academy uses this time to initialize internal data
+        /// structures, initialize the environment and check for the existence
+        /// of a communicator.
+        /// </summary>
+        protected Academy()
+        {
+            Application.quitting += Dispose;
+#if UNITY_EDITOR || UNITY_STANDALONE
+            if (!CommunicatorFactory.CommunicatorRegistered)
+            {
+                Debug.Log("Registered Communicator in Academy.");
+                CommunicatorFactory.Register<ICommunicator>(RpcCommunicator.Create);
+            }
+#endif
+            LazyInitialize();
+
+#if UNITY_EDITOR
+            EditorApplication.playModeStateChanged += HandleOnPlayModeChanged;
+#endif
+        }
+
+#if UNITY_EDITOR
+        /// <summary>
+        /// Clean up the Academy when switching from edit mode to play mode
+        /// </summary>
+        /// <param name="state">State.</param>
+        void HandleOnPlayModeChanged(PlayModeStateChange state)
+        {
+            if (state == PlayModeStateChange.ExitingEditMode)
+            {
+                Dispose();
+            }
+        }
+
+#endif
+
+        /// <summary>
+        /// Initialize the Academy if it hasn't already been initialized.
+        /// This method is always safe to call; it will have no effect if the Academy is already
+        /// initialized.
+        /// </summary>
+        internal void LazyInitialize()
+        {
+            if (!m_Initialized)
+            {
+                InitializeEnvironment();
+                m_Initialized = true;
+            }
+        }
+
+        /// <summary>
+        /// Enable stepping of the Academy during the FixedUpdate phase. This is done by creating
+        /// a temporary GameObject with a MonoBehaviour that calls Academy.EnvironmentStep().
+        /// </summary>
+        void EnableAutomaticStepping()
+        {
+            if (m_FixedUpdateStepper != null)
+            {
+                return;
+            }
+
+            m_StepperObject = new GameObject("AcademyFixedUpdateStepper");
+            // Don't show this object in the hierarchy
+            m_StepperObject.hideFlags = HideFlags.HideInHierarchy;
+            m_FixedUpdateStepper = m_StepperObject.AddComponent<AcademyFixedUpdateStepper>();
+            try
+            {
+                // This try-catch is because DontDestroyOnLoad cannot be used in Editor Tests
+                GameObject.DontDestroyOnLoad(m_StepperObject);
+            }
+            catch { }
+        }
+
+        /// <summary>
+        /// Disable stepping of the Academy during the FixedUpdate phase. If this is called, the Academy must be
+        /// stepped manually by the user by calling Academy.EnvironmentStep().
+        /// </summary>
+        void DisableAutomaticStepping()
+        {
+            if (m_FixedUpdateStepper == null)
+            {
+                return;
+            }
+
+            m_FixedUpdateStepper = null;
+            if (Application.isEditor)
+            {
+                UnityEngine.Object.DestroyImmediate(m_StepperObject);
+            }
+            else
+            {
+                UnityEngine.Object.Destroy(m_StepperObject);
+            }
+
+            m_StepperObject = null;
+        }
+
+        /// <summary>
+        /// Determines whether or not the Academy is automatically stepped during the FixedUpdate phase.
+        /// </summary>
+        /// <value>Set <c>true</c> to enable automatic stepping; <c>false</c> to disable.</value>
+        public bool AutomaticSteppingEnabled
+        {
+            get { return m_FixedUpdateStepper != null; }
+            set
+            {
+                if (value)
+                {
+                    EnableAutomaticStepping();
+                }
+                else
+                {
+                    DisableAutomaticStepping();
+                }
+            }
+        }
+
+        // Used to read Python-provided environment parameters
+        static int ReadPortFromArgs()
+        {
+            var args = Environment.GetCommandLineArgs();
+            var inputPort = "";
+            for (var i = 0; i < args.Length; i++)
+            {
+                if (args[i] == k_PortCommandLineFlag)
+                {
+                    inputPort = args[i + 1];
+                }
+            }
+
+            try
+            {
+                return int.Parse(inputPort);
+            }
+            catch
+            {
+                // No arg passed, or malformed port number.
+#if UNITY_EDITOR
+                // Try connecting on the default editor port
+                return MLAgentsSettingsManager.Settings.ConnectTrainer ? MLAgentsSettingsManager.Settings.EditorPort : -1;
+#else
+                // This is an executable, so we don't try to connect.
+                return -1;
+#endif
+            }
+        }
+
+        EnvironmentParameters m_EnvironmentParameters;
+        StatsRecorder m_StatsRecorder;
+
+        /// <summary>
+        /// Returns the <see cref="EnvironmentParameters"/> instance. If training
+        /// features such as Curriculum Learning or Environment Parameter Randomization are used,
+        /// then the values of the parameters generated from the training process can be
+        /// retrieved here.
+        /// </summary>
+        /// <returns></returns>
+        public EnvironmentParameters EnvironmentParameters
+        {
+            get { return m_EnvironmentParameters; }
+        }
+
+        /// <summary>
+        /// Returns the <see cref="StatsRecorder"/> instance. This instance can be used
+        /// to record any statistics from the Unity environment.
+        /// </summary>
+        /// <returns></returns>
+        public StatsRecorder StatsRecorder
+        {
+            get { return m_StatsRecorder; }
+        }
+
+        /// <summary>
+        /// Initializes the environment, configures it and initializes the Academy.
+        /// </summary>
+        void InitializeEnvironment()
+        {
+            TimerStack.Instance.AddMetadata("communication_protocol_version", k_ApiVersion);
+            TimerStack.Instance.AddMetadata("com.unity.ml-agents_version", k_PackageVersion);
+
+            EnableAutomaticStepping();
+
+            SideChannelManager.RegisterSideChannel(new EngineConfigurationChannel());
+            SideChannelManager.RegisterSideChannel(new TrainingAnalyticsSideChannel());
+            m_EnvironmentParameters = new EnvironmentParameters();
+            m_StatsRecorder = new StatsRecorder();
+
+            // Try to launch the communicator by using the arguments passed at launch
+            var port = ReadPortFromArgs();
+            if (port > 0)
+            {
+                Communicator = CommunicatorFactory.Create();
+            }
+
+            if (Communicator == null && CommunicatorFactory.Enabled && port > 0)
+            {
+                Debug.Log("Communicator failed to start!");
+            }
+
+            if (Communicator != null)
+            {
+                // We try to exchange the first message with Python. If this fails, it means
+                // no Python Process is ready to train the environment. In this case, the
+                // environment must use Inference.
+                bool initSuccessful = false;
+                var communicatorInitParams = new CommunicatorInitParameters
+                {
+                    port = port,
+                    unityCommunicationVersion = k_ApiVersion,
+                    unityPackageVersion = k_PackageVersion,
+                    name = "AcademySingleton",
+                    CSharpCapabilities = new UnityRLCapabilities()
+                };
+
+                try
+                {
+                    initSuccessful = Communicator.Initialize(
+                        communicatorInitParams,
+                        out var unityRlInitParameters
+                    );
+                    if (initSuccessful)
+                    {
+                        UnityEngine.Random.InitState(unityRlInitParameters.seed);
+                        // We might have inference-only Agents, so set the seed for them too.
+                        m_InferenceSeed = unityRlInitParameters.seed;
+                        m_NumAreas = unityRlInitParameters.numAreas;
+                        TrainerCapabilities = unityRlInitParameters.TrainerCapabilities;
+                        TrainerCapabilities.WarnOnPythonMissingBaseRLCapabilities();
+                    }
+                    else
+                    {
+                        Debug.Log($"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. Will perform inference instead.");
+                        Communicator = null;
+                    }
+                }
+                catch (Exception ex)
+                {
+                    Debug.Log($"Unexpected exception when trying to initialize communication: {ex}\nWill perform inference instead.");
+                    Communicator = null;
+                }
+            }
+
+            if (Communicator != null)
+            {
+                Communicator.QuitCommandReceived += OnQuitCommandReceived;
+                Communicator.ResetCommandReceived += OnResetCommand;
+            }
+
+            // If a communicator is enabled/provided, then we assume we are in
+            // training mode. In the absence of a communicator, we assume we are
+            // in inference mode.
+
+            ResetActions();
+        }
+
+        void ResetActions()
+        {
+            DecideAction = () => { };
+            DestroyAction = () => { };
+            AgentPreStep = i => { };
+            AgentSendState = () => { };
+            AgentAct = () => { };
+            AgentForceReset = () => { };
+            OnEnvironmentReset = () => { };
+        }
+
+        static void OnQuitCommandReceived()
+        {
+#if UNITY_EDITOR
+            EditorApplication.isPlaying = false;
+#endif
+            Application.Quit();
+        }
+
+        void OnResetCommand()
+        {
+            ForcedFullReset();
+        }
+
+        /// <summary>
+        /// The current episode count.
+        /// </summary>
+        /// <value>
+        /// Current episode number.
+        /// </value>
+        public int EpisodeCount
+        {
+            get { return m_EpisodeCount; }
+        }
+
+        /// <summary>
+        /// The current step count (within the current episode).
+        /// </summary>
+        /// <value>
+        /// Current step count.
+        /// </value>
+        public int StepCount
+        {
+            get { return m_StepCount; }
+        }
+
+        /// <summary>
+        /// Returns the total step count.
+        /// </summary>
+        /// <value>
+        /// Total step count.
+        /// </value>
+        public int TotalStepCount
+        {
+            get { return m_TotalStepCount; }
+        }
+
+        /// <summary>
+        /// Forces the full reset. The done flags are not affected. Is either
+        /// called the first reset at inference and every external reset
+        /// at training.
+        /// </summary>
+        void ForcedFullReset()
+        {
+            EnvironmentReset();
+            AgentForceReset?.Invoke();
+            m_HadFirstReset = true;
+        }
+
+        /// <summary>
+        /// Performs a single environment update of the Academy and Agent
+        /// objects within the environment.
+        /// </summary>
+        public void EnvironmentStep()
+        {
+            using (m_StepRecursionChecker.Start())
+            {
+                if (!m_HadFirstReset)
+                {
+                    ForcedFullReset();
+                }
+
+                AgentPreStep?.Invoke(m_StepCount);
+
+                m_StepCount += 1;
+                m_TotalStepCount += 1;
+                AgentIncrementStep?.Invoke();
+
+                using (TimerStack.Instance.Scoped("AgentSendState"))
+                {
+                    AgentSendState?.Invoke();
+                }
+
+                using (TimerStack.Instance.Scoped("DecideAction"))
+                {
+                    DecideAction?.Invoke();
+                }
+
+                // If the communicator is not on, we need to clear the SideChannel sending queue
+                if (!IsCommunicatorOn)
+                {
+                    SideChannelManager.GetSideChannelMessage();
+                }
+
+                using (TimerStack.Instance.Scoped("AgentAct"))
+                {
+                    AgentAct?.Invoke();
+                }
+            }
+        }
+
+        /// <summary>
+        /// Resets the environment, including the Academy.
+        /// </summary>
+        void EnvironmentReset()
+        {
+            m_StepCount = 0;
+            m_EpisodeCount++;
+            OnEnvironmentReset?.Invoke();
+        }
+
+        /// <summary>
+        /// Creates or retrieves an existing ModelRunner that uses the same
+        /// NNModel and the InferenceDevice as provided.
+        /// </summary>
+        /// <param name="model">The NNModel the ModelRunner must use.</param>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        /// <param name="inferenceDevice">
+        /// The inference device (CPU or GPU) the ModelRunner will use.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// Deterministic. </param>
+        /// <returns> The ModelRunner compatible with the input settings.</returns>
+        internal ModelRunner GetOrCreateModelRunner(
+            NNModel model, ActionSpec actionSpec, InferenceDevice inferenceDevice, bool deterministicInference = false)
+        {
+            var modelRunner = m_ModelRunners.Find(x => x.HasModel(model, inferenceDevice));
+            if (modelRunner == null)
+            {
+                modelRunner = new ModelRunner(model, actionSpec, inferenceDevice, m_InferenceSeed, deterministicInference);
+                m_ModelRunners.Add(modelRunner);
+                m_InferenceSeed++;
+            }
+            return modelRunner;
+        }
+
+        /// <summary>
+        /// Shut down the Academy.
+        /// </summary>
+        public void Dispose()
+        {
+            DisableAutomaticStepping();
+
+            // Signal to listeners that the academy is being destroyed now
+            DestroyAction?.Invoke();
+
+            Communicator?.Dispose();
+            Communicator = null;
+
+            m_EnvironmentParameters.Dispose();
+            m_StatsRecorder.Dispose();
+            SideChannelManager.UnregisterAllSideChannels();  // unregister custom side channels
+
+            if (m_ModelRunners != null)
+            {
+                foreach (var mr in m_ModelRunners)
+                {
+                    mr.Dispose();
+                }
+
+                m_ModelRunners = null;
+            }
+
+            // Clear out the actions so we're not keeping references to any old objects
+            ResetActions();
+
+            // TODO - Pass worker ID or some other identifier,
+            // so that multiple envs won't overwrite each others stats.
+            TimerStack.Instance.SaveJsonTimers();
+            m_Initialized = false;
+
+            // Reset the Lazy instance
+            s_Lazy = new Lazy<Academy>(() => new Academy());
+        }
+
+        /// <summary>
+        /// Check if the input AcademyFixedUpdateStepper belongs to this Academy.
+        /// </summary>
+        internal bool IsStepperOwner(AcademyFixedUpdateStepper stepper)
+        {
+            return GameObject.ReferenceEquals(stepper.gameObject, Academy.Instance.m_StepperObject);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Academy.cs.meta b/com.unity.ml-agents/Runtime/Academy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b0a5b6ffc4d033791f4fa6a195f9b30acc81571e
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@@ -0,0 +1,12 @@
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+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
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diff --git a/com.unity.ml-agents/Runtime/Actuators.meta b/com.unity.ml-agents/Runtime/Actuators.meta
new file mode 100644
index 0000000000000000000000000000000000000000..96bbfb99b3a8aea380baa89b290a6a19754d81d9
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+++ b/com.unity.ml-agents/Runtime/Actuators.meta
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diff --git a/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs b/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b3026eac39879ec5429115d6f4701c5bb1025efc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs
@@ -0,0 +1,234 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// ActionSegment{T} is a data structure that allows access to a segment of an underlying array
+    /// in order to avoid the copying and allocation of sub-arrays.  The segment is defined by
+    /// the offset into the original array, and an length.
+    /// </summary>
+    /// <typeparam name="T">The type of object stored in the underlying <see cref="Array"/></typeparam>
+    public readonly struct ActionSegment<T> : IEnumerable<T>, IEquatable<ActionSegment<T>>
+        where T : struct
+    {
+        /// <summary>
+        /// The zero-based offset into the original array at which this segment starts.
+        /// </summary>
+        public readonly int Offset;
+
+        /// <summary>
+        /// The number of items this segment can access in the underlying array.
+        /// </summary>
+        public readonly int Length;
+
+        /// <summary>
+        /// An Empty segment which has an offset of 0, a Length of 0, and it's underlying array
+        /// is also empty.
+        /// </summary>
+        public static ActionSegment<T> Empty = new ActionSegment<T>(System.Array.Empty<T>(), 0, 0);
+
+        static void CheckParameters(IReadOnlyCollection<T> actionArray, int offset, int length)
+        {
+#if DEBUG
+            if (offset + length > actionArray.Count)
+            {
+                throw new ArgumentOutOfRangeException(nameof(offset),
+                    $"Arguments offset: {offset} and length: {length} " +
+                    $"are out of bounds of actionArray: {actionArray.Count}.");
+            }
+#endif
+        }
+
+        /// <summary>
+        /// Construct an <see cref="ActionSegment{T}"/> with just an actionArray.  The <see cref="Offset"/> will
+        /// be set to 0 and the <see cref="Length"/> will be set to `actionArray.Length`.
+        /// </summary>
+        /// <param name="actionArray">The action array to use for the this segment.</param>
+        public ActionSegment(T[] actionArray)
+            : this(actionArray ?? System.Array.Empty<T>(), 0, actionArray?.Length ?? 0) { }
+
+        /// <summary>
+        /// Construct an <see cref="ActionSegment{T}"/> with an underlying array
+        /// and offset, and a length.
+        /// </summary>
+        /// <param name="actionArray">The underlying array which this segment has a view into</param>
+        /// <param name="offset">The zero-based offset into the underlying array.</param>
+        /// <param name="length">The length of the segment.</param>
+        public ActionSegment(T[] actionArray, int offset, int length)
+        {
+#if DEBUG
+            CheckParameters(actionArray ?? System.Array.Empty<T>(), offset, length);
+#endif
+            Array = actionArray ?? System.Array.Empty<T>();
+            Offset = offset;
+            Length = length;
+        }
+
+        /// <summary>
+        /// Get the underlying <see cref="Array"/> of this segment.
+        /// </summary>
+        public T[] Array { get; }
+
+        /// <summary>
+        /// Allows access to the underlying array using array syntax.
+        /// </summary>
+        /// <param name="index">The zero-based index of the segment.</param>
+        /// <exception cref="IndexOutOfRangeException">Thrown when the index is less than 0 or
+        /// greater than or equal to <see cref="Length"/></exception>
+        public T this[int index]
+        {
+            get
+            {
+                if (index < 0 || index > Length)
+                {
+                    throw new IndexOutOfRangeException($"Index out of bounds, expected a number between 0 and {Length}");
+                }
+                return Array[Offset + index];
+            }
+            set
+            {
+                if (index < 0 || index > Length)
+                {
+                    throw new IndexOutOfRangeException($"Index out of bounds, expected a number between 0 and {Length}");
+                }
+                Array[Offset + index] = value;
+            }
+        }
+
+        /// <summary>
+        /// Sets the segment of the backing array to all zeros.
+        /// </summary>
+        public void Clear()
+        {
+            System.Array.Clear(Array, Offset, Length);
+        }
+
+        /// <summary>
+        /// Check if the segment is empty.
+        /// </summary>
+        /// <returns>Whether or not the segment is empty.</returns>
+        public bool IsEmpty()
+        {
+            return Array == null || Array.Length == 0;
+        }
+
+        /// <summary>
+        /// Returns an enumerator that iterates through the ActionSegment.
+        /// </summary>
+        /// <returns>An IEnumerator object that can be used to iterate through the ActionSegment.</returns>
+        IEnumerator<T> IEnumerable<T>.GetEnumerator()
+        {
+            return new Enumerator(this);
+        }
+
+        /// <summary>
+        /// Returns an enumerator that iterates through the ActionSegment.
+        /// </summary>
+        /// <returns>An IEnumerator object that can be used to iterate through the ActionSegment.</returns>
+        public IEnumerator GetEnumerator()
+        {
+            return new Enumerator(this);
+        }
+
+        /// <summary>
+        /// Indicates whether the current ActionSegment is equal to another ActionSegment.
+        /// </summary>
+        /// <param name="obj">An ActionSegment to compare with this ActionSegment.</param>
+        /// <returns>true if the current ActionSegment is equal to the other parameter; otherwise, false.</returns>
+        public override bool Equals(object obj)
+        {
+            if (!(obj is ActionSegment<T>))
+            {
+                return false;
+            }
+            return Equals((ActionSegment<T>)obj);
+        }
+
+        /// <summary>
+        /// Indicates whether the current ActionSegment is equal to another ActionSegment.
+        /// </summary>
+        /// <param name="other">An ActionSegment to compare with this ActionSegment.</param>
+        /// <returns>true if the current ActionSegment is equal to the other parameter; otherwise, false.</returns>
+        public bool Equals(ActionSegment<T> other)
+        {
+            return Offset == other.Offset && Length == other.Length && Array.SequenceEqual(other.Array);
+        }
+
+        /// <summary>
+        /// Computes the hash code of the ActionSegment.
+        /// </summary>
+        /// <returns>A hash code for the current ActionSegment.</returns>
+        public override int GetHashCode()
+        {
+            unchecked
+            {
+                var hashCode = Offset;
+                hashCode = (hashCode * 397) ^ Length;
+                hashCode = (hashCode * 397) ^ (Array != null ? Array.GetHashCode() : 0);
+                return hashCode;
+            }
+        }
+
+        /// <summary>
+        /// A private <see cref="IEnumerator{T}"/> for the <see cref="ActionSegment{T}"/> value type which follows its
+        /// rules of being a view into an underlying <see cref="Array"/>.
+        /// </summary>
+        struct Enumerator : IEnumerator<T>
+        {
+            readonly T[] m_Array;
+            readonly int m_Start;
+            readonly int m_End; // cache Offset + Count, since it's a little slow
+            int m_Current;
+
+            internal Enumerator(ActionSegment<T> arraySegment)
+            {
+                Debug.Assert(arraySegment.Array != null);
+                Debug.Assert(arraySegment.Offset >= 0);
+                Debug.Assert(arraySegment.Length >= 0);
+                Debug.Assert(arraySegment.Offset + arraySegment.Length <= arraySegment.Array.Length);
+
+                m_Array = arraySegment.Array;
+                m_Start = arraySegment.Offset;
+                m_End = arraySegment.Offset + arraySegment.Length;
+                m_Current = arraySegment.Offset - 1;
+            }
+
+            public bool MoveNext()
+            {
+                if (m_Current < m_End)
+                {
+                    m_Current++;
+                    return m_Current < m_End;
+                }
+                return false;
+            }
+
+            public T Current
+            {
+                get
+                {
+                    if (m_Current < m_Start)
+                        throw new InvalidOperationException("Enumerator not started.");
+                    if (m_Current >= m_End)
+                        throw new InvalidOperationException("Enumerator has reached the end already.");
+                    return m_Array[m_Current];
+                }
+            }
+
+            object IEnumerator.Current => Current;
+
+            void IEnumerator.Reset()
+            {
+                m_Current = m_Start - 1;
+            }
+
+            public void Dispose()
+            {
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs.meta b/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8e08ed0a4a87a0c83070820b6fa363ad72c3cf08
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 4fa1432c1ba3460caaa84303a9011ef2
+timeCreated: 1595869823
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs b/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6b0a001a7d1441293b3be700517e00c65a16fac0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs
@@ -0,0 +1,137 @@
+using System;
+using System.Linq;
+using UnityEngine;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Defines the structure of the actions to be used by the Actuator system.
+    /// </summary>
+    [Serializable]
+    public struct ActionSpec
+    {
+        [SerializeField]
+        int m_NumContinuousActions;
+
+        /// <summary>
+        /// An array of branch sizes for discrete actions.
+        ///
+        /// For an IActuator that uses discrete actions, the number of
+        /// branches is the Length of the Array and each index contains the branch size.
+        /// The cumulative sum of the total number of discrete actions can be retrieved
+        /// by the <see cref="SumOfDiscreteBranchSizes"/> property.
+        ///
+        /// For an IActuator with a Continuous it will be null.
+        /// </summary>
+        public int[] BranchSizes;
+
+        /// <summary>
+        /// The number of continuous actions that an Agent can take.
+        /// </summary>
+        public int NumContinuousActions { get { return m_NumContinuousActions; } set { m_NumContinuousActions = value; } }
+
+        /// <summary>
+        /// The number of branches for discrete actions that an Agent can take.
+        /// </summary>
+        public int NumDiscreteActions { get { return BranchSizes == null ? 0 : BranchSizes.Length; } }
+
+        /// <summary>
+        /// Get the total number of Discrete Actions that can be taken by calculating the Sum
+        /// of all of the Discrete Action branch sizes.
+        /// </summary>
+        public int SumOfDiscreteBranchSizes { get { return BranchSizes == null ? 0 : BranchSizes.Sum(); } }
+
+        /// <summary>
+        /// Creates a Continuous <see cref="ActionSpec"/> with the number of actions available.
+        /// </summary>
+        /// <param name="numActions">The number of continuous actions available.</param>
+        /// <returns>An Continuous ActionSpec initialized with the number of actions available.</returns>
+        public static ActionSpec MakeContinuous(int numActions)
+        {
+            var actuatorSpace = new ActionSpec(numActions, null);
+            return actuatorSpace;
+        }
+
+        /// <summary>
+        /// Creates a Discrete <see cref="ActionSpec"/> with the array of branch sizes that
+        /// represents the action space.
+        /// </summary>
+        /// <param name="branchSizes">The array of branch sizes for the discrete actions.  Each index
+        /// contains the number of actions available for that branch.</param>
+        /// <returns>An Discrete ActionSpec initialized with the array of branch sizes.</returns>
+        public static ActionSpec MakeDiscrete(params int[] branchSizes)
+        {
+            var actuatorSpace = new ActionSpec(0, branchSizes);
+            return actuatorSpace;
+        }
+
+        /// <summary>
+        /// Create an ActionSpec initialized with the specified action sizes.
+        /// </summary>
+        /// <param name="numContinuousActions">The number of continuous actions available.</param>
+        /// <param name="discreteBranchSizes">The array of branch sizes for the discrete actions.  Each index
+        /// contains the number of actions available for that branch.</param>
+        public ActionSpec(int numContinuousActions = 0, int[] discreteBranchSizes = null)
+        {
+            m_NumContinuousActions = numContinuousActions;
+            BranchSizes = discreteBranchSizes ?? Array.Empty<int>();
+        }
+
+        /// <summary>
+        /// Check that the ActionSpec uses either all continuous or all discrete actions.
+        /// This is only used when connecting to old versions of the trainer that don't support this.
+        /// </summary>
+        /// <exception cref="UnityAgentsException"></exception>
+        internal void CheckAllContinuousOrDiscrete()
+        {
+            if (NumContinuousActions > 0 && NumDiscreteActions > 0)
+            {
+                throw new UnityAgentsException(
+                    "Action spaces with both continuous and discrete actions are not supported by the trainer. " +
+                    "ActionSpecs must be all continuous or all discrete."
+                );
+            }
+        }
+
+        /// <summary>
+        /// Combines a list of actions specs and allocates a new array of branch sizes if needed.
+        /// </summary>
+        /// <param name="specs">The list of action specs to combine.</param>
+        /// <returns>An ActionSpec which represents the aggregate of the ActionSpecs passed in.</returns>
+        public static ActionSpec Combine(params ActionSpec[] specs)
+        {
+            var numContinuous = 0;
+            var numDiscrete = 0;
+            for (var i = 0; i < specs.Length; i++)
+            {
+                var spec = specs[i];
+                numContinuous += spec.NumContinuousActions;
+                numDiscrete += spec.NumDiscreteActions;
+            }
+
+            if (numDiscrete <= 0)
+            {
+                return MakeContinuous(numContinuous);
+            }
+
+            var branchSizes = new int[numDiscrete];
+            var offset = 0;
+            for (var i = 0; i < specs.Length; i++)
+            {
+                var spec = specs[i];
+                if (spec.BranchSizes.Length == 0)
+                {
+                    continue;
+                }
+                var branchSizesLength = spec.BranchSizes.Length;
+                Array.Copy(spec.BranchSizes,
+                    0,
+                    branchSizes,
+                    offset,
+                    branchSizesLength);
+                offset += branchSizesLength;
+            }
+            return new ActionSpec(numContinuous, branchSizes);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs.meta b/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a442a91a5ee2f03a357feafeff6a37aff34c3f99
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ecdd6deefba1416ca149fe09d2a5afd8
+timeCreated: 1595892361
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs b/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..af34bef3a371298f6769a10948d7c40aa9a130f3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs
@@ -0,0 +1,25 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Editor components for creating Actuators. Generally an IActuator component should
+    /// have a corresponding ActuatorComponent.
+    /// </summary>
+    public abstract class ActuatorComponent : MonoBehaviour
+    {
+        /// <summary>
+        /// Create a collection of <see cref="IActuator"/>s.  This is called by the <see cref="Agent"/> during
+        /// initialization.
+        /// </summary>
+        /// <returns>A collection of <see cref="IActuator"/>s</returns>
+        public abstract IActuator[] CreateActuators();
+
+        /// <summary>
+        /// The specification of the possible actions for this ActuatorComponent.
+        /// This must produce the same results as the corresponding IActuator's ActionSpec.
+        /// </summary>
+        /// <seealso cref="ActionSpec"/>
+        public abstract ActionSpec ActionSpec { get; }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs.meta b/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1b7a643ed1637d77203b2a1120e4d0395b902f8e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 77cefae5f6d841be9ff80b41293d271b
+timeCreated: 1593017318
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs b/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d44532b16faf51e67ec78b78c428cb0440bd45fd
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs
@@ -0,0 +1,149 @@
+using System;
+using System.Collections.Generic;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Implementation of IDiscreteActionMask that allows writing to the action mask from an <see cref="IActuator"/>.
+    /// </summary>
+    internal class ActuatorDiscreteActionMask : IDiscreteActionMask
+    {
+        /// When using discrete control, is the starting indices of the actions
+        /// when all the branches are concatenated with each other.
+        int[] m_StartingActionIndices;
+
+        int[] m_BranchSizes;
+
+        bool[] m_CurrentMask;
+
+        IList<IActuator> m_Actuators;
+
+        readonly int m_SumOfDiscreteBranchSizes;
+        readonly int m_NumBranches;
+
+        /// <summary>
+        /// The offset into the branches array that is used when actuators are writing to the action mask.
+        /// </summary>
+        public int CurrentBranchOffset { get; set; }
+
+        internal ActuatorDiscreteActionMask(IList<IActuator> actuators, int sumOfDiscreteBranchSizes, int numBranches, int[] branchSizes = null)
+        {
+            m_Actuators = actuators;
+            m_SumOfDiscreteBranchSizes = sumOfDiscreteBranchSizes;
+            m_NumBranches = numBranches;
+            m_BranchSizes = branchSizes;
+        }
+
+        /// <inheritdoc/>
+        public void SetActionEnabled(int branch, int actionIndex, bool isEnabled)
+        {
+            LazyInitialize();
+#if DEBUG
+            if (branch >= m_NumBranches || actionIndex >= m_BranchSizes[CurrentBranchOffset + branch])
+            {
+                throw new UnityAgentsException(
+                    "Invalid Action Masking: Action Mask is too large for specified branch.");
+            }
+#endif
+            m_CurrentMask[actionIndex + m_StartingActionIndices[CurrentBranchOffset + branch]] = !isEnabled;
+        }
+
+        void LazyInitialize()
+        {
+            if (m_BranchSizes == null)
+            {
+                m_BranchSizes = new int[m_NumBranches];
+                var start = 0;
+                for (var i = 0; i < m_Actuators.Count; i++)
+                {
+                    var actuator = m_Actuators[i];
+                    var branchSizes = actuator.ActionSpec.BranchSizes;
+                    Array.Copy(branchSizes, 0, m_BranchSizes, start, branchSizes.Length);
+                    start += branchSizes.Length;
+                }
+            }
+
+            // By default, the masks are null. If we want to specify a new mask, we initialize
+            // the actionMasks with trues.
+            if (m_CurrentMask == null)
+            {
+                m_CurrentMask = new bool[m_SumOfDiscreteBranchSizes];
+            }
+
+            // If this is the first time the masked actions are used, we generate the starting
+            // indices for each branch.
+            if (m_StartingActionIndices == null)
+            {
+                m_StartingActionIndices = Utilities.CumSum(m_BranchSizes);
+            }
+        }
+
+        /// <summary>
+        /// Get the current mask for an agent.
+        /// </summary>
+        /// <returns>A mask for the agent. A boolean array of length equal to the total number of
+        /// actions.</returns>
+        internal bool[] GetMask()
+        {
+#if DEBUG
+            if (m_CurrentMask != null)
+            {
+                AssertMask();
+            }
+#endif
+            return m_CurrentMask;
+        }
+
+        /// <summary>
+        /// Makes sure that the current mask is usable.
+        /// </summary>
+        void AssertMask()
+        {
+#if DEBUG
+            for (var branchIndex = 0; branchIndex < m_NumBranches; branchIndex++)
+            {
+                if (AreAllActionsMasked(branchIndex))
+                {
+                    throw new UnityAgentsException(
+                        "Invalid Action Masking : All the actions of branch " + branchIndex +
+                        " are masked.");
+                }
+            }
+#endif
+        }
+
+        /// <summary>
+        /// Resets the current mask for an agent.
+        /// </summary>
+        internal void ResetMask()
+        {
+            if (m_CurrentMask != null)
+            {
+                Array.Clear(m_CurrentMask, 0, m_CurrentMask.Length);
+            }
+        }
+
+        /// <summary>
+        /// Checks if all the actions in the input branch are masked.
+        /// </summary>
+        /// <param name="branch"> The index of the branch to check.</param>
+        /// <returns> True if all the actions of the branch are masked.</returns>
+        bool AreAllActionsMasked(int branch)
+        {
+            if (m_CurrentMask == null)
+            {
+                return false;
+            }
+            var start = m_StartingActionIndices[branch];
+            var end = m_StartingActionIndices[branch + 1];
+            for (var i = start; i < end; i++)
+            {
+                if (!m_CurrentMask[i])
+                {
+                    return false;
+                }
+            }
+            return true;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs.meta b/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..09aa4784b07fe5e0b596efd14111092c3c5d76d9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d2a19e2f43fd4637a38d42b2a5f989f3
+timeCreated: 1595459316
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs b/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1ff35557d96970fd160fecabbff67238c21e9699
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs
@@ -0,0 +1,500 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Profiling;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// A class that manages the delegation of events, action buffers, and action mask for a list of IActuators.
+    /// </summary>
+    internal class ActuatorManager : IList<IActuator>
+    {
+        // IActuators managed by this object.
+        List<IActuator> m_Actuators;
+
+        // An implementation of IDiscreteActionMask that allows for writing to it based on an offset.
+        ActuatorDiscreteActionMask m_DiscreteActionMask;
+
+        ActionSpec m_CombinedActionSpec;
+
+        /// <summary>
+        /// Flag used to check if our IActuators are ready for execution.
+        /// </summary>
+        /// <seealso cref="ReadyActuatorsForExecution(IList{IActuator}, int, int, int)"/>
+        bool m_ReadyForExecution;
+
+        /// <summary>
+        /// The sum of all of the discrete branches for all of the <see cref="IActuator"/>s in this manager.
+        /// </summary>
+        internal int SumOfDiscreteBranchSizes { get; private set; }
+
+        /// <summary>
+        /// The number of the discrete branches for all of the <see cref="IActuator"/>s in this manager.
+        /// </summary>
+        internal int NumDiscreteActions { get; private set; }
+
+        /// <summary>
+        /// The number of continuous actions for all of the <see cref="IActuator"/>s in this manager.
+        /// </summary>
+        internal int NumContinuousActions { get; private set; }
+
+        /// <summary>
+        /// Returns the total actions which is calculated by <see cref="NumContinuousActions"/> + <see cref="NumDiscreteActions"/>.
+        /// </summary>
+        public int TotalNumberOfActions => NumContinuousActions + NumDiscreteActions;
+
+        /// <summary>
+        /// Gets the <see cref="IDiscreteActionMask"/> managed by this object.
+        /// </summary>
+        public ActuatorDiscreteActionMask DiscreteActionMask => m_DiscreteActionMask;
+
+        /// <summary>
+        /// The currently stored <see cref="ActionBuffers"/> object for the <see cref="IActuator"/>s managed by this class.
+        /// </summary>
+        public ActionBuffers StoredActions { get; private set; }
+
+        /// <summary>
+        /// Create an ActuatorList with a preset capacity.
+        /// </summary>
+        /// <param name="capacity">The capacity of the list to create.</param>
+        public ActuatorManager(int capacity = 0)
+        {
+            m_Actuators = new List<IActuator>(capacity);
+        }
+
+        /// <summary>
+        /// <see cref="ReadyActuatorsForExecution(IList{IActuator}, int, int, int)"/>
+        /// </summary>
+        void ReadyActuatorsForExecution()
+        {
+            ReadyActuatorsForExecution(m_Actuators, NumContinuousActions, SumOfDiscreteBranchSizes,
+                NumDiscreteActions);
+        }
+
+        /// <summary>
+        /// This method validates that all <see cref="IActuator"/>s have unique names
+        /// if the `DEBUG` preprocessor macro is defined, and allocates the appropriate buffers to manage the actions for
+        /// all of the <see cref="IActuator"/>s that may live on a particular object.
+        /// </summary>
+        /// <param name="actuators">The list of actuators to validate and allocate buffers for.</param>
+        /// <param name="numContinuousActions">The total number of continuous actions for all of the actuators.</param>
+        /// <param name="sumOfDiscreteBranches">The total sum of the discrete branches for all of the actuators in order
+        /// to be able to allocate an <see cref="IDiscreteActionMask"/>.</param>
+        /// <param name="numDiscreteBranches">The number of discrete branches for all of the actuators.</param>
+        internal void ReadyActuatorsForExecution(IList<IActuator> actuators, int numContinuousActions, int sumOfDiscreteBranches, int numDiscreteBranches)
+        {
+            if (m_ReadyForExecution)
+            {
+                return;
+            }
+#if DEBUG
+            // Make sure the names are actually unique
+            ValidateActuators();
+#endif
+
+            // Sort the Actuators by name to ensure determinism
+            SortActuators(m_Actuators);
+            var continuousActions = numContinuousActions == 0 ? ActionSegment<float>.Empty :
+                new ActionSegment<float>(new float[numContinuousActions]);
+            var discreteActions = numDiscreteBranches == 0 ? ActionSegment<int>.Empty : new ActionSegment<int>(new int[numDiscreteBranches]);
+
+            StoredActions = new ActionBuffers(continuousActions, discreteActions);
+            m_CombinedActionSpec = CombineActionSpecs(actuators);
+            m_DiscreteActionMask = new ActuatorDiscreteActionMask(actuators, sumOfDiscreteBranches, numDiscreteBranches, m_CombinedActionSpec.BranchSizes);
+            m_ReadyForExecution = true;
+        }
+
+        internal static ActionSpec CombineActionSpecs(IList<IActuator> actuators)
+        {
+            int numContinuousActions = 0;
+            int numDiscreteActions = 0;
+
+            foreach (var actuator in actuators)
+            {
+                numContinuousActions += actuator.ActionSpec.NumContinuousActions;
+                numDiscreteActions += actuator.ActionSpec.NumDiscreteActions;
+            }
+
+            int[] combinedBranchSizes;
+            if (numDiscreteActions == 0)
+            {
+                combinedBranchSizes = Array.Empty<int>();
+            }
+            else
+            {
+                combinedBranchSizes = new int[numDiscreteActions];
+                var start = 0;
+                for (var i = 0; i < actuators.Count; i++)
+                {
+                    var branchSizes = actuators[i].ActionSpec.BranchSizes;
+                    if (branchSizes != null)
+                    {
+                        Array.Copy(branchSizes, 0, combinedBranchSizes, start, branchSizes.Length);
+                        start += branchSizes.Length;
+                    }
+                }
+            }
+
+            return new ActionSpec(numContinuousActions, combinedBranchSizes);
+        }
+
+        /// <summary>
+        /// Returns an ActionSpec representing the concatenation of all IActuator's ActionSpecs
+        /// </summary>
+        /// <returns></returns>
+        public ActionSpec GetCombinedActionSpec()
+        {
+            ReadyActuatorsForExecution();
+            return m_CombinedActionSpec;
+        }
+
+        /// <summary>
+        /// Updates the local action buffer with the action buffer passed in.  If the buffer
+        /// passed in is null, the local action buffer will be cleared.
+        /// </summary>
+        /// <param name="actions">The <see cref="ActionBuffers"/> object which contains all of the
+        /// actions for the IActuators in this list.</param>
+        public void UpdateActions(ActionBuffers actions)
+        {
+            Profiler.BeginSample("ActuatorManager.UpdateActions");
+            ReadyActuatorsForExecution();
+            UpdateActionArray(actions.ContinuousActions, StoredActions.ContinuousActions);
+            UpdateActionArray(actions.DiscreteActions, StoredActions.DiscreteActions);
+            Profiler.EndSample();
+        }
+
+        static void UpdateActionArray<T>(ActionSegment<T> sourceActionBuffer, ActionSegment<T> destination)
+            where T : struct
+        {
+            if (sourceActionBuffer.Length <= 0)
+            {
+                destination.Clear();
+            }
+            else
+            {
+                if (sourceActionBuffer.Length != destination.Length)
+                {
+                    Debug.AssertFormat(sourceActionBuffer.Length == destination.Length,
+                        "sourceActionBuffer: {0} is a different size than destination: {1}.",
+                        sourceActionBuffer.Length,
+                        destination.Length);
+                }
+
+                Array.Copy(sourceActionBuffer.Array,
+                    sourceActionBuffer.Offset,
+                    destination.Array,
+                    destination.Offset,
+                    destination.Length);
+            }
+        }
+
+        /// <summary>
+        /// This method will trigger the writing to the <see cref="IDiscreteActionMask"/> by all of the actuators
+        /// managed by this object.
+        /// </summary>
+        public void WriteActionMask()
+        {
+            ReadyActuatorsForExecution();
+            m_DiscreteActionMask.ResetMask();
+            var offset = 0;
+            for (var i = 0; i < m_Actuators.Count; i++)
+            {
+                var actuator = m_Actuators[i];
+                if (actuator.ActionSpec.NumDiscreteActions > 0)
+                {
+                    m_DiscreteActionMask.CurrentBranchOffset = offset;
+                    actuator.WriteDiscreteActionMask(m_DiscreteActionMask);
+                    offset += actuator.ActionSpec.NumDiscreteActions;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Iterates through all of the IActuators in this list and calls their
+        /// <see cref="IHeuristicProvider.Heuristic"/> method on them, if implemented, with the appropriate
+        /// <see cref="ActionSegment{T}"/>s depending on their <see cref="ActionSpec"/>.
+        /// </summary>
+        public void ApplyHeuristic(in ActionBuffers actionBuffersOut)
+        {
+            Profiler.BeginSample("ActuatorManager.ApplyHeuristic");
+            var continuousStart = 0;
+            var discreteStart = 0;
+            for (var i = 0; i < m_Actuators.Count; i++)
+            {
+                var actuator = m_Actuators[i];
+                var numContinuousActions = actuator.ActionSpec.NumContinuousActions;
+                var numDiscreteActions = actuator.ActionSpec.NumDiscreteActions;
+
+                if (numContinuousActions == 0 && numDiscreteActions == 0)
+                {
+                    continue;
+                }
+
+                var continuousActions = ActionSegment<float>.Empty;
+                if (numContinuousActions > 0)
+                {
+                    continuousActions = new ActionSegment<float>(actionBuffersOut.ContinuousActions.Array,
+                        continuousStart,
+                        numContinuousActions);
+                }
+
+                var discreteActions = ActionSegment<int>.Empty;
+                if (numDiscreteActions > 0)
+                {
+                    discreteActions = new ActionSegment<int>(actionBuffersOut.DiscreteActions.Array,
+                        discreteStart,
+                        numDiscreteActions);
+                }
+                actuator.Heuristic(new ActionBuffers(continuousActions, discreteActions));
+                continuousStart += numContinuousActions;
+                discreteStart += numDiscreteActions;
+            }
+            Profiler.EndSample();
+        }
+
+        /// <summary>
+        /// Iterates through all of the IActuators in this list and calls their
+        /// <see cref="IActionReceiver.OnActionReceived"/> method on them with the appropriate
+        /// <see cref="ActionSegment{T}"/>s depending on their <see cref="ActionSpec"/>.
+        /// </summary>
+        public void ExecuteActions()
+        {
+            Profiler.BeginSample("ActuatorManager.ExecuteActions");
+            ReadyActuatorsForExecution();
+            var continuousStart = 0;
+            var discreteStart = 0;
+            for (var i = 0; i < m_Actuators.Count; i++)
+            {
+                var actuator = m_Actuators[i];
+                var numContinuousActions = actuator.ActionSpec.NumContinuousActions;
+                var numDiscreteActions = actuator.ActionSpec.NumDiscreteActions;
+
+                if (numContinuousActions == 0 && numDiscreteActions == 0)
+                {
+                    continue;
+                }
+
+                var continuousActions = ActionSegment<float>.Empty;
+                if (numContinuousActions > 0)
+                {
+                    continuousActions = new ActionSegment<float>(StoredActions.ContinuousActions.Array,
+                        continuousStart,
+                        numContinuousActions);
+                }
+
+                var discreteActions = ActionSegment<int>.Empty;
+                if (numDiscreteActions > 0)
+                {
+                    discreteActions = new ActionSegment<int>(StoredActions.DiscreteActions.Array,
+                        discreteStart,
+                        numDiscreteActions);
+                }
+
+                actuator.OnActionReceived(new ActionBuffers(continuousActions, discreteActions));
+                continuousStart += numContinuousActions;
+                discreteStart += numDiscreteActions;
+            }
+            Profiler.EndSample();
+        }
+
+        /// <summary>
+        /// Resets the <see cref="ActionBuffers"/> to be all
+        /// zeros and calls <see cref="IActuator.ResetData"/> on each <see cref="IActuator"/> managed by this object.
+        /// </summary>
+        public void ResetData()
+        {
+            if (!m_ReadyForExecution)
+            {
+                return;
+            }
+            StoredActions.Clear();
+            for (var i = 0; i < m_Actuators.Count; i++)
+            {
+                m_Actuators[i].ResetData();
+            }
+            m_DiscreteActionMask.ResetMask();
+        }
+
+        /// <summary>
+        /// Sorts the <see cref="IActuator"/>s according to their <see cref="IActuator.Name"/> value.
+        /// </summary>
+        internal static void SortActuators(List<IActuator> actuators)
+        {
+            actuators.Sort((x, y) => string.Compare(x.Name, y.Name, StringComparison.InvariantCulture));
+        }
+
+        /// <summary>
+        /// Validates that the IActuators managed by this object have unique names.
+        /// Each Actuator needs to have a unique name in order for this object to ensure that the storage of action
+        /// buffers, and execution of Actuators remains deterministic across different sessions of running.
+        /// </summary>
+        void ValidateActuators()
+        {
+            for (var i = 0; i < m_Actuators.Count - 1; i++)
+            {
+                Debug.Assert(
+                    !m_Actuators[i].Name.Equals(m_Actuators[i + 1].Name),
+                    "Actuator names must be unique.");
+            }
+        }
+
+        /// <summary>
+        /// Helper method to update bookkeeping items around buffer management for actuators added to this object.
+        /// </summary>
+        /// <param name="actuatorItem">The IActuator to keep bookkeeping for.</param>
+        void AddToBufferSizes(IActuator actuatorItem)
+        {
+            if (actuatorItem == null)
+            {
+                return;
+            }
+
+            NumContinuousActions += actuatorItem.ActionSpec.NumContinuousActions;
+            NumDiscreteActions += actuatorItem.ActionSpec.NumDiscreteActions;
+            SumOfDiscreteBranchSizes += actuatorItem.ActionSpec.SumOfDiscreteBranchSizes;
+        }
+
+        /// <summary>
+        /// Helper method to update bookkeeping items around buffer management for actuators removed from this object.
+        /// </summary>
+        /// <param name="actuatorItem">The IActuator to keep bookkeeping for.</param>
+        void SubtractFromBufferSize(IActuator actuatorItem)
+        {
+            if (actuatorItem == null)
+            {
+                return;
+            }
+
+            NumContinuousActions -= actuatorItem.ActionSpec.NumContinuousActions;
+            NumDiscreteActions -= actuatorItem.ActionSpec.NumDiscreteActions;
+            SumOfDiscreteBranchSizes -= actuatorItem.ActionSpec.SumOfDiscreteBranchSizes;
+        }
+
+        /// <summary>
+        /// Sets all of the bookkeeping items back to 0.
+        /// </summary>
+        void ClearBufferSizes()
+        {
+            NumContinuousActions = NumDiscreteActions = SumOfDiscreteBranchSizes = 0;
+        }
+
+        /// <summary>
+        /// Add an array of <see cref="IActuator"/>s at once.
+        /// </summary>
+        /// <param name="actuators">The array of <see cref="IActuator"/>s to add.</param>
+        public void AddActuators(IActuator[] actuators)
+        {
+            for (var i = 0; i < actuators.Length; i++)
+            {
+                Add(actuators[i]);
+            }
+        }
+
+        /*********************************************************************************
+         * IList implementation that delegates to m_Actuators List.                      *
+         *********************************************************************************/
+
+        /// <inheritdoc/>
+        public IEnumerator<IActuator> GetEnumerator()
+        {
+            return m_Actuators.GetEnumerator();
+        }
+
+        /// <inheritdoc/>
+        IEnumerator IEnumerable.GetEnumerator()
+        {
+            return ((IEnumerable)m_Actuators).GetEnumerator();
+        }
+
+        /// <inheritdoc/>
+        public void Add(IActuator item)
+        {
+            Debug.Assert(m_ReadyForExecution == false,
+                "Cannot add to the ActuatorManager after its buffers have been initialized");
+            m_Actuators.Add(item);
+            AddToBufferSizes(item);
+        }
+
+        /// <inheritdoc/>
+        public void Clear()
+        {
+            Debug.Assert(m_ReadyForExecution == false,
+                "Cannot clear the ActuatorManager after its buffers have been initialized");
+            m_Actuators.Clear();
+            ClearBufferSizes();
+        }
+
+        /// <inheritdoc/>
+        public bool Contains(IActuator item)
+        {
+            return m_Actuators.Contains(item);
+        }
+
+        /// <inheritdoc/>
+        public void CopyTo(IActuator[] array, int arrayIndex)
+        {
+            m_Actuators.CopyTo(array, arrayIndex);
+        }
+
+        /// <inheritdoc/>
+        public bool Remove(IActuator item)
+        {
+            Debug.Assert(m_ReadyForExecution == false,
+                "Cannot remove from the ActuatorManager after its buffers have been initialized");
+            if (m_Actuators.Remove(item))
+            {
+                SubtractFromBufferSize(item);
+                return true;
+            }
+            return false;
+        }
+
+        /// <inheritdoc/>
+        public int Count => m_Actuators.Count;
+
+        /// <inheritdoc/>
+        public bool IsReadOnly => false;
+
+        /// <inheritdoc/>
+        public int IndexOf(IActuator item)
+        {
+            return m_Actuators.IndexOf(item);
+        }
+
+        /// <inheritdoc/>
+        public void Insert(int index, IActuator item)
+        {
+            Debug.Assert(m_ReadyForExecution == false,
+                "Cannot insert into the ActuatorManager after its buffers have been initialized");
+            m_Actuators.Insert(index, item);
+            AddToBufferSizes(item);
+        }
+
+        /// <inheritdoc/>
+        public void RemoveAt(int index)
+        {
+            Debug.Assert(m_ReadyForExecution == false,
+                "Cannot remove from the ActuatorManager after its buffers have been initialized");
+            var actuator = m_Actuators[index];
+            SubtractFromBufferSize(actuator);
+            m_Actuators.RemoveAt(index);
+        }
+
+        /// <inheritdoc/>
+        public IActuator this[int index]
+        {
+            get => m_Actuators[index];
+            set
+            {
+                Debug.Assert(m_ReadyForExecution == false,
+                    "Cannot modify the ActuatorManager after its buffers have been initialized");
+                var old = m_Actuators[index];
+                SubtractFromBufferSize(old);
+                m_Actuators[index] = value;
+                AddToBufferSizes(value);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs.meta b/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..aa56b5ca9fba4ee4ed47e8825763280801890169
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 7bb5b1e3779d4342a8e70f6e3c1d67cc
+timeCreated: 1593031463
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs b/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fbdb398ed1b4d231c599083e009e58e1cc24f861
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
@@ -0,0 +1,192 @@
+using System;
+using System.Linq;
+using UnityEngine;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// A structure that wraps the <see cref="ActionSegment{T}"/>s for a particular <see cref="IActionReceiver"/> and is
+    /// used when <see cref="IActionReceiver.OnActionReceived"/> is called.
+    /// </summary>
+    public readonly struct ActionBuffers
+    {
+        /// <summary>
+        /// An empty action buffer.
+        /// </summary>
+        public static ActionBuffers Empty = new ActionBuffers(ActionSegment<float>.Empty, ActionSegment<int>.Empty);
+
+        /// <summary>
+        /// Holds the Continuous <see cref="ActionSegment{T}"/> to be used by an <see cref="IActionReceiver"/>.
+        /// </summary>
+        public ActionSegment<float> ContinuousActions { get; }
+
+        /// <summary>
+        /// Holds the Discrete <see cref="ActionSegment{T}"/> to be used by an <see cref="IActionReceiver"/>.
+        /// </summary>
+        public ActionSegment<int> DiscreteActions { get; }
+
+        /// <summary>
+        /// Create an <see cref="ActionBuffers"/> instance with discrete actions stored as a float array.  This exists
+        /// to achieve backward compatibility with the former Agent methods which used a float array for both continuous
+        /// and discrete actions.
+        /// </summary>
+        /// <param name="discreteActions">The float array of discrete actions.</param>
+        /// <returns>An <see cref="ActionBuffers"/> instance initialized with a <see cref="DiscreteActions"/>
+        /// <see cref="ActionSegment{T}"/> initialized from a float array.</returns>
+        public static ActionBuffers FromDiscreteActions(float[] discreteActions)
+        {
+            return new ActionBuffers(ActionSegment<float>.Empty, discreteActions == null ? ActionSegment<int>.Empty
+                : new ActionSegment<int>(Array.ConvertAll(discreteActions,
+                    x => (int)x)));
+        }
+
+        /// <summary>
+        /// Construct an <see cref="ActionBuffers"/> instance with the continuous and discrete actions that will
+        /// be used.
+        /// /// </summary>
+        /// <param name="continuousActions">The continuous actions to send to an <see cref="IActionReceiver"/>.</param>
+        /// <param name="discreteActions">The discrete actions to send to an <see cref="IActionReceiver"/>.</param>
+        public ActionBuffers(float[] continuousActions, int[] discreteActions)
+            : this(new ActionSegment<float>(continuousActions), new ActionSegment<int>(discreteActions)) { }
+
+        /// <summary>
+        /// Construct an <see cref="ActionBuffers"/> instance with the continuous and discrete actions that will
+        /// be used.
+        /// </summary>
+        /// <param name="continuousActions">The continuous actions to send to an <see cref="IActionReceiver"/>.</param>
+        /// <param name="discreteActions">The discrete actions to send to an <see cref="IActionReceiver"/>.</param>
+        public ActionBuffers(ActionSegment<float> continuousActions, ActionSegment<int> discreteActions)
+        {
+            ContinuousActions = continuousActions;
+            DiscreteActions = discreteActions;
+        }
+
+        /// <summary>
+        /// Construct an <see cref="ActionBuffers"/> instance with <see cref="ActionSpec"/>. All values are initialized to zeros.
+        /// /// </summary>
+        /// <param name="actionSpec">The <see cref="ActionSpec"/>  to send to an <see cref="IActionReceiver"/>.</param>
+        public ActionBuffers(ActionSpec actionSpec)
+            : this(new ActionSegment<float>(new float[actionSpec.NumContinuousActions]),
+            new ActionSegment<int>(new int[actionSpec.NumDiscreteActions]))
+        { }
+
+        /// <summary>
+        /// Create an <see cref="ActionBuffers"/> instance with ActionSpec and all actions stored as a float array.
+        /// </summary>
+        /// <param name="actionSpec"><see cref="ActionSpec"/> of the <see cref="ActionBuffers"/></param>
+        /// <param name="actions">The float array of all actions, including discrete and continuous actions.</param>
+        /// <returns>An <see cref="ActionBuffers"/> instance initialized with a <see cref="ActionSpec"/> and a float array.</returns>
+        internal static ActionBuffers FromActionSpec(ActionSpec actionSpec, float[] actions)
+        {
+            if (actions == null)
+            {
+                return ActionBuffers.Empty;
+            }
+
+            Debug.Assert(actions.Length == actionSpec.NumContinuousActions + actionSpec.NumDiscreteActions,
+                $"The length of '{nameof(actions)}' does not match the total size of ActionSpec.\n" +
+                $"{nameof(actions)}.Length: {actions.Length}\n" +
+                $"{nameof(actionSpec)}: {actionSpec.NumContinuousActions + actionSpec.NumDiscreteActions}");
+
+            ActionSegment<float> continuousActionSegment = ActionSegment<float>.Empty;
+            ActionSegment<int> discreteActionSegment = ActionSegment<int>.Empty;
+            int offset = 0;
+            if (actionSpec.NumContinuousActions > 0)
+            {
+                continuousActionSegment = new ActionSegment<float>(actions, 0, actionSpec.NumContinuousActions);
+                offset += actionSpec.NumContinuousActions;
+            }
+            if (actionSpec.NumDiscreteActions > 0)
+            {
+                int[] discreteActions = new int[actionSpec.NumDiscreteActions];
+                for (var i = 0; i < actionSpec.NumDiscreteActions; i++)
+                {
+                    discreteActions[i] = (int)actions[i + offset];
+                }
+                discreteActionSegment = new ActionSegment<int>(discreteActions);
+            }
+
+            return new ActionBuffers(continuousActionSegment, discreteActionSegment);
+        }
+
+        /// <summary>
+        /// Clear the <see cref="ContinuousActions"/> and <see cref="DiscreteActions"/> segments to be all zeros.
+        /// </summary>
+        public void Clear()
+        {
+            ContinuousActions.Clear();
+            DiscreteActions.Clear();
+        }
+
+        /// <summary>
+        /// Check if the <see cref="ActionBuffers"/> is empty.
+        /// </summary>
+        /// <returns>Whether the buffers are empty.</returns>
+        public bool IsEmpty()
+        {
+            return ContinuousActions.IsEmpty() && DiscreteActions.IsEmpty();
+        }
+
+        /// <summary>
+        /// Indicates whether the current ActionBuffers is equal to another ActionBuffers.
+        /// </summary>
+        /// <param name="obj">An ActionBuffers to compare with this ActionBuffers.</param>
+        /// <returns>true if the current ActionBuffers is equal to the other parameter; otherwise, false.</returns>
+        public override bool Equals(object obj)
+        {
+            if (!(obj is ActionBuffers))
+            {
+                return false;
+            }
+
+            var ab = (ActionBuffers)obj;
+            return ab.ContinuousActions.SequenceEqual(ContinuousActions) &&
+                ab.DiscreteActions.SequenceEqual(DiscreteActions);
+        }
+
+        /// <summary>
+        /// Computes the hash code of the ActionBuffers.
+        /// </summary>
+        /// <returns>A hash code for the current ActionBuffers.</returns>
+        public override int GetHashCode()
+        {
+            unchecked
+            {
+                return (ContinuousActions.GetHashCode() * 397) ^ DiscreteActions.GetHashCode();
+            }
+        }
+    }
+
+    /// <summary>
+    /// An interface that describes an object that can receive actions from a Reinforcement Learning network.
+    /// </summary>
+    public interface IActionReceiver
+    {
+        /// <summary>
+        /// Method called in order too allow object to execute actions based on the
+        /// <see cref="ActionBuffers"/> contents.  The structure of the contents in the <see cref="ActionBuffers"/>
+        /// are defined by the <see cref="ActionSpec"/>.
+        /// </summary>
+        /// <param name="actionBuffers">The data structure containing the action buffers for this object.</param>
+        void OnActionReceived(ActionBuffers actionBuffers);
+
+        /// <summary>
+        /// Implement `WriteDiscreteActionMask()` to modify the masks for discrete
+        /// actions. When using discrete actions, the agent will not perform the masked
+        /// action.
+        /// </summary>
+        /// <param name="actionMask">
+        /// The action mask for the agent.
+        /// </param>
+        /// <remarks>
+        /// When using Discrete Control, you can prevent the Agent from using a certain
+        /// action by masking it with <see cref="IDiscreteActionMask.SetActionEnabled"/>.
+        ///
+        /// See [Agents - Actions] for more information on masking actions.
+        ///
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// </remarks>
+        /// <seealso cref="IActionReceiver.OnActionReceived"/>
+        void WriteDiscreteActionMask(IDiscreteActionMask actionMask);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs.meta b/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b14a69d21c1b1a31e4868a693840581063968f71
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: b25a5b3027c9476ea1a310241be0f10f
+timeCreated: 1594756775
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/IActuator.cs b/com.unity.ml-agents/Runtime/Actuators/IActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..aa2675905a5e34ac70c0bdb197db5746b6b448ea
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IActuator.cs
@@ -0,0 +1,42 @@
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Abstraction that facilitates the execution of actions.
+    /// </summary>
+    public interface IActuator : IActionReceiver, IHeuristicProvider
+    {
+        /// <summary>
+        /// The specification of the actions for this IActuator.
+        /// </summary>
+        /// <seealso cref="ActionSpec"/>
+        ActionSpec ActionSpec { get; }
+
+        /// <summary>
+        /// Gets the name of this IActuator which will be used to sort it.
+        /// </summary>
+        /// <returns></returns>
+        string Name { get; }
+
+        /// <summary>
+        /// Resets the internal state of the actuator. This is called at the end of an Agent's episode.
+        /// Most implementations can leave this empty.
+        /// </summary>
+        void ResetData();
+    }
+
+    /// <summary>
+    /// Helper methods to be shared by all classes that implement <see cref="IActuator"/>.
+    /// </summary>
+    public static class IActuatorExtensions
+    {
+        /// <summary>
+        /// Returns the number of discrete branches + the number of continuous actions.
+        /// </summary>
+        /// <param name="actuator"></param>
+        /// <returns></returns>
+        public static int TotalNumberOfActions(this IActuator actuator)
+        {
+            return actuator.ActionSpec.NumContinuousActions + actuator.ActionSpec.NumDiscreteActions;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/IActuator.cs.meta b/com.unity.ml-agents/Runtime/Actuators/IActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4fd0d172cafd5607cee6c8893c283828bd72c948
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 780d7f0a675f44bfa784b370025b51c3
+timeCreated: 1592848317
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs b/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8b77672d178c546127e24f30d4275e37d14770b4
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs
@@ -0,0 +1,49 @@
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Identifiers for "built in" actuator types.
+    /// These are only used for analytics, and should not be used for any runtime decisions.
+    ///
+    /// NOTE: Do not renumber these, since the values are used for analytics. Renaming is allowed though.
+    /// </summary>
+    public enum BuiltInActuatorType
+    {
+        /// <summary>
+        /// Default Sensor type if it cannot be determined.
+        /// </summary>
+        Unknown = 0,
+
+        /// <summary>
+        /// VectorActuator used by the Agent
+        /// </summary>
+        AgentVectorActuator = 1,
+
+        /// <summary>
+        /// Corresponds to <see cref="VectorActuator"/>
+        /// </summary>
+        VectorActuator = 2,
+
+        /// <summary>
+        /// Corresponds to the Match3Actuator in com.unity.ml-agents.extensions.
+        /// </summary>
+        Match3Actuator = 3,
+
+        /// <summary>
+        /// Corresponds to the InputActionActuator in com.unity.ml-agents.extensions.
+        /// </summary>
+        InputActionActuator = 4,
+    }
+
+    /// <summary>
+    /// Interface for actuators that are provided as part of ML-Agents.
+    /// User-implemented actuators don't need to use this interface.
+    /// </summary>
+    internal interface IBuiltInActuator
+    {
+        /// <summary>
+        /// Return the corresponding BuiltInActuatorType for the actuator.
+        /// </summary>
+        /// <returns>A BuiltInActuatorType corresponding to the actuator.</returns>
+        BuiltInActuatorType GetBuiltInActuatorType();
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs.meta b/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..da1d96f271cd7d00412156c95bfe17cf5625143a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IBuiltInActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e3d7ef9a9a5043549cc5c0bbee520810
+timeCreated: 1613514041
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs b/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs
new file mode 100644
index 0000000000000000000000000000000000000000..713f000f8de20d1450fea6fe4958dfd711240597
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs
@@ -0,0 +1,26 @@
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Interface for writing a mask to disable discrete actions for agents for the next decision.
+    /// </summary>
+    public interface IDiscreteActionMask
+    {
+        /// <summary>
+        /// Set whether or not the action index for the given branch is allowed.
+        /// </summary>
+        /// <remarks>
+        /// By default, all discrete actions are allowed.
+        /// If isEnabled is false, the agent will not be able to perform the actions passed as argument
+        /// at the next decision for the specified action branch. The actionIndex corresponds
+        /// to the action options the agent will be unable to perform.
+        ///
+        /// See [Agents - Actions] for more information on masking actions.
+        ///
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#masking-discrete-actions
+        /// </remarks>
+        /// <param name="branch">The branch for which the actions will be masked.</param>
+        /// <param name="actionIndex">Index of the action.</param>
+        /// <param name="isEnabled">Whether the action is allowed or not.</param>
+        void SetActionEnabled(int branch, int actionIndex, bool isEnabled);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs.meta b/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ebfa10158fcb789feb1ecb814a487f42da026176
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 1bc4e4b71bf4470789488fab2ee65388
+timeCreated: 1595369065
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs b/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b992361c8387498bdc18ca8c40346ff4c450391a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs
@@ -0,0 +1,18 @@
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// Interface that allows objects to fill out an <see cref="ActionBuffers"/> data structure for controlling
+    /// behavior of Agents or Actuators.
+    /// </summary>
+    public interface IHeuristicProvider
+    {
+        /// <summary>
+        /// Method called on objects which are expected to fill out the <see cref="ActionBuffers"/> data structure.
+        /// Object that implement this interface should be careful to be consistent in the placement of their actions
+        /// in the <see cref="ActionBuffers"/> data structure.
+        /// </summary>
+        /// <param name="actionBuffersOut">The <see cref="ActionBuffers"/> data structure to be filled by the
+        /// object implementing this interface.</param>
+        void Heuristic(in ActionBuffers actionBuffersOut);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs.meta b/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ca8338a0720edf0ca39e417fb9bae44e55c6c874
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/IHeuristicProvider.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: be90ffb28f39444a8fb02dfd4a82870c
+timeCreated: 1610057456
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs b/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..586058aad39da30e516dd174f97839e6f165e67c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs
@@ -0,0 +1,105 @@
+using UnityEngine.Profiling;
+
+namespace Unity.MLAgents.Actuators
+{
+    /// <summary>
+    /// IActuator implementation that forwards calls to an <see cref="IActionReceiver"/> and an <see cref="IHeuristicProvider"/>.
+    /// </summary>
+    internal class VectorActuator : IActuator, IBuiltInActuator
+    {
+        IActionReceiver m_ActionReceiver;
+        IHeuristicProvider m_HeuristicProvider;
+
+        ActionBuffers m_ActionBuffers;
+        internal ActionBuffers ActionBuffers
+        {
+            get => m_ActionBuffers;
+            private set => m_ActionBuffers = value;
+        }
+
+        /// <summary>
+        /// Create a VectorActuator that forwards to the provided IActionReceiver.
+        /// </summary>
+        /// <param name="actionReceiver">The <see cref="IActionReceiver"/> used for OnActionReceived and WriteDiscreteActionMask.
+        /// If this parameter also implements <see cref="IHeuristicProvider"/> it will be cast and used to forward calls to
+        /// <see cref="IHeuristicProvider.Heuristic"/>.</param>
+        /// <param name="actionSpec"></param>
+        /// <param name="name"></param>
+        public VectorActuator(IActionReceiver actionReceiver,
+                              ActionSpec actionSpec,
+                              string name = "VectorActuator")
+            : this(actionReceiver, actionReceiver as IHeuristicProvider, actionSpec, name) { }
+
+        /// <summary>
+        /// Create a VectorActuator that forwards to the provided IActionReceiver.
+        /// </summary>
+        /// <param name="actionReceiver">The <see cref="IActionReceiver"/> used for OnActionReceived and WriteDiscreteActionMask.</param>
+        /// <param name="heuristicProvider">The <see cref="IHeuristicProvider"/> used to fill the <see cref="ActionBuffers"/>
+        /// for Heuristic Policies.</param>
+        /// <param name="actionSpec"></param>
+        /// <param name="name"></param>
+        public VectorActuator(IActionReceiver actionReceiver,
+                              IHeuristicProvider heuristicProvider,
+                              ActionSpec actionSpec,
+                              string name = "VectorActuator")
+        {
+            m_ActionReceiver = actionReceiver;
+            m_HeuristicProvider = heuristicProvider;
+            ActionSpec = actionSpec;
+            string suffix;
+            if (actionSpec.NumContinuousActions == 0)
+            {
+                suffix = "-Discrete";
+            }
+            else if (actionSpec.NumDiscreteActions == 0)
+            {
+                suffix = "-Continuous";
+            }
+            else
+            {
+                suffix = $"-Continuous-{actionSpec.NumContinuousActions}-Discrete-{actionSpec.NumDiscreteActions}";
+            }
+            Name = name + suffix;
+        }
+
+        /// <inheritdoc />
+        public void ResetData()
+        {
+            m_ActionBuffers = ActionBuffers.Empty;
+        }
+
+        /// <inheritdoc />
+        public void OnActionReceived(ActionBuffers actionBuffers)
+        {
+            Profiler.BeginSample("VectorActuator.OnActionReceived");
+            m_ActionBuffers = actionBuffers;
+            m_ActionReceiver.OnActionReceived(m_ActionBuffers);
+            Profiler.EndSample();
+        }
+
+        public void Heuristic(in ActionBuffers actionBuffersOut)
+        {
+            Profiler.BeginSample("VectorActuator.Heuristic");
+            m_HeuristicProvider?.Heuristic(actionBuffersOut);
+            Profiler.EndSample();
+        }
+
+        /// <inheritdoc />
+        public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+        {
+            m_ActionReceiver.WriteDiscreteActionMask(actionMask);
+        }
+
+        /// <inheritdoc/>
+        public ActionSpec ActionSpec { get; }
+
+        /// <inheritdoc />
+        public string Name { get; }
+
+        /// <inheritdoc />
+        public virtual BuiltInActuatorType GetBuiltInActuatorType()
+        {
+            return BuiltInActuatorType.VectorActuator;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs.meta b/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6e9f68b913b55433c4b25338c5bd8d732581ce1f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ff7a3292c0b24b23b3f1c0eeb690ec4c
+timeCreated: 1593023833
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Agent.cs b/com.unity.ml-agents/Runtime/Agent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7af3631e827f228ec243ca91987e1bb519663c91
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Agent.cs
@@ -0,0 +1,1427 @@
+using System;
+using System.Collections.Generic;
+using System.Collections.ObjectModel;
+using UnityEngine;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Struct that contains all the information for an Agent, including its
+    /// observations, actions and current status.
+    /// </summary>
+    public struct AgentInfo
+    {
+        /// <summary>
+        /// Keeps track of the last actions taken by the Brain.
+        /// </summary>
+        public ActionBuffers storedActions;
+
+        /// <summary>
+        /// For discrete control, specifies the actions that the agent cannot take.
+        /// An element of the mask array is <c>true</c> if the action is prohibited.
+        /// </summary>
+        public bool[] discreteActionMasks;
+
+        /// <summary>
+        /// The current agent reward.
+        /// </summary>
+        public float reward;
+
+        /// <summary>
+        /// The current group reward received by the agent.
+        /// </summary>
+        public float groupReward;
+
+        /// <summary>
+        /// Whether the agent is done or not.
+        /// </summary>
+        public bool done;
+
+        /// <summary>
+        /// Whether the agent has reached its max step count for this episode.
+        /// </summary>
+        public bool maxStepReached;
+
+        /// <summary>
+        /// Episode identifier each agent receives at every reset. It is used
+        /// to separate between different agents in the environment.
+        /// </summary>
+        public int episodeId;
+
+        /// <summary>
+        /// MultiAgentGroup identifier.
+        /// </summary>
+        public int groupId;
+
+        public void ClearActions()
+        {
+            storedActions.Clear();
+        }
+
+        public void CopyActions(ActionBuffers actionBuffers)
+        {
+            var continuousActions = storedActions.ContinuousActions;
+            for (var i = 0; i < actionBuffers.ContinuousActions.Length; i++)
+            {
+                continuousActions[i] = actionBuffers.ContinuousActions[i];
+            }
+            var discreteActions = storedActions.DiscreteActions;
+            for (var i = 0; i < actionBuffers.DiscreteActions.Length; i++)
+            {
+                discreteActions[i] = actionBuffers.DiscreteActions[i];
+            }
+        }
+    }
+
+    /// <summary>
+    /// Simple wrapper around VectorActuator that overrides GetBuiltInActuatorType
+    /// so that it can be distinguished from a standard VectorActuator.
+    /// </summary>
+    internal class AgentVectorActuator : VectorActuator
+    {
+        public AgentVectorActuator(IActionReceiver actionReceiver,
+                                   IHeuristicProvider heuristicProvider,
+                                   ActionSpec actionSpec,
+                                   string name = "VectorActuator"
+        ) : base(actionReceiver, heuristicProvider, actionSpec, name)
+        { }
+
+        public override BuiltInActuatorType GetBuiltInActuatorType()
+        {
+            return BuiltInActuatorType.AgentVectorActuator;
+        }
+    }
+
+    /// <summary>
+    /// An agent is an actor that can observe its environment, decide on the
+    /// best course of action using those observations, and execute those actions
+    /// within the environment.
+    /// </summary>
+    /// <remarks>
+    /// Use the Agent class as the subclass for implementing your own agents. Add
+    /// your Agent implementation to a [GameObject] in the [Unity scene] that serves
+    /// as the agent's environment.
+    ///
+    /// Agents in an environment operate in *steps*. At each step, an agent collects observations,
+    /// passes them to its decision-making policy, and receives an action vector in response.
+    ///
+    /// Agents make observations using <see cref="ISensor"/> implementations. The ML-Agents
+    /// API provides implementations for visual observations (<see cref="CameraSensor"/>)
+    /// raycast observations (<see cref="RayPerceptionSensor"/>), and arbitrary
+    /// data observations (<see cref="VectorSensor"/>). You can add the
+    /// <see cref="CameraSensorComponent"/> and <see cref="RayPerceptionSensorComponent2D"/> or
+    /// <see cref="RayPerceptionSensorComponent3D"/> components to an agent's [GameObject] to use
+    /// those sensor types. You can implement the <see cref="CollectObservations(VectorSensor)"/>
+    /// function in your Agent subclass to use a vector observation. The Agent class calls this
+    /// function before it uses the observation vector to make a decision. (If you only use
+    /// visual or raycast observations, you do not need to implement
+    /// <see cref="CollectObservations"/>.)
+    ///
+    /// Assign a decision making policy to an agent using a <see cref="BehaviorParameters"/>
+    /// component attached to the agent's [GameObject]. The <see cref="BehaviorType"/> setting
+    /// determines how decisions are made:
+    ///
+    /// * <see cref="BehaviorType.Default"/>: decisions are made by the external process,
+    ///   when connected. Otherwise, decisions are made using inference. If no inference model
+    ///   is specified in the BehaviorParameters component, then heuristic decision
+    ///   making is used.
+    /// * <see cref="BehaviorType.InferenceOnly"/>: decisions are always made using the trained
+    ///   model specified in the <see cref="BehaviorParameters"/> component.
+    /// * <see cref="BehaviorType.HeuristicOnly"/>: when a decision is needed, the agent's
+    ///   <see cref="Heuristic(in ActionBuffers)"/> function is called. Your implementation is responsible for
+    ///   providing the appropriate action.
+    ///
+    /// To trigger an agent decision automatically, you can attach a <see cref="DecisionRequester"/>
+    /// component to the Agent game object. You can also call the agent's <see cref="RequestDecision"/>
+    /// function manually. You only need to call <see cref="RequestDecision"/> when the agent is
+    /// in a position to act upon the decision. In many cases, this will be every [FixedUpdate]
+    /// callback, but could be less frequent. For example, an agent that hops around its environment
+    /// can only take an action when it touches the ground, so several frames might elapse between
+    /// one decision and the need for the next.
+    ///
+    /// Use the <see cref="OnActionReceived(ActionBuffers)"/> function to implement the actions your agent can take,
+    /// such as moving to reach a goal or interacting with its environment.
+    ///
+    /// When you call <see cref="EndEpisode"/> on an agent or the agent reaches its <see cref="MaxStep"/> count,
+    /// its current episode ends. You can reset the agent -- or remove it from the
+    /// environment -- by implementing the <see cref="OnEpisodeBegin"/> function. An agent also
+    /// becomes done when the <see cref="Academy"/> resets the environment, which only happens when
+    /// the <see cref="Academy"/> receives a reset signal from an external process via the
+    /// <see cref="Academy.Communicator"/>.
+    ///
+    /// The Agent class extends the Unity [MonoBehaviour] class. You can implement the
+    /// standard [MonoBehaviour] functions as needed for your agent. Since an agent's
+    /// observations and actions typically take place during the [FixedUpdate] phase, you should
+    /// only use the [MonoBehaviour.Update] function for cosmetic purposes. If you override the [MonoBehaviour]
+    /// methods, [OnEnable()] or [OnDisable()], always call the base Agent class implementations.
+    ///
+    /// You can implement the <see cref="Heuristic(in ActionBuffers)"/> function to specify agent actions using
+    /// your own heuristic algorithm. Implementing a heuristic function can be useful
+    /// for debugging. For example, you can use keyboard input to select agent actions in
+    /// order to manually control an agent's behavior.
+    ///
+    /// Note that you can change the inference model assigned to an agent at any step
+    /// by calling <see cref="SetModel"/>.
+    ///
+    /// See [Agents] and [Reinforcement Learning in Unity] in the [Unity ML-Agents Toolkit manual] for
+    /// more information on creating and training agents.
+    ///
+    /// For sample implementations of agent behavior, see the examples available in the
+    /// [Unity ML-Agents Toolkit] on Github.
+    ///
+    /// [MonoBehaviour]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.html
+    /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+    /// [Unity scene]: https://docs.unity3d.com/Manual/CreatingScenes.html
+    /// [FixedUpdate]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.FixedUpdate.html
+    /// [MonoBehaviour.Update]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.Update.html
+    /// [OnEnable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnEnable.html
+    /// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
+    /// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
+    /// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design.md
+    /// [Unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Readme.md
+    ///
+    /// </remarks>
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/" +
+        "docs/Learning-Environment-Design-Agents.md")]
+    [Serializable]
+    [RequireComponent(typeof(BehaviorParameters))]
+    [DefaultExecutionOrder(-50)]
+    public partial class Agent : MonoBehaviour, ISerializationCallbackReceiver, IActionReceiver, IHeuristicProvider
+    {
+        IPolicy m_Brain;
+        BehaviorParameters m_PolicyFactory;
+
+        /// This code is here to make the upgrade path for users using MaxStep
+        /// easier. We will hook into the Serialization code and make sure that
+        /// agentParameters.maxStep and this.maxStep are in sync.
+        [Serializable]
+        internal struct AgentParameters
+        {
+            public int maxStep;
+        }
+
+        [SerializeField]
+        [HideInInspector]
+        internal AgentParameters agentParameters;
+        [SerializeField]
+        [HideInInspector]
+        internal bool hasUpgradedFromAgentParameters;
+
+        /// <summary>
+        /// The maximum number of steps the agent takes before being done.
+        /// </summary>
+        /// <value>The maximum steps for an agent to take before it resets; or 0 for
+        /// unlimited steps.</value>
+        /// <remarks>
+        /// The max step value determines the maximum length of an agent's episodes.
+        /// Set to a positive integer to limit the episode length to that many steps.
+        /// Set to 0 for unlimited episode length.
+        ///
+        /// When an episode ends and a new one begins, the Agent object's
+        /// <seealso cref="OnEpisodeBegin"/> function is called. You can implement
+        /// <see cref="OnEpisodeBegin"/> to reset the agent or remove it from the
+        /// environment. An agent's episode can also end if you call its <seealso cref="EndEpisode"/>
+        /// method or an external process resets the environment through the <see cref="Academy"/>.
+        ///
+        /// Consider limiting the number of steps in an episode to avoid wasting time during
+        /// training. If you set the max step value to a reasonable estimate of the time it should
+        /// take to complete a task, then agents that haven’t succeeded in that time frame will
+        /// reset and start a new training episode rather than continue to fail.
+        /// </remarks>
+        /// <example>
+        /// To use a step limit when training while allowing agents to run without resetting
+        /// outside of training, you can set the max step to 0 in <see cref="Initialize"/>
+        /// if the <see cref="Academy"/> is not connected to an external process.
+        /// <code>
+        /// using Unity.MLAgents;
+        ///
+        /// public class MyAgent : Agent
+        /// {
+        ///     public override void Initialize()
+        ///     {
+        ///         if (!Academy.Instance.IsCommunicatorOn)
+        ///         {
+        ///             this.MaxStep = 0;
+        ///         }
+        ///     }
+        /// }
+        /// </code>
+        /// **Note:** in general, you should limit the differences between the code you execute
+        /// during training and the code you run during inference.
+        /// </example>
+        [FormerlySerializedAs("maxStep")]
+        [HideInInspector] public int MaxStep;
+
+        /// Current Agent information (message sent to Brain).
+        AgentInfo m_Info;
+
+        /// Represents the reward the agent accumulated during the current step.
+        /// It is reset to 0 at the beginning of every step.
+        /// Should be set to a positive value when the agent performs a "good"
+        /// action that we wish to reinforce/reward, and set to a negative value
+        /// when the agent performs a "bad" action that we wish to punish/deter.
+        /// Additionally, the magnitude of the reward should not exceed 1.0
+        float m_Reward;
+
+        /// Represents the group reward the agent accumulated during the current step.
+        float m_GroupReward;
+
+        /// Keeps track of the cumulative reward in this episode.
+        float m_CumulativeReward;
+
+        /// Whether or not the agent requests an action.
+        bool m_RequestAction;
+
+        /// Whether or not the agent requests a decision.
+        bool m_RequestDecision;
+
+        /// Keeps track of the number of steps taken by the agent in this episode.
+        /// Note that this value is different for each agent, and may not overlap
+        /// with the step counter in the Academy, since agents reset based on
+        /// their own experience.
+        int m_StepCount;
+
+        /// Number of times the Agent has completed an episode.
+        int m_CompletedEpisodes;
+
+        /// Episode identifier each agent receives. It is used
+        /// to separate between different agents in the environment.
+        /// This Id will be changed every time the Agent resets.
+        int m_EpisodeId;
+
+        /// Whether or not the Agent has been initialized already
+        bool m_Initialized;
+
+        /// <summary>
+        /// Set of DemonstrationWriters that the Agent will write its step information to.
+        /// If you use a DemonstrationRecorder component, this will automatically register its DemonstrationWriter.
+        /// You can also add your own DemonstrationWriter by calling
+        /// DemonstrationRecorder.AddDemonstrationWriterToAgent()
+        /// </summary>
+        internal ISet<DemonstrationWriter> DemonstrationWriters = new HashSet<DemonstrationWriter>();
+
+        /// <summary>
+        /// List of sensors used to generate observations.
+        /// Currently generated from attached SensorComponents, and a legacy VectorSensor
+        /// </summary>
+        internal List<ISensor> sensors;
+
+        /// <summary>
+        /// VectorSensor which is written to by AddVectorObs
+        /// </summary>
+        internal VectorSensor collectObservationsSensor;
+
+        /// <summary>
+        /// StackingSensor which is written to by AddVectorObs
+        /// </summary>
+        internal StackingSensor stackedCollectObservationsSensor;
+
+        private RecursionChecker m_CollectObservationsChecker = new RecursionChecker("CollectObservations");
+        private RecursionChecker m_OnEpisodeBeginChecker = new RecursionChecker("OnEpisodeBegin");
+
+        /// <summary>
+        /// List of IActuators that this Agent will delegate actions to if any exist.
+        /// </summary>
+        ActuatorManager m_ActuatorManager;
+
+        /// <summary>
+        /// VectorActuator which is used by default if no other sensors exist on this Agent. This VectorSensor will
+        /// delegate its actions to <see cref="OnActionReceived(ActionBuffers)"/> by default in order to keep backward compatibility
+        /// with the current behavior of Agent.
+        /// </summary>
+        IActuator m_VectorActuator;
+
+        /// Currect MultiAgentGroup ID. Default to 0 (meaning no group)
+        int m_GroupId;
+
+        /// Delegate for the agent to unregister itself from the MultiAgentGroup without cyclic reference
+        /// between agent and the group
+        internal event Action<Agent> OnAgentDisabled;
+
+        /// <summary>
+        /// Called when the Agent is being loaded (before OnEnable()).
+        /// </summary>
+        ///<remarks>
+        /// This function registers the RpcCommunicator delegate if no delegate has been registered with CommunicatorFactory.
+        /// Always call the base Agent class version of this function if you implement `Awake()` in your
+        /// own Agent subclasses.
+        /// </remarks>
+        /// <example>
+        /// <code>
+        /// protected override void Awake()
+        /// {
+        ///     base.Awake();
+        ///     // additional Awake logic...
+        /// }
+        /// </code>
+        /// </example>
+        protected internal virtual void Awake()
+        {
+#if UNITY_EDITOR || UNITY_STANDALONE
+            if (!CommunicatorFactory.CommunicatorRegistered)
+            {
+                Debug.Log("Registered Communicator in Agent.");
+                CommunicatorFactory.Register<ICommunicator>(RpcCommunicator.Create);
+            }
+#endif
+        }
+
+        /// <summary>
+        /// Called when the attached [GameObject] becomes enabled and active.
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </summary>
+        /// <remarks>
+        /// This function initializes the Agent instance, if it hasn't been initialized yet.
+        /// Always call the base Agent class version of this function if you implement `OnEnable()`
+        /// in your own Agent subclasses.
+        /// </remarks>
+        /// <example>
+        /// <code>
+        /// protected override void OnEnable()
+        /// {
+        ///     base.OnEnable();
+        ///     // additional OnEnable logic...
+        /// }
+        /// </code>
+        /// </example>
+        protected virtual void OnEnable()
+        {
+            LazyInitialize();
+        }
+
+        /// <summary>
+        /// Called by Unity immediately before serializing this object.
+        /// </summary>
+        /// <remarks>
+        /// The Agent class uses OnBeforeSerialize() for internal housekeeping. Call the
+        /// base class implementation if you need your own custom serialization logic.
+        ///
+        /// See [OnBeforeSerialize] for more information.
+        ///
+        /// [OnBeforeSerialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
+        /// </remarks>
+        /// <example>
+        /// <code>
+        /// public new void OnBeforeSerialize()
+        /// {
+        ///     base.OnBeforeSerialize();
+        ///     // additional serialization logic...
+        /// }
+        /// </code>
+        /// </example>
+        public void OnBeforeSerialize()
+        {
+            // Manages a serialization upgrade issue from v0.13 to v0.14 where MaxStep moved
+            // from AgentParameters (since removed) to Agent
+            if (MaxStep == 0 && MaxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
+            {
+                MaxStep = agentParameters.maxStep;
+            }
+            hasUpgradedFromAgentParameters = true;
+        }
+
+        /// <summary>
+        /// Called by Unity immediately after deserializing this object.
+        /// </summary>
+        /// <remarks>
+        /// The Agent class uses OnAfterDeserialize() for internal housekeeping. Call the
+        /// base class implementation if you need your own custom deserialization logic.
+        ///
+        /// See [OnAfterDeserialize] for more information.
+        ///
+        /// [OnAfterDeserialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
+        /// </remarks>
+        /// <example>
+        /// <code>
+        /// public new void OnAfterDeserialize()
+        /// {
+        ///     base.OnAfterDeserialize();
+        ///     // additional deserialization logic...
+        /// }
+        /// </code>
+        /// </example>
+        public void OnAfterDeserialize()
+        {
+            // Manages a serialization upgrade issue from v0.13 to v0.14 where MaxStep moved
+            // from AgentParameters (since removed) to Agent
+            if (MaxStep == 0 && MaxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
+            {
+                MaxStep = agentParameters.maxStep;
+            }
+            hasUpgradedFromAgentParameters = true;
+        }
+
+        /// <summary>
+        /// Initializes the agent. Can be safely called multiple times.
+        /// </summary>
+        /// <remarks>
+        /// This function calls your <seealso cref="Initialize"/> implementation, if one exists.
+        /// </remarks>
+        public void LazyInitialize()
+        {
+            if (m_Initialized)
+            {
+                return;
+            }
+            m_Initialized = true;
+
+            // Grab the "static" properties for the Agent.
+            m_EpisodeId = EpisodeIdCounter.GetEpisodeId();
+            m_PolicyFactory = GetComponent<BehaviorParameters>();
+
+            m_Info = new AgentInfo();
+            sensors = new List<ISensor>();
+
+            Academy.Instance.AgentIncrementStep += AgentIncrementStep;
+            Academy.Instance.AgentSendState += SendInfo;
+            Academy.Instance.DecideAction += DecideAction;
+            Academy.Instance.AgentAct += AgentStep;
+            Academy.Instance.AgentForceReset += _AgentReset;
+
+            using (TimerStack.Instance.Scoped("InitializeActuators"))
+            {
+                InitializeActuators();
+            }
+
+            m_Brain = m_PolicyFactory.GeneratePolicy(m_ActuatorManager.GetCombinedActionSpec(), m_ActuatorManager);
+            ResetData();
+            Initialize();
+
+            using (TimerStack.Instance.Scoped("InitializeSensors"))
+            {
+                InitializeSensors();
+            }
+
+            m_Info.storedActions = new ActionBuffers(
+                new float[m_ActuatorManager.NumContinuousActions],
+                new int[m_ActuatorManager.NumDiscreteActions]
+            );
+
+            m_Info.groupId = m_GroupId;
+
+            // The first time the Academy resets, all Agents in the scene will be
+            // forced to reset through the <see cref="AgentForceReset"/> event.
+            // To avoid the Agent resetting twice, the Agents will not begin their
+            // episode when initializing until after the Academy had its first reset.
+            if (Academy.Instance.TotalStepCount != 0)
+            {
+                using (m_OnEpisodeBeginChecker.Start())
+                {
+                    OnEpisodeBegin();
+                }
+            }
+        }
+
+        /// <summary>
+        /// The reason that the Agent has been set to "done".
+        /// </summary>
+        enum DoneReason
+        {
+            /// <summary>
+            /// The episode was ended manually by calling <see cref="EndEpisode"/>.
+            /// </summary>
+            DoneCalled,
+
+            /// <summary>
+            /// The max steps for the Agent were reached.
+            /// </summary>
+            MaxStepReached,
+
+            /// <summary>
+            /// The Agent was disabled.
+            /// </summary>
+            Disabled,
+        }
+
+        /// <summary>
+        /// Called when the attached [GameObject] becomes disabled and inactive.
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </summary>
+        /// <remarks>
+        /// Always call the base Agent class version of this function if you implement `OnDisable()`
+        /// in your own Agent subclasses.
+        /// </remarks>
+        /// <example>
+        /// <code>
+        /// protected override void OnDisable()
+        /// {
+        ///     base.OnDisable();
+        ///     // additional OnDisable logic...
+        /// }
+        /// </code>
+        /// </example>
+        /// <seealso cref="OnEnable"/>
+        protected virtual void OnDisable()
+        {
+            DemonstrationWriters.Clear();
+
+            // If Academy.Dispose has already been called, we don't need to unregister with it.
+            // We don't want to even try, because this will lazily create a new Academy!
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.AgentIncrementStep -= AgentIncrementStep;
+                Academy.Instance.AgentSendState -= SendInfo;
+                Academy.Instance.DecideAction -= DecideAction;
+                Academy.Instance.AgentAct -= AgentStep;
+                Academy.Instance.AgentForceReset -= _AgentReset;
+                NotifyAgentDone(DoneReason.Disabled);
+            }
+
+            CleanupSensors();
+            m_Brain?.Dispose();
+            OnAgentDisabled?.Invoke(this);
+            m_Initialized = false;
+        }
+
+        void NotifyAgentDone(DoneReason doneReason)
+        {
+            if (m_Info.done)
+            {
+                // The Agent was already marked as Done and should not be notified again
+                return;
+            }
+            m_Info.episodeId = m_EpisodeId;
+            m_Info.reward = m_Reward;
+            m_Info.groupReward = m_GroupReward;
+            m_Info.done = true;
+            m_Info.maxStepReached = doneReason == DoneReason.MaxStepReached;
+            m_Info.groupId = m_GroupId;
+            UpdateSensors();
+            // Make sure the latest observations are being passed to training.
+            using (m_CollectObservationsChecker.Start())
+            {
+                CollectObservations(collectObservationsSensor);
+            }
+            // Request the last decision with no callbacks
+            // We request a decision so Python knows the Agent is done immediately
+            m_Brain?.RequestDecision(m_Info, sensors);
+
+            // We also have to write any to any DemonstationStores so that they get the "done" flag.
+            if (DemonstrationWriters.Count != 0)
+            {
+                foreach (var demoWriter in DemonstrationWriters)
+                {
+                    demoWriter.Record(m_Info, sensors);
+                }
+            }
+
+            ResetSensors();
+
+            if (doneReason != DoneReason.Disabled)
+            {
+                // We don't want to update the reward stats when the Agent is disabled, because this will make
+                // the rewards look lower than they actually are during shutdown.
+                m_CompletedEpisodes++;
+                UpdateRewardStats();
+            }
+
+            m_Reward = 0f;
+            m_GroupReward = 0f;
+            m_CumulativeReward = 0f;
+            m_RequestAction = false;
+            m_RequestDecision = false;
+            m_Info.storedActions.Clear();
+        }
+
+        /// <summary>
+        /// Updates the Model assigned to this Agent instance.
+        /// </summary>
+        /// <remarks>
+        /// If the agent already has an assigned model, that model is replaced with the
+        /// the provided one. However, if you call this function with arguments that are
+        /// identical to the current parameters of the agent, then no changes are made.
+        ///
+        /// **Note:** the <paramref name="behaviorName"/> parameter is ignored when not training.
+        /// The <paramref name="model"/> and <paramref name="inferenceDevice"/> parameters
+        /// are ignored when not using inference.
+        /// </remarks>
+        /// <param name="behaviorName"> The identifier of the behavior. This
+        /// will categorize the agent when training.
+        /// </param>
+        /// <param name="model"> The model to use for inference.</param>
+        /// <param name = "inferenceDevice"> Define the device on which the model
+        /// will be run.</param>
+        public void SetModel(
+            string behaviorName,
+            NNModel model,
+            InferenceDevice inferenceDevice = InferenceDevice.Default)
+        {
+            if (behaviorName == m_PolicyFactory.BehaviorName &&
+                model == m_PolicyFactory.Model &&
+                inferenceDevice == m_PolicyFactory.InferenceDevice)
+            {
+                // If everything is the same, don't make any changes.
+                return;
+            }
+            NotifyAgentDone(DoneReason.Disabled);
+            m_PolicyFactory.Model = model;
+            m_PolicyFactory.InferenceDevice = inferenceDevice;
+            m_PolicyFactory.BehaviorName = behaviorName;
+            ReloadPolicy();
+        }
+
+        internal void ReloadPolicy()
+        {
+            if (!m_Initialized)
+            {
+                // If we haven't initialized yet, no need to make any changes now; they'll
+                // happen in LazyInitialize later.
+                return;
+            }
+            m_Brain?.Dispose();
+            m_Brain = m_PolicyFactory.GeneratePolicy(m_ActuatorManager.GetCombinedActionSpec(), m_ActuatorManager);
+        }
+
+        /// <summary>
+        /// Returns the current step counter (within the current episode).
+        /// </summary>
+        /// <returns>
+        /// Current step count.
+        /// </returns>
+        public int StepCount
+        {
+            get { return m_StepCount; }
+        }
+
+        /// <summary>
+        /// Returns the number of episodes that the Agent has completed (either <see cref="Agent.EndEpisode()"/>
+        /// was called, or maxSteps was reached).
+        /// </summary>
+        /// <returns>
+        /// Current episode count.
+        /// </returns>
+        public int CompletedEpisodes
+        {
+            get { return m_CompletedEpisodes; }
+        }
+
+        /// <summary>
+        /// Overrides the current step reward of the agent and updates the episode
+        /// reward accordingly.
+        /// </summary>
+        /// <remarks>
+        /// This function replaces any rewards given to the agent during the current step.
+        /// Use <see cref="AddReward(float)"/> to incrementally change the reward rather than
+        /// overriding it.
+        ///
+        /// Typically, you assign rewards in the Agent subclass's <see cref="OnActionReceived(ActionBuffers)"/>
+        /// implementation after carrying out the received action and evaluating its success.
+        ///
+        /// Rewards are used during reinforcement learning; they are ignored during inference.
+        ///
+        /// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
+        /// for information about mixing reward signals from curiosity and Generative Adversarial
+        /// Imitation Learning (GAIL) with rewards supplied through this method.
+        ///
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// </remarks>
+        /// <param name="reward">The new value of the reward.</param>
+        public void SetReward(float reward)
+        {
+            Utilities.DebugCheckNanAndInfinity(reward, nameof(reward), nameof(SetReward));
+            m_CumulativeReward += (reward - m_Reward);
+            m_Reward = reward;
+        }
+
+        /// <summary>
+        /// Increments the step and episode rewards by the provided value.
+        /// </summary>
+        /// <remarks>Use a positive reward to reinforce desired behavior. You can use a
+        /// negative reward to penalize mistakes. Use <seealso cref="SetReward(float)"/> to
+        /// set the reward assigned to the current step with a specific value rather than
+        /// increasing or decreasing it.
+        ///
+        /// Typically, you assign rewards in the Agent subclass's <see cref="IActionReceiver.OnActionReceived"/>
+        /// implementation after carrying out the received action and evaluating its success.
+        ///
+        /// Rewards are used during reinforcement learning; they are ignored during inference.
+        ///
+        /// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
+        /// for information about mixing reward signals from curiosity and Generative Adversarial
+        /// Imitation Learning (GAIL) with rewards supplied through this method.
+        ///
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        ///</remarks>
+        /// <param name="increment">Incremental reward value.</param>
+        public void AddReward(float increment)
+        {
+            Utilities.DebugCheckNanAndInfinity(increment, nameof(increment), nameof(AddReward));
+            m_Reward += increment;
+            m_CumulativeReward += increment;
+        }
+
+        internal void SetGroupReward(float reward)
+        {
+            Utilities.DebugCheckNanAndInfinity(reward, nameof(reward), nameof(SetGroupReward));
+            m_GroupReward = reward;
+        }
+
+        internal void AddGroupReward(float increment)
+        {
+            Utilities.DebugCheckNanAndInfinity(increment, nameof(increment), nameof(AddGroupReward));
+            m_GroupReward += increment;
+        }
+
+        /// <summary>
+        /// Retrieves the episode reward for the Agent.
+        /// </summary>
+        /// <returns>The episode reward.</returns>
+        public float GetCumulativeReward()
+        {
+            return m_CumulativeReward;
+        }
+
+        void UpdateRewardStats()
+        {
+            var gaugeName = $"{m_PolicyFactory.BehaviorName}.CumulativeReward";
+            TimerStack.Instance.SetGauge(gaugeName, GetCumulativeReward());
+        }
+
+        /// <summary>
+        /// Sets the done flag to true and resets the agent.
+        /// </summary>
+        /// <remarks>
+        /// This should be used when the episode can no longer continue, such as when the Agent
+        /// reaches the goal or fails at the task.
+        /// </remarks>
+        /// <seealso cref="OnEpisodeBegin"/>
+        /// <seealso cref="EpisodeInterrupted"/>
+        public void EndEpisode()
+        {
+            EndEpisodeAndReset(DoneReason.DoneCalled);
+        }
+
+        /// <summary>
+        /// Indicate that the episode is over but not due to the "fault" of the Agent.
+        /// This has the same end result as calling <see cref="EndEpisode"/>, but has a
+        /// slightly different effect on training.
+        /// </summary>
+        /// <remarks>
+        /// This should be used when the episode could continue, but has gone on for
+        /// a sufficient number of steps.
+        /// </remarks>
+        /// <seealso cref="OnEpisodeBegin"/>
+        /// <seealso cref="EndEpisode"/>
+        public void EpisodeInterrupted()
+        {
+            EndEpisodeAndReset(DoneReason.MaxStepReached);
+        }
+
+        /// <summary>
+        /// Internal method to end the episode and reset the Agent.
+        /// </summary>
+        /// <param name="reason"></param>
+        void EndEpisodeAndReset(DoneReason reason)
+        {
+            NotifyAgentDone(reason);
+            _AgentReset();
+        }
+
+        /// <summary>
+        /// Requests a new decision for this agent.
+        /// </summary>
+        /// <remarks>
+        /// Call `RequestDecision()` whenever an agent needs a decision. You often
+        /// want to request a decision every environment step. However, if an agent
+        /// cannot use the decision every step, then you can request a decision less
+        /// frequently.
+        ///
+        /// You can add a <seealso cref="DecisionRequester"/> component to the agent's
+        /// [GameObject] to drive the agent's decision making. When you use this component,
+        /// do not call `RequestDecision()` separately.
+        ///
+        /// Note that this function calls <seealso cref="RequestAction"/>; you do not need to
+        /// call both functions at the same time.
+        ///
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </remarks>
+        public void RequestDecision()
+        {
+            m_RequestDecision = true;
+            RequestAction();
+        }
+
+        /// <summary>
+        /// Requests an action for this agent.
+        /// </summary>
+        /// <remarks>
+        /// Call `RequestAction()` to repeat the previous action returned by the agent's
+        /// most recent decision. A new decision is not requested. When you call this function,
+        /// the Agent instance invokes <seealso cref="IActionReceiver.OnActionReceived"/> with the
+        /// existing action vector.
+        ///
+        /// You can use `RequestAction()` in situations where an agent must take an action
+        /// every update, but doesn't need to make a decision as often. For example, an
+        /// agent that moves through its environment might need to apply an action to keep
+        /// moving, but only needs to make a decision to change course or speed occasionally.
+        ///
+        /// You can add a <seealso cref="DecisionRequester"/> component to the agent's
+        /// [GameObject] to drive the agent's decision making and action frequency. When you
+        /// use this component, do not call `RequestAction()` separately.
+        ///
+        /// Note that <seealso cref="RequestDecision"/> calls `RequestAction()`; you do not need to
+        /// call both functions at the same time.
+        ///
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </remarks>
+        public void RequestAction()
+        {
+            m_RequestAction = true;
+        }
+
+        /// Helper function that resets all the data structures associated with
+        /// the agent. Typically used when the agent is being initialized or reset
+        /// at the end of an episode.
+        void ResetData()
+        {
+            m_ActuatorManager?.ResetData();
+        }
+
+        /// <summary>
+        /// Implement `Initialize()` to perform one-time initialization or set up of the
+        /// Agent instance.
+        /// </summary>
+        /// <remarks>
+        /// `Initialize()` is called once when the agent is first enabled. If, for example,
+        /// the Agent object needs references to other [GameObjects] in the scene, you
+        /// can collect and store those references here.
+        ///
+        /// Note that <seealso cref="OnEpisodeBegin"/> is called at the start of each of
+        /// the agent's "episodes". You can use that function for items that need to be reset
+        /// for each episode.
+        ///
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </remarks>
+        public virtual void Initialize() { }
+
+        /// <summary>
+        /// Implement <see cref="Heuristic"/> to choose an action for this agent using a custom heuristic.
+        /// </summary>
+        /// <remarks>
+        /// Implement this function to provide custom decision making logic or to support manual
+        /// control of an agent using keyboard, mouse, game controller input, or a script.
+        ///
+        /// Your heuristic implementation can use any decision making logic you specify. Assign decision
+        /// values to the <see cref="ActionBuffers.ContinuousActions"/>  and <see cref="ActionBuffers.DiscreteActions"/>
+        /// arrays , passed to your function as a parameter.
+        /// The same array will be reused between steps. It is up to the user to initialize
+        /// the values on each call, for example by calling `Array.Clear(actionsOut, 0, actionsOut.Length);`.
+        /// Add values to the array at the same indexes as they are used in your
+        /// <seealso cref="IActionReceiver.OnActionReceived"/> function, which receives this array and
+        /// implements the corresponding agent behavior. See [Actions] for more information
+        /// about agent actions.
+        /// Note : Do not create a new float array of action in the `Heuristic()` method,
+        /// as this will prevent writing floats to the original action array.
+        ///
+        /// An agent calls this `Heuristic()` function to make a decision when you set its behavior
+        /// type to <see cref="BehaviorType.HeuristicOnly"/>. The agent also calls this function if
+        /// you set its behavior type to <see cref="BehaviorType.Default"/> when the
+        /// <see cref="Academy"/> is not connected to an external training process and you do not
+        /// assign a trained model to the agent.
+        ///
+        /// To perform imitation learning, implement manual control of the agent in the `Heuristic()`
+        /// function so that you can record the demonstrations required for the imitation learning
+        /// algorithms. (Attach a [Demonstration Recorder] component to the agent's [GameObject] to
+        /// record the demonstration session to a file.)
+        ///
+        /// Even when you don’t plan to use heuristic decisions for an agent or imitation learning,
+        /// implementing a simple heuristic function can aid in debugging agent actions and interactions
+        /// with its environment.
+        ///
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// </remarks>
+        /// <example>
+        /// The following example illustrates a `Heuristic()` function that provides WASD-style
+        /// keyboard control for an agent that can move in two dimensions as well as jump. See
+        /// [Input Manager] for more information about the built-in Unity input functions.
+        /// You can also use the [Input System package], which provides a more flexible and
+        /// configurable input system.
+        /// <code>
+        ///     public override void Heuristic(in ActionBuffers actionsOut)
+        ///     {
+        ///         var continuousActionsOut = actionsOut.ContinuousActions;
+        ///         continuousActionsOut[0] = Input.GetAxis("Horizontal");
+        ///         continuousActionsOut[1] = Input.GetKey(KeyCode.Space) ? 1.0f : 0.0f;
+        ///         continuousActionsOut[2] = Input.GetAxis("Vertical");
+        ///     }
+        /// </code>
+        /// [Input Manager]: https://docs.unity3d.com/Manual/class-InputManager.html
+        /// [Input System package]: https://docs.unity3d.com/Packages/com.unity.inputsystem@1.0/manual/index.html
+        /// </example>
+        /// <param name="actionsOut">The <see cref="ActionBuffers"/> which contain the continuous and
+        /// discrete action buffers to write to.</param>
+        /// <seealso cref="IActionReceiver.OnActionReceived"/>
+        public virtual void Heuristic(in ActionBuffers actionsOut)
+        {
+            Debug.LogWarning("Heuristic method called but not implemented. Returning placeholder actions.");
+        }
+
+        /// <summary>
+        /// Set up the list of ISensors on the Agent. By default, this will select any
+        /// SensorComponent's attached to the Agent.
+        /// </summary>
+        internal void InitializeSensors()
+        {
+            if (m_PolicyFactory == null)
+            {
+                m_PolicyFactory = GetComponent<BehaviorParameters>();
+            }
+            if (m_PolicyFactory.ObservableAttributeHandling != ObservableAttributeOptions.Ignore)
+            {
+                var excludeInherited =
+                    m_PolicyFactory.ObservableAttributeHandling == ObservableAttributeOptions.ExcludeInherited;
+                using (TimerStack.Instance.Scoped("CreateObservableSensors"))
+                {
+                    var observableSensors = ObservableAttribute.CreateObservableSensors(this, excludeInherited);
+                    sensors.AddRange(observableSensors);
+                }
+            }
+
+            // Get all attached sensor components
+            SensorComponent[] attachedSensorComponents;
+            if (m_PolicyFactory.UseChildSensors)
+            {
+                attachedSensorComponents = GetComponentsInChildren<SensorComponent>();
+            }
+            else
+            {
+                attachedSensorComponents = GetComponents<SensorComponent>();
+            }
+
+            sensors.Capacity += attachedSensorComponents.Length;
+            foreach (var component in attachedSensorComponents)
+            {
+                sensors.AddRange(component.CreateSensors());
+            }
+
+            // Support legacy CollectObservations
+            var param = m_PolicyFactory.BrainParameters;
+            if (param.VectorObservationSize > 0)
+            {
+                collectObservationsSensor = new VectorSensor(param.VectorObservationSize);
+                if (param.NumStackedVectorObservations > 1)
+                {
+                    stackedCollectObservationsSensor = new StackingSensor(
+                        collectObservationsSensor, param.NumStackedVectorObservations);
+                    sensors.Add(stackedCollectObservationsSensor);
+                }
+                else
+                {
+                    sensors.Add(collectObservationsSensor);
+                }
+            }
+
+            // Sort the Sensors by name to ensure determinism
+            SensorUtils.SortSensors(sensors);
+
+#if DEBUG
+            // Make sure the names are actually unique
+
+            for (var i = 0; i < sensors.Count - 1; i++)
+            {
+                Debug.Assert(
+                    !sensors[i].GetName().Equals(sensors[i + 1].GetName()),
+                    "Sensor names must be unique.");
+            }
+#endif
+        }
+
+        void CleanupSensors()
+        {
+            // Dispose all attached sensor
+            for (var i = 0; i < sensors.Count; i++)
+            {
+                var sensor = sensors[i];
+                if (sensor is IDisposable disposableSensor)
+                {
+                    disposableSensor.Dispose();
+                }
+            }
+        }
+
+        void InitializeActuators()
+        {
+            ActuatorComponent[] attachedActuators;
+            if (m_PolicyFactory.UseChildActuators)
+            {
+                attachedActuators = GetComponentsInChildren<ActuatorComponent>();
+            }
+            else
+            {
+                attachedActuators = GetComponents<ActuatorComponent>();
+            }
+
+            // Support legacy OnActionReceived
+            // TODO don't set this up if the sizes are 0?
+            var param = m_PolicyFactory.BrainParameters;
+            m_VectorActuator = new AgentVectorActuator(this, this, param.ActionSpec);
+            m_ActuatorManager = new ActuatorManager(attachedActuators.Length + 1);
+
+            m_ActuatorManager.Add(m_VectorActuator);
+
+            foreach (var actuatorComponent in attachedActuators)
+            {
+                m_ActuatorManager.AddActuators(actuatorComponent.CreateActuators());
+            }
+        }
+
+        /// <summary>
+        /// Sends the Agent info to the linked Brain.
+        /// </summary>
+        void SendInfoToBrain()
+        {
+            if (!m_Initialized)
+            {
+                throw new UnityAgentsException("Call to SendInfoToBrain when Agent hasn't been initialized." +
+                    "Please ensure that you are calling 'base.OnEnable()' if you have overridden OnEnable.");
+            }
+
+            if (m_Brain == null)
+            {
+                return;
+            }
+
+            if (m_Info.done)
+            {
+                m_Info.ClearActions();
+            }
+            else
+            {
+                m_Info.CopyActions(m_ActuatorManager.StoredActions);
+            }
+
+            UpdateSensors();
+            using (TimerStack.Instance.Scoped("CollectObservations"))
+            {
+                using (m_CollectObservationsChecker.Start())
+                {
+                    CollectObservations(collectObservationsSensor);
+                }
+            }
+            using (TimerStack.Instance.Scoped("WriteActionMask"))
+            {
+                m_ActuatorManager.WriteActionMask();
+            }
+
+            m_Info.discreteActionMasks = m_ActuatorManager.DiscreteActionMask?.GetMask();
+            m_Info.reward = m_Reward;
+            m_Info.groupReward = m_GroupReward;
+            m_Info.done = false;
+            m_Info.maxStepReached = false;
+            m_Info.episodeId = m_EpisodeId;
+            m_Info.groupId = m_GroupId;
+
+            using (TimerStack.Instance.Scoped("RequestDecision"))
+            {
+                m_Brain.RequestDecision(m_Info, sensors);
+            }
+
+            // If we have any DemonstrationWriters, write the AgentInfo and sensors to them.
+            if (DemonstrationWriters.Count != 0)
+            {
+                foreach (var demoWriter in DemonstrationWriters)
+                {
+                    demoWriter.Record(m_Info, sensors);
+                }
+            }
+        }
+
+        void UpdateSensors()
+        {
+            foreach (var sensor in sensors)
+            {
+                sensor.Update();
+            }
+        }
+
+        void ResetSensors()
+        {
+            foreach (var sensor in sensors)
+            {
+                sensor.Reset();
+            }
+        }
+
+        /// <summary>
+        /// Implement `CollectObservations()` to collect the vector observations of
+        /// the agent for the  step. The agent observation describes the current
+        /// environment from the perspective of the agent.
+        /// </summary>
+        /// <param name="sensor">
+        /// The vector observations for the agent.
+        /// </param>
+        /// <remarks>
+        /// An agent's observation is any environment information that helps
+        /// the agent achieve its goal. For example, for a fighting agent, its
+        /// observation could include distances to friends or enemies, or the
+        /// current level of ammunition at its disposal.
+        ///
+        /// You can use a combination of vector, visual, and raycast observations for an
+        /// agent. If you only use visual or raycast observations, you do not need to
+        /// implement a `CollectObservations()` function.
+        ///
+        /// Add vector observations to the <paramref name="sensor"/> parameter passed to
+        /// this method by calling the <seealso cref="VectorSensor"/> helper methods:
+        ///     - <see cref="VectorSensor.AddObservation(int)"/>
+        ///     - <see cref="VectorSensor.AddObservation(float)"/>
+        ///     - <see cref="VectorSensor.AddObservation(Vector3)"/>
+        ///     - <see cref="VectorSensor.AddObservation(Vector2)"/>
+        ///     - <see cref="VectorSensor.AddObservation(Quaternion)"/>
+        ///     - <see cref="VectorSensor.AddObservation(bool)"/>
+        ///     - <see cref="VectorSensor.AddObservation(IList{float})"/>
+        ///     - <see cref="VectorSensor.AddOneHotObservation(int, int)"/>
+        ///
+        /// You can use any combination of these helper functions to build the agent's
+        /// vector of observations. You must build the vector in the same order
+        /// each time `CollectObservations()` is called and the length of the vector
+        /// must always be the same. In addition, the length of the observation must
+        /// match the <see cref="BrainParameters.VectorObservationSize"/>
+        /// attribute of the linked Brain, which is set in the Editor on the
+        /// **Behavior Parameters** component attached to the agent's [GameObject].
+        ///
+        /// For more information about observations, see [Observations and Sensors].
+        ///
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// </remarks>
+        public virtual void CollectObservations(VectorSensor sensor)
+        {
+        }
+
+        /// <summary>
+        /// Returns a read-only view of the observations that were generated in
+        /// <see cref="CollectObservations(VectorSensor)"/>. This is mainly useful inside of a
+        /// <see cref="Heuristic(in ActionBuffers)"/> method to avoid recomputing the observations.
+        /// </summary>
+        /// <returns>A read-only view of the observations list.</returns>
+        public ReadOnlyCollection<float> GetObservations()
+        {
+            return collectObservationsSensor.GetObservations();
+        }
+
+        /// <summary>
+        /// Returns a read-only view of the stacked observations that were generated in
+        /// <see cref="CollectObservations(VectorSensor)"/>. This is mainly useful inside of a
+        /// <see cref="Heuristic(in ActionBuffers)"/> method to avoid recomputing the observations.
+        /// </summary>
+        /// <returns>A read-only view of the stacked observations list.</returns>
+        public ReadOnlyCollection<float> GetStackedObservations()
+        {
+            return stackedCollectObservationsSensor.GetStackedObservations();
+        }
+
+        /// <summary>
+        /// Implement `WriteDiscreteActionMask()` to collects the masks for discrete
+        /// actions. When using discrete actions, the agent will not perform the masked
+        /// action.
+        /// </summary>
+        /// <param name="actionMask">
+        /// The action mask for the agent.
+        /// </param>
+        /// <remarks>
+        /// When using Discrete Control, you can prevent the Agent from using a certain
+        /// action by masking it with <see cref="IDiscreteActionMask.SetActionEnabled"/>.
+        ///
+        /// See [Agents - Actions] for more information on masking actions.
+        ///
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// </remarks>
+        /// <seealso cref="IActionReceiver.OnActionReceived"/>
+        public virtual void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { }
+
+        /// <summary>
+        /// Implement `OnActionReceived()` to specify agent behavior at every step, based
+        /// on the provided action.
+        /// </summary>
+        /// <remarks>
+        /// An action is passed to this function in the form of an  <seealso cref="ActionBuffers"/>.
+        /// Your implementation must use the array to direct the agent's behavior for the
+        /// current step.
+        ///
+        /// You decide how many elements you need in the ActionBuffers to control your
+        /// agent and what each element means. For example, if you want to apply a
+        /// force to move an agent around the environment, you can arbitrarily pick
+        /// three values in ActionBuffers.ContinuousActions array to use as the force components.
+        /// During training, the agent's  policy learns to set those particular elements of
+        /// the array to maximize the training rewards the agent receives. (Of course,
+        /// if you implement a <seealso cref="Agent.Heuristic(in ActionBuffers)"/> function, it must use the same
+        /// elements of the action array for the same purpose since there is no learning
+        /// involved.)
+        ///
+        /// An Agent can use continuous and/or discrete actions. Configure this  along with the size
+        /// of the action array,  in the <see cref="BrainParameters"/> of the agent's associated
+        /// <see cref="BehaviorParameters"/> component.
+        ///
+        /// When an agent uses continuous actions, the values in the ActionBuffers.ContinuousActions
+        /// array are floating point numbers. You should clamp the values to the range,
+        /// -1..1, to increase numerical stability during training.
+        ///
+        /// When an agent uses discrete actions, the values in the ActionBuffers.DiscreteActions array
+        /// are integers that each represent a specific, discrete action. For example,
+        /// you could define a set of discrete actions such as:
+        ///
+        /// <code>
+        /// 0 = Do nothing
+        /// 1 = Move one space left
+        /// 2 = Move one space right
+        /// 3 = Move one space up
+        /// 4 = Move one space down
+        /// </code>
+        ///
+        /// When making a decision, the agent picks one of the five actions and puts the
+        /// corresponding integer value in the ActionBuffers.DiscreteActions array. For example, if the agent
+        /// decided to move left, the ActionBuffers.DiscreteActions parameter would be an array with
+        /// a single element with the value 1.
+        ///
+        /// You can define multiple sets, or branches, of discrete actions to allow an
+        /// agent to perform simultaneous, independent actions. For example, you could
+        /// use one branch for movement and another branch for throwing a ball left, right,
+        /// up, or down, to allow the agent to do both in the same step.
+        ///
+        /// The ActionBuffers.DiscreteActions array of an agent with discrete actions contains one
+        /// element for each  branch. The value of each element is the integer representing the
+        /// chosen action for that branch. The agent always chooses one action for each branch.
+        ///
+        /// When you use the discrete actions, you can prevent the training process
+        /// or the neural network model from choosing specific actions in a step by
+        /// implementing the <see cref="WriteDiscreteActionMask(IDiscreteActionMask)"/>
+        /// method. For example, if your agent is next to a wall, you could mask out any
+        /// actions that would result in the agent trying to move into the wall.
+        ///
+        /// For more information about implementing agent actions see [Agents - Actions].
+        ///
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// </remarks>
+        /// <param name="actions">
+        /// Struct containing the buffers of actions to be executed at this step.
+        /// </param>
+        public virtual void OnActionReceived(ActionBuffers actions) { }
+
+        /// <summary>
+        /// Implement `OnEpisodeBegin()` to set up an Agent instance at the beginning
+        /// of an episode.
+        /// </summary>
+        /// <seealso cref="Initialize"/>
+        /// <seealso cref="EndEpisode"/>
+        public virtual void OnEpisodeBegin() { }
+
+        /// <summary>
+        /// Gets the most recent ActionBuffer for this agent.
+        /// </summary>
+        /// <returns>The most recent ActionBuffer for this agent</returns>
+        public ActionBuffers GetStoredActionBuffers()
+        {
+            return m_ActuatorManager.StoredActions;
+        }
+
+        /// <summary>
+        /// An internal reset method that updates internal data structures in
+        /// addition to calling <see cref="OnEpisodeBegin"/>.
+        /// </summary>
+        void _AgentReset()
+        {
+            ResetData();
+            m_StepCount = 0;
+            using (m_OnEpisodeBeginChecker.Start())
+            {
+                OnEpisodeBegin();
+            }
+        }
+
+        /// <summary>
+        /// Scales continuous action from [-1, 1] to arbitrary range.
+        /// </summary>
+        /// <param name="rawAction">The input action value.</param>
+        /// <param name="min">The minimum output value.</param>
+        /// <param name="max">The maximum output value.</param>
+        /// <returns>The <paramref name="rawAction"/> scaled from [-1,1] to
+        /// [<paramref name="min"/>, <paramref name="max"/>].</returns>
+        protected static float ScaleAction(float rawAction, float min, float max)
+        {
+            var middle = (min + max) / 2;
+            var range = (max - min) / 2;
+            return rawAction * range + middle;
+        }
+
+        /// <summary>
+        /// Signals the agent that it must send its decision to the brain.
+        /// </summary>
+        void SendInfo()
+        {
+            // If the Agent is done, it has just reset and thus requires a new decision
+            if (m_RequestDecision)
+            {
+                SendInfoToBrain();
+                m_Reward = 0f;
+                m_GroupReward = 0f;
+                m_RequestDecision = false;
+            }
+        }
+
+        void AgentIncrementStep()
+        {
+            m_StepCount += 1;
+        }
+
+        /// Used by the brain to make the agent perform a step.
+        void AgentStep()
+        {
+            if ((m_RequestAction) && (m_Brain != null))
+            {
+                m_RequestAction = false;
+                m_ActuatorManager.ExecuteActions();
+            }
+
+            if ((m_StepCount >= MaxStep) && (MaxStep > 0))
+            {
+                NotifyAgentDone(DoneReason.MaxStepReached);
+                _AgentReset();
+            }
+        }
+
+        void DecideAction()
+        {
+            if (m_ActuatorManager.StoredActions.ContinuousActions.Array == null)
+            {
+                ResetData();
+            }
+            var actions = m_Brain?.DecideAction() ?? new ActionBuffers();
+            m_Info.CopyActions(actions);
+            m_ActuatorManager.UpdateActions(actions);
+        }
+
+        internal void SetMultiAgentGroup(IMultiAgentGroup multiAgentGroup)
+        {
+            if (multiAgentGroup == null)
+            {
+                m_GroupId = 0;
+            }
+            else
+            {
+                var newGroupId = multiAgentGroup.GetId();
+                if (m_GroupId == 0 || m_GroupId == newGroupId)
+                {
+                    m_GroupId = newGroupId;
+                }
+                else
+                {
+                    throw new UnityAgentsException("Agent is already registered with a group. Unregister it first.");
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Agent.cs.meta b/com.unity.ml-agents/Runtime/Agent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5463d244fb33540d5f7dfa8b9fada005a763c721
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Agent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 88b6042bc9a5d4aa58d931eae49442e5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Analytics.meta b/com.unity.ml-agents/Runtime/Analytics.meta
new file mode 100644
index 0000000000000000000000000000000000000000..260b85a9b3a13afb47fb6a4a786a6d82663e37e1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 8b12ac54c5224758af88c67e2af4a01e
+timeCreated: 1604359666
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs b/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f0fcdbba191ede69a28a66cb38dab6d8e5dc1fe6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs
@@ -0,0 +1,65 @@
+using System;
+using System.Text;
+using System.Security.Cryptography;
+using UnityEngine;
+
+namespace Unity.MLAgents.Analytics
+{
+    internal static class AnalyticsUtils
+    {
+        /// <summary>
+        /// Conversion function from byte array to hex string
+        /// </summary>
+        /// <param name="array"></param>
+        /// <returns>A byte array to be hex encoded.</returns>
+        private static string ToHexString(byte[] array)
+        {
+            StringBuilder hex = new StringBuilder(array.Length * 2);
+            foreach (byte b in array)
+            {
+                hex.AppendFormat("{0:x2}", b);
+            }
+            return hex.ToString();
+        }
+
+        /// <summary>
+        /// Hash a string to remove PII or secret info before sending to analytics
+        /// </summary>
+        /// <param name="key"></param>
+        /// <returns>A string containing the key to be used for HMAC encoding.</returns>
+        /// <param name="value"></param>
+        /// <returns>A string containing the value to be encoded.</returns>
+        public static string Hash(string key, string value)
+        {
+            string hash;
+            UTF8Encoding encoder = new UTF8Encoding();
+            using (HMACSHA256 hmac = new HMACSHA256(encoder.GetBytes(key)))
+            {
+                Byte[] hmBytes = hmac.ComputeHash(encoder.GetBytes(value));
+                hash = ToHexString(hmBytes);
+            }
+            return hash;
+        }
+
+        internal static bool s_SendEditorAnalytics = true;
+
+        /// <summary>
+        /// Helper class to temporarily disable sending analytics from unit tests.
+        /// </summary>
+        internal class DisableAnalyticsSending : IDisposable
+        {
+            private bool m_PreviousSendEditorAnalytics;
+
+            public DisableAnalyticsSending()
+            {
+                m_PreviousSendEditorAnalytics = s_SendEditorAnalytics;
+                s_SendEditorAnalytics = false;
+            }
+
+            public void Dispose()
+            {
+                s_SendEditorAnalytics = m_PreviousSendEditorAnalytics;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs.meta b/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b00fab1c903ddd31d5c901b779be1b043107cd4b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/AnalyticsUtils.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: af1ef3e70f1242938d7b39284b1a892b
+timeCreated: 1610575760
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Analytics/Events.cs b/com.unity.ml-agents/Runtime/Analytics/Events.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4a34273c049ddc74b2eb04308e52aa60cf339518
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/Events.cs
@@ -0,0 +1,194 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Analytics
+{
+    internal struct InferenceEvent
+    {
+        /// <summary>
+        /// Hash of the BehaviorName.
+        /// </summary>
+        public string BehaviorName;
+        public string BarracudaModelSource;
+        public string BarracudaModelVersion;
+        public string BarracudaModelProducer;
+        public string BarracudaPackageVersion;
+        /// <summary>
+        /// Whether inference is performed on CPU (0) or GPU (1).
+        /// </summary>
+        public int InferenceDevice;
+        public List<EventObservationSpec> ObservationSpecs;
+        public EventActionSpec ActionSpec;
+        public List<EventActuatorInfo> ActuatorInfos;
+        public int MemorySize;
+        public long TotalWeightSizeBytes;
+        public string ModelHash;
+    }
+
+    /// <summary>
+    /// Simplified version of ActionSpec struct for use in analytics
+    /// </summary>
+    [Serializable]
+    internal struct EventActionSpec
+    {
+        public int NumContinuousActions;
+        public int NumDiscreteActions;
+        public int[] BranchSizes;
+
+        public static EventActionSpec FromActionSpec(ActionSpec actionSpec)
+        {
+            var branchSizes = actionSpec.BranchSizes ?? Array.Empty<int>();
+            return new EventActionSpec
+            {
+                NumContinuousActions = actionSpec.NumContinuousActions,
+                NumDiscreteActions = actionSpec.NumDiscreteActions,
+                BranchSizes = branchSizes,
+            };
+        }
+    }
+
+    /// <summary>
+    /// Information about an actuator.
+    /// </summary>
+    [Serializable]
+    internal struct EventActuatorInfo
+    {
+        public int BuiltInActuatorType;
+        public int NumContinuousActions;
+        public int NumDiscreteActions;
+
+        public static EventActuatorInfo FromActuator(IActuator actuator)
+        {
+            BuiltInActuatorType builtInActuatorType = Actuators.BuiltInActuatorType.Unknown;
+            if (actuator is IBuiltInActuator builtInActuator)
+            {
+                builtInActuatorType = builtInActuator.GetBuiltInActuatorType();
+            }
+
+            var actionSpec = actuator.ActionSpec;
+
+            return new EventActuatorInfo
+            {
+                BuiltInActuatorType = (int)builtInActuatorType,
+                NumContinuousActions = actionSpec.NumContinuousActions,
+                NumDiscreteActions = actionSpec.NumDiscreteActions
+            };
+        }
+    }
+
+    /// <summary>
+    /// Information about one dimension of an observation.
+    /// </summary>
+    [Serializable]
+    internal struct EventObservationDimensionInfo
+    {
+        public int Size;
+        public int Flags;
+    }
+
+    /// <summary>
+    /// Simplified summary of Agent observations for use in analytics
+    /// </summary>
+    [Serializable]
+    internal struct EventObservationSpec
+    {
+        public string SensorName;
+        public string CompressionType;
+        public int BuiltInSensorType;
+        public int ObservationType;
+        public EventObservationDimensionInfo[] DimensionInfos;
+
+        public static EventObservationSpec FromSensor(ISensor sensor)
+        {
+            var obsSpec = sensor.GetObservationSpec();
+            var shape = obsSpec.Shape;
+            var dimProps = obsSpec.DimensionProperties;
+            var dimInfos = new EventObservationDimensionInfo[shape.Length];
+            for (var i = 0; i < shape.Length; i++)
+            {
+                dimInfos[i].Size = shape[i];
+                dimInfos[i].Flags = (int)dimProps[i];
+            }
+
+            var builtInSensorType =
+                (sensor as IBuiltInSensor)?.GetBuiltInSensorType() ?? Sensors.BuiltInSensorType.Unknown;
+
+            return new EventObservationSpec
+            {
+                SensorName = sensor.GetName(),
+                CompressionType = sensor.GetCompressionSpec().SensorCompressionType.ToString(),
+                BuiltInSensorType = (int)builtInSensorType,
+                ObservationType = (int)obsSpec.ObservationType,
+                DimensionInfos = dimInfos,
+            };
+        }
+    }
+
+    internal struct RemotePolicyInitializedEvent
+    {
+        public string TrainingSessionGuid;
+        /// <summary>
+        /// Hash of the BehaviorName.
+        /// </summary>
+        public string BehaviorName;
+        public List<EventObservationSpec> ObservationSpecs;
+        public EventActionSpec ActionSpec;
+        public List<EventActuatorInfo> ActuatorInfos;
+
+        /// <summary>
+        /// This will be the same as TrainingEnvironmentInitializedEvent if available, but
+        /// TrainingEnvironmentInitializedEvent maybe not always be available with older trainers.
+        /// </summary>
+        public string MLAgentsEnvsVersion;
+        public string TrainerCommunicationVersion;
+    }
+
+    internal struct TrainingEnvironmentInitializedEvent
+    {
+        public string TrainingSessionGuid;
+
+        public string TrainerPythonVersion;
+        public string MLAgentsVersion;
+        public string MLAgentsEnvsVersion;
+        public string TorchVersion;
+        public string TorchDeviceType;
+        public int NumEnvironments;
+        public int NumEnvironmentParameters;
+        public string RunOptions;
+    }
+
+    [Flags]
+    internal enum RewardSignals
+    {
+        Extrinsic = 1 << 0,
+        Gail = 1 << 1,
+        Curiosity = 1 << 2,
+        Rnd = 1 << 3,
+    }
+
+    [Flags]
+    internal enum TrainingFeatures
+    {
+        BehavioralCloning = 1 << 0,
+        Recurrent = 1 << 1,
+        Threaded = 1 << 2,
+        SelfPlay = 1 << 3,
+        Curriculum = 1 << 4,
+    }
+
+    internal struct TrainingBehaviorInitializedEvent
+    {
+        public string TrainingSessionGuid;
+
+        public string BehaviorName;
+        public string TrainerType;
+        public RewardSignals RewardSignalFlags;
+        public TrainingFeatures TrainingFeatureFlags;
+        public string VisualEncoder;
+        public int NumNetworkLayers;
+        public int NumNetworkHiddenUnits;
+        public string Config;
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Analytics/Events.cs.meta b/com.unity.ml-agents/Runtime/Analytics/Events.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..347eebcd518c4c2b65417d1c08cb73f11eb5217b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/Events.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0a0d7cda6d74425a80775769a9283ba6
+timeCreated: 1604359798
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs b/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b7b466155a12a48e2692284e14ea41bc99647ed4
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs
@@ -0,0 +1,283 @@
+using System.Collections.Generic;
+using System.Diagnostics;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+#if MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+using UnityEngine.Analytics;
+#endif
+
+
+#if UNITY_EDITOR
+using UnityEditor;
+#if MLA_UNITY_ANALYTICS_MODULE
+using UnityEditor.Analytics;
+#endif // MLA_UNITY_ANALYTICS_MODULE
+#endif // UNITY_EDITOR
+
+
+namespace Unity.MLAgents.Analytics
+{
+    internal class InferenceAnalytics
+    {
+        const string k_VendorKey = "unity.ml-agents";
+        const string k_EventName = "ml_agents_inferencemodelset";
+        const int k_EventVersion = 1;
+
+        /// <summary>
+        /// Whether or not we've registered this particular event yet
+        /// </summary>
+        static bool s_EventRegistered;
+
+        /// <summary>
+        /// Hourly limit for this event name
+        /// </summary>
+        const int k_MaxEventsPerHour = 1000;
+
+        /// <summary>
+        /// Maximum number of items in this event.
+        /// </summary>
+        const int k_MaxNumberOfElements = 1000;
+
+
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+        /// <summary>
+        /// Models that we've already sent events for.
+        /// </summary>
+        private static HashSet<NNModel> s_SentModels;
+#endif
+
+        static bool EnableAnalytics()
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            if (s_EventRegistered)
+            {
+                return true;
+            }
+
+            AnalyticsResult result = EditorAnalytics.RegisterEventWithLimit(k_EventName, k_MaxEventsPerHour, k_MaxNumberOfElements, k_VendorKey, k_EventVersion);
+            if (result == AnalyticsResult.Ok)
+            {
+                s_EventRegistered = true;
+            }
+            if (s_EventRegistered && s_SentModels == null)
+            {
+                s_SentModels = new HashSet<NNModel>();
+            }
+
+#else  // no editor, no analytics
+            s_EventRegistered = false;
+#endif
+            return s_EventRegistered;
+        }
+
+        public static bool IsAnalyticsEnabled()
+        {
+#if UNITY_EDITOR
+            return EditorAnalytics.enabled;
+#else
+            return false;
+#endif
+        }
+
+        /// <summary>
+        /// Send an analytics event for the NNModel when it is set up for inference.
+        /// No events will be sent if analytics are disabled, and at most one event
+        /// will be sent per model instance.
+        /// </summary>
+        /// <param name="nnModel">The NNModel being used for inference.</param>
+        /// <param name="behaviorName">The BehaviorName of the Agent using the model</param>
+        /// <param name="inferenceDevice">Whether inference is being performed on the CPU or GPU</param>
+        /// <param name="sensors">List of ISensors for the Agent. Used to generate information about the observation space.</param>
+        /// <param name="actionSpec">ActionSpec for the Agent. Used to generate information about the action space.</param>
+        /// <param name="actuators">List of IActuators for the Agent. Used to generate information about the action space.</param>
+        /// <returns></returns>
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        public static void InferenceModelSet(
+            NNModel nnModel,
+            string behaviorName,
+            InferenceDevice inferenceDevice,
+            IList<ISensor> sensors,
+            ActionSpec actionSpec,
+            IList<IActuator> actuators
+        )
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            // The event shouldn't be able to report if this is disabled but if we know we're not going to report
+            // Lets early out and not waste time gathering all the data
+            if (!IsAnalyticsEnabled())
+                return;
+
+            if (!EnableAnalytics())
+                return;
+
+            var added = s_SentModels.Add(nnModel);
+
+            if (!added)
+            {
+                // We previously added this model. Exit so we don't resend.
+                return;
+            }
+
+            var data = GetEventForModel(nnModel, behaviorName, inferenceDevice, sensors, actionSpec, actuators);
+            // Note - to debug, use JsonUtility.ToJson on the event.
+            // Debug.Log(JsonUtility.ToJson(data, true));
+            if (AnalyticsUtils.s_SendEditorAnalytics)
+            {
+                EditorAnalytics.SendEventWithLimit(k_EventName, data, k_EventVersion);
+            }
+#endif
+        }
+
+        /// <summary>
+        /// Generate an InferenceEvent for the model.
+        /// </summary>
+        /// <param name="nnModel"></param>
+        /// <param name="behaviorName"></param>
+        /// <param name="inferenceDevice"></param>
+        /// <param name="sensors"></param>
+        /// <param name="actionSpec"></param>
+        /// <param name="actuators"></param>
+        /// <returns></returns>
+        internal static InferenceEvent GetEventForModel(
+            NNModel nnModel,
+            string behaviorName,
+            InferenceDevice inferenceDevice,
+            IList<ISensor> sensors,
+            ActionSpec actionSpec,
+            IList<IActuator> actuators
+        )
+        {
+            var barracudaModel = ModelLoader.Load(nnModel);
+            var inferenceEvent = new InferenceEvent();
+
+            // Hash the behavior name so that there's no concern about PII or "secret" data being leaked.
+            inferenceEvent.BehaviorName = AnalyticsUtils.Hash(k_VendorKey, behaviorName);
+
+            inferenceEvent.BarracudaModelSource = barracudaModel.IrSource;
+            inferenceEvent.BarracudaModelVersion = barracudaModel.IrVersion;
+            inferenceEvent.BarracudaModelProducer = barracudaModel.ProducerName;
+            inferenceEvent.MemorySize = (int)barracudaModel.GetTensorByName(TensorNames.MemorySize)[0];
+            inferenceEvent.InferenceDevice = (int)inferenceDevice;
+
+            if (barracudaModel.ProducerName == "Script")
+            {
+                // .nn files don't have these fields set correctly. Assign some placeholder values.
+                inferenceEvent.BarracudaModelSource = "NN";
+                inferenceEvent.BarracudaModelProducer = "tensorflow_to_barracuda.py";
+            }
+
+#if UNITY_EDITOR
+            var barracudaPackageInfo = UnityEditor.PackageManager.PackageInfo.FindForAssembly(typeof(Tensor).Assembly);
+            inferenceEvent.BarracudaPackageVersion = barracudaPackageInfo.version;
+#else
+            inferenceEvent.BarracudaPackageVersion = null;
+#endif
+
+            inferenceEvent.ActionSpec = EventActionSpec.FromActionSpec(actionSpec);
+            inferenceEvent.ObservationSpecs = new List<EventObservationSpec>(sensors.Count);
+            foreach (var sensor in sensors)
+            {
+                inferenceEvent.ObservationSpecs.Add(EventObservationSpec.FromSensor(sensor));
+            }
+
+            inferenceEvent.ActuatorInfos = new List<EventActuatorInfo>(actuators.Count);
+            foreach (var actuator in actuators)
+            {
+                inferenceEvent.ActuatorInfos.Add(EventActuatorInfo.FromActuator(actuator));
+            }
+
+            inferenceEvent.TotalWeightSizeBytes = GetModelWeightSize(barracudaModel);
+            inferenceEvent.ModelHash = GetModelHash(barracudaModel);
+            return inferenceEvent;
+        }
+
+        /// <summary>
+        /// Compute the total model weight size in bytes.
+        /// This corresponds to the "Total weight size" display in the Barracuda inspector,
+        /// and the calculations are the same.
+        /// </summary>
+        /// <param name="barracudaModel"></param>
+        /// <returns></returns>
+        static long GetModelWeightSize(Model barracudaModel)
+        {
+            long totalWeightsSizeInBytes = 0;
+            for (var l = 0; l < barracudaModel.layers.Count; ++l)
+            {
+                for (var d = 0; d < barracudaModel.layers[l].datasets.Length; ++d)
+                {
+                    totalWeightsSizeInBytes += barracudaModel.layers[l].datasets[d].length;
+                }
+            }
+            return totalWeightsSizeInBytes;
+        }
+
+        /// <summary>
+        /// Wrapper around Hash128 that supports Append(float[], int, int)
+        /// </summary>
+        struct MLAgentsHash128
+        {
+            private Hash128 m_Hash;
+
+            public void Append(float[] values, int count)
+            {
+                if (values == null)
+                {
+                    return;
+                }
+
+                // Pre-2020 versions of Unity don't have Hash128.Append() (can only hash strings and scalars)
+                // For these versions, we'll hash element by element.
+#if UNITY_2020_1_OR_NEWER
+                m_Hash.Append(values, 0, count);
+#else
+                for (var i = 0; i < count; i++)
+                {
+                    var tempHash = new Hash128();
+                    HashUtilities.ComputeHash128(ref values[i], ref tempHash);
+                    HashUtilities.AppendHash(ref tempHash, ref m_Hash);
+                }
+#endif
+            }
+
+            public void Append(string value)
+            {
+                var tempHash = Hash128.Compute(value);
+                HashUtilities.AppendHash(ref tempHash, ref m_Hash);
+            }
+
+            public override string ToString()
+            {
+                return m_Hash.ToString();
+            }
+        }
+
+        /// <summary>
+        /// Compute a hash of the model's layer data and return it as a string.
+        /// A subset of the layer weights are used for performance.
+        /// This increases the chance of a collision, but this should still be extremely rare.
+        /// </summary>
+        /// <param name="barracudaModel"></param>
+        /// <returns></returns>
+        static string GetModelHash(Model barracudaModel)
+        {
+            var hash = new MLAgentsHash128();
+
+            // Limit the max number of float bytes that we hash for performance.
+            const int kMaxFloats = 256;
+
+            foreach (var layer in barracudaModel.layers)
+            {
+                hash.Append(layer.name);
+                var numFloatsToHash = Mathf.Min(layer.weights.Length, kMaxFloats);
+                hash.Append(layer.weights, numFloatsToHash);
+            }
+
+            return hash.ToString();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs.meta b/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e81b2ecbb6da4cbaeb14da154b502921669e704c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: ac4c40c2394d481ebf602caa600a32f3
+timeCreated: 1604359787
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs b/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs
new file mode 100644
index 0000000000000000000000000000000000000000..08c205bfc6b8c5f4059220e983f49438568ed11c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs
@@ -0,0 +1,276 @@
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+#if MLA_UNITY_ANALYTICS_MODULE
+
+#if ENABLE_CLOUD_SERVICES_ANALYTICS
+using UnityEngine.Analytics;
+#endif
+
+#if UNITY_EDITOR
+using UnityEditor.Analytics;
+#endif
+#endif
+
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace Unity.MLAgents.Analytics
+{
+    internal static class TrainingAnalytics
+    {
+        const string k_VendorKey = "unity.ml-agents";
+        const string k_TrainingEnvironmentInitializedEventName = "ml_agents_training_environment_initialized";
+        const string k_TrainingBehaviorInitializedEventName = "ml_agents_training_behavior_initialized";
+        const string k_RemotePolicyInitializedEventName = "ml_agents_remote_policy_initialized";
+
+        private static readonly string[] s_EventNames =
+        {
+            k_TrainingEnvironmentInitializedEventName,
+            k_TrainingBehaviorInitializedEventName,
+            k_RemotePolicyInitializedEventName
+        };
+
+        /// <summary>
+        /// Hourly limit for this event name
+        /// </summary>
+        const int k_MaxEventsPerHour = 1000;
+
+        /// <summary>
+        /// Maximum number of items in this event.
+        /// </summary>
+        const int k_MaxNumberOfElements = 1000;
+
+        private static bool s_SentEnvironmentInitialized;
+
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+        /// <summary>
+        /// Whether or not we've registered this particular event yet
+        /// </summary>
+        static bool s_EventsRegistered;
+
+        /// <summary>
+        /// Behaviors that we've already sent events for.
+        /// </summary>
+        private static HashSet<string> s_SentRemotePolicyInitialized;
+        private static HashSet<string> s_SentTrainingBehaviorInitialized;
+#endif
+
+        private static Guid s_TrainingSessionGuid;
+
+        // These are set when the RpcCommunicator connects
+        private static string s_TrainerPackageVersion = "";
+        private static string s_TrainerCommunicationVersion = "";
+
+        internal static bool EnableAnalytics()
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            if (s_EventsRegistered)
+            {
+                return true;
+            }
+            foreach (var eventName in s_EventNames)
+            {
+                AnalyticsResult result = EditorAnalytics.RegisterEventWithLimit(eventName, k_MaxEventsPerHour, k_MaxNumberOfElements, k_VendorKey);
+                if (result != AnalyticsResult.Ok)
+                {
+                    return false;
+                }
+            }
+            s_EventsRegistered = true;
+
+            if (s_SentRemotePolicyInitialized == null)
+            {
+                s_SentRemotePolicyInitialized = new HashSet<string>();
+                s_SentTrainingBehaviorInitialized = new HashSet<string>();
+                s_TrainingSessionGuid = Guid.NewGuid();
+            }
+
+            return s_EventsRegistered;
+#else
+            return false;
+#endif // MLA_UNITY_ANALYTICS_MODULE
+        }
+
+        /// <summary>
+        /// Cache information about the trainer when it becomes available in the RpcCommunicator.
+        /// </summary>
+        /// <param name="communicationVersion"></param>
+        /// <param name="packageVersion"></param>
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        public static void SetTrainerInformation(string packageVersion, string communicationVersion)
+        {
+            s_TrainerPackageVersion = packageVersion;
+            s_TrainerCommunicationVersion = communicationVersion;
+        }
+
+        public static bool IsAnalyticsEnabled()
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            return EditorAnalytics.enabled;
+#else
+            return false;
+#endif
+        }
+
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        public static void TrainingEnvironmentInitialized(TrainingEnvironmentInitializedEvent tbiEvent)
+        {
+            if (!IsAnalyticsEnabled())
+                return;
+
+            if (!EnableAnalytics())
+                return;
+
+            if (s_SentEnvironmentInitialized)
+            {
+                // We already sent an TrainingEnvironmentInitializedEvent. Exit so we don't resend.
+                return;
+            }
+
+            s_SentEnvironmentInitialized = true;
+            tbiEvent.TrainingSessionGuid = s_TrainingSessionGuid.ToString();
+
+            // Note - to debug, use JsonUtility.ToJson on the event.
+            // Debug.Log(
+            //     $"Would send event {k_TrainingEnvironmentInitializedEventName} with body {JsonUtility.ToJson(tbiEvent, true)}"
+            // );
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            if (AnalyticsUtils.s_SendEditorAnalytics)
+            {
+                EditorAnalytics.SendEventWithLimit(k_TrainingEnvironmentInitializedEventName, tbiEvent);
+            }
+#endif
+        }
+
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        public static void RemotePolicyInitialized(
+            string fullyQualifiedBehaviorName,
+            IList<ISensor> sensors,
+            ActionSpec actionSpec,
+            IList<IActuator> actuators
+        )
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            if (!IsAnalyticsEnabled())
+                return;
+
+            if (!EnableAnalytics())
+                return;
+
+            // Extract base behavior name (no team ID)
+            var behaviorName = ParseBehaviorName(fullyQualifiedBehaviorName);
+            var added = s_SentRemotePolicyInitialized.Add(behaviorName);
+
+            if (!added)
+            {
+                // We previously added this model. Exit so we don't resend.
+                return;
+            }
+
+            var data = GetEventForRemotePolicy(behaviorName, sensors, actionSpec, actuators);
+            // Note - to debug, use JsonUtility.ToJson on the event.
+            // Debug.Log(
+            //     $"Would send event {k_RemotePolicyInitializedEventName} with body {JsonUtility.ToJson(data, true)}"
+            // );
+            if (AnalyticsUtils.s_SendEditorAnalytics)
+            {
+                EditorAnalytics.SendEventWithLimit(k_RemotePolicyInitializedEventName, data);
+            }
+#endif
+        }
+
+        internal static string ParseBehaviorName(string fullyQualifiedBehaviorName)
+        {
+            var lastQuestionIndex = fullyQualifiedBehaviorName.LastIndexOf("?");
+            if (lastQuestionIndex < 0)
+            {
+                // Nothing to remove
+                return fullyQualifiedBehaviorName;
+            }
+
+            return fullyQualifiedBehaviorName.Substring(0, lastQuestionIndex);
+        }
+
+        internal static TrainingBehaviorInitializedEvent SanitizeTrainingBehaviorInitializedEvent(TrainingBehaviorInitializedEvent tbiEvent)
+        {
+            // Hash the behavior name if the message version is from an older version of ml-agents that doesn't do trainer-side hashing.
+            // We'll also, for extra safety, verify that the BehaviorName is the size of the expected SHA256 hash.
+            // Context: The config field was added at the same time as trainer side hashing, so messages including it should already be hashed.
+            if (tbiEvent.Config.Length == 0 || tbiEvent.BehaviorName.Length != 64)
+            {
+                tbiEvent.BehaviorName = AnalyticsUtils.Hash(k_VendorKey, tbiEvent.BehaviorName);
+            }
+
+            return tbiEvent;
+        }
+
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        public static void TrainingBehaviorInitialized(TrainingBehaviorInitializedEvent rawTbiEvent)
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE && ENABLE_CLOUD_SERVICES_ANALYTICS
+            if (!IsAnalyticsEnabled())
+                return;
+
+            if (!EnableAnalytics())
+                return;
+
+            var tbiEvent = SanitizeTrainingBehaviorInitializedEvent(rawTbiEvent);
+            var behaviorName = tbiEvent.BehaviorName;
+            var added = s_SentTrainingBehaviorInitialized.Add(behaviorName);
+
+            if (!added)
+            {
+                // We previously added this model. Exit so we don't resend.
+                return;
+            }
+
+            tbiEvent.TrainingSessionGuid = s_TrainingSessionGuid.ToString();
+
+            // Note - to debug, use JsonUtility.ToJson on the event.
+            // Debug.Log(
+            //     $"Would send event {k_TrainingBehaviorInitializedEventName} with body {JsonUtility.ToJson(tbiEvent, true)}"
+            // );
+            if (AnalyticsUtils.s_SendEditorAnalytics)
+            {
+                EditorAnalytics.SendEventWithLimit(k_TrainingBehaviorInitializedEventName, tbiEvent);
+            }
+#endif
+        }
+
+        internal static RemotePolicyInitializedEvent GetEventForRemotePolicy(
+            string behaviorName,
+            IList<ISensor> sensors,
+            ActionSpec actionSpec,
+            IList<IActuator> actuators
+        )
+        {
+            var remotePolicyEvent = new RemotePolicyInitializedEvent();
+
+            // Hash the behavior name so that there's no concern about PII or "secret" data being leaked.
+            remotePolicyEvent.BehaviorName = AnalyticsUtils.Hash(k_VendorKey, behaviorName);
+
+            remotePolicyEvent.TrainingSessionGuid = s_TrainingSessionGuid.ToString();
+            remotePolicyEvent.ActionSpec = EventActionSpec.FromActionSpec(actionSpec);
+            remotePolicyEvent.ObservationSpecs = new List<EventObservationSpec>(sensors.Count);
+            foreach (var sensor in sensors)
+            {
+                remotePolicyEvent.ObservationSpecs.Add(EventObservationSpec.FromSensor(sensor));
+            }
+
+            remotePolicyEvent.ActuatorInfos = new List<EventActuatorInfo>(actuators.Count);
+            foreach (var actuator in actuators)
+            {
+                remotePolicyEvent.ActuatorInfos.Add(EventActuatorInfo.FromActuator(actuator));
+            }
+
+            remotePolicyEvent.MLAgentsEnvsVersion = s_TrainerPackageVersion;
+            remotePolicyEvent.TrainerCommunicationVersion = s_TrainerCommunicationVersion;
+            return remotePolicyEvent;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs.meta b/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9109c265a2fc55fcfe025bbc9e44578a37301533
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Analytics/TrainingAnalytics.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 5ad0bc6b45614bb7929d25dd59d5ac38
+timeCreated: 1608168600
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Areas.meta b/com.unity.ml-agents/Runtime/Areas.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d00b0cf67c53203f2e475fa456af912f30233f0f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Areas.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 4774a04ed09a1405cb957aace235adcb
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs b/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c47383b4a3052cbc9d2ab7aadcb9e1413c0fd8c2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs
@@ -0,0 +1,126 @@
+using System;
+using Unity.Mathematics;
+using UnityEngine;
+
+namespace Unity.MLAgents.Areas
+{
+    /// <summary>
+    /// The Training Ares Replicator allows for a training area object group to be replicated dynamically during runtime.
+    /// </summary>
+    [DefaultExecutionOrder(-5)]
+    public class TrainingAreaReplicator : MonoBehaviour
+    {
+        /// <summary>
+        /// The base training area to be replicated.
+        /// </summary>
+        public GameObject baseArea;
+
+        /// <summary>
+        /// The number of training areas to replicate.
+        /// </summary>
+        public int numAreas = 1;
+
+        /// <summary>
+        /// The separation between each training area.
+        /// </summary>
+        public float separation = 10f;
+
+        /// <summary>
+        /// Whether to replicate in the editor or in a build only. Default = true
+        /// </summary>
+        public bool buildOnly = true;
+
+        int3 m_GridSize = new(1, 1, 1);
+        int m_AreaCount;
+        string m_TrainingAreaName;
+
+        /// <summary>
+        /// The size of the computed grid to pack the training areas into.
+        /// </summary>
+        public int3 GridSize => m_GridSize;
+
+        /// <summary>
+        /// The name of the training area.
+        /// </summary>
+        public string TrainingAreaName => m_TrainingAreaName;
+
+        /// <summary>
+        /// Called before the simulation begins to computed the grid size for distributing
+        /// the replicated training areas and set the area name.
+        /// </summary>
+        public void Awake()
+        {
+            // Computes the Grid Size on Awake
+            ComputeGridSize();
+            // Sets the TrainingArea name to the name of the base area.
+            m_TrainingAreaName = baseArea.name;
+        }
+
+        /// <summary>
+        /// Called after Awake and before the simulation begins and adds the training areas before
+        /// the Academy begins.
+        /// </summary>
+        public void OnEnable()
+        {
+            // Adds the training as replicas during OnEnable to ensure they are added before the Academy begins its work.
+            if (buildOnly)
+            {
+#if UNITY_STANDALONE && !UNITY_EDITOR
+                AddEnvironments();
+#endif
+                return;
+            }
+            AddEnvironments();
+        }
+
+        /// <summary>
+        /// Computes the Grid Size for replicating the training area.
+        /// </summary>
+        void ComputeGridSize()
+        {
+            // check if running inference, if so, use the num areas set through the component,
+            // otherwise, pull it from the academy
+            if (Academy.Instance.Communicator != null)
+                numAreas = Academy.Instance.NumAreas;
+
+            var rootNumAreas = Mathf.Pow(numAreas, 1.0f / 3.0f);
+            m_GridSize.x = Mathf.CeilToInt(rootNumAreas);
+            m_GridSize.y = Mathf.CeilToInt(rootNumAreas);
+            var zSize = Mathf.CeilToInt((float)numAreas / (m_GridSize.x * m_GridSize.y));
+            m_GridSize.z = zSize == 0 ? 1 : zSize;
+        }
+
+        /// <summary>
+        /// Adds replicas of the training area to the scene.
+        /// </summary>
+        /// <exception cref="UnityAgentsException"></exception>
+        void AddEnvironments()
+        {
+            if (numAreas > m_GridSize.x * m_GridSize.y * m_GridSize.z)
+            {
+                throw new UnityAgentsException("The number of training areas that you have specified exceeds the size of the grid.");
+            }
+
+            for (int z = 0; z < m_GridSize.z; z++)
+            {
+                for (int y = 0; y < m_GridSize.y; y++)
+                {
+                    for (int x = 0; x < m_GridSize.x; x++)
+                    {
+                        if (m_AreaCount == 0)
+                        {
+                            // Skip this first area since it already exists.
+                            m_AreaCount = 1;
+                        }
+                        else if (m_AreaCount < numAreas)
+                        {
+                            m_AreaCount++;
+                            var area = Instantiate(baseArea, new Vector3(x * separation, y * separation, z * separation), Quaternion.identity);
+                            area.name = m_TrainingAreaName;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs.meta b/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..84ac36d7899a9a2ed915c1cffe163525142d629e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Areas/TrainingAreaReplicator.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7fc26c3bda6fe4937b2264ffe43190b7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/AssemblyInfo.cs b/com.unity.ml-agents/Runtime/AssemblyInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5bb925003f7e84c190b0023577ff67807f3c46d2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/AssemblyInfo.cs
@@ -0,0 +1,13 @@
+using System.Runtime.CompilerServices;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Sensor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Utils.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.Input")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Pro")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Pro.Tests")]
diff --git a/com.unity.ml-agents/Runtime/AssemblyInfo.cs.meta b/com.unity.ml-agents/Runtime/AssemblyInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1672ad458e41fd3d5819426205e294f6b74660b2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/AssemblyInfo.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: b433ecadea36c4af9a3dc65e359a3ca0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Communicator.meta b/com.unity.ml-agents/Runtime/Communicator.meta
new file mode 100644
index 0000000000000000000000000000000000000000..dc3a8bac9bc79d2cbe5e01e0ff68b84939ecbf88
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 432bb08962b3944c6964c0db6af43669
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs b/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs
new file mode 100644
index 0000000000000000000000000000000000000000..02d1e4efbd92eda73a48d7b69f91f8f2612b31b7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs
@@ -0,0 +1,43 @@
+using System;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Factory class for an ICommunicator instance. This is used to the <see cref="Academy"/> at startup.
+    /// By default, on desktop platforms, an ICommunicator will be created and attempt to connect
+    /// to a trainer. This behavior can be prevented by setting <see cref="CommunicatorFactory.Enabled"/> to false
+    /// *before* the <see cref="Academy"/> is initialized.
+    /// </summary>
+    public static class CommunicatorFactory
+    {
+        static Func<ICommunicator> s_Creator;
+        static bool s_Enabled = true;
+
+        /// <summary>
+        /// Whether or not an ICommunicator instance will be created when the <see cref="Academy"/> is initialized.
+        /// Changing this has no effect after the <see cref="Academy"/> has already been initialized.
+        /// </summary>
+        public static bool Enabled
+        {
+            get => s_Enabled;
+            set => s_Enabled = value;
+        }
+
+        public static bool CommunicatorRegistered => s_Creator != null;
+
+        internal static ICommunicator Create()
+        {
+            return s_Enabled ? s_Creator() : null;
+        }
+
+        public static void Register<T>(Func<T> creator) where T : ICommunicator
+        {
+            s_Creator = () => creator();
+        }
+
+        public static void ClearCreator()
+        {
+            s_Creator = null;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs.meta b/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1d208003e3ee4e139b0c52a63d3bc05e65376e20
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/CommunicatorFactory.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0b604cddc07e4484a2cdaba630a971ea
+timeCreated: 1613617949
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs b/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e5a97cd167c984024b232e631d7a20d0103d07dc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs
@@ -0,0 +1,541 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Google.Protobuf;
+using Unity.MLAgents.CommunicatorObjects;
+using UnityEngine;
+using System.Runtime.CompilerServices;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
+
+using Unity.MLAgents.Analytics;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Utils.Tests")]
+
+namespace Unity.MLAgents
+{
+    internal static class GrpcExtensions
+    {
+        #region AgentInfo
+        /// <summary>
+        /// Static flag to make sure that we only fire the warning once.
+        /// </summary>
+        private static bool s_HaveWarnedTrainerCapabilitiesAgentGroup;
+
+        /// <summary>
+        /// Converts a AgentInfo to a protobuf generated AgentInfoActionPairProto
+        /// </summary>
+        /// <returns>The protobuf version of the AgentInfoActionPairProto.</returns>
+        public static AgentInfoActionPairProto ToInfoActionPairProto(this AgentInfo ai)
+        {
+            var agentInfoProto = ai.ToAgentInfoProto();
+
+            var agentActionProto = new AgentActionProto();
+
+            if (!ai.storedActions.IsEmpty())
+            {
+                if (!ai.storedActions.ContinuousActions.IsEmpty())
+                {
+                    agentActionProto.ContinuousActions.AddRange(ai.storedActions.ContinuousActions.Array);
+                }
+                if (!ai.storedActions.DiscreteActions.IsEmpty())
+                {
+                    agentActionProto.DiscreteActions.AddRange(ai.storedActions.DiscreteActions.Array);
+                }
+            }
+
+            return new AgentInfoActionPairProto
+            {
+                AgentInfo = agentInfoProto,
+                ActionInfo = agentActionProto
+            };
+        }
+
+        /// <summary>
+        /// Converts a AgentInfo to a protobuf generated AgentInfoProto
+        /// </summary>
+        /// <returns>The protobuf version of the AgentInfo.</returns>
+        public static AgentInfoProto ToAgentInfoProto(this AgentInfo ai)
+        {
+            if (ai.groupId > 0)
+            {
+                var trainerCanHandle = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.MultiAgentGroups;
+                if (!trainerCanHandle)
+                {
+                    if (!s_HaveWarnedTrainerCapabilitiesAgentGroup)
+                    {
+                        Debug.LogWarning(
+                            $"Attached trainer doesn't support Multi Agent Groups; group rewards will be ignored." +
+                            "Please find the versions that work best together from our release page: " +
+                            "https://github.com/Unity-Technologies/ml-agents/releases"
+                        );
+                        s_HaveWarnedTrainerCapabilitiesAgentGroup = true;
+                    }
+                }
+            }
+            var agentInfoProto = new AgentInfoProto
+            {
+                Reward = ai.reward,
+                GroupReward = ai.groupReward,
+                MaxStepReached = ai.maxStepReached,
+                Done = ai.done,
+                Id = ai.episodeId,
+                GroupId = ai.groupId,
+            };
+
+            if (ai.discreteActionMasks != null)
+            {
+                agentInfoProto.ActionMask.AddRange(ai.discreteActionMasks);
+            }
+
+            return agentInfoProto;
+        }
+
+        /// <summary>
+        /// Get summaries for the observations in the AgentInfo part of the AgentInfoActionPairProto.
+        /// </summary>
+        /// <param name="infoActionPair"></param>
+        /// <returns></returns>
+        public static List<ObservationSummary> GetObservationSummaries(this AgentInfoActionPairProto infoActionPair)
+        {
+            List<ObservationSummary> summariesOut = new List<ObservationSummary>();
+            var agentInfo = infoActionPair.AgentInfo;
+            foreach (var obs in agentInfo.Observations)
+            {
+                var summary = new ObservationSummary();
+                summary.shape = obs.Shape.ToArray();
+                summariesOut.Add(summary);
+            }
+
+            return summariesOut;
+        }
+
+        #endregion
+
+        #region BrainParameters
+        /// <summary>
+        /// Converts a BrainParameters into to a BrainParametersProto so it can be sent.
+        /// </summary>
+        /// <returns>The BrainInfoProto generated.</returns>
+        /// <param name="bp">The instance of BrainParameter to extend.</param>
+        /// <param name="name">The name of the brain.</param>
+        /// <param name="isTraining">Whether or not the Brain is training.</param>
+        public static BrainParametersProto ToProto(this BrainParameters bp, string name, bool isTraining)
+        {
+            // Disable deprecation warnings so we can set legacy fields
+#pragma warning disable CS0618
+            var brainParametersProto = new BrainParametersProto
+            {
+                VectorActionSpaceTypeDeprecated = (SpaceTypeProto)bp.VectorActionSpaceType,
+                BrainName = name,
+                IsTraining = isTraining,
+                ActionSpec = ToActionSpecProto(bp.ActionSpec),
+            };
+            if (bp.VectorActionSize != null)
+            {
+                brainParametersProto.VectorActionSizeDeprecated.AddRange(bp.VectorActionSize);
+            }
+            if (bp.VectorActionDescriptions != null)
+            {
+                brainParametersProto.VectorActionDescriptionsDeprecated.AddRange(bp.VectorActionDescriptions);
+            }
+#pragma warning restore CS0618
+            return brainParametersProto;
+        }
+
+        /// <summary>
+        /// Converts an ActionSpec into to a Protobuf BrainInfoProto so it can be sent.
+        /// </summary>
+        /// <returns>The BrainInfoProto generated.</returns>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        /// <param name="name">The name of the brain.</param>
+        /// <param name="isTraining">Whether or not the Brain is training.</param>
+        public static BrainParametersProto ToBrainParametersProto(this ActionSpec actionSpec, string name, bool isTraining)
+        {
+            var brainParametersProto = new BrainParametersProto
+            {
+                BrainName = name,
+                IsTraining = isTraining,
+                ActionSpec = ToActionSpecProto(actionSpec),
+            };
+
+            var supportHybrid = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.HybridActions;
+            if (!supportHybrid)
+            {
+                actionSpec.CheckAllContinuousOrDiscrete();
+                if (actionSpec.NumContinuousActions > 0)
+                {
+                    brainParametersProto.VectorActionSizeDeprecated.Add(actionSpec.NumContinuousActions);
+                    brainParametersProto.VectorActionSpaceTypeDeprecated = SpaceTypeProto.Continuous;
+                }
+                else if (actionSpec.NumDiscreteActions > 0)
+                {
+                    brainParametersProto.VectorActionSizeDeprecated.AddRange(actionSpec.BranchSizes);
+                    brainParametersProto.VectorActionSpaceTypeDeprecated = SpaceTypeProto.Discrete;
+                }
+            }
+
+            // TODO handle ActionDescriptions?
+            return brainParametersProto;
+        }
+
+        /// <summary>
+        /// Convert a BrainParametersProto to a BrainParameters struct.
+        /// </summary>
+        /// <param name="bpp">An instance of a brain parameters protobuf object.</param>
+        /// <returns>A BrainParameters struct.</returns>
+        public static BrainParameters ToBrainParameters(this BrainParametersProto bpp)
+        {
+            ActionSpec actionSpec;
+            if (bpp.ActionSpec == null)
+            {
+                // Disable deprecation warnings so we can set legacy fields
+#pragma warning disable CS0618
+                var spaceType = (SpaceType)bpp.VectorActionSpaceTypeDeprecated;
+                if (spaceType == SpaceType.Continuous)
+                {
+                    actionSpec = ActionSpec.MakeContinuous(bpp.VectorActionSizeDeprecated.ToArray()[0]);
+                }
+                else
+                {
+                    actionSpec = ActionSpec.MakeDiscrete(bpp.VectorActionSizeDeprecated.ToArray());
+                }
+#pragma warning restore CS0618
+            }
+            else
+            {
+                actionSpec = ToActionSpec(bpp.ActionSpec);
+            }
+            var bp = new BrainParameters
+            {
+                VectorActionDescriptions = bpp.VectorActionDescriptionsDeprecated.ToArray(),
+                ActionSpec = actionSpec,
+            };
+            return bp;
+        }
+
+        /// <summary>
+        /// Convert a ActionSpecProto to a ActionSpec struct.
+        /// </summary>
+        /// <param name="actionSpecProto">An instance of an action spec protobuf object.</param>
+        /// <returns>An ActionSpec struct.</returns>
+        public static ActionSpec ToActionSpec(this ActionSpecProto actionSpecProto)
+        {
+            var actionSpec = new ActionSpec(actionSpecProto.NumContinuousActions);
+            if (actionSpecProto.DiscreteBranchSizes != null)
+            {
+                actionSpec.BranchSizes = actionSpecProto.DiscreteBranchSizes.ToArray();
+            }
+            return actionSpec;
+        }
+
+        /// <summary>
+        /// Convert a ActionSpec struct to a ActionSpecProto.
+        /// </summary>
+        /// <param name="actionSpec">An instance of an action spec struct.</param>
+        /// <returns>An ActionSpecProto.</returns>
+        public static ActionSpecProto ToActionSpecProto(this ActionSpec actionSpec)
+        {
+            var actionSpecProto = new ActionSpecProto
+            {
+                NumContinuousActions = actionSpec.NumContinuousActions,
+                NumDiscreteActions = actionSpec.NumDiscreteActions,
+            };
+            if (actionSpec.BranchSizes != null)
+            {
+                actionSpecProto.DiscreteBranchSizes.AddRange(actionSpec.BranchSizes);
+            }
+            return actionSpecProto;
+        }
+
+        #endregion
+
+        #region DemonstrationMetaData
+        /// <summary>
+        /// Convert metadata object to proto object.
+        /// </summary>
+        public static DemonstrationMetaProto ToProto(this DemonstrationMetaData dm)
+        {
+            var demonstrationName = dm.demonstrationName ?? "";
+            var demoProto = new DemonstrationMetaProto
+            {
+                ApiVersion = DemonstrationMetaData.ApiVersion,
+                MeanReward = dm.meanReward,
+                NumberSteps = dm.numberSteps,
+                NumberEpisodes = dm.numberEpisodes,
+                DemonstrationName = demonstrationName
+            };
+            return demoProto;
+        }
+
+        /// <summary>
+        /// Initialize metadata values based on proto object.
+        /// </summary>
+        public static DemonstrationMetaData ToDemonstrationMetaData(this DemonstrationMetaProto demoProto)
+        {
+            var dm = new DemonstrationMetaData
+            {
+                numberEpisodes = demoProto.NumberEpisodes,
+                numberSteps = demoProto.NumberSteps,
+                meanReward = demoProto.MeanReward,
+                demonstrationName = demoProto.DemonstrationName
+            };
+            if (demoProto.ApiVersion != DemonstrationMetaData.ApiVersion)
+            {
+                throw new Exception("API versions of demonstration are incompatible.");
+            }
+            return dm;
+        }
+
+        #endregion
+
+        public static UnityRLInitParameters ToUnityRLInitParameters(this UnityRLInitializationInputProto inputProto)
+        {
+            return new UnityRLInitParameters
+            {
+                seed = inputProto.Seed,
+                numAreas = inputProto.NumAreas,
+                pythonLibraryVersion = inputProto.PackageVersion,
+                pythonCommunicationVersion = inputProto.CommunicationVersion,
+                TrainerCapabilities = inputProto.Capabilities.ToRLCapabilities()
+            };
+        }
+
+        #region AgentAction
+        public static List<ActionBuffers> ToAgentActionList(this UnityRLInputProto.Types.ListAgentActionProto proto)
+        {
+            var agentActions = new List<ActionBuffers>(proto.Value.Count);
+            foreach (var ap in proto.Value)
+            {
+                agentActions.Add(ap.ToActionBuffers());
+            }
+            return agentActions;
+        }
+
+        public static ActionBuffers ToActionBuffers(this AgentActionProto proto)
+        {
+            return new ActionBuffers(proto.ContinuousActions.ToArray(), proto.DiscreteActions.ToArray());
+        }
+
+        #endregion
+
+        #region Observations
+        /// <summary>
+        /// Static flag to make sure that we only fire the warning once.
+        /// </summary>
+        private static bool s_HaveWarnedTrainerCapabilitiesMultiPng;
+        private static bool s_HaveWarnedTrainerCapabilitiesMapping;
+
+        /// <summary>
+        /// Generate an ObservationProto for the sensor using the provided ObservationWriter.
+        /// This is equivalent to producing an Observation and calling Observation.ToProto(),
+        /// but avoid some intermediate memory allocations.
+        /// </summary>
+        /// <param name="sensor"></param>
+        /// <param name="observationWriter"></param>
+        /// <returns></returns>
+        public static ObservationProto GetObservationProto(this ISensor sensor, ObservationWriter observationWriter)
+        {
+            var obsSpec = sensor.GetObservationSpec();
+            var shape = obsSpec.Shape;
+            ObservationProto observationProto = null;
+            var compressionSpec = sensor.GetCompressionSpec();
+            var compressionType = compressionSpec.SensorCompressionType;
+            // Check capabilities if we need to concatenate PNGs
+            if (compressionType == SensorCompressionType.PNG && shape.Length == 3 && shape[2] > 3)
+            {
+                var trainerCanHandle = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.ConcatenatedPngObservations;
+                if (!trainerCanHandle)
+                {
+                    if (!s_HaveWarnedTrainerCapabilitiesMultiPng)
+                    {
+                        Debug.LogWarning(
+                            $"Attached trainer doesn't support multiple PNGs. Switching to uncompressed observations for sensor {sensor.GetName()}. " +
+                            "Please find the versions that work best together from our release page: " +
+                            "https://github.com/Unity-Technologies/ml-agents/releases"
+                        );
+                        s_HaveWarnedTrainerCapabilitiesMultiPng = true;
+                    }
+                    compressionType = SensorCompressionType.None;
+                }
+            }
+            // Check capabilities if we need mapping for compressed observations
+            if (compressionType != SensorCompressionType.None && shape.Length == 3 && shape[2] > 3)
+            {
+                var trainerCanHandleMapping = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.CompressedChannelMapping;
+                var isTrivialMapping = compressionSpec.IsTrivialMapping();
+                if (!trainerCanHandleMapping && !isTrivialMapping)
+                {
+                    if (!s_HaveWarnedTrainerCapabilitiesMapping)
+                    {
+                        Debug.LogWarning(
+                            $"The sensor {sensor.GetName()} is using non-trivial mapping and " +
+                            "the attached trainer doesn't support compression mapping. " +
+                            "Switching to uncompressed observations. " +
+                            "Please find the versions that work best together from our release page: " +
+                            "https://github.com/Unity-Technologies/ml-agents/releases"
+                        );
+                        s_HaveWarnedTrainerCapabilitiesMapping = true;
+                    }
+                    compressionType = SensorCompressionType.None;
+                }
+            }
+
+            if (compressionType == SensorCompressionType.None)
+            {
+                var numFloats = sensor.ObservationSize();
+                var floatDataProto = new ObservationProto.Types.FloatData();
+                // Resize the float array
+                // TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
+                for (var i = 0; i < numFloats; i++)
+                {
+                    floatDataProto.Data.Add(0.0f);
+                }
+
+                observationWriter.SetTarget(floatDataProto.Data, sensor.GetObservationSpec(), 0);
+                sensor.Write(observationWriter);
+
+                observationProto = new ObservationProto
+                {
+                    FloatData = floatDataProto,
+                    CompressionType = (CompressionTypeProto)SensorCompressionType.None,
+                };
+            }
+            else
+            {
+                var compressedObs = sensor.GetCompressedObservation();
+                if (compressedObs == null)
+                {
+                    throw new UnityAgentsException(
+                        $"GetCompressedObservation() returned null data for sensor named {sensor.GetName()}. " +
+                        "You must return a byte[]. If you don't want to use compressed observations, " +
+                        "return CompressionSpec.Default() from GetCompressionSpec()."
+                    );
+                }
+                observationProto = new ObservationProto
+                {
+                    CompressedData = ByteString.CopyFrom(compressedObs),
+                    CompressionType = (CompressionTypeProto)sensor.GetCompressionSpec().SensorCompressionType,
+                };
+                if (compressionSpec.CompressedChannelMapping != null)
+                {
+                    observationProto.CompressedChannelMapping.AddRange(compressionSpec.CompressedChannelMapping);
+                }
+            }
+
+            // Add the dimension properties to the observationProto
+            var dimensionProperties = obsSpec.DimensionProperties;
+            for (int i = 0; i < dimensionProperties.Length; i++)
+            {
+                observationProto.DimensionProperties.Add((int)dimensionProperties[i]);
+            }
+
+            // Checking trainer compatibility with variable length observations
+            if (dimensionProperties == new InplaceArray<DimensionProperty>(DimensionProperty.VariableSize, DimensionProperty.None))
+            {
+                var trainerCanHandleVarLenObs = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.VariableLengthObservation;
+                if (!trainerCanHandleVarLenObs)
+                {
+                    throw new UnityAgentsException("Variable Length Observations are not supported by the trainer");
+                }
+            }
+
+            for (var i = 0; i < shape.Length; i++)
+            {
+                observationProto.Shape.Add(shape[i]);
+            }
+
+            var sensorName = sensor.GetName();
+            if (!string.IsNullOrEmpty(sensorName))
+            {
+                observationProto.Name = sensorName;
+            }
+
+            observationProto.ObservationType = (ObservationTypeProto)obsSpec.ObservationType;
+            return observationProto;
+        }
+
+        #endregion
+
+        public static UnityRLCapabilities ToRLCapabilities(this UnityRLCapabilitiesProto proto)
+        {
+            return new UnityRLCapabilities
+            {
+                BaseRLCapabilities = proto.BaseRLCapabilities,
+                ConcatenatedPngObservations = proto.ConcatenatedPngObservations,
+                CompressedChannelMapping = proto.CompressedChannelMapping,
+                HybridActions = proto.HybridActions,
+                TrainingAnalytics = proto.TrainingAnalytics,
+                VariableLengthObservation = proto.VariableLengthObservation,
+                MultiAgentGroups = proto.MultiAgentGroups,
+            };
+        }
+
+        public static UnityRLCapabilitiesProto ToProto(this UnityRLCapabilities rlCaps)
+        {
+            return new UnityRLCapabilitiesProto
+            {
+                BaseRLCapabilities = rlCaps.BaseRLCapabilities,
+                ConcatenatedPngObservations = rlCaps.ConcatenatedPngObservations,
+                CompressedChannelMapping = rlCaps.CompressedChannelMapping,
+                HybridActions = rlCaps.HybridActions,
+                TrainingAnalytics = rlCaps.TrainingAnalytics,
+                VariableLengthObservation = rlCaps.VariableLengthObservation,
+                MultiAgentGroups = rlCaps.MultiAgentGroups,
+            };
+        }
+
+        #region Analytics
+        internal static TrainingEnvironmentInitializedEvent ToTrainingEnvironmentInitializedEvent(
+            this TrainingEnvironmentInitialized inputProto)
+        {
+            return new TrainingEnvironmentInitializedEvent
+            {
+                TrainerPythonVersion = inputProto.PythonVersion,
+                MLAgentsVersion = inputProto.MlagentsVersion,
+                MLAgentsEnvsVersion = inputProto.MlagentsEnvsVersion,
+                TorchVersion = inputProto.TorchVersion,
+                TorchDeviceType = inputProto.TorchDeviceType,
+                NumEnvironments = inputProto.NumEnvs,
+                NumEnvironmentParameters = inputProto.NumEnvironmentParameters,
+                RunOptions = inputProto.RunOptions,
+            };
+        }
+
+        internal static TrainingBehaviorInitializedEvent ToTrainingBehaviorInitializedEvent(
+            this TrainingBehaviorInitialized inputProto)
+        {
+            RewardSignals rewardSignals = 0;
+            rewardSignals |= inputProto.ExtrinsicRewardEnabled ? RewardSignals.Extrinsic : 0;
+            rewardSignals |= inputProto.GailRewardEnabled ? RewardSignals.Gail : 0;
+            rewardSignals |= inputProto.CuriosityRewardEnabled ? RewardSignals.Curiosity : 0;
+            rewardSignals |= inputProto.RndRewardEnabled ? RewardSignals.Rnd : 0;
+
+            TrainingFeatures trainingFeatures = 0;
+            trainingFeatures |= inputProto.BehavioralCloningEnabled ? TrainingFeatures.BehavioralCloning : 0;
+            trainingFeatures |= inputProto.RecurrentEnabled ? TrainingFeatures.Recurrent : 0;
+            trainingFeatures |= inputProto.TrainerThreaded ? TrainingFeatures.Threaded : 0;
+            trainingFeatures |= inputProto.SelfPlayEnabled ? TrainingFeatures.SelfPlay : 0;
+            trainingFeatures |= inputProto.CurriculumEnabled ? TrainingFeatures.Curriculum : 0;
+
+
+            return new TrainingBehaviorInitializedEvent
+            {
+                BehaviorName = inputProto.BehaviorName,
+                TrainerType = inputProto.TrainerType,
+                RewardSignalFlags = rewardSignals,
+                TrainingFeatureFlags = trainingFeatures,
+                VisualEncoder = inputProto.VisualEncoder,
+                NumNetworkLayers = inputProto.NumNetworkLayers,
+                NumNetworkHiddenUnits = inputProto.NumNetworkHiddenUnits,
+                Config = inputProto.Config,
+            };
+        }
+
+        #endregion
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs.meta b/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..43d71078970f01ae128de84a116e23ab8be12032
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 02e8742d8a124607bef3b5ff8b9dd3d0
+timeCreated: 1569444771
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs b/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2036a2aa284447ff8c2e98ecec56936a06017802
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs
@@ -0,0 +1,173 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents
+{
+    public struct CommunicatorInitParameters
+    {
+        /// <summary>
+        /// Port to listen for connections on.
+        /// </summary>
+        public int port;
+
+        /// <summary>
+        /// The name of the environment.
+        /// </summary>
+        public string name;
+
+        /// <summary>
+        /// The version of the Unity SDK.
+        /// </summary>
+        public string unityPackageVersion;
+
+        /// <summary>
+        /// The version of the communication API.
+        /// </summary>
+        public string unityCommunicationVersion;
+
+        /// <summary>
+        /// The RL capabilities of the C# codebase.
+        /// </summary>
+        public UnityRLCapabilities CSharpCapabilities;
+    }
+    public struct UnityRLInitParameters
+    {
+        /// <summary>
+        /// A random number generator (RNG) seed sent from the python process to Unity.
+        /// </summary>
+        public int seed;
+
+        /// <summary>
+        /// The number of areas to replicate if Training Area Replication is used in the scene.
+        /// </summary>
+        public int numAreas;
+
+        /// <summary>
+        /// The library version of the python process.
+        /// </summary>
+        public string pythonLibraryVersion;
+
+        /// <summary>
+        /// The version of the communication API that python is using.
+        /// </summary>
+        public string pythonCommunicationVersion;
+
+        /// <summary>
+        /// The RL capabilities of the Trainer codebase.
+        /// </summary>
+        public UnityRLCapabilities TrainerCapabilities;
+    }
+    internal struct UnityRLInputParameters
+    {
+        /// <summary>
+        /// Boolean sent back from python to indicate whether or not training is happening.
+        /// </summary>
+        public bool isTraining;
+    }
+
+    /// <summary>
+    /// Delegate for handling quit events sent back from the communicator.
+    /// </summary>
+    public delegate void QuitCommandHandler();
+
+    /// <summary>
+    /// Delegate for handling reset parameter updates sent from the communicator.
+    /// </summary>
+    public delegate void ResetCommandHandler();
+
+    /// <summary>
+    /// Delegate to handle UnityRLInputParameters updates from the communicator.
+    /// </summary>
+    /// <param name="inputParams"></param>
+    internal delegate void RLInputReceivedHandler(UnityRLInputParameters inputParams);
+
+    /**
+    This is the interface of the Communicators.
+    This does not need to be modified nor implemented to create a Unity environment.
+
+    When the Unity Communicator is initialized, it will wait for the External Communicator
+    to be initialized as well. The two communicators will then exchange their first messages
+    that will usually contain information for initialization (information that does not need
+    to be resent at each new exchange).
+
+    By convention a Unity input is from External to Unity and a Unity output is from Unity to
+    External. Inputs and outputs are relative to Unity.
+
+    By convention, when the Unity Communicator and External Communicator call exchange, the
+    exchange is NOT simultaneous but sequential. This means that when a side of the
+    communication calls exchange, the other will receive the result of its previous
+    exchange call.
+    This is what happens when A calls exchange a single time:
+    A sends data_1 to B -> B receives data_1 -> B generates and sends data_2 -> A receives data_2
+    When A calls exchange, it sends data_1 and receives data_2
+
+    Since the messages are sent back and forth with exchange and simultaneously when calling
+    initialize, External sends two messages at initialization.
+
+    The structure of the messages is as follows:
+    UnityMessage
+    ...Header
+    ...UnityOutput
+    ......UnityRLOutput
+    ......UnityRLInitializationOutput
+    ...UnityInput
+    ......UnityRLInput
+    ......UnityRLInitializationInput
+
+    UnityOutput and UnityInput can be extended to provide functionalities beyond RL
+    UnityRLOutput and UnityRLInput can be extended to provide new RL functionalities
+     */
+    public interface ICommunicator : IDisposable
+    {
+        /// <summary>
+        /// Quit was received by the communicator.
+        /// </summary>
+        event QuitCommandHandler QuitCommandReceived;
+
+        /// <summary>
+        /// Reset command sent back from the communicator.
+        /// </summary>
+        event ResetCommandHandler ResetCommandReceived;
+
+        /// <summary>
+        /// Sends the academy parameters through the Communicator.
+        /// Is used by the academy to send the AcademyParameters to the communicator.
+        /// </summary>
+        /// <returns>Whether the connection was successful.</returns>
+        /// <param name="initParameters">The Unity Initialization Parameters to be sent.</param>
+        /// <param name="initParametersOut">The External Initialization Parameters received</param>
+        bool Initialize(CommunicatorInitParameters initParameters, out UnityRLInitParameters initParametersOut);
+
+        /// <summary>
+        /// Registers a new Brain to the Communicator.
+        /// </summary>
+        /// <param name="name">The name or key uniquely identifying the Brain.</param>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        void SubscribeBrain(string name, ActionSpec actionSpec);
+
+        /// <summary>
+        /// Sends the observations of one Agent.
+        /// </summary>
+        /// <param name="brainKey">Batch Key.</param>
+        /// <param name="info">Agent info.</param>
+        /// <param name="sensors">The list of ISensors of the Agent.</param>
+        void PutObservations(string brainKey, AgentInfo info, List<ISensor> sensors);
+
+        /// <summary>
+        /// Signals the ICommunicator that the Agents are now ready to receive their action
+        /// and that if the communicator has not yet received an action for one of the Agents
+        /// it needs to get one at this point.
+        /// </summary>
+        void DecideBatch();
+
+        /// <summary>
+        /// Gets the AgentActions based on the batching key.
+        /// </summary>
+        /// <param name="key">A key to identify which behavior actions to get.</param>
+        /// <param name="agentId">A key to identify which Agent actions to get.</param>
+        /// <returns></returns>
+        ActionBuffers GetActions(string key, int agentId);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs.meta b/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b37ca0a83d796bdbe7ec254fb7e85850e2821b9f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 53977f05e5684d4a9e2ef86f225934a2
+timeCreated: 1568395551
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs b/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c1427715bc3c3a42b0c192e46748f8fb1d3bb193
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs
@@ -0,0 +1,607 @@
+#if UNITY_EDITOR || UNITY_STANDALONE
+#define MLA_SUPPORTED_TRAINING_PLATFORM
+#endif
+
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+using Grpc.Core;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using UnityEngine;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.CommunicatorObjects;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.SideChannels;
+using Google.Protobuf;
+
+using Unity.MLAgents.Analytics;
+
+namespace Unity.MLAgents
+{
+    /// Responsible for communication with External using gRPC.
+    public class RpcCommunicator : ICommunicator
+    {
+        public event QuitCommandHandler QuitCommandReceived;
+        public event ResetCommandHandler ResetCommandReceived;
+
+        /// If true, the communication is active.
+        bool m_IsOpen;
+
+        List<string> m_BehaviorNames = new List<string>();
+        bool m_NeedCommunicateThisStep;
+        ObservationWriter m_ObservationWriter = new ObservationWriter();
+        Dictionary<string, SensorShapeValidator> m_SensorShapeValidators = new Dictionary<string, SensorShapeValidator>();
+        Dictionary<string, List<int>> m_OrderedAgentsRequestingDecisions = new Dictionary<string, List<int>>();
+
+        /// The current UnityRLOutput to be sent when all the brains queried the communicator
+        UnityRLOutputProto m_CurrentUnityRlOutput =
+            new UnityRLOutputProto();
+
+        Dictionary<string, Dictionary<int, ActionBuffers>> m_LastActionsReceived =
+            new Dictionary<string, Dictionary<int, ActionBuffers>>();
+
+        // Brains that we have sent over the communicator with agents.
+        HashSet<string> m_SentBrainKeys = new HashSet<string>();
+        Dictionary<string, ActionSpec> m_UnsentBrainKeys = new Dictionary<string, ActionSpec>();
+
+
+        /// The Unity to External client.
+        UnityToExternalProto.UnityToExternalProtoClient m_Client;
+        Channel m_Channel;
+
+        /// <summary>
+        /// Initializes a new instance of the RPCCommunicator class.
+        /// </summary>
+        protected RpcCommunicator()
+        {
+        }
+
+        public static RpcCommunicator Create()
+        {
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+            return new RpcCommunicator();
+#else
+            return null;
+#endif
+        }
+
+#region Initialization
+
+        internal static bool CheckCommunicationVersionsAreCompatible(
+            string unityCommunicationVersion,
+            string pythonApiVersion
+        )
+        {
+            var unityVersion = new Version(unityCommunicationVersion);
+            var pythonVersion = new Version(pythonApiVersion);
+            if (unityVersion.Major == 0)
+            {
+                if (unityVersion.Major != pythonVersion.Major || unityVersion.Minor != pythonVersion.Minor)
+                {
+                    return false;
+                }
+            }
+            else if (unityVersion.Major != pythonVersion.Major)
+            {
+                return false;
+            }
+            else if (unityVersion.Minor != pythonVersion.Minor)
+            {
+                // If a feature is used in Unity but not supported in the trainer,
+                // we will warn at the point it's used. Don't warn here to avoid noise.
+            }
+            return true;
+        }
+
+        /// <summary>
+        /// Sends the initialization parameters through the Communicator.
+        /// Is used by the academy to send initialization parameters to the communicator.
+        /// </summary>
+        /// <returns>Whether the connection was successful.</returns>
+        /// <param name="initParameters">The Unity Initialization Parameters to be sent.</param>
+        /// <param name="initParametersOut">The External Initialization Parameters received.</param>
+        public bool Initialize(CommunicatorInitParameters initParameters, out UnityRLInitParameters initParametersOut)
+        {
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+            var academyParameters = new UnityRLInitializationOutputProto
+            {
+                Name = initParameters.name,
+                PackageVersion = initParameters.unityPackageVersion,
+                CommunicationVersion = initParameters.unityCommunicationVersion,
+                Capabilities = initParameters.CSharpCapabilities.ToProto()
+            };
+
+            UnityInputProto input;
+            UnityInputProto initializationInput;
+            try
+            {
+                initializationInput = Initialize(
+                    initParameters.port,
+                    new UnityOutputProto
+                    {
+                        RlInitializationOutput = academyParameters
+                    },
+                    out input
+                );
+            }
+            catch (Exception ex)
+            {
+                if (ex is RpcException rpcException)
+                {
+                    switch (rpcException.Status.StatusCode)
+                    {
+                        case StatusCode.Unavailable:
+                            // This is the common case where there's no trainer to connect to.
+                            break;
+                        case StatusCode.DeadlineExceeded:
+                            // We don't currently set a deadline for connection, but likely will in the future.
+                            break;
+                        default:
+                            Debug.Log($"Unexpected gRPC exception when trying to initialize communication: {rpcException}");
+                            break;
+                    }
+                }
+                else
+                {
+                    Debug.Log($"Unexpected exception when trying to initialize communication: {ex}");
+                }
+                initParametersOut = new UnityRLInitParameters();
+                NotifyQuitAndShutDownChannel();
+                return false;
+            }
+
+            var pythonPackageVersion = initializationInput.RlInitializationInput.PackageVersion;
+            var pythonCommunicationVersion = initializationInput.RlInitializationInput.CommunicationVersion;
+            TrainingAnalytics.SetTrainerInformation(pythonPackageVersion, pythonCommunicationVersion);
+
+            var communicationIsCompatible = CheckCommunicationVersionsAreCompatible(
+                initParameters.unityCommunicationVersion,
+                pythonCommunicationVersion
+            );
+
+            // Initialization succeeded part-way. The most likely cause is a mismatch between the communicator
+            // API strings, so log an explicit warning if that's the case.
+            if (initializationInput != null && input == null)
+            {
+                if (!communicationIsCompatible)
+                {
+                    Debug.LogWarningFormat(
+                        "Communication protocol between python ({0}) and Unity ({1}) have different " +
+                        "versions which make them incompatible. Python library version: {2}.",
+                        pythonCommunicationVersion, initParameters.unityCommunicationVersion,
+                        pythonPackageVersion
+                    );
+                }
+                else
+                {
+                    Debug.LogWarningFormat(
+                        "Unknown communication error between Python. Python communication protocol: {0}, " +
+                        "Python library version: {1}.",
+                        pythonCommunicationVersion,
+                        pythonPackageVersion
+                    );
+                }
+
+                initParametersOut = new UnityRLInitParameters();
+                return false;
+            }
+
+            UpdateEnvironmentWithInput(input.RlInput);
+            initParametersOut = initializationInput.RlInitializationInput.ToUnityRLInitParameters();
+            // Be sure to shut down the grpc channel when the application is quitting.
+            Application.quitting += NotifyQuitAndShutDownChannel;
+            return true;
+#else
+            initParametersOut = new UnityRLInitParameters();
+            return false;
+#endif
+        }
+
+        /// <summary>
+        /// Adds the brain to the list of brains which will be sending information to External.
+        /// </summary>
+        /// <param name="brainKey">Brain key.</param>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        public void SubscribeBrain(string brainKey, ActionSpec actionSpec)
+        {
+            if (m_BehaviorNames.Contains(brainKey))
+            {
+                return;
+            }
+            m_BehaviorNames.Add(brainKey);
+            m_CurrentUnityRlOutput.AgentInfos.Add(
+                brainKey,
+                new UnityRLOutputProto.Types.ListAgentInfoProto()
+            );
+
+            CacheActionSpec(brainKey, actionSpec);
+        }
+
+        void UpdateEnvironmentWithInput(UnityRLInputProto rlInput)
+        {
+            SideChannelManager.ProcessSideChannelData(rlInput.SideChannel.ToArray());
+            SendCommandEvent(rlInput.Command);
+        }
+
+        UnityInputProto Initialize(int port, UnityOutputProto unityOutput, out UnityInputProto unityInput)
+        {
+            m_IsOpen = true;
+            m_Channel = new Channel($"localhost:{port}", ChannelCredentials.Insecure);
+
+            m_Client = new UnityToExternalProto.UnityToExternalProtoClient(m_Channel);
+            var result = m_Client.Exchange(WrapMessage(unityOutput, 200));
+            var inputMessage = m_Client.Exchange(WrapMessage(null, 200));
+            unityInput = inputMessage.UnityInput;
+#if UNITY_EDITOR
+            EditorApplication.playModeStateChanged += HandleOnPlayModeChanged;
+#endif
+            if (result.Header.Status != 200 || inputMessage.Header.Status != 200)
+            {
+                m_IsOpen = false;
+                NotifyQuitAndShutDownChannel();
+            }
+            return result.UnityInput;
+        }
+
+        void NotifyQuitAndShutDownChannel()
+        {
+            QuitCommandReceived?.Invoke();
+            try
+            {
+                m_Channel.ShutdownAsync().Wait();
+            }
+            catch (Exception)
+            {
+                // do nothing
+            }
+        }
+
+#endregion
+
+#region Destruction
+
+        /// <summary>
+        /// Close the communicator gracefully on both sides of the communication.
+        /// </summary>
+        public void Dispose()
+        {
+            if (!m_IsOpen)
+            {
+                return;
+            }
+
+            try
+            {
+                m_Client.Exchange(WrapMessage(null, 400));
+                m_IsOpen = false;
+            }
+            catch
+            {
+                // ignored
+            }
+        }
+
+#endregion
+
+#region Sending Events
+
+        void SendCommandEvent(CommandProto command)
+        {
+            switch (command)
+            {
+                case CommandProto.Quit:
+                {
+                    NotifyQuitAndShutDownChannel();
+                    return;
+                }
+                case CommandProto.Reset:
+                {
+                    foreach (var brainName in m_OrderedAgentsRequestingDecisions.Keys)
+                    {
+                        m_OrderedAgentsRequestingDecisions[brainName].Clear();
+                    }
+                    ResetCommandReceived?.Invoke();
+                    return;
+                }
+                default:
+                {
+                    return;
+                }
+            }
+        }
+
+#endregion
+
+#region Sending and retreiving data
+
+        public void DecideBatch()
+        {
+            if (!m_NeedCommunicateThisStep)
+            {
+                return;
+            }
+            m_NeedCommunicateThisStep = false;
+
+            SendBatchedMessageHelper();
+        }
+
+        /// <summary>
+        /// Sends the observations of one Agent.
+        /// </summary>
+        /// <param name="behaviorName">Batch Key.</param>
+        /// <param name="info">Agent info.</param>
+        /// <param name="sensors">Sensors that will produce the observations</param>
+        public void PutObservations(string behaviorName, AgentInfo info, List<ISensor> sensors)
+        {
+#if DEBUG
+            if (!m_SensorShapeValidators.ContainsKey(behaviorName))
+            {
+                m_SensorShapeValidators[behaviorName] = new SensorShapeValidator();
+            }
+            m_SensorShapeValidators[behaviorName].ValidateSensors(sensors);
+#endif
+
+            using (TimerStack.Instance.Scoped("AgentInfo.ToProto"))
+            {
+                var agentInfoProto = info.ToAgentInfoProto();
+
+                using (TimerStack.Instance.Scoped("GenerateSensorData"))
+                {
+                    foreach (var sensor in sensors)
+                    {
+                        var obsProto = sensor.GetObservationProto(m_ObservationWriter);
+                        agentInfoProto.Observations.Add(obsProto);
+                    }
+                }
+                m_CurrentUnityRlOutput.AgentInfos[behaviorName].Value.Add(agentInfoProto);
+            }
+
+            m_NeedCommunicateThisStep = true;
+            if (!m_OrderedAgentsRequestingDecisions.ContainsKey(behaviorName))
+            {
+                m_OrderedAgentsRequestingDecisions[behaviorName] = new List<int>();
+            }
+            if (!info.done)
+            {
+                m_OrderedAgentsRequestingDecisions[behaviorName].Add(info.episodeId);
+            }
+            if (!m_LastActionsReceived.ContainsKey(behaviorName))
+            {
+                m_LastActionsReceived[behaviorName] = new Dictionary<int, ActionBuffers>();
+            }
+            m_LastActionsReceived[behaviorName][info.episodeId] = ActionBuffers.Empty;
+            if (info.done)
+            {
+                m_LastActionsReceived[behaviorName].Remove(info.episodeId);
+            }
+        }
+
+        /// <summary>
+        /// Helper method that sends the current UnityRLOutput, receives the next UnityInput and
+        /// Applies the appropriate AgentAction to the agents.
+        /// </summary>
+        void SendBatchedMessageHelper()
+        {
+            var message = new UnityOutputProto
+            {
+                RlOutput = m_CurrentUnityRlOutput,
+            };
+            var tempUnityRlInitializationOutput = GetTempUnityRlInitializationOutput();
+            if (tempUnityRlInitializationOutput != null)
+            {
+                message.RlInitializationOutput = tempUnityRlInitializationOutput;
+            }
+
+            byte[] messageAggregated = SideChannelManager.GetSideChannelMessage();
+            message.RlOutput.SideChannel = ByteString.CopyFrom(messageAggregated);
+
+            var input = Exchange(message);
+            UpdateSentActionSpec(tempUnityRlInitializationOutput);
+
+            foreach (var k in m_CurrentUnityRlOutput.AgentInfos.Keys)
+            {
+                m_CurrentUnityRlOutput.AgentInfos[k].Value.Clear();
+            }
+
+            var rlInput = input?.RlInput;
+
+            if (rlInput?.AgentActions == null)
+            {
+                return;
+            }
+
+            UpdateEnvironmentWithInput(rlInput);
+
+            foreach (var brainName in rlInput.AgentActions.Keys)
+            {
+                if (!m_OrderedAgentsRequestingDecisions[brainName].Any())
+                {
+                    continue;
+                }
+
+                if (!rlInput.AgentActions[brainName].Value.Any())
+                {
+                    continue;
+                }
+
+                var agentActions = rlInput.AgentActions[brainName].ToAgentActionList();
+                var numAgents = m_OrderedAgentsRequestingDecisions[brainName].Count;
+                for (var i = 0; i < numAgents; i++)
+                {
+                    var agentAction = agentActions[i];
+                    var agentId = m_OrderedAgentsRequestingDecisions[brainName][i];
+                    if (m_LastActionsReceived[brainName].ContainsKey(agentId))
+                    {
+                        m_LastActionsReceived[brainName][agentId] = agentAction;
+                    }
+                }
+            }
+            foreach (var brainName in m_OrderedAgentsRequestingDecisions.Keys)
+            {
+                m_OrderedAgentsRequestingDecisions[brainName].Clear();
+            }
+        }
+
+        public ActionBuffers GetActions(string behaviorName, int agentId)
+        {
+            if (m_LastActionsReceived.ContainsKey(behaviorName))
+            {
+                if (m_LastActionsReceived[behaviorName].ContainsKey(agentId))
+                {
+                    return m_LastActionsReceived[behaviorName][agentId];
+                }
+            }
+            return ActionBuffers.Empty;
+        }
+
+        /// <summary>
+        /// Send a UnityOutput and receives a UnityInput.
+        /// </summary>
+        /// <returns>The next UnityInput.</returns>
+        /// <param name="unityOutput">The UnityOutput to be sent.</param>
+        UnityInputProto Exchange(UnityOutputProto unityOutput)
+        {
+            if (!m_IsOpen)
+            {
+                return null;
+            }
+
+            try
+            {
+                var message = m_Client.Exchange(WrapMessage(unityOutput, 200));
+                if (message.Header.Status == 200)
+                {
+                    return message.UnityInput;
+                }
+
+                m_IsOpen = false;
+                // Not sure if the quit command is actually sent when a
+                // non 200 message is received.  Notify that we are indeed
+                // quitting.
+                NotifyQuitAndShutDownChannel();
+                return message.UnityInput;
+            }
+            catch (Exception ex)
+            {
+                if (ex is RpcException rpcException)
+                {
+                    // Log more verbose errors if they're something the user can possibly do something about.
+                    switch (rpcException.Status.StatusCode)
+                    {
+                        case StatusCode.Unavailable:
+                            // This can happen when python disconnects. Ignore it to avoid noisy logs.
+                            break;
+                        case StatusCode.ResourceExhausted:
+                            // This happens is the message body is too large. There's no way to
+                            // gracefully handle this, but at least we can show the message and the
+                            // user can try to reduce the number of agents or observation sizes.
+                            Debug.LogError($"GRPC Exception: {rpcException.Message}. Disconnecting from trainer.");
+                            break;
+                        default:
+                            // Other unknown errors. Log at INFO level.
+                            Debug.Log($"GRPC Exception: {rpcException.Message}. Disconnecting from trainer.");
+                            break;
+                    }
+                }
+                else
+                {
+                    // Fall-through for other error types
+                    Debug.LogError($"Communication Exception: {ex.Message}. Disconnecting from trainer.");
+                }
+
+                m_IsOpen = false;
+                NotifyQuitAndShutDownChannel();
+                return null;
+            }
+        }
+
+        /// <summary>
+        /// Wraps the UnityOutput into a message with the appropriate status.
+        /// </summary>
+        /// <returns>The UnityMessage corresponding.</returns>
+        /// <param name="content">The UnityOutput to be wrapped.</param>
+        /// <param name="status">The status of the message.</param>
+        static UnityMessageProto WrapMessage(UnityOutputProto content, int status)
+        {
+            return new UnityMessageProto
+            {
+                Header = new HeaderProto { Status = status },
+                UnityOutput = content
+            };
+        }
+
+        void CacheActionSpec(string behaviorName, ActionSpec actionSpec)
+        {
+            if (m_SentBrainKeys.Contains(behaviorName))
+            {
+                return;
+            }
+
+            // TODO We should check that if m_unsentBrainKeys has brainKey, it equals actionSpec
+            m_UnsentBrainKeys[behaviorName] = actionSpec;
+        }
+
+        UnityRLInitializationOutputProto GetTempUnityRlInitializationOutput()
+        {
+            UnityRLInitializationOutputProto output = null;
+            foreach (var behaviorName in m_UnsentBrainKeys.Keys)
+            {
+                if (m_CurrentUnityRlOutput.AgentInfos.ContainsKey(behaviorName))
+                {
+                    if (m_CurrentUnityRlOutput.AgentInfos[behaviorName].CalculateSize() > 0)
+                    {
+                        // Only send the actionSpec if there is a non empty list of
+                        // AgentInfos ready to be sent.
+                        // This is to ensure that The Python side will always have a first
+                        // observation when receiving the ActionSpec
+                        if (output == null)
+                        {
+                            output = new UnityRLInitializationOutputProto();
+                        }
+
+                        var actionSpec = m_UnsentBrainKeys[behaviorName];
+                        output.BrainParameters.Add(actionSpec.ToBrainParametersProto(behaviorName, true));
+                    }
+                }
+            }
+
+            return output;
+        }
+
+        void UpdateSentActionSpec(UnityRLInitializationOutputProto output)
+        {
+            if (output == null)
+            {
+                return;
+            }
+
+            foreach (var brainProto in output.BrainParameters)
+            {
+                m_SentBrainKeys.Add(brainProto.BrainName);
+                m_UnsentBrainKeys.Remove(brainProto.BrainName);
+            }
+        }
+
+#endregion
+
+#if UNITY_EDITOR
+        /// <summary>
+        /// When the editor exits, the communicator must be closed
+        /// </summary>
+        /// <param name="state">State.</param>
+        void HandleOnPlayModeChanged(PlayModeStateChange state)
+        {
+            // This method is run whenever the playmode state is changed.
+            if (state == PlayModeStateChange.ExitingPlayMode)
+            {
+                Dispose();
+            }
+        }
+
+#endif
+    }
+}
+#endif // UNITY_EDITOR || UNITY_STANDALONE
diff --git a/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs.meta b/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d1903d74ccd9b9f6eaa4fd9ad5a34a01cd3ce963
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs.meta
@@ -0,0 +1,13 @@
+fileFormatVersion: 2
+guid: 57a3dc12d3b88408688bb490b65a838e
+timeCreated: 1523046536
+licenseType: Free
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs b/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3dffcf52b7f5b85b997533c91c91aa05ff995ab3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs
@@ -0,0 +1,54 @@
+using UnityEngine;
+
+namespace Unity.MLAgents
+{
+    public class UnityRLCapabilities
+    {
+        public bool BaseRLCapabilities;
+        public bool ConcatenatedPngObservations;
+        public bool CompressedChannelMapping;
+        public bool HybridActions;
+        public bool TrainingAnalytics;
+        public bool VariableLengthObservation;
+        public bool MultiAgentGroups;
+
+        /// <summary>
+        /// A class holding the capabilities flags for Reinforcement Learning across C# and the Trainer codebase.  This
+        /// struct will be used to inform users if and when they are using C# / Trainer features that are mismatched.
+        /// </summary>
+        public UnityRLCapabilities(
+            bool baseRlCapabilities = true,
+            bool concatenatedPngObservations = true,
+            bool compressedChannelMapping = true,
+            bool hybridActions = true,
+            bool trainingAnalytics = true,
+            bool variableLengthObservation = true,
+            bool multiAgentGroups = true)
+        {
+            BaseRLCapabilities = baseRlCapabilities;
+            ConcatenatedPngObservations = concatenatedPngObservations;
+            CompressedChannelMapping = compressedChannelMapping;
+            HybridActions = hybridActions;
+            TrainingAnalytics = trainingAnalytics;
+            VariableLengthObservation = variableLengthObservation;
+            MultiAgentGroups = multiAgentGroups;
+        }
+
+        /// <summary>
+        /// Will print a warning to the console if Python does not support base capabilities and will
+        /// return <value>true</value> if the warning was printed.
+        /// </summary>
+        /// <returns></returns>
+        public bool WarnOnPythonMissingBaseRLCapabilities()
+        {
+            if (BaseRLCapabilities)
+            {
+                return false;
+            }
+            Debug.LogWarning("Unity has connected to a Training process that does not support" +
+                "Base Reinforcement Learning Capabilities.  Please make sure you have the" +
+                " latest training codebase installed for this version of the ML-Agents package.");
+            return true;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs.meta b/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6cdc57628e5e02a1a4970ea394b83d9db567d99c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f95d271af72d4b75aa94d308222f79d8
+timeCreated: 1587670989
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Constants.cs b/com.unity.ml-agents/Runtime/Constants.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4be9eba0428483470308d6cc53490bdafd1cb3d8
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Constants.cs
@@ -0,0 +1,12 @@
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Grouping for use in AddComponentMenu (instead of nesting the menus).
+    /// </summary>
+    internal enum MenuGroup
+    {
+        Default = 0,
+        Sensors = 50,
+        Actuators = 100
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Constants.cs.meta b/com.unity.ml-agents/Runtime/Constants.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2b2825b7470a656d1be6cdb1723d8c2f352c606f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Constants.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0622d88401ec464d9d2cf2fb03ce17b5
+timeCreated: 1579215785
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/DecisionRequester.cs b/com.unity.ml-agents/Runtime/DecisionRequester.cs
new file mode 100644
index 0000000000000000000000000000000000000000..37b5ad299cf72e85b9d785b146aaf875eec5d643
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/DecisionRequester.cs
@@ -0,0 +1,135 @@
+using System;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// The DecisionRequester component automatically request decisions for an
+    /// <see cref="Agent"/> instance at regular intervals.
+    /// </summary>
+    /// <remarks>
+    /// Attach a DecisionRequester component to the same [GameObject] as the
+    /// <see cref="Agent"/> component.
+    ///
+    /// The DecisionRequester component provides a convenient and flexible way to
+    /// trigger the agent decision making process. Without a DecisionRequester,
+    /// your <see cref="Agent"/> implementation must manually call its
+    /// <seealso cref="Agent.RequestDecision"/> function.
+    /// </remarks>
+    [AddComponentMenu("ML Agents/Decision Requester", (int)MenuGroup.Default)]
+    [RequireComponent(typeof(Agent))]
+    [DefaultExecutionOrder(-10)]
+    public class DecisionRequester : MonoBehaviour
+    {
+        /// <summary>
+        /// The frequency with which the agent requests a decision. A DecisionPeriod of 5 means
+        /// that the Agent will request a decision every 5 Academy steps. /// </summary>
+        [Range(1, 20)]
+        [Tooltip("The frequency with which the agent requests a decision. A DecisionPeriod " +
+            "of 5 means that the Agent will request a decision every 5 Academy steps.")]
+        public int DecisionPeriod = 5;
+
+        /// <summary>
+        /// Indicates when to requests a decision. By changing this value, the timing of decision
+        /// can be shifted even among agents with the same decision period. The value can be
+        /// from 0 to DecisionPeriod - 1.
+        /// </summary>
+        [Range(0, 19)]
+        [Tooltip("Indicates when to requests a decision. By changing this value, the timing " +
+            "of decision can be shifted even among agents with the same decision period. " +
+            "The value can be from 0 to DecisionPeriod - 1.")]
+        public int DecisionStep = 0;
+
+        /// <summary>
+        /// Indicates whether or not the agent will take an action during the Academy steps where
+        /// it does not request a decision. Has no effect when DecisionPeriod is set to 1.
+        /// </summary>
+        [Tooltip("Indicates whether or not the agent will take an action during the Academy " +
+            "steps where it does not request a decision. Has no effect when DecisionPeriod " +
+            "is set to 1.")]
+        [FormerlySerializedAs("RepeatAction")]
+        public bool TakeActionsBetweenDecisions = true;
+
+        [NonSerialized]
+        Agent m_Agent;
+
+        /// <summary>
+        /// Get the Agent attached to the DecisionRequester.
+        /// </summary>
+        public Agent Agent
+        {
+            get => m_Agent;
+        }
+
+        internal void Awake()
+        {
+            Debug.Assert(DecisionStep < DecisionPeriod, "DecisionStep must be between 0 and DecisionPeriod - 1.");
+            m_Agent = gameObject.GetComponent<Agent>();
+            Debug.Assert(m_Agent != null, "Agent component was not found on this gameObject and is required.");
+            Academy.Instance.AgentPreStep += MakeRequests;
+        }
+
+        void OnDestroy()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.AgentPreStep -= MakeRequests;
+            }
+        }
+
+        /// <summary>
+        /// Information about Academy step used to make decisions about whether to request a decision.
+        /// </summary>
+        public struct DecisionRequestContext
+        {
+            /// <summary>
+            /// The current step count of the Academy, equivalent to Academy.StepCount.
+            /// </summary>
+            public int AcademyStepCount;
+        }
+
+        /// <summary>
+        /// Method that hooks into the Academy in order inform the Agent on whether or not it should request a
+        /// decision, and whether or not it should take actions between decisions.
+        /// </summary>
+        /// <param name="academyStepCount">The current step count of the academy.</param>
+        void MakeRequests(int academyStepCount)
+        {
+            var context = new DecisionRequestContext
+            {
+                AcademyStepCount = academyStepCount
+            };
+
+            if (ShouldRequestDecision(context))
+            {
+                m_Agent?.RequestDecision();
+            }
+
+            if (ShouldRequestAction(context))
+            {
+                m_Agent?.RequestAction();
+            }
+        }
+
+        /// <summary>
+        /// Whether Agent.RequestDecision should be called on this update step.
+        /// </summary>
+        /// <param name="context"></param>
+        /// <returns></returns>
+        protected virtual bool ShouldRequestDecision(DecisionRequestContext context)
+        {
+            return context.AcademyStepCount % DecisionPeriod == DecisionStep;
+        }
+
+        /// <summary>
+        /// Whether Agent.RequestAction should be called on this update step.
+        /// </summary>
+        /// <param name="context"></param>
+        /// <returns></returns>
+        protected virtual bool ShouldRequestAction(DecisionRequestContext context)
+        {
+            return TakeActionsBetweenDecisions;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/DecisionRequester.cs.meta b/com.unity.ml-agents/Runtime/DecisionRequester.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bdc416b94bdd180da99f74031932083059c5ed8e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/DecisionRequester.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3a5c9d521e5ef4759a8246a07d52221e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Demonstrations.meta b/com.unity.ml-agents/Runtime/Demonstrations.meta
new file mode 100644
index 0000000000000000000000000000000000000000..85288b5325dacbb54270ab23fc6d6b4092336702
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 85e02c21d231b4f5fa0c5f87e5f907a2
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs
new file mode 100644
index 0000000000000000000000000000000000000000..42f67733df995cfee9b46719fef9212210628e42
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs
@@ -0,0 +1,20 @@
+using System;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Demonstrations
+{
+    /// <summary>
+    /// Demonstration meta-data.
+    /// Kept in a struct for easy serialization and deserialization.
+    /// </summary>
+    [Serializable]
+    internal class DemonstrationMetaData
+    {
+        [FormerlySerializedAs("numberExperiences")]
+        public int numberSteps;
+        public int numberEpisodes;
+        public float meanReward;
+        public string demonstrationName;
+        public const int ApiVersion = 1;
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs.meta b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8e6ff392755eae0586f91f8eba29e1c5bfcc8b05
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationMetaData.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: af5f3b4258a2d4ead90e733f30cfaa7a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c8a02ba7084d9e3f813c282c537724fe749e73a6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs
@@ -0,0 +1,228 @@
+using System.IO.Abstractions;
+using System.Text.RegularExpressions;
+using UnityEngine;
+using System.IO;
+using Unity.MLAgents.Policies;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Demonstrations
+{
+    /// <summary>
+    /// The Demonstration Recorder component facilitates the recording of demonstrations
+    /// used for imitation learning.
+    /// </summary>
+    /// <remarks>Add this component to the [GameObject] containing an <see cref="Agent"/>
+    /// to enable recording the agent for imitation learning. You must implement the
+    /// <see cref="Agent.Heuristic"/> function of the agent to provide manual control
+    /// in order to record demonstrations.
+    ///
+    /// See [Imitation Learning - Recording Demonstrations] for more information.
+    ///
+    /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
+    /// </remarks>
+    [RequireComponent(typeof(Agent))]
+    [AddComponentMenu("ML Agents/Demonstration Recorder", (int)MenuGroup.Default)]
+    public class DemonstrationRecorder : MonoBehaviour
+    {
+        /// <summary>
+        /// Whether or not to record demonstrations.
+        /// </summary>
+        [FormerlySerializedAs("record")]
+        [Tooltip("Whether or not to record demonstrations.")]
+        public bool Record;
+
+        /// <summary>
+        /// Number of steps to record. The editor will stop playing when it reaches this threshold.
+        /// Set to zero to record indefinitely.
+        /// </summary>
+        [Tooltip("Number of steps to record. The editor will stop playing when it reaches this threshold. " +
+            "Set to zero to record indefinitely.")]
+        public int NumStepsToRecord;
+
+        /// <summary>
+        /// Base demonstration file name. If multiple files are saved, the additional filenames
+        /// will have a sequence of unique numbers appended.
+        /// </summary>
+        [FormerlySerializedAs("demonstrationName")]
+        [Tooltip("Base demonstration file name. If multiple files are saved, the additional " +
+            "filenames will have a unique number appended.")]
+        public string DemonstrationName;
+
+        /// <summary>
+        /// Directory to save the demo files. Will default to a "Demonstrations/" folder in the
+        /// Application data path if not specified.
+        /// </summary>
+        [FormerlySerializedAs("demonstrationDirectory")]
+        [Tooltip("Directory to save the demo files. Will default to " +
+            "{Application.dataPath}/Demonstrations if not specified.")]
+        public string DemonstrationDirectory;
+
+        DemonstrationWriter m_DemoWriter;
+        internal const int MaxNameLength = 16;
+
+        const string k_ExtensionType = ".demo";
+        const string k_DefaultDirectoryName = "Demonstrations";
+        IFileSystem m_FileSystem;
+
+        Agent m_Agent;
+
+        void OnEnable()
+        {
+            m_Agent = GetComponent<Agent>();
+        }
+
+        void Update()
+        {
+            if (!Record)
+            {
+                return;
+            }
+
+            LazyInitialize();
+
+            // Quit when num steps to record is reached
+            if (NumStepsToRecord > 0 && m_DemoWriter.NumSteps >= NumStepsToRecord)
+            {
+                Application.Quit(0);
+#if UNITY_EDITOR
+                UnityEditor.EditorApplication.isPlaying = false;
+#endif
+            }
+        }
+
+        /// <summary>
+        /// Creates demonstration store for use in recording.
+        /// Has no effect if the demonstration store was already created.
+        /// </summary>
+        internal DemonstrationWriter LazyInitialize(IFileSystem fileSystem = null)
+        {
+            if (m_DemoWriter != null)
+            {
+                return m_DemoWriter;
+            }
+
+            if (m_Agent == null)
+            {
+                m_Agent = GetComponent<Agent>();
+            }
+
+            m_FileSystem = fileSystem ?? new FileSystem();
+            var behaviorParams = GetComponent<BehaviorParameters>();
+            if (string.IsNullOrEmpty(DemonstrationName))
+            {
+                DemonstrationName = behaviorParams.BehaviorName;
+            }
+            if (string.IsNullOrEmpty(DemonstrationDirectory))
+            {
+                DemonstrationDirectory = Path.Combine(Application.dataPath, k_DefaultDirectoryName);
+            }
+
+            DemonstrationName = SanitizeName(DemonstrationName, MaxNameLength);
+            var filePath = MakeDemonstrationFilePath(m_FileSystem, DemonstrationDirectory, DemonstrationName);
+            var stream = m_FileSystem.File.Create(filePath);
+            m_DemoWriter = new DemonstrationWriter(stream);
+
+            AddDemonstrationWriterToAgent(m_DemoWriter);
+
+            return m_DemoWriter;
+        }
+
+        /// <summary>
+        /// Removes all characters except alphanumerics from demonstration name.
+        /// Shorten name if it is longer than the maxNameLength.
+        /// </summary>
+        internal static string SanitizeName(string demoName, int maxNameLength)
+        {
+            var rgx = new Regex("[^a-zA-Z0-9 -]");
+            demoName = rgx.Replace(demoName, "");
+            // If the string is too long, it will overflow the metadata.
+            if (demoName.Length > maxNameLength)
+            {
+                demoName = demoName.Substring(0, maxNameLength);
+            }
+            return demoName;
+        }
+
+        /// <summary>
+        /// Gets a unique path for the DemonstrationName in the DemonstrationDirectory.
+        /// </summary>
+        /// <param name="fileSystem"></param>
+        /// <param name="demonstrationDirectory"></param>
+        /// <param name="demonstrationName"></param>
+        /// <returns></returns>
+        internal static string MakeDemonstrationFilePath(
+            IFileSystem fileSystem, string demonstrationDirectory, string demonstrationName
+        )
+        {
+            // Create the directory if it doesn't already exist
+            if (!fileSystem.Directory.Exists(demonstrationDirectory))
+            {
+                fileSystem.Directory.CreateDirectory(demonstrationDirectory);
+            }
+
+            var literalName = demonstrationName;
+            var filePath = Path.Combine(demonstrationDirectory, literalName + k_ExtensionType);
+            var uniqueNameCounter = 0;
+            while (fileSystem.File.Exists(filePath))
+            {
+                // TODO should we use a timestamp instead of a counter here? This loops an increasing number of times
+                // as the number of demos increases.
+                literalName = demonstrationName + "_" + uniqueNameCounter;
+                filePath = Path.Combine(demonstrationDirectory, literalName + k_ExtensionType);
+                uniqueNameCounter++;
+            }
+
+            return filePath;
+        }
+
+        /// <summary>
+        /// Close the DemonstrationWriter and remove it from the Agent.
+        /// Has no effect if the DemonstrationWriter is already closed (or wasn't opened)
+        /// </summary>
+        public void Close()
+        {
+            if (m_DemoWriter != null)
+            {
+                RemoveDemonstrationWriterFromAgent(m_DemoWriter);
+
+                m_DemoWriter.Close();
+                m_DemoWriter = null;
+            }
+        }
+
+        /// <summary>
+        /// Clean up the DemonstrationWriter when shutting down or destroying the Agent.
+        /// </summary>
+        void OnDestroy()
+        {
+            Close();
+        }
+
+        /// <summary>
+        /// Add additional DemonstrationWriter to the Agent. It is still up to the user to Close this
+        /// DemonstrationWriters when recording is done.
+        /// </summary>
+        /// <param name="demoWriter"></param>
+        public void AddDemonstrationWriterToAgent(DemonstrationWriter demoWriter)
+        {
+            var behaviorParams = GetComponent<BehaviorParameters>();
+            demoWriter.Initialize(
+                DemonstrationName,
+                behaviorParams.BrainParameters,
+                behaviorParams.FullyQualifiedBehaviorName
+            );
+            m_Agent.DemonstrationWriters.Add(demoWriter);
+        }
+
+        /// <summary>
+        /// Remove additional DemonstrationWriter to the Agent. It is still up to the user to Close this
+        /// DemonstrationWriters when recording is done.
+        /// </summary>
+        /// <param name="demoWriter"></param>
+        public void RemoveDemonstrationWriterFromAgent(DemonstrationWriter demoWriter)
+        {
+            m_Agent.DemonstrationWriters.Remove(demoWriter);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs.meta b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cde4db8f20fb8dfd296392a5215e63494ba4fe71
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: f2902496c0120472b90269f94a0aec7e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs
new file mode 100644
index 0000000000000000000000000000000000000000..cb32409913179026c4c80aad9426a2a5dfb9ac87
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs
@@ -0,0 +1,37 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Demonstrations
+{
+    /// <summary>
+    /// Summary of a loaded Demonstration file. Only used for display in the Inspector.
+    /// </summary>
+    [Serializable]
+    internal class DemonstrationSummary : ScriptableObject
+    {
+        public DemonstrationMetaData metaData;
+        public BrainParameters brainParameters;
+        public List<ObservationSummary> observationSummaries;
+
+        public void Initialize(BrainParameters brainParams,
+            DemonstrationMetaData demonstrationMetaData, List<ObservationSummary> obsSummaries)
+        {
+            brainParameters = brainParams;
+            metaData = demonstrationMetaData;
+            observationSummaries = obsSummaries;
+        }
+    }
+
+
+    /// <summary>
+    /// Summary of a loaded Observation. Currently only contains the shape of the Observation.
+    /// </summary>
+    /// <remarks>This is necessary because serialization doesn't support nested containers or arrays.</remarks>
+    [Serializable]
+    internal struct ObservationSummary
+    {
+        public int[] shape;
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs.meta b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..91e53800d573ae6cf0aa8a46432396d48885886a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationSummary.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a5e0cbcbc514b473399c262dd37541ea
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..bd4426102f83b70f544e9956ae922b3683eac075
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs
@@ -0,0 +1,160 @@
+using System.IO;
+using Google.Protobuf;
+using System.Collections.Generic;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Demonstrations
+{
+    /// <summary>
+    /// Responsible for writing demonstration data to stream (typically a file stream).
+    /// </summary>
+    /// <seealso cref="DemonstrationRecorder"/>
+    public class DemonstrationWriter
+    {
+        /// <summary>
+        /// Number of bytes reserved for the <see cref="DemonstrationMetaData"/> at the start of the demo file.
+        /// </summary>
+        internal const int MetaDataBytes = 32;
+
+        DemonstrationMetaData m_MetaData;
+        Stream m_Writer;
+        float m_CumulativeReward;
+        ObservationWriter m_ObservationWriter = new ObservationWriter();
+
+        /// <summary>
+        /// Create a DemonstrationWriter that will write to the specified stream.
+        /// The stream must support writes and seeking.
+        /// </summary>
+        /// <param name="stream"></param>
+        public DemonstrationWriter(Stream stream)
+        {
+            m_Writer = stream;
+        }
+
+        /// <summary>
+        /// Number of steps written so far.
+        /// </summary>
+        internal int NumSteps
+        {
+            get { return m_MetaData.numberSteps; }
+        }
+
+        /// <summary>
+        /// Writes the initial data to the stream.
+        /// </summary>
+        /// <param name="demonstrationName">Base name of the demonstration file(s).</param>
+        /// <param name="brainName">The name of the Brain the agent is attached to.</param>
+        /// <param name="brainParameters">The parameters of the Brain the agent is attached to.</param>
+        internal void Initialize(
+            string demonstrationName, BrainParameters brainParameters, string brainName)
+        {
+            if (m_Writer == null)
+            {
+                // Already closed
+                return;
+            }
+
+            m_MetaData = new DemonstrationMetaData { demonstrationName = demonstrationName };
+            var metaProto = m_MetaData.ToProto();
+            metaProto.WriteDelimitedTo(m_Writer);
+
+            WriteBrainParameters(brainName, brainParameters);
+        }
+
+        /// <summary>
+        /// Writes meta-data. Note that this is called at the *end* of recording, but writes to the
+        /// beginning of the file.
+        /// </summary>
+        void WriteMetadata()
+        {
+            if (m_Writer == null)
+            {
+                // Already closed
+                return;
+            }
+
+            var metaProto = m_MetaData.ToProto();
+            var metaProtoBytes = metaProto.ToByteArray();
+            m_Writer.Write(metaProtoBytes, 0, metaProtoBytes.Length);
+            m_Writer.Seek(0, 0);
+            metaProto.WriteDelimitedTo(m_Writer);
+        }
+
+        /// <summary>
+        /// Writes brain parameters to file.
+        /// </summary>
+        /// <param name="brainName">The name of the Brain the agent is attached to.</param>
+        /// <param name="brainParameters">The parameters of the Brain the agent is attached to.</param>
+        void WriteBrainParameters(string brainName, BrainParameters brainParameters)
+        {
+            if (m_Writer == null)
+            {
+                // Already closed
+                return;
+            }
+
+            // Writes BrainParameters to file.
+            m_Writer.Seek(MetaDataBytes + 1, 0);
+            var brainProto = brainParameters.ToProto(brainName, false);
+            brainProto.WriteDelimitedTo(m_Writer);
+        }
+
+        /// <summary>
+        /// Write AgentInfo experience to file.
+        /// </summary>
+        /// <param name="info"> <see cref="AgentInfo"/> for the agent being recorded.</param>
+        /// <param name="sensors">List of sensors to record for the agent.</param>
+        internal void Record(AgentInfo info, List<ISensor> sensors)
+        {
+            if (m_Writer == null)
+            {
+                // Already closed
+                return;
+            }
+
+            // Increment meta-data counters.
+            m_MetaData.numberSteps++;
+            m_CumulativeReward += info.reward;
+            if (info.done)
+            {
+                EndEpisode();
+            }
+
+            // Generate observations and add AgentInfo to file.
+            var agentProto = info.ToInfoActionPairProto();
+            foreach (var sensor in sensors)
+            {
+                agentProto.AgentInfo.Observations.Add(sensor.GetObservationProto(m_ObservationWriter));
+            }
+
+            agentProto.WriteDelimitedTo(m_Writer);
+        }
+
+        /// <summary>
+        /// Performs all clean-up necessary.
+        /// </summary>
+        public void Close()
+        {
+            if (m_Writer == null)
+            {
+                // Already closed
+                return;
+            }
+
+            EndEpisode();
+            m_MetaData.meanReward = m_CumulativeReward / m_MetaData.numberEpisodes;
+            WriteMetadata();
+            m_Writer.Close();
+            m_Writer = null;
+        }
+
+        /// <summary>
+        /// Performs necessary episode-completion steps.
+        /// </summary>
+        void EndEpisode()
+        {
+            m_MetaData.numberEpisodes += 1;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs.meta b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f30f1b22c19c516bb783f4c60704746e3b615f42
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ebaf7878a8cc74ee3aae07daf9e1b6f2
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/EnvironmentParameters.cs b/com.unity.ml-agents/Runtime/EnvironmentParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fc1c667cd68dd01afeefa49d70200f09fbda5a8a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/EnvironmentParameters.cs
@@ -0,0 +1,70 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.SideChannels;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// A container for the Environment Parameters that may be modified during training.
+    /// The keys for those parameters are defined in the trainer configurations and the
+    /// the values are generated from the training process in features such as Curriculum Learning
+    /// and Environment Parameter Randomization.
+    ///
+    /// One current assumption for all the environment parameters is that they are of type float.
+    /// </summary>
+    public sealed class EnvironmentParameters
+    {
+        /// <summary>
+        /// The side channel that is used to receive the new parameter values.
+        /// </summary>
+        readonly EnvironmentParametersChannel m_Channel;
+
+        /// <summary>
+        /// Constructor.
+        /// </summary>
+        internal EnvironmentParameters()
+        {
+            m_Channel = new EnvironmentParametersChannel();
+            SideChannelManager.RegisterSideChannel(m_Channel);
+        }
+
+        /// <summary>
+        /// Returns the parameter value for the specified key. Returns the default value provided
+        /// if this parameter key does not have a value. Only returns a parameter value if it is
+        /// of type float.
+        /// </summary>
+        /// <param name="key">The parameter key</param>
+        /// <param name="defaultValue">Default value for this parameter.</param>
+        /// <returns></returns>
+        public float GetWithDefault(string key, float defaultValue)
+        {
+            return m_Channel.GetWithDefault(key, defaultValue);
+        }
+
+        /// <summary>
+        /// Registers a callback action for the provided parameter key. Will overwrite any
+        /// existing action for that parameter. The callback will be called whenever the parameter
+        /// receives a value from the training process.
+        /// </summary>
+        /// <param name="key">The parameter key</param>
+        /// <param name="action">The callback action</param>
+        public void RegisterCallback(string key, Action<float> action)
+        {
+            m_Channel.RegisterCallback(key, action);
+        }
+
+        /// <summary>
+        /// Returns a list of all the parameter keys that have received values.
+        /// </summary>
+        /// <returns>List of parameter keys.</returns>
+        public IList<string> Keys()
+        {
+            return m_Channel.ListParameters();
+        }
+
+        internal void Dispose()
+        {
+            SideChannelManager.UnregisterSideChannel(m_Channel);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/EnvironmentParameters.cs.meta b/com.unity.ml-agents/Runtime/EnvironmentParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9e7a85f810cc9006fedaf248d2e6e35379fd515f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/EnvironmentParameters.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 90ce0b26bef35484890eac0633b85eed
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs b/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..735c7fff96c19638c17a88f138d1d7845176ba10
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs
@@ -0,0 +1,11 @@
+namespace Unity.MLAgents
+{
+    internal static class EpisodeIdCounter
+    {
+        static int s_Counter;
+        public static int GetEpisodeId()
+        {
+            return s_Counter++;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs.meta b/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c377f5004bac0e59a67cd1b0207927b03fa7cc37
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 847786b7bcf9d4817b3f3879d57517c7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc.meta b/com.unity.ml-agents/Runtime/Grpc.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e8760ec24e79de7b6aa92fd2f9a9fe402ba7aa8d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 418327e202c7464bb6649d025df1b539
+timeCreated: 1569444731
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs b/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b740e05db8222778e6a9c03814c187469ace18b1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs
@@ -0,0 +1,7 @@
+using System.Runtime.CompilerServices;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Sensor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Utils.Tests")]
diff --git a/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs.meta b/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cf7b4f0f10f2c1178136acea667960aaf609ffc9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/AssemblyInfo.cs.meta
@@ -0,0 +1,11 @@
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+guid: 54959ce8e2e574f09b91f80a516acee3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cef92044c3752acd5a6fd60b72f73a3f96070791
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 7ebeef5df83b74a048b7f99681672f3b
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3eb0a357a2991dcbeffbb6e94131200dba3a18d5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs
@@ -0,0 +1,242 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/agent_action.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_action.proto</summary>
+  internal static partial class AgentActionReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_action.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static AgentActionReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2Fj",
+            "dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMijAEKEEFnZW50QWN0",
+            "aW9uUHJvdG8SIQoZdmVjdG9yX2FjdGlvbnNfZGVwcmVjYXRlZBgBIAMoAhIN",
+            "CgV2YWx1ZRgEIAEoAhIaChJjb250aW51b3VzX2FjdGlvbnMYBiADKAISGAoQ",
+            "ZGlzY3JldGVfYWN0aW9ucxgHIAMoBUoECAIQA0oECAMQBEoECAUQBkIlqgIi",
+            "VW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IGcHJvdG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentActionProto), global::Unity.MLAgents.CommunicatorObjects.AgentActionProto.Parser, new[]{ "VectorActionsDeprecated", "Value", "ContinuousActions", "DiscreteActions" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class AgentActionProto : pb::IMessage<AgentActionProto> {
+    private static readonly pb::MessageParser<AgentActionProto> _parser = new pb::MessageParser<AgentActionProto>(() => new AgentActionProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<AgentActionProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.AgentActionReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentActionProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentActionProto(AgentActionProto other) : this() {
+      vectorActionsDeprecated_ = other.vectorActionsDeprecated_.Clone();
+      value_ = other.value_;
+      continuousActions_ = other.continuousActions_.Clone();
+      discreteActions_ = other.discreteActions_.Clone();
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentActionProto Clone() {
+      return new AgentActionProto(this);
+    }
+
+    /// <summary>Field number for the "vector_actions_deprecated" field.</summary>
+    public const int VectorActionsDeprecatedFieldNumber = 1;
+    private static readonly pb::FieldCodec<float> _repeated_vectorActionsDeprecated_codec
+        = pb::FieldCodec.ForFloat(10);
+    private readonly pbc::RepeatedField<float> vectorActionsDeprecated_ = new pbc::RepeatedField<float>();
+    /// <summary>
+    /// mark as deprecated in communicator v1.3.0
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<float> VectorActionsDeprecated {
+      get { return vectorActionsDeprecated_; }
+    }
+
+    /// <summary>Field number for the "value" field.</summary>
+    public const int ValueFieldNumber = 4;
+    private float value_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public float Value {
+      get { return value_; }
+      set {
+        value_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "continuous_actions" field.</summary>
+    public const int ContinuousActionsFieldNumber = 6;
+    private static readonly pb::FieldCodec<float> _repeated_continuousActions_codec
+        = pb::FieldCodec.ForFloat(50);
+    private readonly pbc::RepeatedField<float> continuousActions_ = new pbc::RepeatedField<float>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<float> ContinuousActions {
+      get { return continuousActions_; }
+    }
+
+    /// <summary>Field number for the "discrete_actions" field.</summary>
+    public const int DiscreteActionsFieldNumber = 7;
+    private static readonly pb::FieldCodec<int> _repeated_discreteActions_codec
+        = pb::FieldCodec.ForInt32(58);
+    private readonly pbc::RepeatedField<int> discreteActions_ = new pbc::RepeatedField<int>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> DiscreteActions {
+      get { return discreteActions_; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as AgentActionProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(AgentActionProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if(!vectorActionsDeprecated_.Equals(other.vectorActionsDeprecated_)) return false;
+      if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(Value, other.Value)) return false;
+      if(!continuousActions_.Equals(other.continuousActions_)) return false;
+      if(!discreteActions_.Equals(other.discreteActions_)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      hash ^= vectorActionsDeprecated_.GetHashCode();
+      if (Value != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(Value);
+      hash ^= continuousActions_.GetHashCode();
+      hash ^= discreteActions_.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      vectorActionsDeprecated_.WriteTo(output, _repeated_vectorActionsDeprecated_codec);
+      if (Value != 0F) {
+        output.WriteRawTag(37);
+        output.WriteFloat(Value);
+      }
+      continuousActions_.WriteTo(output, _repeated_continuousActions_codec);
+      discreteActions_.WriteTo(output, _repeated_discreteActions_codec);
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      size += vectorActionsDeprecated_.CalculateSize(_repeated_vectorActionsDeprecated_codec);
+      if (Value != 0F) {
+        size += 1 + 4;
+      }
+      size += continuousActions_.CalculateSize(_repeated_continuousActions_codec);
+      size += discreteActions_.CalculateSize(_repeated_discreteActions_codec);
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(AgentActionProto other) {
+      if (other == null) {
+        return;
+      }
+      vectorActionsDeprecated_.Add(other.vectorActionsDeprecated_);
+      if (other.Value != 0F) {
+        Value = other.Value;
+      }
+      continuousActions_.Add(other.continuousActions_);
+      discreteActions_.Add(other.discreteActions_);
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10:
+          case 13: {
+            vectorActionsDeprecated_.AddEntriesFrom(input, _repeated_vectorActionsDeprecated_codec);
+            break;
+          }
+          case 37: {
+            Value = input.ReadFloat();
+            break;
+          }
+          case 50:
+          case 53: {
+            continuousActions_.AddEntriesFrom(input, _repeated_continuousActions_codec);
+            break;
+          }
+          case 58:
+          case 56: {
+            discreteActions_.AddEntriesFrom(input, _repeated_discreteActions_codec);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f47d94375b5191d3b894d6ef84061ee260d07736
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: b1fa94db54b734224927bb4b322227cd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs
new file mode 100644
index 0000000000000000000000000000000000000000..187f2fdab75cef786d1936cc99dc509c4b8227d7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs
@@ -0,0 +1,361 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/agent_info.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_info.proto</summary>
+  internal static partial class AgentInfoReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_info.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static AgentInfoReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjNtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
+            "Zm8ucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjRtbGFnZW50c19lbnZz",
+            "L2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0aW9uLnByb3RvIvkBCg5B",
+            "Z2VudEluZm9Qcm90bxIOCgZyZXdhcmQYByABKAISDAoEZG9uZRgIIAEoCBIY",
+            "ChBtYXhfc3RlcF9yZWFjaGVkGAkgASgIEgoKAmlkGAogASgFEhMKC2FjdGlv",
+            "bl9tYXNrGAsgAygIEjwKDG9ic2VydmF0aW9ucxgNIAMoCzImLmNvbW11bmlj",
+            "YXRvcl9vYmplY3RzLk9ic2VydmF0aW9uUHJvdG8SEAoIZ3JvdXBfaWQYDiAB",
+            "KAUSFAoMZ3JvdXBfcmV3YXJkGA8gASgCSgQIARACSgQIAhADSgQIAxAESgQI",
+            "BBAFSgQIBRAGSgQIBhAHSgQIDBANQiWqAiJVbml0eS5NTEFnZW50cy5Db21t",
+            "dW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.ObservationReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto), global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto.Parser, new[]{ "Reward", "Done", "MaxStepReached", "Id", "ActionMask", "Observations", "GroupId", "GroupReward" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class AgentInfoProto : pb::IMessage<AgentInfoProto> {
+    private static readonly pb::MessageParser<AgentInfoProto> _parser = new pb::MessageParser<AgentInfoProto>(() => new AgentInfoProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<AgentInfoProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.AgentInfoReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoProto(AgentInfoProto other) : this() {
+      reward_ = other.reward_;
+      done_ = other.done_;
+      maxStepReached_ = other.maxStepReached_;
+      id_ = other.id_;
+      actionMask_ = other.actionMask_.Clone();
+      observations_ = other.observations_.Clone();
+      groupId_ = other.groupId_;
+      groupReward_ = other.groupReward_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoProto Clone() {
+      return new AgentInfoProto(this);
+    }
+
+    /// <summary>Field number for the "reward" field.</summary>
+    public const int RewardFieldNumber = 7;
+    private float reward_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public float Reward {
+      get { return reward_; }
+      set {
+        reward_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "done" field.</summary>
+    public const int DoneFieldNumber = 8;
+    private bool done_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Done {
+      get { return done_; }
+      set {
+        done_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "max_step_reached" field.</summary>
+    public const int MaxStepReachedFieldNumber = 9;
+    private bool maxStepReached_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool MaxStepReached {
+      get { return maxStepReached_; }
+      set {
+        maxStepReached_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "id" field.</summary>
+    public const int IdFieldNumber = 10;
+    private int id_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int Id {
+      get { return id_; }
+      set {
+        id_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "action_mask" field.</summary>
+    public const int ActionMaskFieldNumber = 11;
+    private static readonly pb::FieldCodec<bool> _repeated_actionMask_codec
+        = pb::FieldCodec.ForBool(90);
+    private readonly pbc::RepeatedField<bool> actionMask_ = new pbc::RepeatedField<bool>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<bool> ActionMask {
+      get { return actionMask_; }
+    }
+
+    /// <summary>Field number for the "observations" field.</summary>
+    public const int ObservationsFieldNumber = 13;
+    private static readonly pb::FieldCodec<global::Unity.MLAgents.CommunicatorObjects.ObservationProto> _repeated_observations_codec
+        = pb::FieldCodec.ForMessage(106, global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser);
+    private readonly pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.ObservationProto> observations_ = new pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.ObservationProto>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.ObservationProto> Observations {
+      get { return observations_; }
+    }
+
+    /// <summary>Field number for the "group_id" field.</summary>
+    public const int GroupIdFieldNumber = 14;
+    private int groupId_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int GroupId {
+      get { return groupId_; }
+      set {
+        groupId_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "group_reward" field.</summary>
+    public const int GroupRewardFieldNumber = 15;
+    private float groupReward_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public float GroupReward {
+      get { return groupReward_; }
+      set {
+        groupReward_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as AgentInfoProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(AgentInfoProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(Reward, other.Reward)) return false;
+      if (Done != other.Done) return false;
+      if (MaxStepReached != other.MaxStepReached) return false;
+      if (Id != other.Id) return false;
+      if(!actionMask_.Equals(other.actionMask_)) return false;
+      if(!observations_.Equals(other.observations_)) return false;
+      if (GroupId != other.GroupId) return false;
+      if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(GroupReward, other.GroupReward)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (Reward != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(Reward);
+      if (Done != false) hash ^= Done.GetHashCode();
+      if (MaxStepReached != false) hash ^= MaxStepReached.GetHashCode();
+      if (Id != 0) hash ^= Id.GetHashCode();
+      hash ^= actionMask_.GetHashCode();
+      hash ^= observations_.GetHashCode();
+      if (GroupId != 0) hash ^= GroupId.GetHashCode();
+      if (GroupReward != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(GroupReward);
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (Reward != 0F) {
+        output.WriteRawTag(61);
+        output.WriteFloat(Reward);
+      }
+      if (Done != false) {
+        output.WriteRawTag(64);
+        output.WriteBool(Done);
+      }
+      if (MaxStepReached != false) {
+        output.WriteRawTag(72);
+        output.WriteBool(MaxStepReached);
+      }
+      if (Id != 0) {
+        output.WriteRawTag(80);
+        output.WriteInt32(Id);
+      }
+      actionMask_.WriteTo(output, _repeated_actionMask_codec);
+      observations_.WriteTo(output, _repeated_observations_codec);
+      if (GroupId != 0) {
+        output.WriteRawTag(112);
+        output.WriteInt32(GroupId);
+      }
+      if (GroupReward != 0F) {
+        output.WriteRawTag(125);
+        output.WriteFloat(GroupReward);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (Reward != 0F) {
+        size += 1 + 4;
+      }
+      if (Done != false) {
+        size += 1 + 1;
+      }
+      if (MaxStepReached != false) {
+        size += 1 + 1;
+      }
+      if (Id != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(Id);
+      }
+      size += actionMask_.CalculateSize(_repeated_actionMask_codec);
+      size += observations_.CalculateSize(_repeated_observations_codec);
+      if (GroupId != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(GroupId);
+      }
+      if (GroupReward != 0F) {
+        size += 1 + 4;
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(AgentInfoProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.Reward != 0F) {
+        Reward = other.Reward;
+      }
+      if (other.Done != false) {
+        Done = other.Done;
+      }
+      if (other.MaxStepReached != false) {
+        MaxStepReached = other.MaxStepReached;
+      }
+      if (other.Id != 0) {
+        Id = other.Id;
+      }
+      actionMask_.Add(other.actionMask_);
+      observations_.Add(other.observations_);
+      if (other.GroupId != 0) {
+        GroupId = other.GroupId;
+      }
+      if (other.GroupReward != 0F) {
+        GroupReward = other.GroupReward;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 61: {
+            Reward = input.ReadFloat();
+            break;
+          }
+          case 64: {
+            Done = input.ReadBool();
+            break;
+          }
+          case 72: {
+            MaxStepReached = input.ReadBool();
+            break;
+          }
+          case 80: {
+            Id = input.ReadInt32();
+            break;
+          }
+          case 90:
+          case 88: {
+            actionMask_.AddEntriesFrom(input, _repeated_actionMask_codec);
+            break;
+          }
+          case 106: {
+            observations_.AddEntriesFrom(input, _repeated_observations_codec);
+            break;
+          }
+          case 112: {
+            GroupId = input.ReadInt32();
+            break;
+          }
+          case 125: {
+            GroupReward = input.ReadFloat();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..07ed36145616b7d7117c457890192b91f248f893
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ecaddd3a8141a4854a4d2c7fe8bd6a75
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs
new file mode 100644
index 0000000000000000000000000000000000000000..37cd219c7339d8d0f19db4096dc9a5b25f827a1b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs
@@ -0,0 +1,219 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/agent_info_action_pair.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_info_action_pair.proto</summary>
+  internal static partial class AgentInfoActionPairReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_info_action_pair.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static AgentInfoActionPairReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "Cj9tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
+            "Zm9fYWN0aW9uX3BhaXIucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjNt",
+            "bGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2luZm8u",
+            "cHJvdG8aNW1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdl",
+            "bnRfYWN0aW9uLnByb3RvIpEBChhBZ2VudEluZm9BY3Rpb25QYWlyUHJvdG8S",
+            "OAoKYWdlbnRfaW5mbxgBIAEoCzIkLmNvbW11bmljYXRvcl9vYmplY3RzLkFn",
+            "ZW50SW5mb1Byb3RvEjsKC2FjdGlvbl9pbmZvGAIgASgLMiYuY29tbXVuaWNh",
+            "dG9yX29iamVjdHMuQWdlbnRBY3Rpb25Qcm90b0IlqgIiVW5pdHkuTUxBZ2Vu",
+            "dHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IGcHJvdG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.AgentInfoReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.AgentActionReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentInfoActionPairProto), global::Unity.MLAgents.CommunicatorObjects.AgentInfoActionPairProto.Parser, new[]{ "AgentInfo", "ActionInfo" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class AgentInfoActionPairProto : pb::IMessage<AgentInfoActionPairProto> {
+    private static readonly pb::MessageParser<AgentInfoActionPairProto> _parser = new pb::MessageParser<AgentInfoActionPairProto>(() => new AgentInfoActionPairProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<AgentInfoActionPairProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.AgentInfoActionPairReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoActionPairProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoActionPairProto(AgentInfoActionPairProto other) : this() {
+      AgentInfo = other.agentInfo_ != null ? other.AgentInfo.Clone() : null;
+      ActionInfo = other.actionInfo_ != null ? other.ActionInfo.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public AgentInfoActionPairProto Clone() {
+      return new AgentInfoActionPairProto(this);
+    }
+
+    /// <summary>Field number for the "agent_info" field.</summary>
+    public const int AgentInfoFieldNumber = 1;
+    private global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto agentInfo_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto AgentInfo {
+      get { return agentInfo_; }
+      set {
+        agentInfo_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "action_info" field.</summary>
+    public const int ActionInfoFieldNumber = 2;
+    private global::Unity.MLAgents.CommunicatorObjects.AgentActionProto actionInfo_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.AgentActionProto ActionInfo {
+      get { return actionInfo_; }
+      set {
+        actionInfo_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as AgentInfoActionPairProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(AgentInfoActionPairProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!object.Equals(AgentInfo, other.AgentInfo)) return false;
+      if (!object.Equals(ActionInfo, other.ActionInfo)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (agentInfo_ != null) hash ^= AgentInfo.GetHashCode();
+      if (actionInfo_ != null) hash ^= ActionInfo.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (agentInfo_ != null) {
+        output.WriteRawTag(10);
+        output.WriteMessage(AgentInfo);
+      }
+      if (actionInfo_ != null) {
+        output.WriteRawTag(18);
+        output.WriteMessage(ActionInfo);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (agentInfo_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(AgentInfo);
+      }
+      if (actionInfo_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(ActionInfo);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(AgentInfoActionPairProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.agentInfo_ != null) {
+        if (agentInfo_ == null) {
+          agentInfo_ = new global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto();
+        }
+        AgentInfo.MergeFrom(other.AgentInfo);
+      }
+      if (other.actionInfo_ != null) {
+        if (actionInfo_ == null) {
+          actionInfo_ = new global::Unity.MLAgents.CommunicatorObjects.AgentActionProto();
+        }
+        ActionInfo.MergeFrom(other.ActionInfo);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            if (agentInfo_ == null) {
+              agentInfo_ = new global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto();
+            }
+            input.ReadMessage(agentInfo_);
+            break;
+          }
+          case 18: {
+            if (actionInfo_ == null) {
+              actionInfo_ = new global::Unity.MLAgents.CommunicatorObjects.AgentActionProto();
+            }
+            input.ReadMessage(actionInfo_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7474dcae69cd61b0356386c0e7cb04f8a097b659
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfoActionPair.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 29577366657494c678558b0643abcb30
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..65b57f4ea3c8ba5637650064330a6ea619434c87
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs
@@ -0,0 +1,524 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/brain_parameters.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/brain_parameters.proto</summary>
+  internal static partial class BrainParametersReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/brain_parameters.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static BrainParametersReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjltbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2JyYWluX3Bh",
+            "cmFtZXRlcnMucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjNtbGFnZW50",
+            "c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5cGUucHJvdG8i",
+            "iwEKD0FjdGlvblNwZWNQcm90bxIeChZudW1fY29udGludW91c19hY3Rpb25z",
+            "GAEgASgFEhwKFG51bV9kaXNjcmV0ZV9hY3Rpb25zGAIgASgFEh0KFWRpc2Ny",
+            "ZXRlX2JyYW5jaF9zaXplcxgDIAMoBRIbChNhY3Rpb25fZGVzY3JpcHRpb25z",
+            "GAQgAygJIrYCChRCcmFpblBhcmFtZXRlcnNQcm90bxIlCh12ZWN0b3JfYWN0",
+            "aW9uX3NpemVfZGVwcmVjYXRlZBgDIAMoBRItCiV2ZWN0b3JfYWN0aW9uX2Rl",
+            "c2NyaXB0aW9uc19kZXByZWNhdGVkGAUgAygJElEKI3ZlY3Rvcl9hY3Rpb25f",
+            "c3BhY2VfdHlwZV9kZXByZWNhdGVkGAYgASgOMiQuY29tbXVuaWNhdG9yX29i",
+            "amVjdHMuU3BhY2VUeXBlUHJvdG8SEgoKYnJhaW5fbmFtZRgHIAEoCRITCgtp",
+            "c190cmFpbmluZxgIIAEoCBI6CgthY3Rpb25fc3BlYxgJIAEoCzIlLmNvbW11",
+            "bmljYXRvcl9vYmplY3RzLkFjdGlvblNwZWNQcm90b0oECAEQAkoECAIQA0oE",
+            "CAQQBUIlqgIiVW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IG",
+            "cHJvdG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.SpaceTypeReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto), global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto.Parser, new[]{ "NumContinuousActions", "NumDiscreteActions", "DiscreteBranchSizes", "ActionDescriptions" }, null, null, null),
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto), global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto.Parser, new[]{ "VectorActionSizeDeprecated", "VectorActionDescriptionsDeprecated", "VectorActionSpaceTypeDeprecated", "BrainName", "IsTraining", "ActionSpec" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class ActionSpecProto : pb::IMessage<ActionSpecProto> {
+    private static readonly pb::MessageParser<ActionSpecProto> _parser = new pb::MessageParser<ActionSpecProto>(() => new ActionSpecProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<ActionSpecProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ActionSpecProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ActionSpecProto(ActionSpecProto other) : this() {
+      numContinuousActions_ = other.numContinuousActions_;
+      numDiscreteActions_ = other.numDiscreteActions_;
+      discreteBranchSizes_ = other.discreteBranchSizes_.Clone();
+      actionDescriptions_ = other.actionDescriptions_.Clone();
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ActionSpecProto Clone() {
+      return new ActionSpecProto(this);
+    }
+
+    /// <summary>Field number for the "num_continuous_actions" field.</summary>
+    public const int NumContinuousActionsFieldNumber = 1;
+    private int numContinuousActions_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumContinuousActions {
+      get { return numContinuousActions_; }
+      set {
+        numContinuousActions_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "num_discrete_actions" field.</summary>
+    public const int NumDiscreteActionsFieldNumber = 2;
+    private int numDiscreteActions_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumDiscreteActions {
+      get { return numDiscreteActions_; }
+      set {
+        numDiscreteActions_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "discrete_branch_sizes" field.</summary>
+    public const int DiscreteBranchSizesFieldNumber = 3;
+    private static readonly pb::FieldCodec<int> _repeated_discreteBranchSizes_codec
+        = pb::FieldCodec.ForInt32(26);
+    private readonly pbc::RepeatedField<int> discreteBranchSizes_ = new pbc::RepeatedField<int>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> DiscreteBranchSizes {
+      get { return discreteBranchSizes_; }
+    }
+
+    /// <summary>Field number for the "action_descriptions" field.</summary>
+    public const int ActionDescriptionsFieldNumber = 4;
+    private static readonly pb::FieldCodec<string> _repeated_actionDescriptions_codec
+        = pb::FieldCodec.ForString(34);
+    private readonly pbc::RepeatedField<string> actionDescriptions_ = new pbc::RepeatedField<string>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<string> ActionDescriptions {
+      get { return actionDescriptions_; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as ActionSpecProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(ActionSpecProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (NumContinuousActions != other.NumContinuousActions) return false;
+      if (NumDiscreteActions != other.NumDiscreteActions) return false;
+      if(!discreteBranchSizes_.Equals(other.discreteBranchSizes_)) return false;
+      if(!actionDescriptions_.Equals(other.actionDescriptions_)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (NumContinuousActions != 0) hash ^= NumContinuousActions.GetHashCode();
+      if (NumDiscreteActions != 0) hash ^= NumDiscreteActions.GetHashCode();
+      hash ^= discreteBranchSizes_.GetHashCode();
+      hash ^= actionDescriptions_.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (NumContinuousActions != 0) {
+        output.WriteRawTag(8);
+        output.WriteInt32(NumContinuousActions);
+      }
+      if (NumDiscreteActions != 0) {
+        output.WriteRawTag(16);
+        output.WriteInt32(NumDiscreteActions);
+      }
+      discreteBranchSizes_.WriteTo(output, _repeated_discreteBranchSizes_codec);
+      actionDescriptions_.WriteTo(output, _repeated_actionDescriptions_codec);
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (NumContinuousActions != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumContinuousActions);
+      }
+      if (NumDiscreteActions != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumDiscreteActions);
+      }
+      size += discreteBranchSizes_.CalculateSize(_repeated_discreteBranchSizes_codec);
+      size += actionDescriptions_.CalculateSize(_repeated_actionDescriptions_codec);
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(ActionSpecProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.NumContinuousActions != 0) {
+        NumContinuousActions = other.NumContinuousActions;
+      }
+      if (other.NumDiscreteActions != 0) {
+        NumDiscreteActions = other.NumDiscreteActions;
+      }
+      discreteBranchSizes_.Add(other.discreteBranchSizes_);
+      actionDescriptions_.Add(other.actionDescriptions_);
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            NumContinuousActions = input.ReadInt32();
+            break;
+          }
+          case 16: {
+            NumDiscreteActions = input.ReadInt32();
+            break;
+          }
+          case 26:
+          case 24: {
+            discreteBranchSizes_.AddEntriesFrom(input, _repeated_discreteBranchSizes_codec);
+            break;
+          }
+          case 34: {
+            actionDescriptions_.AddEntriesFrom(input, _repeated_actionDescriptions_codec);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  internal sealed partial class BrainParametersProto : pb::IMessage<BrainParametersProto> {
+    private static readonly pb::MessageParser<BrainParametersProto> _parser = new pb::MessageParser<BrainParametersProto>(() => new BrainParametersProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<BrainParametersProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor.MessageTypes[1]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public BrainParametersProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public BrainParametersProto(BrainParametersProto other) : this() {
+      vectorActionSizeDeprecated_ = other.vectorActionSizeDeprecated_.Clone();
+      vectorActionDescriptionsDeprecated_ = other.vectorActionDescriptionsDeprecated_.Clone();
+      vectorActionSpaceTypeDeprecated_ = other.vectorActionSpaceTypeDeprecated_;
+      brainName_ = other.brainName_;
+      isTraining_ = other.isTraining_;
+      ActionSpec = other.actionSpec_ != null ? other.ActionSpec.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public BrainParametersProto Clone() {
+      return new BrainParametersProto(this);
+    }
+
+    /// <summary>Field number for the "vector_action_size_deprecated" field.</summary>
+    public const int VectorActionSizeDeprecatedFieldNumber = 3;
+    private static readonly pb::FieldCodec<int> _repeated_vectorActionSizeDeprecated_codec
+        = pb::FieldCodec.ForInt32(26);
+    private readonly pbc::RepeatedField<int> vectorActionSizeDeprecated_ = new pbc::RepeatedField<int>();
+    /// <summary>
+    /// mark as deprecated in communicator v1.3.0
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> VectorActionSizeDeprecated {
+      get { return vectorActionSizeDeprecated_; }
+    }
+
+    /// <summary>Field number for the "vector_action_descriptions_deprecated" field.</summary>
+    public const int VectorActionDescriptionsDeprecatedFieldNumber = 5;
+    private static readonly pb::FieldCodec<string> _repeated_vectorActionDescriptionsDeprecated_codec
+        = pb::FieldCodec.ForString(42);
+    private readonly pbc::RepeatedField<string> vectorActionDescriptionsDeprecated_ = new pbc::RepeatedField<string>();
+    /// <summary>
+    /// mark as deprecated in communicator v1.3.0
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<string> VectorActionDescriptionsDeprecated {
+      get { return vectorActionDescriptionsDeprecated_; }
+    }
+
+    /// <summary>Field number for the "vector_action_space_type_deprecated" field.</summary>
+    public const int VectorActionSpaceTypeDeprecatedFieldNumber = 6;
+    private global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto vectorActionSpaceTypeDeprecated_ = 0;
+    /// <summary>
+    /// mark as deprecated in communicator v1.3.0
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto VectorActionSpaceTypeDeprecated {
+      get { return vectorActionSpaceTypeDeprecated_; }
+      set {
+        vectorActionSpaceTypeDeprecated_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "brain_name" field.</summary>
+    public const int BrainNameFieldNumber = 7;
+    private string brainName_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string BrainName {
+      get { return brainName_; }
+      set {
+        brainName_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "is_training" field.</summary>
+    public const int IsTrainingFieldNumber = 8;
+    private bool isTraining_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool IsTraining {
+      get { return isTraining_; }
+      set {
+        isTraining_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "action_spec" field.</summary>
+    public const int ActionSpecFieldNumber = 9;
+    private global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto actionSpec_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto ActionSpec {
+      get { return actionSpec_; }
+      set {
+        actionSpec_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as BrainParametersProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(BrainParametersProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if(!vectorActionSizeDeprecated_.Equals(other.vectorActionSizeDeprecated_)) return false;
+      if(!vectorActionDescriptionsDeprecated_.Equals(other.vectorActionDescriptionsDeprecated_)) return false;
+      if (VectorActionSpaceTypeDeprecated != other.VectorActionSpaceTypeDeprecated) return false;
+      if (BrainName != other.BrainName) return false;
+      if (IsTraining != other.IsTraining) return false;
+      if (!object.Equals(ActionSpec, other.ActionSpec)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      hash ^= vectorActionSizeDeprecated_.GetHashCode();
+      hash ^= vectorActionDescriptionsDeprecated_.GetHashCode();
+      if (VectorActionSpaceTypeDeprecated != 0) hash ^= VectorActionSpaceTypeDeprecated.GetHashCode();
+      if (BrainName.Length != 0) hash ^= BrainName.GetHashCode();
+      if (IsTraining != false) hash ^= IsTraining.GetHashCode();
+      if (actionSpec_ != null) hash ^= ActionSpec.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      vectorActionSizeDeprecated_.WriteTo(output, _repeated_vectorActionSizeDeprecated_codec);
+      vectorActionDescriptionsDeprecated_.WriteTo(output, _repeated_vectorActionDescriptionsDeprecated_codec);
+      if (VectorActionSpaceTypeDeprecated != 0) {
+        output.WriteRawTag(48);
+        output.WriteEnum((int) VectorActionSpaceTypeDeprecated);
+      }
+      if (BrainName.Length != 0) {
+        output.WriteRawTag(58);
+        output.WriteString(BrainName);
+      }
+      if (IsTraining != false) {
+        output.WriteRawTag(64);
+        output.WriteBool(IsTraining);
+      }
+      if (actionSpec_ != null) {
+        output.WriteRawTag(74);
+        output.WriteMessage(ActionSpec);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      size += vectorActionSizeDeprecated_.CalculateSize(_repeated_vectorActionSizeDeprecated_codec);
+      size += vectorActionDescriptionsDeprecated_.CalculateSize(_repeated_vectorActionDescriptionsDeprecated_codec);
+      if (VectorActionSpaceTypeDeprecated != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) VectorActionSpaceTypeDeprecated);
+      }
+      if (BrainName.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(BrainName);
+      }
+      if (IsTraining != false) {
+        size += 1 + 1;
+      }
+      if (actionSpec_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(ActionSpec);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(BrainParametersProto other) {
+      if (other == null) {
+        return;
+      }
+      vectorActionSizeDeprecated_.Add(other.vectorActionSizeDeprecated_);
+      vectorActionDescriptionsDeprecated_.Add(other.vectorActionDescriptionsDeprecated_);
+      if (other.VectorActionSpaceTypeDeprecated != 0) {
+        VectorActionSpaceTypeDeprecated = other.VectorActionSpaceTypeDeprecated;
+      }
+      if (other.BrainName.Length != 0) {
+        BrainName = other.BrainName;
+      }
+      if (other.IsTraining != false) {
+        IsTraining = other.IsTraining;
+      }
+      if (other.actionSpec_ != null) {
+        if (actionSpec_ == null) {
+          actionSpec_ = new global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto();
+        }
+        ActionSpec.MergeFrom(other.ActionSpec);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 26:
+          case 24: {
+            vectorActionSizeDeprecated_.AddEntriesFrom(input, _repeated_vectorActionSizeDeprecated_codec);
+            break;
+          }
+          case 42: {
+            vectorActionDescriptionsDeprecated_.AddEntriesFrom(input, _repeated_vectorActionDescriptionsDeprecated_codec);
+            break;
+          }
+          case 48: {
+            vectorActionSpaceTypeDeprecated_ = (global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto) input.ReadEnum();
+            break;
+          }
+          case 58: {
+            BrainName = input.ReadString();
+            break;
+          }
+          case 64: {
+            IsTraining = input.ReadBool();
+            break;
+          }
+          case 74: {
+            if (actionSpec_ == null) {
+              actionSpec_ = new global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto();
+            }
+            input.ReadMessage(actionSpec_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..447602fcc2aa4de103c4324c41916701e807f8be
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 26f9a93df956e4ee88c1cf5f31017f0e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ac267f4c2f06cdecbbd9e156f6597a81700ad640
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs
@@ -0,0 +1,373 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/capabilities.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/capabilities.proto</summary>
+  internal static partial class CapabilitiesReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/capabilities.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static CapabilitiesReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2NhcGFiaWxp",
+            "dGllcy5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMi7AEKGFVuaXR5UkxD",
+            "YXBhYmlsaXRpZXNQcm90bxIaChJiYXNlUkxDYXBhYmlsaXRpZXMYASABKAgS",
+            "IwobY29uY2F0ZW5hdGVkUG5nT2JzZXJ2YXRpb25zGAIgASgIEiAKGGNvbXBy",
+            "ZXNzZWRDaGFubmVsTWFwcGluZxgDIAEoCBIVCg1oeWJyaWRBY3Rpb25zGAQg",
+            "ASgIEhkKEXRyYWluaW5nQW5hbHl0aWNzGAUgASgIEiEKGXZhcmlhYmxlTGVu",
+            "Z3RoT2JzZXJ2YXRpb24YBiABKAgSGAoQbXVsdGlBZ2VudEdyb3VwcxgHIAEo",
+            "CEIlqgIiVW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IGcHJv",
+            "dG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto.Parser, new[]{ "BaseRLCapabilities", "ConcatenatedPngObservations", "CompressedChannelMapping", "HybridActions", "TrainingAnalytics", "VariableLengthObservation", "MultiAgentGroups" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  /// <summary>
+  ///
+  /// A Capabilities message that will communicate both C# and Python
+  /// what features are available to both.
+  /// </summary>
+  internal sealed partial class UnityRLCapabilitiesProto : pb::IMessage<UnityRLCapabilitiesProto> {
+    private static readonly pb::MessageParser<UnityRLCapabilitiesProto> _parser = new pb::MessageParser<UnityRLCapabilitiesProto>(() => new UnityRLCapabilitiesProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityRLCapabilitiesProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.CapabilitiesReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLCapabilitiesProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLCapabilitiesProto(UnityRLCapabilitiesProto other) : this() {
+      baseRLCapabilities_ = other.baseRLCapabilities_;
+      concatenatedPngObservations_ = other.concatenatedPngObservations_;
+      compressedChannelMapping_ = other.compressedChannelMapping_;
+      hybridActions_ = other.hybridActions_;
+      trainingAnalytics_ = other.trainingAnalytics_;
+      variableLengthObservation_ = other.variableLengthObservation_;
+      multiAgentGroups_ = other.multiAgentGroups_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLCapabilitiesProto Clone() {
+      return new UnityRLCapabilitiesProto(this);
+    }
+
+    /// <summary>Field number for the "baseRLCapabilities" field.</summary>
+    public const int BaseRLCapabilitiesFieldNumber = 1;
+    private bool baseRLCapabilities_;
+    /// <summary>
+    /// These are the 1.0 capabilities.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool BaseRLCapabilities {
+      get { return baseRLCapabilities_; }
+      set {
+        baseRLCapabilities_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "concatenatedPngObservations" field.</summary>
+    public const int ConcatenatedPngObservationsFieldNumber = 2;
+    private bool concatenatedPngObservations_;
+    /// <summary>
+    /// concatenated PNG files for compressed visual observations with >3 channels.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool ConcatenatedPngObservations {
+      get { return concatenatedPngObservations_; }
+      set {
+        concatenatedPngObservations_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "compressedChannelMapping" field.</summary>
+    public const int CompressedChannelMappingFieldNumber = 3;
+    private bool compressedChannelMapping_;
+    /// <summary>
+    /// compression mapping for stacking compressed observations.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool CompressedChannelMapping {
+      get { return compressedChannelMapping_; }
+      set {
+        compressedChannelMapping_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "hybridActions" field.</summary>
+    public const int HybridActionsFieldNumber = 4;
+    private bool hybridActions_;
+    /// <summary>
+    /// support for hybrid action spaces (discrete + continuous)
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool HybridActions {
+      get { return hybridActions_; }
+      set {
+        hybridActions_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "trainingAnalytics" field.</summary>
+    public const int TrainingAnalyticsFieldNumber = 5;
+    private bool trainingAnalytics_;
+    /// <summary>
+    /// support for training analytics
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool TrainingAnalytics {
+      get { return trainingAnalytics_; }
+      set {
+        trainingAnalytics_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "variableLengthObservation" field.</summary>
+    public const int VariableLengthObservationFieldNumber = 6;
+    private bool variableLengthObservation_;
+    /// <summary>
+    /// Support for variable length observations of rank 2
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool VariableLengthObservation {
+      get { return variableLengthObservation_; }
+      set {
+        variableLengthObservation_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "multiAgentGroups" field.</summary>
+    public const int MultiAgentGroupsFieldNumber = 7;
+    private bool multiAgentGroups_;
+    /// <summary>
+    /// Support for multi agent groups and group rewards
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool MultiAgentGroups {
+      get { return multiAgentGroups_; }
+      set {
+        multiAgentGroups_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityRLCapabilitiesProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityRLCapabilitiesProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (BaseRLCapabilities != other.BaseRLCapabilities) return false;
+      if (ConcatenatedPngObservations != other.ConcatenatedPngObservations) return false;
+      if (CompressedChannelMapping != other.CompressedChannelMapping) return false;
+      if (HybridActions != other.HybridActions) return false;
+      if (TrainingAnalytics != other.TrainingAnalytics) return false;
+      if (VariableLengthObservation != other.VariableLengthObservation) return false;
+      if (MultiAgentGroups != other.MultiAgentGroups) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (BaseRLCapabilities != false) hash ^= BaseRLCapabilities.GetHashCode();
+      if (ConcatenatedPngObservations != false) hash ^= ConcatenatedPngObservations.GetHashCode();
+      if (CompressedChannelMapping != false) hash ^= CompressedChannelMapping.GetHashCode();
+      if (HybridActions != false) hash ^= HybridActions.GetHashCode();
+      if (TrainingAnalytics != false) hash ^= TrainingAnalytics.GetHashCode();
+      if (VariableLengthObservation != false) hash ^= VariableLengthObservation.GetHashCode();
+      if (MultiAgentGroups != false) hash ^= MultiAgentGroups.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (BaseRLCapabilities != false) {
+        output.WriteRawTag(8);
+        output.WriteBool(BaseRLCapabilities);
+      }
+      if (ConcatenatedPngObservations != false) {
+        output.WriteRawTag(16);
+        output.WriteBool(ConcatenatedPngObservations);
+      }
+      if (CompressedChannelMapping != false) {
+        output.WriteRawTag(24);
+        output.WriteBool(CompressedChannelMapping);
+      }
+      if (HybridActions != false) {
+        output.WriteRawTag(32);
+        output.WriteBool(HybridActions);
+      }
+      if (TrainingAnalytics != false) {
+        output.WriteRawTag(40);
+        output.WriteBool(TrainingAnalytics);
+      }
+      if (VariableLengthObservation != false) {
+        output.WriteRawTag(48);
+        output.WriteBool(VariableLengthObservation);
+      }
+      if (MultiAgentGroups != false) {
+        output.WriteRawTag(56);
+        output.WriteBool(MultiAgentGroups);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (BaseRLCapabilities != false) {
+        size += 1 + 1;
+      }
+      if (ConcatenatedPngObservations != false) {
+        size += 1 + 1;
+      }
+      if (CompressedChannelMapping != false) {
+        size += 1 + 1;
+      }
+      if (HybridActions != false) {
+        size += 1 + 1;
+      }
+      if (TrainingAnalytics != false) {
+        size += 1 + 1;
+      }
+      if (VariableLengthObservation != false) {
+        size += 1 + 1;
+      }
+      if (MultiAgentGroups != false) {
+        size += 1 + 1;
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityRLCapabilitiesProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.BaseRLCapabilities != false) {
+        BaseRLCapabilities = other.BaseRLCapabilities;
+      }
+      if (other.ConcatenatedPngObservations != false) {
+        ConcatenatedPngObservations = other.ConcatenatedPngObservations;
+      }
+      if (other.CompressedChannelMapping != false) {
+        CompressedChannelMapping = other.CompressedChannelMapping;
+      }
+      if (other.HybridActions != false) {
+        HybridActions = other.HybridActions;
+      }
+      if (other.TrainingAnalytics != false) {
+        TrainingAnalytics = other.TrainingAnalytics;
+      }
+      if (other.VariableLengthObservation != false) {
+        VariableLengthObservation = other.VariableLengthObservation;
+      }
+      if (other.MultiAgentGroups != false) {
+        MultiAgentGroups = other.MultiAgentGroups;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            BaseRLCapabilities = input.ReadBool();
+            break;
+          }
+          case 16: {
+            ConcatenatedPngObservations = input.ReadBool();
+            break;
+          }
+          case 24: {
+            CompressedChannelMapping = input.ReadBool();
+            break;
+          }
+          case 32: {
+            HybridActions = input.ReadBool();
+            break;
+          }
+          case 40: {
+            TrainingAnalytics = input.ReadBool();
+            break;
+          }
+          case 48: {
+            VariableLengthObservation = input.ReadBool();
+            break;
+          }
+          case 56: {
+            MultiAgentGroups = input.ReadBool();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1e65cf6ee3020ad1e3afd275bf31944d3ad2c135
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e8388443b440343299cab2e88988e14e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1220f9f9eec87040d387566cc1695005aee6f29e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs
@@ -0,0 +1,49 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/command.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/command.proto</summary>
+  internal static partial class CommandReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/command.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static CommandReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjBtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2NvbW1hbmQu",
+            "cHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzKi0KDENvbW1hbmRQcm90bxII",
+            "CgRTVEVQEAASCQoFUkVTRVQQARIICgRRVUlUEAJCJaoCIlVuaXR5Lk1MQWdl",
+            "bnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.CommandProto), }, null));
+    }
+    #endregion
+
+  }
+  #region Enums
+  internal enum CommandProto {
+    [pbr::OriginalName("STEP")] Step = 0,
+    [pbr::OriginalName("RESET")] Reset = 1,
+    [pbr::OriginalName("QUIT")] Quit = 2,
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f47033a7c17960f7a8fc7fa7b73c7e63b17fd6b7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Command.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 9be6f5025f61540eabbc831436642adc
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..45099b04c7f8545c5bc4ebc39f6b6294025a999d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs
@@ -0,0 +1,146 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/custom_reset_parameters.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/custom_reset_parameters.proto</summary>
+  internal static partial class CustomResetParametersReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/custom_reset_parameters.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static CustomResetParametersReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CkBtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2N1c3RvbV9y",
+            "ZXNldF9wYXJhbWV0ZXJzLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyIc",
+            "ChpDdXN0b21SZXNldFBhcmFtZXRlcnNQcm90b0IlqgIiVW5pdHkuTUxBZ2Vu",
+            "dHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IGcHJvdG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.CustomResetParametersProto), global::Unity.MLAgents.CommunicatorObjects.CustomResetParametersProto.Parser, null, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class CustomResetParametersProto : pb::IMessage<CustomResetParametersProto> {
+    private static readonly pb::MessageParser<CustomResetParametersProto> _parser = new pb::MessageParser<CustomResetParametersProto>(() => new CustomResetParametersProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<CustomResetParametersProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.CustomResetParametersReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public CustomResetParametersProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public CustomResetParametersProto(CustomResetParametersProto other) : this() {
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public CustomResetParametersProto Clone() {
+      return new CustomResetParametersProto(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as CustomResetParametersProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(CustomResetParametersProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(CustomResetParametersProto other) {
+      if (other == null) {
+        return;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..aa357195f6fd8ad46bb822394c92532fdf4b4289
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/CustomResetParameters.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 62f03717ee98042bf8990733358f2dbd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs
new file mode 100644
index 0000000000000000000000000000000000000000..58f8ad80225946c0319a8582e5f90394485a7e62
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs
@@ -0,0 +1,289 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/demonstration_meta.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/demonstration_meta.proto</summary>
+  internal static partial class DemonstrationMetaReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/demonstration_meta.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static DemonstrationMetaReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjttbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2RlbW9uc3Ry",
+            "YXRpb25fbWV0YS5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMijQEKFkRl",
+            "bW9uc3RyYXRpb25NZXRhUHJvdG8SEwoLYXBpX3ZlcnNpb24YASABKAUSGgoS",
+            "ZGVtb25zdHJhdGlvbl9uYW1lGAIgASgJEhQKDG51bWJlcl9zdGVwcxgDIAEo",
+            "BRIXCg9udW1iZXJfZXBpc29kZXMYBCABKAUSEwoLbWVhbl9yZXdhcmQYBSAB",
+            "KAJCJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnBy",
+            "b3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.DemonstrationMetaProto), global::Unity.MLAgents.CommunicatorObjects.DemonstrationMetaProto.Parser, new[]{ "ApiVersion", "DemonstrationName", "NumberSteps", "NumberEpisodes", "MeanReward" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class DemonstrationMetaProto : pb::IMessage<DemonstrationMetaProto> {
+    private static readonly pb::MessageParser<DemonstrationMetaProto> _parser = new pb::MessageParser<DemonstrationMetaProto>(() => new DemonstrationMetaProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<DemonstrationMetaProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.DemonstrationMetaReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public DemonstrationMetaProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public DemonstrationMetaProto(DemonstrationMetaProto other) : this() {
+      apiVersion_ = other.apiVersion_;
+      demonstrationName_ = other.demonstrationName_;
+      numberSteps_ = other.numberSteps_;
+      numberEpisodes_ = other.numberEpisodes_;
+      meanReward_ = other.meanReward_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public DemonstrationMetaProto Clone() {
+      return new DemonstrationMetaProto(this);
+    }
+
+    /// <summary>Field number for the "api_version" field.</summary>
+    public const int ApiVersionFieldNumber = 1;
+    private int apiVersion_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int ApiVersion {
+      get { return apiVersion_; }
+      set {
+        apiVersion_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "demonstration_name" field.</summary>
+    public const int DemonstrationNameFieldNumber = 2;
+    private string demonstrationName_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string DemonstrationName {
+      get { return demonstrationName_; }
+      set {
+        demonstrationName_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "number_steps" field.</summary>
+    public const int NumberStepsFieldNumber = 3;
+    private int numberSteps_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumberSteps {
+      get { return numberSteps_; }
+      set {
+        numberSteps_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "number_episodes" field.</summary>
+    public const int NumberEpisodesFieldNumber = 4;
+    private int numberEpisodes_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumberEpisodes {
+      get { return numberEpisodes_; }
+      set {
+        numberEpisodes_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "mean_reward" field.</summary>
+    public const int MeanRewardFieldNumber = 5;
+    private float meanReward_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public float MeanReward {
+      get { return meanReward_; }
+      set {
+        meanReward_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as DemonstrationMetaProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(DemonstrationMetaProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (ApiVersion != other.ApiVersion) return false;
+      if (DemonstrationName != other.DemonstrationName) return false;
+      if (NumberSteps != other.NumberSteps) return false;
+      if (NumberEpisodes != other.NumberEpisodes) return false;
+      if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(MeanReward, other.MeanReward)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (ApiVersion != 0) hash ^= ApiVersion.GetHashCode();
+      if (DemonstrationName.Length != 0) hash ^= DemonstrationName.GetHashCode();
+      if (NumberSteps != 0) hash ^= NumberSteps.GetHashCode();
+      if (NumberEpisodes != 0) hash ^= NumberEpisodes.GetHashCode();
+      if (MeanReward != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(MeanReward);
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (ApiVersion != 0) {
+        output.WriteRawTag(8);
+        output.WriteInt32(ApiVersion);
+      }
+      if (DemonstrationName.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(DemonstrationName);
+      }
+      if (NumberSteps != 0) {
+        output.WriteRawTag(24);
+        output.WriteInt32(NumberSteps);
+      }
+      if (NumberEpisodes != 0) {
+        output.WriteRawTag(32);
+        output.WriteInt32(NumberEpisodes);
+      }
+      if (MeanReward != 0F) {
+        output.WriteRawTag(45);
+        output.WriteFloat(MeanReward);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (ApiVersion != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(ApiVersion);
+      }
+      if (DemonstrationName.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(DemonstrationName);
+      }
+      if (NumberSteps != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumberSteps);
+      }
+      if (NumberEpisodes != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumberEpisodes);
+      }
+      if (MeanReward != 0F) {
+        size += 1 + 4;
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(DemonstrationMetaProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.ApiVersion != 0) {
+        ApiVersion = other.ApiVersion;
+      }
+      if (other.DemonstrationName.Length != 0) {
+        DemonstrationName = other.DemonstrationName;
+      }
+      if (other.NumberSteps != 0) {
+        NumberSteps = other.NumberSteps;
+      }
+      if (other.NumberEpisodes != 0) {
+        NumberEpisodes = other.NumberEpisodes;
+      }
+      if (other.MeanReward != 0F) {
+        MeanReward = other.MeanReward;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            ApiVersion = input.ReadInt32();
+            break;
+          }
+          case 18: {
+            DemonstrationName = input.ReadString();
+            break;
+          }
+          case 24: {
+            NumberSteps = input.ReadInt32();
+            break;
+          }
+          case 32: {
+            NumberEpisodes = input.ReadInt32();
+            break;
+          }
+          case 45: {
+            MeanReward = input.ReadFloat();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..41176197e92339aa36b3cee1557aabca018cd557
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/DemonstrationMeta.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7248e2660150f4a39bb99dfabb9bae7d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6a05c09f28b4058d14481d5baaf5a92cc758e6ea
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs
@@ -0,0 +1,317 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/engine_configuration.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/engine_configuration.proto</summary>
+  internal static partial class EngineConfigurationReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/engine_configuration.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static EngineConfigurationReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "Cj1tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2VuZ2luZV9j",
+            "b25maWd1cmF0aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKVAQoY",
+            "RW5naW5lQ29uZmlndXJhdGlvblByb3RvEg0KBXdpZHRoGAEgASgFEg4KBmhl",
+            "aWdodBgCIAEoBRIVCg1xdWFsaXR5X2xldmVsGAMgASgFEhIKCnRpbWVfc2Nh",
+            "bGUYBCABKAISGQoRdGFyZ2V0X2ZyYW1lX3JhdGUYBSABKAUSFAoMc2hvd19t",
+            "b25pdG9yGAYgASgIQiWqAiJVbml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JP",
+            "YmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.EngineConfigurationProto), global::Unity.MLAgents.CommunicatorObjects.EngineConfigurationProto.Parser, new[]{ "Width", "Height", "QualityLevel", "TimeScale", "TargetFrameRate", "ShowMonitor" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class EngineConfigurationProto : pb::IMessage<EngineConfigurationProto> {
+    private static readonly pb::MessageParser<EngineConfigurationProto> _parser = new pb::MessageParser<EngineConfigurationProto>(() => new EngineConfigurationProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<EngineConfigurationProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.EngineConfigurationReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public EngineConfigurationProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public EngineConfigurationProto(EngineConfigurationProto other) : this() {
+      width_ = other.width_;
+      height_ = other.height_;
+      qualityLevel_ = other.qualityLevel_;
+      timeScale_ = other.timeScale_;
+      targetFrameRate_ = other.targetFrameRate_;
+      showMonitor_ = other.showMonitor_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public EngineConfigurationProto Clone() {
+      return new EngineConfigurationProto(this);
+    }
+
+    /// <summary>Field number for the "width" field.</summary>
+    public const int WidthFieldNumber = 1;
+    private int width_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int Width {
+      get { return width_; }
+      set {
+        width_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "height" field.</summary>
+    public const int HeightFieldNumber = 2;
+    private int height_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int Height {
+      get { return height_; }
+      set {
+        height_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "quality_level" field.</summary>
+    public const int QualityLevelFieldNumber = 3;
+    private int qualityLevel_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int QualityLevel {
+      get { return qualityLevel_; }
+      set {
+        qualityLevel_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "time_scale" field.</summary>
+    public const int TimeScaleFieldNumber = 4;
+    private float timeScale_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public float TimeScale {
+      get { return timeScale_; }
+      set {
+        timeScale_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "target_frame_rate" field.</summary>
+    public const int TargetFrameRateFieldNumber = 5;
+    private int targetFrameRate_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int TargetFrameRate {
+      get { return targetFrameRate_; }
+      set {
+        targetFrameRate_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "show_monitor" field.</summary>
+    public const int ShowMonitorFieldNumber = 6;
+    private bool showMonitor_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool ShowMonitor {
+      get { return showMonitor_; }
+      set {
+        showMonitor_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as EngineConfigurationProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(EngineConfigurationProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (Width != other.Width) return false;
+      if (Height != other.Height) return false;
+      if (QualityLevel != other.QualityLevel) return false;
+      if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(TimeScale, other.TimeScale)) return false;
+      if (TargetFrameRate != other.TargetFrameRate) return false;
+      if (ShowMonitor != other.ShowMonitor) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (Width != 0) hash ^= Width.GetHashCode();
+      if (Height != 0) hash ^= Height.GetHashCode();
+      if (QualityLevel != 0) hash ^= QualityLevel.GetHashCode();
+      if (TimeScale != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(TimeScale);
+      if (TargetFrameRate != 0) hash ^= TargetFrameRate.GetHashCode();
+      if (ShowMonitor != false) hash ^= ShowMonitor.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (Width != 0) {
+        output.WriteRawTag(8);
+        output.WriteInt32(Width);
+      }
+      if (Height != 0) {
+        output.WriteRawTag(16);
+        output.WriteInt32(Height);
+      }
+      if (QualityLevel != 0) {
+        output.WriteRawTag(24);
+        output.WriteInt32(QualityLevel);
+      }
+      if (TimeScale != 0F) {
+        output.WriteRawTag(37);
+        output.WriteFloat(TimeScale);
+      }
+      if (TargetFrameRate != 0) {
+        output.WriteRawTag(40);
+        output.WriteInt32(TargetFrameRate);
+      }
+      if (ShowMonitor != false) {
+        output.WriteRawTag(48);
+        output.WriteBool(ShowMonitor);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (Width != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(Width);
+      }
+      if (Height != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(Height);
+      }
+      if (QualityLevel != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(QualityLevel);
+      }
+      if (TimeScale != 0F) {
+        size += 1 + 4;
+      }
+      if (TargetFrameRate != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(TargetFrameRate);
+      }
+      if (ShowMonitor != false) {
+        size += 1 + 1;
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(EngineConfigurationProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.Width != 0) {
+        Width = other.Width;
+      }
+      if (other.Height != 0) {
+        Height = other.Height;
+      }
+      if (other.QualityLevel != 0) {
+        QualityLevel = other.QualityLevel;
+      }
+      if (other.TimeScale != 0F) {
+        TimeScale = other.TimeScale;
+      }
+      if (other.TargetFrameRate != 0) {
+        TargetFrameRate = other.TargetFrameRate;
+      }
+      if (other.ShowMonitor != false) {
+        ShowMonitor = other.ShowMonitor;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            Width = input.ReadInt32();
+            break;
+          }
+          case 16: {
+            Height = input.ReadInt32();
+            break;
+          }
+          case 24: {
+            QualityLevel = input.ReadInt32();
+            break;
+          }
+          case 37: {
+            TimeScale = input.ReadFloat();
+            break;
+          }
+          case 40: {
+            TargetFrameRate = input.ReadInt32();
+            break;
+          }
+          case 48: {
+            ShowMonitor = input.ReadBool();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cb08edae85681dbd41a138627a25d403f57703c6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/EngineConfiguration.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 129a5bbec69fc4f42bc70e422660c8f0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2f38cc8f444972a308d55edf59f3c1879bc61313
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs
@@ -0,0 +1,202 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/header.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/header.proto</summary>
+  internal static partial class HeaderReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/header.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static HeaderReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "Ci9tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2hlYWRlci5w",
+            "cm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMiLgoLSGVhZGVyUHJvdG8SDgoG",
+            "c3RhdHVzGAEgASgFEg8KB21lc3NhZ2UYAiABKAlCJaoCIlVuaXR5Lk1MQWdl",
+            "bnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.HeaderProto), global::Unity.MLAgents.CommunicatorObjects.HeaderProto.Parser, new[]{ "Status", "Message" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class HeaderProto : pb::IMessage<HeaderProto> {
+    private static readonly pb::MessageParser<HeaderProto> _parser = new pb::MessageParser<HeaderProto>(() => new HeaderProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<HeaderProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.HeaderReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public HeaderProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public HeaderProto(HeaderProto other) : this() {
+      status_ = other.status_;
+      message_ = other.message_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public HeaderProto Clone() {
+      return new HeaderProto(this);
+    }
+
+    /// <summary>Field number for the "status" field.</summary>
+    public const int StatusFieldNumber = 1;
+    private int status_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int Status {
+      get { return status_; }
+      set {
+        status_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "message" field.</summary>
+    public const int MessageFieldNumber = 2;
+    private string message_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string Message {
+      get { return message_; }
+      set {
+        message_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as HeaderProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(HeaderProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (Status != other.Status) return false;
+      if (Message != other.Message) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (Status != 0) hash ^= Status.GetHashCode();
+      if (Message.Length != 0) hash ^= Message.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (Status != 0) {
+        output.WriteRawTag(8);
+        output.WriteInt32(Status);
+      }
+      if (Message.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(Message);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (Status != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(Status);
+      }
+      if (Message.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(Message);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(HeaderProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.Status != 0) {
+        Status = other.Status;
+      }
+      if (other.Message.Length != 0) {
+        Message = other.Message;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            Status = input.ReadInt32();
+            break;
+          }
+          case 18: {
+            Message = input.ReadString();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3084742c9566118c3c8fcb6beb5bd712d7c2ea4b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Header.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 870996bd75a1a4fbcbb120b1e1e66c37
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3e23c8d991b742e2a67b9655e9c60899a898088c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs
@@ -0,0 +1,546 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/observation.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/observation.proto</summary>
+  internal static partial class ObservationReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/observation.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static ObservationReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0",
+            "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKPAwoQT2JzZXJ2YXRp",
+            "b25Qcm90bxINCgVzaGFwZRgBIAMoBRJEChBjb21wcmVzc2lvbl90eXBlGAIg",
+            "ASgOMiouY29tbXVuaWNhdG9yX29iamVjdHMuQ29tcHJlc3Npb25UeXBlUHJv",
+            "dG8SGQoPY29tcHJlc3NlZF9kYXRhGAMgASgMSAASRgoKZmxvYXRfZGF0YRgE",
+            "IAEoCzIwLmNvbW11bmljYXRvcl9vYmplY3RzLk9ic2VydmF0aW9uUHJvdG8u",
+            "RmxvYXREYXRhSAASIgoaY29tcHJlc3NlZF9jaGFubmVsX21hcHBpbmcYBSAD",
+            "KAUSHAoUZGltZW5zaW9uX3Byb3BlcnRpZXMYBiADKAUSRAoQb2JzZXJ2YXRp",
+            "b25fdHlwZRgHIAEoDjIqLmNvbW11bmljYXRvcl9vYmplY3RzLk9ic2VydmF0",
+            "aW9uVHlwZVByb3RvEgwKBG5hbWUYCCABKAkaGQoJRmxvYXREYXRhEgwKBGRh",
+            "dGEYASADKAJCEgoQb2JzZXJ2YXRpb25fZGF0YSopChRDb21wcmVzc2lvblR5",
+            "cGVQcm90bxIICgROT05FEAASBwoDUE5HEAEqQAoUT2JzZXJ2YXRpb25UeXBl",
+            "UHJvdG8SCwoHREVGQVVMVBAAEg8KC0dPQUxfU0lHTkFMEAEiBAgCEAIiBAgD",
+            "EANCJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnBy",
+            "b3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto), typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto), }, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser, new[]{ "Shape", "CompressionType", "CompressedData", "FloatData", "CompressedChannelMapping", "DimensionProperties", "ObservationType", "Name" }, new[]{ "ObservationData" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData.Parser, new[]{ "Data" }, null, null, null)})
+          }));
+    }
+    #endregion
+
+  }
+  #region Enums
+  internal enum CompressionTypeProto {
+    [pbr::OriginalName("NONE")] None = 0,
+    [pbr::OriginalName("PNG")] Png = 1,
+  }
+
+  internal enum ObservationTypeProto {
+    [pbr::OriginalName("DEFAULT")] Default = 0,
+    [pbr::OriginalName("GOAL_SIGNAL")] GoalSignal = 1,
+  }
+
+  #endregion
+
+  #region Messages
+  internal sealed partial class ObservationProto : pb::IMessage<ObservationProto> {
+    private static readonly pb::MessageParser<ObservationProto> _parser = new pb::MessageParser<ObservationProto>(() => new ObservationProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<ObservationProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.ObservationReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ObservationProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ObservationProto(ObservationProto other) : this() {
+      shape_ = other.shape_.Clone();
+      compressionType_ = other.compressionType_;
+      compressedChannelMapping_ = other.compressedChannelMapping_.Clone();
+      dimensionProperties_ = other.dimensionProperties_.Clone();
+      observationType_ = other.observationType_;
+      name_ = other.name_;
+      switch (other.ObservationDataCase) {
+        case ObservationDataOneofCase.CompressedData:
+          CompressedData = other.CompressedData;
+          break;
+        case ObservationDataOneofCase.FloatData:
+          FloatData = other.FloatData.Clone();
+          break;
+      }
+
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ObservationProto Clone() {
+      return new ObservationProto(this);
+    }
+
+    /// <summary>Field number for the "shape" field.</summary>
+    public const int ShapeFieldNumber = 1;
+    private static readonly pb::FieldCodec<int> _repeated_shape_codec
+        = pb::FieldCodec.ForInt32(10);
+    private readonly pbc::RepeatedField<int> shape_ = new pbc::RepeatedField<int>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> Shape {
+      get { return shape_; }
+    }
+
+    /// <summary>Field number for the "compression_type" field.</summary>
+    public const int CompressionTypeFieldNumber = 2;
+    private global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto compressionType_ = 0;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto CompressionType {
+      get { return compressionType_; }
+      set {
+        compressionType_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "compressed_data" field.</summary>
+    public const int CompressedDataFieldNumber = 3;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pb::ByteString CompressedData {
+      get { return observationDataCase_ == ObservationDataOneofCase.CompressedData ? (pb::ByteString) observationData_ : pb::ByteString.Empty; }
+      set {
+        observationData_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+        observationDataCase_ = ObservationDataOneofCase.CompressedData;
+      }
+    }
+
+    /// <summary>Field number for the "float_data" field.</summary>
+    public const int FloatDataFieldNumber = 4;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData FloatData {
+      get { return observationDataCase_ == ObservationDataOneofCase.FloatData ? (global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData) observationData_ : null; }
+      set {
+        observationData_ = value;
+        observationDataCase_ = value == null ? ObservationDataOneofCase.None : ObservationDataOneofCase.FloatData;
+      }
+    }
+
+    /// <summary>Field number for the "compressed_channel_mapping" field.</summary>
+    public const int CompressedChannelMappingFieldNumber = 5;
+    private static readonly pb::FieldCodec<int> _repeated_compressedChannelMapping_codec
+        = pb::FieldCodec.ForInt32(42);
+    private readonly pbc::RepeatedField<int> compressedChannelMapping_ = new pbc::RepeatedField<int>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> CompressedChannelMapping {
+      get { return compressedChannelMapping_; }
+    }
+
+    /// <summary>Field number for the "dimension_properties" field.</summary>
+    public const int DimensionPropertiesFieldNumber = 6;
+    private static readonly pb::FieldCodec<int> _repeated_dimensionProperties_codec
+        = pb::FieldCodec.ForInt32(50);
+    private readonly pbc::RepeatedField<int> dimensionProperties_ = new pbc::RepeatedField<int>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<int> DimensionProperties {
+      get { return dimensionProperties_; }
+    }
+
+    /// <summary>Field number for the "observation_type" field.</summary>
+    public const int ObservationTypeFieldNumber = 7;
+    private global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto observationType_ = 0;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto ObservationType {
+      get { return observationType_; }
+      set {
+        observationType_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "name" field.</summary>
+    public const int NameFieldNumber = 8;
+    private string name_ = "";
+    /// <summary>
+    /// Optional name of the observation.
+    /// This will be set to the ISensor name when writing,
+    /// and read into the ObservationSpec in the low-level API
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string Name {
+      get { return name_; }
+      set {
+        name_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    private object observationData_;
+    /// <summary>Enum of possible cases for the "observation_data" oneof.</summary>
+    public enum ObservationDataOneofCase {
+      None = 0,
+      CompressedData = 3,
+      FloatData = 4,
+    }
+    private ObservationDataOneofCase observationDataCase_ = ObservationDataOneofCase.None;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public ObservationDataOneofCase ObservationDataCase {
+      get { return observationDataCase_; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void ClearObservationData() {
+      observationDataCase_ = ObservationDataOneofCase.None;
+      observationData_ = null;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as ObservationProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(ObservationProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if(!shape_.Equals(other.shape_)) return false;
+      if (CompressionType != other.CompressionType) return false;
+      if (CompressedData != other.CompressedData) return false;
+      if (!object.Equals(FloatData, other.FloatData)) return false;
+      if(!compressedChannelMapping_.Equals(other.compressedChannelMapping_)) return false;
+      if(!dimensionProperties_.Equals(other.dimensionProperties_)) return false;
+      if (ObservationType != other.ObservationType) return false;
+      if (Name != other.Name) return false;
+      if (ObservationDataCase != other.ObservationDataCase) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      hash ^= shape_.GetHashCode();
+      if (CompressionType != 0) hash ^= CompressionType.GetHashCode();
+      if (observationDataCase_ == ObservationDataOneofCase.CompressedData) hash ^= CompressedData.GetHashCode();
+      if (observationDataCase_ == ObservationDataOneofCase.FloatData) hash ^= FloatData.GetHashCode();
+      hash ^= compressedChannelMapping_.GetHashCode();
+      hash ^= dimensionProperties_.GetHashCode();
+      if (ObservationType != 0) hash ^= ObservationType.GetHashCode();
+      if (Name.Length != 0) hash ^= Name.GetHashCode();
+      hash ^= (int) observationDataCase_;
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      shape_.WriteTo(output, _repeated_shape_codec);
+      if (CompressionType != 0) {
+        output.WriteRawTag(16);
+        output.WriteEnum((int) CompressionType);
+      }
+      if (observationDataCase_ == ObservationDataOneofCase.CompressedData) {
+        output.WriteRawTag(26);
+        output.WriteBytes(CompressedData);
+      }
+      if (observationDataCase_ == ObservationDataOneofCase.FloatData) {
+        output.WriteRawTag(34);
+        output.WriteMessage(FloatData);
+      }
+      compressedChannelMapping_.WriteTo(output, _repeated_compressedChannelMapping_codec);
+      dimensionProperties_.WriteTo(output, _repeated_dimensionProperties_codec);
+      if (ObservationType != 0) {
+        output.WriteRawTag(56);
+        output.WriteEnum((int) ObservationType);
+      }
+      if (Name.Length != 0) {
+        output.WriteRawTag(66);
+        output.WriteString(Name);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      size += shape_.CalculateSize(_repeated_shape_codec);
+      if (CompressionType != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) CompressionType);
+      }
+      if (observationDataCase_ == ObservationDataOneofCase.CompressedData) {
+        size += 1 + pb::CodedOutputStream.ComputeBytesSize(CompressedData);
+      }
+      if (observationDataCase_ == ObservationDataOneofCase.FloatData) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(FloatData);
+      }
+      size += compressedChannelMapping_.CalculateSize(_repeated_compressedChannelMapping_codec);
+      size += dimensionProperties_.CalculateSize(_repeated_dimensionProperties_codec);
+      if (ObservationType != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ObservationType);
+      }
+      if (Name.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(Name);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(ObservationProto other) {
+      if (other == null) {
+        return;
+      }
+      shape_.Add(other.shape_);
+      if (other.CompressionType != 0) {
+        CompressionType = other.CompressionType;
+      }
+      compressedChannelMapping_.Add(other.compressedChannelMapping_);
+      dimensionProperties_.Add(other.dimensionProperties_);
+      if (other.ObservationType != 0) {
+        ObservationType = other.ObservationType;
+      }
+      if (other.Name.Length != 0) {
+        Name = other.Name;
+      }
+      switch (other.ObservationDataCase) {
+        case ObservationDataOneofCase.CompressedData:
+          CompressedData = other.CompressedData;
+          break;
+        case ObservationDataOneofCase.FloatData:
+          if (FloatData == null) {
+            FloatData = new global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData();
+          }
+          FloatData.MergeFrom(other.FloatData);
+          break;
+      }
+
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10:
+          case 8: {
+            shape_.AddEntriesFrom(input, _repeated_shape_codec);
+            break;
+          }
+          case 16: {
+            compressionType_ = (global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto) input.ReadEnum();
+            break;
+          }
+          case 26: {
+            CompressedData = input.ReadBytes();
+            break;
+          }
+          case 34: {
+            global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData subBuilder = new global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData();
+            if (observationDataCase_ == ObservationDataOneofCase.FloatData) {
+              subBuilder.MergeFrom(FloatData);
+            }
+            input.ReadMessage(subBuilder);
+            FloatData = subBuilder;
+            break;
+          }
+          case 42:
+          case 40: {
+            compressedChannelMapping_.AddEntriesFrom(input, _repeated_compressedChannelMapping_codec);
+            break;
+          }
+          case 50:
+          case 48: {
+            dimensionProperties_.AddEntriesFrom(input, _repeated_dimensionProperties_codec);
+            break;
+          }
+          case 56: {
+            observationType_ = (global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto) input.ReadEnum();
+            break;
+          }
+          case 66: {
+            Name = input.ReadString();
+            break;
+          }
+        }
+      }
+    }
+
+    #region Nested types
+    /// <summary>Container for nested types declared in the ObservationProto message type.</summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static partial class Types {
+      internal sealed partial class FloatData : pb::IMessage<FloatData> {
+        private static readonly pb::MessageParser<FloatData> _parser = new pb::MessageParser<FloatData>(() => new FloatData());
+        private pb::UnknownFieldSet _unknownFields;
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pb::MessageParser<FloatData> Parser { get { return _parser; } }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pbr::MessageDescriptor Descriptor {
+          get { return global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Descriptor.NestedTypes[0]; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        pbr::MessageDescriptor pb::IMessage.Descriptor {
+          get { return Descriptor; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public FloatData() {
+          OnConstruction();
+        }
+
+        partial void OnConstruction();
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public FloatData(FloatData other) : this() {
+          data_ = other.data_.Clone();
+          _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public FloatData Clone() {
+          return new FloatData(this);
+        }
+
+        /// <summary>Field number for the "data" field.</summary>
+        public const int DataFieldNumber = 1;
+        private static readonly pb::FieldCodec<float> _repeated_data_codec
+            = pb::FieldCodec.ForFloat(10);
+        private readonly pbc::RepeatedField<float> data_ = new pbc::RepeatedField<float>();
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public pbc::RepeatedField<float> Data {
+          get { return data_; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override bool Equals(object other) {
+          return Equals(other as FloatData);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public bool Equals(FloatData other) {
+          if (ReferenceEquals(other, null)) {
+            return false;
+          }
+          if (ReferenceEquals(other, this)) {
+            return true;
+          }
+          if(!data_.Equals(other.data_)) return false;
+          return Equals(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override int GetHashCode() {
+          int hash = 1;
+          hash ^= data_.GetHashCode();
+          if (_unknownFields != null) {
+            hash ^= _unknownFields.GetHashCode();
+          }
+          return hash;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override string ToString() {
+          return pb::JsonFormatter.ToDiagnosticString(this);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void WriteTo(pb::CodedOutputStream output) {
+          data_.WriteTo(output, _repeated_data_codec);
+          if (_unknownFields != null) {
+            _unknownFields.WriteTo(output);
+          }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public int CalculateSize() {
+          int size = 0;
+          size += data_.CalculateSize(_repeated_data_codec);
+          if (_unknownFields != null) {
+            size += _unknownFields.CalculateSize();
+          }
+          return size;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(FloatData other) {
+          if (other == null) {
+            return;
+          }
+          data_.Add(other.data_);
+          _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(pb::CodedInputStream input) {
+          uint tag;
+          while ((tag = input.ReadTag()) != 0) {
+            switch(tag) {
+              default:
+                _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+                break;
+              case 10:
+              case 13: {
+                data_.AddEntriesFrom(input, _repeated_data_codec);
+                break;
+              }
+            }
+          }
+        }
+
+      }
+
+    }
+    #endregion
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..971fead69c473f5f6bec650515da5e0d2ea366d5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 9fbba5f80821d4f02b4239a8e16eebfa
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d3bf7cf22002df033bf5e928c88fe6dce4bae192
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs
@@ -0,0 +1,48 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/space_type.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/space_type.proto</summary>
+  internal static partial class SpaceTypeReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/space_type.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static SpaceTypeReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjNtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5",
+            "cGUucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzKi4KDlNwYWNlVHlwZVBy",
+            "b3RvEgwKCGRpc2NyZXRlEAASDgoKY29udGludW91cxABQiWqAiJVbml0eS5N",
+            "TEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto), }, null));
+    }
+    #endregion
+
+  }
+  #region Enums
+  internal enum SpaceTypeProto {
+    [pbr::OriginalName("discrete")] Discrete = 0,
+    [pbr::OriginalName("continuous")] Continuous = 1,
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7b6ada73eaf63b66b8d1995eabbd76fb614439ec
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/SpaceType.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3934602aadbe9471ca973685059ef04a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs
new file mode 100644
index 0000000000000000000000000000000000000000..042357f28062745fc7ce0c1067235a6f8d17c023
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs
@@ -0,0 +1,907 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/training_analytics.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/training_analytics.proto</summary>
+  internal static partial class TrainingAnalyticsReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/training_analytics.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static TrainingAnalyticsReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjttbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3RyYWluaW5n",
+            "X2FuYWx5dGljcy5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMi7gEKHlRy",
+            "YWluaW5nRW52aXJvbm1lbnRJbml0aWFsaXplZBIYChBtbGFnZW50c192ZXJz",
+            "aW9uGAEgASgJEh0KFW1sYWdlbnRzX2VudnNfdmVyc2lvbhgCIAEoCRIWCg5w",
+            "eXRob25fdmVyc2lvbhgDIAEoCRIVCg10b3JjaF92ZXJzaW9uGAQgASgJEhkK",
+            "EXRvcmNoX2RldmljZV90eXBlGAUgASgJEhAKCG51bV9lbnZzGAYgASgFEiIK",
+            "Gm51bV9lbnZpcm9ubWVudF9wYXJhbWV0ZXJzGAcgASgFEhMKC3J1bl9vcHRp",
+            "b25zGAggASgJIr0DChtUcmFpbmluZ0JlaGF2aW9ySW5pdGlhbGl6ZWQSFQoN",
+            "YmVoYXZpb3JfbmFtZRgBIAEoCRIUCgx0cmFpbmVyX3R5cGUYAiABKAkSIAoY",
+            "ZXh0cmluc2ljX3Jld2FyZF9lbmFibGVkGAMgASgIEhsKE2dhaWxfcmV3YXJk",
+            "X2VuYWJsZWQYBCABKAgSIAoYY3VyaW9zaXR5X3Jld2FyZF9lbmFibGVkGAUg",
+            "ASgIEhoKEnJuZF9yZXdhcmRfZW5hYmxlZBgGIAEoCBIiChpiZWhhdmlvcmFs",
+            "X2Nsb25pbmdfZW5hYmxlZBgHIAEoCBIZChFyZWN1cnJlbnRfZW5hYmxlZBgI",
+            "IAEoCBIWCg52aXN1YWxfZW5jb2RlchgJIAEoCRIaChJudW1fbmV0d29ya19s",
+            "YXllcnMYCiABKAUSIAoYbnVtX25ldHdvcmtfaGlkZGVuX3VuaXRzGAsgASgF",
+            "EhgKEHRyYWluZXJfdGhyZWFkZWQYDCABKAgSGQoRc2VsZl9wbGF5X2VuYWJs",
+            "ZWQYDSABKAgSGgoSY3VycmljdWx1bV9lbmFibGVkGA4gASgIEg4KBmNvbmZp",
+            "ZxgPIAEoCUIlqgIiVW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0",
+            "c2IGcHJvdG8z"));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.TrainingEnvironmentInitialized), global::Unity.MLAgents.CommunicatorObjects.TrainingEnvironmentInitialized.Parser, new[]{ "MlagentsVersion", "MlagentsEnvsVersion", "PythonVersion", "TorchVersion", "TorchDeviceType", "NumEnvs", "NumEnvironmentParameters", "RunOptions" }, null, null, null),
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.TrainingBehaviorInitialized), global::Unity.MLAgents.CommunicatorObjects.TrainingBehaviorInitialized.Parser, new[]{ "BehaviorName", "TrainerType", "ExtrinsicRewardEnabled", "GailRewardEnabled", "CuriosityRewardEnabled", "RndRewardEnabled", "BehavioralCloningEnabled", "RecurrentEnabled", "VisualEncoder", "NumNetworkLayers", "NumNetworkHiddenUnits", "TrainerThreaded", "SelfPlayEnabled", "CurriculumEnabled", "Config" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class TrainingEnvironmentInitialized : pb::IMessage<TrainingEnvironmentInitialized> {
+    private static readonly pb::MessageParser<TrainingEnvironmentInitialized> _parser = new pb::MessageParser<TrainingEnvironmentInitialized>(() => new TrainingEnvironmentInitialized());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<TrainingEnvironmentInitialized> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.TrainingAnalyticsReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingEnvironmentInitialized() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingEnvironmentInitialized(TrainingEnvironmentInitialized other) : this() {
+      mlagentsVersion_ = other.mlagentsVersion_;
+      mlagentsEnvsVersion_ = other.mlagentsEnvsVersion_;
+      pythonVersion_ = other.pythonVersion_;
+      torchVersion_ = other.torchVersion_;
+      torchDeviceType_ = other.torchDeviceType_;
+      numEnvs_ = other.numEnvs_;
+      numEnvironmentParameters_ = other.numEnvironmentParameters_;
+      runOptions_ = other.runOptions_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingEnvironmentInitialized Clone() {
+      return new TrainingEnvironmentInitialized(this);
+    }
+
+    /// <summary>Field number for the "mlagents_version" field.</summary>
+    public const int MlagentsVersionFieldNumber = 1;
+    private string mlagentsVersion_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string MlagentsVersion {
+      get { return mlagentsVersion_; }
+      set {
+        mlagentsVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "mlagents_envs_version" field.</summary>
+    public const int MlagentsEnvsVersionFieldNumber = 2;
+    private string mlagentsEnvsVersion_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string MlagentsEnvsVersion {
+      get { return mlagentsEnvsVersion_; }
+      set {
+        mlagentsEnvsVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "python_version" field.</summary>
+    public const int PythonVersionFieldNumber = 3;
+    private string pythonVersion_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string PythonVersion {
+      get { return pythonVersion_; }
+      set {
+        pythonVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "torch_version" field.</summary>
+    public const int TorchVersionFieldNumber = 4;
+    private string torchVersion_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string TorchVersion {
+      get { return torchVersion_; }
+      set {
+        torchVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "torch_device_type" field.</summary>
+    public const int TorchDeviceTypeFieldNumber = 5;
+    private string torchDeviceType_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string TorchDeviceType {
+      get { return torchDeviceType_; }
+      set {
+        torchDeviceType_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "num_envs" field.</summary>
+    public const int NumEnvsFieldNumber = 6;
+    private int numEnvs_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumEnvs {
+      get { return numEnvs_; }
+      set {
+        numEnvs_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "num_environment_parameters" field.</summary>
+    public const int NumEnvironmentParametersFieldNumber = 7;
+    private int numEnvironmentParameters_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumEnvironmentParameters {
+      get { return numEnvironmentParameters_; }
+      set {
+        numEnvironmentParameters_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "run_options" field.</summary>
+    public const int RunOptionsFieldNumber = 8;
+    private string runOptions_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string RunOptions {
+      get { return runOptions_; }
+      set {
+        runOptions_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as TrainingEnvironmentInitialized);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(TrainingEnvironmentInitialized other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (MlagentsVersion != other.MlagentsVersion) return false;
+      if (MlagentsEnvsVersion != other.MlagentsEnvsVersion) return false;
+      if (PythonVersion != other.PythonVersion) return false;
+      if (TorchVersion != other.TorchVersion) return false;
+      if (TorchDeviceType != other.TorchDeviceType) return false;
+      if (NumEnvs != other.NumEnvs) return false;
+      if (NumEnvironmentParameters != other.NumEnvironmentParameters) return false;
+      if (RunOptions != other.RunOptions) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (MlagentsVersion.Length != 0) hash ^= MlagentsVersion.GetHashCode();
+      if (MlagentsEnvsVersion.Length != 0) hash ^= MlagentsEnvsVersion.GetHashCode();
+      if (PythonVersion.Length != 0) hash ^= PythonVersion.GetHashCode();
+      if (TorchVersion.Length != 0) hash ^= TorchVersion.GetHashCode();
+      if (TorchDeviceType.Length != 0) hash ^= TorchDeviceType.GetHashCode();
+      if (NumEnvs != 0) hash ^= NumEnvs.GetHashCode();
+      if (NumEnvironmentParameters != 0) hash ^= NumEnvironmentParameters.GetHashCode();
+      if (RunOptions.Length != 0) hash ^= RunOptions.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (MlagentsVersion.Length != 0) {
+        output.WriteRawTag(10);
+        output.WriteString(MlagentsVersion);
+      }
+      if (MlagentsEnvsVersion.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(MlagentsEnvsVersion);
+      }
+      if (PythonVersion.Length != 0) {
+        output.WriteRawTag(26);
+        output.WriteString(PythonVersion);
+      }
+      if (TorchVersion.Length != 0) {
+        output.WriteRawTag(34);
+        output.WriteString(TorchVersion);
+      }
+      if (TorchDeviceType.Length != 0) {
+        output.WriteRawTag(42);
+        output.WriteString(TorchDeviceType);
+      }
+      if (NumEnvs != 0) {
+        output.WriteRawTag(48);
+        output.WriteInt32(NumEnvs);
+      }
+      if (NumEnvironmentParameters != 0) {
+        output.WriteRawTag(56);
+        output.WriteInt32(NumEnvironmentParameters);
+      }
+      if (RunOptions.Length != 0) {
+        output.WriteRawTag(66);
+        output.WriteString(RunOptions);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (MlagentsVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(MlagentsVersion);
+      }
+      if (MlagentsEnvsVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(MlagentsEnvsVersion);
+      }
+      if (PythonVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(PythonVersion);
+      }
+      if (TorchVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(TorchVersion);
+      }
+      if (TorchDeviceType.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(TorchDeviceType);
+      }
+      if (NumEnvs != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumEnvs);
+      }
+      if (NumEnvironmentParameters != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumEnvironmentParameters);
+      }
+      if (RunOptions.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(RunOptions);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(TrainingEnvironmentInitialized other) {
+      if (other == null) {
+        return;
+      }
+      if (other.MlagentsVersion.Length != 0) {
+        MlagentsVersion = other.MlagentsVersion;
+      }
+      if (other.MlagentsEnvsVersion.Length != 0) {
+        MlagentsEnvsVersion = other.MlagentsEnvsVersion;
+      }
+      if (other.PythonVersion.Length != 0) {
+        PythonVersion = other.PythonVersion;
+      }
+      if (other.TorchVersion.Length != 0) {
+        TorchVersion = other.TorchVersion;
+      }
+      if (other.TorchDeviceType.Length != 0) {
+        TorchDeviceType = other.TorchDeviceType;
+      }
+      if (other.NumEnvs != 0) {
+        NumEnvs = other.NumEnvs;
+      }
+      if (other.NumEnvironmentParameters != 0) {
+        NumEnvironmentParameters = other.NumEnvironmentParameters;
+      }
+      if (other.RunOptions.Length != 0) {
+        RunOptions = other.RunOptions;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            MlagentsVersion = input.ReadString();
+            break;
+          }
+          case 18: {
+            MlagentsEnvsVersion = input.ReadString();
+            break;
+          }
+          case 26: {
+            PythonVersion = input.ReadString();
+            break;
+          }
+          case 34: {
+            TorchVersion = input.ReadString();
+            break;
+          }
+          case 42: {
+            TorchDeviceType = input.ReadString();
+            break;
+          }
+          case 48: {
+            NumEnvs = input.ReadInt32();
+            break;
+          }
+          case 56: {
+            NumEnvironmentParameters = input.ReadInt32();
+            break;
+          }
+          case 66: {
+            RunOptions = input.ReadString();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  internal sealed partial class TrainingBehaviorInitialized : pb::IMessage<TrainingBehaviorInitialized> {
+    private static readonly pb::MessageParser<TrainingBehaviorInitialized> _parser = new pb::MessageParser<TrainingBehaviorInitialized>(() => new TrainingBehaviorInitialized());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<TrainingBehaviorInitialized> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.TrainingAnalyticsReflection.Descriptor.MessageTypes[1]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingBehaviorInitialized() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingBehaviorInitialized(TrainingBehaviorInitialized other) : this() {
+      behaviorName_ = other.behaviorName_;
+      trainerType_ = other.trainerType_;
+      extrinsicRewardEnabled_ = other.extrinsicRewardEnabled_;
+      gailRewardEnabled_ = other.gailRewardEnabled_;
+      curiosityRewardEnabled_ = other.curiosityRewardEnabled_;
+      rndRewardEnabled_ = other.rndRewardEnabled_;
+      behavioralCloningEnabled_ = other.behavioralCloningEnabled_;
+      recurrentEnabled_ = other.recurrentEnabled_;
+      visualEncoder_ = other.visualEncoder_;
+      numNetworkLayers_ = other.numNetworkLayers_;
+      numNetworkHiddenUnits_ = other.numNetworkHiddenUnits_;
+      trainerThreaded_ = other.trainerThreaded_;
+      selfPlayEnabled_ = other.selfPlayEnabled_;
+      curriculumEnabled_ = other.curriculumEnabled_;
+      config_ = other.config_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public TrainingBehaviorInitialized Clone() {
+      return new TrainingBehaviorInitialized(this);
+    }
+
+    /// <summary>Field number for the "behavior_name" field.</summary>
+    public const int BehaviorNameFieldNumber = 1;
+    private string behaviorName_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string BehaviorName {
+      get { return behaviorName_; }
+      set {
+        behaviorName_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "trainer_type" field.</summary>
+    public const int TrainerTypeFieldNumber = 2;
+    private string trainerType_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string TrainerType {
+      get { return trainerType_; }
+      set {
+        trainerType_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "extrinsic_reward_enabled" field.</summary>
+    public const int ExtrinsicRewardEnabledFieldNumber = 3;
+    private bool extrinsicRewardEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool ExtrinsicRewardEnabled {
+      get { return extrinsicRewardEnabled_; }
+      set {
+        extrinsicRewardEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "gail_reward_enabled" field.</summary>
+    public const int GailRewardEnabledFieldNumber = 4;
+    private bool gailRewardEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool GailRewardEnabled {
+      get { return gailRewardEnabled_; }
+      set {
+        gailRewardEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "curiosity_reward_enabled" field.</summary>
+    public const int CuriosityRewardEnabledFieldNumber = 5;
+    private bool curiosityRewardEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool CuriosityRewardEnabled {
+      get { return curiosityRewardEnabled_; }
+      set {
+        curiosityRewardEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "rnd_reward_enabled" field.</summary>
+    public const int RndRewardEnabledFieldNumber = 6;
+    private bool rndRewardEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool RndRewardEnabled {
+      get { return rndRewardEnabled_; }
+      set {
+        rndRewardEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "behavioral_cloning_enabled" field.</summary>
+    public const int BehavioralCloningEnabledFieldNumber = 7;
+    private bool behavioralCloningEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool BehavioralCloningEnabled {
+      get { return behavioralCloningEnabled_; }
+      set {
+        behavioralCloningEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "recurrent_enabled" field.</summary>
+    public const int RecurrentEnabledFieldNumber = 8;
+    private bool recurrentEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool RecurrentEnabled {
+      get { return recurrentEnabled_; }
+      set {
+        recurrentEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "visual_encoder" field.</summary>
+    public const int VisualEncoderFieldNumber = 9;
+    private string visualEncoder_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string VisualEncoder {
+      get { return visualEncoder_; }
+      set {
+        visualEncoder_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "num_network_layers" field.</summary>
+    public const int NumNetworkLayersFieldNumber = 10;
+    private int numNetworkLayers_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumNetworkLayers {
+      get { return numNetworkLayers_; }
+      set {
+        numNetworkLayers_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "num_network_hidden_units" field.</summary>
+    public const int NumNetworkHiddenUnitsFieldNumber = 11;
+    private int numNetworkHiddenUnits_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumNetworkHiddenUnits {
+      get { return numNetworkHiddenUnits_; }
+      set {
+        numNetworkHiddenUnits_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "trainer_threaded" field.</summary>
+    public const int TrainerThreadedFieldNumber = 12;
+    private bool trainerThreaded_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool TrainerThreaded {
+      get { return trainerThreaded_; }
+      set {
+        trainerThreaded_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "self_play_enabled" field.</summary>
+    public const int SelfPlayEnabledFieldNumber = 13;
+    private bool selfPlayEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool SelfPlayEnabled {
+      get { return selfPlayEnabled_; }
+      set {
+        selfPlayEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "curriculum_enabled" field.</summary>
+    public const int CurriculumEnabledFieldNumber = 14;
+    private bool curriculumEnabled_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool CurriculumEnabled {
+      get { return curriculumEnabled_; }
+      set {
+        curriculumEnabled_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "config" field.</summary>
+    public const int ConfigFieldNumber = 15;
+    private string config_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string Config {
+      get { return config_; }
+      set {
+        config_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as TrainingBehaviorInitialized);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(TrainingBehaviorInitialized other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (BehaviorName != other.BehaviorName) return false;
+      if (TrainerType != other.TrainerType) return false;
+      if (ExtrinsicRewardEnabled != other.ExtrinsicRewardEnabled) return false;
+      if (GailRewardEnabled != other.GailRewardEnabled) return false;
+      if (CuriosityRewardEnabled != other.CuriosityRewardEnabled) return false;
+      if (RndRewardEnabled != other.RndRewardEnabled) return false;
+      if (BehavioralCloningEnabled != other.BehavioralCloningEnabled) return false;
+      if (RecurrentEnabled != other.RecurrentEnabled) return false;
+      if (VisualEncoder != other.VisualEncoder) return false;
+      if (NumNetworkLayers != other.NumNetworkLayers) return false;
+      if (NumNetworkHiddenUnits != other.NumNetworkHiddenUnits) return false;
+      if (TrainerThreaded != other.TrainerThreaded) return false;
+      if (SelfPlayEnabled != other.SelfPlayEnabled) return false;
+      if (CurriculumEnabled != other.CurriculumEnabled) return false;
+      if (Config != other.Config) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (BehaviorName.Length != 0) hash ^= BehaviorName.GetHashCode();
+      if (TrainerType.Length != 0) hash ^= TrainerType.GetHashCode();
+      if (ExtrinsicRewardEnabled != false) hash ^= ExtrinsicRewardEnabled.GetHashCode();
+      if (GailRewardEnabled != false) hash ^= GailRewardEnabled.GetHashCode();
+      if (CuriosityRewardEnabled != false) hash ^= CuriosityRewardEnabled.GetHashCode();
+      if (RndRewardEnabled != false) hash ^= RndRewardEnabled.GetHashCode();
+      if (BehavioralCloningEnabled != false) hash ^= BehavioralCloningEnabled.GetHashCode();
+      if (RecurrentEnabled != false) hash ^= RecurrentEnabled.GetHashCode();
+      if (VisualEncoder.Length != 0) hash ^= VisualEncoder.GetHashCode();
+      if (NumNetworkLayers != 0) hash ^= NumNetworkLayers.GetHashCode();
+      if (NumNetworkHiddenUnits != 0) hash ^= NumNetworkHiddenUnits.GetHashCode();
+      if (TrainerThreaded != false) hash ^= TrainerThreaded.GetHashCode();
+      if (SelfPlayEnabled != false) hash ^= SelfPlayEnabled.GetHashCode();
+      if (CurriculumEnabled != false) hash ^= CurriculumEnabled.GetHashCode();
+      if (Config.Length != 0) hash ^= Config.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (BehaviorName.Length != 0) {
+        output.WriteRawTag(10);
+        output.WriteString(BehaviorName);
+      }
+      if (TrainerType.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(TrainerType);
+      }
+      if (ExtrinsicRewardEnabled != false) {
+        output.WriteRawTag(24);
+        output.WriteBool(ExtrinsicRewardEnabled);
+      }
+      if (GailRewardEnabled != false) {
+        output.WriteRawTag(32);
+        output.WriteBool(GailRewardEnabled);
+      }
+      if (CuriosityRewardEnabled != false) {
+        output.WriteRawTag(40);
+        output.WriteBool(CuriosityRewardEnabled);
+      }
+      if (RndRewardEnabled != false) {
+        output.WriteRawTag(48);
+        output.WriteBool(RndRewardEnabled);
+      }
+      if (BehavioralCloningEnabled != false) {
+        output.WriteRawTag(56);
+        output.WriteBool(BehavioralCloningEnabled);
+      }
+      if (RecurrentEnabled != false) {
+        output.WriteRawTag(64);
+        output.WriteBool(RecurrentEnabled);
+      }
+      if (VisualEncoder.Length != 0) {
+        output.WriteRawTag(74);
+        output.WriteString(VisualEncoder);
+      }
+      if (NumNetworkLayers != 0) {
+        output.WriteRawTag(80);
+        output.WriteInt32(NumNetworkLayers);
+      }
+      if (NumNetworkHiddenUnits != 0) {
+        output.WriteRawTag(88);
+        output.WriteInt32(NumNetworkHiddenUnits);
+      }
+      if (TrainerThreaded != false) {
+        output.WriteRawTag(96);
+        output.WriteBool(TrainerThreaded);
+      }
+      if (SelfPlayEnabled != false) {
+        output.WriteRawTag(104);
+        output.WriteBool(SelfPlayEnabled);
+      }
+      if (CurriculumEnabled != false) {
+        output.WriteRawTag(112);
+        output.WriteBool(CurriculumEnabled);
+      }
+      if (Config.Length != 0) {
+        output.WriteRawTag(122);
+        output.WriteString(Config);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (BehaviorName.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(BehaviorName);
+      }
+      if (TrainerType.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(TrainerType);
+      }
+      if (ExtrinsicRewardEnabled != false) {
+        size += 1 + 1;
+      }
+      if (GailRewardEnabled != false) {
+        size += 1 + 1;
+      }
+      if (CuriosityRewardEnabled != false) {
+        size += 1 + 1;
+      }
+      if (RndRewardEnabled != false) {
+        size += 1 + 1;
+      }
+      if (BehavioralCloningEnabled != false) {
+        size += 1 + 1;
+      }
+      if (RecurrentEnabled != false) {
+        size += 1 + 1;
+      }
+      if (VisualEncoder.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(VisualEncoder);
+      }
+      if (NumNetworkLayers != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumNetworkLayers);
+      }
+      if (NumNetworkHiddenUnits != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumNetworkHiddenUnits);
+      }
+      if (TrainerThreaded != false) {
+        size += 1 + 1;
+      }
+      if (SelfPlayEnabled != false) {
+        size += 1 + 1;
+      }
+      if (CurriculumEnabled != false) {
+        size += 1 + 1;
+      }
+      if (Config.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(Config);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(TrainingBehaviorInitialized other) {
+      if (other == null) {
+        return;
+      }
+      if (other.BehaviorName.Length != 0) {
+        BehaviorName = other.BehaviorName;
+      }
+      if (other.TrainerType.Length != 0) {
+        TrainerType = other.TrainerType;
+      }
+      if (other.ExtrinsicRewardEnabled != false) {
+        ExtrinsicRewardEnabled = other.ExtrinsicRewardEnabled;
+      }
+      if (other.GailRewardEnabled != false) {
+        GailRewardEnabled = other.GailRewardEnabled;
+      }
+      if (other.CuriosityRewardEnabled != false) {
+        CuriosityRewardEnabled = other.CuriosityRewardEnabled;
+      }
+      if (other.RndRewardEnabled != false) {
+        RndRewardEnabled = other.RndRewardEnabled;
+      }
+      if (other.BehavioralCloningEnabled != false) {
+        BehavioralCloningEnabled = other.BehavioralCloningEnabled;
+      }
+      if (other.RecurrentEnabled != false) {
+        RecurrentEnabled = other.RecurrentEnabled;
+      }
+      if (other.VisualEncoder.Length != 0) {
+        VisualEncoder = other.VisualEncoder;
+      }
+      if (other.NumNetworkLayers != 0) {
+        NumNetworkLayers = other.NumNetworkLayers;
+      }
+      if (other.NumNetworkHiddenUnits != 0) {
+        NumNetworkHiddenUnits = other.NumNetworkHiddenUnits;
+      }
+      if (other.TrainerThreaded != false) {
+        TrainerThreaded = other.TrainerThreaded;
+      }
+      if (other.SelfPlayEnabled != false) {
+        SelfPlayEnabled = other.SelfPlayEnabled;
+      }
+      if (other.CurriculumEnabled != false) {
+        CurriculumEnabled = other.CurriculumEnabled;
+      }
+      if (other.Config.Length != 0) {
+        Config = other.Config;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            BehaviorName = input.ReadString();
+            break;
+          }
+          case 18: {
+            TrainerType = input.ReadString();
+            break;
+          }
+          case 24: {
+            ExtrinsicRewardEnabled = input.ReadBool();
+            break;
+          }
+          case 32: {
+            GailRewardEnabled = input.ReadBool();
+            break;
+          }
+          case 40: {
+            CuriosityRewardEnabled = input.ReadBool();
+            break;
+          }
+          case 48: {
+            RndRewardEnabled = input.ReadBool();
+            break;
+          }
+          case 56: {
+            BehavioralCloningEnabled = input.ReadBool();
+            break;
+          }
+          case 64: {
+            RecurrentEnabled = input.ReadBool();
+            break;
+          }
+          case 74: {
+            VisualEncoder = input.ReadString();
+            break;
+          }
+          case 80: {
+            NumNetworkLayers = input.ReadInt32();
+            break;
+          }
+          case 88: {
+            NumNetworkHiddenUnits = input.ReadInt32();
+            break;
+          }
+          case 96: {
+            TrainerThreaded = input.ReadBool();
+            break;
+          }
+          case 104: {
+            SelfPlayEnabled = input.ReadBool();
+            break;
+          }
+          case 112: {
+            CurriculumEnabled = input.ReadBool();
+            break;
+          }
+          case 122: {
+            Config = input.ReadString();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8e9d358feba7f08adcd8bf509e173c354308d707
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/TrainingAnalytics.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 9e6ac06a3931742d798cf922de6b99f0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9497aa0ad8f5caba9513d5d4a80c9fcb22c0f2e2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs
@@ -0,0 +1,220 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_input.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_input.proto</summary>
+  internal static partial class UnityInputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_input.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityInputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X2lu",
+            "cHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo3bWxhZ2VudHNfZW52",
+            "cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9ybF9pbnB1dC5wcm90bxpG",
+            "bWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9ybF9p",
+            "bml0aWFsaXphdGlvbl9pbnB1dC5wcm90byKkAQoPVW5pdHlJbnB1dFByb3Rv",
+            "EjkKCHJsX2lucHV0GAEgASgLMicuY29tbXVuaWNhdG9yX29iamVjdHMuVW5p",
+            "dHlSTElucHV0UHJvdG8SVgoXcmxfaW5pdGlhbGl6YXRpb25faW5wdXQYAiAB",
+            "KAsyNS5jb21tdW5pY2F0b3Jfb2JqZWN0cy5Vbml0eVJMSW5pdGlhbGl6YXRp",
+            "b25JbnB1dFByb3RvQiWqAiJVbml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JP",
+            "YmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.UnityRlInputReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.UnityRlInitializationInputReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityInputProto), global::Unity.MLAgents.CommunicatorObjects.UnityInputProto.Parser, new[]{ "RlInput", "RlInitializationInput" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class UnityInputProto : pb::IMessage<UnityInputProto> {
+    private static readonly pb::MessageParser<UnityInputProto> _parser = new pb::MessageParser<UnityInputProto>(() => new UnityInputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityInputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityInputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityInputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityInputProto(UnityInputProto other) : this() {
+      RlInput = other.rlInput_ != null ? other.RlInput.Clone() : null;
+      RlInitializationInput = other.rlInitializationInput_ != null ? other.RlInitializationInput.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityInputProto Clone() {
+      return new UnityInputProto(this);
+    }
+
+    /// <summary>Field number for the "rl_input" field.</summary>
+    public const int RlInputFieldNumber = 1;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto rlInput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto RlInput {
+      get { return rlInput_; }
+      set {
+        rlInput_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "rl_initialization_input" field.</summary>
+    public const int RlInitializationInputFieldNumber = 2;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto rlInitializationInput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto RlInitializationInput {
+      get { return rlInitializationInput_; }
+      set {
+        rlInitializationInput_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityInputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityInputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!object.Equals(RlInput, other.RlInput)) return false;
+      if (!object.Equals(RlInitializationInput, other.RlInitializationInput)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (rlInput_ != null) hash ^= RlInput.GetHashCode();
+      if (rlInitializationInput_ != null) hash ^= RlInitializationInput.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (rlInput_ != null) {
+        output.WriteRawTag(10);
+        output.WriteMessage(RlInput);
+      }
+      if (rlInitializationInput_ != null) {
+        output.WriteRawTag(18);
+        output.WriteMessage(RlInitializationInput);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (rlInput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(RlInput);
+      }
+      if (rlInitializationInput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(RlInitializationInput);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityInputProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.rlInput_ != null) {
+        if (rlInput_ == null) {
+          rlInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto();
+        }
+        RlInput.MergeFrom(other.RlInput);
+      }
+      if (other.rlInitializationInput_ != null) {
+        if (rlInitializationInput_ == null) {
+          rlInitializationInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto();
+        }
+        RlInitializationInput.MergeFrom(other.RlInitializationInput);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            if (rlInput_ == null) {
+              rlInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto();
+            }
+            input.ReadMessage(rlInput_);
+            break;
+          }
+          case 18: {
+            if (rlInitializationInput_ == null) {
+              rlInitializationInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto();
+            }
+            input.ReadMessage(rlInitializationInput_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..32f1aa8334a622d59ca498818df9ecad2d673661
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 13de5026cc0834f558fe971eb93c850e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b98264dc44a0eaf89d7ae48726c57f4bf884d25a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs
@@ -0,0 +1,255 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_message.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_message.proto</summary>
+  internal static partial class UnityMessageReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_message.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityMessageReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjZtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X21l",
+            "c3NhZ2UucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjVtbGFnZW50c19l",
+            "bnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X291dHB1dC5wcm90bxo0",
+            "bWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9pbnB1",
+            "dC5wcm90bxovbWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy9o",
+            "ZWFkZXIucHJvdG8iwAEKEVVuaXR5TWVzc2FnZVByb3RvEjEKBmhlYWRlchgB",
+            "IAEoCzIhLmNvbW11bmljYXRvcl9vYmplY3RzLkhlYWRlclByb3RvEjwKDHVu",
+            "aXR5X291dHB1dBgCIAEoCzImLmNvbW11bmljYXRvcl9vYmplY3RzLlVuaXR5",
+            "T3V0cHV0UHJvdG8SOgoLdW5pdHlfaW5wdXQYAyABKAsyJS5jb21tdW5pY2F0",
+            "b3Jfb2JqZWN0cy5Vbml0eUlucHV0UHJvdG9CJaoCIlVuaXR5Lk1MQWdlbnRz",
+            "LkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.UnityOutputReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.UnityInputReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.HeaderReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto), global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto.Parser, new[]{ "Header", "UnityOutput", "UnityInput" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class UnityMessageProto : pb::IMessage<UnityMessageProto> {
+    private static readonly pb::MessageParser<UnityMessageProto> _parser = new pb::MessageParser<UnityMessageProto>(() => new UnityMessageProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityMessageProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityMessageReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityMessageProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityMessageProto(UnityMessageProto other) : this() {
+      Header = other.header_ != null ? other.Header.Clone() : null;
+      UnityOutput = other.unityOutput_ != null ? other.UnityOutput.Clone() : null;
+      UnityInput = other.unityInput_ != null ? other.UnityInput.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityMessageProto Clone() {
+      return new UnityMessageProto(this);
+    }
+
+    /// <summary>Field number for the "header" field.</summary>
+    public const int HeaderFieldNumber = 1;
+    private global::Unity.MLAgents.CommunicatorObjects.HeaderProto header_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.HeaderProto Header {
+      get { return header_; }
+      set {
+        header_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "unity_output" field.</summary>
+    public const int UnityOutputFieldNumber = 2;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto unityOutput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto UnityOutput {
+      get { return unityOutput_; }
+      set {
+        unityOutput_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "unity_input" field.</summary>
+    public const int UnityInputFieldNumber = 3;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityInputProto unityInput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityInputProto UnityInput {
+      get { return unityInput_; }
+      set {
+        unityInput_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityMessageProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityMessageProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!object.Equals(Header, other.Header)) return false;
+      if (!object.Equals(UnityOutput, other.UnityOutput)) return false;
+      if (!object.Equals(UnityInput, other.UnityInput)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (header_ != null) hash ^= Header.GetHashCode();
+      if (unityOutput_ != null) hash ^= UnityOutput.GetHashCode();
+      if (unityInput_ != null) hash ^= UnityInput.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (header_ != null) {
+        output.WriteRawTag(10);
+        output.WriteMessage(Header);
+      }
+      if (unityOutput_ != null) {
+        output.WriteRawTag(18);
+        output.WriteMessage(UnityOutput);
+      }
+      if (unityInput_ != null) {
+        output.WriteRawTag(26);
+        output.WriteMessage(UnityInput);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (header_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(Header);
+      }
+      if (unityOutput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(UnityOutput);
+      }
+      if (unityInput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(UnityInput);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityMessageProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.header_ != null) {
+        if (header_ == null) {
+          header_ = new global::Unity.MLAgents.CommunicatorObjects.HeaderProto();
+        }
+        Header.MergeFrom(other.Header);
+      }
+      if (other.unityOutput_ != null) {
+        if (unityOutput_ == null) {
+          unityOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto();
+        }
+        UnityOutput.MergeFrom(other.UnityOutput);
+      }
+      if (other.unityInput_ != null) {
+        if (unityInput_ == null) {
+          unityInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityInputProto();
+        }
+        UnityInput.MergeFrom(other.UnityInput);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            if (header_ == null) {
+              header_ = new global::Unity.MLAgents.CommunicatorObjects.HeaderProto();
+            }
+            input.ReadMessage(header_);
+            break;
+          }
+          case 18: {
+            if (unityOutput_ == null) {
+              unityOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto();
+            }
+            input.ReadMessage(unityOutput_);
+            break;
+          }
+          case 26: {
+            if (unityInput_ == null) {
+              unityInput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityInputProto();
+            }
+            input.ReadMessage(unityInput_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fe03de4e4ff49f68b7ff775fe4fb7a515c947678
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityMessage.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e2189c32296994576b0ef0aaa2b78142
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..efb255d25138291a7d69227cd747f1d21a339b3b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs
@@ -0,0 +1,220 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_output.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_output.proto</summary>
+  internal static partial class UnityOutputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_output.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityOutputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X291",
+            "dHB1dC5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMaOG1sYWdlbnRzX2Vu",
+            "dnMvY29tbXVuaWNhdG9yX29iamVjdHMvdW5pdHlfcmxfb3V0cHV0LnByb3Rv",
+            "GkdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X2luaXRpYWxpemF0aW9uX291dHB1dC5wcm90byKpAQoQVW5pdHlPdXRwdXRQ",
+            "cm90bxI7CglybF9vdXRwdXQYASABKAsyKC5jb21tdW5pY2F0b3Jfb2JqZWN0",
+            "cy5Vbml0eVJMT3V0cHV0UHJvdG8SWAoYcmxfaW5pdGlhbGl6YXRpb25fb3V0",
+            "cHV0GAIgASgLMjYuY29tbXVuaWNhdG9yX29iamVjdHMuVW5pdHlSTEluaXRp",
+            "YWxpemF0aW9uT3V0cHV0UHJvdG9CJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11",
+            "bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.UnityRlOutputReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.UnityRlInitializationOutputReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto), global::Unity.MLAgents.CommunicatorObjects.UnityOutputProto.Parser, new[]{ "RlOutput", "RlInitializationOutput" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class UnityOutputProto : pb::IMessage<UnityOutputProto> {
+    private static readonly pb::MessageParser<UnityOutputProto> _parser = new pb::MessageParser<UnityOutputProto>(() => new UnityOutputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityOutputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityOutputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityOutputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityOutputProto(UnityOutputProto other) : this() {
+      RlOutput = other.rlOutput_ != null ? other.RlOutput.Clone() : null;
+      RlInitializationOutput = other.rlInitializationOutput_ != null ? other.RlInitializationOutput.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityOutputProto Clone() {
+      return new UnityOutputProto(this);
+    }
+
+    /// <summary>Field number for the "rl_output" field.</summary>
+    public const int RlOutputFieldNumber = 1;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto rlOutput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto RlOutput {
+      get { return rlOutput_; }
+      set {
+        rlOutput_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "rl_initialization_output" field.</summary>
+    public const int RlInitializationOutputFieldNumber = 2;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto rlInitializationOutput_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto RlInitializationOutput {
+      get { return rlInitializationOutput_; }
+      set {
+        rlInitializationOutput_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityOutputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityOutputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!object.Equals(RlOutput, other.RlOutput)) return false;
+      if (!object.Equals(RlInitializationOutput, other.RlInitializationOutput)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (rlOutput_ != null) hash ^= RlOutput.GetHashCode();
+      if (rlInitializationOutput_ != null) hash ^= RlInitializationOutput.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (rlOutput_ != null) {
+        output.WriteRawTag(10);
+        output.WriteMessage(RlOutput);
+      }
+      if (rlInitializationOutput_ != null) {
+        output.WriteRawTag(18);
+        output.WriteMessage(RlInitializationOutput);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (rlOutput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(RlOutput);
+      }
+      if (rlInitializationOutput_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(RlInitializationOutput);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityOutputProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.rlOutput_ != null) {
+        if (rlOutput_ == null) {
+          rlOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto();
+        }
+        RlOutput.MergeFrom(other.RlOutput);
+      }
+      if (other.rlInitializationOutput_ != null) {
+        if (rlInitializationOutput_ == null) {
+          rlInitializationOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto();
+        }
+        RlInitializationOutput.MergeFrom(other.RlInitializationOutput);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            if (rlOutput_ == null) {
+              rlOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto();
+            }
+            input.ReadMessage(rlOutput_);
+            break;
+          }
+          case 18: {
+            if (rlInitializationOutput_ == null) {
+              rlInitializationOutput_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto();
+            }
+            input.ReadMessage(rlInitializationOutput_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e1ae7344594d1e6b4b4b1548cb292ae7728b3317
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityOutput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e1c19e75c7657497fbc05cfa40dd6783
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1b83a17e6859b098393eb0310e89d8fb60515a94
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs
@@ -0,0 +1,312 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_initialization_input.proto</summary>
+  internal static partial class UnityRlInitializationInputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_initialization_input.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityRlInitializationInputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CkZtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X2luaXRpYWxpemF0aW9uX2lucHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2Jq",
+            "ZWN0cxo1bWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy9jYXBh",
+            "YmlsaXRpZXMucHJvdG8iwAEKH1VuaXR5UkxJbml0aWFsaXphdGlvbklucHV0",
+            "UHJvdG8SDAoEc2VlZBgBIAEoBRIdChVjb21tdW5pY2F0aW9uX3ZlcnNpb24Y",
+            "AiABKAkSFwoPcGFja2FnZV92ZXJzaW9uGAMgASgJEkQKDGNhcGFiaWxpdGll",
+            "cxgEIAEoCzIuLmNvbW11bmljYXRvcl9vYmplY3RzLlVuaXR5UkxDYXBhYmls",
+            "aXRpZXNQcm90bxIRCgludW1fYXJlYXMYBSABKAVCJaoCIlVuaXR5Lk1MQWdl",
+            "bnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.CapabilitiesReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationInputProto.Parser, new[]{ "Seed", "CommunicationVersion", "PackageVersion", "Capabilities", "NumAreas" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  /// <summary>
+  /// The initializaiton message - this is typically sent from the Python trainer to the C# environment.
+  /// </summary>
+  internal sealed partial class UnityRLInitializationInputProto : pb::IMessage<UnityRLInitializationInputProto> {
+    private static readonly pb::MessageParser<UnityRLInitializationInputProto> _parser = new pb::MessageParser<UnityRLInitializationInputProto>(() => new UnityRLInitializationInputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityRLInitializationInputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityRlInitializationInputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationInputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationInputProto(UnityRLInitializationInputProto other) : this() {
+      seed_ = other.seed_;
+      communicationVersion_ = other.communicationVersion_;
+      packageVersion_ = other.packageVersion_;
+      Capabilities = other.capabilities_ != null ? other.Capabilities.Clone() : null;
+      numAreas_ = other.numAreas_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationInputProto Clone() {
+      return new UnityRLInitializationInputProto(this);
+    }
+
+    /// <summary>Field number for the "seed" field.</summary>
+    public const int SeedFieldNumber = 1;
+    private int seed_;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int Seed {
+      get { return seed_; }
+      set {
+        seed_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "communication_version" field.</summary>
+    public const int CommunicationVersionFieldNumber = 2;
+    private string communicationVersion_ = "";
+    /// <summary>
+    /// Communication protocol version that the initiating side (typically the Python trainer) is using.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string CommunicationVersion {
+      get { return communicationVersion_; }
+      set {
+        communicationVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "package_version" field.</summary>
+    public const int PackageVersionFieldNumber = 3;
+    private string packageVersion_ = "";
+    /// <summary>
+    /// Package/library version that the initiating side (typically the Python trainer) is using.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string PackageVersion {
+      get { return packageVersion_; }
+      set {
+        packageVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "capabilities" field.</summary>
+    public const int CapabilitiesFieldNumber = 4;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto capabilities_;
+    /// <summary>
+    /// The RL Capabilities of the Python trainer.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto Capabilities {
+      get { return capabilities_; }
+      set {
+        capabilities_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "num_areas" field.</summary>
+    public const int NumAreasFieldNumber = 5;
+    private int numAreas_;
+    /// <summary>
+    /// The number of training areas to instantiate
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int NumAreas {
+      get { return numAreas_; }
+      set {
+        numAreas_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityRLInitializationInputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityRLInitializationInputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (Seed != other.Seed) return false;
+      if (CommunicationVersion != other.CommunicationVersion) return false;
+      if (PackageVersion != other.PackageVersion) return false;
+      if (!object.Equals(Capabilities, other.Capabilities)) return false;
+      if (NumAreas != other.NumAreas) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (Seed != 0) hash ^= Seed.GetHashCode();
+      if (CommunicationVersion.Length != 0) hash ^= CommunicationVersion.GetHashCode();
+      if (PackageVersion.Length != 0) hash ^= PackageVersion.GetHashCode();
+      if (capabilities_ != null) hash ^= Capabilities.GetHashCode();
+      if (NumAreas != 0) hash ^= NumAreas.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (Seed != 0) {
+        output.WriteRawTag(8);
+        output.WriteInt32(Seed);
+      }
+      if (CommunicationVersion.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(CommunicationVersion);
+      }
+      if (PackageVersion.Length != 0) {
+        output.WriteRawTag(26);
+        output.WriteString(PackageVersion);
+      }
+      if (capabilities_ != null) {
+        output.WriteRawTag(34);
+        output.WriteMessage(Capabilities);
+      }
+      if (NumAreas != 0) {
+        output.WriteRawTag(40);
+        output.WriteInt32(NumAreas);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (Seed != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(Seed);
+      }
+      if (CommunicationVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(CommunicationVersion);
+      }
+      if (PackageVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(PackageVersion);
+      }
+      if (capabilities_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(Capabilities);
+      }
+      if (NumAreas != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumAreas);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityRLInitializationInputProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.Seed != 0) {
+        Seed = other.Seed;
+      }
+      if (other.CommunicationVersion.Length != 0) {
+        CommunicationVersion = other.CommunicationVersion;
+      }
+      if (other.PackageVersion.Length != 0) {
+        PackageVersion = other.PackageVersion;
+      }
+      if (other.capabilities_ != null) {
+        if (capabilities_ == null) {
+          capabilities_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto();
+        }
+        Capabilities.MergeFrom(other.Capabilities);
+      }
+      if (other.NumAreas != 0) {
+        NumAreas = other.NumAreas;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 8: {
+            Seed = input.ReadInt32();
+            break;
+          }
+          case 18: {
+            CommunicationVersion = input.ReadString();
+            break;
+          }
+          case 26: {
+            PackageVersion = input.ReadString();
+            break;
+          }
+          case 34: {
+            if (capabilities_ == null) {
+              capabilities_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto();
+            }
+            input.ReadMessage(capabilities_);
+            break;
+          }
+          case 40: {
+            NumAreas = input.ReadInt32();
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c0c9fc2fd422d4d09f599dfa950234a5830c32a1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e1542ad34ffb34317b74b239135d0477
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1e073a596532f18412453948ab6323a49babcaab
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs
@@ -0,0 +1,332 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_initialization_output.proto</summary>
+  internal static partial class UnityRlInitializationOutputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_initialization_output.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityRlInitializationOutputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CkdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X2luaXRpYWxpemF0aW9uX291dHB1dC5wcm90bxIUY29tbXVuaWNhdG9yX29i",
+            "amVjdHMaNW1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvY2Fw",
+            "YWJpbGl0aWVzLnByb3RvGjltbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9v",
+            "YmplY3RzL2JyYWluX3BhcmFtZXRlcnMucHJvdG8ijAIKIFVuaXR5UkxJbml0",
+            "aWFsaXphdGlvbk91dHB1dFByb3RvEgwKBG5hbWUYASABKAkSHQoVY29tbXVu",
+            "aWNhdGlvbl92ZXJzaW9uGAIgASgJEhAKCGxvZ19wYXRoGAMgASgJEkQKEGJy",
+            "YWluX3BhcmFtZXRlcnMYBSADKAsyKi5jb21tdW5pY2F0b3Jfb2JqZWN0cy5C",
+            "cmFpblBhcmFtZXRlcnNQcm90bxIXCg9wYWNrYWdlX3ZlcnNpb24YByABKAkS",
+            "RAoMY2FwYWJpbGl0aWVzGAggASgLMi4uY29tbXVuaWNhdG9yX29iamVjdHMu",
+            "VW5pdHlSTENhcGFiaWxpdGllc1Byb3RvSgQIBhAHQiWqAiJVbml0eS5NTEFn",
+            "ZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.CapabilitiesReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLInitializationOutputProto.Parser, new[]{ "Name", "CommunicationVersion", "LogPath", "BrainParameters", "PackageVersion", "Capabilities" }, null, null, null)
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  /// <summary>
+  /// The request message containing the academy's parameters.
+  /// </summary>
+  internal sealed partial class UnityRLInitializationOutputProto : pb::IMessage<UnityRLInitializationOutputProto> {
+    private static readonly pb::MessageParser<UnityRLInitializationOutputProto> _parser = new pb::MessageParser<UnityRLInitializationOutputProto>(() => new UnityRLInitializationOutputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityRLInitializationOutputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityRlInitializationOutputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationOutputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationOutputProto(UnityRLInitializationOutputProto other) : this() {
+      name_ = other.name_;
+      communicationVersion_ = other.communicationVersion_;
+      logPath_ = other.logPath_;
+      brainParameters_ = other.brainParameters_.Clone();
+      packageVersion_ = other.packageVersion_;
+      Capabilities = other.capabilities_ != null ? other.Capabilities.Clone() : null;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInitializationOutputProto Clone() {
+      return new UnityRLInitializationOutputProto(this);
+    }
+
+    /// <summary>Field number for the "name" field.</summary>
+    public const int NameFieldNumber = 1;
+    private string name_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string Name {
+      get { return name_; }
+      set {
+        name_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "communication_version" field.</summary>
+    public const int CommunicationVersionFieldNumber = 2;
+    private string communicationVersion_ = "";
+    /// <summary>
+    /// Communication protocol version that the responding side (typically the C# environment) is using.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string CommunicationVersion {
+      get { return communicationVersion_; }
+      set {
+        communicationVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "log_path" field.</summary>
+    public const int LogPathFieldNumber = 3;
+    private string logPath_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string LogPath {
+      get { return logPath_; }
+      set {
+        logPath_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "brain_parameters" field.</summary>
+    public const int BrainParametersFieldNumber = 5;
+    private static readonly pb::FieldCodec<global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto> _repeated_brainParameters_codec
+        = pb::FieldCodec.ForMessage(42, global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto.Parser);
+    private readonly pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto> brainParameters_ = new pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto> BrainParameters {
+      get { return brainParameters_; }
+    }
+
+    /// <summary>Field number for the "package_version" field.</summary>
+    public const int PackageVersionFieldNumber = 7;
+    private string packageVersion_ = "";
+    /// <summary>
+    /// Package/library version that the responding side (typically the C# environment) is using.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string PackageVersion {
+      get { return packageVersion_; }
+      set {
+        packageVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    /// <summary>Field number for the "capabilities" field.</summary>
+    public const int CapabilitiesFieldNumber = 8;
+    private global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto capabilities_;
+    /// <summary>
+    /// The RL Capabilities of the C# package.
+    /// </summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto Capabilities {
+      get { return capabilities_; }
+      set {
+        capabilities_ = value;
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityRLInitializationOutputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityRLInitializationOutputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (Name != other.Name) return false;
+      if (CommunicationVersion != other.CommunicationVersion) return false;
+      if (LogPath != other.LogPath) return false;
+      if(!brainParameters_.Equals(other.brainParameters_)) return false;
+      if (PackageVersion != other.PackageVersion) return false;
+      if (!object.Equals(Capabilities, other.Capabilities)) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      if (Name.Length != 0) hash ^= Name.GetHashCode();
+      if (CommunicationVersion.Length != 0) hash ^= CommunicationVersion.GetHashCode();
+      if (LogPath.Length != 0) hash ^= LogPath.GetHashCode();
+      hash ^= brainParameters_.GetHashCode();
+      if (PackageVersion.Length != 0) hash ^= PackageVersion.GetHashCode();
+      if (capabilities_ != null) hash ^= Capabilities.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      if (Name.Length != 0) {
+        output.WriteRawTag(10);
+        output.WriteString(Name);
+      }
+      if (CommunicationVersion.Length != 0) {
+        output.WriteRawTag(18);
+        output.WriteString(CommunicationVersion);
+      }
+      if (LogPath.Length != 0) {
+        output.WriteRawTag(26);
+        output.WriteString(LogPath);
+      }
+      brainParameters_.WriteTo(output, _repeated_brainParameters_codec);
+      if (PackageVersion.Length != 0) {
+        output.WriteRawTag(58);
+        output.WriteString(PackageVersion);
+      }
+      if (capabilities_ != null) {
+        output.WriteRawTag(66);
+        output.WriteMessage(Capabilities);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      if (Name.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(Name);
+      }
+      if (CommunicationVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(CommunicationVersion);
+      }
+      if (LogPath.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(LogPath);
+      }
+      size += brainParameters_.CalculateSize(_repeated_brainParameters_codec);
+      if (PackageVersion.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(PackageVersion);
+      }
+      if (capabilities_ != null) {
+        size += 1 + pb::CodedOutputStream.ComputeMessageSize(Capabilities);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityRLInitializationOutputProto other) {
+      if (other == null) {
+        return;
+      }
+      if (other.Name.Length != 0) {
+        Name = other.Name;
+      }
+      if (other.CommunicationVersion.Length != 0) {
+        CommunicationVersion = other.CommunicationVersion;
+      }
+      if (other.LogPath.Length != 0) {
+        LogPath = other.LogPath;
+      }
+      brainParameters_.Add(other.brainParameters_);
+      if (other.PackageVersion.Length != 0) {
+        PackageVersion = other.PackageVersion;
+      }
+      if (other.capabilities_ != null) {
+        if (capabilities_ == null) {
+          capabilities_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto();
+        }
+        Capabilities.MergeFrom(other.Capabilities);
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            Name = input.ReadString();
+            break;
+          }
+          case 18: {
+            CommunicationVersion = input.ReadString();
+            break;
+          }
+          case 26: {
+            LogPath = input.ReadString();
+            break;
+          }
+          case 42: {
+            brainParameters_.AddEntriesFrom(input, _repeated_brainParameters_codec);
+            break;
+          }
+          case 58: {
+            PackageVersion = input.ReadString();
+            break;
+          }
+          case 66: {
+            if (capabilities_ == null) {
+              capabilities_ = new global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto();
+            }
+            input.ReadMessage(capabilities_);
+            break;
+          }
+        }
+      }
+    }
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bbc4dba7c4c3e8cd916932094ccd3e54aaf32f2d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e0bcb88495d5d48229140a2080dfd297
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e8553e8b8eae85d473390c3103dee1084cfd7fdb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs
@@ -0,0 +1,361 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_rl_input.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_input.proto</summary>
+  internal static partial class UnityRlInputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_input.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityRlInputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X2lucHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo1bWxhZ2VudHNf",
+            "ZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy9hZ2VudF9hY3Rpb24ucHJvdG8a",
+            "MG1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvY29tbWFuZC5w",
+            "cm90byL+AgoRVW5pdHlSTElucHV0UHJvdG8SUAoNYWdlbnRfYWN0aW9ucxgB",
+            "IAMoCzI5LmNvbW11bmljYXRvcl9vYmplY3RzLlVuaXR5UkxJbnB1dFByb3Rv",
+            "LkFnZW50QWN0aW9uc0VudHJ5EjMKB2NvbW1hbmQYBCABKA4yIi5jb21tdW5p",
+            "Y2F0b3Jfb2JqZWN0cy5Db21tYW5kUHJvdG8SFAoMc2lkZV9jaGFubmVsGAUg",
+            "ASgMGk0KFExpc3RBZ2VudEFjdGlvblByb3RvEjUKBXZhbHVlGAEgAygLMiYu",
+            "Y29tbXVuaWNhdG9yX29iamVjdHMuQWdlbnRBY3Rpb25Qcm90bxpxChFBZ2Vu",
+            "dEFjdGlvbnNFbnRyeRILCgNrZXkYASABKAkSSwoFdmFsdWUYAiABKAsyPC5j",
+            "b21tdW5pY2F0b3Jfb2JqZWN0cy5Vbml0eVJMSW5wdXRQcm90by5MaXN0QWdl",
+            "bnRBY3Rpb25Qcm90bzoCOAFKBAgCEANKBAgDEARCJaoCIlVuaXR5Lk1MQWdl",
+            "bnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.AgentActionReflection.Descriptor, global::Unity.MLAgents.CommunicatorObjects.CommandReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Parser, new[]{ "AgentActions", "Command", "SideChannel" }, null, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto.Parser, new[]{ "Value" }, null, null, null),
+            null, })
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class UnityRLInputProto : pb::IMessage<UnityRLInputProto> {
+    private static readonly pb::MessageParser<UnityRLInputProto> _parser = new pb::MessageParser<UnityRLInputProto>(() => new UnityRLInputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityRLInputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityRlInputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInputProto(UnityRLInputProto other) : this() {
+      agentActions_ = other.agentActions_.Clone();
+      command_ = other.command_;
+      sideChannel_ = other.sideChannel_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLInputProto Clone() {
+      return new UnityRLInputProto(this);
+    }
+
+    /// <summary>Field number for the "agent_actions" field.</summary>
+    public const int AgentActionsFieldNumber = 1;
+    private static readonly pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto>.Codec _map_agentActions_codec
+        = new pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto>.Codec(pb::FieldCodec.ForString(10), pb::FieldCodec.ForMessage(18, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto.Parser), 10);
+    private readonly pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto> agentActions_ = new pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Types.ListAgentActionProto> AgentActions {
+      get { return agentActions_; }
+    }
+
+    /// <summary>Field number for the "command" field.</summary>
+    public const int CommandFieldNumber = 4;
+    private global::Unity.MLAgents.CommunicatorObjects.CommandProto command_ = 0;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.CommandProto Command {
+      get { return command_; }
+      set {
+        command_ = value;
+      }
+    }
+
+    /// <summary>Field number for the "side_channel" field.</summary>
+    public const int SideChannelFieldNumber = 5;
+    private pb::ByteString sideChannel_ = pb::ByteString.Empty;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pb::ByteString SideChannel {
+      get { return sideChannel_; }
+      set {
+        sideChannel_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityRLInputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityRLInputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!AgentActions.Equals(other.AgentActions)) return false;
+      if (Command != other.Command) return false;
+      if (SideChannel != other.SideChannel) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      hash ^= AgentActions.GetHashCode();
+      if (Command != 0) hash ^= Command.GetHashCode();
+      if (SideChannel.Length != 0) hash ^= SideChannel.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      agentActions_.WriteTo(output, _map_agentActions_codec);
+      if (Command != 0) {
+        output.WriteRawTag(32);
+        output.WriteEnum((int) Command);
+      }
+      if (SideChannel.Length != 0) {
+        output.WriteRawTag(42);
+        output.WriteBytes(SideChannel);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      size += agentActions_.CalculateSize(_map_agentActions_codec);
+      if (Command != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) Command);
+      }
+      if (SideChannel.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeBytesSize(SideChannel);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityRLInputProto other) {
+      if (other == null) {
+        return;
+      }
+      agentActions_.Add(other.agentActions_);
+      if (other.Command != 0) {
+        Command = other.Command;
+      }
+      if (other.SideChannel.Length != 0) {
+        SideChannel = other.SideChannel;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 10: {
+            agentActions_.AddEntriesFrom(input, _map_agentActions_codec);
+            break;
+          }
+          case 32: {
+            command_ = (global::Unity.MLAgents.CommunicatorObjects.CommandProto) input.ReadEnum();
+            break;
+          }
+          case 42: {
+            SideChannel = input.ReadBytes();
+            break;
+          }
+        }
+      }
+    }
+
+    #region Nested types
+    /// <summary>Container for nested types declared in the UnityRLInputProto message type.</summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static partial class Types {
+      internal sealed partial class ListAgentActionProto : pb::IMessage<ListAgentActionProto> {
+        private static readonly pb::MessageParser<ListAgentActionProto> _parser = new pb::MessageParser<ListAgentActionProto>(() => new ListAgentActionProto());
+        private pb::UnknownFieldSet _unknownFields;
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pb::MessageParser<ListAgentActionProto> Parser { get { return _parser; } }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pbr::MessageDescriptor Descriptor {
+          get { return global::Unity.MLAgents.CommunicatorObjects.UnityRLInputProto.Descriptor.NestedTypes[0]; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        pbr::MessageDescriptor pb::IMessage.Descriptor {
+          get { return Descriptor; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentActionProto() {
+          OnConstruction();
+        }
+
+        partial void OnConstruction();
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentActionProto(ListAgentActionProto other) : this() {
+          value_ = other.value_.Clone();
+          _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentActionProto Clone() {
+          return new ListAgentActionProto(this);
+        }
+
+        /// <summary>Field number for the "value" field.</summary>
+        public const int ValueFieldNumber = 1;
+        private static readonly pb::FieldCodec<global::Unity.MLAgents.CommunicatorObjects.AgentActionProto> _repeated_value_codec
+            = pb::FieldCodec.ForMessage(10, global::Unity.MLAgents.CommunicatorObjects.AgentActionProto.Parser);
+        private readonly pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentActionProto> value_ = new pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentActionProto>();
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentActionProto> Value {
+          get { return value_; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override bool Equals(object other) {
+          return Equals(other as ListAgentActionProto);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public bool Equals(ListAgentActionProto other) {
+          if (ReferenceEquals(other, null)) {
+            return false;
+          }
+          if (ReferenceEquals(other, this)) {
+            return true;
+          }
+          if(!value_.Equals(other.value_)) return false;
+          return Equals(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override int GetHashCode() {
+          int hash = 1;
+          hash ^= value_.GetHashCode();
+          if (_unknownFields != null) {
+            hash ^= _unknownFields.GetHashCode();
+          }
+          return hash;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override string ToString() {
+          return pb::JsonFormatter.ToDiagnosticString(this);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void WriteTo(pb::CodedOutputStream output) {
+          value_.WriteTo(output, _repeated_value_codec);
+          if (_unknownFields != null) {
+            _unknownFields.WriteTo(output);
+          }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public int CalculateSize() {
+          int size = 0;
+          size += value_.CalculateSize(_repeated_value_codec);
+          if (_unknownFields != null) {
+            size += _unknownFields.CalculateSize();
+          }
+          return size;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(ListAgentActionProto other) {
+          if (other == null) {
+            return;
+          }
+          value_.Add(other.value_);
+          _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(pb::CodedInputStream input) {
+          uint tag;
+          while ((tag = input.ReadTag()) != 0) {
+            switch(tag) {
+              default:
+                _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+                break;
+              case 10: {
+                value_.AddEntriesFrom(input, _repeated_value_codec);
+                break;
+              }
+            }
+          }
+        }
+
+      }
+
+    }
+    #endregion
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c2e8eb03e9764681c217379dd821f6bb8d09a792
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlInput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: c9d247f0bc49d468da0f9f0cc6484d34
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0971d50299328c405536886760e85dd0962838e0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs
@@ -0,0 +1,331 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_rl_output.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_output.proto</summary>
+  internal static partial class UnityRlOutputReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_output.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityRlOutputReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjhtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X291dHB1dC5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMaM21sYWdlbnRz",
+            "X2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdlbnRfaW5mby5wcm90byK5",
+            "AgoSVW5pdHlSTE91dHB1dFByb3RvEkwKCmFnZW50SW5mb3MYAiADKAsyOC5j",
+            "b21tdW5pY2F0b3Jfb2JqZWN0cy5Vbml0eVJMT3V0cHV0UHJvdG8uQWdlbnRJ",
+            "bmZvc0VudHJ5EhQKDHNpZGVfY2hhbm5lbBgDIAEoDBpJChJMaXN0QWdlbnRJ",
+            "bmZvUHJvdG8SMwoFdmFsdWUYASADKAsyJC5jb21tdW5pY2F0b3Jfb2JqZWN0",
+            "cy5BZ2VudEluZm9Qcm90bxpuCg9BZ2VudEluZm9zRW50cnkSCwoDa2V5GAEg",
+            "ASgJEkoKBXZhbHVlGAIgASgLMjsuY29tbXVuaWNhdG9yX29iamVjdHMuVW5p",
+            "dHlSTE91dHB1dFByb3RvLkxpc3RBZ2VudEluZm9Qcm90bzoCOAFKBAgBEAJC",
+            "JaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3Rv",
+            "Mw=="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.AgentInfoReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Parser, new[]{ "AgentInfos", "SideChannel" }, null, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto.Parser, new[]{ "Value" }, null, null, null),
+            null, })
+          }));
+    }
+    #endregion
+
+  }
+  #region Messages
+  internal sealed partial class UnityRLOutputProto : pb::IMessage<UnityRLOutputProto> {
+    private static readonly pb::MessageParser<UnityRLOutputProto> _parser = new pb::MessageParser<UnityRLOutputProto>(() => new UnityRLOutputProto());
+    private pb::UnknownFieldSet _unknownFields;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pb::MessageParser<UnityRLOutputProto> Parser { get { return _parser; } }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static pbr::MessageDescriptor Descriptor {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityRlOutputReflection.Descriptor.MessageTypes[0]; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    pbr::MessageDescriptor pb::IMessage.Descriptor {
+      get { return Descriptor; }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLOutputProto() {
+      OnConstruction();
+    }
+
+    partial void OnConstruction();
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLOutputProto(UnityRLOutputProto other) : this() {
+      agentInfos_ = other.agentInfos_.Clone();
+      sideChannel_ = other.sideChannel_;
+      _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public UnityRLOutputProto Clone() {
+      return new UnityRLOutputProto(this);
+    }
+
+    /// <summary>Field number for the "agentInfos" field.</summary>
+    public const int AgentInfosFieldNumber = 2;
+    private static readonly pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto>.Codec _map_agentInfos_codec
+        = new pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto>.Codec(pb::FieldCodec.ForString(10), pb::FieldCodec.ForMessage(18, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto.Parser), 18);
+    private readonly pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto> agentInfos_ = new pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto>();
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pbc::MapField<string, global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Types.ListAgentInfoProto> AgentInfos {
+      get { return agentInfos_; }
+    }
+
+    /// <summary>Field number for the "side_channel" field.</summary>
+    public const int SideChannelFieldNumber = 3;
+    private pb::ByteString sideChannel_ = pb::ByteString.Empty;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public pb::ByteString SideChannel {
+      get { return sideChannel_; }
+      set {
+        sideChannel_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override bool Equals(object other) {
+      return Equals(other as UnityRLOutputProto);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public bool Equals(UnityRLOutputProto other) {
+      if (ReferenceEquals(other, null)) {
+        return false;
+      }
+      if (ReferenceEquals(other, this)) {
+        return true;
+      }
+      if (!AgentInfos.Equals(other.AgentInfos)) return false;
+      if (SideChannel != other.SideChannel) return false;
+      return Equals(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override int GetHashCode() {
+      int hash = 1;
+      hash ^= AgentInfos.GetHashCode();
+      if (SideChannel.Length != 0) hash ^= SideChannel.GetHashCode();
+      if (_unknownFields != null) {
+        hash ^= _unknownFields.GetHashCode();
+      }
+      return hash;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public override string ToString() {
+      return pb::JsonFormatter.ToDiagnosticString(this);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void WriteTo(pb::CodedOutputStream output) {
+      agentInfos_.WriteTo(output, _map_agentInfos_codec);
+      if (SideChannel.Length != 0) {
+        output.WriteRawTag(26);
+        output.WriteBytes(SideChannel);
+      }
+      if (_unknownFields != null) {
+        _unknownFields.WriteTo(output);
+      }
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public int CalculateSize() {
+      int size = 0;
+      size += agentInfos_.CalculateSize(_map_agentInfos_codec);
+      if (SideChannel.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeBytesSize(SideChannel);
+      }
+      if (_unknownFields != null) {
+        size += _unknownFields.CalculateSize();
+      }
+      return size;
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(UnityRLOutputProto other) {
+      if (other == null) {
+        return;
+      }
+      agentInfos_.Add(other.agentInfos_);
+      if (other.SideChannel.Length != 0) {
+        SideChannel = other.SideChannel;
+      }
+      _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+    }
+
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public void MergeFrom(pb::CodedInputStream input) {
+      uint tag;
+      while ((tag = input.ReadTag()) != 0) {
+        switch(tag) {
+          default:
+            _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+            break;
+          case 18: {
+            agentInfos_.AddEntriesFrom(input, _map_agentInfos_codec);
+            break;
+          }
+          case 26: {
+            SideChannel = input.ReadBytes();
+            break;
+          }
+        }
+      }
+    }
+
+    #region Nested types
+    /// <summary>Container for nested types declared in the UnityRLOutputProto message type.</summary>
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public static partial class Types {
+      internal sealed partial class ListAgentInfoProto : pb::IMessage<ListAgentInfoProto> {
+        private static readonly pb::MessageParser<ListAgentInfoProto> _parser = new pb::MessageParser<ListAgentInfoProto>(() => new ListAgentInfoProto());
+        private pb::UnknownFieldSet _unknownFields;
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pb::MessageParser<ListAgentInfoProto> Parser { get { return _parser; } }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public static pbr::MessageDescriptor Descriptor {
+          get { return global::Unity.MLAgents.CommunicatorObjects.UnityRLOutputProto.Descriptor.NestedTypes[0]; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        pbr::MessageDescriptor pb::IMessage.Descriptor {
+          get { return Descriptor; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentInfoProto() {
+          OnConstruction();
+        }
+
+        partial void OnConstruction();
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentInfoProto(ListAgentInfoProto other) : this() {
+          value_ = other.value_.Clone();
+          _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public ListAgentInfoProto Clone() {
+          return new ListAgentInfoProto(this);
+        }
+
+        /// <summary>Field number for the "value" field.</summary>
+        public const int ValueFieldNumber = 1;
+        private static readonly pb::FieldCodec<global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto> _repeated_value_codec
+            = pb::FieldCodec.ForMessage(10, global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto.Parser);
+        private readonly pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto> value_ = new pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto>();
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public pbc::RepeatedField<global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto> Value {
+          get { return value_; }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override bool Equals(object other) {
+          return Equals(other as ListAgentInfoProto);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public bool Equals(ListAgentInfoProto other) {
+          if (ReferenceEquals(other, null)) {
+            return false;
+          }
+          if (ReferenceEquals(other, this)) {
+            return true;
+          }
+          if(!value_.Equals(other.value_)) return false;
+          return Equals(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override int GetHashCode() {
+          int hash = 1;
+          hash ^= value_.GetHashCode();
+          if (_unknownFields != null) {
+            hash ^= _unknownFields.GetHashCode();
+          }
+          return hash;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public override string ToString() {
+          return pb::JsonFormatter.ToDiagnosticString(this);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void WriteTo(pb::CodedOutputStream output) {
+          value_.WriteTo(output, _repeated_value_codec);
+          if (_unknownFields != null) {
+            _unknownFields.WriteTo(output);
+          }
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public int CalculateSize() {
+          int size = 0;
+          size += value_.CalculateSize(_repeated_value_codec);
+          if (_unknownFields != null) {
+            size += _unknownFields.CalculateSize();
+          }
+          return size;
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(ListAgentInfoProto other) {
+          if (other == null) {
+            return;
+          }
+          value_.Add(other.value_);
+          _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
+        }
+
+        [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+        public void MergeFrom(pb::CodedInputStream input) {
+          uint tag;
+          while ((tag = input.ReadTag()) != 0) {
+            switch(tag) {
+              default:
+                _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
+                break;
+              case 10: {
+                value_.AddEntriesFrom(input, _repeated_value_codec);
+                break;
+              }
+            }
+          }
+        }
+
+      }
+
+    }
+    #endregion
+
+  }
+
+  #endregion
+
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d2607bf220c3d063d0ef2fe027bef4cd1171ba47
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityRlOutput.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7b039d6d52b5142a78431d1758f5bf53
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ddb47dea96b93a482dae01c0717295ef8387f6bb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs
@@ -0,0 +1,43 @@
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_to_external.proto
+// </auto-generated>
+#pragma warning disable 1591, 0612, 3021
+#region Designer generated code
+
+using pb = global::Google.Protobuf;
+using pbc = global::Google.Protobuf.Collections;
+using pbr = global::Google.Protobuf.Reflection;
+using scg = global::System.Collections.Generic;
+namespace Unity.MLAgents.CommunicatorObjects {
+
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_to_external.proto</summary>
+  public static partial class UnityToExternalReflection {
+
+    #region Descriptor
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_to_external.proto</summary>
+    public static pbr::FileDescriptor Descriptor {
+      get { return descriptor; }
+    }
+    private static pbr::FileDescriptor descriptor;
+
+    static UnityToExternalReflection() {
+      byte[] descriptorData = global::System.Convert.FromBase64String(
+          string.Concat(
+            "CjptbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Rv",
+            "X2V4dGVybmFsLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo2bWxhZ2Vu",
+            "dHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9tZXNzYWdlLnBy",
+            "b3RvMnYKFFVuaXR5VG9FeHRlcm5hbFByb3RvEl4KCEV4Y2hhbmdlEicuY29t",
+            "bXVuaWNhdG9yX29iamVjdHMuVW5pdHlNZXNzYWdlUHJvdG8aJy5jb21tdW5p",
+            "Y2F0b3Jfb2JqZWN0cy5Vbml0eU1lc3NhZ2VQcm90byIAQiWqAiJVbml0eS5N",
+            "TEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
+      descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
+          new pbr::FileDescriptor[] { global::Unity.MLAgents.CommunicatorObjects.UnityMessageReflection.Descriptor, },
+          new pbr::GeneratedClrTypeInfo(null, null));
+    }
+    #endregion
+
+  }
+}
+
+#endregion Designer generated code
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e8ad13fd02dc180375d98da3084a3149baf1595b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternal.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: c03819ddc4c30416ab6ecc83c9cee562
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs
new file mode 100644
index 0000000000000000000000000000000000000000..273c57cb1d1fab4c2db8cb84a70d8e420f7a233e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs
@@ -0,0 +1,135 @@
+#if UNITY_EDITOR || UNITY_STANDALONE
+#define MLA_SUPPORTED_TRAINING_PLATFORM
+#endif
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+// <auto-generated>
+//     Generated by the protocol buffer compiler.  DO NOT EDIT!
+//     source: mlagents_envs/communicator_objects/unity_to_external.proto
+// </auto-generated>
+#pragma warning disable 0414, 1591
+#region Designer generated code
+
+using grpc = global::Grpc.Core;
+
+namespace Unity.MLAgents.CommunicatorObjects {
+  internal static partial class UnityToExternalProto
+  {
+    static readonly string __ServiceName = "communicator_objects.UnityToExternalProto";
+
+    static readonly grpc::Marshaller<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto> __Marshaller_communicator_objects_UnityMessageProto = grpc::Marshallers.Create((arg) => global::Google.Protobuf.MessageExtensions.ToByteArray(arg), global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto.Parser.ParseFrom);
+
+    static readonly grpc::Method<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto, global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto> __Method_Exchange = new grpc::Method<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto, global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto>(
+        grpc::MethodType.Unary,
+        __ServiceName,
+        "Exchange",
+        __Marshaller_communicator_objects_UnityMessageProto,
+        __Marshaller_communicator_objects_UnityMessageProto);
+
+    /// <summary>Service descriptor</summary>
+    public static global::Google.Protobuf.Reflection.ServiceDescriptor Descriptor
+    {
+      get { return global::Unity.MLAgents.CommunicatorObjects.UnityToExternalReflection.Descriptor.Services[0]; }
+    }
+
+    /// <summary>Base class for server-side implementations of UnityToExternalProto</summary>
+    public abstract partial class UnityToExternalProtoBase
+    {
+      /// <summary>
+      /// Sends the academy parameters
+      /// </summary>
+      /// <param name="request">The request received from the client.</param>
+      /// <param name="context">The context of the server-side call handler being invoked.</param>
+      /// <returns>The response to send back to the client (wrapped by a task).</returns>
+      public virtual global::System.Threading.Tasks.Task<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto> Exchange(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto request, grpc::ServerCallContext context)
+      {
+        throw new grpc::RpcException(new grpc::Status(grpc::StatusCode.Unimplemented, ""));
+      }
+
+    }
+
+    /// <summary>Client for UnityToExternalProto</summary>
+    public partial class UnityToExternalProtoClient : grpc::ClientBase<UnityToExternalProtoClient>
+    {
+      /// <summary>Creates a new client for UnityToExternalProto</summary>
+      /// <param name="channel">The channel to use to make remote calls.</param>
+      public UnityToExternalProtoClient(grpc::Channel channel) : base(channel)
+      {
+      }
+      /// <summary>Creates a new client for UnityToExternalProto that uses a custom <c>CallInvoker</c>.</summary>
+      /// <param name="callInvoker">The callInvoker to use to make remote calls.</param>
+      public UnityToExternalProtoClient(grpc::CallInvoker callInvoker) : base(callInvoker)
+      {
+      }
+      /// <summary>Protected parameterless constructor to allow creation of test doubles.</summary>
+      protected UnityToExternalProtoClient() : base()
+      {
+      }
+      /// <summary>Protected constructor to allow creation of configured clients.</summary>
+      /// <param name="configuration">The client configuration.</param>
+      protected UnityToExternalProtoClient(ClientBaseConfiguration configuration) : base(configuration)
+      {
+      }
+
+      /// <summary>
+      /// Sends the academy parameters
+      /// </summary>
+      /// <param name="request">The request to send to the server.</param>
+      /// <param name="headers">The initial metadata to send with the call. This parameter is optional.</param>
+      /// <param name="deadline">An optional deadline for the call. The call will be cancelled if deadline is hit.</param>
+      /// <param name="cancellationToken">An optional token for canceling the call.</param>
+      /// <returns>The response received from the server.</returns>
+      public virtual global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto Exchange(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto request, grpc::Metadata headers = null, global::System.DateTime? deadline = null, global::System.Threading.CancellationToken cancellationToken = default(global::System.Threading.CancellationToken))
+      {
+        return Exchange(request, new grpc::CallOptions(headers, deadline, cancellationToken));
+      }
+      /// <summary>
+      /// Sends the academy parameters
+      /// </summary>
+      /// <param name="request">The request to send to the server.</param>
+      /// <param name="options">The options for the call.</param>
+      /// <returns>The response received from the server.</returns>
+      public virtual global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto Exchange(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto request, grpc::CallOptions options)
+      {
+        return CallInvoker.BlockingUnaryCall(__Method_Exchange, null, options, request);
+      }
+      /// <summary>
+      /// Sends the academy parameters
+      /// </summary>
+      /// <param name="request">The request to send to the server.</param>
+      /// <param name="headers">The initial metadata to send with the call. This parameter is optional.</param>
+      /// <param name="deadline">An optional deadline for the call. The call will be cancelled if deadline is hit.</param>
+      /// <param name="cancellationToken">An optional token for canceling the call.</param>
+      /// <returns>The call object.</returns>
+      public virtual grpc::AsyncUnaryCall<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto> ExchangeAsync(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto request, grpc::Metadata headers = null, global::System.DateTime? deadline = null, global::System.Threading.CancellationToken cancellationToken = default(global::System.Threading.CancellationToken))
+      {
+        return ExchangeAsync(request, new grpc::CallOptions(headers, deadline, cancellationToken));
+      }
+      /// <summary>
+      /// Sends the academy parameters
+      /// </summary>
+      /// <param name="request">The request to send to the server.</param>
+      /// <param name="options">The options for the call.</param>
+      /// <returns>The call object.</returns>
+      public virtual grpc::AsyncUnaryCall<global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto> ExchangeAsync(global::Unity.MLAgents.CommunicatorObjects.UnityMessageProto request, grpc::CallOptions options)
+      {
+        return CallInvoker.AsyncUnaryCall(__Method_Exchange, null, options, request);
+      }
+      /// <summary>Creates a new instance of client from given <c>ClientBaseConfiguration</c>.</summary>
+      protected override UnityToExternalProtoClient NewInstance(ClientBaseConfiguration configuration)
+      {
+        return new UnityToExternalProtoClient(configuration);
+      }
+    }
+
+    /// <summary>Creates service definition that can be registered with a server</summary>
+    /// <param name="serviceImpl">An object implementing the server-side handling logic.</param>
+    public static grpc::ServerServiceDefinition BindService(UnityToExternalProtoBase serviceImpl)
+    {
+      return grpc::ServerServiceDefinition.CreateBuilder()
+          .AddMethod(__Method_Exchange, serviceImpl.Exchange).Build();
+    }
+
+  }
+}
+#endregion
+#endif
diff --git a/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs.meta b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..620a3f1d448cbe7d7d04dfd35e0aa5d1d19e4565
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 6c0f560328e7343499ad203c75c11741
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef b/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..16f49b5dc63eb4fa1f5821c67891bb11e78de0d6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef
@@ -0,0 +1,18 @@
+{
+    "name": "Unity.ML-Agents.CommunicatorObjects",
+    "rootNamespace": "",
+    "references": [],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": false,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "Google.Protobuf.dll",
+        "Grpc.Core.dll"
+    ],
+    "autoReferenced": true,
+    "defineConstraints": [],
+    "versionDefines": [],
+    "noEngineReferences": false
+}
diff --git a/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef.meta b/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1bf58ee5ea5ce2f3833ea6b2465083633450b567
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Grpc/Unity.ML-Agents.CommunicatorObjects.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
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diff --git a/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs b/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0dcdd0a5afeba794feb5c37c144a68a0b0168f4f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs
@@ -0,0 +1,28 @@
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// MultiAgentGroup interface for grouping agents to support multi-agent training.
+    /// </summary>
+    public interface IMultiAgentGroup
+    {
+        /// <summary>
+        /// Get the ID of MultiAgentGroup.
+        /// </summary>
+        /// <returns>
+        /// MultiAgentGroup ID.
+        /// </returns>
+        int GetId();
+
+        /// <summary>
+        /// Register agent to the MultiAgentGroup.
+        /// </summary>
+        /// <param name="agent">The Agent to register.</param>
+        void RegisterAgent(Agent agent);
+
+        /// <summary>
+        /// Unregister agent from the MultiAgentGroup.
+        /// </summary>
+        /// <param name="agent">The Agent to unregister.</param>
+        void UnregisterAgent(Agent agent);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs.meta b/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b9171ab0408bbbb6e18ebf5c360caba9dccff25e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/IMultiAgentGroup.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3744ac27d956e43e1a39c7ba2550ab82
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference.meta b/com.unity.ml-agents/Runtime/Inference.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cb7450c10e8be7c090ee9ff1db0f0b7092a34067
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: ccb5b186c34bc48d8bd81e9d9bd5cd95
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs b/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e80ccd9fdd7729d8269017477cb9b91d4e68efc5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs
@@ -0,0 +1,207 @@
+using System.Collections.Generic;
+using System.Linq;
+using Unity.MLAgents.Inference.Utils;
+using Unity.MLAgents.Actuators;
+using Unity.Barracuda;
+using UnityEngine;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// The Applier for the Continuous Action output tensor. Tensor is assumed to contain the
+    /// continuous action data of the agents in the batch.
+    /// </summary>
+    internal class ContinuousActionOutputApplier : TensorApplier.IApplier
+    {
+        readonly ActionSpec m_ActionSpec;
+
+        public ContinuousActionOutputApplier(ActionSpec actionSpec)
+        {
+            m_ActionSpec = actionSpec;
+        }
+
+        public void Apply(TensorProxy tensorProxy, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
+        {
+            var actionSize = tensorProxy.shape[tensorProxy.shape.Length - 1];
+            var agentIndex = 0;
+            for (var i = 0; i < actionIds.Count; i++)
+            {
+                var agentId = actionIds[i];
+                if (lastActions.ContainsKey(agentId))
+                {
+                    var actionBuffer = lastActions[agentId];
+                    if (actionBuffer.IsEmpty())
+                    {
+                        actionBuffer = new ActionBuffers(m_ActionSpec);
+                        lastActions[agentId] = actionBuffer;
+                    }
+                    var continuousBuffer = actionBuffer.ContinuousActions;
+                    for (var j = 0; j < actionSize; j++)
+                    {
+                        continuousBuffer[j] = tensorProxy.data[agentIndex, j];
+                    }
+                }
+                agentIndex++;
+            }
+        }
+    }
+
+    /// <summary>
+    /// The Applier for the Discrete Action output tensor.
+    /// </summary>
+    internal class DiscreteActionOutputApplier : TensorApplier.IApplier
+    {
+        readonly ActionSpec m_ActionSpec;
+
+
+        public DiscreteActionOutputApplier(ActionSpec actionSpec, int seed, ITensorAllocator allocator)
+        {
+            m_ActionSpec = actionSpec;
+        }
+
+        public void Apply(TensorProxy tensorProxy, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
+        {
+            var agentIndex = 0;
+            var actionSize = tensorProxy.shape[tensorProxy.shape.Length - 1];
+            for (var i = 0; i < actionIds.Count; i++)
+            {
+                var agentId = actionIds[i];
+                if (lastActions.ContainsKey(agentId))
+                {
+                    var actionBuffer = lastActions[agentId];
+                    if (actionBuffer.IsEmpty())
+                    {
+                        actionBuffer = new ActionBuffers(m_ActionSpec);
+                        lastActions[agentId] = actionBuffer;
+                    }
+                    var discreteBuffer = actionBuffer.DiscreteActions;
+                    for (var j = 0; j < actionSize; j++)
+                    {
+                        discreteBuffer[j] = (int)tensorProxy.data[agentIndex, j];
+                    }
+                }
+                agentIndex++;
+            }
+        }
+    }
+
+
+    /// <summary>
+    /// The Applier for the Discrete Action output tensor. Uses multinomial to sample discrete
+    /// actions from the logits contained in the tensor.
+    /// </summary>
+    internal class LegacyDiscreteActionOutputApplier : TensorApplier.IApplier
+    {
+        readonly int[] m_ActionSize;
+        readonly Multinomial m_Multinomial;
+        readonly ActionSpec m_ActionSpec;
+        readonly int[] m_StartActionIndices;
+        readonly float[] m_CdfBuffer;
+
+
+        public LegacyDiscreteActionOutputApplier(ActionSpec actionSpec, int seed, ITensorAllocator allocator)
+        {
+            m_ActionSize = actionSpec.BranchSizes;
+            m_Multinomial = new Multinomial(seed);
+            m_ActionSpec = actionSpec;
+            m_StartActionIndices = Utilities.CumSum(m_ActionSize);
+
+            // Scratch space for computing the cumulative distribution function.
+            // In order to reuse it, make it the size of the largest branch.
+            var largestBranch = Mathf.Max(m_ActionSize);
+            m_CdfBuffer = new float[largestBranch];
+        }
+
+        public void Apply(TensorProxy tensorProxy, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
+        {
+            var agentIndex = 0;
+            for (var i = 0; i < actionIds.Count; i++)
+            {
+                var agentId = actionIds[i];
+                if (lastActions.ContainsKey(agentId))
+                {
+                    var actionBuffer = lastActions[agentId];
+                    if (actionBuffer.IsEmpty())
+                    {
+                        actionBuffer = new ActionBuffers(m_ActionSpec);
+                        lastActions[agentId] = actionBuffer;
+                    }
+                    var discreteBuffer = actionBuffer.DiscreteActions;
+                    for (var j = 0; j < m_ActionSize.Length; j++)
+                    {
+                        ComputeCdf(tensorProxy, agentIndex, m_StartActionIndices[j], m_ActionSize[j]);
+                        discreteBuffer[j] = m_Multinomial.Sample(m_CdfBuffer, m_ActionSize[j]);
+                    }
+                }
+                agentIndex++;
+            }
+        }
+
+        /// <summary>
+        /// Compute the cumulative distribution function for a given agent's action
+        /// given the log-probabilities.
+        /// The results are stored in m_CdfBuffer, which is the size of the largest action's number of branches.
+        /// </summary>
+        /// <param name="logProbs"></param>
+        /// <param name="batch">Index of the agent being considered</param>
+        /// <param name="channelOffset">Offset into the tensor's channel.</param>
+        /// <param name="branchSize"></param>
+        internal void ComputeCdf(TensorProxy logProbs, int batch, int channelOffset, int branchSize)
+        {
+            // Find the class maximum
+            var maxProb = float.NegativeInfinity;
+            for (var cls = 0; cls < branchSize; ++cls)
+            {
+                maxProb = Mathf.Max(logProbs.data[batch, cls + channelOffset], maxProb);
+            }
+
+            // Sum the log probabilities and compute CDF
+            var sumProb = 0.0f;
+            for (var cls = 0; cls < branchSize; ++cls)
+            {
+                sumProb += Mathf.Exp(logProbs.data[batch, cls + channelOffset] - maxProb);
+                m_CdfBuffer[cls] = sumProb;
+            }
+        }
+    }
+
+    /// <summary>
+    /// The Applier for the Memory output tensor. Tensor is assumed to contain the new
+    /// memory data of the agents in the batch.
+    /// </summary>
+    internal class MemoryOutputApplier : TensorApplier.IApplier
+    {
+        Dictionary<int, List<float>> m_Memories;
+
+        public MemoryOutputApplier(
+            Dictionary<int, List<float>> memories)
+        {
+            m_Memories = memories;
+        }
+
+        public void Apply(TensorProxy tensorProxy, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
+        {
+            var agentIndex = 0;
+            var memorySize = tensorProxy.data.width;
+            for (var i = 0; i < actionIds.Count; i++)
+            {
+                var agentId = actionIds[i];
+                List<float> memory;
+                if (!m_Memories.TryGetValue(agentId, out memory)
+                    || memory.Count < memorySize)
+                {
+                    memory = new List<float>();
+                    memory.AddRange(Enumerable.Repeat(0f, memorySize));
+                }
+
+                for (var j = 0; j < memorySize; j++)
+                {
+                    memory[j] = tensorProxy.data[agentIndex, 0, j, 0];
+                }
+
+                m_Memories[agentId] = memory;
+                agentIndex++;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs.meta b/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..251ad8ac4a76cdf85a94acc7fa04c18c1c863721
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 99d5dc2d52e442d1a1f466a246cfb28d
+timeCreated: 1539118675
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs b/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0bc1eba2b6163bd2eb72b47cbcd83a6752379edd
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs
@@ -0,0 +1,443 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Unity.Barracuda;
+using FailedCheck = Unity.MLAgents.Inference.BarracudaModelParamLoader.FailedCheck;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Barracuda Model extension methods.
+    /// </summary>
+    internal static class BarracudaModelExtensions
+    {
+        /// <summary>
+        /// Get array of the input tensor names of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>Array of the input tensor names of the model</returns>
+        public static string[] GetInputNames(this Model model)
+        {
+            var names = new List<string>();
+
+            if (model == null)
+                return names.ToArray();
+
+            foreach (var input in model.inputs)
+            {
+                names.Add(input.name);
+            }
+
+            foreach (var mem in model.memories)
+            {
+                names.Add(mem.input);
+            }
+
+            names.Sort(StringComparer.InvariantCulture);
+
+            return names.ToArray();
+        }
+
+        /// <summary>
+        /// Get the version of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>The api version of the model</returns>
+        public static int GetVersion(this Model model)
+        {
+            return (int)model.GetTensorByName(TensorNames.VersionNumber)[0];
+        }
+
+        /// <summary>
+        /// Generates the Tensor inputs that are expected to be present in the Model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>TensorProxy IEnumerable with the expected Tensor inputs.</returns>
+        public static IReadOnlyList<TensorProxy> GetInputTensors(this Model model)
+        {
+            var tensors = new List<TensorProxy>();
+
+            if (model == null)
+                return tensors;
+
+            foreach (var input in model.inputs)
+            {
+                tensors.Add(new TensorProxy
+                {
+                    name = input.name,
+                    valueType = TensorProxy.TensorType.FloatingPoint,
+                    data = null,
+                    shape = input.shape.Select(i => (long)i).ToArray()
+                });
+            }
+
+            tensors.Sort((el1, el2) => string.Compare(el1.name, el2.name, StringComparison.InvariantCulture));
+
+            return tensors;
+        }
+
+        /// <summary>
+        /// Get number of visual observation inputs to the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>Number of visual observation inputs to the model</returns>
+        public static int GetNumVisualInputs(this Model model)
+        {
+            var count = 0;
+            if (model == null)
+                return count;
+
+            foreach (var input in model.inputs)
+            {
+                if (input.name.StartsWith(TensorNames.VisualObservationPlaceholderPrefix))
+                {
+                    count++;
+                }
+            }
+
+            return count;
+        }
+
+        /// <summary>
+        /// Get array of the output tensor names of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>Array of the output tensor names of the model</returns>
+        public static string[] GetOutputNames(this Model model, bool deterministicInference = false)
+        {
+            var names = new List<string>();
+
+            if (model == null)
+            {
+                return names.ToArray();
+            }
+
+            if (model.HasContinuousOutputs(deterministicInference))
+            {
+                names.Add(model.ContinuousOutputName(deterministicInference));
+            }
+            if (model.HasDiscreteOutputs(deterministicInference))
+            {
+                names.Add(model.DiscreteOutputName(deterministicInference));
+            }
+
+            var modelVersion = model.GetVersion();
+            var memory = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
+            if (memory > 0)
+            {
+                names.Add(TensorNames.RecurrentOutput);
+            }
+
+            names.Sort(StringComparer.InvariantCulture);
+
+            return names.ToArray();
+        }
+
+        /// <summary>
+        /// Check if the model has continuous action outputs.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>True if the model has continuous action outputs.</returns>
+        public static bool HasContinuousOutputs(this Model model, bool deterministicInference = false)
+        {
+            if (model == null)
+                return false;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0;
+            }
+            else
+            {
+                bool hasStochasticOutput = !deterministicInference &&
+                    model.outputs.Contains(TensorNames.ContinuousActionOutput);
+                bool hasDeterministicOutput = deterministicInference &&
+                    model.outputs.Contains(TensorNames.DeterministicContinuousActionOutput);
+
+                return (hasStochasticOutput || hasDeterministicOutput) &&
+                    (int)model.GetTensorByName(TensorNames.ContinuousActionOutputShape)[0] > 0;
+            }
+        }
+
+        /// <summary>
+        /// Continuous action output size of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>Size of continuous action output.</returns>
+        public static int ContinuousOutputSize(this Model model)
+        {
+            if (model == null)
+                return 0;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0 ?
+                    (int)model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated)[0] : 0;
+            }
+            else
+            {
+                var continuousOutputShape = model.GetTensorByName(TensorNames.ContinuousActionOutputShape);
+                return continuousOutputShape == null ? 0 : (int)continuousOutputShape[0];
+            }
+        }
+
+        /// <summary>
+        /// Continuous action output tensor name of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>Tensor name of continuous action output.</returns>
+        public static string ContinuousOutputName(this Model model, bool deterministicInference = false)
+        {
+            if (model == null)
+                return null;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return TensorNames.ActionOutputDeprecated;
+            }
+            else
+            {
+                return deterministicInference ? TensorNames.DeterministicContinuousActionOutput : TensorNames.ContinuousActionOutput;
+            }
+        }
+
+        /// <summary>
+        /// Check if the model has discrete action outputs.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>True if the model has discrete action outputs.</returns>
+        public static bool HasDiscreteOutputs(this Model model, bool deterministicInference = false)
+        {
+            if (model == null)
+                return false;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] == 0;
+            }
+            else
+            {
+                bool hasStochasticOutput = !deterministicInference &&
+                    model.outputs.Contains(TensorNames.DiscreteActionOutput);
+                bool hasDeterministicOutput = deterministicInference &&
+                    model.outputs.Contains(TensorNames.DeterministicDiscreteActionOutput);
+                return (hasStochasticOutput || hasDeterministicOutput) &&
+                    model.DiscreteOutputSize() > 0;
+            }
+        }
+
+        /// <summary>
+        /// Discrete action output size of the model. This is equal to the sum of the branch sizes.
+        /// This method gets the tensor representing the list of branch size and returns the
+        /// sum of all the elements in the Tensor.
+        ///  - In version 1.X this tensor contains a single number, the sum of all branch
+        /// size values.
+        ///  - In version 2.X this tensor contains a 1D Tensor with each element corresponding
+        /// to a branch size.
+        /// Since this method does the sum of all elements in the tensor, the output
+        /// will be the same on both 1.X and 2.X.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>Size of discrete action output.</returns>
+        public static int DiscreteOutputSize(this Model model)
+        {
+            if (model == null)
+                return 0;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0 ?
+                    0 : (int)model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated)[0];
+            }
+            else
+            {
+                var discreteOutputShape = model.GetTensorByName(TensorNames.DiscreteActionOutputShape);
+                if (discreteOutputShape == null)
+                {
+                    return 0;
+                }
+                else
+                {
+                    int result = 0;
+                    for (int i = 0; i < discreteOutputShape.length; i++)
+                    {
+                        result += (int)discreteOutputShape[i];
+                    }
+                    return result;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Discrete action output tensor name of the model.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>Tensor name of discrete action output.</returns>
+        public static string DiscreteOutputName(this Model model, bool deterministicInference = false)
+        {
+            if (model == null)
+                return null;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                return TensorNames.ActionOutputDeprecated;
+            }
+            else
+            {
+                return deterministicInference ? TensorNames.DeterministicDiscreteActionOutput : TensorNames.DiscreteActionOutput;
+            }
+        }
+
+        /// <summary>
+        /// Check if the model supports both continuous and discrete actions.
+        /// If not, the model should be handled differently and use the deprecated fields.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <returns>True if the model supports both continuous and discrete actions.</returns>
+        public static bool SupportsContinuousAndDiscrete(this Model model)
+        {
+            return model == null ||
+                model.outputs.Contains(TensorNames.ContinuousActionOutput) ||
+                model.outputs.Contains(TensorNames.DiscreteActionOutput);
+        }
+
+        /// <summary>
+        /// Check if the model contains all the expected input/output tensors.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters.
+        /// </param>
+        /// <param name="failedModelChecks">Output list of failure messages</param>
+        ///<param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>True if the model contains all the expected tensors.</returns>
+        /// TODO: add checks for deterministic actions
+        public static bool CheckExpectedTensors(this Model model, List<FailedCheck> failedModelChecks, bool deterministicInference = false)
+        {
+            // Check the presence of model version
+            var modelApiVersionTensor = model.GetTensorByName(TensorNames.VersionNumber);
+            if (modelApiVersionTensor == null)
+            {
+                failedModelChecks.Add(
+                    FailedCheck.Warning($"Required constant \"{TensorNames.VersionNumber}\" was not found in the model file.")
+                );
+                return false;
+            }
+
+            // Check the presence of memory size
+            var memorySizeTensor = model.GetTensorByName(TensorNames.MemorySize);
+            if (memorySizeTensor == null)
+            {
+                failedModelChecks.Add(
+                    FailedCheck.Warning($"Required constant \"{TensorNames.MemorySize}\" was not found in the model file.")
+                );
+                return false;
+            }
+
+            // Check the presence of action output tensor
+            if (!model.outputs.Contains(TensorNames.ActionOutputDeprecated) &&
+                !model.outputs.Contains(TensorNames.ContinuousActionOutput) &&
+                !model.outputs.Contains(TensorNames.DiscreteActionOutput) &&
+                !model.outputs.Contains(TensorNames.DeterministicContinuousActionOutput) &&
+                !model.outputs.Contains(TensorNames.DeterministicDiscreteActionOutput))
+            {
+                failedModelChecks.Add(
+                    FailedCheck.Warning("The model does not contain any Action Output Node.")
+                );
+                return false;
+            }
+
+            // Check the presence of action output shape tensor
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                if (model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated) == null)
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain any Action Output Shape Node.")
+                    );
+                    return false;
+                }
+                if (model.GetTensorByName(TensorNames.IsContinuousControlDeprecated) == null)
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning($"Required constant \"{TensorNames.IsContinuousControlDeprecated}\" was " +
+                            "not found in the model file. " +
+                            "This is only required for model that uses a deprecated model format.")
+                    );
+                    return false;
+                }
+            }
+            else
+            {
+                if (model.outputs.Contains(TensorNames.ContinuousActionOutput))
+                {
+                    if (model.GetTensorByName(TensorNames.ContinuousActionOutputShape) == null)
+                    {
+                        failedModelChecks.Add(
+                            FailedCheck.Warning("The model uses continuous action but does not contain Continuous Action Output Shape Node.")
+                        );
+                        return false;
+                    }
+                    else if (!model.HasContinuousOutputs(deterministicInference))
+                    {
+                        var actionType = deterministicInference ? "deterministic" : "stochastic";
+                        var actionName = deterministicInference ? "Deterministic" : "";
+                        failedModelChecks.Add(
+                            FailedCheck.Warning($"The model uses {actionType} inference but does not contain {actionName} Continuous Action Output Tensor. Uncheck `Deterministic inference` flag..")
+                        );
+                        return false;
+                    }
+                }
+
+                if (model.outputs.Contains(TensorNames.DiscreteActionOutput))
+                {
+                    if (model.GetTensorByName(TensorNames.DiscreteActionOutputShape) == null)
+                    {
+                        failedModelChecks.Add(
+                            FailedCheck.Warning("The model uses discrete action but does not contain Discrete Action Output Shape Node.")
+                        );
+                        return false;
+                    }
+                    else if (!model.HasDiscreteOutputs(deterministicInference))
+                    {
+                        var actionType = deterministicInference ? "deterministic" : "stochastic";
+                        var actionName = deterministicInference ? "Deterministic" : "";
+                        failedModelChecks.Add(
+                            FailedCheck.Warning($"The model uses {actionType} inference but does not contain {actionName} Discrete Action Output Tensor. Uncheck `Deterministic inference` flag.")
+                        );
+                        return false;
+                    }
+                }
+            }
+            return true;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs.meta b/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d43474fb469e071d23fdbf940fbac3023fe9b740
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 1193c3bef93464baca0d8ba2d6ce1754
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs b/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2d04c04e696e2f407850389d10c7a849ea0476dc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs
@@ -0,0 +1,904 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Prepares the Tensors for the Learning Brain and exposes a list of failed checks if Model
+    /// and BrainParameters are incompatible.
+    /// </summary>
+    internal class BarracudaModelParamLoader
+    {
+        internal enum ModelApiVersion
+        {
+            /// <summary>
+            /// ML-Agents model version for versions 1.x.y
+            /// The observations are split between vector and visual observations
+            /// There are legacy action outputs for discrete and continuous actions
+            /// LSTM inputs and outputs are handled by Barracuda
+            /// </summary>
+            MLAgents1_0 = 2,
+
+            /// <summary>
+            /// All observations are treated the same and named obs_{i} with i being
+            /// the sensor index
+            /// Legacy "action" output is no longer present
+            /// LSTM inputs and outputs are treated like regular inputs and outputs
+            /// and no longer managed by Barracuda
+            /// </summary>
+            MLAgents2_0 = 3,
+            MinSupportedVersion = MLAgents1_0,
+            MaxSupportedVersion = MLAgents2_0
+        }
+
+        internal class FailedCheck
+        {
+            public enum CheckTypeEnum
+            {
+                Info = 0,
+                Warning = 1,
+                Error = 2
+            }
+            public CheckTypeEnum CheckType;
+            public string Message;
+            public static FailedCheck Info(string message)
+            {
+                return new FailedCheck { CheckType = CheckTypeEnum.Info, Message = message };
+            }
+
+            public static FailedCheck Warning(string message)
+            {
+                return new FailedCheck { CheckType = CheckTypeEnum.Warning, Message = message };
+            }
+
+            public static FailedCheck Error(string message)
+            {
+                return new FailedCheck { CheckType = CheckTypeEnum.Error, Message = message };
+            }
+        }
+
+        /// <summary>
+        /// Checks that a model has the appropriate version.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <returns>A FailedCheck containing the error message if the version of the model does not mach, else null</returns>
+        public static FailedCheck CheckModelVersion(Model model)
+        {
+            var modelApiVersion = model.GetVersion();
+            if (modelApiVersion < (int)ModelApiVersion.MinSupportedVersion)
+            {
+                return FailedCheck.Error(
+                    "Model was trained with a older version of the trainer than is supported. " +
+                    "Either retrain with an newer trainer, or use an older version of com.unity.ml-agents.\n" +
+                    $"Model version: {modelApiVersion} Minimum supported version: {(int)ModelApiVersion.MinSupportedVersion}"
+                );
+            }
+
+            if (modelApiVersion > (int)ModelApiVersion.MaxSupportedVersion)
+            {
+                return FailedCheck.Error(
+                    "Model was trained with a newer version of the trainer than is supported. " +
+                    "Either retrain with an older trainer, or update to a newer version of com.unity.ml-agents.\n" +
+                    $"Model version: {modelApiVersion}  Maximum supported version: {(int)ModelApiVersion.MaxSupportedVersion}"
+                );
+            }
+
+            var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
+
+            if (modelApiVersion == (int)ModelApiVersion.MLAgents1_0 && memorySize > 0)
+            {
+                // This block is to make sure that models that are trained with MLAgents version 1.x and have
+                // an LSTM (i.e. use the barracuda _c and _h inputs and outputs) will not work with MLAgents version
+                // 2.x. This is because Barracuda version 2.x will eventually drop support for the _c and _h inputs
+                // and only ML-Agents 2.x models will be compatible.
+                return FailedCheck.Error(
+                    "Models from com.unity.ml-agents 1.x that use recurrent neural networks are not supported in newer versions. " +
+                    "Either retrain with an newer trainer, or use an older version of com.unity.ml-agents.\n"
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Factory for the ModelParamLoader : Creates a ModelParamLoader and runs the checks
+        /// on it.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="sensors">Attached sensor components</param>
+        /// <param name="actuatorComponents">Attached actuator components</param>
+        /// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
+        /// <param name="behaviorType">BehaviorType or the Agent to check.</param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>A IEnumerable of the checks that failed</returns>
+        public static IEnumerable<FailedCheck> CheckModel(
+            Model model,
+            BrainParameters brainParameters,
+            ISensor[] sensors,
+            ActuatorComponent[] actuatorComponents,
+            int observableAttributeTotalSize = 0,
+            BehaviorType behaviorType = BehaviorType.Default,
+            bool deterministicInference = false
+        )
+        {
+            List<FailedCheck> failedModelChecks = new List<FailedCheck>();
+            if (model == null)
+            {
+                var errorMsg = "There is no model for this Brain; cannot run inference. ";
+                if (behaviorType == BehaviorType.InferenceOnly)
+                {
+                    errorMsg += "Either assign a model, or change to a different Behavior Type.";
+                }
+                else
+                {
+                    errorMsg += "(But can still train)";
+                }
+                failedModelChecks.Add(FailedCheck.Info(errorMsg));
+                return failedModelChecks;
+            }
+
+            var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks, deterministicInference);
+            if (!hasExpectedTensors)
+            {
+                return failedModelChecks;
+            }
+
+            var modelApiVersion = model.GetVersion();
+            var versionCheck = CheckModelVersion(model);
+            if (versionCheck != null)
+            {
+                failedModelChecks.Add(versionCheck);
+            }
+
+            var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
+            if (memorySize == -1)
+            {
+                failedModelChecks.Add(FailedCheck.Warning($"Missing node in the model provided : {TensorNames.MemorySize}"
+                ));
+                return failedModelChecks;
+            }
+
+            if (modelApiVersion == (int)ModelApiVersion.MLAgents1_0)
+            {
+                failedModelChecks.AddRange(
+                    CheckInputTensorPresenceLegacy(model, brainParameters, memorySize, sensors)
+                );
+                failedModelChecks.AddRange(
+                    CheckInputTensorShapeLegacy(model, brainParameters, sensors, observableAttributeTotalSize)
+                );
+            }
+            else if (modelApiVersion == (int)ModelApiVersion.MLAgents2_0)
+            {
+                failedModelChecks.AddRange(
+                    CheckInputTensorPresence(model, brainParameters, memorySize, sensors, deterministicInference)
+                );
+                failedModelChecks.AddRange(
+                    CheckInputTensorShape(model, brainParameters, sensors, observableAttributeTotalSize)
+                );
+            }
+
+
+            failedModelChecks.AddRange(
+                CheckOutputTensorShape(model, brainParameters, actuatorComponents)
+            );
+
+            failedModelChecks.AddRange(
+                CheckOutputTensorPresence(model, memorySize, deterministicInference)
+            );
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to inputs expected by the model that are not
+        /// present in the BrainParameters. Tests the models created with the API of version 1.X
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="memory">
+        /// The memory size that the model is expecting.
+        /// </param>
+        /// <param name="sensors">Array of attached sensor components</param>
+        /// <returns>
+        /// A IEnumerable of the checks that failed
+        /// </returns>
+        static IEnumerable<FailedCheck> CheckInputTensorPresenceLegacy(
+            Model model,
+            BrainParameters brainParameters,
+            int memory,
+            ISensor[] sensors
+        )
+        {
+            var failedModelChecks = new List<FailedCheck>();
+            var tensorsNames = model.GetInputNames();
+
+            // If there is no Vector Observation Input but the Brain Parameters expect one.
+            if ((brainParameters.VectorObservationSize != 0) &&
+                (!tensorsNames.Contains(TensorNames.VectorObservationPlaceholder)))
+            {
+                failedModelChecks.Add(
+                    FailedCheck.Warning("The model does not contain a Vector Observation Placeholder Input. " +
+                        "You must set the Vector Observation Space Size to 0.")
+                );
+            }
+
+            // If there are not enough Visual Observation Input compared to what the
+            // sensors expect.
+            var visObsIndex = 0;
+            for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
+            {
+                var sensor = sensors[sensorIndex];
+                if (sensor.GetObservationSpec().Shape.Length == 3)
+                {
+                    if (!tensorsNames.Contains(
+                        TensorNames.GetVisualObservationName(visObsIndex)))
+                    {
+                        failedModelChecks.Add(
+                            FailedCheck.Warning("The model does not contain a Visual Observation Placeholder Input " +
+                                $"for sensor component {visObsIndex} ({sensor.GetType().Name}).")
+                        );
+                    }
+                    visObsIndex++;
+                }
+                if (sensor.GetObservationSpec().Shape.Length == 2)
+                {
+                    if (!tensorsNames.Contains(
+                        TensorNames.GetObservationName(sensorIndex)))
+                    {
+                        failedModelChecks.Add(
+                            FailedCheck.Warning("The model does not contain an Observation Placeholder Input " +
+                                $"for sensor component {sensorIndex} ({sensor.GetType().Name}).")
+                        );
+                    }
+                }
+            }
+
+            var expectedVisualObs = model.GetNumVisualInputs();
+            // Check if there's not enough visual sensors (too many would be handled above)
+            if (expectedVisualObs > visObsIndex)
+            {
+                failedModelChecks.Add(
+                    FailedCheck.Warning($"The model expects {expectedVisualObs} visual inputs," +
+                        $" but only found {visObsIndex} visual sensors.")
+                );
+            }
+
+            // If the model has a non-negative memory size but requires a recurrent input
+            if (memory > 0)
+            {
+                if (!tensorsNames.Any(x => x.EndsWith("_h")) ||
+                    !tensorsNames.Any(x => x.EndsWith("_c")))
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain a Recurrent Input Node but has memory_size.")
+                    );
+                }
+            }
+
+            // If the model uses discrete control but does not have an input for action masks
+            if (model.HasDiscreteOutputs())
+            {
+                if (!tensorsNames.Contains(TensorNames.ActionMaskPlaceholder))
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain an Action Mask but is using Discrete Control.")
+                    );
+                }
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to inputs expected by the model that are not
+        /// present in the BrainParameters.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="memory">
+        /// The memory size that the model is expecting.
+        /// </param>
+        /// <param name="sensors">Array of attached sensor components</param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// Deterministic. </param>
+        /// <returns>
+        /// A IEnumerable of the checks that failed
+        /// </returns>
+        static IEnumerable<FailedCheck> CheckInputTensorPresence(
+            Model model,
+            BrainParameters brainParameters,
+            int memory,
+            ISensor[] sensors,
+            bool deterministicInference = false
+        )
+        {
+            var failedModelChecks = new List<FailedCheck>();
+            var tensorsNames = model.GetInputNames();
+            for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
+            {
+                if (!tensorsNames.Contains(
+                    TensorNames.GetObservationName(sensorIndex)))
+                {
+                    var sensor = sensors[sensorIndex];
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain an Observation Placeholder Input " +
+                            $"for sensor component {sensorIndex} ({sensor.GetType().Name}).")
+                    );
+                }
+            }
+
+            // If the model has a non-negative memory size but requires a recurrent input
+            if (memory > 0)
+            {
+                var modelVersion = model.GetVersion();
+                if (!tensorsNames.Any(x => x == TensorNames.RecurrentInPlaceholder))
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain a Recurrent Input Node but has memory_size.")
+                    );
+                }
+            }
+
+            // If the model uses discrete control but does not have an input for action masks
+            if (model.HasDiscreteOutputs(deterministicInference))
+            {
+                if (!tensorsNames.Contains(TensorNames.ActionMaskPlaceholder))
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain an Action Mask but is using Discrete Control.")
+                    );
+                }
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to outputs expected by the model that are not
+        /// present in the BrainParameters.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="memory">The memory size that the model is expecting/</param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <returns>
+        /// A IEnumerable of the checks that failed
+        /// </returns>
+        static IEnumerable<FailedCheck> CheckOutputTensorPresence(Model model, int memory, bool deterministicInference = false)
+        {
+            var failedModelChecks = new List<FailedCheck>();
+
+            // If there is no Recurrent Output but the model is Recurrent.
+            if (memory > 0)
+            {
+                var allOutputs = model.GetOutputNames(deterministicInference).ToList();
+                if (!allOutputs.Any(x => x == TensorNames.RecurrentOutput))
+                {
+                    failedModelChecks.Add(
+                        FailedCheck.Warning("The model does not contain a Recurrent Output Node but has memory_size.")
+                    );
+                }
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the visual observation input placeholder is the same as the corresponding sensor.
+        /// </summary>
+        /// <param name="tensorProxy">The tensor that is expected by the model</param>
+        /// <param name="sensor">The sensor that produces the visual observation.</param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckVisualObsShape(
+            TensorProxy tensorProxy, ISensor sensor)
+        {
+            var shape = sensor.GetObservationSpec().Shape;
+            var heightBp = shape[0];
+            var widthBp = shape[1];
+            var pixelBp = shape[2];
+            var heightT = tensorProxy.Height;
+            var widthT = tensorProxy.Width;
+            var pixelT = tensorProxy.Channels;
+            if ((widthBp != widthT) || (heightBp != heightT) || (pixelBp != pixelT))
+            {
+                return FailedCheck.Warning($"The visual Observation of the model does not match. " +
+                    $"Received TensorProxy of shape [?x{widthBp}x{heightBp}x{pixelBp}] but " +
+                    $"was expecting [?x{widthT}x{heightT}x{pixelT}] for the {sensor.GetName()} Sensor."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the rank 2 observation input placeholder is the same as the corresponding sensor.
+        /// </summary>
+        /// <param name="tensorProxy">The tensor that is expected by the model</param>
+        /// <param name="sensor">The sensor that produces the visual observation.</param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckRankTwoObsShape(
+            TensorProxy tensorProxy, ISensor sensor)
+        {
+            var shape = sensor.GetObservationSpec().Shape;
+            var dim1Bp = shape[0];
+            var dim2Bp = shape[1];
+            var dim1T = tensorProxy.Channels;
+            var dim2T = tensorProxy.Width;
+            var dim3T = tensorProxy.Height;
+            if ((dim1Bp != dim1T) || (dim2Bp != dim2T))
+            {
+                var proxyDimStr = $"[?x{dim1T}x{dim2T}]";
+                if (dim3T > 1)
+                {
+                    proxyDimStr = $"[?x{dim3T}x{dim2T}x{dim1T}]";
+                }
+                return FailedCheck.Warning($"An Observation of the model does not match. " +
+                    $"Received TensorProxy of shape [?x{dim1Bp}x{dim2Bp}] but " +
+                    $"was expecting {proxyDimStr} for the {sensor.GetName()} Sensor."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the rank 2 observation input placeholder is the same as the corresponding sensor.
+        /// </summary>
+        /// <param name="tensorProxy">The tensor that is expected by the model</param>
+        /// <param name="sensor">The sensor that produces the visual observation.</param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckRankOneObsShape(
+            TensorProxy tensorProxy, ISensor sensor)
+        {
+            var shape = sensor.GetObservationSpec().Shape;
+            var dim1Bp = shape[0];
+            var dim1T = tensorProxy.Channels;
+            var dim2T = tensorProxy.Width;
+            var dim3T = tensorProxy.Height;
+            if ((dim1Bp != dim1T))
+            {
+                var proxyDimStr = $"[?x{dim1T}]";
+                if (dim2T > 1)
+                {
+                    proxyDimStr = $"[?x{dim1T}x{dim2T}]";
+                }
+                if (dim3T > 1)
+                {
+                    proxyDimStr = $"[?x{dim3T}x{dim2T}x{dim1T}]";
+                }
+                return FailedCheck.Warning($"An Observation of the model does not match. " +
+                    $"Received TensorProxy of shape [?x{dim1Bp}] but " +
+                    $"was expecting {proxyDimStr} for the {sensor.GetName()} Sensor."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to inputs shapes incompatibilities between
+        /// the model and the BrainParameters. Tests the models created with the API of version 1.X
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="sensors">Attached sensors</param>
+        /// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
+        /// <returns>A IEnumerable of the checks that failed</returns>
+        static IEnumerable<FailedCheck> CheckInputTensorShapeLegacy(
+            Model model, BrainParameters brainParameters, ISensor[] sensors,
+            int observableAttributeTotalSize)
+        {
+            var failedModelChecks = new List<FailedCheck>();
+            var tensorTester =
+                new Dictionary<string, Func<BrainParameters, TensorProxy, ISensor[], int, FailedCheck>>()
+            {
+                {TensorNames.VectorObservationPlaceholder, CheckVectorObsShapeLegacy},
+                {TensorNames.PreviousActionPlaceholder, CheckPreviousActionShape},
+                {TensorNames.RandomNormalEpsilonPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.ActionMaskPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.SequenceLengthPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.RecurrentInPlaceholder, ((bp, tensor, scs, i) => null)},
+            };
+
+            foreach (var mem in model.memories)
+            {
+                tensorTester[mem.input] = ((bp, tensor, scs, i) => null);
+            }
+
+            var visObsIndex = 0;
+            for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
+            {
+                var sens = sensors[sensorIndex];
+                if (sens.GetObservationSpec().Shape.Length == 3)
+                {
+                    tensorTester[TensorNames.GetVisualObservationName(visObsIndex)] =
+                        (bp, tensor, scs, i) => CheckVisualObsShape(tensor, sens);
+                    visObsIndex++;
+                }
+                if (sens.GetObservationSpec().Shape.Length == 2)
+                {
+                    tensorTester[TensorNames.GetObservationName(sensorIndex)] =
+                        (bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sens);
+                }
+            }
+
+            // If the model expects an input but it is not in this list
+            foreach (var tensor in model.GetInputTensors())
+            {
+                if (!tensorTester.ContainsKey(tensor.name))
+                {
+                    if (!tensor.name.Contains("visual_observation"))
+                    {
+                        failedModelChecks.Add(
+                            FailedCheck.Warning("Model contains an unexpected input named : " + tensor.name)
+                        );
+                    }
+                }
+                else
+                {
+                    var tester = tensorTester[tensor.name];
+                    var error = tester.Invoke(brainParameters, tensor, sensors, observableAttributeTotalSize);
+                    if (error != null)
+                    {
+                        failedModelChecks.Add(error);
+                    }
+                }
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the Vector Observation input placeholder is the same in the
+        /// model and in the Brain Parameters. Tests the models created with the API of version 1.X
+        /// </summary>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="tensorProxy">The tensor that is expected by the model</param>
+        /// <param name="sensors">Array of attached sensor components</param>
+        /// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckVectorObsShapeLegacy(
+            BrainParameters brainParameters, TensorProxy tensorProxy, ISensor[] sensors,
+            int observableAttributeTotalSize)
+        {
+            var vecObsSizeBp = brainParameters.VectorObservationSize;
+            var numStackedVector = brainParameters.NumStackedVectorObservations;
+            var totalVecObsSizeT = tensorProxy.shape[tensorProxy.shape.Length - 1];
+
+            var totalVectorSensorSize = 0;
+            foreach (var sens in sensors)
+            {
+                if ((sens.GetObservationSpec().Shape.Length == 1))
+                {
+                    totalVectorSensorSize += sens.GetObservationSpec().Shape[0];
+                }
+            }
+
+            if (totalVectorSensorSize != totalVecObsSizeT)
+            {
+                var sensorSizes = "";
+                foreach (var sensorComp in sensors)
+                {
+                    if (sensorComp.GetObservationSpec().Shape.Length == 1)
+                    {
+                        var vecSize = sensorComp.GetObservationSpec().Shape[0];
+                        if (sensorSizes.Length == 0)
+                        {
+                            sensorSizes = $"[{vecSize}";
+                        }
+                        else
+                        {
+                            sensorSizes += $", {vecSize}";
+                        }
+                    }
+                }
+
+                sensorSizes += "]";
+                return FailedCheck.Warning(
+                    $"Vector Observation Size of the model does not match. Was expecting {totalVecObsSizeT} " +
+                    $"but received: \n" +
+                    $"Vector observations: {vecObsSizeBp} x {numStackedVector}\n" +
+                    $"Total [Observable] attributes: {observableAttributeTotalSize}\n" +
+                    $"Sensor sizes: {sensorSizes}."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to inputs shapes incompatibilities between
+        /// the model and the BrainParameters.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="sensors">Attached sensors</param>
+        /// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
+        /// <returns>A IEnumerable of the checks that failed</returns>
+        static IEnumerable<FailedCheck> CheckInputTensorShape(
+            Model model, BrainParameters brainParameters, ISensor[] sensors,
+            int observableAttributeTotalSize)
+        {
+            var failedModelChecks = new List<FailedCheck>();
+            var tensorTester =
+                new Dictionary<string, Func<BrainParameters, TensorProxy, ISensor[], int, FailedCheck>>()
+            {
+                {TensorNames.PreviousActionPlaceholder, CheckPreviousActionShape},
+                {TensorNames.RandomNormalEpsilonPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.ActionMaskPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.SequenceLengthPlaceholder, ((bp, tensor, scs, i) => null)},
+                {TensorNames.RecurrentInPlaceholder, ((bp, tensor, scs, i) => null)},
+            };
+
+            foreach (var mem in model.memories)
+            {
+                tensorTester[mem.input] = ((bp, tensor, scs, i) => null);
+            }
+
+            for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
+            {
+                var sens = sensors[sensorIndex];
+                if (sens.GetObservationSpec().Rank == 3)
+                {
+                    tensorTester[TensorNames.GetObservationName(sensorIndex)] =
+                        (bp, tensor, scs, i) => CheckVisualObsShape(tensor, sens);
+                }
+                if (sens.GetObservationSpec().Rank == 2)
+                {
+                    tensorTester[TensorNames.GetObservationName(sensorIndex)] =
+                        (bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sens);
+                }
+                if (sens.GetObservationSpec().Rank == 1)
+                {
+                    tensorTester[TensorNames.GetObservationName(sensorIndex)] =
+                        (bp, tensor, scs, i) => CheckRankOneObsShape(tensor, sens);
+                }
+            }
+
+            // If the model expects an input but it is not in this list
+            foreach (var tensor in model.GetInputTensors())
+            {
+                if (!tensorTester.ContainsKey(tensor.name))
+                {
+                    failedModelChecks.Add(FailedCheck.Warning("Model contains an unexpected input named : " + tensor.name
+                    ));
+                }
+                else
+                {
+                    var tester = tensorTester[tensor.name];
+                    var error = tester.Invoke(brainParameters, tensor, sensors, observableAttributeTotalSize);
+                    if (error != null)
+                    {
+                        failedModelChecks.Add(error);
+                    }
+                }
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the Previous Vector Action input placeholder is the same in the
+        /// model and in the Brain Parameters.
+        /// </summary>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="tensorProxy"> The tensor that is expected by the model</param>
+        /// <param name="sensors">Array of attached sensor components (unused).</param>
+        /// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes (unused).</param>
+        /// <returns>If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.</returns>
+        static FailedCheck CheckPreviousActionShape(
+            BrainParameters brainParameters, TensorProxy tensorProxy,
+            ISensor[] sensors, int observableAttributeTotalSize)
+        {
+            var numberActionsBp = brainParameters.ActionSpec.NumDiscreteActions;
+            var numberActionsT = tensorProxy.shape[tensorProxy.shape.Length - 1];
+            if (numberActionsBp != numberActionsT)
+            {
+                return FailedCheck.Warning("Previous Action Size of the model does not match. " +
+                    $"Received {numberActionsBp} but was expecting {numberActionsT}."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Generates failed checks that correspond to output shapes incompatibilities between
+        /// the model and the BrainParameters.
+        /// </summary>
+        /// <param name="model">
+        /// The Barracuda engine model for loading static parameters
+        /// </param>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
+        /// <returns>
+        /// A IEnumerable of error messages corresponding to the incompatible shapes between model
+        /// and BrainParameters.
+        /// </returns>
+        static IEnumerable<FailedCheck> CheckOutputTensorShape(
+            Model model,
+            BrainParameters brainParameters,
+            ActuatorComponent[] actuatorComponents)
+        {
+            var failedModelChecks = new List<FailedCheck>();
+
+            // If the model expects an output but it is not in this list
+            var modelContinuousActionSize = model.ContinuousOutputSize();
+            var continuousError = CheckContinuousActionOutputShape(brainParameters, actuatorComponents, modelContinuousActionSize);
+            if (continuousError != null)
+            {
+                failedModelChecks.Add(continuousError);
+            }
+            FailedCheck discreteError = null;
+            var modelApiVersion = model.GetVersion();
+            if (modelApiVersion == (int)ModelApiVersion.MLAgents1_0)
+            {
+                var modelSumDiscreteBranchSizes = model.DiscreteOutputSize();
+                discreteError = CheckDiscreteActionOutputShapeLegacy(brainParameters, actuatorComponents, modelSumDiscreteBranchSizes);
+            }
+            if (modelApiVersion == (int)ModelApiVersion.MLAgents2_0)
+            {
+                var modelDiscreteBranches = model.GetTensorByName(TensorNames.DiscreteActionOutputShape);
+                discreteError = CheckDiscreteActionOutputShape(brainParameters, actuatorComponents, modelDiscreteBranches);
+            }
+
+            if (discreteError != null)
+            {
+                failedModelChecks.Add(discreteError);
+            }
+            return failedModelChecks;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the discrete action output is the same in the
+        /// model and in the Brain Parameters.
+        /// </summary>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
+        /// <param name="modelDiscreteBranches"> The Tensor of branch sizes.
+        /// </param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckDiscreteActionOutputShape(
+            BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, Tensor modelDiscreteBranches)
+        {
+            var discreteActionBranches = brainParameters.ActionSpec.BranchSizes.ToList();
+            foreach (var actuatorComponent in actuatorComponents)
+            {
+                var actionSpec = actuatorComponent.ActionSpec;
+                discreteActionBranches.AddRange(actionSpec.BranchSizes);
+            }
+
+            int modelDiscreteBranchesLength = modelDiscreteBranches?.length ?? 0;
+            if (modelDiscreteBranchesLength != discreteActionBranches.Count)
+            {
+                return FailedCheck.Warning("Discrete Action Size of the model does not match. The BrainParameters expect " +
+                    $"{discreteActionBranches.Count} branches but the model contains {modelDiscreteBranchesLength}."
+                );
+            }
+
+            for (int i = 0; i < modelDiscreteBranchesLength; i++)
+            {
+                if (modelDiscreteBranches != null && modelDiscreteBranches[i] != discreteActionBranches[i])
+                {
+                    return FailedCheck.Warning($"The number of Discrete Actions of branch {i} does not match. " +
+                        $"Was expecting {discreteActionBranches[i]} but the model contains {modelDiscreteBranches[i]} "
+                    );
+                }
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the discrete action output is the same in the
+        /// model and in the Brain Parameters. Tests the models created with the API of version 1.X
+        /// </summary>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
+        /// <param name="modelSumDiscreteBranchSizes">
+        /// The size of the discrete action output that is expected by the model.
+        /// </param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static FailedCheck CheckDiscreteActionOutputShapeLegacy(
+            BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, int modelSumDiscreteBranchSizes)
+        {
+            // TODO: check each branch size instead of sum of branch sizes
+            var sumOfDiscreteBranchSizes = brainParameters.ActionSpec.SumOfDiscreteBranchSizes;
+
+            foreach (var actuatorComponent in actuatorComponents)
+            {
+                var actionSpec = actuatorComponent.ActionSpec;
+                sumOfDiscreteBranchSizes += actionSpec.SumOfDiscreteBranchSizes;
+            }
+
+            if (modelSumDiscreteBranchSizes != sumOfDiscreteBranchSizes)
+            {
+                return FailedCheck.Warning("Discrete Action Size of the model does not match. The BrainParameters expect " +
+                    $"{sumOfDiscreteBranchSizes} but the model contains {modelSumDiscreteBranchSizes}."
+                );
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Checks that the shape of the continuous action output is the same in the
+        /// model and in the Brain Parameters.
+        /// </summary>
+        /// <param name="brainParameters">
+        /// The BrainParameters that are used verify the compatibility with the InferenceEngine
+        /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
+        /// <param name="modelContinuousActionSize">
+        /// The size of the continuous action output that is expected by the model.
+        /// </param>
+        /// <returns>If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.</returns>
+        static FailedCheck CheckContinuousActionOutputShape(
+            BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, int modelContinuousActionSize)
+        {
+            var numContinuousActions = brainParameters.ActionSpec.NumContinuousActions;
+
+            foreach (var actuatorComponent in actuatorComponents)
+            {
+                var actionSpec = actuatorComponent.ActionSpec;
+                numContinuousActions += actionSpec.NumContinuousActions;
+            }
+
+            if (modelContinuousActionSize != numContinuousActions)
+            {
+                return FailedCheck.Warning(
+                    "Continuous Action Size of the model does not match. The BrainParameters and ActuatorComponents expect " +
+                    $"{numContinuousActions} but the model contains {modelContinuousActionSize}."
+                );
+            }
+            return null;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs.meta b/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8958faf1e5d4faeb568daa860e94f28fb37d6419
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 399c5e92395a1484cb2808ac397745e1
+timeCreated: 1539197357
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs b/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs
new file mode 100644
index 0000000000000000000000000000000000000000..68a997f2bbc9f2bdb6eadbe06243feed9b78a6e8
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs
@@ -0,0 +1,284 @@
+using System.Collections.Generic;
+using System;
+using Unity.Barracuda;
+using Unity.MLAgents.Inference.Utils;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Reshapes a Tensor so that its first dimension becomes equal to the current batch size
+    /// and initializes its content to be zeros. Will only work on 2-dimensional tensors.
+    /// The second dimension of the Tensor will not be modified.
+    /// </summary>
+    internal class BiDimensionalOutputGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+
+        public BiDimensionalOutputGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the BatchSize input : Will be a one dimensional
+    /// integer array of size 1 containing the batch size.
+    /// </summary>
+    internal class BatchSizeGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+
+        public BatchSizeGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            tensorProxy.data?.Dispose();
+            tensorProxy.data = m_Allocator.Alloc(new TensorShape(1, 1));
+            tensorProxy.data[0] = batchSize;
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the SequenceLength input : Will be a one
+    /// dimensional integer array of size 1 containing 1.
+    /// Note : the sequence length is always one since recurrent networks only predict for
+    /// one step at the time.
+    /// </summary>
+    internal class SequenceLengthGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+
+        public SequenceLengthGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            tensorProxy.shape = new long[0];
+            tensorProxy.data?.Dispose();
+            tensorProxy.data = m_Allocator.Alloc(new TensorShape(1, 1));
+            tensorProxy.data[0] = 1;
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the Recurrent input : Will be a two
+    /// dimensional float array of dimension [batchSize x memorySize].
+    /// It will use the Memory data contained in the agentInfo to fill the data
+    /// of the tensor.
+    /// </summary>
+    internal class RecurrentInputGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+        Dictionary<int, List<float>> m_Memories;
+
+        public RecurrentInputGenerator(
+            ITensorAllocator allocator,
+            Dictionary<int, List<float>> memories)
+        {
+            m_Allocator = allocator;
+            m_Memories = memories;
+        }
+
+        public void Generate(
+            TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+
+            var memorySize = tensorProxy.data.width;
+
+            var agentIndex = 0;
+            for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++)
+            {
+                var infoSensorPair = infos[infoIndex];
+                var info = infoSensorPair.agentInfo;
+                List<float> memory;
+
+                if (info.done)
+                {
+                    m_Memories.Remove(info.episodeId);
+                }
+                if (!m_Memories.TryGetValue(info.episodeId, out memory))
+                {
+                    for (var j = 0; j < memorySize; j++)
+                    {
+                        tensorProxy.data[agentIndex, 0, j, 0] = 0;
+                    }
+                    agentIndex++;
+                    continue;
+                }
+                for (var j = 0; j < Math.Min(memorySize, memory.Count); j++)
+                {
+                    if (j >= memory.Count)
+                    {
+                        break;
+                    }
+                    tensorProxy.data[agentIndex, 0, j, 0] = memory[j];
+                }
+                agentIndex++;
+            }
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the Previous Action input : Will be a two
+    /// dimensional integer array of dimension [batchSize x actionSize].
+    /// It will use the previous action data contained in the agentInfo to fill the data
+    /// of the tensor.
+    /// </summary>
+    internal class PreviousActionInputGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+
+        public PreviousActionInputGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+
+            var actionSize = tensorProxy.shape[tensorProxy.shape.Length - 1];
+            var agentIndex = 0;
+            for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++)
+            {
+                var infoSensorPair = infos[infoIndex];
+                var info = infoSensorPair.agentInfo;
+                var pastAction = info.storedActions.DiscreteActions;
+                if (!pastAction.IsEmpty())
+                {
+                    for (var j = 0; j < actionSize; j++)
+                    {
+                        tensorProxy.data[agentIndex, j] = pastAction[j];
+                    }
+                }
+
+                agentIndex++;
+            }
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the Action Mask input : Will be a two
+    /// dimensional float array of dimension [batchSize x numActionLogits].
+    /// It will use the Action Mask data contained in the agentInfo to fill the data
+    /// of the tensor.
+    /// </summary>
+    internal class ActionMaskInputGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+
+        public ActionMaskInputGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+
+            var maskSize = tensorProxy.shape[tensorProxy.shape.Length - 1];
+            var agentIndex = 0;
+            for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++)
+            {
+                var infoSensorPair = infos[infoIndex];
+                var agentInfo = infoSensorPair.agentInfo;
+                var maskList = agentInfo.discreteActionMasks;
+                for (var j = 0; j < maskSize; j++)
+                {
+                    var isUnmasked = (maskList != null && maskList[j]) ? 0.0f : 1.0f;
+                    tensorProxy.data[agentIndex, j] = isUnmasked;
+                }
+                agentIndex++;
+            }
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the Epsilon input : Will be a two
+    /// dimensional float array of dimension [batchSize x actionSize].
+    /// It will use the generate random input data from a normal Distribution.
+    /// </summary>
+    internal class RandomNormalInputGenerator : TensorGenerator.IGenerator
+    {
+        readonly RandomNormal m_RandomNormal;
+        readonly ITensorAllocator m_Allocator;
+
+        public RandomNormalInputGenerator(int seed, ITensorAllocator allocator)
+        {
+            m_RandomNormal = new RandomNormal(seed);
+            m_Allocator = allocator;
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+            TensorUtils.FillTensorWithRandomNormal(tensorProxy, m_RandomNormal);
+        }
+    }
+
+    /// <summary>
+    /// Generates the Tensor corresponding to the Observation input : Will be a multi
+    /// dimensional float array.
+    /// It will use the Observation data contained in the sensors to fill the data
+    /// of the tensor.
+    /// </summary>
+    internal class ObservationGenerator : TensorGenerator.IGenerator
+    {
+        readonly ITensorAllocator m_Allocator;
+        List<int> m_SensorIndices = new List<int>();
+        ObservationWriter m_ObservationWriter = new ObservationWriter();
+
+        public ObservationGenerator(ITensorAllocator allocator)
+        {
+            m_Allocator = allocator;
+        }
+
+        public void AddSensorIndex(int sensorIndex)
+        {
+            m_SensorIndices.Add(sensorIndex);
+        }
+
+        public void Generate(TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
+            var agentIndex = 0;
+            for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++)
+            {
+                var info = infos[infoIndex];
+                if (info.agentInfo.done)
+                {
+                    // If the agent is done, we might have a stale reference to the sensors
+                    // e.g. a dependent object might have been disposed.
+                    // To avoid this, just fill observation with zeroes instead of calling sensor.Write.
+                    TensorUtils.FillTensorBatch(tensorProxy, agentIndex, 0.0f);
+                }
+                else
+                {
+                    var tensorOffset = 0;
+                    // Write each sensor consecutively to the tensor
+                    for (var sensorIndexIndex = 0; sensorIndexIndex < m_SensorIndices.Count; sensorIndexIndex++)
+                    {
+                        var sensorIndex = m_SensorIndices[sensorIndexIndex];
+                        var sensor = info.sensors[sensorIndex];
+                        m_ObservationWriter.SetTarget(tensorProxy, agentIndex, tensorOffset);
+                        var numWritten = sensor.Write(m_ObservationWriter);
+                        tensorOffset += numWritten;
+                    }
+                }
+                agentIndex++;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs.meta b/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5f0b9469c208d1e6b48126ad9cb7a311bbc31e58
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c57a4989c7e54b93ab56293698d7d237
+timeCreated: 1539109542
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs b/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e7d4c1c646313424ca475b4fe32e7f536b2f984e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs
@@ -0,0 +1,253 @@
+using System.Collections.Generic;
+using Unity.Barracuda;
+using UnityEngine.Profiling;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Inference
+{
+    internal struct AgentInfoSensorsPair
+    {
+        public AgentInfo agentInfo;
+        public List<ISensor> sensors;
+    }
+
+    internal class ModelRunner
+    {
+        List<AgentInfoSensorsPair> m_Infos = new List<AgentInfoSensorsPair>();
+        Dictionary<int, ActionBuffers> m_LastActionsReceived = new Dictionary<int, ActionBuffers>();
+        List<int> m_OrderedAgentsRequestingDecisions = new List<int>();
+
+        ITensorAllocator m_TensorAllocator;
+        TensorGenerator m_TensorGenerator;
+        TensorApplier m_TensorApplier;
+
+        NNModel m_Model;
+        string m_ModelName;
+        InferenceDevice m_InferenceDevice;
+        IWorker m_Engine;
+        bool m_Verbose = false;
+        bool m_DeterministicInference;
+        string[] m_OutputNames;
+        IReadOnlyList<TensorProxy> m_InferenceInputs;
+        List<TensorProxy> m_InferenceOutputs;
+        Dictionary<string, Tensor> m_InputsByName;
+        Dictionary<int, List<float>> m_Memories = new Dictionary<int, List<float>>();
+
+        SensorShapeValidator m_SensorShapeValidator = new SensorShapeValidator();
+
+        bool m_ObservationsInitialized;
+
+        /// <summary>
+        /// Initializes the Brain with the Model that it will use when selecting actions for
+        /// the agents
+        /// </summary>
+        /// <param name="model"> The Barracuda model to load </param>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        /// <param name="inferenceDevice"> Inference execution device. CPU is the fastest
+        /// option for most of ML Agents models. </param>
+        /// <param name="seed"> The seed that will be used to initialize the RandomNormal
+        /// and Multinomial objects used when running inference.</param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        /// <exception cref="UnityAgentsException">Throws an error when the model is null
+        /// </exception>
+        public ModelRunner(
+            NNModel model,
+            ActionSpec actionSpec,
+            InferenceDevice inferenceDevice,
+            int seed = 0,
+            bool deterministicInference = false)
+        {
+            Model barracudaModel;
+            m_Model = model;
+            m_ModelName = model?.name;
+            m_InferenceDevice = inferenceDevice;
+            m_DeterministicInference = deterministicInference;
+            m_TensorAllocator = new TensorCachingAllocator();
+            if (model != null)
+            {
+#if BARRACUDA_VERBOSE
+                m_Verbose = true;
+#endif
+
+                D.logEnabled = m_Verbose;
+
+                barracudaModel = ModelLoader.Load(model);
+
+                var failedCheck = BarracudaModelParamLoader.CheckModelVersion(
+                    barracudaModel
+                );
+                if (failedCheck != null)
+                {
+                    if (failedCheck.CheckType == BarracudaModelParamLoader.FailedCheck.CheckTypeEnum.Error)
+                    {
+                        throw new UnityAgentsException(failedCheck.Message);
+                    }
+                }
+
+                WorkerFactory.Type executionDevice;
+                switch (inferenceDevice)
+                {
+                    case InferenceDevice.CPU:
+                        executionDevice = WorkerFactory.Type.CSharp;
+                        break;
+                    case InferenceDevice.GPU:
+                        executionDevice = WorkerFactory.Type.ComputePrecompiled;
+                        break;
+                    case InferenceDevice.Burst:
+                        executionDevice = WorkerFactory.Type.CSharpBurst;
+                        break;
+                    case InferenceDevice.Default: // fallthrough
+                    default:
+                        executionDevice = WorkerFactory.Type.CSharpBurst;
+                        break;
+                }
+                m_Engine = WorkerFactory.CreateWorker(executionDevice, barracudaModel, m_Verbose);
+            }
+            else
+            {
+                barracudaModel = null;
+                m_Engine = null;
+            }
+
+            m_InferenceInputs = barracudaModel.GetInputTensors();
+            m_OutputNames = barracudaModel.GetOutputNames(m_DeterministicInference);
+
+            m_TensorGenerator = new TensorGenerator(
+                seed, m_TensorAllocator, m_Memories, barracudaModel, m_DeterministicInference);
+            m_TensorApplier = new TensorApplier(
+                actionSpec, seed, m_TensorAllocator, m_Memories, barracudaModel, m_DeterministicInference);
+            m_InputsByName = new Dictionary<string, Tensor>();
+            m_InferenceOutputs = new List<TensorProxy>();
+        }
+
+        public InferenceDevice InferenceDevice
+        {
+            get { return m_InferenceDevice; }
+        }
+
+        public NNModel Model
+        {
+            get { return m_Model; }
+        }
+
+        void PrepareBarracudaInputs(IReadOnlyList<TensorProxy> infInputs)
+        {
+            m_InputsByName.Clear();
+            for (var i = 0; i < infInputs.Count; i++)
+            {
+                var inp = infInputs[i];
+                m_InputsByName[inp.name] = inp.data;
+            }
+        }
+
+        public void Dispose()
+        {
+            if (m_Engine != null)
+                m_Engine.Dispose();
+            m_TensorAllocator?.Reset(false);
+        }
+
+        void FetchBarracudaOutputs(string[] names)
+        {
+            m_InferenceOutputs.Clear();
+            foreach (var n in names)
+            {
+                var output = m_Engine.PeekOutput(n);
+                m_InferenceOutputs.Add(TensorUtils.TensorProxyFromBarracuda(output, n));
+            }
+        }
+
+        public void PutObservations(AgentInfo info, List<ISensor> sensors)
+        {
+#if DEBUG
+            m_SensorShapeValidator.ValidateSensors(sensors);
+#endif
+            m_Infos.Add(new AgentInfoSensorsPair
+            {
+                agentInfo = info,
+                sensors = sensors
+            });
+
+            // We add the episodeId to this list to maintain the order in which the decisions were requested
+            m_OrderedAgentsRequestingDecisions.Add(info.episodeId);
+
+            if (!m_LastActionsReceived.ContainsKey(info.episodeId))
+            {
+                m_LastActionsReceived[info.episodeId] = ActionBuffers.Empty;
+            }
+            if (info.done)
+            {
+                // If the agent is done, we remove the key from the last action dictionary since no action
+                // should be taken.
+                m_LastActionsReceived.Remove(info.episodeId);
+            }
+        }
+
+        public void DecideBatch()
+        {
+            var currentBatchSize = m_Infos.Count;
+            if (currentBatchSize == 0)
+            {
+                return;
+            }
+            if (!m_ObservationsInitialized)
+            {
+                // Just grab the first agent in the collection (any will suffice, really).
+                // We check for an empty Collection above, so this will always return successfully.
+                var firstInfo = m_Infos[0];
+                m_TensorGenerator.InitializeObservations(firstInfo.sensors, m_TensorAllocator);
+                m_ObservationsInitialized = true;
+            }
+
+            Profiler.BeginSample("ModelRunner.DecideAction");
+            Profiler.BeginSample(m_ModelName);
+
+            Profiler.BeginSample($"GenerateTensors");
+            // Prepare the input tensors to be feed into the engine
+            m_TensorGenerator.GenerateTensors(m_InferenceInputs, currentBatchSize, m_Infos);
+            Profiler.EndSample();
+
+            Profiler.BeginSample($"PrepareBarracudaInputs");
+            PrepareBarracudaInputs(m_InferenceInputs);
+            Profiler.EndSample();
+
+            // Execute the Model
+            Profiler.BeginSample($"ExecuteGraph");
+            m_Engine.Execute(m_InputsByName);
+            Profiler.EndSample();
+
+            Profiler.BeginSample($"FetchBarracudaOutputs");
+            FetchBarracudaOutputs(m_OutputNames);
+            Profiler.EndSample();
+
+            Profiler.BeginSample($"ApplyTensors");
+            // Update the outputs
+            m_TensorApplier.ApplyTensors(m_InferenceOutputs, m_OrderedAgentsRequestingDecisions, m_LastActionsReceived);
+            Profiler.EndSample();
+
+            Profiler.EndSample(); // end name
+            Profiler.EndSample(); // end ModelRunner.DecideAction
+
+            m_Infos.Clear();
+
+            m_OrderedAgentsRequestingDecisions.Clear();
+        }
+
+        public bool HasModel(NNModel other, InferenceDevice otherInferenceDevice)
+        {
+            return m_Model == other && m_InferenceDevice == otherInferenceDevice;
+        }
+
+        public ActionBuffers GetAction(int agentId)
+        {
+            if (m_LastActionsReceived.ContainsKey(agentId))
+            {
+                return m_LastActionsReceived[agentId];
+            }
+            return ActionBuffers.Empty;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs.meta b/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e4e8e67539617360ecc0cb9d2a72d3a29f59991d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/ModelRunner.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8f3f4b630ca3f4a4ba74922ec8249046
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs b/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a03b3d927e5cfa939bc3630f0271740d1c81e514
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs
@@ -0,0 +1,112 @@
+using System.Collections.Generic;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Mapping between the output tensor names and the method that will use the
+    /// output tensors and the Agents present in the batch to update their action, memories and
+    /// value estimates.
+    /// A TensorApplier implements a Dictionary of strings (node names) to an Action.
+    /// This action takes as input the tensor and the Dictionary of Agent to AgentInfo for
+    /// the current batch.
+    /// </summary>
+    internal class TensorApplier
+    {
+        /// <summary>
+        /// A tensor Applier's Execute method takes a tensor and a Dictionary of Agent to AgentInfo.
+        /// Uses the data contained inside the tensor to modify the state of the Agent. The Tensors
+        /// are assumed to have the batch size on the first dimension and the agents to be ordered
+        /// the same way in the dictionary and in the tensor.
+        /// </summary>
+        public interface IApplier
+        {
+            /// <summary>
+            /// Applies the values in the Tensor to the Agents present in the agentInfos
+            /// </summary>
+            /// <param name="tensorProxy">
+            /// The Tensor containing the data to be applied to the Agents
+            /// </param>
+            /// <param name="actionIds"> List of Agents Ids that will be updated using the tensor's data</param>
+            /// <param name="lastActions"> Dictionary of AgentId to Actions to be updated</param>
+            void Apply(TensorProxy tensorProxy, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions);
+        }
+
+        readonly Dictionary<string, IApplier> m_Dict = new Dictionary<string, IApplier>();
+
+        /// <summary>
+        /// Returns a new TensorAppliers object.
+        /// </summary>
+        /// <param name="actionSpec"> Description of the actions for the Agent.</param>
+        /// <param name="seed"> The seed the Appliers will be initialized with.</param>
+        /// <param name="allocator"> Tensor allocator</param>
+        /// <param name="memories">Dictionary of AgentInfo.id to memory used to pass to the inference model.</param>
+        /// <param name="barracudaModel"></param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic.</param>
+        public TensorApplier(
+            ActionSpec actionSpec,
+            int seed,
+            ITensorAllocator allocator,
+            Dictionary<int, List<float>> memories,
+            object barracudaModel = null,
+            bool deterministicInference = false)
+        {
+            // If model is null, no inference to run and exception is thrown before reaching here.
+            if (barracudaModel == null)
+            {
+                return;
+            }
+
+            var model = (Model)barracudaModel;
+            if (!model.SupportsContinuousAndDiscrete())
+            {
+                actionSpec.CheckAllContinuousOrDiscrete();
+            }
+            if (actionSpec.NumContinuousActions > 0)
+            {
+                var tensorName = model.ContinuousOutputName(deterministicInference);
+                m_Dict[tensorName] = new ContinuousActionOutputApplier(actionSpec);
+            }
+            var modelVersion = model.GetVersion();
+            if (actionSpec.NumDiscreteActions > 0)
+            {
+                var tensorName = model.DiscreteOutputName(deterministicInference);
+                if (modelVersion == (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents1_0)
+                {
+                    m_Dict[tensorName] = new LegacyDiscreteActionOutputApplier(actionSpec, seed, allocator);
+                }
+                if (modelVersion == (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents2_0)
+                {
+                    m_Dict[tensorName] = new DiscreteActionOutputApplier(actionSpec, seed, allocator);
+                }
+            }
+            m_Dict[TensorNames.RecurrentOutput] = new MemoryOutputApplier(memories);
+        }
+
+        /// <summary>
+        /// Updates the state of the agents based on the data present in the tensor.
+        /// </summary>
+        /// <param name="tensors"> Enumerable of tensors containing the data.</param>
+        /// <param name="actionIds"> List of Agents Ids that will be updated using the tensor's data</param>
+        /// <param name="lastActions"> Dictionary of AgentId to Actions to be updated</param>
+        /// <exception cref="UnityAgentsException"> One of the tensor does not have an
+        /// associated applier.</exception>
+        public void ApplyTensors(
+            IReadOnlyList<TensorProxy> tensors, IList<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
+        {
+            for (var tensorIndex = 0; tensorIndex < tensors.Count; tensorIndex++)
+            {
+                var tensor = tensors[tensorIndex];
+                if (!m_Dict.ContainsKey(tensor.name))
+                {
+                    throw new UnityAgentsException(
+                        $"Unknown tensorProxy expected as output : {tensor.name}");
+                }
+                m_Dict[tensor.name].Apply(tensor, actionIds, lastActions);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs.meta b/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d95eb26a15d74d5f307831e58af4183a51177c60
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorApplier.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d1bef4f4ae72645108f16614355473e8
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs b/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f1a3578bdb737450b1169b71701f494aa30d4331
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs
@@ -0,0 +1,179 @@
+using System.Collections.Generic;
+using Unity.Barracuda;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Mapping between Tensor names and generators.
+    /// A TensorGenerator implements a Dictionary of strings (node names) to an Action.
+    /// The Action take as argument the tensor, the current batch size and a Dictionary of
+    /// Agent to AgentInfo corresponding to the current batch.
+    /// Each Generator reshapes and fills the data of the tensor based of the data of the batch.
+    /// When the TensorProxy is an Input to the model, the shape of the Tensor will be modified
+    /// depending on the current batch size and the data of the Tensor will be filled using the
+    /// Dictionary of Agent to AgentInfo.
+    /// When the TensorProxy is an Output of the model, only the shape of the Tensor will be
+    /// modified using the current batch size. The data will be pre-filled with zeros.
+    /// </summary>
+    internal class TensorGenerator
+    {
+        public interface IGenerator
+        {
+            /// <summary>
+            /// Modifies the data inside a Tensor according to the information contained in the
+            /// AgentInfos contained in the current batch.
+            /// </summary>
+            /// <param name="tensorProxy"> The tensor the data and shape will be modified.</param>
+            /// <param name="batchSize"> The number of agents present in the current batch.</param>
+            /// <param name="infos">
+            /// List of AgentInfos containing the information that will be used to populate
+            /// the tensor's data.
+            /// </param>
+            void Generate(
+                TensorProxy tensorProxy, int batchSize, IList<AgentInfoSensorsPair> infos);
+        }
+
+        readonly Dictionary<string, IGenerator> m_Dict = new Dictionary<string, IGenerator>();
+        int m_ApiVersion;
+
+        /// <summary>
+        /// Returns a new TensorGenerators object.
+        /// </summary>
+        /// <param name="seed"> The seed the Generators will be initialized with.</param>
+        /// <param name="allocator"> Tensor allocator.</param>
+        /// <param name="memories">Dictionary of AgentInfo.id to memory for use in the inference model.</param>
+        /// <param name="barracudaModel"></param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        public TensorGenerator(
+            int seed,
+            ITensorAllocator allocator,
+            Dictionary<int, List<float>> memories,
+            object barracudaModel = null,
+            bool deterministicInference = false)
+        {
+            // If model is null, no inference to run and exception is thrown before reaching here.
+            if (barracudaModel == null)
+            {
+                return;
+            }
+            var model = (Model)barracudaModel;
+
+            m_ApiVersion = model.GetVersion();
+
+            // Generator for Inputs
+            m_Dict[TensorNames.BatchSizePlaceholder] =
+                new BatchSizeGenerator(allocator);
+            m_Dict[TensorNames.SequenceLengthPlaceholder] =
+                new SequenceLengthGenerator(allocator);
+            m_Dict[TensorNames.RecurrentInPlaceholder] =
+                new RecurrentInputGenerator(allocator, memories);
+
+            m_Dict[TensorNames.PreviousActionPlaceholder] =
+                new PreviousActionInputGenerator(allocator);
+            m_Dict[TensorNames.ActionMaskPlaceholder] =
+                new ActionMaskInputGenerator(allocator);
+            m_Dict[TensorNames.RandomNormalEpsilonPlaceholder] =
+                new RandomNormalInputGenerator(seed, allocator);
+
+
+            // Generators for Outputs
+            if (model.HasContinuousOutputs(deterministicInference))
+            {
+                m_Dict[model.ContinuousOutputName(deterministicInference)] = new BiDimensionalOutputGenerator(allocator);
+            }
+            if (model.HasDiscreteOutputs(deterministicInference))
+            {
+                m_Dict[model.DiscreteOutputName(deterministicInference)] = new BiDimensionalOutputGenerator(allocator);
+            }
+            m_Dict[TensorNames.RecurrentOutput] = new BiDimensionalOutputGenerator(allocator);
+            m_Dict[TensorNames.ValueEstimateOutput] = new BiDimensionalOutputGenerator(allocator);
+        }
+
+        public void InitializeObservations(List<ISensor> sensors, ITensorAllocator allocator)
+        {
+            if (m_ApiVersion == (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents1_0)
+            {
+                // Loop through the sensors on a representative agent.
+                // All vector observations use a shared ObservationGenerator since they are concatenated.
+                // All other observations use a unique ObservationInputGenerator
+                var visIndex = 0;
+                ObservationGenerator vecObsGen = null;
+                for (var sensorIndex = 0; sensorIndex < sensors.Count; sensorIndex++)
+                {
+                    var sensor = sensors[sensorIndex];
+                    var rank = sensor.GetObservationSpec().Rank;
+                    ObservationGenerator obsGen = null;
+                    string obsGenName = null;
+                    switch (rank)
+                    {
+                        case 1:
+                            if (vecObsGen == null)
+                            {
+                                vecObsGen = new ObservationGenerator(allocator);
+                            }
+                            obsGen = vecObsGen;
+                            obsGenName = TensorNames.VectorObservationPlaceholder;
+                            break;
+                        case 2:
+                            // If the tensor is of rank 2, we use the index of the sensor
+                            // to create the name
+                            obsGen = new ObservationGenerator(allocator);
+                            obsGenName = TensorNames.GetObservationName(sensorIndex);
+                            break;
+                        case 3:
+                            // If the tensor is of rank 3, we use the "visual observation
+                            // index", which only counts the rank 3 sensors
+                            obsGen = new ObservationGenerator(allocator);
+                            obsGenName = TensorNames.GetVisualObservationName(visIndex);
+                            visIndex++;
+                            break;
+                        default:
+                            throw new UnityAgentsException(
+                                $"Sensor {sensor.GetName()} have an invalid rank {rank}");
+                    }
+                    obsGen.AddSensorIndex(sensorIndex);
+                    m_Dict[obsGenName] = obsGen;
+                }
+            }
+
+            if (m_ApiVersion == (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents2_0)
+            {
+                for (var sensorIndex = 0; sensorIndex < sensors.Count; sensorIndex++)
+                {
+                    var obsGen = new ObservationGenerator(allocator);
+                    var obsGenName = TensorNames.GetObservationName(sensorIndex);
+                    obsGen.AddSensorIndex(sensorIndex);
+                    m_Dict[obsGenName] = obsGen;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Populates the data of the tensor inputs given the data contained in the current batch
+        /// of agents.
+        /// </summary>
+        /// <param name="tensors"> Enumerable of tensors that will be modified.</param>
+        /// <param name="currentBatchSize"> The number of agents present in the current batch
+        /// </param>
+        /// <param name="infos"> List of AgentsInfos and Sensors that contains the
+        /// data that will be used to modify the tensors</param>
+        /// <exception cref="UnityAgentsException"> One of the tensor does not have an
+        /// associated generator.</exception>
+        public void GenerateTensors(
+            IReadOnlyList<TensorProxy> tensors, int currentBatchSize, IList<AgentInfoSensorsPair> infos)
+        {
+            for (var tensorIndex = 0; tensorIndex < tensors.Count; tensorIndex++)
+            {
+                var tensor = tensors[tensorIndex];
+                if (!m_Dict.ContainsKey(tensor.name))
+                {
+                    throw new UnityAgentsException(
+                        $"Unknown tensorProxy expected as input : {tensor.name}");
+                }
+                m_Dict[tensor.name].Generate(tensor, currentBatchSize, infos);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs.meta b/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..db25d5da72293399f9ba00ce150dae8d699114e9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6a24e86bc77c4a5088a5fd04d6d30e81
+timeCreated: 1537484304
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diff --git a/com.unity.ml-agents/Runtime/Inference/TensorNames.cs b/com.unity.ml-agents/Runtime/Inference/TensorNames.cs
new file mode 100644
index 0000000000000000000000000000000000000000..48ae04b5f6e192a111ccd8f42e8c8438e8a4c04e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorNames.cs
@@ -0,0 +1,50 @@
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Contains the names of the input and output tensors for the Inference Brain.
+    /// </summary>
+    internal static class TensorNames
+    {
+        public const string BatchSizePlaceholder = "batch_size";
+        public const string SequenceLengthPlaceholder = "sequence_length";
+        public const string VectorObservationPlaceholder = "vector_observation";
+        public const string RecurrentInPlaceholder = "recurrent_in";
+        public const string VisualObservationPlaceholderPrefix = "visual_observation_";
+        public const string ObservationPlaceholderPrefix = "obs_";
+        public const string PreviousActionPlaceholder = "prev_action";
+        public const string ActionMaskPlaceholder = "action_masks";
+        public const string RandomNormalEpsilonPlaceholder = "epsilon";
+
+        public const string ValueEstimateOutput = "value_estimate";
+        public const string RecurrentOutput = "recurrent_out";
+        public const string MemorySize = "memory_size";
+        public const string VersionNumber = "version_number";
+        public const string ContinuousActionOutputShape = "continuous_action_output_shape";
+        public const string DiscreteActionOutputShape = "discrete_action_output_shape";
+        public const string ContinuousActionOutput = "continuous_actions";
+        public const string DiscreteActionOutput = "discrete_actions";
+        public const string DeterministicContinuousActionOutput = "deterministic_continuous_actions";
+        public const string DeterministicDiscreteActionOutput = "deterministic_discrete_actions";
+
+        // Deprecated TensorNames entries for backward compatibility
+        public const string IsContinuousControlDeprecated = "is_continuous_control";
+        public const string ActionOutputDeprecated = "action";
+        public const string ActionOutputShapeDeprecated = "action_output_shape";
+
+        /// <summary>
+        /// Returns the name of the visual observation with a given index
+        /// </summary>
+        public static string GetVisualObservationName(int index)
+        {
+            return VisualObservationPlaceholderPrefix + index;
+        }
+
+        /// <summary>
+        /// Returns the name of the observation with a given index
+        /// </summary>
+        public static string GetObservationName(int index)
+        {
+            return ObservationPlaceholderPrefix + index;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorNames.cs.meta b/com.unity.ml-agents/Runtime/Inference/TensorNames.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6ddb324bf3803aac6207efda5049180b8c562893
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorNames.cs.meta
@@ -0,0 +1,3 @@
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+guid: b28a46ea97c2445794d29d5a8a718a4a
+timeCreated: 1538158527
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorProxy.cs b/com.unity.ml-agents/Runtime/Inference/TensorProxy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b36b765ca8273604b2dc578bddf68c36094d1988
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/TensorProxy.cs
@@ -0,0 +1,161 @@
+using System;
+using System.Collections.Generic;
+using Unity.Barracuda;
+using Unity.MLAgents.Inference.Utils;
+
+namespace Unity.MLAgents.Inference
+{
+    /// <summary>
+    /// Tensor - A class to encapsulate a Tensor used for inference.
+    ///
+    /// This class contains the Array that holds the data array, the shapes, type and the
+    /// placeholder in the execution graph. All the fields are editable in the inspector,
+    /// allowing the user to specify everything but the data in a graphical way.
+    /// </summary>
+    [Serializable]
+    internal class TensorProxy
+    {
+        public enum TensorType
+        {
+            Integer,
+            FloatingPoint
+        };
+
+        static readonly Dictionary<TensorType, Type> k_TypeMap =
+            new Dictionary<TensorType, Type>()
+        {
+            {TensorType.FloatingPoint, typeof(float)},
+            {TensorType.Integer, typeof(int)}
+        };
+
+        public string name;
+        public TensorType valueType;
+
+        // Since Type is not serializable, we use the DisplayType for the Inspector
+        public Type DataType => k_TypeMap[valueType];
+        public long[] shape;
+        public Tensor data;
+
+        public long Height
+        {
+            get { return shape.Length == 4 ? shape[1] : shape[5]; }
+        }
+
+        public long Width
+        {
+            get { return shape.Length == 4 ? shape[2] : shape[6]; }
+        }
+
+        public long Channels
+        {
+            get { return shape.Length == 4 ? shape[3] : shape[7]; }
+        }
+    }
+
+    internal static class TensorUtils
+    {
+        public static void ResizeTensor(TensorProxy tensor, int batch, ITensorAllocator allocator)
+        {
+            if (tensor.shape[0] == batch &&
+                tensor.data != null && tensor.data.batch == batch)
+            {
+                return;
+            }
+
+            tensor.data?.Dispose();
+            tensor.shape[0] = batch;
+
+            if (tensor.shape.Length == 4 || tensor.shape.Length == 8)
+            {
+                tensor.data = allocator.Alloc(
+                    new TensorShape(
+                        batch,
+                        (int)tensor.Height,
+                        (int)tensor.Width,
+                        (int)tensor.Channels));
+            }
+            else
+            {
+                tensor.data = allocator.Alloc(
+                    new TensorShape(
+                        batch,
+                        (int)tensor.shape[tensor.shape.Length - 1]));
+            }
+        }
+
+        internal static long[] TensorShapeFromBarracuda(TensorShape src)
+        {
+            if (src.height == 1 && src.width == 1)
+            {
+                return new long[] { src.batch, src.channels };
+            }
+
+            return new long[] { src.batch, src.height, src.width, src.channels };
+        }
+
+        public static TensorProxy TensorProxyFromBarracuda(Tensor src, string nameOverride = null)
+        {
+            var shape = TensorShapeFromBarracuda(src.shape);
+            return new TensorProxy
+            {
+                name = nameOverride ?? src.name,
+                valueType = TensorProxy.TensorType.FloatingPoint,
+                shape = shape,
+                data = src
+            };
+        }
+
+        /// <summary>
+        /// Fill a specific batch of a TensorProxy with a given value
+        /// </summary>
+        /// <param name="tensorProxy"></param>
+        /// <param name="batch">The batch index to fill.</param>
+        /// <param name="fillValue"></param>
+        public static void FillTensorBatch(TensorProxy tensorProxy, int batch, float fillValue)
+        {
+            var height = tensorProxy.data.height;
+            var width = tensorProxy.data.width;
+            var channels = tensorProxy.data.channels;
+            for (var h = 0; h < height; h++)
+            {
+                for (var w = 0; w < width; w++)
+                {
+                    for (var c = 0; c < channels; c++)
+                    {
+                        tensorProxy.data[batch, h, w, c] = fillValue;
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// Fill a pre-allocated Tensor with random numbers
+        /// </summary>
+        /// <param name="tensorProxy">The pre-allocated Tensor to fill</param>
+        /// <param name="randomNormal">RandomNormal object used to populate tensor</param>
+        /// <exception cref="NotImplementedException">
+        /// Throws when trying to fill a Tensor of type other than float
+        /// </exception>
+        /// <exception cref="ArgumentNullException">
+        /// Throws when the Tensor is not allocated
+        /// </exception>
+        public static void FillTensorWithRandomNormal(
+            TensorProxy tensorProxy, RandomNormal randomNormal)
+        {
+            if (tensorProxy.DataType != typeof(float))
+            {
+                throw new NotImplementedException("Only float data types are currently supported");
+            }
+
+            if (tensorProxy.data == null)
+            {
+                throw new ArgumentNullException();
+            }
+
+            for (var i = 0; i < tensorProxy.data.length; i++)
+            {
+                tensorProxy.data[i] = (float)randomNormal.NextDouble();
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/TensorProxy.cs.meta b/com.unity.ml-agents/Runtime/Inference/TensorProxy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5e0dd9d08d2c4b29a3777b243787a97e140ec414
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@@ -0,0 +1,11 @@
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+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/Inference/Utils.meta b/com.unity.ml-agents/Runtime/Inference/Utils.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5431b3d6468328200572f93c093a8083cdcd75b1
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+++ b/com.unity.ml-agents/Runtime/Inference/Utils.meta
@@ -0,0 +1,8 @@
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diff --git a/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs b/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs
new file mode 100644
index 0000000000000000000000000000000000000000..41603dd3baf718e931897815e40d52866ff8833b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs
@@ -0,0 +1,59 @@
+namespace Unity.MLAgents.Inference.Utils
+{
+    /// <summary>
+    /// Multinomial - Draws samples from a multinomial distribution given a (potentially unscaled)
+    /// cumulative mass function (CMF). This means that the CMF need not "end" with probability
+    /// mass of 1.0. For instance: [0.1, 0.2, 0.5] is a valid (unscaled). What is important is
+    /// that it is a cumulative function, not a probability function. In other words,
+    /// entry[i] = P(x \le i), NOT P(i - 1 \le x \lt i).
+    /// (\le stands for less than or equal to while \lt is strictly less than).
+    /// </summary>
+    internal class Multinomial
+    {
+        readonly System.Random m_Random;
+
+        /// <summary>
+        /// Constructor.
+        /// </summary>
+        /// <param name="seed">
+        /// Seed for the random number generator used in the sampling process.
+        /// </param>
+        public Multinomial(int seed)
+        {
+            m_Random = new System.Random(seed);
+        }
+
+        /// <summary>
+        /// Samples from the Multinomial distribution defined by the provided cumulative
+        /// mass function.
+        /// </summary>
+        /// <param name="cmf">
+        /// Cumulative mass function, which may be unscaled. The entries in this array need
+        /// to be monotonic (always increasing). If the CMF is scaled, then the last entry in
+        /// the array will be 1.0.
+        /// </param>
+        /// <param name="branchSize">The number of possible branches, i.e. the effective size of the cmf array.</param>
+        /// <returns>A sampled index from the CMF ranging from 0 to branchSize-1.</returns>
+        public int Sample(float[] cmf, int branchSize)
+        {
+            var p = (float)m_Random.NextDouble() * cmf[branchSize - 1];
+            var cls = 0;
+            while (cmf[cls] < p)
+            {
+                ++cls;
+            }
+
+            return cls;
+        }
+
+        /// <summary>
+        /// Samples from the Multinomial distribution defined by the provided cumulative
+        /// mass function.
+        /// </summary>
+        /// <returns>A sampled index from the CMF ranging from 0 to cmf.Length-1.</returns>
+        public int Sample(float[] cmf)
+        {
+            return Sample(cmf, cmf.Length);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs.meta b/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2467eb50b3356986b13b461f52b5fe0efda2ea74
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/Utils/Multinomial.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5c9e297dad748408db9e5ce26b940fe3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs b/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4b0e1e7e2fa230f8cdf610065c5a2b437af0933c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs
@@ -0,0 +1,56 @@
+using System;
+
+namespace Unity.MLAgents.Inference.Utils
+{
+    /// <summary>
+    /// RandomNormal - A random number generator that produces normally distributed random
+    /// numbers using the Marsaglia polar method:
+    /// https://en.wikipedia.org/wiki/Marsaglia_polar_method
+    /// TODO: worth overriding System.Random instead of aggregating?
+    /// </summary>
+    internal class RandomNormal
+    {
+        readonly double m_Mean;
+        readonly double m_Stddev;
+        readonly Random m_Random;
+
+        public RandomNormal(int seed, float mean = 0.0f, float stddev = 1.0f)
+        {
+            m_Mean = mean;
+            m_Stddev = stddev;
+            m_Random = new Random(seed);
+        }
+
+        // Each iteration produces two numbers. Hold one here for next call
+        bool m_HasSpare;
+        double m_SpareUnscaled;
+
+        /// <summary>
+        /// Return the next random double number.
+        /// </summary>
+        /// <returns>Next random double number.</returns>
+        public double NextDouble()
+        {
+            if (m_HasSpare)
+            {
+                m_HasSpare = false;
+                return m_SpareUnscaled * m_Stddev + m_Mean;
+            }
+
+            double u, v, s;
+            do
+            {
+                u = m_Random.NextDouble() * 2.0 - 1.0;
+                v = m_Random.NextDouble() * 2.0 - 1.0;
+                s = u * u + v * v;
+            }
+            while (s >= 1.0 || Math.Abs(s) < double.Epsilon);
+
+            s = Math.Sqrt(-2.0 * Math.Log(s) / s);
+            m_SpareUnscaled = u * s;
+            m_HasSpare = true;
+
+            return v * s * m_Stddev + m_Mean;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs.meta b/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3cd152462dc785134ddb0915fe3602b0e08e8c20
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Inference/Utils/RandomNormal.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: df8528cf20f0e4c64a4a7596eccc1631
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/InplaceArray.cs b/com.unity.ml-agents/Runtime/InplaceArray.cs
new file mode 100644
index 0000000000000000000000000000000000000000..91208136d8511420a6c1aa5a5f78a3a2bac6f16b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/InplaceArray.cs
@@ -0,0 +1,240 @@
+using System;
+using System.Collections.Generic;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// An array-like object that stores up to four elements.
+    /// This is a value type that does not allocate any additional memory.
+    /// </summary>
+    /// <remarks>
+    /// This does not implement any interfaces such as IList, in order to avoid any accidental boxing allocations.
+    /// </remarks>
+    /// <typeparam name="T"></typeparam>
+    public struct InplaceArray<T> : IEquatable<InplaceArray<T>> where T : struct
+    {
+        private const int k_MaxLength = 4;
+        private readonly int m_Length;
+
+        private T m_Elem0;
+        private T m_Elem1;
+        private T m_Elem2;
+        private T m_Elem3;
+
+        /// <summary>
+        /// Create a length-1 array.
+        /// </summary>
+        /// <param name="elem0"></param>
+        public InplaceArray(T elem0)
+        {
+            m_Length = 1;
+            m_Elem0 = elem0;
+            m_Elem1 = new T();
+            m_Elem2 = new T();
+            m_Elem3 = new T();
+        }
+
+        /// <summary>
+        /// Create a length-2 array.
+        /// </summary>
+        /// <param name="elem0"></param>
+        /// <param name="elem1"></param>
+        public InplaceArray(T elem0, T elem1)
+        {
+            m_Length = 2;
+            m_Elem0 = elem0;
+            m_Elem1 = elem1;
+            m_Elem2 = new T();
+            m_Elem3 = new T();
+        }
+
+        /// <summary>
+        /// Create a length-3 array.
+        /// </summary>
+        /// <param name="elem0"></param>
+        /// <param name="elem1"></param>
+        /// <param name="elem2"></param>
+        public InplaceArray(T elem0, T elem1, T elem2)
+        {
+            m_Length = 3;
+            m_Elem0 = elem0;
+            m_Elem1 = elem1;
+            m_Elem2 = elem2;
+            m_Elem3 = new T();
+        }
+
+        /// <summary>
+        /// Create a length-3 array.
+        /// </summary>
+        /// <param name="elem0"></param>
+        /// <param name="elem1"></param>
+        /// <param name="elem2"></param>
+        /// <param name="elem3"></param>
+        public InplaceArray(T elem0, T elem1, T elem2, T elem3)
+        {
+            m_Length = 4;
+            m_Elem0 = elem0;
+            m_Elem1 = elem1;
+            m_Elem2 = elem2;
+            m_Elem3 = elem3;
+        }
+
+        /// <summary>
+        /// Construct an InplaceArray from an IList (e.g. Array or List).
+        /// The source must be non-empty and have at most 4 elements.
+        /// </summary>
+        /// <param name="elems"></param>
+        /// <returns></returns>
+        /// <exception cref="ArgumentOutOfRangeException"></exception>
+        public static InplaceArray<T> FromList(IList<T> elems)
+        {
+            switch (elems.Count)
+            {
+                case 1:
+                    return new InplaceArray<T>(elems[0]);
+                case 2:
+                    return new InplaceArray<T>(elems[0], elems[1]);
+                case 3:
+                    return new InplaceArray<T>(elems[0], elems[1], elems[2]);
+                case 4:
+                    return new InplaceArray<T>(elems[0], elems[1], elems[2], elems[3]);
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// Per-element access.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <exception cref="IndexOutOfRangeException"></exception>
+        public T this[int index]
+        {
+            get
+            {
+                if (index >= Length)
+                {
+                    throw new IndexOutOfRangeException();
+                }
+
+                switch (index)
+                {
+                    case 0:
+                        return m_Elem0;
+                    case 1:
+                        return m_Elem1;
+                    case 2:
+                        return m_Elem2;
+                    case 3:
+                        return m_Elem3;
+                    default:
+                        throw new IndexOutOfRangeException();
+                }
+            }
+
+            set
+            {
+                if (index >= Length)
+                {
+                    throw new IndexOutOfRangeException();
+                }
+
+                switch (index)
+                {
+                    case 0:
+                        m_Elem0 = value;
+                        break;
+                    case 1:
+                        m_Elem1 = value;
+                        break;
+                    case 2:
+                        m_Elem2 = value;
+                        break;
+                    case 3:
+                        m_Elem3 = value;
+                        break;
+                    default:
+                        throw new IndexOutOfRangeException();
+                }
+            }
+        }
+
+        /// <summary>
+        /// The length of the array.
+        /// </summary>
+        public int Length
+        {
+            get => m_Length;
+        }
+
+        /// <summary>
+        /// Returns a string representation of the array's elements.
+        /// </summary>
+        /// <returns></returns>
+        /// <exception cref="IndexOutOfRangeException"></exception>
+        public override string ToString()
+        {
+            switch (m_Length)
+            {
+                case 1:
+                    return $"[{m_Elem0}]";
+                case 2:
+                    return $"[{m_Elem0}, {m_Elem1}]";
+                case 3:
+                    return $"[{m_Elem0}, {m_Elem1}, {m_Elem2}]";
+                case 4:
+                    return $"[{m_Elem0}, {m_Elem1}, {m_Elem2}, {m_Elem3}]";
+                default:
+                    throw new IndexOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// Check that the arrays have the same length and have all equal values.
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>Whether the arrays are equivalent.</returns>
+        public static bool operator ==(InplaceArray<T> lhs, InplaceArray<T> rhs)
+        {
+            return lhs.Equals(rhs);
+        }
+
+        /// <summary>
+        /// Check that the arrays are not equivalent.
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>Whether the arrays are not equivalent</returns>
+        public static bool operator !=(InplaceArray<T> lhs, InplaceArray<T> rhs) => !lhs.Equals(rhs);
+
+        /// <summary>
+        /// Check that the arrays are equivalent.
+        /// </summary>
+        /// <param name="other"></param>
+        /// <returns>Whether the arrays are not equivalent</returns>
+        public override bool Equals(object other) => other is InplaceArray<T> other1 && this.Equals(other1);
+
+        /// <summary>
+        /// Check that the arrays are equivalent.
+        /// </summary>
+        /// <param name="other"></param>
+        /// <returns>Whether the arrays are not equivalent</returns>
+        public bool Equals(InplaceArray<T> other)
+        {
+            // See https://montemagno.com/optimizing-c-struct-equality-with-iequatable/
+            var thisTuple = (m_Elem0, m_Elem1, m_Elem2, m_Elem3, Length);
+            var otherTuple = (other.m_Elem0, other.m_Elem1, other.m_Elem2, other.m_Elem3, other.Length);
+            return thisTuple.Equals(otherTuple);
+        }
+
+        /// <summary>
+        /// Get a hashcode for the array.
+        /// </summary>
+        /// <returns></returns>
+        public override int GetHashCode()
+        {
+            return (m_Elem0, m_Elem1, m_Elem2, m_Elem3, Length).GetHashCode();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/InplaceArray.cs.meta b/com.unity.ml-agents/Runtime/InplaceArray.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3e4ab0c9287cbbfb41ce9d089b3c35eb3494cae1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/InplaceArray.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c1a80abee18a41c8aee89aeb33f5985d
+timeCreated: 1615506199
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations.meta b/com.unity.ml-agents/Runtime/Integrations.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f218be2521d7e64cf9072cc4f5d3b600b498b4cb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f8f4fd0bc35f4e8f9867228591f663e3
+timeCreated: 1618359419
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3.meta b/com.unity.ml-agents/Runtime/Integrations/Match3.meta
new file mode 100644
index 0000000000000000000000000000000000000000..27b09a9eff9083a935aaf3db28bdf80dae66eba9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 569f8fa2b7dd477c9b71f09e9d633832
+timeCreated: 1600465975
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d2f218a3f752af6e5172e1728a6c54c61d8e3b06
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs
@@ -0,0 +1,312 @@
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using UnityEngine;
+using Debug = UnityEngine.Debug;
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Representation of the AbstractBoard dimensions, and number of cell and special types.
+    /// </summary>
+    public struct BoardSize
+    {
+        /// <summary>
+        /// Number of rows on the board
+        /// </summary>
+        public int Rows;
+
+        /// <summary>
+        /// Number of columns on the board
+        /// </summary>
+        public int Columns;
+
+        /// <summary>
+        /// Maximum number of different types of cells (colors, pieces, etc).
+        /// </summary>
+        public int NumCellTypes;
+
+        /// <summary>
+        /// Maximum number of special types. This can be zero, in which case
+        /// all cells of the same type are assumed to be equivalent.
+        /// </summary>
+        public int NumSpecialTypes;
+
+        /// <summary>
+        /// Check that all fields of the left-hand BoardSize are less than or equal to the field of the right-hand BoardSize
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>True if all fields are less than or equal.</returns>
+        public static bool operator <=(BoardSize lhs, BoardSize rhs)
+        {
+            return lhs.Rows <= rhs.Rows && lhs.Columns <= rhs.Columns && lhs.NumCellTypes <= rhs.NumCellTypes &&
+                lhs.NumSpecialTypes <= rhs.NumSpecialTypes;
+        }
+
+        /// <summary>
+        /// Check that all fields of the left-hand BoardSize are greater than or equal to the field of the right-hand BoardSize
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>True if all fields are greater than or equal.</returns>
+        public static bool operator >=(BoardSize lhs, BoardSize rhs)
+        {
+            return lhs.Rows >= rhs.Rows && lhs.Columns >= rhs.Columns && lhs.NumCellTypes >= rhs.NumCellTypes &&
+                lhs.NumSpecialTypes >= rhs.NumSpecialTypes;
+        }
+
+        /// <summary>
+        /// Return a string representation of the BoardSize.
+        /// </summary>
+        /// <returns></returns>
+        public override string ToString()
+        {
+            return
+                $"Rows: {Rows}, Columns: {Columns}, NumCellTypes: {NumCellTypes}, NumSpecialTypes: {NumSpecialTypes}";
+        }
+    }
+
+    /// <summary>
+    /// An adapter between ML Agents and a Match-3 game.
+    /// </summary>
+    public abstract class AbstractBoard : MonoBehaviour
+    {
+        /// <summary>
+        /// Return the maximum size of the board. This is used to determine the size of observations and actions,
+        /// so the returned values must not change.
+        /// </summary>
+        /// <returns></returns>
+        public abstract BoardSize GetMaxBoardSize();
+
+        /// <summary>
+        /// Return the current size of the board. The values must less than or equal to the values returned from
+        /// <see cref="GetMaxBoardSize"/>.
+        /// By default, this will return <see cref="GetMaxBoardSize"/>; if your board doesn't change size, you don't need to
+        /// override it.
+        /// </summary>
+        /// <returns></returns>
+        public virtual BoardSize GetCurrentBoardSize()
+        {
+            return GetMaxBoardSize();
+        }
+
+        /// <summary>
+        /// Returns the "color" of the piece at the given row and column.
+        /// This should be between 0 and BoardSize.NumCellTypes-1 (inclusive).
+        /// The actual order of the values doesn't matter.
+        /// </summary>
+        /// <param name="row"></param>
+        /// <param name="col"></param>
+        /// <returns></returns>
+        public abstract int GetCellType(int row, int col);
+
+        /// <summary>
+        /// Returns the special type of the piece at the given row and column.
+        /// This should be between 0 and BoardSize.NumSpecialTypes (inclusive).
+        /// The actual order of the values doesn't matter.
+        /// </summary>
+        /// <param name="row"></param>
+        /// <param name="col"></param>
+        /// <returns></returns>
+        public abstract int GetSpecialType(int row, int col);
+
+        /// <summary>
+        /// Check whether the particular Move is valid for the game.
+        /// The actual results will depend on the rules of the game, but we provide <see cref="SimpleIsMoveValid(Move)"/>
+        /// that handles basic match3 rules with no special or immovable pieces.
+        /// </summary>
+        /// <remarks>
+        /// Moves that would go outside of <see cref="GetCurrentBoardSize"/> are filtered out before they are
+        /// passed to IsMoveValid().
+        /// </remarks>
+        /// <param name="m">The move to check.</param>
+        /// <returns></returns>
+        public abstract bool IsMoveValid(Move m);
+
+        /// <summary>
+        /// Instruct the game to make the given <see cref="Move"/>. Returns true if the move was made.
+        /// Note that during training, a move that was marked as invalid may occasionally still be
+        /// requested. If this happens, it is safe to do nothing and request another move.
+        /// </summary>
+        /// <param name="m">The move to carry out.</param>
+        /// <returns></returns>
+        public abstract bool MakeMove(Move m);
+
+        /// <summary>
+        /// Return the total number of moves possible for the board.
+        /// </summary>
+        /// <returns></returns>
+        public int NumMoves()
+        {
+            return Move.NumPotentialMoves(GetMaxBoardSize());
+        }
+
+        /// <summary>
+        /// An optional callback for when the all moves are invalid. Ideally, the game state should
+        /// be changed before this happens, but this is a way to get notified if not.
+        /// </summary>
+        public Action OnNoValidMovesAction;
+
+        /// <summary>
+        /// Iterate through all moves on the board.
+        /// </summary>
+        /// <returns></returns>
+        public IEnumerable<Move> AllMoves()
+        {
+            var maxBoardSize = GetMaxBoardSize();
+            var currentBoardSize = GetCurrentBoardSize();
+
+            var currentMove = Move.FromMoveIndex(0, maxBoardSize);
+            for (var i = 0; i < NumMoves(); i++)
+            {
+                if (currentMove.InRangeForBoard(currentBoardSize))
+                {
+                    yield return currentMove;
+                }
+                currentMove.Next(maxBoardSize);
+            }
+        }
+
+        /// <summary>
+        /// Iterate through all valid moves on the board.
+        /// </summary>
+        /// <returns></returns>
+        public IEnumerable<Move> ValidMoves()
+        {
+            var maxBoardSize = GetMaxBoardSize();
+            var currentBoardSize = GetCurrentBoardSize();
+
+            var currentMove = Move.FromMoveIndex(0, maxBoardSize);
+            for (var i = 0; i < NumMoves(); i++)
+            {
+                if (currentMove.InRangeForBoard(currentBoardSize) && IsMoveValid(currentMove))
+                {
+                    yield return currentMove;
+                }
+                currentMove.Next(maxBoardSize);
+            }
+        }
+
+        /// <summary>
+        /// Returns true if swapping the cells specified by the move would result in
+        /// 3 or more cells of the same type in a row. This assumes that all pieces are allowed
+        /// to be moved; to add extra logic, incorporate it into your <see cref="IsMoveValid"/> method.
+        /// </summary>
+        /// <param name="move"></param>
+        /// <returns></returns>
+        public bool SimpleIsMoveValid(Move move)
+        {
+            using (TimerStack.Instance.Scoped("SimpleIsMoveValid"))
+            {
+                var moveVal = GetCellType(move.Row, move.Column);
+                var (otherRow, otherCol) = move.OtherCell();
+                var oppositeVal = GetCellType(otherRow, otherCol);
+
+                // Simple check - if the values are the same, don't match
+                // This might not be valid for all games
+                {
+                    if (moveVal == oppositeVal)
+                    {
+                        return false;
+                    }
+                }
+
+                bool moveMatches = CheckHalfMove(otherRow, otherCol, moveVal, move.Direction);
+                if (moveMatches)
+                {
+                    // early out
+                    return true;
+                }
+
+                bool otherMatches = CheckHalfMove(move.Row, move.Column, oppositeVal, move.OtherDirection());
+                return otherMatches;
+            }
+        }
+
+        /// <summary>
+        /// Check if one of the cells that is swapped during a move matches 3 or more.
+        /// Since these checks are similar for each cell, we consider the move as two "half moves".
+        /// </summary>
+        /// <param name="newRow"></param>
+        /// <param name="newCol"></param>
+        /// <param name="newValue"></param>
+        /// <param name="incomingDirection"></param>
+        /// <returns></returns>
+        bool CheckHalfMove(int newRow, int newCol, int newValue, Direction incomingDirection)
+        {
+            var currentBoardSize = GetCurrentBoardSize();
+            int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
+
+            if (incomingDirection != Direction.Right)
+            {
+                for (var c = newCol - 1; c >= 0; c--)
+                {
+                    if (GetCellType(newRow, c) == newValue)
+                        matchedLeft++;
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Left)
+            {
+                for (var c = newCol + 1; c < currentBoardSize.Columns; c++)
+                {
+                    if (GetCellType(newRow, c) == newValue)
+                        matchedRight++;
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Down)
+            {
+                for (var r = newRow + 1; r < currentBoardSize.Rows; r++)
+                {
+                    if (GetCellType(r, newCol) == newValue)
+                        matchedUp++;
+                    else
+                        break;
+                }
+            }
+
+            if (incomingDirection != Direction.Up)
+            {
+                for (var r = newRow - 1; r >= 0; r--)
+                {
+                    if (GetCellType(r, newCol) == newValue)
+                        matchedDown++;
+                    else
+                        break;
+                }
+            }
+
+            if ((matchedUp + matchedDown >= 2) || (matchedLeft + matchedRight >= 2))
+            {
+                return true;
+            }
+
+            return false;
+        }
+
+        /// <summary>
+        /// Make sure that the current BoardSize isn't larger than the original value of <see cref="GetMaxBoardSize"/>.
+        /// If it is, log a warning.
+        /// </summary>
+        /// <param name="originalMaxBoardSize"></param>
+        [Conditional("DEBUG")]
+        internal void CheckBoardSizes(BoardSize originalMaxBoardSize)
+        {
+            var currentBoardSize = GetCurrentBoardSize();
+            if (!(currentBoardSize <= originalMaxBoardSize))
+            {
+                Debug.LogWarning(
+                    "Current BoardSize is larger than maximum board size was on initialization. This may cause unexpected results.\n" +
+                    $"Original GetMaxBoardSize() result: {originalMaxBoardSize}\n" +
+                    $"GetCurrentBoardSize() result: {currentBoardSize}"
+                );
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..42019368a138e8d64c5a8017c161fc47fa980b8b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/AbstractBoard.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6222defa70dc4c08aaeafd0be4e821d2
+timeCreated: 1600466051
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9bd60bd5717e6f0ccd73714a960d10c39115a8d1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs
@@ -0,0 +1,188 @@
+using Unity.MLAgents.Actuators;
+using Debug = UnityEngine.Debug;
+
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Actuator for a Match3 game. It translates valid moves (defined by AbstractBoard.IsMoveValid())
+    /// in action masks, and applies the action to the board via AbstractBoard.MakeMove().
+    /// </summary>
+    public class Match3Actuator : IActuator, IBuiltInActuator
+    {
+        AbstractBoard m_Board;
+        System.Random m_Random;
+        ActionSpec m_ActionSpec;
+        bool m_ForceHeuristic;
+        BoardSize m_MaxBoardSize;
+
+        /// <summary>
+        /// Create a Match3Actuator.
+        /// </summary>
+        /// <param name="board"></param>
+        /// <param name="forceHeuristic">Whether the inference action should be ignored and the Agent's Heuristic
+        /// should be called. This should only be used for generating comparison stats of the Heuristic.</param>
+        /// <param name="seed">The seed used to initialize <see cref="System.Random"/>.</param>
+        /// <param name="name"></param>
+        public Match3Actuator(AbstractBoard board,
+                              bool forceHeuristic,
+                              int seed,
+                              string name)
+        {
+            m_Board = board;
+            m_MaxBoardSize = m_Board.GetMaxBoardSize();
+            Name = name;
+
+            m_ForceHeuristic = forceHeuristic;
+
+            var numMoves = Move.NumPotentialMoves(m_MaxBoardSize);
+            m_ActionSpec = ActionSpec.MakeDiscrete(numMoves);
+            m_Random = new System.Random(seed);
+        }
+
+        /// <inheritdoc/>
+        public ActionSpec ActionSpec => m_ActionSpec;
+
+        /// <inheritdoc/>
+        public void OnActionReceived(ActionBuffers actions)
+        {
+            m_Board.CheckBoardSizes(m_MaxBoardSize);
+            if (m_ForceHeuristic)
+            {
+                Heuristic(actions);
+            }
+            var moveIndex = actions.DiscreteActions[0];
+
+            Move move = Move.FromMoveIndex(moveIndex, m_MaxBoardSize);
+            m_Board.MakeMove(move);
+        }
+
+        /// <inheritdoc/>
+        public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+        {
+            var currentBoardSize = m_Board.GetCurrentBoardSize();
+            m_Board.CheckBoardSizes(m_MaxBoardSize);
+            const int branch = 0;
+            bool foundValidMove = false;
+            using (TimerStack.Instance.Scoped("WriteDiscreteActionMask"))
+            {
+                var numMoves = m_Board.NumMoves();
+
+                var currentMove = Move.FromMoveIndex(0, m_MaxBoardSize);
+                for (var i = 0; i < numMoves; i++)
+                {
+                    // Check that the move is allowed for the current boardSize (e.g. it won't move a piece out of
+                    // bounds), and that it's allowed by the game itself.
+                    if (currentMove.InRangeForBoard(currentBoardSize) && m_Board.IsMoveValid(currentMove))
+                    {
+                        foundValidMove = true;
+                    }
+                    else
+                    {
+                        actionMask.SetActionEnabled(branch, i, false);
+                    }
+                    currentMove.Next(m_MaxBoardSize);
+                }
+
+                if (!foundValidMove)
+                {
+                    // If all the moves are invalid and we mask all the actions out, this will cause an assert
+                    // later on in IDiscreteActionMask. Instead, fire a callback to the user if they provided one,
+                    // (or log a warning if not) and leave the last action unmasked. This isn't great, but
+                    // an invalid move should be easier to handle than an exception..
+                    if (m_Board.OnNoValidMovesAction != null)
+                    {
+                        m_Board.OnNoValidMovesAction();
+                    }
+                    else
+                    {
+                        Debug.LogWarning(
+                            "No valid moves are available. The last action will be left unmasked, so " +
+                            "an invalid move will be passed to AbstractBoard.MakeMove()."
+                        );
+                    }
+                    actionMask.SetActionEnabled(branch, numMoves - 1, true);
+                }
+            }
+        }
+
+        /// <inheritdoc/>
+        public string Name { get; }
+
+        /// <inheritdoc/>
+        public void ResetData()
+        {
+        }
+
+        /// <inheritdoc/>
+        public BuiltInActuatorType GetBuiltInActuatorType()
+        {
+            return BuiltInActuatorType.Match3Actuator;
+        }
+
+        /// <inheritdoc/>
+        public void Heuristic(in ActionBuffers actionsOut)
+        {
+            var discreteActions = actionsOut.DiscreteActions;
+            discreteActions[0] = GreedyMove();
+        }
+
+        /// <summary>
+        /// Returns a valid move that gives the highest value for EvalMovePoints(). If multiple moves have the same
+        /// value, one of them will be chosen with uniform probability.
+        /// </summary>
+        /// <remarks>
+        /// By default, EvalMovePoints() returns 1, so all valid moves are equally likely. Inherit from this class and
+        /// override EvalMovePoints() to use your game's scoring as a better estimate.
+        /// </remarks>
+        /// <returns></returns>
+        internal int GreedyMove()
+        {
+            var bestMoveIndex = 0;
+            var bestMovePoints = -1;
+            var numMovesAtCurrentScore = 0;
+
+            foreach (var move in m_Board.ValidMoves())
+            {
+                var movePoints = EvalMovePoints(move);
+                if (movePoints < bestMovePoints)
+                {
+                    // Worse, skip
+                    continue;
+                }
+
+                if (movePoints > bestMovePoints)
+                {
+                    // Better, keep
+                    bestMovePoints = movePoints;
+                    bestMoveIndex = move.MoveIndex;
+                    numMovesAtCurrentScore = 1;
+                }
+                else
+                {
+                    // Tied for best - use reservoir sampling to make sure we select from equal moves uniformly.
+                    // See https://en.wikipedia.org/wiki/Reservoir_sampling#Simple_algorithm
+                    numMovesAtCurrentScore++;
+                    var randVal = m_Random.Next(0, numMovesAtCurrentScore);
+                    if (randVal == 0)
+                    {
+                        // Keep the new one
+                        bestMoveIndex = move.MoveIndex;
+                    }
+                }
+            }
+
+            return bestMoveIndex;
+        }
+
+        /// <summary>
+        /// Method to be overridden when evaluating how many points a specific move will generate.
+        /// </summary>
+        /// <param name="move">The move to evaluate.</param>
+        /// <returns>The number of points the move generates.</returns>
+        protected virtual int EvalMovePoints(Move move)
+        {
+            return 1;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4052f5e51fa4da65a4cd0b21d36953e74d0b1113
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Actuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 9083fa4c35dc499aa5a86d8e7447c7cf
+timeCreated: 1600906373
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8dfa7e496e749d83f190048d6242d174700d9b9b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs
@@ -0,0 +1,81 @@
+using System;
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Actuator component for a Match3 game. Generates a Match3Actuator at runtime.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Match 3 Actuator", (int)MenuGroup.Actuators)]
+    public class Match3ActuatorComponent : ActuatorComponent
+    {
+        [HideInInspector, SerializeField, FormerlySerializedAs("ActuatorName")]
+        string m_ActuatorName = "Match3 Actuator";
+
+        /// <summary>
+        /// Name of the generated Match3Actuator object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the actuators.
+        /// </summary>
+        public string ActuatorName
+        {
+            get => m_ActuatorName;
+            set => m_ActuatorName = value;
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("RandomSeed")]
+        int m_RandomSeed = -1;
+
+        /// <summary>
+        /// A random seed used in the actuator's heuristic, if needed.
+        /// </summary>
+        public int RandomSeed
+        {
+            get => m_RandomSeed;
+            set => m_RandomSeed = value;
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("ForceHeuristic")]
+        [Tooltip("Force using the Agent's Heuristic() method to decide the action. This should only be used in testing.")]
+        bool m_ForceHeuristic;
+
+        /// <summary>
+        /// Force using the Agent's Heuristic() method to decide the action. This should only be used in testing.
+        /// </summary>
+        public bool ForceHeuristic
+        {
+            get => m_ForceHeuristic;
+            set => m_ForceHeuristic = value;
+        }
+
+        /// <inheritdoc/>
+        public override IActuator[] CreateActuators()
+        {
+            var board = GetComponent<AbstractBoard>();
+            if (!board)
+            {
+                return Array.Empty<IActuator>();
+            }
+
+            var seed = m_RandomSeed == -1 ? gameObject.GetInstanceID() : m_RandomSeed + 1;
+            return new IActuator[] { new Match3Actuator(board, m_ForceHeuristic, seed, m_ActuatorName) };
+        }
+
+        /// <inheritdoc/>
+        public override ActionSpec ActionSpec
+        {
+            get
+            {
+                var board = GetComponent<AbstractBoard>();
+                if (board == null)
+                {
+                    return ActionSpec.MakeContinuous(0);
+                }
+
+                var numMoves = Move.NumPotentialMoves(board.GetMaxBoardSize());
+                return ActionSpec.MakeDiscrete(numMoves);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c592f8be7a4ac6f587251d6c4627152ab26b2f01
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3ActuatorComponent.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 08e4b0da54cb4d56bfcbae22dd49ab8d
+timeCreated: 1600906388
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4252bd9b50d4db6abd15331372c32a1463e20f91
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs
@@ -0,0 +1,350 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Delegate that provides integer values at a given (x,y) coordinate.
+    /// </summary>
+    /// <param name="x"></param>
+    /// <param name="y"></param>
+    public delegate int GridValueProvider(int x, int y);
+
+    /// <summary>
+    /// Type of observations to generate.
+    ///
+    /// </summary>
+    public enum Match3ObservationType
+    {
+        /// <summary>
+        /// Generate a one-hot encoding of the cell type for each cell on the board. If there are special types,
+        /// these will also be one-hot encoded.
+        /// </summary>
+        Vector,
+
+        /// <summary>
+        /// Generate a one-hot encoding of the cell type for each cell on the board, but arranged as
+        /// a Rows x Columns visual observation. If there are special types, these will also be one-hot encoded.
+        /// </summary>
+        UncompressedVisual,
+
+        /// <summary>
+        /// Generate a one-hot encoding of the cell type for each cell on the board, but arranged as
+        /// a Rows x Columns visual observation. If there are special types, these will also be one-hot encoded.
+        /// During training, these will be sent as a concatenated series of PNG images, with 3 channels per image.
+        /// </summary>
+        CompressedVisual
+    }
+
+    /// <summary>
+    /// Sensor for Match3 games. Can generate either vector, compressed visual,
+    /// or uncompressed visual observations. Uses a GridValueProvider to determine the observation values.
+    /// </summary>
+    public class Match3Sensor : ISensor, IBuiltInSensor, IDisposable
+    {
+        Match3ObservationType m_ObservationType;
+        ObservationSpec m_ObservationSpec;
+        string m_Name;
+
+        AbstractBoard m_Board;
+        BoardSize m_MaxBoardSize;
+        GridValueProvider m_GridValues;
+        int m_OneHotSize;
+
+        Texture2D m_ObservationTexture;
+        OneHotToTextureUtil m_TextureUtil;
+
+        /// <summary>
+        /// Create a sensor for the GridValueProvider with the specified observation type.
+        /// </summary>
+        /// <remarks>
+        /// Use Match3Sensor.CellTypeSensor() or Match3Sensor.SpecialTypeSensor() instead of calling
+        /// the constructor directly.
+        /// </remarks>
+        /// <param name="board">The abstract board.</param>
+        /// <param name="gvp">The GridValueProvider, should be either board.GetCellType or board.GetSpecialType.</param>
+        /// <param name="oneHotSize">The number of possible values that the GridValueProvider can return.</param>
+        /// <param name="obsType">Whether to produce vector or visual observations</param>
+        /// <param name="name">Name of the sensor.</param>
+        public Match3Sensor(AbstractBoard board, GridValueProvider gvp, int oneHotSize, Match3ObservationType obsType, string name)
+        {
+            var maxBoardSize = board.GetMaxBoardSize();
+            m_Name = name;
+            m_MaxBoardSize = maxBoardSize;
+            m_GridValues = gvp;
+            m_OneHotSize = oneHotSize;
+            m_Board = board;
+
+            m_ObservationType = obsType;
+            m_ObservationSpec = obsType == Match3ObservationType.Vector
+                ? ObservationSpec.Vector(maxBoardSize.Rows * maxBoardSize.Columns * oneHotSize)
+                : ObservationSpec.Visual(maxBoardSize.Rows, maxBoardSize.Columns, oneHotSize);
+        }
+
+        /// <summary>
+        /// Create a sensor that encodes the board cells as observations.
+        /// </summary>
+        /// <param name="board">The abstract board.</param>
+        /// <param name="obsType">Whether to produce vector or visual observations</param>
+        /// <param name="name">Name of the sensor.</param>
+        /// <returns></returns>
+        public static Match3Sensor CellTypeSensor(AbstractBoard board, Match3ObservationType obsType, string name)
+        {
+            var maxBoardSize = board.GetMaxBoardSize();
+            return new Match3Sensor(board, board.GetCellType, maxBoardSize.NumCellTypes, obsType, name);
+        }
+
+        /// <summary>
+        /// Create a sensor that encodes the cell special types as observations. Returns null if the board's
+        /// NumSpecialTypes is 0 (indicating the sensor isn't needed).
+        /// </summary>
+        /// <param name="board">The abstract board.</param>
+        /// <param name="obsType">Whether to produce vector or visual observations</param>
+        /// <param name="name">Name of the sensor.</param>
+        /// <returns></returns>
+        public static Match3Sensor SpecialTypeSensor(AbstractBoard board, Match3ObservationType obsType, string name)
+        {
+            var maxBoardSize = board.GetMaxBoardSize();
+            if (maxBoardSize.NumSpecialTypes == 0)
+            {
+                return null;
+            }
+            var specialSize = maxBoardSize.NumSpecialTypes + 1;
+            return new Match3Sensor(board, board.GetSpecialType, specialSize, obsType, name);
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            m_Board.CheckBoardSizes(m_MaxBoardSize);
+            var currentBoardSize = m_Board.GetCurrentBoardSize();
+
+            int offset = 0;
+            var isVisual = m_ObservationType != Match3ObservationType.Vector;
+
+            // This is equivalent to
+            // for (var r = 0; r < m_MaxBoardSize.Rows; r++)
+            //     for (var c = 0; c < m_MaxBoardSize.Columns; c++)
+            //          if (r < currentBoardSize.Rows && c < currentBoardSize.Columns)
+            //              WriteOneHot
+            //          else
+            //              WriteZero
+            // but rearranged to avoid the branching.
+
+            for (var r = 0; r < currentBoardSize.Rows; r++)
+            {
+                for (var c = 0; c < currentBoardSize.Columns; c++)
+                {
+                    var val = m_GridValues(r, c);
+                    writer.WriteOneHot(offset, r, c, val, m_OneHotSize, isVisual);
+                    offset += m_OneHotSize;
+                }
+
+                for (var c = currentBoardSize.Columns; c < m_MaxBoardSize.Columns; c++)
+                {
+                    writer.WriteZero(offset, r, c, m_OneHotSize, isVisual);
+                    offset += m_OneHotSize;
+                }
+            }
+
+            for (var r = currentBoardSize.Rows; r < m_MaxBoardSize.Columns; r++)
+            {
+                for (var c = 0; c < m_MaxBoardSize.Columns; c++)
+                {
+                    writer.WriteZero(offset, r, c, m_OneHotSize, isVisual);
+                    offset += m_OneHotSize;
+                }
+            }
+
+            return offset;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            m_Board.CheckBoardSizes(m_MaxBoardSize);
+            var height = m_MaxBoardSize.Rows;
+            var width = m_MaxBoardSize.Columns;
+            if (ReferenceEquals(null, m_ObservationTexture))
+            {
+                m_ObservationTexture = new Texture2D(width, height, TextureFormat.RGB24, false);
+            }
+
+            if (ReferenceEquals(null, m_TextureUtil))
+            {
+                m_TextureUtil = new OneHotToTextureUtil(height, width);
+            }
+            var bytesOut = new List<byte>();
+            var currentBoardSize = m_Board.GetCurrentBoardSize();
+
+            // Encode the cell types or special types as batches of PNGs
+            // This is potentially wasteful, e.g. if there are 4 cell types and 1 special type, we could
+            // fit in in 2 images, but we'll use 3 total (2 PNGs for the 4 cell type channels, and 1 for
+            // the special types).
+            var numCellImages = (m_OneHotSize + 2) / 3;
+            for (var i = 0; i < numCellImages; i++)
+            {
+                m_TextureUtil.EncodeToTexture(
+                    m_GridValues,
+                    m_ObservationTexture,
+                    3 * i,
+                    currentBoardSize.Rows,
+                    currentBoardSize.Columns
+                );
+                bytesOut.AddRange(m_ObservationTexture.EncodeToPNG());
+            }
+
+            return bytesOut.ToArray();
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+        }
+
+        /// <inheritdoc/>
+        public void Reset()
+        {
+        }
+
+        internal SensorCompressionType GetCompressionType()
+        {
+            return m_ObservationType == Match3ObservationType.CompressedVisual ?
+                SensorCompressionType.PNG :
+                SensorCompressionType.None;
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return new CompressionSpec(GetCompressionType());
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.Match3Sensor;
+        }
+
+        /// <summary>
+        /// Clean up the owned Texture2D.
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(null, m_ObservationTexture))
+            {
+                Utilities.DestroyTexture(m_ObservationTexture);
+                m_ObservationTexture = null;
+            }
+        }
+    }
+
+    /// <summary>
+    /// Utility class for converting a 2D array of ints representing a one-hot encoding into
+    /// a texture, suitable for conversion to PNGs for observations.
+    /// Works by encoding 3 values at a time as pixels in the texture, thus it should be
+    /// called (maxValue + 2) / 3 times, increasing the channelOffset by 3 each time.
+    /// </summary>
+    internal class OneHotToTextureUtil
+    {
+        Color[] m_Colors;
+        int m_MaxHeight;
+        int m_MaxWidth;
+        private static Color[] s_OneHotColors = { Color.red, Color.green, Color.blue };
+
+        public OneHotToTextureUtil(int maxHeight, int maxWidth)
+        {
+            m_Colors = new Color[maxHeight * maxWidth];
+            m_MaxHeight = maxHeight;
+            m_MaxWidth = maxWidth;
+        }
+
+        public void EncodeToTexture(
+            GridValueProvider gridValueProvider,
+            Texture2D texture,
+            int channelOffset,
+            int currentHeight,
+            int currentWidth
+        )
+        {
+            var i = 0;
+            // There's an implicit flip converting to PNG from texture, so make sure we
+            // counteract that when forming the texture by iterating through h in reverse.
+            for (var h = m_MaxHeight - 1; h >= 0; h--)
+            {
+                for (var w = 0; w < m_MaxWidth; w++)
+                {
+                    var colorVal = Color.black;
+                    if (h < currentHeight && w < currentWidth)
+                    {
+                        int oneHotValue = gridValueProvider(h, w);
+                        if (oneHotValue >= channelOffset && oneHotValue < channelOffset + 3)
+                        {
+                            colorVal = s_OneHotColors[oneHotValue - channelOffset];
+                        }
+                    }
+                    m_Colors[i++] = colorVal;
+                }
+            }
+            texture.SetPixels(m_Colors);
+        }
+    }
+
+    /// <summary>
+    /// Utility methods for writing one-hot observations.
+    /// </summary>
+    internal static class ObservationWriterMatch3Extensions
+    {
+        public static void WriteOneHot(this ObservationWriter writer, int offset, int row, int col, int value, int oneHotSize, bool isVisual)
+        {
+            if (isVisual)
+            {
+                for (var i = 0; i < oneHotSize; i++)
+                {
+                    writer[row, col, i] = (i == value) ? 1.0f : 0.0f;
+                }
+            }
+            else
+            {
+                for (var i = 0; i < oneHotSize; i++)
+                {
+                    writer[offset] = (i == value) ? 1.0f : 0.0f;
+                    offset++;
+                }
+            }
+        }
+
+        public static void WriteZero(this ObservationWriter writer, int offset, int row, int col, int oneHotSize, bool isVisual)
+        {
+            if (isVisual)
+            {
+                for (var i = 0; i < oneHotSize; i++)
+                {
+                    writer[row, col, i] = 0.0f;
+                }
+            }
+            else
+            {
+                for (var i = 0; i < oneHotSize; i++)
+                {
+                    writer[offset] = 0.0f;
+                    offset++;
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b440cac0fc7bd9e81f502d6a3becc8bdcde5db20
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3Sensor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 795ad5f211e344e5bf3049abd9499721
+timeCreated: 1600906663
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8afd4d0edcc83964ed7261eb5bee67b374e567d3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs
@@ -0,0 +1,77 @@
+using System;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Sensor component for a Match3 game.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Match 3 Sensor", (int)MenuGroup.Sensors)]
+    public class Match3SensorComponent : SensorComponent, IDisposable
+    {
+        [HideInInspector, SerializeField, FormerlySerializedAs("SensorName")]
+        string m_SensorName = "Match3 Sensor";
+
+        /// <summary>
+        /// Name of the generated Match3Sensor object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get => m_SensorName;
+            set => m_SensorName = value;
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("ObservationType")]
+        Match3ObservationType m_ObservationType = Match3ObservationType.Vector;
+
+        /// <summary>
+        /// Type of observation to generate.
+        /// </summary>
+        public Match3ObservationType ObservationType
+        {
+            get => m_ObservationType;
+            set => m_ObservationType = value;
+        }
+
+        private ISensor[] m_Sensors;
+
+        /// <inheritdoc/>
+        public override ISensor[] CreateSensors()
+        {
+            // Clean up any existing sensors
+            Dispose();
+
+            var board = GetComponent<AbstractBoard>();
+            if (!board)
+            {
+                return Array.Empty<ISensor>();
+            }
+            var cellSensor = Match3Sensor.CellTypeSensor(board, m_ObservationType, m_SensorName + " (cells)");
+            // This can be null if BoardSize.NumSpecialTypes is 0
+            var specialSensor = Match3Sensor.SpecialTypeSensor(board, m_ObservationType, m_SensorName + " (special)");
+            m_Sensors = specialSensor != null
+                ? new ISensor[] { cellSensor, specialSensor }
+            : new ISensor[] { cellSensor };
+            return m_Sensors;
+        }
+
+        /// <summary>
+        /// Clean up the sensors created by CreateSensors().
+        /// </summary>
+        public void Dispose()
+        {
+            if (m_Sensors != null)
+            {
+                for (var i = 0; i < m_Sensors.Length; i++)
+                {
+                    ((Match3Sensor)m_Sensors[i]).Dispose();
+                }
+
+                m_Sensors = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d2d2713eefad4b5dbe30173a33186389cafd45c4
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Match3SensorComponent.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 530d2f105aa145bd8a00e021bdd925fd
+timeCreated: 1600906676
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs b/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d4aac5f854cc99815e71e722d8a23d89a8268254
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs
@@ -0,0 +1,277 @@
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.Integrations.Match3
+{
+    /// <summary>
+    /// Directions for a Move.
+    /// </summary>
+    public enum Direction
+    {
+        /// <summary>
+        /// Move up (increasing row direction).
+        /// </summary>
+        Up,
+
+        /// <summary>
+        /// Move down (decreasing row direction).
+        /// </summary>
+        Down, // -row direction
+
+        /// <summary>
+        /// Move left (decreasing column direction).
+        /// </summary>
+        Left, // -column direction
+
+        /// <summary>
+        /// Move right (increasing column direction).
+        /// </summary>
+        Right, // +column direction
+    }
+
+    /// <summary>
+    /// Struct that encapsulates a swap of adjacent cells.
+    /// A Move can be constructed from either a starting row, column, and direction,
+    /// or from a "move index" between 0 and NumPotentialMoves()-1.
+    /// Moves are enumerated as the internal edges of the game grid.
+    /// Left/right moves come first. There are (maxCols - 1) * maxRows of these.
+    /// Up/down moves are next. There are (maxRows - 1) * maxCols of these.
+    /// </summary>
+    public struct Move
+    {
+        /// <summary>
+        /// Index of the move, from 0 to NumPotentialMoves-1.
+        /// </summary>
+        public int MoveIndex;
+
+        /// <summary>
+        /// Row of the cell that will be moved.
+        /// </summary>
+        public int Row;
+
+        /// <summary>
+        /// Column of the cell that will be moved.
+        /// </summary>
+        public int Column;
+
+        /// <summary>
+        /// Direction that the cell will be moved.
+        /// </summary>
+        public Direction Direction;
+
+        /// <summary>
+        /// Construct a Move from its move index and the board size.
+        /// This is useful for iterating through all the Moves on a board, or constructing
+        /// the Move corresponding to an Agent decision.
+        /// </summary>
+        /// <param name="moveIndex">Must be between 0 and NumPotentialMoves(maxRows, maxCols).</param>
+        /// <param name="maxBoardSize"></param>
+        /// <returns></returns>
+        /// <exception cref="ArgumentOutOfRangeException"></exception>
+        public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize)
+        {
+            var maxRows = maxBoardSize.Rows;
+            var maxCols = maxBoardSize.Columns;
+
+            if (moveIndex < 0 || moveIndex >= NumPotentialMoves(maxBoardSize))
+            {
+                throw new ArgumentOutOfRangeException("moveIndex");
+            }
+            Direction dir;
+            int row, col;
+            if (moveIndex < (maxCols - 1) * maxRows)
+            {
+                dir = Direction.Right;
+                col = moveIndex % (maxCols - 1);
+                row = moveIndex / (maxCols - 1);
+            }
+            else
+            {
+                dir = Direction.Up;
+                var offset = moveIndex - (maxCols - 1) * maxRows;
+                col = offset % maxCols;
+                row = offset / maxCols;
+            }
+            return new Move
+            {
+                MoveIndex = moveIndex,
+                Direction = dir,
+                Row = row,
+                Column = col
+            };
+        }
+
+        /// <summary>
+        /// Increment the Move to the next MoveIndex, and update the Row, Column, and Direction accordingly.
+        /// </summary>
+        /// <param name="maxBoardSize"></param>
+        public void Next(BoardSize maxBoardSize)
+        {
+            var maxRows = maxBoardSize.Rows;
+            var maxCols = maxBoardSize.Columns;
+
+            var switchoverIndex = (maxCols - 1) * maxRows;
+
+            MoveIndex++;
+            if (MoveIndex < switchoverIndex)
+            {
+                Column++;
+                if (Column == maxCols - 1)
+                {
+                    Row++;
+                    Column = 0;
+                }
+            }
+            else if (MoveIndex == switchoverIndex)
+            {
+                // switch from moving right to moving up
+                Row = 0;
+                Column = 0;
+                Direction = Direction.Up;
+            }
+            else
+            {
+                Column++;
+                if (Column == maxCols)
+                {
+                    Row++;
+                    Column = 0;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Construct a Move from the row, column, direction, and board size.
+        /// </summary>
+        /// <param name="row"></param>
+        /// <param name="col"></param>
+        /// <param name="dir"></param>
+        /// <param name="maxBoardSize"></param>
+        /// <returns></returns>
+        public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize)
+        {
+            // Check for out-of-bounds
+            if (row < 0 || row >= maxBoardSize.Rows)
+            {
+                throw new IndexOutOfRangeException($"row was {row}, but must be between 0 and {maxBoardSize.Rows - 1}.");
+            }
+
+            if (col < 0 || col >= maxBoardSize.Columns)
+            {
+                throw new IndexOutOfRangeException($"col was {col}, but must be between 0 and {maxBoardSize.Columns - 1}.");
+            }
+
+            // Check moves that would go out of bounds e.g. col == 0 and dir == Left
+            if (
+                row == 0 && dir == Direction.Down ||
+                row == maxBoardSize.Rows - 1 && dir == Direction.Up ||
+                col == 0 && dir == Direction.Left ||
+                col == maxBoardSize.Columns - 1 && dir == Direction.Right
+            )
+            {
+                throw new IndexOutOfRangeException($"Cannot move cell at row={row} col={col} in Direction={dir}");
+            }
+
+            // Normalize - only consider Right and Up
+            if (dir == Direction.Left)
+            {
+                dir = Direction.Right;
+                col = col - 1;
+            }
+            else if (dir == Direction.Down)
+            {
+                dir = Direction.Up;
+                row = row - 1;
+            }
+
+            int moveIndex;
+            if (dir == Direction.Right)
+            {
+                moveIndex = col + row * (maxBoardSize.Columns - 1);
+            }
+            else
+            {
+                var offset = (maxBoardSize.Columns - 1) * maxBoardSize.Rows;
+                moveIndex = offset + col + row * maxBoardSize.Columns;
+            }
+
+            return new Move
+            {
+                Row = row,
+                Column = col,
+                Direction = dir,
+                MoveIndex = moveIndex,
+            };
+        }
+
+        /// <summary>
+        /// Check if the move is valid for the given board size.
+        /// This will be passed the return value from AbstractBoard.GetCurrentBoardSize().
+        /// </summary>
+        /// <param name="boardSize"></param>
+        /// <returns></returns>
+        public bool InRangeForBoard(BoardSize boardSize)
+        {
+            var (otherRow, otherCol) = OtherCell();
+            // Get the maximum row and column this move would affect.
+            var maxMoveRow = Mathf.Max(Row, otherRow);
+            var maxMoveCol = Mathf.Max(Column, otherCol);
+            return maxMoveRow < boardSize.Rows && maxMoveCol < boardSize.Columns;
+        }
+
+        /// <summary>
+        /// Get the other row and column that correspond to this move.
+        /// </summary>
+        /// <returns></returns>
+        /// <exception cref="ArgumentOutOfRangeException"></exception>
+        public (int Row, int Column) OtherCell()
+        {
+            switch (Direction)
+            {
+                case Direction.Up:
+                    return (Row + 1, Column);
+                case Direction.Down:
+                    return (Row - 1, Column);
+                case Direction.Left:
+                    return (Row, Column - 1);
+                case Direction.Right:
+                    return (Row, Column + 1);
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// Get the opposite direction of this move.
+        /// </summary>
+        /// <returns></returns>
+        /// <exception cref="ArgumentOutOfRangeException"></exception>
+        public Direction OtherDirection()
+        {
+            switch (Direction)
+            {
+                case Direction.Up:
+                    return Direction.Down;
+                case Direction.Down:
+                    return Direction.Up;
+                case Direction.Left:
+                    return Direction.Right;
+                case Direction.Right:
+                    return Direction.Left;
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// Return the number of potential moves for a board of the given size.
+        /// This is equivalent to the number of internal edges in the board.
+        /// </summary>
+        /// <param name="maxBoardSize"></param>
+        /// <returns></returns>
+        public static int NumPotentialMoves(BoardSize maxBoardSize)
+        {
+            return maxBoardSize.Rows * (maxBoardSize.Columns - 1) + (maxBoardSize.Rows - 1) * (maxBoardSize.Columns);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs.meta b/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1457c24b13647dcb16a00be1cfc10caf769df8ce
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 41d6d7b9e07c4ef1ae075c74a906906b
+timeCreated: 1600466100
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/MLAgentsSettings.cs b/com.unity.ml-agents/Runtime/MLAgentsSettings.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a86cb3635cbed03413de73a835d23b972eeac8d9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MLAgentsSettings.cs
@@ -0,0 +1,41 @@
+using UnityEngine;
+using System.Runtime.CompilerServices;
+
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.DevTests.Editor")]
+namespace Unity.MLAgents
+{
+    internal class MLAgentsSettings : ScriptableObject
+    {
+        [SerializeField]
+        private bool m_ConnectTrainer = true;
+        [SerializeField]
+        private int m_EditorPort = 5004;
+
+        public bool ConnectTrainer
+        {
+            get { return m_ConnectTrainer; }
+            set
+            {
+                m_ConnectTrainer = value;
+                OnChange();
+            }
+        }
+
+        public int EditorPort
+        {
+            get { return m_EditorPort; }
+            set
+            {
+                m_EditorPort = value;
+                OnChange();
+            }
+        }
+
+        internal void OnChange()
+        {
+            if (MLAgentsSettingsManager.Settings == this)
+                MLAgentsSettingsManager.ApplySettings();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/MLAgentsSettings.cs.meta b/com.unity.ml-agents/Runtime/MLAgentsSettings.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..90c1507a50764545784db79c7b58d5d331430af0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MLAgentsSettings.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 71515ce028aaa4b4cb6bee13e96ef6f3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs b/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs
new file mode 100644
index 0000000000000000000000000000000000000000..86abeed74a0217d34c2023f344fc5b87491cb2ed
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs
@@ -0,0 +1,94 @@
+using System;
+using UnityEngine;
+#if UNITY_EDITOR
+using UnityEditor;
+#else
+using System.Linq;
+#endif
+
+namespace Unity.MLAgents
+{
+#if UNITY_EDITOR
+    [InitializeOnLoad]
+#endif
+    internal static class MLAgentsSettingsManager
+    {
+        internal static event Action OnSettingsChange;
+        internal const string EditorBuildSettingsConfigKey = "com.unity.ml-agents.settings";
+        private static MLAgentsSettings s_Settings;
+
+
+        // setter will trigger callback for refreshing editor UI if using editor
+        public static MLAgentsSettings Settings
+        {
+            get
+            {
+                if (s_Settings == null)
+                {
+                    Initialize();
+                }
+                return s_Settings;
+            }
+            set
+            {
+                Debug.Assert(value != null);
+#if UNITY_EDITOR
+                if (!string.IsNullOrEmpty(AssetDatabase.GetAssetPath(value)))
+                {
+                    EditorBuildSettings.AddConfigObject(EditorBuildSettingsConfigKey, value, true);
+                }
+#endif
+                s_Settings = value;
+                ApplySettings();
+            }
+        }
+
+        static MLAgentsSettingsManager()
+        {
+            Initialize();
+        }
+
+        static void Initialize()
+        {
+#if UNITY_EDITOR
+            InitializeInEditor();
+#else
+            InitializeInPlayer();
+#endif
+        }
+
+#if UNITY_EDITOR
+        internal static void InitializeInEditor()
+        {
+            var settings = ScriptableObject.CreateInstance<MLAgentsSettings>();
+            if (EditorBuildSettings.TryGetConfigObject(EditorBuildSettingsConfigKey,
+                out MLAgentsSettings settingsAsset))
+            {
+                if (settingsAsset != null)
+                {
+                    settings = settingsAsset;
+                }
+            }
+            Settings = settings;
+        }
+
+#else
+        internal static void InitializeInPlayer()
+        {
+            Settings = Resources.FindObjectsOfTypeAll<MLAgentsSettings>().FirstOrDefault() ?? ScriptableObject.CreateInstance<MLAgentsSettings>();
+        }
+
+#endif
+
+        internal static void ApplySettings()
+        {
+            OnSettingsChange?.Invoke();
+        }
+
+        internal static void Destroy()
+        {
+            s_Settings = null;
+            OnSettingsChange = null;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs.meta b/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9a6f0c3a12fa659618406d8081a8d9ed317dd3d2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MLAgentsSettingsManager.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: be40451993af54e3c84c7113140fdf2c
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs b/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..47aa61299e040f2c4e606ce859abdce920f78f94
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs
@@ -0,0 +1,13 @@
+using System.Threading;
+
+namespace Unity.MLAgents
+{
+    internal static class MultiAgentGroupIdCounter
+    {
+        static int s_Counter;
+        public static int GetGroupId()
+        {
+            return Interlocked.Increment(ref s_Counter);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs.meta b/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b4298cdc957d4448065604bbedc2330033b5b560
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5661ffdb6c7704e84bc785572dcd5bd1
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Policies.meta b/com.unity.ml-agents/Runtime/Policies.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6353357f7bce8fbda1e88e420d6f26835ba0d27d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 3c3d1b36de8f74c9e8ab29c8f23f58ab
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs b/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..96a15b50d85876d7779933725f0b9e6130236441
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs
@@ -0,0 +1,144 @@
+using Unity.Barracuda;
+using System.Collections.Generic;
+using System.Diagnostics;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// Where to perform inference.
+    /// </summary>
+    public enum InferenceDevice
+    {
+        /// <summary>
+        /// Default inference. This is currently the same as Burst, but may change in the future.
+        /// </summary>
+        Default = 0,
+
+        /// <summary>
+        /// GPU inference. Corresponds to WorkerFactory.Type.ComputePrecompiled in Barracuda.
+        /// </summary>
+        GPU = 1,
+
+        /// <summary>
+        /// CPU inference using Burst. Corresponds to WorkerFactory.Type.CSharpBurst in Barracuda.
+        /// </summary>
+        Burst = 2,
+
+        /// <summary>
+        /// CPU inference. Corresponds to in WorkerFactory.Type.CSharp Barracuda.
+        /// Burst is recommended instead; this is kept for legacy compatibility.
+        /// </summary>
+        CPU = 3,
+    }
+
+    /// <summary>
+    /// The Barracuda Policy uses a Barracuda Model to make decisions at
+    /// every step. It uses a ModelRunner that is shared across all
+    /// Barracuda Policies that use the same model and inference devices.
+    /// </summary>
+    internal class BarracudaPolicy : IPolicy
+    {
+        protected ModelRunner m_ModelRunner;
+        ActionBuffers m_LastActionBuffer;
+
+        int m_AgentId;
+        /// <summary>
+        /// Inference only: set to true if the action selection from model should be
+        /// deterministic.
+        /// </summary>
+        bool m_DeterministicInference;
+
+        /// <summary>
+        /// Sensor shapes for the associated Agents. All Agents must have the same shapes for their Sensors.
+        /// </summary>
+        List<int[]> m_SensorShapes;
+        ActionSpec m_ActionSpec;
+
+        private string m_BehaviorName;
+
+        /// <summary>
+        /// List of actuators, only used for analytics
+        /// </summary>
+        private IList<IActuator> m_Actuators;
+
+        /// <summary>
+        /// Whether or not we've tried to send analytics for this model. We only ever try to send once per policy,
+        /// and do additional deduplication in the analytics code.
+        /// </summary>
+        private bool m_AnalyticsSent;
+
+        /// <summary>
+        /// Instantiate a BarracudaPolicy with the necessary objects for it to run.
+        /// </summary>
+        /// <param name="actionSpec">The action spec of the behavior.</param>
+        /// <param name="actuators">The actuators used for this behavior.</param>
+        /// <param name="model">The Neural Network to use.</param>
+        /// <param name="inferenceDevice">Which device Barracuda will run on.</param>
+        /// <param name="behaviorName">The name of the behavior.</param>
+        /// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
+        /// deterministic. </param>
+        public BarracudaPolicy(
+            ActionSpec actionSpec,
+            IList<IActuator> actuators,
+            NNModel model,
+            InferenceDevice inferenceDevice,
+            string behaviorName,
+            bool deterministicInference = false
+        )
+        {
+            var modelRunner = Academy.Instance.GetOrCreateModelRunner(model, actionSpec, inferenceDevice, deterministicInference);
+            m_ModelRunner = modelRunner;
+            m_BehaviorName = behaviorName;
+            m_ActionSpec = actionSpec;
+            m_Actuators = actuators;
+            m_DeterministicInference = deterministicInference;
+        }
+
+        /// <inheritdoc />
+        public void RequestDecision(AgentInfo info, List<ISensor> sensors)
+        {
+            SendAnalytics(sensors);
+            m_AgentId = info.episodeId;
+            m_ModelRunner?.PutObservations(info, sensors);
+        }
+
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        void SendAnalytics(IList<ISensor> sensors)
+        {
+            if (!m_AnalyticsSent)
+            {
+                m_AnalyticsSent = true;
+                Analytics.InferenceAnalytics.InferenceModelSet(
+                    m_ModelRunner.Model,
+                    m_BehaviorName,
+                    m_ModelRunner.InferenceDevice,
+                    sensors,
+                    m_ActionSpec,
+                    m_Actuators
+                );
+            }
+        }
+
+        /// <inheritdoc />
+        public ref readonly ActionBuffers DecideAction()
+        {
+            if (m_ModelRunner == null)
+            {
+                m_LastActionBuffer = ActionBuffers.Empty;
+            }
+            else
+            {
+                m_ModelRunner?.DecideBatch();
+                m_LastActionBuffer = m_ModelRunner.GetAction(m_AgentId);
+            }
+            return ref m_LastActionBuffer;
+        }
+
+        public void Dispose()
+        {
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs.meta b/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..014a05302df5ba5118dd65ff307fe2d3a891c4a3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8eb047b11855142d2be2cc458bef3264
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs b/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b0d369b910a082e0cdba7b3088ea5d53b08820cc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs
@@ -0,0 +1,294 @@
+using Unity.Barracuda;
+using System;
+using UnityEngine;
+using UnityEngine.Serialization;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors.Reflection;
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// Defines what type of behavior the Agent will be using
+    /// </summary>
+    [Serializable]
+    public enum BehaviorType
+    {
+        /// <summary>
+        /// The Agent will use the remote process for decision making.
+        /// if unavailable, will use inference and if no model is provided, will use
+        /// the heuristic.
+        /// </summary>
+        Default,
+
+        /// <summary>
+        /// The Agent will always use its heuristic
+        /// </summary>
+        HeuristicOnly,
+
+        /// <summary>
+        /// The Agent will always use inference with the provided
+        /// neural network model.
+        /// </summary>
+        InferenceOnly
+    }
+
+    /// <summary>
+    /// Options for controlling how the Agent class is searched for <see cref="ObservableAttribute"/>s.
+    /// </summary>
+    public enum ObservableAttributeOptions
+    {
+        /// <summary>
+        /// All ObservableAttributes on the Agent will be ignored. This is the
+        /// default behavior. If there are no  ObservableAttributes on the
+        /// Agent, this will result in the fastest initialization time.
+        /// </summary>
+        Ignore,
+
+        /// <summary>
+        /// Only members on the declared class will be examined; members that are
+        /// inherited are ignored. This is a reasonable tradeoff between
+        /// performance and flexibility.
+        /// </summary>
+        /// <remarks>This corresponds to setting the
+        /// [BindingFlags.DeclaredOnly](https://docs.microsoft.com/en-us/dotnet/api/system.reflection.bindingflags?view=netcore-3.1)
+        /// when examining the fields and properties of the Agent class instance.
+        /// </remarks>
+        ExcludeInherited,
+
+        /// <summary>
+        /// All members on the class will be examined. This can lead to slower
+        /// startup times.
+        /// </summary>
+        ExamineAll
+    }
+
+    /// <summary>
+    /// A component for setting an <seealso cref="Agent"/> instance's behavior and
+    /// brain properties.
+    /// </summary>
+    /// <remarks>At runtime, this component generates the agent's policy objects
+    /// according to the settings you specified in the Editor.</remarks>
+    [AddComponentMenu("ML Agents/Behavior Parameters", (int)MenuGroup.Default)]
+    public class BehaviorParameters : MonoBehaviour
+    {
+        [HideInInspector, SerializeField]
+        BrainParameters m_BrainParameters = new BrainParameters();
+
+        /// <summary>
+        /// Delegate for receiving events about Policy Updates.
+        /// </summary>
+        /// <param name="isInHeuristicMode">Whether or not the current policy is running in heuristic mode.</param>
+        public delegate void PolicyUpdated(bool isInHeuristicMode);
+
+        /// <summary>
+        /// Event that fires when an Agent's policy is updated.
+        /// </summary>
+        internal event PolicyUpdated OnPolicyUpdated;
+
+        /// <summary>
+        /// The associated <see cref="Policies.BrainParameters"/> for this behavior.
+        /// </summary>
+        public BrainParameters BrainParameters
+        {
+            get { return m_BrainParameters; }
+            internal set { m_BrainParameters = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        NNModel m_Model;
+
+        /// <summary>
+        /// The neural network model used when in inference mode.
+        /// This should not be set at runtime; use <see cref="Agent.SetModel(string,NNModel,Policies.InferenceDevice)"/>
+        /// to set it instead.
+        /// </summary>
+        public NNModel Model
+        {
+            get { return m_Model; }
+            set { m_Model = value; UpdateAgentPolicy(); }
+        }
+
+        [HideInInspector, SerializeField]
+        InferenceDevice m_InferenceDevice = InferenceDevice.Default;
+
+        /// <summary>
+        /// How inference is performed for this Agent's model.
+        /// This should not be set at runtime; use <see cref="Agent.SetModel(string,NNModel,Policies.InferenceDevice)"/>
+        /// to set it instead.
+        /// </summary>
+        public InferenceDevice InferenceDevice
+        {
+            get { return m_InferenceDevice; }
+            set { m_InferenceDevice = value; UpdateAgentPolicy(); }
+        }
+
+        [HideInInspector, SerializeField]
+        BehaviorType m_BehaviorType;
+
+        /// <summary>
+        /// The BehaviorType for the Agent.
+        /// </summary>
+        public BehaviorType BehaviorType
+        {
+            get { return m_BehaviorType; }
+            set { m_BehaviorType = value; UpdateAgentPolicy(); }
+        }
+
+        [HideInInspector, SerializeField]
+        string m_BehaviorName = "My Behavior";
+
+        /// <summary>
+        /// The name of this behavior, which is used as a base name. See
+        /// <see cref="FullyQualifiedBehaviorName"/> for the full name.
+        /// This should not be set at runtime; use <see cref="Agent.SetModel(string,NNModel,Policies.InferenceDevice)"/>
+        /// to set it instead.
+        /// </summary>
+        public string BehaviorName
+        {
+            get { return m_BehaviorName; }
+            set { m_BehaviorName = value; UpdateAgentPolicy(); }
+        }
+
+        /// <summary>
+        /// The team ID for this behavior.
+        /// </summary>
+        [HideInInspector, SerializeField, FormerlySerializedAs("m_TeamID")]
+        public int TeamId;
+        // TODO properties here instead of Agent
+
+        [FormerlySerializedAs("m_useChildSensors")]
+        [HideInInspector]
+        [SerializeField]
+        [Tooltip("Use all Sensor components attached to child GameObjects of this Agent.")]
+        bool m_UseChildSensors = true;
+
+        [HideInInspector]
+        [SerializeField]
+        [Tooltip("Use all Actuator components attached to child GameObjects of this Agent.")]
+        bool m_UseChildActuators = true;
+
+        /// <summary>
+        /// Whether or not to use all the sensor components attached to child GameObjects of the agent.
+        /// Note that changing this after the Agent has been initialized will not have any effect.
+        /// </summary>
+        public bool UseChildSensors
+        {
+            get { return m_UseChildSensors; }
+            set { m_UseChildSensors = value; }
+        }
+
+        [HideInInspector]
+        [SerializeField]
+        [Tooltip("Set action selection to deterministic, Only applies to inference from within unity.")]
+        private bool m_DeterministicInference = false;
+
+        /// <summary>
+        /// Whether to select actions deterministically during inference from the provided neural network.
+        /// </summary>
+        public bool DeterministicInference
+        {
+            get { return m_DeterministicInference; }
+            set { m_DeterministicInference = value; }
+        }
+
+        /// <summary>
+        /// Whether or not to use all the actuator components attached to child GameObjects of the agent.
+        /// Note that changing this after the Agent has been initialized will not have any effect.
+        /// </summary>
+        public bool UseChildActuators
+        {
+            get { return m_UseChildActuators; }
+            set { m_UseChildActuators = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        ObservableAttributeOptions m_ObservableAttributeHandling = ObservableAttributeOptions.Ignore;
+
+        /// <summary>
+        /// Determines how the Agent class is searched for <see cref="ObservableAttribute"/>s.
+        /// </summary>
+        public ObservableAttributeOptions ObservableAttributeHandling
+        {
+            get { return m_ObservableAttributeHandling; }
+            set { m_ObservableAttributeHandling = value; }
+        }
+
+        /// <summary>
+        /// Returns the behavior name, concatenated with any other metadata (i.e. team id).
+        /// </summary>
+        public string FullyQualifiedBehaviorName
+        {
+            get { return m_BehaviorName + "?team=" + TeamId; }
+        }
+
+        void Awake()
+        {
+            OnPolicyUpdated += mode => { };
+        }
+
+        internal IPolicy GeneratePolicy(ActionSpec actionSpec, ActuatorManager actuatorManager)
+        {
+            switch (m_BehaviorType)
+            {
+                case BehaviorType.HeuristicOnly:
+                    return new HeuristicPolicy(actuatorManager, actionSpec);
+                case BehaviorType.InferenceOnly:
+                    {
+                        if (m_Model == null)
+                        {
+                            var behaviorType = BehaviorType.InferenceOnly.ToString();
+                            throw new UnityAgentsException(
+                                $"Can't use Behavior Type {behaviorType} without a model. " +
+                                "Either assign a model, or change to a different Behavior Type."
+                            );
+                        }
+                        return new BarracudaPolicy(actionSpec, actuatorManager, m_Model, m_InferenceDevice, m_BehaviorName, m_DeterministicInference);
+                    }
+                case BehaviorType.Default:
+                    if (Academy.Instance.IsCommunicatorOn)
+                    {
+                        return new RemotePolicy(actionSpec, actuatorManager, FullyQualifiedBehaviorName);
+                    }
+                    if (m_Model != null)
+                    {
+                        return new BarracudaPolicy(actionSpec, actuatorManager, m_Model, m_InferenceDevice, m_BehaviorName, m_DeterministicInference);
+                    }
+                    else
+                    {
+                        return new HeuristicPolicy(actuatorManager, actionSpec);
+                    }
+                default:
+                    return new HeuristicPolicy(actuatorManager, actionSpec);
+            }
+        }
+
+        /// <summary>
+        /// Query the behavior parameters in order to see if the Agent is running in Heuristic Mode.
+        /// </summary>
+        /// <returns>true if the Agent is running in Heuristic mode.</returns>
+        public bool IsInHeuristicMode()
+        {
+            if (BehaviorType == BehaviorType.HeuristicOnly)
+            {
+                return true;
+            }
+
+            return BehaviorType == BehaviorType.Default &&
+                ReferenceEquals(Model, null) &&
+                (!Academy.IsInitialized ||
+                    Academy.IsInitialized &&
+                    !Academy.Instance.IsCommunicatorOn);
+        }
+
+        internal void UpdateAgentPolicy()
+        {
+            var agent = GetComponent<Agent>();
+            if (agent == null)
+            {
+                return;
+            }
+            agent.ReloadPolicy();
+            OnPolicyUpdated?.Invoke(IsInHeuristicMode());
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs.meta b/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..507c417e97363973a49783181ec92a2a9de28f41
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5d1c4e0b1822b495aa52bc52839ecb30
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs b/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..882521a892d0c63a24517e59209a32ea9bc0568d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs
@@ -0,0 +1,192 @@
+using System;
+using UnityEngine;
+using UnityEngine.Serialization;
+using Unity.MLAgents.Actuators;
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// This is deprecated. Agents can now use both continuous and discrete actions together.
+    /// </summary>
+    [Obsolete("Continuous and discrete actions on the same Agent are now supported; see ActionSpec.")]
+    internal enum SpaceType
+    {
+        /// <summary>
+        /// Discrete action space: a fixed number of options are available.
+        /// </summary>
+        Discrete,
+
+        /// <summary>
+        /// Continuous action space: each action can take on a float value.
+        /// </summary>
+        Continuous
+    }
+
+    /// <summary>
+    /// Holds information about the brain. It defines what are the inputs and outputs of the
+    /// decision process.
+    /// </summary>
+    /// <remarks>
+    /// Set brain parameters for an <see cref="Agent"/> instance using the
+    /// <seealso cref="BehaviorParameters"/> component attached to the agent's [GameObject].
+    ///
+    /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+    /// </remarks>
+    [Serializable]
+    public class BrainParameters : ISerializationCallbackReceiver
+    {
+        /// <summary>
+        /// The number of the observations that are added in
+        /// <see cref="Agent.CollectObservations(Sensors.VectorSensor)"/>
+        /// </summary>
+        /// <value>
+        /// The length of the vector containing observation values.
+        /// </value>
+        [FormerlySerializedAs("vectorObservationSize")]
+        public int VectorObservationSize = 1;
+
+        /// <summary>
+        /// Stacking refers to concatenating the observations across multiple frames. This field
+        /// indicates the number of frames to concatenate across.
+        /// </summary>
+        [FormerlySerializedAs("numStackedVectorObservations")]
+        [Range(1, 50)] public int NumStackedVectorObservations = 1;
+
+        [SerializeField]
+        internal ActionSpec m_ActionSpec = new ActionSpec(0, null);
+
+        /// <summary>
+        /// The specification of the Actions for the BrainParameters.
+        /// </summary>
+        public ActionSpec ActionSpec
+        {
+            get { return m_ActionSpec; }
+            set
+            {
+                m_ActionSpec.NumContinuousActions = value.NumContinuousActions;
+                m_ActionSpec.BranchSizes = value.BranchSizes;
+                SyncDeprecatedActionFields();
+            }
+        }
+
+        /// <summary>
+        /// (Deprecated) The number of possible actions.
+        /// </summary>
+        /// <remarks>The size specified is interpreted differently depending on whether
+        /// the agent uses the continuous or the discrete actions.</remarks>
+        /// <value>
+        /// For the continuous actions: the length of the float vector that represents
+        /// the action.
+        /// For the discrete actions: the number of branches.
+        /// </value>
+        [Obsolete("VectorActionSize has been deprecated, please use ActionSpec instead.")]
+        [SerializeField]
+        [FormerlySerializedAs("vectorActionSize")]
+        internal int[] VectorActionSize = new[] { 1 };
+
+        /// <summary>
+        /// The list of strings describing what the actions correspond to.
+        /// </summary>
+        [FormerlySerializedAs("vectorActionDescriptions")]
+        public string[] VectorActionDescriptions;
+
+        /// <summary>
+        /// (Deprecated) Defines if the action is discrete or continuous.
+        /// </summary>
+        [Obsolete("VectorActionSpaceType has been deprecated, please use ActionSpec instead.")]
+        [SerializeField]
+        [FormerlySerializedAs("vectorActionSpaceType")]
+        internal SpaceType VectorActionSpaceType = SpaceType.Discrete;
+
+        [SerializeField]
+        [HideInInspector]
+        internal bool hasUpgradedBrainParametersWithActionSpec;
+
+        /// <summary>
+        /// Deep clones the BrainParameter object.
+        /// </summary>
+        /// <returns> A new BrainParameter object with the same values as the original.</returns>
+        public BrainParameters Clone()
+        {
+            // Disable deprecation warnings so we can read/write the old fields.
+#pragma warning disable CS0618
+            return new BrainParameters
+            {
+                VectorObservationSize = VectorObservationSize,
+                NumStackedVectorObservations = NumStackedVectorObservations,
+                VectorActionDescriptions = (string[])VectorActionDescriptions.Clone(),
+                ActionSpec = new ActionSpec(ActionSpec.NumContinuousActions, ActionSpec.BranchSizes),
+                VectorActionSize = (int[])VectorActionSize.Clone(),
+                VectorActionSpaceType = VectorActionSpaceType,
+            };
+#pragma warning restore CS0618
+        }
+
+        /// <summary>
+        /// Propagate ActionSpec fields from deprecated fields
+        /// </summary>
+        private void UpdateToActionSpec()
+        {
+            // Disable deprecation warnings so we can read the old fields.
+#pragma warning disable CS0618
+            if (!hasUpgradedBrainParametersWithActionSpec
+                && m_ActionSpec.NumContinuousActions == 0
+                && m_ActionSpec.NumDiscreteActions == 0)
+            {
+                if (VectorActionSpaceType == SpaceType.Continuous)
+                {
+                    m_ActionSpec.NumContinuousActions = VectorActionSize[0];
+                }
+                if (VectorActionSpaceType == SpaceType.Discrete)
+                {
+                    m_ActionSpec.BranchSizes = (int[])VectorActionSize.Clone();
+                }
+            }
+            hasUpgradedBrainParametersWithActionSpec = true;
+#pragma warning restore CS0618
+        }
+
+        /// <summary>
+        /// Sync values in ActionSpec fields to deprecated fields
+        /// </summary>
+        private void SyncDeprecatedActionFields()
+        {
+            // Disable deprecation warnings so we can read the old fields.
+#pragma warning disable CS0618
+
+            if (m_ActionSpec.NumContinuousActions == 0)
+            {
+                VectorActionSize = (int[])ActionSpec.BranchSizes.Clone();
+                VectorActionSpaceType = SpaceType.Discrete;
+            }
+            else if (m_ActionSpec.NumDiscreteActions == 0)
+            {
+                VectorActionSize = new[] { m_ActionSpec.NumContinuousActions };
+                VectorActionSpaceType = SpaceType.Continuous;
+            }
+            else
+            {
+                VectorActionSize = null;
+            }
+#pragma warning restore CS0618
+        }
+
+        /// <summary>
+        /// Called by Unity immediately before serializing this object.
+        /// </summary>
+        public void OnBeforeSerialize()
+        {
+            UpdateToActionSpec();
+            SyncDeprecatedActionFields();
+        }
+
+        /// <summary>
+        /// Called by Unity immediately after deserializing this object.
+        /// </summary>
+        public void OnAfterDeserialize()
+        {
+            UpdateToActionSpec();
+            SyncDeprecatedActionFields();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs.meta b/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f6305491734d44d0ebdb5915e518edcfff9f99b6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/BrainParameters.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6108a41e9be04c238d7babaed4476134
+timeCreated: 1538758934
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs b/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8e5333874a68c4646c7875fd6d7efe891da7994a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs
@@ -0,0 +1,142 @@
+using System.Collections.Generic;
+using System;
+using System.Collections;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// The Heuristic Policy uses a hard-coded Heuristic method
+    /// to take decisions each time the RequestDecision method is
+    /// called.
+    /// </summary>
+    internal class HeuristicPolicy : IPolicy
+    {
+        ActuatorManager m_ActuatorManager;
+        ActionBuffers m_ActionBuffers;
+        bool m_Done;
+        bool m_DecisionRequested;
+
+        ObservationWriter m_ObservationWriter = new ObservationWriter();
+        NullList m_NullList = new NullList();
+
+
+        public HeuristicPolicy(ActuatorManager actuatorManager, ActionSpec actionSpec)
+        {
+            m_ActuatorManager = actuatorManager;
+            var numContinuousActions = actionSpec.NumContinuousActions;
+            var numDiscreteActions = actionSpec.NumDiscreteActions;
+            var continuousDecision = new ActionSegment<float>(new float[numContinuousActions], 0, numContinuousActions);
+            var discreteDecision = new ActionSegment<int>(new int[numDiscreteActions], 0, numDiscreteActions);
+            m_ActionBuffers = new ActionBuffers(continuousDecision, discreteDecision);
+        }
+
+        /// <inheritdoc />
+        public void RequestDecision(AgentInfo info, List<ISensor> sensors)
+        {
+            StepSensors(sensors);
+            m_Done = info.done;
+            m_DecisionRequested = true;
+        }
+
+        /// <inheritdoc />
+        public ref readonly ActionBuffers DecideAction()
+        {
+            if (!m_Done && m_DecisionRequested)
+            {
+                m_ActionBuffers.Clear();
+                m_ActuatorManager.ApplyHeuristic(m_ActionBuffers);
+            }
+            m_DecisionRequested = false;
+            return ref m_ActionBuffers;
+        }
+
+        public void Dispose()
+        {
+        }
+
+        /// <summary>
+        /// Trivial implementation of the IList interface that does nothing.
+        /// This is only used for "writing" observations that we will discard.
+        /// </summary>
+        internal class NullList : IList<float>
+        {
+            public IEnumerator<float> GetEnumerator()
+            {
+                throw new NotImplementedException();
+            }
+
+            IEnumerator IEnumerable.GetEnumerator()
+            {
+                return GetEnumerator();
+            }
+
+            public void Add(float item)
+            {
+            }
+
+            public void Clear()
+            {
+            }
+
+            public bool Contains(float item)
+            {
+                return false;
+            }
+
+            public void CopyTo(float[] array, int arrayIndex)
+            {
+                throw new NotImplementedException();
+            }
+
+            public bool Remove(float item)
+            {
+                return false;
+            }
+
+            public int Count { get; }
+            public bool IsReadOnly { get; }
+            public int IndexOf(float item)
+            {
+                return -1;
+            }
+
+            public void Insert(int index, float item)
+            {
+            }
+
+            public void RemoveAt(int index)
+            {
+            }
+
+            public float this[int index]
+            {
+                get { return 0.0f; }
+                set { }
+            }
+        }
+
+        /// <summary>
+        /// Run ISensor.Write or ISensor.GetCompressedObservation for each sensor
+        /// The output is currently unused, but this makes the sensor usage consistent
+        /// between training and inference.
+        /// </summary>
+        /// <param name="sensors"></param>
+        void StepSensors(List<ISensor> sensors)
+        {
+            foreach (var sensor in sensors)
+            {
+                if (sensor.GetCompressionSpec().SensorCompressionType == SensorCompressionType.None)
+                {
+                    m_ObservationWriter.SetTarget(m_NullList, sensor.GetObservationSpec(), 0);
+                    sensor.Write(m_ObservationWriter);
+                }
+                else
+                {
+                    sensor.GetCompressedObservation();
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs.meta b/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ae074f5727d993fb7ee184a4a5d8b9e1ea0b6b6d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8a55e3cea7fd643109e42f5f4c9a1425
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+  serializedVersion: 2
+  defaultReferences: []
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diff --git a/com.unity.ml-agents/Runtime/Policies/IPolicy.cs b/com.unity.ml-agents/Runtime/Policies/IPolicy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4079a1f25ae418ee6b08e7801c2009c2161c58ee
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/IPolicy.cs
@@ -0,0 +1,32 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// IPolicy is connected to a single Agent. Each time the agent needs
+    /// a decision, it will request a decision to the Policy. The decision
+    /// will not be taken immediately but will be taken before or when
+    /// DecideAction is called.
+    /// </summary>
+    internal interface IPolicy : IDisposable
+    {
+        /// <summary>
+        /// Signals the Brain that the Agent needs a Decision. The Policy
+        /// will make the decision at a later time to allow possible
+        /// batching of requests.
+        /// </summary>
+        /// <param name="info"></param>
+        /// <param name="sensors"></param>
+        void RequestDecision(AgentInfo info, List<ISensor> sensors);
+
+        /// <summary>
+        /// Signals the Policy that if the Decision has not been taken yet,
+        /// it must be taken now. The Brain is expected to update the actions
+        /// of the Agents at this point the latest.
+        /// </summary>
+        ref readonly ActionBuffers DecideAction();
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/IPolicy.cs.meta b/com.unity.ml-agents/Runtime/Policies/IPolicy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f43c4ddc8bb13b945853588983c2a5810ca875a2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/IPolicy.cs.meta
@@ -0,0 +1,11 @@
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+guid: 56e263dd566be41d6b81d0b46895a0dd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs b/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs
new file mode 100644
index 0000000000000000000000000000000000000000..faa8a37e60ae843fe0b41ebf467ad0b655ec6340
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs
@@ -0,0 +1,77 @@
+using System.Collections.Generic;
+using System.Diagnostics;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Analytics;
+
+
+namespace Unity.MLAgents.Policies
+{
+    /// <summary>
+    /// The Remote Policy only works when training.
+    /// When training your Agents, the RemotePolicy will be controlled by Python.
+    /// </summary>
+    internal class RemotePolicy : IPolicy
+    {
+        int m_AgentId;
+        string m_FullyQualifiedBehaviorName;
+        ActionSpec m_ActionSpec;
+        ActionBuffers m_LastActionBuffer;
+        bool m_AnalyticsSent;
+
+        internal ICommunicator m_Communicator;
+
+        /// <summary>
+        /// List of actuators, only used for analytics
+        /// </summary>
+        private IList<IActuator> m_Actuators;
+
+        public RemotePolicy(
+            ActionSpec actionSpec,
+            IList<IActuator> actuators,
+            string fullyQualifiedBehaviorName)
+        {
+            m_FullyQualifiedBehaviorName = fullyQualifiedBehaviorName;
+            m_Communicator = Academy.Instance.Communicator;
+            m_Communicator?.SubscribeBrain(m_FullyQualifiedBehaviorName, actionSpec);
+            m_ActionSpec = actionSpec;
+            m_Actuators = actuators;
+        }
+
+        /// <inheritdoc />
+        public void RequestDecision(AgentInfo info, List<ISensor> sensors)
+        {
+            SendAnalytics(sensors);
+            m_AgentId = info.episodeId;
+            m_Communicator?.PutObservations(m_FullyQualifiedBehaviorName, info, sensors);
+        }
+
+        [Conditional("MLA_UNITY_ANALYTICS_MODULE")]
+        void SendAnalytics(IList<ISensor> sensors)
+        {
+            if (!m_AnalyticsSent)
+            {
+                m_AnalyticsSent = true;
+                TrainingAnalytics.RemotePolicyInitialized(
+                    m_FullyQualifiedBehaviorName,
+                    sensors,
+                    m_ActionSpec,
+                    m_Actuators
+                );
+            }
+        }
+
+        /// <inheritdoc />
+        public ref readonly ActionBuffers DecideAction()
+        {
+            m_Communicator?.DecideBatch();
+            var actions = m_Communicator?.GetActions(m_FullyQualifiedBehaviorName, m_AgentId);
+            m_LastActionBuffer = actions == null ? ActionBuffers.Empty : (ActionBuffers)actions;
+            return ref m_LastActionBuffer;
+        }
+
+        public void Dispose()
+        {
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs.meta b/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..08996fa8a24457935858ab3b1a7b5aae474a4bd0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2f1ffc0e0bec14a1eaca4c709b3ba230
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/RecursionChecker.cs b/com.unity.ml-agents/Runtime/RecursionChecker.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ac411fa837149370201dede2a9362da7b191a0d2
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/RecursionChecker.cs
@@ -0,0 +1,35 @@
+using System;
+
+namespace Unity.MLAgents
+{
+    internal class RecursionChecker : IDisposable
+    {
+        private bool m_IsRunning;
+        private string m_MethodName;
+
+        public RecursionChecker(string methodName)
+        {
+            m_MethodName = methodName;
+        }
+
+        public IDisposable Start()
+        {
+            if (m_IsRunning)
+            {
+                throw new UnityAgentsException(
+                    $"{m_MethodName} called recursively. " +
+                    "This might happen if you call EnvironmentStep() or EndEpisode() from custom " +
+                    "code such as CollectObservations() or OnActionReceived()."
+                );
+            }
+            m_IsRunning = true;
+            return this;
+        }
+
+        public void Dispose()
+        {
+            // Reset the flag when we're done (or if an exception occurred).
+            m_IsRunning = false;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/RecursionChecker.cs.meta b/com.unity.ml-agents/Runtime/RecursionChecker.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4b2363f80906b8ccf68a5dfcd06d80e7f43d2ce7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/RecursionChecker.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 49ebd06532b24078a6edda823aeff5d2
+timeCreated: 1602731302
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sampler.cs b/com.unity.ml-agents/Runtime/Sampler.cs
new file mode 100644
index 0000000000000000000000000000000000000000..15a83937d1a1b78ffb6b41402b094185d7e5a7ff
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sampler.cs
@@ -0,0 +1,68 @@
+using System;
+using System.Collections.Generic;
+using Unity.MLAgents.Inference.Utils;
+using Random = System.Random;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Takes a list of floats that encode a sampling distribution and returns the sampling function.
+    /// </summary>
+    internal static class SamplerFactory
+    {
+        public static Func<float> CreateUniformSampler(float min, float max, int seed)
+        {
+            Random distr = new Random(seed);
+            return () => min + (float)distr.NextDouble() * (max - min);
+        }
+
+        public static Func<float> CreateGaussianSampler(float mean, float stddev, int seed)
+        {
+            RandomNormal distr = new RandomNormal(seed, mean, stddev);
+            return () => (float)distr.NextDouble();
+        }
+
+        public static Func<float> CreateMultiRangeUniformSampler(IList<float> intervals, int seed)
+        {
+            //RNG
+            Random distr = new Random(seed);
+            // Will be used to normalize intervalFuncs
+            float sumIntervalSizes = 0;
+            //The number of intervals
+            int numIntervals = (intervals.Count / 2);
+            // List that will store interval lengths
+            float[] intervalSizes = new float[numIntervals];
+            // List that will store uniform distributions
+            IList<Func<float>> intervalFuncs = new Func<float>[numIntervals];
+            // Collect all intervals and store as uniform distrus
+            // Collect all interval sizes
+            for (int i = 0; i < numIntervals; i++)
+            {
+                var min = intervals[2 * i];
+                var max = intervals[2 * i + 1];
+                var intervalSize = max - min;
+                sumIntervalSizes += intervalSize;
+                intervalSizes[i] = intervalSize;
+                intervalFuncs[i] = () => min + (float)distr.NextDouble() * intervalSize;
+            }
+            // Normalize interval lengths
+            for (int i = 0; i < numIntervals; i++)
+            {
+                intervalSizes[i] = intervalSizes[i] / sumIntervalSizes;
+            }
+            // Build cmf for intervals
+            for (int i = 1; i < numIntervals; i++)
+            {
+                intervalSizes[i] += intervalSizes[i - 1];
+            }
+            Multinomial intervalDistr = new Multinomial(seed + 1);
+            float MultiRange()
+            {
+                int sampledInterval = intervalDistr.Sample(intervalSizes);
+                return intervalFuncs[sampledInterval].Invoke();
+            }
+
+            return MultiRange;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sampler.cs.meta b/com.unity.ml-agents/Runtime/Sampler.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..950e28c5b679a43193a313640b699ff9a46e446f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sampler.cs.meta
@@ -0,0 +1,11 @@
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+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/SensorHelper.cs b/com.unity.ml-agents/Runtime/SensorHelper.cs
new file mode 100644
index 0000000000000000000000000000000000000000..45cf0fe9353d5feee6b1da797f741f09fbd618e5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SensorHelper.cs
@@ -0,0 +1,129 @@
+using Unity.Barracuda;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Utility methods related to <see cref="ISensor"/> implementations.
+    /// </summary>
+    public static class SensorHelper
+    {
+        /// <summary>
+        /// Generates the observations for the provided sensor, and returns true if they equal the
+        /// expected values. If they are unequal, errorMessage is also set.
+        /// This should not generally be used in production code. It is only intended for
+        /// simplifying unit tests.
+        /// </summary>
+        /// <param name="sensor"></param>
+        /// <param name="expected"></param>
+        /// <param name="errorMessage"></param>
+        /// <returns></returns>
+        public static bool CompareObservation(ISensor sensor, float[] expected, out string errorMessage)
+        {
+            var numExpected = expected.Length;
+            const float fill = -1337f;
+            var output = new float[numExpected];
+            for (var i = 0; i < numExpected; i++)
+            {
+                output[i] = fill;
+            }
+
+            if (numExpected > 0)
+            {
+                if (fill != output[0])
+                {
+                    errorMessage = "Error setting output buffer.";
+                    return false;
+                }
+            }
+
+            ObservationWriter writer = new ObservationWriter();
+            writer.SetTarget(output, sensor.GetObservationSpec(), 0);
+
+            // Make sure ObservationWriter didn't touch anything
+            if (numExpected > 0)
+            {
+                if (fill != output[0])
+                {
+                    errorMessage = "ObservationWriter.SetTarget modified a buffer it shouldn't have.";
+                    return false;
+                }
+            }
+
+            sensor.Write(writer);
+            for (var i = 0; i < output.Length; i++)
+            {
+                if (expected[i] != output[i])
+                {
+                    errorMessage = $"Expected and actual differed in position {i}. Expected: {expected[i]}  Actual: {output[i]} ";
+                    return false;
+                }
+            }
+
+            errorMessage = null;
+            return true;
+        }
+
+        /// <summary>
+        /// Generates the observations for the provided sensor, and returns true if they equal the
+        /// expected values. If they are unequal, errorMessage is also set.
+        /// This should not generally be used in production code. It is only intended for
+        /// simplifying unit tests.
+        /// </summary>
+        /// <param name="sensor"></param>
+        /// <param name="expected"></param>
+        /// <param name="errorMessage"></param>
+        /// <returns></returns>
+        public static bool CompareObservation(ISensor sensor, float[,,] expected, out string errorMessage)
+        {
+            var tensorShape = new TensorShape(0, expected.GetLength(0), expected.GetLength(1), expected.GetLength(2));
+            var numExpected = tensorShape.height * tensorShape.width * tensorShape.channels;
+            const float fill = -1337f;
+            var output = new float[numExpected];
+            for (var i = 0; i < numExpected; i++)
+            {
+                output[i] = fill;
+            }
+
+            if (numExpected > 0)
+            {
+                if (fill != output[0])
+                {
+                    errorMessage = "Error setting output buffer.";
+                    return false;
+                }
+            }
+
+            ObservationWriter writer = new ObservationWriter();
+            writer.SetTarget(output, sensor.GetObservationSpec(), 0);
+
+            // Make sure ObservationWriter didn't touch anything
+            if (numExpected > 0)
+            {
+                if (fill != output[0])
+                {
+                    errorMessage = "ObservationWriter.SetTarget modified a buffer it shouldn't have.";
+                    return false;
+                }
+            }
+
+            sensor.Write(writer);
+            for (var h = 0; h < tensorShape.height; h++)
+            {
+                for (var w = 0; w < tensorShape.width; w++)
+                {
+                    for (var c = 0; c < tensorShape.channels; c++)
+                    {
+                        if (expected[h, w, c] != output[tensorShape.Index(0, h, w, c)])
+                        {
+                            errorMessage = $"Expected and actual differed in position [{h}, {w}, {c}]. " +
+                                $"Expected: {expected[h, w, c]}  Actual: {output[tensorShape.Index(0, h, w, c)]} ";
+                            return false;
+                        }
+                    }
+                }
+            }
+            errorMessage = null;
+            return true;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SensorHelper.cs.meta b/com.unity.ml-agents/Runtime/SensorHelper.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c331abd0b6a4be0050e5fe40f8cdc0b18f43b578
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SensorHelper.cs.meta
@@ -0,0 +1,11 @@
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+  serializedVersion: 2
+  defaultReferences: []
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diff --git a/com.unity.ml-agents/Runtime/Sensors.meta b/com.unity.ml-agents/Runtime/Sensors.meta
new file mode 100644
index 0000000000000000000000000000000000000000..06dbaab14820fb87e11ebda54b8d459604b4d8c9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors.meta
@@ -0,0 +1,8 @@
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diff --git a/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs b/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5ec58b13589f14a5c3e50247d7bfc2932b48438c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs
@@ -0,0 +1,264 @@
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// The grid perception strategy that uses box overlap to detect objects.
+    /// </summary>
+    internal class BoxOverlapChecker : IGridPerception
+    {
+        Vector3 m_CellScale;
+        Vector3Int m_GridSize;
+        bool m_RotateWithAgent;
+        LayerMask m_ColliderMask;
+        GameObject m_CenterObject;
+        GameObject m_AgentGameObject;
+        string[] m_DetectableTags;
+        int m_InitialColliderBufferSize;
+        int m_MaxColliderBufferSize;
+
+        int m_NumCells;
+        Vector3 m_HalfCellScale;
+        Vector3 m_CellCenterOffset;
+        Vector3[] m_CellLocalPositions;
+
+#if MLA_UNITY_PHYSICS_MODULE
+        Collider[] m_ColliderBuffer;
+
+        public event Action<GameObject, int> GridOverlapDetectedAll;
+        public event Action<GameObject, int> GridOverlapDetectedClosest;
+        public event Action<GameObject, int> GridOverlapDetectedDebug;
+#endif
+
+        public BoxOverlapChecker(
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            bool rotateWithAgent,
+            LayerMask colliderMask,
+            GameObject centerObject,
+            GameObject agentGameObject,
+            string[] detectableTags,
+            int initialColliderBufferSize,
+            int maxColliderBufferSize)
+        {
+            m_CellScale = cellScale;
+            m_GridSize = gridSize;
+            m_RotateWithAgent = rotateWithAgent;
+            m_ColliderMask = colliderMask;
+            m_CenterObject = centerObject;
+            m_AgentGameObject = agentGameObject;
+            m_DetectableTags = detectableTags;
+            m_InitialColliderBufferSize = initialColliderBufferSize;
+            m_MaxColliderBufferSize = maxColliderBufferSize;
+
+            m_NumCells = gridSize.x * gridSize.z;
+            m_HalfCellScale = new Vector3(cellScale.x / 2f, cellScale.y, cellScale.z / 2f);
+            m_CellCenterOffset = new Vector3((gridSize.x - 1f) / 2, 0, (gridSize.z - 1f) / 2);
+#if MLA_UNITY_PHYSICS_MODULE
+            m_ColliderBuffer = new Collider[Math.Min(m_MaxColliderBufferSize, m_InitialColliderBufferSize)];
+#endif
+
+            InitCellLocalPositions();
+        }
+
+        public bool RotateWithAgent
+        {
+            get { return m_RotateWithAgent; }
+            set { m_RotateWithAgent = value; }
+        }
+
+        public LayerMask ColliderMask
+        {
+            get { return m_ColliderMask; }
+            set { m_ColliderMask = value; }
+        }
+
+        /// <summary>
+        /// Initializes the local location of the cells
+        /// </summary>
+        void InitCellLocalPositions()
+        {
+            m_CellLocalPositions = new Vector3[m_NumCells];
+
+            for (int i = 0; i < m_NumCells; i++)
+            {
+                m_CellLocalPositions[i] = GetCellLocalPosition(i);
+            }
+        }
+
+        public Vector3 GetCellLocalPosition(int cellIndex)
+        {
+            float x = (cellIndex / m_GridSize.z - m_CellCenterOffset.x) * m_CellScale.x;
+            float z = (cellIndex % m_GridSize.z - m_CellCenterOffset.z) * m_CellScale.z;
+            return new Vector3(x, 0, z);
+        }
+
+        public Vector3 GetCellGlobalPosition(int cellIndex)
+        {
+            if (m_RotateWithAgent)
+            {
+                return m_CenterObject.transform.TransformPoint(m_CellLocalPositions[cellIndex]);
+            }
+            else
+            {
+                return m_CellLocalPositions[cellIndex] + m_CenterObject.transform.position;
+            }
+        }
+
+        public Quaternion GetGridRotation()
+        {
+            return m_RotateWithAgent ? m_CenterObject.transform.rotation : Quaternion.identity;
+        }
+
+        public void Perceive()
+        {
+#if MLA_UNITY_PHYSICS_MODULE
+            for (var cellIndex = 0; cellIndex < m_NumCells; cellIndex++)
+            {
+                var cellCenter = GetCellGlobalPosition(cellIndex);
+                var numFound = BufferResizingOverlapBoxNonAlloc(cellCenter, m_HalfCellScale, GetGridRotation());
+
+                if (GridOverlapDetectedAll != null)
+                {
+                    ParseCollidersAll(m_ColliderBuffer, numFound, cellIndex, cellCenter, GridOverlapDetectedAll);
+                }
+                if (GridOverlapDetectedClosest != null)
+                {
+                    ParseCollidersClosest(m_ColliderBuffer, numFound, cellIndex, cellCenter, GridOverlapDetectedClosest);
+                }
+            }
+#endif
+        }
+
+        public void UpdateGizmo()
+        {
+#if MLA_UNITY_PHYSICS_MODULE
+            for (var cellIndex = 0; cellIndex < m_NumCells; cellIndex++)
+            {
+                var cellCenter = GetCellGlobalPosition(cellIndex);
+                var numFound = BufferResizingOverlapBoxNonAlloc(cellCenter, m_HalfCellScale, GetGridRotation());
+
+                ParseCollidersClosest(m_ColliderBuffer, numFound, cellIndex, cellCenter, GridOverlapDetectedDebug);
+            }
+#endif
+        }
+
+#if MLA_UNITY_PHYSICS_MODULE
+        /// <summary>
+        /// This method attempts to perform the Physics.OverlapBoxNonAlloc and will double the size of the Collider buffer
+        /// if the number of Colliders in the buffer after the call is equal to the length of the buffer.
+        /// </summary>
+        /// <param name="cellCenter"></param>
+        /// <param name="halfCellScale"></param>
+        /// <param name="rotation"></param>
+        /// <returns></returns>
+        int BufferResizingOverlapBoxNonAlloc(Vector3 cellCenter, Vector3 halfCellScale, Quaternion rotation)
+        {
+            int numFound;
+            // Since we can only get a fixed number of results, requery
+            // until we're sure we can hold them all (or until we hit the max size).
+            while (true)
+            {
+                numFound = Physics.OverlapBoxNonAlloc(cellCenter, halfCellScale, m_ColliderBuffer, rotation, m_ColliderMask);
+                if (numFound == m_ColliderBuffer.Length && m_ColliderBuffer.Length < m_MaxColliderBufferSize)
+                {
+                    m_ColliderBuffer = new Collider[Math.Min(m_MaxColliderBufferSize, m_ColliderBuffer.Length * 2)];
+                    m_InitialColliderBufferSize = m_ColliderBuffer.Length;
+                }
+                else
+                {
+                    break;
+                }
+            }
+            return numFound;
+        }
+
+        /// <summary>
+        /// Parses the array of colliders found within a cell. Finds the closest gameobject to the agent root reference within the cell
+        /// </summary>
+        void ParseCollidersClosest(Collider[] foundColliders, int numFound, int cellIndex, Vector3 cellCenter, Action<GameObject, int> detectedAction)
+        {
+            GameObject closestColliderGo = null;
+            var minDistanceSquared = float.MaxValue;
+
+            for (var i = 0; i < numFound; i++)
+            {
+                var currentColliderGo = foundColliders[i].gameObject;
+
+                // Continue if the current collider go is the root reference
+                if (ReferenceEquals(currentColliderGo, m_AgentGameObject))
+                {
+                    continue;
+                }
+
+                var closestColliderPoint = foundColliders[i].ClosestPointOnBounds(cellCenter);
+                var currentDistanceSquared = (closestColliderPoint - m_CenterObject.transform.position).sqrMagnitude;
+
+                if (currentDistanceSquared >= minDistanceSquared)
+                {
+                    continue;
+                }
+
+                // Checks if our colliders contain a detectable object
+                var index = -1;
+                for (var ii = 0; ii < m_DetectableTags.Length; ii++)
+                {
+                    if (currentColliderGo.CompareTag(m_DetectableTags[ii]))
+                    {
+                        index = ii;
+                        break;
+                    }
+                }
+                if (index > -1 && currentDistanceSquared < minDistanceSquared)
+                {
+                    minDistanceSquared = currentDistanceSquared;
+                    closestColliderGo = currentColliderGo;
+                }
+            }
+
+            if (!ReferenceEquals(closestColliderGo, null))
+            {
+                detectedAction.Invoke(closestColliderGo, cellIndex);
+            }
+        }
+
+        /// <summary>
+        /// Parses all colliders in the array of colliders found within a cell.
+        /// </summary>
+        void ParseCollidersAll(Collider[] foundColliders, int numFound, int cellIndex, Vector3 cellCenter, Action<GameObject, int> detectedAction)
+        {
+            for (int i = 0; i < numFound; i++)
+            {
+                var currentColliderGo = foundColliders[i].gameObject;
+                if (!ReferenceEquals(currentColliderGo, m_AgentGameObject))
+                {
+                    detectedAction.Invoke(currentColliderGo, cellIndex);
+                }
+            }
+        }
+
+#endif
+
+        public void RegisterSensor(GridSensorBase sensor)
+        {
+#if MLA_UNITY_PHYSICS_MODULE
+            if (sensor.GetProcessCollidersMethod() == ProcessCollidersMethod.ProcessAllColliders)
+            {
+                GridOverlapDetectedAll += sensor.ProcessDetectedObject;
+            }
+            else
+            {
+                GridOverlapDetectedClosest += sensor.ProcessDetectedObject;
+            }
+#endif
+        }
+
+        public void RegisterDebugSensor(GridSensorBase debugSensor)
+        {
+#if MLA_UNITY_PHYSICS_MODULE
+            GridOverlapDetectedDebug += debugSensor.ProcessDetectedObject;
+#endif
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs.meta b/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1d20815c0ec63004e0972c2906c030cee0287a32
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BoxOverlapChecker.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e039296229a084578823e21bce9cf834
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs b/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..09db85d22405ee69a0d9b393ddb88a0daf82bbf8
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs
@@ -0,0 +1,114 @@
+using System;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A Sensor that allows to observe a variable number of entities.
+    /// </summary>
+    public class BufferSensor : ISensor, IBuiltInSensor
+    {
+        private string m_Name;
+        private int m_MaxNumObs;
+        private int m_ObsSize;
+        float[] m_ObservationBuffer;
+        int m_CurrentNumObservables;
+        ObservationSpec m_ObservationSpec;
+
+
+        /// <summary>
+        /// Creates the BufferSensor.
+        /// </summary>
+        /// <param name="maxNumberObs">The maximum number of observations to be appended to this BufferSensor.</param>
+        /// <param name="obsSize">The size of each observation appended to the BufferSensor.</param>
+        /// <param name="name">The name of the sensor.</param>
+        public BufferSensor(int maxNumberObs, int obsSize, string name)
+        {
+            m_Name = name;
+            m_MaxNumObs = maxNumberObs;
+            m_ObsSize = obsSize;
+            m_ObservationBuffer = new float[m_ObsSize * m_MaxNumObs];
+            m_CurrentNumObservables = 0;
+            m_ObservationSpec = ObservationSpec.VariableLength(m_MaxNumObs, m_ObsSize);
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <summary>
+        /// Appends an observation to the buffer. If the buffer is full (maximum number
+        /// of observation is reached) the observation will be ignored. the length of
+        /// the provided observation array must be equal to the observation size of
+        /// the buffer sensor.
+        /// </summary>
+        /// <param name="obs"> The float array observation</param>
+        public void AppendObservation(float[] obs)
+        {
+            if (obs.Length != m_ObsSize)
+            {
+                throw new UnityAgentsException(
+                    "The BufferSensor was expecting an observation of size " +
+                    $"{m_ObsSize} but received {obs.Length} observations instead."
+                );
+            }
+            if (m_CurrentNumObservables >= m_MaxNumObs)
+            {
+                return;
+            }
+            for (int i = 0; i < obs.Length; i++)
+            {
+                m_ObservationBuffer[m_CurrentNumObservables * m_ObsSize + i] = obs[i];
+            }
+            m_CurrentNumObservables++;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            for (int i = 0; i < m_ObsSize * m_MaxNumObs; i++)
+            {
+                writer[i] = m_ObservationBuffer[i];
+            }
+            return m_ObsSize * m_MaxNumObs;
+        }
+
+        /// <inheritdoc/>
+        public virtual byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            Reset();
+        }
+
+        /// <inheritdoc/>
+        public void Reset()
+        {
+            m_CurrentNumObservables = 0;
+            Array.Clear(m_ObservationBuffer, 0, m_ObservationBuffer.Length);
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.BufferSensor;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..327456d2ee4613204f2328959a30aa3ab8f12066
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 034f05c858e684e5498d9a548c9d1fc5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8be049e8a2dc1ad918b65f8f79cdc8211af7691f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs
@@ -0,0 +1,68 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A SensorComponent that creates a <see cref="BufferSensor"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Buffer Sensor", (int)MenuGroup.Sensors)]
+    public class BufferSensorComponent : SensorComponent
+    {
+        /// <summary>
+        /// Name of the generated <see cref="BufferSensor"/> object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+        [HideInInspector, SerializeField]
+        private string m_SensorName = "BufferSensor";
+
+        /// <summary>
+        /// This is how many floats each entities will be represented with. This number
+        /// is fixed and all entities must have the same representation.
+        /// </summary>
+        public int ObservableSize
+        {
+            get { return m_ObservableSize; }
+            set { m_ObservableSize = value; }
+        }
+        [HideInInspector, SerializeField]
+        private int m_ObservableSize;
+
+        /// <summary>
+        /// This is the maximum number of entities the `BufferSensor` will be able to
+        /// collect.
+        /// </summary>
+        public int MaxNumObservables
+        {
+            get { return m_MaxNumObservables; }
+            set { m_MaxNumObservables = value; }
+        }
+        [HideInInspector, SerializeField]
+        private int m_MaxNumObservables;
+
+        private BufferSensor m_Sensor;
+
+        /// <inheritdoc/>
+        public override ISensor[] CreateSensors()
+        {
+            m_Sensor = new BufferSensor(MaxNumObservables, ObservableSize, m_SensorName);
+            return new ISensor[] { m_Sensor };
+        }
+
+        /// <summary>
+        /// Appends an observation to the buffer. If the buffer is full (maximum number
+        /// of observation is reached) the observation will be ignored. the length of
+        /// the provided observation array must be equal to the observation size of
+        /// the buffer sensor.
+        /// </summary>
+        /// <param name="obs"> The float array observation</param>
+        public void AppendObservation(float[] obs)
+        {
+            m_Sensor.AppendObservation(obs);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..69bee5ca08963d2114010ba87de1b28487d0929d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: dd8012d5925524537b27131fef517017
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs b/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ddf3d0000be0c2326f53d603da2e9e1ab4e581dc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs
@@ -0,0 +1,184 @@
+using System;
+using UnityEngine;
+using UnityEngine.Rendering;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A sensor that wraps a Camera object to generate visual observations for an agent.
+    /// </summary>
+    public class CameraSensor : ISensor, IBuiltInSensor, IDisposable
+    {
+        Camera m_Camera;
+        int m_Width;
+        int m_Height;
+        bool m_Grayscale;
+        string m_Name;
+        private ObservationSpec m_ObservationSpec;
+        SensorCompressionType m_CompressionType;
+        Texture2D m_Texture;
+
+        /// <summary>
+        /// The Camera used for rendering the sensor observations.
+        /// </summary>
+        public Camera Camera
+        {
+            get { return m_Camera; }
+            set { m_Camera = value; }
+        }
+
+        /// <summary>
+        /// The compression type used by the sensor.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_CompressionType; }
+            set { m_CompressionType = value; }
+        }
+
+        /// <summary>
+        /// Creates and returns the camera sensor.
+        /// </summary>
+        /// <param name="camera">Camera object to capture images from.</param>
+        /// <param name="width">The width of the generated visual observation.</param>
+        /// <param name="height">The height of the generated visual observation.</param>
+        /// <param name="grayscale">Whether to convert the generated image to grayscale or keep color.</param>
+        /// <param name="name">The name of the camera sensor.</param>
+        /// <param name="compression">The compression to apply to the generated image.</param>
+        /// <param name="observationType">The type of observation.</param>
+        public CameraSensor(
+            Camera camera, int width, int height, bool grayscale, string name, SensorCompressionType compression, ObservationType observationType = ObservationType.Default)
+        {
+            m_Camera = camera;
+            m_Width = width;
+            m_Height = height;
+            m_Grayscale = grayscale;
+            m_Name = name;
+            var channels = grayscale ? 1 : 3;
+            m_ObservationSpec = ObservationSpec.Visual(height, width, channels, observationType);
+            m_CompressionType = compression;
+            m_Texture = new Texture2D(width, height, TextureFormat.RGB24, false);
+        }
+
+        /// <summary>
+        /// Accessor for the name of the sensor.
+        /// </summary>
+        /// <returns>Sensor name.</returns>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <summary>
+        /// Returns a description of the observations that will be generated by the sensor.
+        /// The shape will be h x w x 1 for grayscale and h x w x 3 for color.
+        /// The dimensions have translational equivariance along width and height,
+        /// and no property along the channels dimension.
+        /// </summary>
+        /// <returns></returns>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <summary>
+        /// Generates a compressed image. This can be valuable in speeding-up training.
+        /// </summary>
+        /// <returns>Compressed image.</returns>
+        public byte[] GetCompressedObservation()
+        {
+            using (TimerStack.Instance.Scoped("CameraSensor.GetCompressedObservation"))
+            {
+                ObservationToTexture(m_Camera, m_Texture, m_Width, m_Height);
+                // TODO support more types here, e.g. JPG
+                var compressed = m_Texture.EncodeToPNG();
+                return compressed;
+            }
+        }
+
+        /// <summary>
+        /// Writes out the generated, uncompressed image to the provided <see cref="ObservationWriter"/>.
+        /// </summary>
+        /// <param name="writer">Where the observation is written to.</param>
+        /// <returns></returns>
+        public int Write(ObservationWriter writer)
+        {
+            using (TimerStack.Instance.Scoped("CameraSensor.WriteToTensor"))
+            {
+                ObservationToTexture(m_Camera, m_Texture, m_Width, m_Height);
+                var numWritten = writer.WriteTexture(m_Texture, m_Grayscale);
+                return numWritten;
+            }
+        }
+
+        /// <inheritdoc/>
+        public void Update() { }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return new CompressionSpec(m_CompressionType);
+        }
+
+        /// <summary>
+        /// Renders a Camera instance to a 2D texture at the corresponding resolution.
+        /// </summary>
+        /// <param name="obsCamera">Camera.</param>
+        /// <param name="texture2D">Texture2D to render to.</param>
+        /// <param name="width">Width of resulting 2D texture.</param>
+        /// <param name="height">Height of resulting 2D texture.</param>
+        public static void ObservationToTexture(Camera obsCamera, Texture2D texture2D, int width, int height)
+        {
+            if (SystemInfo.graphicsDeviceType == GraphicsDeviceType.Null)
+            {
+                Debug.LogError("GraphicsDeviceType is Null. This will likely crash when trying to render.");
+            }
+
+            var oldRec = obsCamera.rect;
+            obsCamera.rect = new Rect(0f, 0f, 1f, 1f);
+            var depth = 24;
+            var format = RenderTextureFormat.Default;
+            var readWrite = RenderTextureReadWrite.Default;
+
+            var tempRt =
+                RenderTexture.GetTemporary(width, height, depth, format, readWrite);
+
+            var prevActiveRt = RenderTexture.active;
+            var prevCameraRt = obsCamera.targetTexture;
+
+            // render to offscreen texture (readonly from CPU side)
+            RenderTexture.active = tempRt;
+            obsCamera.targetTexture = tempRt;
+
+            obsCamera.Render();
+
+            texture2D.ReadPixels(new Rect(0, 0, texture2D.width, texture2D.height), 0, 0);
+
+            obsCamera.targetTexture = prevCameraRt;
+            obsCamera.rect = oldRec;
+            RenderTexture.active = prevActiveRt;
+            RenderTexture.ReleaseTemporary(tempRt);
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.CameraSensor;
+        }
+
+        /// <summary>
+        /// Clean up the owned Texture2D.
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(null, m_Texture))
+            {
+                Utilities.DestroyTexture(m_Texture);
+                m_Texture = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1a0314b8f776bfc2ec2ef60f4a0cf9cbb5ec419c
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 41cb6bf4b09974bf583f5b2fef0c08a7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f6b53f087eaa062308dea49c074893d53df32011
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs
@@ -0,0 +1,179 @@
+using System;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A SensorComponent that creates a <see cref="CameraSensor"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Camera Sensor", (int)MenuGroup.Sensors)]
+    public class CameraSensorComponent : SensorComponent, IDisposable
+    {
+        [HideInInspector, SerializeField, FormerlySerializedAs("camera")]
+        Camera m_Camera;
+
+        CameraSensor m_Sensor;
+
+        /// <summary>
+        /// Camera object that provides the data to the sensor.
+        /// </summary>
+        public Camera Camera
+        {
+            get { return m_Camera; }
+            set { m_Camera = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("sensorName")]
+        string m_SensorName = "CameraSensor";
+
+        /// <summary>
+        /// Name of the generated <see cref="CameraSensor"/> object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("width")]
+        int m_Width = 84;
+
+        /// <summary>
+        /// Width of the generated observation.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int Width
+        {
+            get { return m_Width; }
+            set { m_Width = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("height")]
+        int m_Height = 84;
+
+        /// <summary>
+        /// Height of the generated observation.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int Height
+        {
+            get { return m_Height; }
+            set { m_Height = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("grayscale")]
+        bool m_Grayscale;
+
+        /// <summary>
+        /// Whether to generate grayscale images or color.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public bool Grayscale
+        {
+            get { return m_Grayscale; }
+            set { m_Grayscale = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        ObservationType m_ObservationType;
+
+        /// <summary>
+        /// The type of the observation.
+        /// </summary>
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField]
+        bool m_RuntimeCameraEnable;
+
+
+        /// <summary>
+        /// Controls the whether the camera sensor's attached camera
+        /// is enabled during runtime. Overrides the camera object enabled status.
+        /// Disabled for improved performance. Disabled by default.
+        /// </summary>
+        public bool RuntimeCameraEnable
+        {
+            get { return m_RuntimeCameraEnable; }
+            set { m_RuntimeCameraEnable = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField]
+        [Range(1, 50)]
+        [Tooltip("Number of camera frames that will be stacked before being fed to the neural network.")]
+        int m_ObservationStacks = 1;
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("compression")]
+        SensorCompressionType m_Compression = SensorCompressionType.PNG;
+
+        /// <summary>
+        /// The compression type to use for the sensor.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_Compression; }
+            set { m_Compression = value; UpdateSensor(); }
+        }
+
+        /// <summary>
+        /// Whether to stack previous observations. Using 1 means no previous observations.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int ObservationStacks
+        {
+            get { return m_ObservationStacks; }
+            set { m_ObservationStacks = value; }
+        }
+
+        void Start()
+        {
+            UpdateSensor();
+        }
+
+        /// <summary>
+        /// Creates the <see cref="CameraSensor"/>
+        /// </summary>
+        /// <returns>The created <see cref="CameraSensor"/> object for this component.</returns>
+        public override ISensor[] CreateSensors()
+        {
+            Dispose();
+            m_Sensor = new CameraSensor(m_Camera, m_Width, m_Height, Grayscale, m_SensorName, m_Compression, m_ObservationType);
+
+            if (ObservationStacks != 1)
+            {
+                return new ISensor[] { new StackingSensor(m_Sensor, ObservationStacks) };
+            }
+            return new ISensor[] { m_Sensor };
+        }
+
+        /// <summary>
+        /// Update fields that are safe to change on the Sensor at runtime.
+        /// </summary>
+        internal void UpdateSensor()
+        {
+            if (m_Sensor != null)
+            {
+                m_Sensor.Camera = m_Camera;
+                m_Sensor.CompressionType = m_Compression;
+                m_Sensor.Camera.enabled = m_RuntimeCameraEnable;
+            }
+        }
+
+        /// <summary>
+        /// Clean up the sensor created by CreateSensors().
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(m_Sensor, null))
+            {
+                m_Sensor.Dispose();
+                m_Sensor = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..307dc6495244fec3cb7e340d0cad52ce036a411b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs.meta
@@ -0,0 +1,11 @@
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+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs b/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs
new file mode 100644
index 0000000000000000000000000000000000000000..76e283a14a94db777efcb574ea30b53912b21a00
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs
@@ -0,0 +1,113 @@
+using System.Linq;
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// The compression setting for visual/camera observations.
+    /// </summary>
+    public enum SensorCompressionType
+    {
+        /// <summary>
+        /// No compression. Data is preserved as float arrays.
+        /// </summary>
+        None,
+
+        /// <summary>
+        /// PNG format. Data will be stored in binary format.
+        /// </summary>
+        PNG
+    }
+
+    /// <summary>
+    /// A description of the compression used for observations.
+    /// </summary>
+    /// <remarks>
+    /// Most ISensor implementations can't take advantage of compression,
+    /// and should return CompressionSpec.Default() from their ISensor.GetCompressionSpec() methods.
+    /// Visual observations, or mulitdimensional categorical observations (for example, image segmentation
+    /// or the piece types in a match-3 game board) can use PNG compression reduce the amount of
+    /// data transferred between Unity and the trainer.
+    /// </remarks>
+    public struct CompressionSpec
+    {
+        internal SensorCompressionType m_SensorCompressionType;
+
+        /// <summary>
+        /// The compression type that the sensor will use for its observations.
+        /// </summary>
+        public SensorCompressionType SensorCompressionType
+        {
+            get => m_SensorCompressionType;
+        }
+
+        internal int[] m_CompressedChannelMapping;
+
+        /// <summary>
+        /// The mapping of the channels in compressed data to the actual channel after decompression.
+        /// </summary>
+        /// <remarks>
+        /// The mapping is a list of integer index with the same length as
+        /// the number of output observation layers (channels), including padding if there's any.
+        /// Each index indicates the actual channel the layer will go into.
+        /// Layers with the same index will be averaged, and layers with negative index will be dropped.
+        /// For example, mapping for CameraSensor using grayscale and stacking of two: [0, 0, 0, 1, 1, 1]
+        /// Mapping for GridSensor of 4 channels and stacking of two: [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
+        /// </remarks>
+        public int[] CompressedChannelMapping
+        {
+            get => m_CompressedChannelMapping;
+        }
+
+        /// <summary>
+        /// Return a CompressionSpec indicating possible compression.
+        /// </summary>
+        /// <param name="sensorCompressionType">The compression type to use.</param>
+        /// <param name="compressedChannelMapping">Optional mapping mapping of the channels in compressed data to the
+        /// actual channel after decompression.</param>
+        public CompressionSpec(SensorCompressionType sensorCompressionType, int[] compressedChannelMapping = null)
+        {
+            m_SensorCompressionType = sensorCompressionType;
+            m_CompressedChannelMapping = compressedChannelMapping;
+        }
+
+        /// <summary>
+        /// Return a CompressionSpec indicating no compression. This is recommended for most sensors.
+        /// </summary>
+        /// <returns></returns>
+        public static CompressionSpec Default()
+        {
+            return new CompressionSpec
+            {
+                m_SensorCompressionType = SensorCompressionType.None,
+                m_CompressedChannelMapping = null
+            };
+        }
+
+        /// <summary>
+        /// Return whether the compressed channel mapping is "trivial"; if so it doesn't need to be sent to the
+        /// trainer.
+        /// </summary>
+        /// <returns></returns>
+        internal bool IsTrivialMapping()
+        {
+            var mapping = CompressedChannelMapping;
+            if (mapping == null)
+            {
+                return true;
+            }
+            // check if mapping equals zero mapping
+            if (mapping.Length == 3 && mapping.All(m => m == 0))
+            {
+                return true;
+            }
+            // check if mapping equals identity mapping
+            for (var i = 0; i < mapping.Length; i++)
+            {
+                if (mapping[i] != i)
+                {
+                    return false;
+                }
+            }
+            return true;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs.meta b/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3bbac496d7e37c0973b07b2b880f8a645394a9d4
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/CompressionSpec.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0ddff1d1b7ad4170acb1a10272d4a8c2
+timeCreated: 1616006929
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs b/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a37861e2716a2acfe454f4c28dadb42550e5cbc8
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs
@@ -0,0 +1,348 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Profiling;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// The way the GridSensor process detected colliders in a cell.
+    /// </summary>
+    public enum ProcessCollidersMethod
+    {
+        /// <summary>
+        /// Get data from all colliders detected in a cell
+        /// </summary>
+        ProcessAllColliders,
+
+        /// <summary>
+        /// Get data from the collider closest to the agent
+        /// </summary>
+        ProcessClosestColliders
+    }
+
+    /// <summary>
+    /// Grid-based sensor.
+    /// </summary>
+    public class GridSensorBase : ISensor, IBuiltInSensor, IDisposable
+    {
+        string m_Name;
+        Vector3 m_CellScale;
+        Vector3Int m_GridSize;
+        string[] m_DetectableTags;
+        SensorCompressionType m_CompressionType;
+        ObservationSpec m_ObservationSpec;
+        internal IGridPerception m_GridPerception;
+
+        // Buffers
+        float[] m_PerceptionBuffer;
+        Color[] m_PerceptionColors;
+        Texture2D m_PerceptionTexture;
+        float[] m_CellDataBuffer;
+
+        // Utility Constants Calculated on Init
+        int m_NumCells;
+        int m_CellObservationSize;
+        Vector3 m_CellCenterOffset;
+
+
+        /// <summary>
+        /// Create a GridSensorBase with the specified configuration.
+        /// </summary>
+        /// <param name="name">The sensor name</param>
+        /// <param name="cellScale">The scale of each cell in the grid</param>
+        /// <param name="gridSize">Number of cells on each side of the grid</param>
+        /// <param name="detectableTags">Tags to be detected by the sensor</param>
+        /// <param name="compression">Compression type</param>
+        public GridSensorBase(
+            string name,
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            string[] detectableTags,
+            SensorCompressionType compression
+        )
+        {
+            m_Name = name;
+            m_CellScale = cellScale;
+            m_GridSize = gridSize;
+            m_DetectableTags = detectableTags;
+            CompressionType = compression;
+
+            if (m_GridSize.y != 1)
+            {
+                throw new UnityAgentsException("GridSensor only supports 2D grids.");
+            }
+
+            m_NumCells = m_GridSize.x * m_GridSize.z;
+            m_CellObservationSize = GetCellObservationSize();
+            m_ObservationSpec = ObservationSpec.Visual(m_GridSize.x, m_GridSize.z, m_CellObservationSize);
+            m_PerceptionTexture = new Texture2D(m_GridSize.x, m_GridSize.z, TextureFormat.RGB24, false);
+
+            ResetPerceptionBuffer();
+        }
+
+        /// <summary>
+        /// The compression type used by the sensor.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_CompressionType; }
+            set
+            {
+                if (!IsDataNormalized() && value == SensorCompressionType.PNG)
+                {
+                    Debug.LogWarning($"Compression type {value} is only supported with normalized data. " +
+                        "The sensor will not compress the data.");
+                    return;
+                }
+                m_CompressionType = value;
+            }
+        }
+
+        internal float[] PerceptionBuffer
+        {
+            get { return m_PerceptionBuffer; }
+        }
+
+        /// <summary>
+        /// The tags which the sensor dectects.
+        /// </summary>
+        protected string[] DetectableTags
+        {
+            get { return m_DetectableTags; }
+        }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <summary>
+        /// Clears the perception buffer before loading in new data.
+        /// </summary>
+        public void ResetPerceptionBuffer()
+        {
+            if (m_PerceptionBuffer != null)
+            {
+                Array.Clear(m_PerceptionBuffer, 0, m_PerceptionBuffer.Length);
+                Array.Clear(m_CellDataBuffer, 0, m_CellDataBuffer.Length);
+            }
+            else
+            {
+                m_PerceptionBuffer = new float[m_CellObservationSize * m_NumCells];
+                m_CellDataBuffer = new float[m_CellObservationSize];
+                m_PerceptionColors = new Color[m_NumCells];
+            }
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return new CompressionSpec(CompressionType);
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.GridSensor;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            using (TimerStack.Instance.Scoped("GridSensor.GetCompressedObservation"))
+            {
+                var allBytes = new List<byte>();
+                var numImages = (m_CellObservationSize + 2) / 3;
+                for (int i = 0; i < numImages; i++)
+                {
+                    var channelIndex = 3 * i;
+                    GridValuesToTexture(channelIndex, Math.Min(3, m_CellObservationSize - channelIndex));
+                    allBytes.AddRange(m_PerceptionTexture.EncodeToPNG());
+                }
+
+                return allBytes.ToArray();
+            }
+        }
+
+        /// <summary>
+        /// Convert observation values to texture for PNG compression.
+        /// </summary>
+        void GridValuesToTexture(int channelIndex, int numChannelsToAdd)
+        {
+            for (int i = 0; i < m_NumCells; i++)
+            {
+                for (int j = 0; j < numChannelsToAdd; j++)
+                {
+                    m_PerceptionColors[i][j] = m_PerceptionBuffer[i * m_CellObservationSize + channelIndex + j];
+                }
+            }
+            m_PerceptionTexture.SetPixels(m_PerceptionColors);
+        }
+
+        /// <summary>
+        /// Get the observation values of the detected game object.
+        /// Default is to record the detected tag index.
+        ///
+        /// This method can be overridden to encode the observation differently or get custom data from the object.
+        /// When overriding this method, <seealso cref="GetCellObservationSize"/> and <seealso cref="IsDataNormalized"/>
+        /// might also need to change accordingly.
+        /// </summary>
+        /// <param name="detectedObject">The game object that was detected within a certain cell</param>
+        /// <param name="tagIndex">The index of the detectedObject's tag in the DetectableObjects list</param>
+        /// <param name="dataBuffer">The buffer to write the observation values.
+        ///         The buffer size is configured by <seealso cref="GetCellObservationSize"/>.
+        /// </param>
+        /// <example>
+        ///   Here is an example of overriding GetObjectData to get the velocity of a potential Rigidbody:
+        ///   <code>
+        ///     protected override void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+        ///     {
+        ///         if (tagIndex == Array.IndexOf(DetectableTags, "RigidBodyObject"))
+        ///         {
+        ///             Rigidbody rigidbody = detectedObject.GetComponent&lt;Rigidbody&gt;();
+        ///             dataBuffer[0] = rigidbody.velocity.x;
+        ///             dataBuffer[1] = rigidbody.velocity.y;
+        ///             dataBuffer[2] = rigidbody.velocity.z;
+        ///         }
+        ///     }
+        ///  </code>
+        /// </example>
+        protected virtual void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+        {
+            dataBuffer[0] = tagIndex + 1;
+        }
+
+        /// <summary>
+        /// Get the observation size for each cell. This will be the size of dataBuffer for <seealso cref="GetObjectData"/>.
+        /// If overriding <seealso cref="GetObjectData"/>, override this method as well to the custom observation size.
+        /// </summary>
+        /// <returns>The observation size of each cell.</returns>
+        protected virtual int GetCellObservationSize()
+        {
+            return 1;
+        }
+
+        /// <summary>
+        /// Whether the data is normalized within [0, 1]. The sensor can only use PNG compression if the data is normailzed.
+        /// If overriding <seealso cref="GetObjectData"/>, override this method as well according to the custom observation values.
+        /// </summary>
+        /// <returns>Bool value indicating whether data is normalized.</returns>
+        protected virtual bool IsDataNormalized()
+        {
+            return false;
+        }
+
+        /// <summary>
+        /// Whether to process all detected colliders in a cell. Default to false and only use the one closest to the agent.
+        /// If overriding <seealso cref="GetObjectData"/>, consider override this method when needed.
+        /// </summary>
+        /// <returns>Bool value indicating whether to process all detected colliders in a cell.</returns>
+        protected internal virtual ProcessCollidersMethod GetProcessCollidersMethod()
+        {
+            return ProcessCollidersMethod.ProcessClosestColliders;
+        }
+
+        /// <summary>
+        /// If using PNG compression, check if the values are normalized.
+        /// </summary>
+        void ValidateValues(float[] dataValues, GameObject detectedObject)
+        {
+            if (m_CompressionType != SensorCompressionType.PNG)
+            {
+                return;
+            }
+
+            for (int j = 0; j < dataValues.Length; j++)
+            {
+                if (dataValues[j] < 0 || dataValues[j] > 1)
+                    throw new UnityAgentsException($"When using compression type {m_CompressionType} the data value has to be normalized between 0-1. " +
+                        $"Received value[{dataValues[j]}] for {detectedObject.name}");
+            }
+        }
+
+        /// <summary>
+        /// Collect data from the detected object if a detectable tag is matched.
+        /// </summary>
+        internal void ProcessDetectedObject(GameObject detectedObject, int cellIndex)
+        {
+            Profiler.BeginSample("GridSensor.ProcessDetectedObject");
+            for (var i = 0; i < m_DetectableTags.Length; i++)
+            {
+                if (!ReferenceEquals(detectedObject, null) && detectedObject.CompareTag(m_DetectableTags[i]))
+                {
+                    if (GetProcessCollidersMethod() == ProcessCollidersMethod.ProcessAllColliders)
+                    {
+                        Array.Copy(m_PerceptionBuffer, cellIndex * m_CellObservationSize, m_CellDataBuffer, 0, m_CellObservationSize);
+                    }
+                    else
+                    {
+                        Array.Clear(m_CellDataBuffer, 0, m_CellDataBuffer.Length);
+                    }
+
+                    GetObjectData(detectedObject, i, m_CellDataBuffer);
+                    ValidateValues(m_CellDataBuffer, detectedObject);
+                    Array.Copy(m_CellDataBuffer, 0, m_PerceptionBuffer, cellIndex * m_CellObservationSize, m_CellObservationSize);
+                    break;
+                }
+            }
+            Profiler.EndSample();
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            ResetPerceptionBuffer();
+            using (TimerStack.Instance.Scoped("GridSensor.Update"))
+            {
+                if (m_GridPerception != null)
+                {
+                    m_GridPerception.Perceive();
+                }
+            }
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            using (TimerStack.Instance.Scoped("GridSensor.Write"))
+            {
+                int index = 0;
+                for (var h = m_GridSize.z - 1; h >= 0; h--)
+                {
+                    for (var w = 0; w < m_GridSize.x; w++)
+                    {
+                        for (var d = 0; d < m_CellObservationSize; d++)
+                        {
+                            writer[h, w, d] = m_PerceptionBuffer[index];
+                            index++;
+                        }
+                    }
+                }
+                return index;
+            }
+        }
+
+        /// <summary>
+        /// Clean up the internal objects.
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(null, m_PerceptionTexture))
+            {
+                Utilities.DestroyTexture(m_PerceptionTexture);
+                m_PerceptionTexture = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs.meta b/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..623d17202d5056f3cbaa7e2f59c020a0199c85d9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/GridSensorBase.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2454efb6b02aa414dae2cb8573e87682
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..004a04f39637dc6490f2215e2c5c0722496caf24
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs
@@ -0,0 +1,310 @@
+using System.Collections.Generic;
+using System.Linq;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A SensorComponent that creates a <see cref="GridSensorBase"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Grid Sensor", (int)MenuGroup.Sensors)]
+    public class GridSensorComponent : SensorComponent
+    {
+        // dummy sensor only used for debug gizmo
+        GridSensorBase m_DebugSensor;
+        List<GridSensorBase> m_Sensors;
+        internal IGridPerception m_GridPerception;
+
+        [HideInInspector, SerializeField]
+        protected internal string m_SensorName = "GridSensor";
+        /// <summary>
+        /// Name of the generated <see cref="GridSensorBase"/> object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal Vector3 m_CellScale = new Vector3(1f, 0.01f, 1f);
+
+        /// <summary>
+        /// The scale of each grid cell.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public Vector3 CellScale
+        {
+            get { return m_CellScale; }
+            set { m_CellScale = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal Vector3Int m_GridSize = new Vector3Int(16, 1, 16);
+        /// <summary>
+        /// The number of grid on each side.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public Vector3Int GridSize
+        {
+            get { return m_GridSize; }
+            set
+            {
+                if (value.y != 1)
+                {
+                    m_GridSize = new Vector3Int(value.x, 1, value.z);
+                }
+                else
+                {
+                    m_GridSize = value;
+                }
+            }
+        }
+
+        [HideInInspector, SerializeField]
+        internal bool m_RotateWithAgent = true;
+        /// <summary>
+        /// Rotate the grid based on the direction the agent is facing.
+        /// </summary>
+        public bool RotateWithAgent
+        {
+            get { return m_RotateWithAgent; }
+            set { m_RotateWithAgent = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal GameObject m_AgentGameObject;
+        /// <summary>
+        /// The reference of the root of the agent. This is used to disambiguate objects with
+        /// the same tag as the agent. Defaults to current GameObject.
+        /// </summary>
+        public GameObject AgentGameObject
+        {
+            get { return (m_AgentGameObject == null ? gameObject : m_AgentGameObject); }
+            set { m_AgentGameObject = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal string[] m_DetectableTags;
+        /// <summary>
+        /// List of tags that are detected.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public string[] DetectableTags
+        {
+            get { return m_DetectableTags; }
+            set { m_DetectableTags = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal LayerMask m_ColliderMask;
+        /// <summary>
+        /// The layer mask.
+        /// </summary>
+        public LayerMask ColliderMask
+        {
+            get { return m_ColliderMask; }
+            set { m_ColliderMask = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal int m_MaxColliderBufferSize = 500;
+        /// <summary>
+        /// The absolute max size of the Collider buffer used in the non-allocating Physics calls.  In other words
+        /// the Collider buffer will never grow beyond this number even if there are more Colliders in the Grid Cell.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int MaxColliderBufferSize
+        {
+            get { return m_MaxColliderBufferSize; }
+            set { m_MaxColliderBufferSize = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal int m_InitialColliderBufferSize = 4;
+        /// <summary>
+        /// The Estimated Max Number of Colliders to expect per cell.  This number is used to
+        /// pre-allocate an array of Colliders in order to take advantage of the OverlapBoxNonAlloc
+        /// Physics API.  If the number of colliders found is >= InitialColliderBufferSize the array
+        /// will be resized to double its current size.  The hard coded absolute size is 500.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int InitialColliderBufferSize
+        {
+            get { return m_InitialColliderBufferSize; }
+            set { m_InitialColliderBufferSize = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal Color[] m_DebugColors;
+        /// <summary>
+        /// Array of Colors used for the grid gizmos.
+        /// </summary>
+        public Color[] DebugColors
+        {
+            get { return m_DebugColors; }
+            set { m_DebugColors = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal float m_GizmoYOffset = 0f;
+        /// <summary>
+        /// The height of the gizmos grid.
+        /// </summary>
+        public float GizmoYOffset
+        {
+            get { return m_GizmoYOffset; }
+            set { m_GizmoYOffset = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal bool m_ShowGizmos = false;
+        /// <summary>
+        /// Whether to show gizmos or not.
+        /// </summary>
+        public bool ShowGizmos
+        {
+            get { return m_ShowGizmos; }
+            set { m_ShowGizmos = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        internal SensorCompressionType m_CompressionType = SensorCompressionType.PNG;
+        /// <summary>
+        /// The compression type to use for the sensor.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_CompressionType; }
+            set { m_CompressionType = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField]
+        [Range(1, 50)]
+        [Tooltip("Number of frames of observations that will be stacked before being fed to the neural network.")]
+        internal int m_ObservationStacks = 1;
+        /// <summary>
+        /// Whether to stack previous observations. Using 1 means no previous observations.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int ObservationStacks
+        {
+            get { return m_ObservationStacks; }
+            set { m_ObservationStacks = value; }
+        }
+
+        /// <inheritdoc/>
+        public override ISensor[] CreateSensors()
+        {
+            m_GridPerception = new BoxOverlapChecker(
+                m_CellScale,
+                m_GridSize,
+                m_RotateWithAgent,
+                m_ColliderMask,
+                gameObject,
+                AgentGameObject,
+                m_DetectableTags,
+                m_InitialColliderBufferSize,
+                m_MaxColliderBufferSize
+            );
+
+            // debug data is positive int value and will trigger data validation exception if SensorCompressionType is not None.
+            m_DebugSensor = new GridSensorBase("DebugGridSensor", m_CellScale, m_GridSize, m_DetectableTags, SensorCompressionType.None);
+            m_GridPerception.RegisterDebugSensor(m_DebugSensor);
+
+            m_Sensors = GetGridSensors().ToList();
+            if (m_Sensors == null || m_Sensors.Count < 1)
+            {
+                throw new UnityAgentsException("GridSensorComponent received no sensors. Specify at least one observation type (OneHot/Counting) to use grid sensors." +
+                    "If you're overriding GridSensorComponent.GetGridSensors(), return at least one grid sensor.");
+            }
+
+            // Only one sensor needs to reference the boxOverlapChecker, so that it gets updated exactly once
+            m_Sensors[0].m_GridPerception = m_GridPerception;
+            foreach (var sensor in m_Sensors)
+            {
+                m_GridPerception.RegisterSensor(sensor);
+            }
+
+            if (ObservationStacks != 1)
+            {
+                var sensors = new ISensor[m_Sensors.Count];
+                for (var i = 0; i < m_Sensors.Count; i++)
+                {
+                    sensors[i] = new StackingSensor(m_Sensors[i], ObservationStacks);
+                }
+                return sensors;
+            }
+            else
+            {
+                return m_Sensors.ToArray();
+            }
+        }
+
+        /// <summary>
+        /// Get an array of GridSensors to be added in this component.
+        /// Override this method and return custom GridSensor implementations.
+        /// </summary>
+        /// <returns>Array of grid sensors to be added to the component.</returns>
+        protected virtual GridSensorBase[] GetGridSensors()
+        {
+            List<GridSensorBase> sensorList = new List<GridSensorBase>();
+            var sensor = new OneHotGridSensor(m_SensorName + "-OneHot", m_CellScale, m_GridSize, m_DetectableTags, m_CompressionType);
+            sensorList.Add(sensor);
+            return sensorList.ToArray();
+        }
+
+        /// <summary>
+        /// Update fields that are safe to change on the Sensor at runtime.
+        /// </summary>
+        internal void UpdateSensor()
+        {
+            if (m_Sensors != null)
+            {
+                m_GridPerception.RotateWithAgent = m_RotateWithAgent;
+                m_GridPerception.ColliderMask = m_ColliderMask;
+                foreach (var sensor in m_Sensors)
+                {
+                    sensor.CompressionType = m_CompressionType;
+                }
+            }
+        }
+
+        void OnDrawGizmos()
+        {
+            if (m_ShowGizmos)
+            {
+                if (m_GridPerception == null || m_DebugSensor == null)
+                {
+                    return;
+                }
+
+                m_DebugSensor.ResetPerceptionBuffer();
+                m_GridPerception.UpdateGizmo();
+                var cellColors = m_DebugSensor.PerceptionBuffer;
+                var rotation = m_GridPerception.GetGridRotation();
+
+                var scale = new Vector3(m_CellScale.x, m_CellScale.y, m_CellScale.z);
+                var gizmoYOffset = new Vector3(0, m_GizmoYOffset, 0);
+                var oldGizmoMatrix = Gizmos.matrix;
+                for (var i = 0; i < m_DebugSensor.PerceptionBuffer.Length; i++)
+                {
+                    var cellPosition = m_GridPerception.GetCellGlobalPosition(i);
+                    var cubeTransform = Matrix4x4.TRS(cellPosition + gizmoYOffset, rotation, scale);
+                    Gizmos.matrix = oldGizmoMatrix * cubeTransform;
+                    var colorIndex = cellColors[i] - 1;
+                    var debugRayColor = Color.white;
+                    if (colorIndex > -1 && m_DebugColors.Length > colorIndex)
+                    {
+                        debugRayColor = m_DebugColors[(int)colorIndex];
+                    }
+                    Gizmos.color = new Color(debugRayColor.r, debugRayColor.g, debugRayColor.b, .5f);
+                    Gizmos.DrawCube(Vector3.zero, Vector3.one);
+                }
+
+                Gizmos.matrix = oldGizmoMatrix;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8090977a3af2fa7f85b64a2a24904cc2c81ca0eb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/GridSensorComponent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2a501962d056745d1a30e99146ee39fe
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs b/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d69164d8c9b14e3e8a0b574c5fd91cc410b20cab
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs
@@ -0,0 +1,69 @@
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Identifiers for "built in" sensor types.
+    /// These are only used for analytics, and should not be used for any runtime decisions.
+    ///
+    /// NOTE: Do not renumber these, since the values are used for analytics. Renaming is allowed though.
+    /// </summary>
+    public enum BuiltInSensorType
+    {
+        /// <summary>
+        /// Default Sensor type if it cannot be determined.
+        /// </summary>
+        Unknown = 0,
+        /// <summary>
+        /// The Vector sensor used by the agent.
+        /// </summary>
+        VectorSensor = 1,
+        /// <summary>
+        /// The Stacking Sensor type. NOTE: StackingSensor actually returns the wrapped sensor's type.
+        /// </summary>
+        StackingSensor = 2,
+        /// <summary>
+        /// The RayPerception Sensor types, both 3D and 2D.
+        /// </summary>
+        RayPerceptionSensor = 3,
+        /// <summary>
+        /// The observable attribute sensor type.
+        /// </summary>
+        ReflectionSensor = 4,
+        /// <summary>
+        /// Sensors that use the Camera for observations.
+        /// </summary>
+        CameraSensor = 5,
+        /// <summary>
+        /// Sensors that use RenderTextures for observations.
+        /// </summary>
+        RenderTextureSensor = 6,
+        /// <summary>
+        /// Sensors that use buffers or tensors for observations.
+        /// </summary>
+        BufferSensor = 7,
+        /// <summary>
+        /// The sensors that observe properties of rigid bodies.
+        /// </summary>
+        PhysicsBodySensor = 8,
+        /// <summary>
+        /// The sensors that observe Match 3 boards.
+        /// </summary>
+        Match3Sensor = 9,
+        /// <summary>
+        /// Sensors that break down the world into a grid of colliders to observe an area at a pre-defined granularity.
+        /// </summary>
+        GridSensor = 10
+    }
+
+    /// <summary>
+    /// Interface for sensors that are provided as part of ML-Agents.
+    /// User-implemented sensors don't need to use this interface.
+    /// </summary>
+    internal interface IBuiltInSensor
+    {
+        /// <summary>
+        /// Return the corresponding BuiltInSensorType for the sensor.
+        /// </summary>
+        /// <returns>A BuiltInSensorType corresponding to the sensor.</returns>
+        BuiltInSensorType GetBuiltInSensorType();
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..93dd08d1f194ab5e65476170704351ccaad7db00
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/IBuiltInSensor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c0c4a98bf1c941b381917cb65209beee
+timeCreated: 1611096525
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs b/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs
new file mode 100644
index 0000000000000000000000000000000000000000..bbb981efa510cf73848da5bec428c3260263c57d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs
@@ -0,0 +1,62 @@
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// An interface for GridSensor perception that defines the grid cells and collider detecting strategies.
+    /// </summary>
+    internal interface IGridPerception
+    {
+        bool RotateWithAgent
+        {
+            get;
+            set;
+        }
+
+        LayerMask ColliderMask
+        {
+            get;
+            set;
+        }
+
+        /// <summary>Converts the index of the cell to the 3D point (y is zero) relative to grid center</summary>
+        /// <returns>Vector3 of the position of the center of the cell relative to grid center</returns>
+        /// <param name="cellIndex">The index of the cell</param>
+        Vector3 GetCellLocalPosition(int cellIndex);
+
+        /// <summary>
+        /// Converts the index of the cell to the 3D point (y is zero) in world space
+        /// based on the result from GetCellLocalPosition()
+        /// </summary>
+        /// <returns>Vector3 of the position of the center of the cell in world space</returns>
+        /// <param name="cellIndex">The index of the cell</param>
+        Vector3 GetCellGlobalPosition(int cellIndex);
+
+        Quaternion GetGridRotation();
+
+        /// <summary>
+        /// Perceive the latest grid status. Detect colliders for each cell, parse the collider arrays,
+        /// then trigger registered sensors to encode and update with the new grid status.
+        /// </summary>
+        void Perceive();
+
+        /// <summary>
+        /// Same as Perceive(), but only load data for debug gizmo.
+        /// </summary>
+        void UpdateGizmo();
+
+        /// <summary>
+        /// Register a sensor to this GridPerception to receive the grid perception results.
+        /// When the GridPerception perceive a new observation, registered sensors will be triggered
+        /// to encode the new observation and update its data.
+        /// </summary>
+        void RegisterSensor(GridSensorBase sensor);
+
+        /// <summary>
+        /// Register an internal debug sensor.
+        /// Debug sensors will only be triggered when drawing debug gizmos.
+        /// </summary>
+        void RegisterDebugSensor(GridSensorBase debugSensor);
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs.meta b/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b08ed6449d545a290eaa349d4a1e9f3641564f4f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/IGridPerception.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 87820d9eb927c4fa483dff9289d983f1
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/ISensor.cs b/com.unity.ml-agents/Runtime/Sensors/ISensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ed93910fa0b910f57f44cef19472f93e42cc0bc0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ISensor.cs
@@ -0,0 +1,139 @@
+using System;
+using System.Collections.Generic;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// The Dimension property flags of the observations
+    /// </summary>
+    [Flags]
+    public enum DimensionProperty
+    {
+        /// <summary>
+        /// No properties specified.
+        /// </summary>
+        Unspecified = 0,
+
+        /// <summary>
+        /// No Property of the observation in that dimension. Observation can be processed with
+        /// fully connected networks.
+        /// </summary>
+        None = 1,
+
+        /// <summary>
+        /// Means it is suitable to do a convolution in this dimension.
+        /// </summary>
+        TranslationalEquivariance = 2,
+
+        /// <summary>
+        /// Means that there can be a variable number of observations in this dimension.
+        /// The observations are unordered.
+        /// </summary>
+        VariableSize = 4,
+    }
+
+    /// <summary>
+    /// The ObservationType enum of the Sensor.
+    /// </summary>
+    public enum ObservationType
+    {
+        /// <summary>
+        /// Collected observations are generic.
+        /// </summary>
+        Default = 0,
+
+        /// <summary>
+        /// Collected observations contain goal information.
+        /// </summary>
+        GoalSignal = 1,
+    }
+
+    /// <summary>
+    /// Sensor interface for generating observations.
+    /// </summary>
+    public interface ISensor
+    {
+        /// <summary>
+        /// Returns a description of the observations that will be generated by the sensor.
+        /// See <see cref="ObservationSpec"/> for more details, and helper methods to create one.
+        /// </summary>
+        /// <returns>An object describing the observation.</returns>
+        ObservationSpec GetObservationSpec();
+
+        /// <summary>
+        /// Write the observation data directly to the <see cref="ObservationWriter"/>.
+        /// Note that this (and  <see cref="GetCompressedObservation"/>) may
+        /// be called multiple times per agent step, so should not mutate any internal state.
+        /// </summary>
+        /// <param name="writer">Where the observations will be written to.</param>
+        /// <returns>The number of elements written.</returns>
+        int Write(ObservationWriter writer);
+
+        /// <summary>
+        /// Return a compressed representation of the observation. For small observations,
+        /// this should generally not be implemented. However, compressing large observations
+        /// (such as visual results) can significantly improve model training time.
+        /// </summary>
+        /// <returns>Compressed observation.</returns>
+        byte[] GetCompressedObservation();
+
+        /// <summary>
+        /// Update any internal state of the sensor. This is called once per each agent step.
+        /// </summary>
+        void Update();
+
+        /// <summary>
+        /// Resets the internal state of the sensor. This is called at the end of an Agent's episode.
+        /// Most implementations can leave this empty.
+        /// </summary>
+        void Reset();
+
+        /// <summary>
+        /// Return information on the compression type being used. If no compression is used, return
+        /// <see cref="CompressionSpec.Default()"/>.
+        /// </summary>
+        /// <returns>An object describing the compression used by the sensor.</returns>
+        CompressionSpec GetCompressionSpec();
+
+        /// <summary>
+        /// Get the name of the sensor. This is used to ensure deterministic sorting of the sensors
+        /// on an Agent, so the naming must be consistent across all sensors and agents.
+        /// </summary>
+        /// <returns>The name of the sensor.</returns>
+        string GetName();
+    }
+
+
+    /// <summary>
+    /// Helper methods to be shared by all classes that implement <see cref="ISensor"/>.
+    /// </summary>
+    public static class SensorExtensions
+    {
+        /// <summary>
+        /// Get the total number of elements in the ISensor's observation (i.e. the product of the
+        /// shape elements).
+        /// </summary>
+        /// <param name="sensor"></param>
+        /// <returns></returns>
+        public static int ObservationSize(this ISensor sensor)
+        {
+            var obsSpec = sensor.GetObservationSpec();
+            var count = 1;
+            for (var i = 0; i < obsSpec.Rank; i++)
+            {
+                count *= obsSpec.Shape[i];
+            }
+
+            return count;
+        }
+    }
+
+    internal static class SensorUtils
+    {
+        internal static void SortSensors(List<ISensor> sensors)
+        {
+            // Use InvariantCulture to ensure consistent sorting between different culture settings.
+            sensors.Sort((x, y) => string.Compare(x.GetName(), y.GetName(), StringComparison.InvariantCulture));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/ISensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/ISensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d8ceedec70635e68eaa14738f3d174c343e257fd
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ISensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 4bb5e09a94c6d4cd9a46c60b084e4952
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs b/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs
new file mode 100644
index 0000000000000000000000000000000000000000..59c7f2eb4e6a53f1ece4023f2835f1a5225378a3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs
@@ -0,0 +1,138 @@
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A description of the observations that an ISensor produces.
+    /// This includes the size of the observation, the properties of each dimension, and how the observation
+    /// should be used for training.
+    /// </summary>
+    public struct ObservationSpec
+    {
+        internal readonly InplaceArray<int> m_Shape;
+
+        /// <summary>
+        /// The size of the observations that will be generated.
+        /// For example, a sensor that observes the velocity of a rigid body (in 3D) would use [3].
+        /// A sensor that returns an RGB image would use [Height, Width, 3].
+        /// </summary>
+        public InplaceArray<int> Shape
+        {
+            get => m_Shape;
+        }
+
+        internal readonly InplaceArray<DimensionProperty> m_DimensionProperties;
+
+        /// <summary>
+        /// The properties of each dimensions of the observation.
+        /// The length of the array must be equal to the rank of the observation tensor.
+        /// </summary>
+        /// <remarks>
+        /// It is generally recommended to use default values provided by helper functions,
+        /// as not all combinations of DimensionProperty may be supported by the trainer.
+        /// </remarks>
+        public InplaceArray<DimensionProperty> DimensionProperties
+        {
+            get => m_DimensionProperties;
+        }
+
+        internal ObservationType m_ObservationType;
+
+        /// <summary>
+        /// The type of the observation, e.g. whether they are generic or
+        /// help determine the goal for the Agent.
+        /// </summary>
+        public ObservationType ObservationType
+        {
+            get => m_ObservationType;
+        }
+
+        /// <summary>
+        /// The number of dimensions of the observation.
+        /// </summary>
+        public int Rank
+        {
+            get { return Shape.Length; }
+        }
+
+        /// <summary>
+        /// Construct an ObservationSpec for 1-D observations of the requested length.
+        /// </summary>
+        /// <param name="length"></param>
+        /// <param name="obsType"></param>
+        /// <returns></returns>
+        public static ObservationSpec Vector(int length, ObservationType obsType = ObservationType.Default)
+        {
+            return new ObservationSpec(
+                new InplaceArray<int>(length),
+                new InplaceArray<DimensionProperty>(DimensionProperty.None),
+                obsType
+            );
+        }
+
+        /// <summary>
+        /// Construct an ObservationSpec for variable-length observations.
+        /// </summary>
+        /// <param name="obsSize"></param>
+        /// <param name="maxNumObs"></param>
+        /// <returns></returns>
+        public static ObservationSpec VariableLength(int obsSize, int maxNumObs)
+        {
+            var dimProps = new InplaceArray<DimensionProperty>(
+                DimensionProperty.VariableSize,
+                DimensionProperty.None
+            );
+            return new ObservationSpec(
+                new InplaceArray<int>(obsSize, maxNumObs),
+                dimProps
+            );
+        }
+
+        /// <summary>
+        /// Construct an ObservationSpec for visual-like observations, e.g. observations
+        /// with a height, width, and possible multiple channels.
+        /// </summary>
+        /// <param name="height"></param>
+        /// <param name="width"></param>
+        /// <param name="channels"></param>
+        /// <param name="obsType"></param>
+        /// <returns></returns>
+        public static ObservationSpec Visual(int height, int width, int channels, ObservationType obsType = ObservationType.Default)
+        {
+            var dimProps = new InplaceArray<DimensionProperty>(
+                DimensionProperty.TranslationalEquivariance,
+                DimensionProperty.TranslationalEquivariance,
+                DimensionProperty.None
+            );
+            return new ObservationSpec(
+                new InplaceArray<int>(height, width, channels),
+                dimProps,
+                obsType
+            );
+        }
+
+        /// <summary>
+        /// Create a general ObservationSpec from the shape, dimension properties, and observation type.
+        /// </summary>
+        /// <remarks>
+        /// Note that not all combinations of DimensionProperty may be supported by the trainer.
+        /// shape and dimensionProperties must have the same size.
+        /// </remarks>
+        /// <param name="shape"></param>
+        /// <param name="dimensionProperties"></param>
+        /// <param name="observationType"></param>
+        /// <exception cref="UnityAgentsException"></exception>
+        public ObservationSpec(
+            InplaceArray<int> shape,
+            InplaceArray<DimensionProperty> dimensionProperties,
+            ObservationType observationType = ObservationType.Default
+        )
+        {
+            if (shape.Length != dimensionProperties.Length)
+            {
+                throw new UnityAgentsException("shape and dimensionProperties must have the same length.");
+            }
+            m_Shape = shape;
+            m_DimensionProperties = dimensionProperties;
+            m_ObservationType = observationType;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs.meta b/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..691fdf61727a9cf69a2fb39ce188a012fa844e96
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ObservationSpec.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: cc1734d60fd5485ead94247cb206aa35
+timeCreated: 1615412644
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs b/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs
new file mode 100644
index 0000000000000000000000000000000000000000..02fbae8997ebf7ed9413c364e66cba6ecf8dea22
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs
@@ -0,0 +1,321 @@
+using System;
+using System.Collections.Generic;
+using Unity.Barracuda;
+using Unity.MLAgents.Inference;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Allows sensors to write to both TensorProxy and float arrays/lists.
+    /// </summary>
+    public class ObservationWriter
+    {
+        IList<float> m_Data;
+        int m_Offset;
+
+        TensorProxy m_Proxy;
+        int m_Batch;
+
+        TensorShape m_TensorShape;
+
+        public ObservationWriter() { }
+
+        /// <summary>
+        /// Set the writer to write to an IList at the given channelOffset.
+        /// </summary>
+        /// <param name="data">Float array or list that will be written to.</param>
+        /// <param name="observationSpec">ObservationSpec of the observation to be written</param>
+        /// <param name="offset">Offset from the start of the float data to write to.</param>
+        internal void SetTarget(IList<float> data, ObservationSpec observationSpec, int offset)
+        {
+            SetTarget(data, observationSpec.Shape, offset);
+        }
+
+        /// <summary>
+        /// Set the writer to write to an IList at the given channelOffset.
+        /// </summary>
+        /// <param name="data">Float array or list that will be written to.</param>
+        /// <param name="shape">Shape of the observations to be written.</param>
+        /// <param name="offset">Offset from the start of the float data to write to.</param>
+        internal void SetTarget(IList<float> data, InplaceArray<int> shape, int offset)
+        {
+            m_Data = data;
+            m_Offset = offset;
+            m_Proxy = null;
+            m_Batch = 0;
+
+            if (shape.Length == 1)
+            {
+                m_TensorShape = new TensorShape(m_Batch, shape[0]);
+            }
+            else if (shape.Length == 2)
+            {
+                m_TensorShape = new TensorShape(new[] { m_Batch, 1, shape[0], shape[1] });
+            }
+            else
+            {
+                m_TensorShape = new TensorShape(m_Batch, shape[0], shape[1], shape[2]);
+            }
+        }
+
+        /// <summary>
+        /// Set the writer to write to a TensorProxy at the given batch and channel offset.
+        /// </summary>
+        /// <param name="tensorProxy">Tensor proxy that will be written to.</param>
+        /// <param name="batchIndex">Batch index in the tensor proxy (i.e. the index of the Agent).</param>
+        /// <param name="channelOffset">Offset from the start of the channel to write to.</param>
+        internal void SetTarget(TensorProxy tensorProxy, int batchIndex, int channelOffset)
+        {
+            m_Proxy = tensorProxy;
+            m_Batch = batchIndex;
+            m_Offset = channelOffset;
+            m_Data = null;
+            m_TensorShape = m_Proxy.data.shape;
+        }
+
+        /// <summary>
+        /// 1D write access at a specified index. Use AddList if possible instead.
+        /// </summary>
+        /// <param name="index">Index to write to.</param>
+        public float this[int index]
+        {
+            set
+            {
+                if (m_Data != null)
+                {
+                    m_Data[index + m_Offset] = value;
+                }
+                else
+                {
+                    m_Proxy.data[m_Batch, index + m_Offset] = value;
+                }
+            }
+        }
+
+        /// <summary>
+        /// 3D write access at the specified height, width, and channel.
+        /// </summary>
+        /// <param name="h"></param>
+        /// <param name="w"></param>
+        /// <param name="ch"></param>
+        public float this[int h, int w, int ch]
+        {
+            set
+            {
+                if (m_Data != null)
+                {
+                    if (h < 0 || h >= m_TensorShape.height)
+                    {
+                        throw new IndexOutOfRangeException($"height value {h} must be in range [0, {m_TensorShape.height - 1}]");
+                    }
+                    if (w < 0 || w >= m_TensorShape.width)
+                    {
+                        throw new IndexOutOfRangeException($"width value {w} must be in range [0, {m_TensorShape.width - 1}]");
+                    }
+                    if (ch < 0 || ch >= m_TensorShape.channels)
+                    {
+                        throw new IndexOutOfRangeException($"channel value {ch} must be in range [0, {m_TensorShape.channels - 1}]");
+                    }
+
+                    var index = m_TensorShape.Index(m_Batch, h, w, ch + m_Offset);
+                    m_Data[index] = value;
+                }
+                else
+                {
+                    m_Proxy.data[m_Batch, h, w, ch + m_Offset] = value;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Write the list of floats.
+        /// </summary>
+        /// <param name="data">The actual list of floats to write.</param>
+        /// <param name="writeOffset">Optional write offset to start writing from.</param>
+        public void AddList(IList<float> data, int writeOffset = 0)
+        {
+            if (m_Data != null)
+            {
+                for (var index = 0; index < data.Count; index++)
+                {
+                    var val = data[index];
+                    m_Data[index + m_Offset + writeOffset] = val;
+                }
+            }
+            else
+            {
+                for (var index = 0; index < data.Count; index++)
+                {
+                    var val = data[index];
+                    m_Proxy.data[m_Batch, index + m_Offset + writeOffset] = val;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Write the Vector3 components.
+        /// </summary>
+        /// <param name="vec">The Vector3 to be written.</param>
+        /// <param name="writeOffset">Optional write offset.</param>
+        public void Add(Vector3 vec, int writeOffset = 0)
+        {
+            if (m_Data != null)
+            {
+                m_Data[m_Offset + writeOffset + 0] = vec.x;
+                m_Data[m_Offset + writeOffset + 1] = vec.y;
+                m_Data[m_Offset + writeOffset + 2] = vec.z;
+            }
+            else
+            {
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 0] = vec.x;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 1] = vec.y;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 2] = vec.z;
+            }
+        }
+
+        /// <summary>
+        /// Write the Vector4 components.
+        /// </summary>
+        /// <param name="vec">The Vector4 to be written.</param>
+        /// <param name="writeOffset">Optional write offset.</param>
+        public void Add(Vector4 vec, int writeOffset = 0)
+        {
+            if (m_Data != null)
+            {
+                m_Data[m_Offset + writeOffset + 0] = vec.x;
+                m_Data[m_Offset + writeOffset + 1] = vec.y;
+                m_Data[m_Offset + writeOffset + 2] = vec.z;
+                m_Data[m_Offset + writeOffset + 3] = vec.w;
+            }
+            else
+            {
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 0] = vec.x;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 1] = vec.y;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 2] = vec.z;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 3] = vec.w;
+            }
+        }
+
+        /// <summary>
+        /// Write the Quaternion components.
+        /// </summary>
+        /// <param name="quat">The Quaternion to be written.</param>
+        /// <param name="writeOffset">Optional write offset.</param>
+
+        public void Add(Quaternion quat, int writeOffset = 0)
+        {
+            if (m_Data != null)
+            {
+                m_Data[m_Offset + writeOffset + 0] = quat.x;
+                m_Data[m_Offset + writeOffset + 1] = quat.y;
+                m_Data[m_Offset + writeOffset + 2] = quat.z;
+                m_Data[m_Offset + writeOffset + 3] = quat.w;
+            }
+            else
+            {
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 0] = quat.x;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 1] = quat.y;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 2] = quat.z;
+                m_Proxy.data[m_Batch, m_Offset + writeOffset + 3] = quat.w;
+            }
+        }
+    }
+
+    /// <summary>
+    /// Provides extension methods for the ObservationWriter.
+    /// </summary>
+    public static class ObservationWriterExtension
+    {
+        /// <summary>
+        /// Writes a Texture2D into a ObservationWriter.
+        /// </summary>
+        /// <param name="obsWriter">
+        /// Writer to fill with Texture data.
+        /// </param>
+        /// <param name="texture">
+        /// The texture to be put into the tensor.
+        /// </param>
+        /// <param name="grayScale">
+        /// If set to <c>true</c> the textures will be converted to grayscale before
+        /// being stored in the tensor.
+        /// </param>
+        /// <returns>The number of floats written</returns>
+        public static int WriteTexture(
+            this ObservationWriter obsWriter,
+            Texture2D texture,
+            bool grayScale)
+        {
+            if (texture.format == TextureFormat.RGB24)
+            {
+                return obsWriter.WriteTextureRGB24(texture, grayScale);
+            }
+            var width = texture.width;
+            var height = texture.height;
+
+            var texturePixels = texture.GetPixels32();
+            // During training, we convert from Texture to PNG before sending to the trainer, which has the
+            // effect of flipping the image. We need another flip here at inference time to match this.
+            for (var h = height - 1; h >= 0; h--)
+            {
+                for (var w = 0; w < width; w++)
+                {
+                    var currentPixel = texturePixels[(height - h - 1) * width + w];
+
+                    if (grayScale)
+                    {
+                        obsWriter[h, w, 0] =
+                            (currentPixel.r + currentPixel.g + currentPixel.b) / 3f / 255.0f;
+                    }
+                    else
+                    {
+                        // For Color32, the r, g and b values are between 0 and 255.
+                        obsWriter[h, w, 0] = currentPixel.r / 255.0f;
+                        obsWriter[h, w, 1] = currentPixel.g / 255.0f;
+                        obsWriter[h, w, 2] = currentPixel.b / 255.0f;
+                    }
+                }
+            }
+
+            return height * width * (grayScale ? 1 : 3);
+        }
+
+        internal static int WriteTextureRGB24(
+            this ObservationWriter obsWriter,
+            Texture2D texture,
+            bool grayScale
+        )
+        {
+            var width = texture.width;
+            var height = texture.height;
+
+            var rawBytes = texture.GetRawTextureData<byte>();
+            // During training, we convert from Texture to PNG before sending to the trainer, which has the
+            // effect of flipping the image. We need another flip here at inference time to match this.
+            for (var h = height - 1; h >= 0; h--)
+            {
+                for (var w = 0; w < width; w++)
+                {
+                    var offset = (height - h - 1) * width + w;
+                    var r = rawBytes[3 * offset];
+                    var g = rawBytes[3 * offset + 1];
+                    var b = rawBytes[3 * offset + 2];
+
+                    if (grayScale)
+                    {
+                        obsWriter[h, w, 0] = (r + g + b) / 3f / 255.0f;
+                    }
+                    else
+                    {
+                        // For Color32, the r, g and b values are between 0 and 255.
+                        obsWriter[h, w, 0] = r / 255.0f;
+                        obsWriter[h, w, 1] = g / 255.0f;
+                        obsWriter[h, w, 2] = b / 255.0f;
+                    }
+                }
+            }
+
+            return height * width * (grayScale ? 1 : 3);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs.meta b/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..424707a882dfc62a4f442358ed2159e284bb4266
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/ObservationWriter.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 86bad2e6dded4a62853752a1713981f2
+timeCreated: 1572540197
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs b/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..648c702d80c94194382db7250ff84b8511813ff5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs
@@ -0,0 +1,59 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Grid-based sensor with one-hot observations.
+    /// </summary>
+    public class OneHotGridSensor : GridSensorBase
+    {
+        /// <summary>
+        /// Create a OneHotGridSensor with the specified configuration.
+        /// </summary>
+        /// <param name="name">The sensor name</param>
+        /// <param name="cellScale">The scale of each cell in the grid</param>
+        /// <param name="gridSize">Number of cells on each side of the grid</param>
+        /// <param name="detectableTags">Tags to be detected by the sensor</param>
+        /// <param name="compression">Compression type</param>
+        public OneHotGridSensor(
+            string name,
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            string[] detectableTags,
+            SensorCompressionType compression
+        ) : base(name, cellScale, gridSize, detectableTags, compression)
+        {
+        }
+
+        /// <inheritdoc/>
+        protected override int GetCellObservationSize()
+        {
+            return DetectableTags == null ? 0 : DetectableTags.Length;
+        }
+
+        /// <inheritdoc/>
+        protected override bool IsDataNormalized()
+        {
+            return true;
+        }
+
+        /// <inheritdoc/>
+        protected internal override ProcessCollidersMethod GetProcessCollidersMethod()
+        {
+            return ProcessCollidersMethod.ProcessClosestColliders;
+        }
+
+        /// <summary>
+        /// Get the one-hot representation of the detected game object's tag.
+        /// </summary>
+        /// <param name="detectedObject">The game object that was detected within a certain cell</param>
+        /// <param name="tagIndex">The index of the detectedObject's tag in the DetectableObjects list</param>
+        /// <param name="dataBuffer">The buffer to write the observation values.
+        ///         The buffer size is configured by <seealso cref="GetCellObservationSize"/>.
+        /// </param>
+        protected override void GetObjectData(GameObject detectedObject, int tagIndex, float[] dataBuffer)
+        {
+            dataBuffer[tagIndex] = 1;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c21f87cba487491aaa0baff45cd475ce97ecd315
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/OneHotGridSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 914f5ab90be9e411d83642035abebc2c
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..158d2a03918bfdd47850d55704de306598c6ec0a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs
@@ -0,0 +1,673 @@
+using System;
+using System.Collections.Generic;
+using Unity.Collections;
+using Unity.Jobs;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Determines which dimensions the sensor will perform the casts in.
+    /// </summary>
+    public enum RayPerceptionCastType
+    {
+        /// <summary>
+        /// Cast in 2 dimensions, using Physics2D.CircleCast or Physics2D.RayCast.
+        /// </summary>
+        Cast2D,
+
+        /// <summary>
+        /// Cast in 3 dimensions, using Physics.SphereCast or Physics.RayCast.
+        /// </summary>
+        Cast3D,
+    }
+
+    /// <summary>
+    /// Contains the elements that define a ray perception sensor.
+    /// </summary>
+    public struct RayPerceptionInput
+    {
+        /// <summary>
+        /// Length of the rays to cast. This will be scaled up or down based on the scale of the transform.
+        /// </summary>
+        public float RayLength;
+
+        /// <summary>
+        /// List of tags which correspond to object types agent can see.
+        /// </summary>
+        public IReadOnlyList<string> DetectableTags;
+
+        /// <summary>
+        /// List of angles (in degrees) used to define the rays.
+        /// 90 degrees is considered "forward" relative to the game object.
+        /// </summary>
+        public IReadOnlyList<float> Angles;
+
+        /// <summary>
+        /// Starting height offset of ray from center of agent
+        /// </summary>
+        public float StartOffset;
+
+        /// <summary>
+        /// Ending height offset of ray from center of agent.
+        /// </summary>
+        public float EndOffset;
+
+        /// <summary>
+        /// Radius of the sphere to use for spherecasting.
+        /// If 0 or less, rays are used instead - this may be faster, especially for complex environments.
+        /// </summary>
+        public float CastRadius;
+
+        /// <summary>
+        /// Transform of the GameObject.
+        /// </summary>
+        public Transform Transform;
+
+        /// <summary>
+        /// Whether to perform the casts in 2D or 3D.
+        /// </summary>
+        public RayPerceptionCastType CastType;
+
+        /// <summary>
+        /// Filtering options for the casts.
+        /// </summary>
+        public int LayerMask;
+
+        /// <summary>
+        ///  Whether to use batched raycasts.
+        /// </summary>
+        public bool UseBatchedRaycasts;
+
+        /// <summary>
+        /// Returns the expected number of floats in the output.
+        /// </summary>
+        /// <returns></returns>
+        public int OutputSize()
+        {
+            return ((DetectableTags?.Count ?? 0) + 2) * (Angles?.Count ?? 0);
+        }
+
+        /// <summary>
+        /// Get the cast start and end points for the given ray index/
+        /// </summary>
+        /// <param name="rayIndex"></param>
+        /// <returns>A tuple of the start and end positions in world space.</returns>
+        public (Vector3 StartPositionWorld, Vector3 EndPositionWorld) RayExtents(int rayIndex)
+        {
+            var angle = Angles[rayIndex];
+            Vector3 startPositionLocal, endPositionLocal;
+            if (CastType == RayPerceptionCastType.Cast3D)
+            {
+                startPositionLocal = new Vector3(0, StartOffset, 0);
+                endPositionLocal = PolarToCartesian3D(RayLength, angle);
+                endPositionLocal.y += EndOffset;
+            }
+            else
+            {
+                // Vector2s here get converted to Vector3s (and back to Vector2s for casting)
+                startPositionLocal = new Vector2();
+                endPositionLocal = PolarToCartesian2D(RayLength, angle);
+            }
+
+            var startPositionWorld = Transform.TransformPoint(startPositionLocal);
+            var endPositionWorld = Transform.TransformPoint(endPositionLocal);
+
+            return (StartPositionWorld: startPositionWorld, EndPositionWorld: endPositionWorld);
+        }
+
+        /// <summary>
+        /// Converts polar coordinate to cartesian coordinate.
+        /// </summary>
+        static internal Vector3 PolarToCartesian3D(float radius, float angleDegrees)
+        {
+            var x = radius * Mathf.Cos(Mathf.Deg2Rad * angleDegrees);
+            var z = radius * Mathf.Sin(Mathf.Deg2Rad * angleDegrees);
+            return new Vector3(x, 0f, z);
+        }
+
+        /// <summary>
+        /// Converts polar coordinate to cartesian coordinate.
+        /// </summary>
+        static internal Vector2 PolarToCartesian2D(float radius, float angleDegrees)
+        {
+            var x = radius * Mathf.Cos(Mathf.Deg2Rad * angleDegrees);
+            var y = radius * Mathf.Sin(Mathf.Deg2Rad * angleDegrees);
+            return new Vector2(x, y);
+        }
+    }
+
+    /// <summary>
+    /// Contains the data generated/produced from a ray perception sensor.
+    /// </summary>
+    public class RayPerceptionOutput
+    {
+        /// <summary>
+        /// Contains the data generated from a single ray of a ray perception sensor.
+        /// </summary>
+        public struct RayOutput
+        {
+            /// <summary>
+            /// Whether or not the ray hit anything.
+            /// </summary>
+            public bool HasHit;
+
+            /// <summary>
+            /// Whether or not the ray hit an object whose tag is in the input's DetectableTags list.
+            /// </summary>
+            public bool HitTaggedObject;
+
+            /// <summary>
+            /// The index of the hit object's tag in the DetectableTags list, or -1 if there was no hit, or the
+            /// hit object has a different tag.
+            /// </summary>
+            public int HitTagIndex;
+
+            /// <summary>
+            /// Normalized distance to the hit object.
+            /// </summary>
+            public float HitFraction;
+
+            /// <summary>
+            /// The hit GameObject (or null if there was no hit).
+            /// </summary>
+            public GameObject HitGameObject;
+
+            /// <summary>
+            /// Start position of the ray in world space.
+            /// </summary>
+            public Vector3 StartPositionWorld;
+
+            /// <summary>
+            /// End position of the ray in world space.
+            /// </summary>
+            public Vector3 EndPositionWorld;
+
+            /// <summary>
+            /// The scaled length of the ray.
+            /// </summary>
+            /// <remarks>
+            /// If there is non-(1,1,1) scale, |EndPositionWorld - StartPositionWorld| will be different from
+            /// the input rayLength.
+            /// </remarks>
+            public float ScaledRayLength
+            {
+                get
+                {
+                    var rayDirection = EndPositionWorld - StartPositionWorld;
+                    return rayDirection.magnitude;
+                }
+            }
+
+            /// <summary>
+            /// The scaled size of the cast.
+            /// </summary>
+            /// <remarks>
+            /// If there is non-(1,1,1) scale, the cast radius will be also be scaled.
+            /// </remarks>
+            public float ScaledCastRadius;
+
+            /// <summary>
+            /// Writes the ray output information to a subset of the float array.  Each element in the rayAngles array
+            /// determines a sublist of data to the observation. The sublist contains the observation data for a single cast.
+            /// The list is composed of the following:
+            /// 1. A one-hot encoding for detectable tags. For example, if DetectableTags.Length = n, the
+            ///    first n elements of the sublist will be a one-hot encoding of the detectableTag that was hit, or
+            ///    all zeroes otherwise.
+            /// 2. The 'numDetectableTags' element of the sublist will be 1 if the ray missed everything, or 0 if it hit
+            ///    something (detectable or not).
+            /// 3. The 'numDetectableTags+1' element of the sublist will contain the normalized distance to the object
+            ///    hit, or 1.0 if nothing was hit.
+            /// </summary>
+            /// <param name="numDetectableTags"></param>
+            /// <param name="rayIndex"></param>
+            /// <param name="buffer">Output buffer. The size must be equal to (numDetectableTags+2) * RayOutputs.Length</param>
+            public void ToFloatArray(int numDetectableTags, int rayIndex, float[] buffer)
+            {
+                var bufferOffset = (numDetectableTags + 2) * rayIndex;
+                if (HitTaggedObject)
+                {
+                    buffer[bufferOffset + HitTagIndex] = 1f;
+                }
+                buffer[bufferOffset + numDetectableTags] = HasHit ? 0f : 1f;
+                buffer[bufferOffset + numDetectableTags + 1] = HitFraction;
+            }
+        }
+
+        /// <summary>
+        /// RayOutput for each ray that was cast.
+        /// </summary>
+        public RayOutput[] RayOutputs;
+    }
+
+    /// <summary>
+    /// A sensor implementation that supports ray cast-based observations.
+    /// </summary>
+    public class RayPerceptionSensor : ISensor, IBuiltInSensor
+    {
+        float[] m_Observations;
+        ObservationSpec m_ObservationSpec;
+        string m_Name;
+
+        RayPerceptionInput m_RayPerceptionInput;
+        RayPerceptionOutput m_RayPerceptionOutput;
+
+        bool m_UseBatchedRaycasts;
+
+        /// <summary>
+        /// Time.frameCount at the last time Update() was called. This is only used for display in gizmos.
+        /// </summary>
+        int m_DebugLastFrameCount;
+
+        internal int DebugLastFrameCount
+        {
+            get { return m_DebugLastFrameCount; }
+        }
+
+        /// <summary>
+        /// Creates the RayPerceptionSensor.
+        /// </summary>
+        /// <param name="name">The name of the sensor.</param>
+        /// <param name="rayInput">The inputs for the sensor.</param>
+        public RayPerceptionSensor(string name, RayPerceptionInput rayInput)
+        {
+            m_Name = name;
+            m_RayPerceptionInput = rayInput;
+            m_UseBatchedRaycasts = rayInput.UseBatchedRaycasts;
+
+            SetNumObservations(rayInput.OutputSize());
+
+            m_DebugLastFrameCount = Time.frameCount;
+            m_RayPerceptionOutput = new RayPerceptionOutput();
+        }
+
+        /// <summary>
+        /// The most recent raycast results.
+        /// </summary>
+        public RayPerceptionOutput RayPerceptionOutput
+        {
+            get { return m_RayPerceptionOutput; }
+        }
+
+        void SetNumObservations(int numObservations)
+        {
+            m_ObservationSpec = ObservationSpec.Vector(numObservations);
+            m_Observations = new float[numObservations];
+        }
+
+        internal void SetRayPerceptionInput(RayPerceptionInput rayInput)
+        {
+            // Note that change the number of rays or tags doesn't directly call this,
+            // but changing them and then changing another field will.
+            if (m_RayPerceptionInput.OutputSize() != rayInput.OutputSize())
+            {
+                Debug.Log(
+                    "Changing the number of tags or rays at runtime is not " +
+                    "supported and may cause errors in training or inference."
+                );
+                // Changing the shape will probably break things downstream, but we can at least
+                // keep this consistent.
+                SetNumObservations(rayInput.OutputSize());
+            }
+            m_RayPerceptionInput = rayInput;
+        }
+
+        /// <summary>
+        /// Computes the ray perception observations and saves them to the provided
+        /// <see cref="ObservationWriter"/>.
+        /// </summary>
+        /// <param name="writer">Where the ray perception observations are written to.</param>
+        /// <returns></returns>
+        public int Write(ObservationWriter writer)
+        {
+            using (TimerStack.Instance.Scoped("RayPerceptionSensor.Perceive"))
+            {
+                Array.Clear(m_Observations, 0, m_Observations.Length);
+                var numRays = m_RayPerceptionInput.Angles.Count;
+                var numDetectableTags = m_RayPerceptionInput.DetectableTags.Count;
+
+                // For each ray, write the information to the observation buffer
+                for (var rayIndex = 0; rayIndex < numRays; rayIndex++)
+                {
+                    m_RayPerceptionOutput.RayOutputs?[rayIndex].ToFloatArray(numDetectableTags, rayIndex, m_Observations);
+                }
+
+                // Finally, add the observations to the ObservationWriter
+                writer.AddList(m_Observations);
+            }
+            return m_Observations.Length;
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            m_DebugLastFrameCount = Time.frameCount;
+            var numRays = m_RayPerceptionInput.Angles.Count;
+
+            if (m_RayPerceptionOutput.RayOutputs == null || m_RayPerceptionOutput.RayOutputs.Length != numRays)
+            {
+                m_RayPerceptionOutput.RayOutputs = new RayPerceptionOutput.RayOutput[numRays];
+            }
+
+            if (m_UseBatchedRaycasts && m_RayPerceptionInput.CastType == RayPerceptionCastType.Cast3D)
+            {
+                PerceiveBatchedRays(ref m_RayPerceptionOutput.RayOutputs, m_RayPerceptionInput);
+            }
+            else
+            {
+                // For each ray, do the casting and save the results.
+                for (var rayIndex = 0; rayIndex < numRays; rayIndex++)
+                {
+                    m_RayPerceptionOutput.RayOutputs[rayIndex] = PerceiveSingleRay(m_RayPerceptionInput, rayIndex);
+                }
+            }
+        }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public virtual byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.RayPerceptionSensor;
+        }
+
+        /// <summary>
+        /// Evaluates the raycasts to be used as part of an observation of an agent.
+        /// </summary>
+        /// <param name="input">Input defining the rays that will be cast.</param>
+        /// <param name="batched">Use batched raycasts.</param>
+        /// <returns>Output struct containing the raycast results.</returns>
+        public static RayPerceptionOutput Perceive(RayPerceptionInput input, bool batched)
+        {
+            RayPerceptionOutput output = new RayPerceptionOutput();
+            output.RayOutputs = new RayPerceptionOutput.RayOutput[input.Angles.Count];
+
+            if (batched)
+            {
+                PerceiveBatchedRays(ref output.RayOutputs, input);
+            }
+            else
+            {
+                for (var rayIndex = 0; rayIndex < input.Angles.Count; rayIndex++)
+                {
+                    output.RayOutputs[rayIndex] = PerceiveSingleRay(input, rayIndex);
+                }
+            }
+
+            return output;
+        }
+
+        /// <summary>
+        /// Evaluate the raycast results of all the rays from the RayPerceptionInput as a batch.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="rayIndex"></param>
+        /// <returns></returns>
+        internal static void PerceiveBatchedRays(ref RayPerceptionOutput.RayOutput[] batchedRaycastOutputs, RayPerceptionInput input)
+        {
+            var numRays = input.Angles.Count;
+            var results = new NativeArray<RaycastHit>(numRays, Allocator.TempJob);
+            var unscaledRayLength = input.RayLength;
+            var unscaledCastRadius = input.CastRadius;
+
+            var raycastCommands = new NativeArray<RaycastCommand>(unscaledCastRadius <= 0f ? numRays : 0, Allocator.TempJob);
+            var spherecastCommands = new NativeArray<SpherecastCommand>(unscaledCastRadius > 0f ? numRays : 0, Allocator.TempJob);
+
+            // this is looped
+
+            for (int i = 0; i < numRays; i++)
+            {
+                var extents = input.RayExtents(i);
+                var startPositionWorld = extents.StartPositionWorld;
+                var endPositionWorld = extents.EndPositionWorld;
+
+                var rayDirection = endPositionWorld - startPositionWorld;
+                // If there is non-unity scale, |rayDirection| will be different from rayLength.
+                // We want to use this transformed ray length for determining cast length, hit fraction etc.
+                // We also it to scale up or down the sphere or circle radii
+                var scaledRayLength = rayDirection.magnitude;
+                // Avoid 0/0 if unscaledRayLength is 0
+                var scaledCastRadius = unscaledRayLength > 0 ?
+                    unscaledCastRadius * scaledRayLength / unscaledRayLength :
+                    unscaledCastRadius;
+
+                var queryParameters = QueryParameters.Default;
+                queryParameters.layerMask = input.LayerMask;
+
+                var rayDirectionNormalized = rayDirection.normalized;
+
+                if (scaledCastRadius > 0f)
+                {
+                    spherecastCommands[i] = new SpherecastCommand(startPositionWorld, scaledCastRadius, rayDirectionNormalized, queryParameters, scaledRayLength);
+                }
+                else
+                {
+                    raycastCommands[i] = new RaycastCommand(startPositionWorld, rayDirectionNormalized, queryParameters, scaledRayLength);
+                }
+
+                batchedRaycastOutputs[i] = new RayPerceptionOutput.RayOutput
+                {
+                    HitTaggedObject = false,
+                    HitTagIndex = -1,
+                    StartPositionWorld = startPositionWorld,
+                    EndPositionWorld = endPositionWorld,
+                    ScaledCastRadius = scaledCastRadius
+                };
+
+            }
+
+            if (unscaledCastRadius > 0f)
+            {
+                JobHandle handle = SpherecastCommand.ScheduleBatch(spherecastCommands, results, 1, 1, default(JobHandle));
+                handle.Complete();
+            }
+            else
+            {
+                JobHandle handle = RaycastCommand.ScheduleBatch(raycastCommands, results, 1, 1, default(JobHandle));
+                handle.Complete();
+            }
+
+            for (int i = 0; i < results.Length; i++)
+            {
+                var castHit = results[i].collider != null;
+                var hitFraction = 1.0f;
+                GameObject hitObject = null;
+                float scaledRayLength;
+                float scaledCastRadius = batchedRaycastOutputs[i].ScaledCastRadius;
+                if (scaledCastRadius > 0f)
+                {
+                    scaledRayLength = spherecastCommands[i].distance;
+                }
+                else
+                {
+                    scaledRayLength = raycastCommands[i].distance;
+                }
+
+                // hitFraction = castHit ? (scaledRayLength > 0 ? results[i].distance / scaledRayLength : 0.0f) : 1.0f;
+                // Debug.Log(results[i].distance);
+                hitFraction = castHit ? (scaledRayLength > 0 ? results[i].distance / scaledRayLength : 0.0f) : 1.0f;
+                hitObject = castHit ? results[i].collider.gameObject : null;
+
+                if (castHit)
+                {
+                    var numTags = input.DetectableTags?.Count ?? 0;
+                    for (int j = 0; j < numTags; j++)
+                    {
+                        var tagsEqual = false;
+                        try
+                        {
+                            var tag = input.DetectableTags[j];
+                            if (!string.IsNullOrEmpty(tag))
+                            {
+                                tagsEqual = hitObject.CompareTag(tag);
+                            }
+                        }
+                        catch (UnityException)
+                        {
+                        }
+
+                        if (tagsEqual)
+                        {
+                            batchedRaycastOutputs[i].HitTaggedObject = true;
+                            batchedRaycastOutputs[i].HitTagIndex = j;
+                            break;
+                        }
+                    }
+                }
+
+                batchedRaycastOutputs[i].HasHit = castHit;
+                batchedRaycastOutputs[i].HitFraction = hitFraction;
+                batchedRaycastOutputs[i].HitGameObject = hitObject;
+
+            }
+
+            results.Dispose();
+            raycastCommands.Dispose();
+            spherecastCommands.Dispose();
+        }
+
+        /// <summary>
+        /// Evaluate the raycast results of a single ray from the RayPerceptionInput.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="rayIndex"></param>
+        /// <returns></returns>
+        internal static RayPerceptionOutput.RayOutput PerceiveSingleRay(
+            RayPerceptionInput input,
+            int rayIndex
+        )
+        {
+            var unscaledRayLength = input.RayLength;
+            var unscaledCastRadius = input.CastRadius;
+
+            var extents = input.RayExtents(rayIndex);
+            var startPositionWorld = extents.StartPositionWorld;
+            var endPositionWorld = extents.EndPositionWorld;
+
+            var rayDirection = endPositionWorld - startPositionWorld;
+            // If there is non-unity scale, |rayDirection| will be different from rayLength.
+            // We want to use this transformed ray length for determining cast length, hit fraction etc.
+            // We also it to scale up or down the sphere or circle radii
+            var scaledRayLength = rayDirection.magnitude;
+            // Avoid 0/0 if unscaledRayLength is 0
+            var scaledCastRadius = unscaledRayLength > 0 ?
+                unscaledCastRadius * scaledRayLength / unscaledRayLength :
+                unscaledCastRadius;
+
+            // Do the cast and assign the hit information for each detectable tag.
+            var castHit = false;
+            var hitFraction = 1.0f;
+            GameObject hitObject = null;
+
+            if (input.CastType == RayPerceptionCastType.Cast3D)
+            {
+#if MLA_UNITY_PHYSICS_MODULE
+                RaycastHit rayHit;
+                if (scaledCastRadius > 0f)
+                {
+                    castHit = Physics.SphereCast(startPositionWorld, scaledCastRadius, rayDirection, out rayHit,
+                        scaledRayLength, input.LayerMask);
+                }
+                else
+                {
+                    castHit = Physics.Raycast(startPositionWorld, rayDirection, out rayHit,
+                        scaledRayLength, input.LayerMask);
+                }
+
+                // If scaledRayLength is 0, we still could have a hit with sphere casts (maybe?).
+                // To avoid 0/0, set the fraction to 0.
+                hitFraction = castHit ? (scaledRayLength > 0 ? rayHit.distance / scaledRayLength : 0.0f) : 1.0f;
+                hitObject = castHit ? rayHit.collider.gameObject : null;
+#endif
+            }
+            else
+            {
+#if MLA_UNITY_PHYSICS2D_MODULE
+                RaycastHit2D rayHit;
+                if (scaledCastRadius > 0f)
+                {
+                    rayHit = Physics2D.CircleCast(startPositionWorld, scaledCastRadius, rayDirection,
+                        scaledRayLength, input.LayerMask);
+                }
+                else
+                {
+                    rayHit = Physics2D.Raycast(startPositionWorld, rayDirection, scaledRayLength, input.LayerMask);
+                }
+
+                castHit = rayHit;
+                hitFraction = castHit ? rayHit.fraction : 1.0f;
+                hitObject = castHit ? rayHit.collider.gameObject : null;
+#endif
+            }
+
+            var rayOutput = new RayPerceptionOutput.RayOutput
+            {
+                HasHit = castHit,
+                HitFraction = hitFraction,
+                HitTaggedObject = false,
+                HitTagIndex = -1,
+                HitGameObject = hitObject,
+                StartPositionWorld = startPositionWorld,
+                EndPositionWorld = endPositionWorld,
+                ScaledCastRadius = scaledCastRadius
+            };
+
+            if (castHit)
+            {
+                // Find the index of the tag of the object that was hit.
+                var numTags = input.DetectableTags?.Count ?? 0;
+                for (var i = 0; i < numTags; i++)
+                {
+                    var tagsEqual = false;
+                    try
+                    {
+                        var tag = input.DetectableTags[i];
+                        if (!string.IsNullOrEmpty(tag))
+                        {
+                            tagsEqual = hitObject.CompareTag(tag);
+                        }
+                    }
+                    catch (UnityException)
+                    {
+                        // If the tag is null, empty, or not a valid tag, just ignore it.
+                    }
+
+                    if (tagsEqual)
+                    {
+                        rayOutput.HitTaggedObject = true;
+                        rayOutput.HitTagIndex = i;
+                        break;
+                    }
+                }
+            }
+
+
+            return rayOutput;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..be40d29e519d97bb40fe0d19567f9daa3df53d01
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 71417cdf8dd542e19ec22822b001b884
+timeCreated: 1573089052
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3abee3d46b1f60d7166322d5a614bffa252d48f6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs
@@ -0,0 +1,17 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A component for 2D Ray Perception.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Ray Perception Sensor 2D", (int)MenuGroup.Sensors)]
+    public class RayPerceptionSensorComponent2D : RayPerceptionSensorComponentBase
+    {
+        /// <inheritdoc/>
+        public override RayPerceptionCastType GetCastType()
+        {
+            return RayPerceptionCastType.Cast2D;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6453f25a42ce71d51871fa7f8d9e611d30559510
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent2D.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f67c7e722ba14acd9153bb4488bff6e4
+timeCreated: 1573769662
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs
new file mode 100644
index 0000000000000000000000000000000000000000..34bde4814058500dab7f05a418b35cbe9a518b31
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs
@@ -0,0 +1,58 @@
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A component for 3D Ray Perception.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Ray Perception Sensor 3D", (int)MenuGroup.Sensors)]
+    public class RayPerceptionSensorComponent3D : RayPerceptionSensorComponentBase
+    {
+        [HideInInspector, SerializeField, FormerlySerializedAs("startVerticalOffset")]
+        [Range(-10f, 10f)]
+        [Tooltip("Ray start is offset up or down by this amount.")]
+        float m_StartVerticalOffset;
+
+        /// <summary>
+        /// Ray start is offset up or down by this amount.
+        /// </summary>
+        public float StartVerticalOffset
+        {
+            get => m_StartVerticalOffset;
+            set { m_StartVerticalOffset = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("endVerticalOffset")]
+        [Range(-10f, 10f)]
+        [Tooltip("Ray end is offset up or down by this amount.")]
+        float m_EndVerticalOffset;
+
+        /// <summary>
+        /// Ray end is offset up or down by this amount.
+        /// </summary>
+        public float EndVerticalOffset
+        {
+            get => m_EndVerticalOffset;
+            set { m_EndVerticalOffset = value; UpdateSensor(); }
+        }
+
+        /// <inheritdoc/>
+        public override RayPerceptionCastType GetCastType()
+        {
+            return RayPerceptionCastType.Cast3D;
+        }
+
+        /// <inheritdoc/>
+        public override float GetStartVerticalOffset()
+        {
+            return StartVerticalOffset;
+        }
+
+        /// <inheritdoc/>
+        public override float GetEndVerticalOffset()
+        {
+            return EndVerticalOffset;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b2a1d48cd8d6d8c4f69333ad5f3cc1918f7f5fae
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponent3D.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 6bb6b867a41448888c1cd4f99643ad71
+timeCreated: 1573764567
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs
new file mode 100644
index 0000000000000000000000000000000000000000..35c4fd53203f06021ead4e186ebaa14129ef90e6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs
@@ -0,0 +1,398 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A base class to support sensor components for raycast-based sensors.
+    /// </summary>
+    public abstract class RayPerceptionSensorComponentBase : SensorComponent
+    {
+        [HideInInspector, SerializeField, FormerlySerializedAs("sensorName")]
+        string m_SensorName = "RayPerceptionSensor";
+
+        /// <summary>
+        /// The name of the Sensor that this component wraps.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+
+        [SerializeField, FormerlySerializedAs("detectableTags")]
+        [Tooltip("List of tags in the scene to compare against.")]
+        List<string> m_DetectableTags;
+
+        /// <summary>
+        /// List of tags in the scene to compare against.
+        /// Note that this should not be changed at runtime.
+        /// </summary>
+        public List<string> DetectableTags
+        {
+            get { return m_DetectableTags; }
+            set { m_DetectableTags = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("raysPerDirection")]
+        [Range(0, 50)]
+        [Tooltip("Number of rays to the left and right of center.")]
+        int m_RaysPerDirection = 3;
+
+        /// <summary>
+        /// Number of rays to the left and right of center.
+        /// Note that this should not be changed at runtime.
+        /// </summary>
+        public int RaysPerDirection
+        {
+            get { return m_RaysPerDirection; }
+            // Note: can't change at runtime
+            set { m_RaysPerDirection = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("maxRayDegrees")]
+        [Range(0, 180)]
+        [Tooltip("Cone size for rays. Using 90 degrees will cast rays to the left and right. " +
+            "Greater than 90 degrees will go backwards.")]
+        float m_MaxRayDegrees = 70;
+
+        /// <summary>
+        /// Cone size for rays. Using 90 degrees will cast rays to the left and right.
+        /// Greater than 90 degrees will go backwards.
+        /// </summary>
+        public float MaxRayDegrees
+        {
+            get => m_MaxRayDegrees;
+            set { m_MaxRayDegrees = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("sphereCastRadius")]
+        [Range(0f, 10f)]
+        [Tooltip("Radius of sphere to cast. Set to zero for raycasts.")]
+        float m_SphereCastRadius = 0.5f;
+
+        /// <summary>
+        /// Radius of sphere to cast. Set to zero for raycasts.
+        /// </summary>
+        public float SphereCastRadius
+        {
+            get => m_SphereCastRadius;
+            set { m_SphereCastRadius = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("rayLength")]
+        [Range(1, 1000)]
+        [Tooltip("Length of the rays to cast.")]
+        float m_RayLength = 20f;
+
+        /// <summary>
+        /// Length of the rays to cast.
+        /// </summary>
+        public float RayLength
+        {
+            get => m_RayLength;
+            set { m_RayLength = value; UpdateSensor(); }
+        }
+
+        // The value of the default layers.
+        const int k_PhysicsDefaultLayers = -5;
+        [HideInInspector, SerializeField, FormerlySerializedAs("rayLayerMask")]
+        [Tooltip("Controls which layers the rays can hit.")]
+        LayerMask m_RayLayerMask = k_PhysicsDefaultLayers;
+
+        /// <summary>
+        /// Controls which layers the rays can hit.
+        /// </summary>
+        public LayerMask RayLayerMask
+        {
+            get => m_RayLayerMask;
+            set { m_RayLayerMask = value; UpdateSensor(); }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("observationStacks")]
+        [Range(1, 50)]
+        [Tooltip("Number of raycast results that will be stacked before being fed to the neural network.")]
+        int m_ObservationStacks = 1;
+
+        /// <summary>
+        /// Whether to stack previous observations. Using 1 means no previous observations.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int ObservationStacks
+        {
+            get { return m_ObservationStacks; }
+            set { m_ObservationStacks = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        [Tooltip("Disable to provide the rays in left to right order.  Warning: Alternating order will be deprecated, disable it to ensure compatibility with future versions of ML-Agents.")]
+        public bool m_AlternatingRayOrder = true;
+
+        /// <summary>
+        /// Determines how the rays are ordered.  By default the ordering is as follows: middle ray is first;
+        /// then alternates outward adding rays to the left and right.  If set to false, then the rays are
+        /// ordered from left to right (viewed from above) which is more amenable to processing with
+        /// conv nets.
+        /// This property will be deprecated with the next major version update and the left to right ordering
+        /// will be used thereafter.
+        /// </summary>
+        public bool AlternatingRayOrder
+        {
+            get { return m_AlternatingRayOrder; }
+            set { m_AlternatingRayOrder = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        [Tooltip("Enable to use batched raycasts and the jobs system.")]
+        public bool m_UseBatchedRaycasts = false;
+
+        /// <summary>
+        /// Determines whether to use batched raycasts and the jobs system. Default = false.
+        /// </summary>
+        public bool UseBatchedRaycasts
+        {
+            get { return m_UseBatchedRaycasts; }
+            set { m_UseBatchedRaycasts = value; }
+        }
+
+
+        /// <summary>
+        /// Color to code a ray that hits another object.
+        /// </summary>
+        [HideInInspector]
+        [SerializeField]
+        [Header("Debug Gizmos", order = 999)]
+        internal Color rayHitColor = Color.red;
+
+        /// <summary>
+        /// Color to code a ray that avoid or misses all other objects.
+        /// </summary>
+        [HideInInspector]
+        [SerializeField]
+        internal Color rayMissColor = Color.white;
+
+        [NonSerialized]
+        RayPerceptionSensor m_RaySensor;
+
+        /// <summary>
+        /// Get the RayPerceptionSensor that was created.
+        /// </summary>
+        public RayPerceptionSensor RaySensor
+        {
+            get => m_RaySensor;
+        }
+
+        /// <summary>
+        /// Returns the <see cref="RayPerceptionCastType"/> for the associated raycast sensor.
+        /// </summary>
+        /// <returns></returns>
+        public abstract RayPerceptionCastType GetCastType();
+
+        /// <summary>
+        /// Returns the amount that the ray start is offset up or down by.
+        /// </summary>
+        /// <returns></returns>
+        public virtual float GetStartVerticalOffset()
+        {
+            return 0f;
+        }
+
+        /// <summary>
+        /// Returns the amount that the ray end is offset up or down by.
+        /// </summary>
+        /// <returns></returns>
+        public virtual float GetEndVerticalOffset()
+        {
+            return 0f;
+        }
+
+        /// <summary>
+        /// Returns an initialized raycast sensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            var rayPerceptionInput = GetRayPerceptionInput();
+
+            m_RaySensor = new RayPerceptionSensor(m_SensorName, rayPerceptionInput);
+
+            if (ObservationStacks != 1)
+            {
+                var stackingSensor = new StackingSensor(m_RaySensor, ObservationStacks);
+                return new ISensor[] { stackingSensor };
+            }
+
+            return new ISensor[] { m_RaySensor };
+        }
+
+        /// <summary>
+        /// Returns the specific ray angles given the number of rays per direction and the
+        /// cone size for the rays.
+        /// </summary>
+        /// <param name="raysPerDirection">Number of rays to the left and right of center.</param>
+        /// <param name="maxRayDegrees">
+        /// Cone size for rays. Using 90 degrees will cast rays to the left and right.
+        /// Greater than 90 degrees will go backwards.
+        /// Orders the rays starting with the centermost and alternating to the left and right.
+        /// Should be deprecated with a future major version release (doing so will break existing
+        /// models).
+        /// </param>
+        /// <returns></returns>
+        internal static float[] GetRayAnglesAlternating(int raysPerDirection, float maxRayDegrees)
+        {
+            // Example:
+            // { 90, 90 - delta, 90 + delta, 90 - 2*delta, 90 + 2*delta }
+            var anglesOut = new float[2 * raysPerDirection + 1];
+            var delta = maxRayDegrees / raysPerDirection;
+            anglesOut[0] = 90f;
+            for (var i = 0; i < raysPerDirection; i++)
+            {
+                anglesOut[2 * i + 1] = 90 - (i + 1) * delta;
+                anglesOut[2 * i + 2] = 90 + (i + 1) * delta;
+            }
+            return anglesOut;
+        }
+
+        /// <summary>
+        /// Returns the specific ray angles given the number of rays per direction and the
+        /// cone size for the rays.
+        /// </summary>
+        /// <param name="raysPerDirection">Number of rays to the left and right of center.</param>
+        /// <param name="maxRayDegrees">
+        /// Cone size for rays. Using 90 degrees will cast rays to the left and right.
+        /// Greater than 90 degrees will go backwards.
+        /// Orders the rays from the left-most to the right-most which makes using a convolution
+        /// in the model easier.
+        /// </param>
+        /// <returns></returns>
+        internal static float[] GetRayAngles(int raysPerDirection, float maxRayDegrees)
+        {
+            // Example:
+            // { 90 - 3*delta, 90 - 2*delta, ..., 90, 90 + delta, ..., 90 + 3*delta }
+            var anglesOut = new float[2 * raysPerDirection + 1];
+            var delta = maxRayDegrees / raysPerDirection;
+
+            for (var i = 0; i < 2 * raysPerDirection + 1; i++)
+            {
+                anglesOut[i] = 90 + (i - raysPerDirection) * delta;
+            }
+
+            return anglesOut;
+        }
+
+        /// <summary>
+        /// Get the RayPerceptionInput that is used by the <see cref="RayPerceptionSensor"/>.
+        /// </summary>
+        /// <returns></returns>
+        public RayPerceptionInput GetRayPerceptionInput()
+        {
+            var rayAngles = m_AlternatingRayOrder ?
+                GetRayAnglesAlternating(RaysPerDirection, MaxRayDegrees) :
+                GetRayAngles(RaysPerDirection, MaxRayDegrees);
+
+            var rayPerceptionInput = new RayPerceptionInput();
+            rayPerceptionInput.RayLength = RayLength;
+            rayPerceptionInput.DetectableTags = DetectableTags;
+            rayPerceptionInput.Angles = rayAngles;
+            rayPerceptionInput.StartOffset = GetStartVerticalOffset();
+            rayPerceptionInput.EndOffset = GetEndVerticalOffset();
+            rayPerceptionInput.CastRadius = SphereCastRadius;
+            rayPerceptionInput.Transform = transform;
+            rayPerceptionInput.CastType = GetCastType();
+            rayPerceptionInput.LayerMask = RayLayerMask;
+            rayPerceptionInput.UseBatchedRaycasts = UseBatchedRaycasts;
+
+            return rayPerceptionInput;
+        }
+
+        internal void UpdateSensor()
+        {
+            if (m_RaySensor != null)
+            {
+                var rayInput = GetRayPerceptionInput();
+                m_RaySensor.SetRayPerceptionInput(rayInput);
+            }
+        }
+
+        internal int SensorObservationAge()
+        {
+            if (m_RaySensor != null)
+            {
+                return Time.frameCount - m_RaySensor.DebugLastFrameCount;
+            }
+
+            return 0;
+        }
+
+        void OnDrawGizmosSelected()
+        {
+            if (m_RaySensor?.RayPerceptionOutput?.RayOutputs != null)
+            {
+                // If we have cached debug info from the sensor, draw that.
+                // Draw "old" observations in a lighter color.
+                // Since the agent may not step every frame, this helps de-emphasize "stale" hit information.
+                var alpha = Mathf.Pow(.5f, SensorObservationAge());
+
+                foreach (var rayInfo in m_RaySensor.RayPerceptionOutput.RayOutputs)
+                {
+                    DrawRaycastGizmos(rayInfo, alpha);
+                }
+            }
+            else
+            {
+                var rayInput = GetRayPerceptionInput();
+                // We don't actually need the tags here, since they don't affect the display of the rays.
+                // Additionally, the user might be in the middle of typing the tag name when this is called,
+                // and there's no way to turn off the "Tag ... is not defined" error logs.
+                // So just don't use any tags here.
+                rayInput.DetectableTags = null;
+                if (m_UseBatchedRaycasts && rayInput.CastType == RayPerceptionCastType.Cast3D)
+                {
+                    // TODO add call to PerceiveBatchedRays()
+                    var rayOutputs = new RayPerceptionOutput.RayOutput[rayInput.Angles.Count];
+                    RayPerceptionSensor.PerceiveBatchedRays(ref rayOutputs, rayInput);
+                    for (var rayIndex = 0; rayIndex < rayInput.Angles.Count; rayIndex++)
+                    {
+                        DrawRaycastGizmos(rayOutputs[rayIndex]);
+                    }
+                }
+                else
+                {
+                    for (var rayIndex = 0; rayIndex < rayInput.Angles.Count; rayIndex++)
+                    {
+                        var rayOutput = RayPerceptionSensor.PerceiveSingleRay(rayInput, rayIndex);
+                        DrawRaycastGizmos(rayOutput);
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// Draw the debug information from the sensor (if available).
+        /// </summary>
+        void DrawRaycastGizmos(RayPerceptionOutput.RayOutput rayOutput, float alpha = 1.0f)
+        {
+            var startPositionWorld = rayOutput.StartPositionWorld;
+            var endPositionWorld = rayOutput.EndPositionWorld;
+            var rayDirection = endPositionWorld - startPositionWorld;
+            rayDirection *= rayOutput.HitFraction;
+
+            // hit fraction ^2 will shift "far" hits closer to the hit color
+            var lerpT = rayOutput.HitFraction * rayOutput.HitFraction;
+            var color = Color.Lerp(rayHitColor, rayMissColor, lerpT);
+            color.a *= alpha;
+            Gizmos.color = color;
+            Gizmos.DrawRay(startPositionWorld, rayDirection);
+
+            // Draw the hit point as a sphere. If using rays to cast (0 radius), use a small sphere.
+            if (rayOutput.HasHit)
+            {
+                var hitRadius = Mathf.Max(rayOutput.ScaledCastRadius, .05f);
+                Gizmos.DrawWireSphere(startPositionWorld + rayDirection, hitRadius);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fb886554053bf8fc72698e1d686286b5c7e33871
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RayPerceptionSensorComponentBase.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 45243967d8c0419b953c02bccb7c2768
+timeCreated: 1573087062
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fb7288f7178a8f35ef31ae11979f24520cef13ad
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 08ece3d7e9bb94089a9d59c6f269ab0a
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..606656ecb3976f14252d9917c42ce8658afb7c3d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a boolean field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class BoolReflectionSensor : ReflectionSensorBase
+    {
+        public BoolReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 1)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var boolVal = (System.Boolean)GetReflectedValue();
+            writer[0] = boolVal ? 1.0f : 0.0f;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5cac420f1160c36ccd0acb1651360aa6be3466a0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: be795c90750a6420d93f569b69ddc1ba
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2d92df0369e3569d25c17685651eaf1b2b112242
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs
@@ -0,0 +1,61 @@
+using System;
+
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    internal class EnumReflectionSensor : ReflectionSensorBase
+    {
+        Array m_Values;
+        bool m_IsFlags;
+
+        internal EnumReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, GetEnumObservationSize(reflectionSensorInfo.GetMemberType()))
+        {
+            var memberType = reflectionSensorInfo.GetMemberType();
+            m_Values = Enum.GetValues(memberType);
+            m_IsFlags = memberType.IsDefined(typeof(FlagsAttribute), false);
+        }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            // Write the enum value as a one-hot encoding.
+            // Note that unknown enum values will record all 0's.
+            // Flags will get treated as a sequence of bools.
+            var enumValue = (Enum)GetReflectedValue();
+
+            int i = 0;
+            foreach (var val in m_Values)
+            {
+                if (m_IsFlags)
+                {
+                    if (enumValue.HasFlag((Enum)val))
+                    {
+                        writer[i] = 1.0f;
+                    }
+                    else
+                    {
+                        writer[i] = 0.0f;
+                    }
+                }
+                else
+                {
+                    if (val.Equals(enumValue))
+                    {
+                        writer[i] = 1.0f;
+                    }
+                    else
+                    {
+                        writer[i] = 0.0f;
+                    }
+                }
+                i++;
+            }
+        }
+
+        internal static int GetEnumObservationSize(Type t)
+        {
+            var values = Enum.GetValues(t);
+            // Account for all enum values
+            return values.Length;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d42cce5521050af2dab0678985aff59be92ce5f0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/EnumReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7d86e42cede474ec28dc3b1ef1c7a63c
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a488a9ed5c280a6c5a47c8af9272c5800b77ce06
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a float field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class FloatReflectionSensor : ReflectionSensorBase
+    {
+        public FloatReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 1)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var floatVal = (System.Single)GetReflectedValue();
+            writer[0] = floatVal;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2de8b18c7c983ba42f393bbc66b0a9437f5281b0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/FloatReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 51ed837d5b7cd44349287ac8066120fc
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6c1b10f45a4ae94ffdb4ce9b6dbd7881eb92f63f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps an integer field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class IntReflectionSensor : ReflectionSensorBase
+    {
+        public IntReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 1)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var intVal = (System.Int32)GetReflectedValue();
+            writer[0] = intVal;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a07726937f76c77df0dfdba7d1816853bc028466
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/IntReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5cae4c843cc074d11a549aaa3904c898
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a88528c87343dcf0cefc8cdd025eeb454c5dcd38
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs
@@ -0,0 +1,285 @@
+using System;
+using System.Collections.Generic;
+using System.Reflection;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Specify that a field or property should be used to generate observations for an Agent.
+    /// For each field or property that uses ObservableAttribute, a corresponding
+    /// <see cref="ISensor"/> will be created during Agent initialization, and this
+    /// sensor will read the values during training and inference.
+    /// </summary>
+    /// <remarks>
+    /// ObservableAttribute is intended to make initial setup of an Agent easier. Because it
+    /// uses reflection to read the values of fields and properties at runtime, this may
+    /// be much slower than reading the values directly. If the performance of
+    /// ObservableAttribute is an issue, you can get the same functionality by overriding
+    /// <see cref="Agent.CollectObservations(VectorSensor)"/> or creating a custom
+    /// <see cref="ISensor"/> implementation to read the values without reflection.
+    ///
+    /// Note that you do not need to adjust the VectorObservationSize in
+    /// <see cref="Unity.MLAgents.Policies.BrainParameters"/> when adding ObservableAttribute
+    /// to fields or properties.
+    /// </remarks>
+    /// <example>
+    /// This sample class will produce two observations, one for the m_Health field, and one
+    /// for the HealthPercent property.
+    /// <code>
+    /// using Unity.MLAgents;
+    /// using Unity.MLAgents.Sensors.Reflection;
+    ///
+    /// public class MyAgent : Agent
+    /// {
+    ///     [Observable]
+    ///     int m_Health;
+    ///
+    ///     [Observable]
+    ///     float HealthPercent
+    ///     {
+    ///         get => return 100.0f * m_Health / float(m_MaxHealth);
+    ///     }
+    /// }
+    /// </code>
+    /// </example>
+    [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property)]
+    public class ObservableAttribute : Attribute
+    {
+        string m_Name;
+        int m_NumStackedObservations;
+
+        /// <summary>
+        /// Default binding flags used for reflection of members and properties.
+        /// </summary>
+        const BindingFlags k_BindingFlags = BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic;
+
+        /// <summary>
+        /// Supported types and their observation sizes and corresponding sensor type.
+        /// </summary>
+        static Dictionary<Type, (int, Type)> s_TypeToSensorInfo = new Dictionary<Type, (int, Type)>()
+        {
+            {typeof(int), (1, typeof(IntReflectionSensor))},
+            {typeof(bool), (1, typeof(BoolReflectionSensor))},
+            {typeof(float), (1, typeof(FloatReflectionSensor))},
+
+            {typeof(Vector2), (2, typeof(Vector2ReflectionSensor))},
+            {typeof(Vector3), (3, typeof(Vector3ReflectionSensor))},
+            {typeof(Vector4), (4, typeof(Vector4ReflectionSensor))},
+            {typeof(Quaternion), (4, typeof(QuaternionReflectionSensor))},
+        };
+
+        /// <summary>
+        /// ObservableAttribute constructor.
+        /// </summary>
+        /// <param name="name">Optional override for the sensor name. Note that all sensors for an Agent
+        /// must have a unique name.</param>
+        /// <param name="numStackedObservations">Number of frames to concatenate observations from.</param>
+        public ObservableAttribute(string name = null, int numStackedObservations = 1)
+        {
+            m_Name = name;
+            m_NumStackedObservations = numStackedObservations;
+        }
+
+        /// <summary>
+        /// Returns a FieldInfo for all fields that have an ObservableAttribute
+        /// </summary>
+        /// <param name="o">Object being reflected</param>
+        /// <param name="excludeInherited">Whether to exclude inherited properties or not.</param>
+        /// <returns></returns>
+        static IEnumerable<(FieldInfo, ObservableAttribute)> GetObservableFields(object o, bool excludeInherited)
+        {
+            // TODO cache these (and properties) by type, so that we only have to reflect once.
+            var bindingFlags = k_BindingFlags | (excludeInherited ? BindingFlags.DeclaredOnly : 0);
+            var fields = o.GetType().GetFields(bindingFlags);
+            foreach (var field in fields)
+            {
+                var attr = (ObservableAttribute)GetCustomAttribute(field, typeof(ObservableAttribute));
+                if (attr != null)
+                {
+                    yield return (field, attr);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Returns a PropertyInfo for all fields that have an ObservableAttribute
+        /// </summary>
+        /// <param name="o">Object being reflected</param>
+        /// <param name="excludeInherited">Whether to exclude inherited properties or not.</param>
+        /// <returns></returns>
+        static IEnumerable<(PropertyInfo, ObservableAttribute)> GetObservableProperties(object o, bool excludeInherited)
+        {
+            var bindingFlags = k_BindingFlags | (excludeInherited ? BindingFlags.DeclaredOnly : 0);
+            var properties = o.GetType().GetProperties(bindingFlags);
+            foreach (var prop in properties)
+            {
+                var attr = (ObservableAttribute)GetCustomAttribute(prop, typeof(ObservableAttribute));
+                if (attr != null)
+                {
+                    yield return (prop, attr);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Creates sensors for each field and property with ObservableAttribute.
+        /// </summary>
+        /// <param name="o">Object being reflected</param>
+        /// <param name="excludeInherited">Whether to exclude inherited properties or not.</param>
+        /// <returns></returns>
+        internal static List<ISensor> CreateObservableSensors(object o, bool excludeInherited)
+        {
+            var sensorsOut = new List<ISensor>();
+            foreach (var (field, attr) in GetObservableFields(o, excludeInherited))
+            {
+                var sensor = CreateReflectionSensor(o, field, null, attr);
+                if (sensor != null)
+                {
+                    sensorsOut.Add(sensor);
+                }
+            }
+
+            foreach (var (prop, attr) in GetObservableProperties(o, excludeInherited))
+            {
+                if (!prop.CanRead)
+                {
+                    // Skip unreadable properties.
+                    continue;
+                }
+                var sensor = CreateReflectionSensor(o, null, prop, attr);
+                if (sensor != null)
+                {
+                    sensorsOut.Add(sensor);
+                }
+            }
+
+            return sensorsOut;
+        }
+
+        /// <summary>
+        /// Create the ISensor for either the field or property on the provided object.
+        /// If the data type is unsupported, or the property is write-only, returns null.
+        /// </summary>
+        /// <param name="o"></param>
+        /// <param name="fieldInfo"></param>
+        /// <param name="propertyInfo"></param>
+        /// <param name="observableAttribute"></param>
+        /// <returns></returns>
+        /// <exception cref="UnityAgentsException"></exception>
+        static ISensor CreateReflectionSensor(object o, FieldInfo fieldInfo, PropertyInfo propertyInfo, ObservableAttribute observableAttribute)
+        {
+            string memberName;
+            string declaringTypeName;
+            Type memberType;
+            if (fieldInfo != null)
+            {
+                declaringTypeName = fieldInfo.DeclaringType.Name;
+                memberName = fieldInfo.Name;
+                memberType = fieldInfo.FieldType;
+            }
+            else
+            {
+                declaringTypeName = propertyInfo.DeclaringType.Name;
+                memberName = propertyInfo.Name;
+                memberType = propertyInfo.PropertyType;
+            }
+
+            if (!s_TypeToSensorInfo.ContainsKey(memberType) && !memberType.IsEnum)
+            {
+                // For unsupported types, return null and we'll filter them out later.
+                return null;
+            }
+
+            string sensorName;
+            if (string.IsNullOrEmpty(observableAttribute.m_Name))
+            {
+                sensorName = $"ObservableAttribute:{declaringTypeName}.{memberName}";
+            }
+            else
+            {
+                sensorName = observableAttribute.m_Name;
+            }
+
+            var reflectionSensorInfo = new ReflectionSensorInfo
+            {
+                Object = o,
+                FieldInfo = fieldInfo,
+                PropertyInfo = propertyInfo,
+                ObservableAttribute = observableAttribute,
+                SensorName = sensorName
+            };
+
+            ISensor sensor = null;
+            if (memberType.IsEnum)
+            {
+                sensor = new EnumReflectionSensor(reflectionSensorInfo);
+            }
+            else
+            {
+                var (_, sensorType) = s_TypeToSensorInfo[memberType];
+                sensor = (ISensor)Activator.CreateInstance(sensorType, reflectionSensorInfo);
+            }
+
+            // Wrap the base sensor in a StackingSensor if we're using stacking.
+            if (observableAttribute.m_NumStackedObservations > 1)
+            {
+                return new StackingSensor(sensor, observableAttribute.m_NumStackedObservations);
+            }
+
+            return sensor;
+        }
+
+        /// <summary>
+        /// Gets the sum of the observation sizes of the Observable fields and properties on an object.
+        /// Also appends errors to the errorsOut array.
+        /// </summary>
+        /// <param name="o"></param>
+        /// <param name="excludeInherited"></param>
+        /// <param name="errorsOut"></param>
+        /// <returns></returns>
+        internal static int GetTotalObservationSize(object o, bool excludeInherited, List<string> errorsOut)
+        {
+            int sizeOut = 0;
+            foreach (var (field, attr) in GetObservableFields(o, excludeInherited))
+            {
+                if (s_TypeToSensorInfo.ContainsKey(field.FieldType))
+                {
+                    var (obsSize, _) = s_TypeToSensorInfo[field.FieldType];
+                    sizeOut += obsSize * attr.m_NumStackedObservations;
+                }
+                else if (field.FieldType.IsEnum)
+                {
+                    sizeOut += EnumReflectionSensor.GetEnumObservationSize(field.FieldType);
+                }
+                else
+                {
+                    errorsOut.Add($"Unsupported Observable type {field.FieldType.Name} on field {field.Name}");
+                }
+            }
+
+            foreach (var (prop, attr) in GetObservableProperties(o, excludeInherited))
+            {
+                if (!prop.CanRead)
+                {
+                    errorsOut.Add($"Observable property {prop.Name} is write-only.");
+                }
+                else if (s_TypeToSensorInfo.ContainsKey(prop.PropertyType))
+                {
+                    var (obsSize, _) = s_TypeToSensorInfo[prop.PropertyType];
+                    sizeOut += obsSize * attr.m_NumStackedObservations;
+                }
+                else if (prop.PropertyType.IsEnum)
+                {
+                    sizeOut += EnumReflectionSensor.GetEnumObservationSize(prop.PropertyType);
+                }
+                else
+                {
+                    errorsOut.Add($"Unsupported Observable type {prop.PropertyType.Name} on property {prop.Name}");
+                }
+            }
+
+            return sizeOut;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..41659283dac1a5073cf4889845e1f926310cc8da
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/ObservableAttribute.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a75086dc66a594baea6b8b2935f5dacf
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5cd92ee68fecde0e84ab28964c7b129143cec2fa
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a quaternion field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class QuaternionReflectionSensor : ReflectionSensorBase
+    {
+        public QuaternionReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 4)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var quatVal = (UnityEngine.Quaternion)GetReflectedValue();
+            writer.Add(quatVal);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f3970e6b51331ac9047eeab752e4a3e5f68ab2fb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/QuaternionReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d38241d74074d459bb4590f7f5d16c80
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9a0219146ec69ced574bebc28dd212582c55faa4
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs
@@ -0,0 +1,111 @@
+using System;
+using System.Reflection;
+
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Construction info for a ReflectionSensorBase.
+    /// </summary>
+    internal struct ReflectionSensorInfo
+    {
+        public object Object;
+
+        public FieldInfo FieldInfo;
+        public PropertyInfo PropertyInfo;
+        public ObservableAttribute ObservableAttribute;
+        public string SensorName;
+
+        public Type GetMemberType()
+        {
+            return FieldInfo != null ? FieldInfo.FieldType : PropertyInfo.PropertyType;
+        }
+    }
+
+    /// <summary>
+    /// Abstract base class for reflection-based sensors.
+    /// </summary>
+    internal abstract class ReflectionSensorBase : ISensor, IBuiltInSensor
+    {
+        protected object m_Object;
+
+        // Exactly one of m_FieldInfo and m_PropertyInfo should be non-null.
+        protected FieldInfo m_FieldInfo;
+        protected PropertyInfo m_PropertyInfo;
+
+        // Not currently used, but might want later.
+        protected ObservableAttribute m_ObservableAttribute;
+
+        // Cached sensor names and shapes.
+        string m_SensorName;
+        ObservationSpec m_ObservationSpec;
+        int m_NumFloats;
+
+        public ReflectionSensorBase(ReflectionSensorInfo reflectionSensorInfo, int size)
+        {
+            m_Object = reflectionSensorInfo.Object;
+            m_FieldInfo = reflectionSensorInfo.FieldInfo;
+            m_PropertyInfo = reflectionSensorInfo.PropertyInfo;
+            m_ObservableAttribute = reflectionSensorInfo.ObservableAttribute;
+            m_SensorName = reflectionSensorInfo.SensorName;
+            m_ObservationSpec = ObservationSpec.Vector(size);
+            m_NumFloats = size;
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            WriteReflectedField(writer);
+            return m_NumFloats;
+        }
+
+        internal abstract void WriteReflectedField(ObservationWriter writer);
+
+        /// <summary>
+        /// Get either the reflected field, or return the reflected property.
+        /// This should be used by implementations in their WriteReflectedField() method.
+        /// </summary>
+        /// <returns></returns>
+        protected object GetReflectedValue()
+        {
+            return m_FieldInfo != null ?
+                m_FieldInfo.GetValue(m_Object) :
+                m_PropertyInfo.GetMethod.Invoke(m_Object, null);
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public void Update() { }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_SensorName;
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.ReflectionSensor;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cef19bb5986347c9feedaa9915894d09948a2942
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/ReflectionSensorBase.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 6b68d855fb94a45fbbeb0dbe968a35f8
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..85c6dea8ef825075b817bdadfa4556e292ceec23
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs
@@ -0,0 +1,20 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a Vector2 field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class Vector2ReflectionSensor : ReflectionSensorBase
+    {
+        public Vector2ReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 2)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var vecVal = (UnityEngine.Vector2)GetReflectedValue();
+            writer[0] = vecVal.x;
+            writer[1] = vecVal.y;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2b78c25ffec864f9d3e2dcb7f905da3633b3604e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector2ReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: da06ff33f6f2d409cbf240cffa2ba0be
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8fa28b73ee57e3334110132367d87f5cf559d091
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a Vector3 field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class Vector3ReflectionSensor : ReflectionSensorBase
+    {
+        public Vector3ReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 3)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var vecVal = (UnityEngine.Vector3)GetReflectedValue();
+            writer.Add(vecVal);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..771b690b07529ac6d55a17c4cf327ce1239f46fe
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e756976ec2a0943cfbc0f97a6550a85b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..76b5c39c2767d9538838f591463255d5b1cfa366
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs
@@ -0,0 +1,19 @@
+namespace Unity.MLAgents.Sensors.Reflection
+{
+    /// <summary>
+    /// Sensor that wraps a Vector4 field or property of an object, and returns
+    /// that as an observation.
+    /// </summary>
+    internal class Vector4ReflectionSensor : ReflectionSensorBase
+    {
+        public Vector4ReflectionSensor(ReflectionSensorInfo reflectionSensorInfo)
+            : base(reflectionSensorInfo, 4)
+        { }
+
+        internal override void WriteReflectedField(ObservationWriter writer)
+        {
+            var vecVal = (UnityEngine.Vector4)GetReflectedValue();
+            writer.Add(vecVal);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3d938af6c8e0427f33333605e8c1f6996641054e
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector4ReflectionSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 01d93aaa1b42b47b8960d303d7c498d3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..678e7222209b54c7a199ae1432a7bbf3e592c5f1
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs
@@ -0,0 +1,130 @@
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Sensor class that wraps a [RenderTexture](https://docs.unity3d.com/ScriptReference/RenderTexture.html) instance.
+    /// </summary>
+    public class RenderTextureSensor : ISensor, IBuiltInSensor, IDisposable
+    {
+        RenderTexture m_RenderTexture;
+        bool m_Grayscale;
+        string m_Name;
+        private ObservationSpec m_ObservationSpec;
+        SensorCompressionType m_CompressionType;
+        Texture2D m_Texture;
+
+        /// <summary>
+        /// The compression type used by the sensor.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_CompressionType; }
+            set { m_CompressionType = value; }
+        }
+
+
+        /// <summary>
+        /// Initializes the sensor.
+        /// </summary>
+        /// <param name="renderTexture">The [RenderTexture](https://docs.unity3d.com/ScriptReference/RenderTexture.html)
+        /// instance to wrap.</param>
+        /// <param name="grayscale">Whether to convert it to grayscale or not.</param>
+        /// <param name="name">Name of the sensor.</param>
+        /// <param name="compressionType">Compression method for the render texture.</param>
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
+        public RenderTextureSensor(
+            RenderTexture renderTexture, bool grayscale, string name, SensorCompressionType compressionType)
+        {
+            m_RenderTexture = renderTexture;
+            var width = renderTexture != null ? renderTexture.width : 0;
+            var height = renderTexture != null ? renderTexture.height : 0;
+            m_Grayscale = grayscale;
+            m_Name = name;
+            m_ObservationSpec = ObservationSpec.Visual(height, width, grayscale ? 1 : 3);
+            m_CompressionType = compressionType;
+            m_Texture = new Texture2D(width, height, TextureFormat.RGB24, false);
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            using (TimerStack.Instance.Scoped("RenderTextureSensor.GetCompressedObservation"))
+            {
+                ObservationToTexture(m_RenderTexture, m_Texture);
+                // TODO support more types here, e.g. JPG
+                var compressed = m_Texture.EncodeToPNG();
+                return compressed;
+            }
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            using (TimerStack.Instance.Scoped("RenderTextureSensor.Write"))
+            {
+                ObservationToTexture(m_RenderTexture, m_Texture);
+                var numWritten = writer.WriteTexture(m_Texture, m_Grayscale);
+                return numWritten;
+            }
+        }
+
+        /// <inheritdoc/>
+        public void Update() { }
+
+        /// <inheritdoc/>
+        public void Reset() { }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return new CompressionSpec(m_CompressionType);
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.RenderTextureSensor;
+        }
+
+        /// <summary>
+        /// Converts a RenderTexture to a 2D texture.
+        /// </summary>
+        /// <param name="obsTexture">RenderTexture.</param>
+        /// <param name="texture2D">Texture2D to render to.</param>
+        public static void ObservationToTexture(RenderTexture obsTexture, Texture2D texture2D)
+        {
+            var prevActiveRt = RenderTexture.active;
+            RenderTexture.active = obsTexture;
+
+            texture2D.ReadPixels(new Rect(0, 0, texture2D.width, texture2D.height), 0, 0);
+            texture2D.Apply();
+            RenderTexture.active = prevActiveRt;
+        }
+
+        /// <summary>
+        /// Clean up the owned Texture2D.
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(null, m_Texture))
+            {
+                Utilities.DestroyTexture(m_Texture);
+                m_Texture = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..28a1dff7674843fc3f5b133f4fe211fd12e97985
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 958f1f6bb9058405cae3c03266ad9899
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8e58617dd3be6a05ed1b41d903bdc241154692ec
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs
@@ -0,0 +1,120 @@
+using System;
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Component that wraps a <see cref="RenderTextureSensor"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Render Texture Sensor", (int)MenuGroup.Sensors)]
+    public class RenderTextureSensorComponent : SensorComponent, IDisposable
+    {
+        RenderTextureSensor m_Sensor;
+
+        /// <summary>
+        /// The [RenderTexture](https://docs.unity3d.com/ScriptReference/RenderTexture.html) instance
+        /// that the associated <see cref="RenderTextureSensor"/> wraps.
+        /// </summary>
+        [HideInInspector, SerializeField, FormerlySerializedAs("renderTexture")]
+        RenderTexture m_RenderTexture;
+
+        /// <summary>
+        /// Stores the [RenderTexture](https://docs.unity3d.com/ScriptReference/RenderTexture.html)
+        /// associated with this sensor.
+        /// </summary>
+        public RenderTexture RenderTexture
+        {
+            get { return m_RenderTexture; }
+            set { m_RenderTexture = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("sensorName")]
+        string m_SensorName = "RenderTextureSensor";
+
+        /// <summary>
+        /// Name of the generated <see cref="RenderTextureSensor"/>.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("grayscale")]
+        bool m_Grayscale;
+
+        /// <summary>
+        /// Whether the RenderTexture observation should be converted to grayscale or not.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public bool Grayscale
+        {
+            get { return m_Grayscale; }
+            set { m_Grayscale = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        [Range(1, 50)]
+        [Tooltip("Number of frames that will be stacked before being fed to the neural network.")]
+        int m_ObservationStacks = 1;
+
+        [HideInInspector, SerializeField, FormerlySerializedAs("compression")]
+        SensorCompressionType m_Compression = SensorCompressionType.PNG;
+
+        /// <summary>
+        /// Compression type for the render texture observation.
+        /// </summary>
+        public SensorCompressionType CompressionType
+        {
+            get { return m_Compression; }
+            set { m_Compression = value; UpdateSensor(); }
+        }
+
+        /// <summary>
+        /// Whether to stack previous observations. Using 1 means no previous observations.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int ObservationStacks
+        {
+            get { return m_ObservationStacks; }
+            set { m_ObservationStacks = value; }
+        }
+
+        /// <inheritdoc/>
+        public override ISensor[] CreateSensors()
+        {
+            Dispose();
+            m_Sensor = new RenderTextureSensor(RenderTexture, Grayscale, SensorName, m_Compression);
+            if (ObservationStacks != 1)
+            {
+                return new ISensor[] { new StackingSensor(m_Sensor, ObservationStacks) };
+            }
+            return new ISensor[] { m_Sensor };
+        }
+
+        /// <summary>
+        /// Update fields that are safe to change on the Sensor at runtime.
+        /// </summary>
+        internal void UpdateSensor()
+        {
+            if (m_Sensor != null)
+            {
+                m_Sensor.CompressionType = m_Compression;
+            }
+        }
+
+        /// <summary>
+        /// Clean up the sensor created by CreateSensors().
+        /// </summary>
+        public void Dispose()
+        {
+            if (!ReferenceEquals(null, m_Sensor))
+            {
+                m_Sensor.Dispose();
+                m_Sensor = null;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..542ca3e278b0ce1c7ab2c3ee9fa0bd2b7eafa8df
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/RenderTextureSensorComponent.cs.meta
@@ -0,0 +1,11 @@
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+guid: 132e1194facb64429b007ea1edf562d0
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+  externalObjects: {}
+  serializedVersion: 2
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diff --git a/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4ddcaabb74a84057600aa110871555548f34375f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs
@@ -0,0 +1,17 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Editor components for creating Sensors. Generally an ISensor implementation should have a
+    /// corresponding SensorComponent to create it.
+    /// </summary>
+    public abstract class SensorComponent : MonoBehaviour
+    {
+        /// <summary>
+        /// Create the ISensors. This is called by the Agent when it is initialized.
+        /// </summary>
+        /// <returns>Created ISensor objects.</returns>
+        public abstract ISensor[] CreateSensors();
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5576281e12aadd57fbf3b977e76006b86f56460d
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 4f1dad589959a4b598d09e54f61fbe02
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs b/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2d80d75234a0d2fadf8b163ee7094881b8505dbc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs
@@ -0,0 +1,54 @@
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    public class SensorShapeValidator
+    {
+        List<ObservationSpec> m_SensorShapes;
+
+        /// <summary>
+        /// Check that the List Sensors are the same shape as the previous ones.
+        /// If this is the first List of Sensors being checked, its Sensor sizes will be saved.
+        /// </summary>
+        public void ValidateSensors(List<ISensor> sensors)
+        {
+            if (m_SensorShapes == null)
+            {
+                m_SensorShapes = new List<ObservationSpec>(sensors.Count);
+                // First agent, save the sensor sizes
+                foreach (var sensor in sensors)
+                {
+                    m_SensorShapes.Add(sensor.GetObservationSpec());
+                }
+            }
+            else
+            {
+                // Check for compatibility with the other Agents' Sensors
+                if (m_SensorShapes.Count != sensors.Count)
+                {
+                    Debug.AssertFormat(
+                        m_SensorShapes.Count == sensors.Count,
+                        "Number of Sensors must match. {0} != {1}",
+                        m_SensorShapes.Count,
+                        sensors.Count
+                    );
+                }
+                for (var i = 0; i < Mathf.Min(m_SensorShapes.Count, sensors.Count); i++)
+                {
+                    var cachedSpec = m_SensorShapes[i];
+                    var sensorSpec = sensors[i].GetObservationSpec();
+                    if (cachedSpec.Shape != sensorSpec.Shape)
+                    {
+                        Debug.AssertFormat(
+                            cachedSpec.Shape == sensorSpec.Shape,
+                            "Sensor shapes must match. {0} != {1}",
+                            cachedSpec.Shape,
+                            sensorSpec.Shape
+                        );
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs.meta b/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6ce44665cc61ac44dd1bf53758f0f3058e220852
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/SensorShapeValidator.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a7b5a4560ee254be497321527f92c174
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs b/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..710c58a821ce3a9f6849bd947017b10d4e0bd2eb
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs
@@ -0,0 +1,300 @@
+using System;
+using System.Collections.Generic;
+using System.Collections.ObjectModel;
+using System.Linq;
+using UnityEngine;
+using Unity.Barracuda;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// Sensor that wraps around another Sensor to provide temporal stacking.
+    /// Conceptually, consecutive observations are stored left-to-right, which is how they're output
+    /// For example, 4 stacked sets of observations would be output like
+    ///   |  t = now - 3  |  t = now -3  |  t = now - 2  |  t = now  |
+    /// Internally, a circular buffer of arrays is used. The m_CurrentIndex represents the most recent observation.
+    /// Currently, observations are stacked on the last dimension.
+    /// </summary>
+    public class StackingSensor : ISensor, IBuiltInSensor
+    {
+        /// <summary>
+        /// The wrapped sensor.
+        /// </summary>
+        ISensor m_WrappedSensor;
+
+        /// <summary>
+        /// Number of stacks to save
+        /// </summary>
+        int m_NumStackedObservations;
+        int m_UnstackedObservationSize;
+
+        string m_Name;
+        private ObservationSpec m_ObservationSpec;
+        private ObservationSpec m_WrappedSpec;
+
+        /// <summary>
+        /// Buffer of previous observations
+        /// </summary>
+        float[][] m_StackedObservations;
+
+        byte[][] m_StackedCompressedObservations;
+
+        int m_CurrentIndex;
+        ObservationWriter m_LocalWriter = new ObservationWriter();
+
+        byte[] m_EmptyCompressedObservation;
+        int[] m_CompressionMapping;
+        TensorShape m_tensorShape;
+
+        /// <summary>
+        /// Initializes the sensor.
+        /// </summary>
+        /// <param name="wrapped">The wrapped sensor.</param>
+        /// <param name="numStackedObservations">Number of stacked observations to keep.</param>
+        public StackingSensor(ISensor wrapped, int numStackedObservations)
+        {
+            // TODO ensure numStackedObservations > 1
+            m_WrappedSensor = wrapped;
+            m_NumStackedObservations = numStackedObservations;
+
+            m_Name = $"StackingSensor_size{numStackedObservations}_{wrapped.GetName()}";
+
+            m_WrappedSpec = wrapped.GetObservationSpec();
+
+            m_UnstackedObservationSize = wrapped.ObservationSize();
+
+            // Set up the cached observation spec for the StackingSensor
+            var newShape = m_WrappedSpec.Shape;
+            // TODO support arbitrary stacking dimension
+            newShape[newShape.Length - 1] *= numStackedObservations;
+            m_ObservationSpec = new ObservationSpec(
+                newShape, m_WrappedSpec.DimensionProperties, m_WrappedSpec.ObservationType
+            );
+
+            // Initialize uncompressed buffer anyway in case python trainer does not
+            // support the compression mapping and has to fall back to uncompressed obs.
+            m_StackedObservations = new float[numStackedObservations][];
+            for (var i = 0; i < numStackedObservations; i++)
+            {
+                m_StackedObservations[i] = new float[m_UnstackedObservationSize];
+            }
+
+            if (m_WrappedSensor.GetCompressionSpec().SensorCompressionType != SensorCompressionType.None)
+            {
+                m_StackedCompressedObservations = new byte[numStackedObservations][];
+                m_EmptyCompressedObservation = CreateEmptyPNG();
+                for (var i = 0; i < numStackedObservations; i++)
+                {
+                    m_StackedCompressedObservations[i] = m_EmptyCompressedObservation;
+                }
+                m_CompressionMapping = ConstructStackedCompressedChannelMapping(wrapped);
+            }
+
+            if (m_WrappedSpec.Rank != 1)
+            {
+                var wrappedShape = m_WrappedSpec.Shape;
+                m_tensorShape = new TensorShape(0, wrappedShape[0], wrappedShape[1], wrappedShape[2]);
+            }
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            // First, call the wrapped sensor's write method. Make sure to use our own writer, not the passed one.
+            m_LocalWriter.SetTarget(m_StackedObservations[m_CurrentIndex], m_WrappedSpec, 0);
+            m_WrappedSensor.Write(m_LocalWriter);
+
+            // Now write the saved observations (oldest first)
+            var numWritten = 0;
+            if (m_WrappedSpec.Rank == 1)
+            {
+                for (var i = 0; i < m_NumStackedObservations; i++)
+                {
+                    var obsIndex = (m_CurrentIndex + 1 + i) % m_NumStackedObservations;
+                    writer.AddList(m_StackedObservations[obsIndex], numWritten);
+                    numWritten += m_UnstackedObservationSize;
+                }
+            }
+            else
+            {
+                for (var i = 0; i < m_NumStackedObservations; i++)
+                {
+                    var obsIndex = (m_CurrentIndex + 1 + i) % m_NumStackedObservations;
+                    for (var h = 0; h < m_WrappedSpec.Shape[0]; h++)
+                    {
+                        for (var w = 0; w < m_WrappedSpec.Shape[1]; w++)
+                        {
+                            for (var c = 0; c < m_WrappedSpec.Shape[2]; c++)
+                            {
+                                writer[h, w, i * m_WrappedSpec.Shape[2] + c] = m_StackedObservations[obsIndex][m_tensorShape.Index(0, h, w, c)];
+                            }
+                        }
+                    }
+                }
+                numWritten = m_WrappedSpec.Shape[0] * m_WrappedSpec.Shape[1] * m_WrappedSpec.Shape[2] * m_NumStackedObservations;
+            }
+
+            return numWritten;
+        }
+
+        /// <summary>
+        /// Updates the index of the "current" buffer.
+        /// </summary>
+        public void Update()
+        {
+            m_WrappedSensor.Update();
+            m_CurrentIndex = (m_CurrentIndex + 1) % m_NumStackedObservations;
+        }
+
+        /// <inheritdoc/>
+        public void Reset()
+        {
+            m_WrappedSensor.Reset();
+            // Zero out the buffer.
+            for (var i = 0; i < m_NumStackedObservations; i++)
+            {
+                Array.Clear(m_StackedObservations[i], 0, m_StackedObservations[i].Length);
+            }
+            if (m_WrappedSensor.GetCompressionSpec().SensorCompressionType != SensorCompressionType.None)
+            {
+                for (var i = 0; i < m_NumStackedObservations; i++)
+                {
+                    m_StackedCompressedObservations[i] = m_EmptyCompressedObservation;
+                }
+            }
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            var compressed = m_WrappedSensor.GetCompressedObservation();
+            m_StackedCompressedObservations[m_CurrentIndex] = compressed;
+
+            int bytesLength = 0;
+            foreach (byte[] compressedObs in m_StackedCompressedObservations)
+            {
+                bytesLength += compressedObs.Length;
+            }
+
+            byte[] outputBytes = new byte[bytesLength];
+            int offset = 0;
+            for (var i = 0; i < m_NumStackedObservations; i++)
+            {
+                var obsIndex = (m_CurrentIndex + 1 + i) % m_NumStackedObservations;
+                Buffer.BlockCopy(m_StackedCompressedObservations[obsIndex],
+                    0, outputBytes, offset, m_StackedCompressedObservations[obsIndex].Length);
+                offset += m_StackedCompressedObservations[obsIndex].Length;
+            }
+
+            return outputBytes;
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            var wrappedSpec = m_WrappedSensor.GetCompressionSpec();
+            return new CompressionSpec(wrappedSpec.SensorCompressionType, m_CompressionMapping);
+        }
+
+        /// <summary>
+        /// Create Empty PNG for initializing the buffer for stacking.
+        /// </summary>
+        internal byte[] CreateEmptyPNG()
+        {
+            var shape = m_WrappedSpec.Shape;
+            int height = shape[0];
+            int width = shape[1];
+            var texture2D = new Texture2D(width, height, TextureFormat.RGB24, false);
+            Color32[] resetColorArray = texture2D.GetPixels32();
+            Color32 black = new Color32(0, 0, 0, 0);
+            for (int i = 0; i < resetColorArray.Length; i++)
+            {
+                resetColorArray[i] = black;
+            }
+            texture2D.SetPixels32(resetColorArray);
+            texture2D.Apply();
+            return texture2D.EncodeToPNG();
+        }
+
+        /// <summary>
+        /// Construct stacked CompressedChannelMapping.
+        /// </summary>
+        internal int[] ConstructStackedCompressedChannelMapping(ISensor wrappedSenesor)
+        {
+            // Get CompressedChannelMapping of the wrapped sensor. If the
+            // wrapped sensor doesn't have one, use default mapping.
+            // Default mapping: {0, 0, 0} for grayscale, identity mapping {1, 2, ..., n} otherwise.
+            int[] wrappedMapping = null;
+            int wrappedNumChannel = m_WrappedSpec.Shape[2];
+
+            wrappedMapping = wrappedSenesor.GetCompressionSpec().CompressedChannelMapping;
+            if (wrappedMapping == null)
+            {
+                if (wrappedNumChannel == 1)
+                {
+                    wrappedMapping = new[] { 0, 0, 0 };
+                }
+                else
+                {
+                    wrappedMapping = Enumerable.Range(0, wrappedNumChannel).ToArray();
+                }
+            }
+
+            // Construct stacked mapping using the mapping of wrapped sensor.
+            // First pad the wrapped mapping to multiple of 3, then repeat
+            // and add offset to each copy to form the stacked mapping.
+            int paddedMapLength = (wrappedMapping.Length + 2) / 3 * 3;
+            var compressionMapping = new int[paddedMapLength * m_NumStackedObservations];
+            for (var i = 0; i < m_NumStackedObservations; i++)
+            {
+                var offset = wrappedNumChannel * i;
+                for (var j = 0; j < paddedMapLength; j++)
+                {
+                    if (j < wrappedMapping.Length)
+                    {
+                        compressionMapping[j + paddedMapLength * i] = wrappedMapping[j] >= 0 ? wrappedMapping[j] + offset : -1;
+                    }
+                    else
+                    {
+                        compressionMapping[j + paddedMapLength * i] = -1;
+                    }
+                }
+            }
+            return compressionMapping;
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            IBuiltInSensor wrappedBuiltInSensor = m_WrappedSensor as IBuiltInSensor;
+            return wrappedBuiltInSensor?.GetBuiltInSensorType() ?? BuiltInSensorType.Unknown;
+        }
+
+        /// <summary>
+        /// Returns the stacked observations as a read-only collection.
+        /// </summary>
+        /// <returns>The stacked observations as a read-only collection.</returns>
+        internal ReadOnlyCollection<float> GetStackedObservations()
+        {
+            List<float> observations = new List<float>();
+            for (var i = 0; i < m_NumStackedObservations; i++)
+            {
+                var obsIndex = (m_CurrentIndex + 1 + i) % m_NumStackedObservations;
+                observations.AddRange(m_StackedObservations[obsIndex].ToList());
+            }
+            return observations.AsReadOnly();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..52dbbcc9bafbe8d45450df859fe2f9253810d6ac
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/StackingSensor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 8b7a6e88d47d4438ad67e1862566462c
+timeCreated: 1572299581
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs b/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d4bd0507c498f541a72cdf606637c803439e92d3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs
@@ -0,0 +1,218 @@
+using System.Collections.Generic;
+using System.Collections.ObjectModel;
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A sensor implementation for vector observations.
+    /// </summary>
+    public class VectorSensor : ISensor, IBuiltInSensor
+    {
+        // TODO use float[] instead
+        // TODO allow setting float[]
+        List<float> m_Observations;
+        ObservationSpec m_ObservationSpec;
+        string m_Name;
+
+        /// <summary>
+        /// Initializes the sensor.
+        /// </summary>
+        /// <param name="observationSize">Number of vector observations.</param>
+        /// <param name="name">Name of the sensor.</param>
+        /// <param name="observationType"></param>
+        public VectorSensor(int observationSize, string name = null, ObservationType observationType = ObservationType.Default)
+        {
+            if (string.IsNullOrEmpty(name))
+            {
+                name = $"VectorSensor_size{observationSize}";
+                if (observationType != ObservationType.Default)
+                {
+                    name += $"_{observationType.ToString()}";
+                }
+            }
+
+            m_Observations = new List<float>(observationSize);
+            m_Name = name;
+            m_ObservationSpec = ObservationSpec.Vector(observationSize, observationType);
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            var expectedObservations = m_ObservationSpec.Shape[0];
+            if (m_Observations.Count > expectedObservations)
+            {
+                // Too many observations, truncate
+                Debug.LogWarningFormat(
+                    "More observations ({0}) made than vector observation size ({1}). The observations will be truncated.",
+                    m_Observations.Count, expectedObservations
+                );
+                m_Observations.RemoveRange(expectedObservations, m_Observations.Count - expectedObservations);
+            }
+            else if (m_Observations.Count < expectedObservations)
+            {
+                // Not enough observations; pad with zeros.
+                Debug.LogWarningFormat(
+                    "Fewer observations ({0}) made than vector observation size ({1}). The observations will be padded.",
+                    m_Observations.Count, expectedObservations
+                );
+                for (int i = m_Observations.Count; i < expectedObservations; i++)
+                {
+                    m_Observations.Add(0);
+                }
+            }
+            writer.AddList(m_Observations);
+            return expectedObservations;
+        }
+
+        /// <summary>
+        /// Returns a read-only view of the observations that added.
+        /// </summary>
+        /// <returns>A read-only view of the observations list.</returns>
+        internal ReadOnlyCollection<float> GetObservations()
+        {
+            return m_Observations.AsReadOnly();
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            Clear();
+        }
+
+        /// <inheritdoc/>
+        public void Reset()
+        {
+            Clear();
+        }
+
+        /// <inheritdoc/>
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public virtual byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        /// <inheritdoc/>
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return BuiltInSensorType.VectorSensor;
+        }
+
+        void Clear()
+        {
+            m_Observations.Clear();
+        }
+
+        void AddFloatObs(float obs)
+        {
+            Utilities.DebugCheckNanAndInfinity(obs, nameof(obs), nameof(AddFloatObs));
+            m_Observations.Add(obs);
+        }
+
+        // Compatibility methods with Agent observation. These should be removed eventually.
+
+        /// <summary>
+        /// Adds a float observation to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(float observation)
+        {
+            AddFloatObs(observation);
+        }
+
+        /// <summary>
+        /// Adds an integer observation to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(int observation)
+        {
+            AddFloatObs(observation);
+        }
+
+        /// <summary>
+        /// Adds an Vector3 observation to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(Vector3 observation)
+        {
+            AddFloatObs(observation.x);
+            AddFloatObs(observation.y);
+            AddFloatObs(observation.z);
+        }
+
+        /// <summary>
+        /// Adds an Vector2 observation to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(Vector2 observation)
+        {
+            AddFloatObs(observation.x);
+            AddFloatObs(observation.y);
+        }
+
+        /// <summary>
+        /// Adds a list or array of float observations to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(IList<float> observation)
+        {
+            for (var i = 0; i < observation.Count; i++)
+            {
+                AddFloatObs(observation[i]);
+            }
+        }
+
+        /// <summary>
+        /// Adds a quaternion observation to the vector observations of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(Quaternion observation)
+        {
+            AddFloatObs(observation.x);
+            AddFloatObs(observation.y);
+            AddFloatObs(observation.z);
+            AddFloatObs(observation.w);
+        }
+
+        /// <summary>
+        /// Adds a boolean observation to the vector observation of the agent.
+        /// </summary>
+        /// <param name="observation">Observation.</param>
+        public void AddObservation(bool observation)
+        {
+            AddFloatObs(observation ? 1f : 0f);
+        }
+
+        /// <summary>
+        /// Adds a one-hot encoding observation.
+        /// </summary>
+        /// <param name="observation">The index of this observation.</param>
+        /// <param name="range">The upper limit on the value observation can take (exclusive).</param>
+        public void AddOneHotObservation(int observation, int range)
+        {
+            for (var i = 0; i < range; i++)
+            {
+                AddFloatObs(i == observation ? 1.0f : 0.0f);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs.meta b/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e47bef0e54347f3a7736b4895b6628b10d1477c6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e3966c9961b343108808d91a4d140a68
+timeCreated: 1572300800
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs b/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs
new file mode 100644
index 0000000000000000000000000000000000000000..26deb7434f1677dc0d47d4bb426fa691ec0b9fd7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs
@@ -0,0 +1,87 @@
+using UnityEngine;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A SensorComponent that creates a <see cref="VectorSensor"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Vector Sensor", (int)MenuGroup.Sensors)]
+    public class VectorSensorComponent : SensorComponent
+    {
+        /// <summary>
+        /// Name of the generated <see cref="VectorSensor"/> object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+        [HideInInspector, SerializeField]
+        private string m_SensorName = "VectorSensor";
+
+        /// <summary>
+        /// The number of float observations in the VectorSensor
+        /// </summary>
+        public int ObservationSize
+        {
+            get { return m_ObservationSize; }
+            set { m_ObservationSize = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        int m_ObservationSize;
+
+        [HideInInspector, SerializeField]
+        ObservationType m_ObservationType;
+
+        VectorSensor m_Sensor;
+
+        /// <summary>
+        /// The type of the observation.
+        /// </summary>
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        [Range(1, 50)]
+        [Tooltip("Number of camera frames that will be stacked before being fed to the neural network.")]
+        int m_ObservationStacks = 1;
+
+        /// <summary>
+        /// Whether to stack previous observations. Using 1 means no previous observations.
+        /// Note that changing this after the sensor is created has no effect.
+        /// </summary>
+        public int ObservationStacks
+        {
+            get { return m_ObservationStacks; }
+            set { m_ObservationStacks = value; }
+        }
+
+        /// <summary>
+        /// Creates a VectorSensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            m_Sensor = new VectorSensor(m_ObservationSize, m_SensorName, m_ObservationType);
+            if (ObservationStacks != 1)
+            {
+                return new ISensor[] { new StackingSensor(m_Sensor, ObservationStacks) };
+            }
+            return new ISensor[] { m_Sensor };
+        }
+
+        /// <summary>
+        /// Returns the underlying VectorSensor
+        /// </summary>
+        /// <returns></returns>
+        public VectorSensor GetSensor()
+        {
+            return m_Sensor;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs.meta b/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c867a60f2b2b1375f17c949a6eef501f530bb7d6
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs.meta
@@ -0,0 +1,11 @@
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+guid: 38b7cc1f5819445aa85e9a9b054552dc
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
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diff --git a/com.unity.ml-agents/Runtime/SideChannels.meta b/com.unity.ml-agents/Runtime/SideChannels.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6bff982a90afbb98a3d35f8424c8d7e17254984b
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels.meta
@@ -0,0 +1,8 @@
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+guid: 9de9d822922c6454ca88483e2b9eeeac
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+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..246ef50074e4da650639662d305e5409023e59ef
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs
@@ -0,0 +1,75 @@
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Side channel that supports modifying attributes specific to the Unity Engine.
+    /// </summary>
+    internal class EngineConfigurationChannel : SideChannel
+    {
+        internal enum ConfigurationType : int
+        {
+            ScreenResolution = 0,
+            QualityLevel = 1,
+            TimeScale = 2,
+            TargetFrameRate = 3,
+            CaptureFrameRate = 4
+        }
+
+        const string k_EngineConfigId = "e951342c-4f7e-11ea-b238-784f4387d1f7";
+
+        /// <summary>
+        /// Initializes the side channel. The constructor is internal because only one instance is
+        /// supported at a time, and is created by the Academy.
+        /// </summary>
+        internal EngineConfigurationChannel()
+        {
+            ChannelId = new Guid(k_EngineConfigId);
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            var messageType = (ConfigurationType)msg.ReadInt32();
+            switch (messageType)
+            {
+                case ConfigurationType.ScreenResolution:
+                    var width = msg.ReadInt32();
+                    var height = msg.ReadInt32();
+                    Screen.SetResolution(width, height, false);
+                    break;
+                case ConfigurationType.QualityLevel:
+                    var qualityLevel = msg.ReadInt32();
+                    QualitySettings.SetQualityLevel(qualityLevel, true);
+                    break;
+                case ConfigurationType.TimeScale:
+                    var timeScale = msg.ReadFloat32();
+
+                    // There's an upper limit for the timeScale in the editor (but not in the player)
+                    // Always ensure that timeScale >= 1 also,
+#if UNITY_EDITOR
+                    const float maxTimeScale = 100f;
+#else
+                    const float maxTimeScale = float.PositiveInfinity;
+#endif
+                    timeScale = Mathf.Clamp(timeScale, 1, maxTimeScale);
+                    Time.timeScale = timeScale;
+                    break;
+                case ConfigurationType.TargetFrameRate:
+                    var targetFrameRate = msg.ReadInt32();
+                    Application.targetFrameRate = targetFrameRate;
+                    break;
+                case ConfigurationType.CaptureFrameRate:
+                    var captureFrameRate = msg.ReadInt32();
+                    Time.captureFramerate = captureFrameRate;
+                    break;
+                default:
+                    Debug.LogWarning(
+                        "Unknown engine configuration received from Python. Make sure" +
+                        " your Unity and Python versions are compatible.");
+                    break;
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8f6335e9b05bddcf8dc7b9d80f0796c820dc6fbf
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/EngineConfigurationChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 18ccdf3ce76784f2db68016fa284c33f
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0e2b88b6ecce790ae11b0fa2d3a7ebe7f59b1730
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs
@@ -0,0 +1,142 @@
+using System.Collections.Generic;
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Lists the different data types supported.
+    /// </summary>
+    internal enum EnvironmentDataTypes
+    {
+        Float = 0,
+        Sampler = 1
+    }
+
+    /// <summary>
+    /// The types of distributions from which to sample reset parameters.
+    /// </summary>
+    internal enum SamplerType
+    {
+        /// <summary>
+        /// Samples a reset parameter from a uniform distribution.
+        /// </summary>
+        Uniform = 0,
+
+        /// <summary>
+        /// Samples a reset parameter from a Gaussian distribution.
+        /// </summary>
+        Gaussian = 1,
+
+        /// <summary>
+        /// Samples a reset parameter from a MultiRangeUniform distribution.
+        /// </summary>
+        MultiRangeUniform = 2
+    }
+
+    /// <summary>
+    /// A side channel that manages the environment parameter values from Python. Currently
+    /// limited to parameters of type float.
+    /// </summary>
+    internal class EnvironmentParametersChannel : SideChannel
+    {
+        Dictionary<string, Func<float>> m_Parameters = new Dictionary<string, Func<float>>();
+        Dictionary<string, Action<float>> m_RegisteredActions =
+            new Dictionary<string, Action<float>>();
+
+        const string k_EnvParamsId = "534c891e-810f-11ea-a9d0-822485860400";
+
+        /// <summary>
+        /// Initializes the side channel. The constructor is internal because only one instance is
+        /// supported at a time, and is created by the Academy.
+        /// </summary>
+        internal EnvironmentParametersChannel()
+        {
+            ChannelId = new Guid(k_EnvParamsId);
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            var key = msg.ReadString();
+            var type = msg.ReadInt32();
+            if ((int)EnvironmentDataTypes.Float == type)
+            {
+                var value = msg.ReadFloat32();
+
+                m_Parameters[key] = () => value;
+
+                Action<float> action;
+                m_RegisteredActions.TryGetValue(key, out action);
+                action?.Invoke(value);
+            }
+            else if ((int)EnvironmentDataTypes.Sampler == type)
+            {
+                int seed = msg.ReadInt32();
+                int samplerType = msg.ReadInt32();
+                Func<float> sampler = () => 0.0f;
+                if ((int)SamplerType.Uniform == samplerType)
+                {
+                    float min = msg.ReadFloat32();
+                    float max = msg.ReadFloat32();
+                    sampler = SamplerFactory.CreateUniformSampler(min, max, seed);
+                }
+                else if ((int)SamplerType.Gaussian == samplerType)
+                {
+                    float mean = msg.ReadFloat32();
+                    float stddev = msg.ReadFloat32();
+
+                    sampler = SamplerFactory.CreateGaussianSampler(mean, stddev, seed);
+                }
+                else if ((int)SamplerType.MultiRangeUniform == samplerType)
+                {
+                    IList<float> intervals = msg.ReadFloatList();
+                    sampler = SamplerFactory.CreateMultiRangeUniformSampler(intervals, seed);
+                }
+                else
+                {
+                    Debug.LogWarning("EnvironmentParametersChannel received an unknown data type.");
+                }
+                m_Parameters[key] = sampler;
+            }
+            else
+            {
+                Debug.LogWarning("EnvironmentParametersChannel received an unknown data type.");
+            }
+        }
+
+        /// <summary>
+        /// Returns the parameter value associated with the provided key. Returns the default
+        /// value if one doesn't exist.
+        /// </summary>
+        /// <param name="key">Parameter key.</param>
+        /// <param name="defaultValue">Default value to return.</param>
+        /// <returns></returns>
+        public float GetWithDefault(string key, float defaultValue)
+        {
+            Func<float> valueOut;
+            bool hasKey = m_Parameters.TryGetValue(key, out valueOut);
+            return hasKey ? valueOut.Invoke() : defaultValue;
+        }
+
+        /// <summary>
+        /// Registers a callback for the associated parameter key. Will overwrite any existing
+        /// actions for this parameter key.
+        /// </summary>
+        /// <param name="key">The parameter key.</param>
+        /// <param name="action">The callback.</param>
+        public void RegisterCallback(string key, Action<float> action)
+        {
+            m_RegisteredActions[key] = action;
+        }
+
+        /// <summary>
+        /// Returns all parameter keys that have a registered value.
+        /// </summary>
+        /// <returns></returns>
+        public IList<string> ListParameters()
+        {
+            return new List<string>(m_Parameters.Keys);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f118b1f99fe6d8e9d5dafebae18d653b84af2067
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/EnvironmentParametersChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a849760d5bec946b884984e35c66fcfa
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
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+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f4c293547bef6a7f6595f04dae02ceba426560c5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs
@@ -0,0 +1,97 @@
+using System.Collections.Generic;
+using System;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Side channel that is comprised of a collection of float variables.
+    /// </summary>
+    public class FloatPropertiesChannel : SideChannel
+    {
+        Dictionary<string, float> m_FloatProperties = new Dictionary<string, float>();
+        Dictionary<string, Action<float>> m_RegisteredActions = new Dictionary<string, Action<float>>();
+        const string k_FloatPropertiesDefaultId = "60ccf7d0-4f7e-11ea-b238-784f4387d1f7";
+
+        /// <summary>
+        /// Initializes the side channel with the provided channel ID.
+        /// </summary>
+        /// <param name="channelId">ID for the side channel.</param>
+        public FloatPropertiesChannel(Guid channelId = default(Guid))
+        {
+            if (channelId == default(Guid))
+            {
+                ChannelId = new Guid(k_FloatPropertiesDefaultId);
+            }
+            else
+            {
+                ChannelId = channelId;
+            }
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            var key = msg.ReadString();
+            var value = msg.ReadFloat32();
+
+            m_FloatProperties[key] = value;
+
+            Action<float> action;
+            m_RegisteredActions.TryGetValue(key, out action);
+            action?.Invoke(value);
+        }
+
+        /// <summary>
+        /// Sets one of the float properties of the environment. This data will be sent to Python.
+        /// </summary>
+        /// <param name="key"> The string identifier of the property.</param>
+        /// <param name="value"> The float value of the property.</param>
+        public void Set(string key, float value)
+        {
+            m_FloatProperties[key] = value;
+            using (var msgOut = new OutgoingMessage())
+            {
+                msgOut.WriteString(key);
+                msgOut.WriteFloat32(value);
+                QueueMessageToSend(msgOut);
+            }
+
+            Action<float> action;
+            m_RegisteredActions.TryGetValue(key, out action);
+            action?.Invoke(value);
+        }
+
+        /// <summary>
+        /// Get an Environment property with a default value. If there is a value for this property,
+        /// it will be returned, otherwise, the default value will be returned.
+        /// </summary>
+        /// <param name="key"> The string identifier of the property.</param>
+        /// <param name="defaultValue"> The default value of the property.</param>
+        /// <returns></returns>
+        public float GetWithDefault(string key, float defaultValue)
+        {
+            float valueOut;
+            bool hasKey = m_FloatProperties.TryGetValue(key, out valueOut);
+            return hasKey ? valueOut : defaultValue;
+        }
+
+        /// <summary>
+        /// Registers an action to be performed everytime the property is changed.
+        /// </summary>
+        /// <param name="key"> The string identifier of the property.</param>
+        /// <param name="action"> The action that ill be performed. Takes a float as input.</param>
+        public void RegisterCallback(string key, Action<float> action)
+        {
+            m_RegisteredActions[key] = action;
+        }
+
+        /// <summary>
+        /// Returns a list of all the string identifiers of the properties currently present.
+        /// </summary>
+        /// <returns> The list of string identifiers </returns>
+        public IList<string> Keys()
+        {
+            return new List<string>(m_FloatProperties.Keys);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d4b87eb1e4d9f38407e6894ab380a998f2f26327
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/FloatPropertiesChannel.cs.meta
@@ -0,0 +1,11 @@
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+guid: 452f8b3c01c4642aba645dcf0b6bfc6e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs b/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs
new file mode 100644
index 0000000000000000000000000000000000000000..90425955de79c424224d923000630e202b34f7f7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs
@@ -0,0 +1,127 @@
+using System.Collections.Generic;
+using System.Runtime.CompilerServices;
+using System;
+using System.IO;
+using System.Text;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Utility class for reading the data sent to the SideChannel.
+    /// </summary>
+    public class IncomingMessage : IDisposable
+    {
+        byte[] m_Data;
+        Stream m_Stream;
+        BinaryReader m_Reader;
+
+        /// <summary>
+        /// Construct an IncomingMessage from the byte array.
+        /// </summary>
+        /// <param name="data"></param>
+        public IncomingMessage(byte[] data)
+        {
+            m_Data = data;
+            m_Stream = new MemoryStream(data);
+            m_Reader = new BinaryReader(m_Stream);
+        }
+
+        /// <summary>
+        /// Read a boolean value from the message.
+        /// </summary>
+        /// <param name="defaultValue">Default value to use if the end of the message is reached.</param>
+        /// <returns></returns>
+        public bool ReadBoolean(bool defaultValue = false)
+        {
+            return CanReadMore() ? m_Reader.ReadBoolean() : defaultValue;
+        }
+
+        /// <summary>
+        /// Read an integer value from the message.
+        /// </summary>
+        /// <param name="defaultValue">Default value to use if the end of the message is reached.</param>
+        /// <returns></returns>
+        public int ReadInt32(int defaultValue = 0)
+        {
+            return CanReadMore() ? m_Reader.ReadInt32() : defaultValue;
+        }
+
+        /// <summary>
+        /// Read a float value from the message.
+        /// </summary>
+        /// <param name="defaultValue">Default value to use if the end of the message is reached.</param>
+        /// <returns></returns>
+        public float ReadFloat32(float defaultValue = 0.0f)
+        {
+            return CanReadMore() ? m_Reader.ReadSingle() : defaultValue;
+        }
+
+        /// <summary>
+        /// Read a string value from the message.
+        /// </summary>
+        /// <param name="defaultValue">Default value to use if the end of the message is reached.</param>
+        /// <returns></returns>
+        public string ReadString(string defaultValue = default)
+        {
+            if (!CanReadMore())
+            {
+                return defaultValue;
+            }
+
+            var strLength = ReadInt32();
+            var str = Encoding.ASCII.GetString(m_Reader.ReadBytes(strLength));
+            return str;
+        }
+
+        /// <summary>
+        /// Reads a list of floats from the message. The length of the list is stored in the message.
+        /// </summary>
+        /// <param name="defaultValue">Default value to use if the end of the message is reached.</param>
+        /// <returns></returns>
+        public IList<float> ReadFloatList(IList<float> defaultValue = default)
+        {
+            if (!CanReadMore())
+            {
+                return defaultValue;
+            }
+
+            var len = ReadInt32();
+            var output = new float[len];
+            for (var i = 0; i < len; i++)
+            {
+                output[i] = ReadFloat32();
+            }
+
+            return output;
+        }
+
+        /// <summary>
+        /// Gets the original data of the message. Note that this will return all of the data,
+        /// even if part of it has already been read.
+        /// </summary>
+        /// <returns></returns>
+        public byte[] GetRawBytes()
+        {
+            return m_Data;
+        }
+
+        /// <summary>
+        /// Clean up the internal storage.
+        /// </summary>
+        public void Dispose()
+        {
+            m_Reader?.Dispose();
+            m_Stream?.Dispose();
+        }
+
+        /// <summary>
+        /// Whether or not there is more data left in the stream that can be read.
+        /// </summary>
+        /// <returns></returns>
+        [MethodImpl(MethodImplOptions.AggressiveInlining)]
+        bool CanReadMore()
+        {
+            return m_Stream.Position < m_Stream.Length;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f70c658d1a2931367cd3437bfdd83b6f219eef17
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/IncomingMessage.cs.meta
@@ -0,0 +1,11 @@
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+guid: c8043cec65aeb4ec09db1d25ad694328
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
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diff --git a/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs b/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs
new file mode 100644
index 0000000000000000000000000000000000000000..70a7948e2222c7e6d0ed0c5a9bd6914a848843fc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs
@@ -0,0 +1,110 @@
+using System.Collections.Generic;
+using System;
+using System.IO;
+using System.Text;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Utility class for forming the data that is sent to the SideChannel.
+    /// </summary>
+    public class OutgoingMessage : IDisposable
+    {
+        BinaryWriter m_Writer;
+        MemoryStream m_Stream;
+
+        /// <summary>
+        /// Create a new empty OutgoingMessage.
+        /// </summary>
+        public OutgoingMessage()
+        {
+            m_Stream = new MemoryStream();
+            m_Writer = new BinaryWriter(m_Stream);
+        }
+
+        /// <summary>
+        /// Clean up the internal storage.
+        /// </summary>
+        public void Dispose()
+        {
+            m_Writer?.Dispose();
+            m_Stream?.Dispose();
+        }
+
+        /// <summary>
+        /// Write a boolean value to the message.
+        /// </summary>
+        /// <param name="b"></param>
+        public void WriteBoolean(bool b)
+        {
+            m_Writer.Write(b);
+        }
+
+        /// <summary>
+        /// Write an interger value to the message.
+        /// </summary>
+        /// <param name="i"></param>
+        public void WriteInt32(int i)
+        {
+            m_Writer.Write(i);
+        }
+
+        /// <summary>
+        /// Write a float values to the message.
+        /// </summary>
+        /// <param name="f"></param>
+        public void WriteFloat32(float f)
+        {
+            m_Writer.Write(f);
+        }
+
+        /// <summary>
+        /// Write a string value to the message.
+        /// </summary>
+        /// <param name="s"></param>
+        public void WriteString(string s)
+        {
+            var stringEncoded = Encoding.ASCII.GetBytes(s);
+            m_Writer.Write(stringEncoded.Length);
+            m_Writer.Write(stringEncoded);
+        }
+
+        /// <summary>
+        /// Write a list or array of floats to the message.
+        /// </summary>
+        /// <param name="floatList"></param>
+        public void WriteFloatList(IList<float> floatList)
+        {
+            WriteInt32(floatList.Count);
+            foreach (var f in floatList)
+            {
+                WriteFloat32(f);
+            }
+        }
+
+        /// <summary>
+        /// Overwrite the message with a specific byte array.
+        /// </summary>
+        /// <param name="data"></param>
+        public void SetRawBytes(byte[] data)
+        {
+            // Reset first. Set the length to zero so that if there's more data than we're going to
+            // write, we don't have any of the original data.
+            m_Stream.Seek(0, SeekOrigin.Begin);
+            m_Stream.SetLength(0);
+
+            // Then append the data. Increase the capacity if needed (but don't shrink it).
+            m_Stream.Capacity = (m_Stream.Capacity < data.Length) ? data.Length : m_Stream.Capacity;
+            m_Stream.Write(data, 0, data.Length);
+        }
+
+        /// <summary>
+        /// Read the byte array of the message.
+        /// </summary>
+        /// <returns></returns>
+        internal byte[] ToByteArray()
+        {
+            return m_Stream.ToArray();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..348b80de80bd043fc5c9879c24100421a3330579
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/OutgoingMessage.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 1a007135a9a1e49849eb2d295f4c3879
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..133832447b9d6578cbd45db0c366238cacf8d49a
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs
@@ -0,0 +1,70 @@
+using System.Collections.Generic;
+using System;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Side channel for managing raw bytes of data. It is up to the clients of this side channel
+    /// to interpret the messages.
+    /// </summary>
+    public class RawBytesChannel : SideChannel
+    {
+        List<byte[]> m_MessagesReceived = new List<byte[]>();
+
+        /// <summary>
+        /// RawBytesChannel provides a way to exchange raw byte arrays between Unity and Python.
+        /// </summary>
+        /// <param name="channelId"> The identifier for the RawBytesChannel. Must be
+        /// the same on Python and Unity.</param>
+        public RawBytesChannel(Guid channelId)
+        {
+            ChannelId = channelId;
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            m_MessagesReceived.Add(msg.GetRawBytes());
+        }
+
+        /// <summary>
+        /// Sends the byte array message to the Python side channel. The message will be sent
+        /// alongside the simulation step.
+        /// </summary>
+        /// <param name="data"> The byte array of data to send to Python.</param>
+        public void SendRawBytes(byte[] data)
+        {
+            using (var msg = new OutgoingMessage())
+            {
+                msg.SetRawBytes(data);
+                QueueMessageToSend(msg);
+            }
+        }
+
+        /// <summary>
+        /// Gets the messages that were sent by python since the last call to
+        /// GetAndClearReceivedMessages.
+        /// </summary>
+        /// <returns> a list of byte array messages that Python has sent.</returns>
+        public IList<byte[]> GetAndClearReceivedMessages()
+        {
+            var result = new List<byte[]>();
+            result.AddRange(m_MessagesReceived);
+            m_MessagesReceived.Clear();
+            return result;
+        }
+
+        /// <summary>
+        /// Gets the messages that were sent by python since the last call to
+        /// GetAndClearReceivedMessages. Note that the messages received will not
+        /// be cleared with a call to GetReceivedMessages.
+        /// </summary>
+        /// <returns> a list of byte array messages that Python has sent.</returns>
+        public IList<byte[]> GetReceivedMessages()
+        {
+            var result = new List<byte[]>();
+            result.AddRange(m_MessagesReceived);
+            return result;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..90a49234ba4e36af1ac3cc4c47a7b180c019b6d3
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/RawBytesChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 40b01e9cdbfd94865b54ebeb4e5aeaa5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..250e638b0f857a3569f0f5e871693e2f3dccee27
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs
@@ -0,0 +1,69 @@
+using System.Collections.Generic;
+using System;
+using UnityEngine;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Side channels provide an alternative mechanism of sending/receiving data from Unity
+    /// to Python that is outside of the traditional machine learning loop. ML-Agents provides
+    /// some specific implementations of side channels, but users can create their own.
+    ///
+    /// To create your own, you'll need to create two, new mirrored classes, one in Unity (by
+    /// extending <see cref="SideChannel"/>) and another in Python by extending a Python class
+    /// also called SideChannel. Then, within your project, use
+    /// <see cref="SideChannelManager.RegisterSideChannel"/> and
+    /// <see cref="SideChannelManager.UnregisterSideChannel"/> to register and unregister your
+    /// custom side channel.
+    /// </summary>
+    public abstract class SideChannel
+    {
+        // The list of messages (byte arrays) that need to be sent to Python via the communicator.
+        // Should only ever be read and cleared by a ICommunicator object.
+        internal List<byte[]> MessageQueue = new List<byte[]>();
+
+        /// <summary>
+        /// An int identifier for the SideChannel. Ensures that there is only ever one side channel
+        /// of each type. Ensure the Unity side channels will be linked to their Python equivalent.
+        /// </summary>
+        /// <returns> The integer identifier of the SideChannel.</returns>
+        public Guid ChannelId
+        {
+            get;
+            protected set;
+        }
+
+        internal void ProcessMessage(byte[] msg)
+        {
+            try
+            {
+                using (var incomingMsg = new IncomingMessage(msg))
+                {
+                    OnMessageReceived(incomingMsg);
+                }
+            }
+            catch (Exception ex)
+            {
+                // Catch all errors in the sidechannel processing, so that a single
+                // bad SideChannel implementation doesn't take everything down with it.
+                Debug.LogError($"Error processing SideChannel message: {ex}.\nThe message will be skipped.");
+            }
+        }
+
+        /// <summary>
+        /// Is called by the communicator every time a message is received from Python by the SideChannel.
+        /// Can be called multiple times per simulation step if multiple messages were sent.
+        /// </summary>
+        /// <param name="msg">The incoming message.</param>
+        protected abstract void OnMessageReceived(IncomingMessage msg);
+
+        /// <summary>
+        /// Queues a message to be sent to Python during the next simulation step.
+        /// </summary>
+        /// <param name="msg"> The byte array of data to be sent to Python.</param>
+        protected void QueueMessageToSend(OutgoingMessage msg)
+        {
+            MessageQueue.Add(msg.ToByteArray());
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c668b0187ffb2ec337c78aa2a3f3778f61b33e39
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/SideChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 77b7d19dd6ce343eeba907540b5a2286
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs b/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs
new file mode 100644
index 0000000000000000000000000000000000000000..bdcc596b85231314971612f0ecc75a870dabbf8f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs
@@ -0,0 +1,244 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using System.IO;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Collection of static utilities for managing the registering/unregistering of
+    /// <see cref="SideChannels"/> and the sending/receiving of messages for all the channels.
+    /// </summary>
+    public static class SideChannelManager
+    {
+        static Dictionary<Guid, SideChannel> s_RegisteredChannels = new Dictionary<Guid, SideChannel>();
+
+        struct CachedSideChannelMessage
+        {
+            public Guid ChannelId;
+            public byte[] Message;
+        }
+
+        static readonly Queue<CachedSideChannelMessage> s_CachedMessages =
+            new Queue<CachedSideChannelMessage>();
+
+        /// <summary>
+        /// Register a side channel to begin sending and receiving messages. This method is
+        /// available for environments that have custom side channels. All built-in side
+        /// channels within the ML-Agents Toolkit are managed internally and do not need to
+        /// be explicitly registered/unregistered. A side channel may only be registered once.
+        /// </summary>
+        /// <param name="sideChannel">The side channel to register.</param>
+        public static void RegisterSideChannel(SideChannel sideChannel)
+        {
+            var channelId = sideChannel.ChannelId;
+            if (s_RegisteredChannels.ContainsKey(channelId))
+            {
+                throw new UnityAgentsException(
+                    $"A side channel with id {channelId} is already registered. " +
+                    "You cannot register multiple side channels of the same id.");
+            }
+
+            // Process any messages that we've already received for this channel ID.
+            var numMessages = s_CachedMessages.Count;
+            for (var i = 0; i < numMessages; i++)
+            {
+                var cachedMessage = s_CachedMessages.Dequeue();
+                if (channelId == cachedMessage.ChannelId)
+                {
+                    sideChannel.ProcessMessage(cachedMessage.Message);
+                }
+                else
+                {
+                    s_CachedMessages.Enqueue(cachedMessage);
+                }
+            }
+            s_RegisteredChannels.Add(channelId, sideChannel);
+        }
+
+        /// <summary>
+        /// Unregister a side channel to stop sending and receiving messages. This method is
+        /// available for environments that have custom side channels. All built-in side
+        /// channels within the ML-Agents Toolkit are managed internally and do not need to
+        /// be explicitly registered/unregistered. Unregistering a side channel that has already
+        /// been unregistered (or never registered in the first place) has no negative side effects.
+        /// Note that unregistering a side channel may not stop the Python side
+        /// from sending messages, but it does mean that sent messages with not result in a call
+        /// to <see cref="SideChannel.OnMessageReceived(IncomingMessage)"/>. Furthermore,
+        /// those messages will not be buffered and will, in essence, be lost.
+        /// </summary>
+        /// <param name="sideChannel">The side channel to unregister.</param>
+        public static void UnregisterSideChannel(SideChannel sideChannel)
+        {
+            if (s_RegisteredChannels.ContainsKey(sideChannel.ChannelId))
+            {
+                s_RegisteredChannels.Remove(sideChannel.ChannelId);
+            }
+        }
+
+        /// <summary>
+        /// Unregisters all the side channels from the communicator.
+        /// </summary>
+        internal static void UnregisterAllSideChannels()
+        {
+            s_RegisteredChannels = new Dictionary<Guid, SideChannel>();
+        }
+
+        /// <summary>
+        /// Returns the SideChannel of Type T if there is one registered, or null if it doesn't.
+        /// If there are multiple SideChannels of the same type registered, the returned instance is arbitrary.
+        /// </summary>
+        /// <typeparam name="T"></typeparam>
+        /// <returns></returns>
+        internal static T GetSideChannel<T>() where T : SideChannel
+        {
+            foreach (var sc in s_RegisteredChannels.Values)
+            {
+                if (sc.GetType() == typeof(T))
+                {
+                    return (T)sc;
+                }
+            }
+            return null;
+        }
+
+        /// <summary>
+        /// Grabs the messages that the registered side channels will send to Python at the current step
+        /// into a singe byte array.
+        /// </summary>
+        /// <returns></returns>
+        internal static byte[] GetSideChannelMessage()
+        {
+            return GetSideChannelMessage(s_RegisteredChannels);
+        }
+
+        /// <summary>
+        /// Grabs the messages that the registered side channels will send to Python at the current step
+        /// into a singe byte array.
+        /// </summary>
+        /// <param name="sideChannels"> A dictionary of channel type to channel.</param>
+        /// <returns></returns>
+        internal static byte[] GetSideChannelMessage(Dictionary<Guid, SideChannel> sideChannels)
+        {
+            if (!HasOutgoingMessages(sideChannels))
+            {
+                // Early out so that we don't create the MemoryStream or BinaryWriter.
+                // This is the most common case.
+                return Array.Empty<byte>();
+            }
+
+            using (var memStream = new MemoryStream())
+            {
+                using (var binaryWriter = new BinaryWriter(memStream))
+                {
+                    foreach (var sideChannel in sideChannels.Values)
+                    {
+                        var messageList = sideChannel.MessageQueue;
+                        foreach (var message in messageList)
+                        {
+                            binaryWriter.Write(sideChannel.ChannelId.ToByteArray());
+                            binaryWriter.Write(message.Length);
+                            binaryWriter.Write(message);
+                        }
+                        sideChannel.MessageQueue.Clear();
+                    }
+                    return memStream.ToArray();
+                }
+            }
+        }
+
+        /// <summary>
+        /// Check whether any of the sidechannels have queued messages.
+        /// </summary>
+        /// <param name="sideChannels"></param>
+        /// <returns></returns>
+        static bool HasOutgoingMessages(Dictionary<Guid, SideChannel> sideChannels)
+        {
+            foreach (var sideChannel in sideChannels.Values)
+            {
+                var messageList = sideChannel.MessageQueue;
+                if (messageList.Count > 0)
+                {
+                    return true;
+                }
+            }
+
+            return false;
+        }
+
+        /// <summary>
+        /// Separates the data received from Python into individual messages for each registered side channel.
+        /// </summary>
+        /// <param name="dataReceived">The byte array of data received from Python.</param>
+        internal static void ProcessSideChannelData(byte[] dataReceived)
+        {
+            ProcessSideChannelData(s_RegisteredChannels, dataReceived);
+        }
+
+        /// <summary>
+        /// Separates the data received from Python into individual messages for each registered side channel.
+        /// </summary>
+        /// <param name="sideChannels">A dictionary of channel type to channel.</param>
+        /// <param name="dataReceived">The byte array of data received from Python.</param>
+        internal static void ProcessSideChannelData(Dictionary<Guid, SideChannel> sideChannels, byte[] dataReceived)
+        {
+            while (s_CachedMessages.Count != 0)
+            {
+                var cachedMessage = s_CachedMessages.Dequeue();
+                if (sideChannels.ContainsKey(cachedMessage.ChannelId))
+                {
+                    sideChannels[cachedMessage.ChannelId].ProcessMessage(cachedMessage.Message);
+                }
+                else
+                {
+                    Debug.Log(string.Format(
+                        "Unknown side channel data received. Channel Id is "
+                        + ": {0}", cachedMessage.ChannelId));
+                }
+            }
+
+            if (dataReceived.Length == 0)
+            {
+                return;
+            }
+            using (var memStream = new MemoryStream(dataReceived))
+            {
+                using (var binaryReader = new BinaryReader(memStream))
+                {
+                    while (memStream.Position < memStream.Length)
+                    {
+                        Guid channelId = Guid.Empty;
+                        byte[] message = null;
+                        try
+                        {
+                            channelId = new Guid(binaryReader.ReadBytes(16));
+                            var messageLength = binaryReader.ReadInt32();
+                            message = binaryReader.ReadBytes(messageLength);
+                        }
+                        catch (Exception ex)
+                        {
+                            throw new UnityAgentsException(
+                                "There was a problem reading a message in a SideChannel. Please make sure the " +
+                                "version of MLAgents in Unity is compatible with the Python version. Original error : "
+                                + ex.Message);
+                        }
+                        if (sideChannels.ContainsKey(channelId))
+                        {
+                            sideChannels[channelId].ProcessMessage(message);
+                        }
+                        else
+                        {
+                            // Don't recognize this ID, but cache it in case the SideChannel that can handle
+                            // it is registered before the next call to ProcessSideChannelData.
+                            s_CachedMessages.Enqueue(new CachedSideChannelMessage
+                            {
+                                ChannelId = channelId,
+                                Message = message
+                            });
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..251cc146329425acddc70f731ca9cb7b1bcbd5cc
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/SideChannelManager.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ccc0d134445f947349c68a6d07e3cdc2
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..dbe1b5aeec3c8d4e2865eea60dcfa6fcf36fccad
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs
@@ -0,0 +1,43 @@
+using System;
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// A Side Channel for sending <see cref="StatsRecorder"/> data.
+    /// </summary>
+    internal class StatsSideChannel : SideChannel
+    {
+        const string k_StatsSideChannelDefaultId = "a1d8f7b7-cec8-50f9-b78b-d3e165a78520";
+
+        /// <summary>
+        /// Initializes the side channel. The constructor is internal because only one instance is
+        /// supported at a time.
+        /// </summary>
+        internal StatsSideChannel()
+        {
+            ChannelId = new Guid(k_StatsSideChannelDefaultId);
+        }
+
+        /// <summary>
+        /// Add a stat value for reporting.
+        /// </summary>
+        /// <param name="key">The stat name.</param>
+        /// <param name="value">The stat value.</param>
+        /// <param name="aggregationMethod">How multiple values should be treated.</param>
+        public void AddStat(string key, float value, StatAggregationMethod aggregationMethod)
+        {
+            using (var msg = new OutgoingMessage())
+            {
+                msg.WriteString(key);
+                msg.WriteFloat32(value);
+                msg.WriteInt32((int)aggregationMethod);
+                QueueMessageToSend(msg);
+            }
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            throw new UnityAgentsException("StatsSideChannel should never receive messages.");
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ebc11e7092a6d4d376f227bcfbc171a79aff0de7
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/StatsSideChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 83a07fdb9e8f04536908a51447dfe548
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs b/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0c880e4be991989000694dd69aa501452ed73f84
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs
@@ -0,0 +1,52 @@
+using System;
+using Unity.MLAgents.Analytics;
+using Unity.MLAgents.CommunicatorObjects;
+
+namespace Unity.MLAgents.SideChannels
+{
+    /// <summary>
+    /// Side Channel implementation for recording which training features are being used.
+    /// </summary>
+    internal class TrainingAnalyticsSideChannel : SideChannel
+    {
+        const string k_TrainingAnalyticsConfigId = "b664a4a9-d86f-5a5f-95cb-e8353a7e8356";
+
+        /// <summary>
+        /// Initializes the side channel. The constructor is internal because only one instance is
+        /// supported at a time, and is created by the Academy.
+        /// </summary>
+        internal TrainingAnalyticsSideChannel()
+        {
+            ChannelId = new Guid(k_TrainingAnalyticsConfigId);
+        }
+
+        /// <inheritdoc/>
+        protected override void OnMessageReceived(IncomingMessage msg)
+        {
+            Google.Protobuf.WellKnownTypes.Any anyMessage = null;
+            try
+            {
+                anyMessage = Google.Protobuf.WellKnownTypes.Any.Parser.ParseFrom(msg.GetRawBytes());
+            }
+            catch (Google.Protobuf.InvalidProtocolBufferException)
+            {
+                // Bad message, nothing we can do about it, so just ignore.
+                return;
+            }
+
+            if (anyMessage.Is(TrainingEnvironmentInitialized.Descriptor))
+            {
+                var envInitProto = anyMessage.Unpack<TrainingEnvironmentInitialized>();
+                var envInitEvent = envInitProto.ToTrainingEnvironmentInitializedEvent();
+                TrainingAnalytics.TrainingEnvironmentInitialized(envInitEvent);
+            }
+            else if (anyMessage.Is(TrainingBehaviorInitialized.Descriptor))
+            {
+                var behaviorInitProto = anyMessage.Unpack<TrainingBehaviorInitialized>();
+                var behaviorTrainingEvent = behaviorInitProto.ToTrainingBehaviorInitializedEvent();
+                TrainingAnalytics.TrainingBehaviorInitialized(behaviorTrainingEvent);
+            }
+            // Don't do anything for unknown types, since the user probably can't do anything about it.
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs.meta b/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..757d0d0d4fbb697a2a83214b1d71d6a30e83e514
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SideChannels/TrainingAnalyticsSideChannel.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 13c87198bbd54b40a0b93308eb37933e
+timeCreated: 1608337471
\ No newline at end of file
diff --git a/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs b/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c5fe6ce83587b268e8b4cf557e984900f60d2959
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs
@@ -0,0 +1,145 @@
+using System;
+using System.Linq;
+using System.Collections.Generic;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// A basic class implementation of MultiAgentGroup.
+    /// </summary>
+    public class SimpleMultiAgentGroup : IMultiAgentGroup, IDisposable
+    {
+        readonly int m_Id = MultiAgentGroupIdCounter.GetGroupId();
+        HashSet<Agent> m_Agents = new HashSet<Agent>();
+
+        /// <summary>
+        /// Disposes of the SimpleMultiAgentGroup.
+        /// </summary>
+        public virtual void Dispose()
+        {
+            while (m_Agents.Count > 0)
+            {
+                UnregisterAgent(m_Agents.First());
+            }
+        }
+
+        /// <inheritdoc />
+        public virtual void RegisterAgent(Agent agent)
+        {
+            if (!m_Agents.Contains(agent))
+            {
+                agent.SetMultiAgentGroup(this);
+                m_Agents.Add(agent);
+                agent.OnAgentDisabled += UnregisterAgent;
+            }
+        }
+
+        /// <inheritdoc />
+        public virtual void UnregisterAgent(Agent agent)
+        {
+            if (m_Agents.Contains(agent))
+            {
+                agent.SetMultiAgentGroup(null);
+                m_Agents.Remove(agent);
+                agent.OnAgentDisabled -= UnregisterAgent;
+            }
+        }
+
+        /// <inheritdoc />
+        public int GetId()
+        {
+            return m_Id;
+        }
+
+        /// <summary>
+        /// Get list of all agents currently registered to this MultiAgentGroup.
+        /// </summary>
+        /// <returns>
+        /// List of agents registered to the MultiAgentGroup.
+        /// </returns>
+        public IReadOnlyCollection<Agent> GetRegisteredAgents()
+        {
+            return m_Agents;
+        }
+
+        /// <summary>
+        /// Increments the group rewards for all agents in this MultiAgentGroup.
+        /// </summary>
+        /// <remarks>
+        /// This function increases or decreases the group rewards by a given amount for all agents
+        /// in the group. Use <see cref="SetGroupReward(float)"/> to set the group reward assigned
+        /// to the current step with a specific value rather than increasing or decreasing it.
+        ///
+        /// A positive group reward indicates the whole group's accomplishments or desired behaviors.
+        /// Every agent in the group will receive the same group reward no matter whether the
+        /// agent's act directly leads to the reward. Group rewards are meant to reinforce agents
+        /// to act in the group's best interest instead of individual ones.
+        /// Group rewards are treated differently than individual agent rewards during training, so
+        /// calling AddGroupReward() is not equivalent to calling agent.AddReward() on each agent in the group.
+        /// </remarks>
+        /// <param name="reward">Incremental group reward value.</param>
+        public void AddGroupReward(float reward)
+        {
+            foreach (var agent in m_Agents)
+            {
+                agent.AddGroupReward(reward);
+            }
+        }
+
+        /// <summary>
+        /// Set the group rewards for all agents in this MultiAgentGroup.
+        /// </summary>
+        /// <remarks>
+        /// This function replaces any group rewards given during the current step for all agents in the group.
+        /// Use <see cref="AddGroupReward(float)"/> to incrementally change the group reward rather than
+        /// overriding it.
+        ///
+        /// A positive group reward indicates the whole group's accomplishments or desired behaviors.
+        /// Every agent in the group will receive the same group reward no matter whether the
+        /// agent's act directly leads to the reward. Group rewards are meant to reinforce agents
+        /// to act in the group's best interest instead of indivisual ones.
+        /// Group rewards are treated differently than individual agent rewards during training, so
+        /// calling SetGroupReward() is not equivalent to calling agent.SetReward() on each agent in the group.
+        /// </remarks>
+        /// <param name="reward">The new value of the group reward.</param>
+        public void SetGroupReward(float reward)
+        {
+            foreach (var agent in m_Agents)
+            {
+                agent.SetGroupReward(reward);
+            }
+        }
+
+        /// <summary>
+        /// End episodes for all agents in this MultiAgentGroup.
+        /// </summary>
+        /// <remarks>
+        /// This should be used when the episode can no longer continue, such as when the group
+        /// reaches the goal or fails at the task.
+        /// </remarks>
+        public void EndGroupEpisode()
+        {
+            foreach (var agent in m_Agents)
+            {
+                agent.EndEpisode();
+            }
+        }
+
+        /// <summary>
+        /// Indicate that the episode is over but not due to the "fault" of the group.
+        /// This has the same end result as calling <see cref="EndGroupEpisode"/>, but has a
+        /// slightly different effect on training.
+        /// </summary>
+        /// <remarks>
+        /// This should be used when the episode could continue, but has gone on for
+        /// a sufficient number of steps, such as if the environment hits some maximum number of steps.
+        /// </remarks>
+        public void GroupEpisodeInterrupted()
+        {
+            foreach (var agent in m_Agents)
+            {
+                agent.EpisodeInterrupted();
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs.meta b/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..33b0a0559e2a6b80935e1cb13facdb25dcf195b9
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/SimpleMultiAgentGroup.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3454e3c3c70964dca93b63ee4b650095
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/StatsRecorder.cs b/com.unity.ml-agents/Runtime/StatsRecorder.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d7250862b975eeb161f57b1d55ade1990fc526ae
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/StatsRecorder.cs
@@ -0,0 +1,80 @@
+using Unity.MLAgents.SideChannels;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Determines the behavior of how multiple stats within the same summary period are combined.
+    /// </summary>
+    public enum StatAggregationMethod
+    {
+        /// <summary>
+        /// Values within the summary period are averaged before reporting.
+        /// </summary>
+        Average = 0,
+
+        /// <summary>
+        /// Only the most recent value is reported.
+        /// To avoid conflicts when training with multiple concurrent environments, only
+        /// stats from worker index 0 will be tracked.
+        /// </summary>
+        MostRecent = 1,
+
+        /// <summary>
+        /// Values within the summary period are summed up before reporting.
+        /// </summary>
+        Sum = 2,
+
+        /// <summary>
+        /// Values within the summary period are reported as a histogram.
+        /// </summary>
+        Histogram = 3
+    }
+
+    /// <summary>
+    /// Add stats (key-value pairs) for reporting. These values will sent these to a StatsReporter
+    /// instance, which means the values will appear in the TensorBoard summary, as well as trainer
+    /// gauges. You can nest stats in TensorBoard by adding "/" in the name (e.g. "Agent/Health"
+    /// and "Agent/Wallet"). Note that stats are only written to TensorBoard each summary_frequency
+    /// steps (a trainer configuration). If a stat is received multiple times, within that period
+    /// then the values will be aggregated using the <see cref="StatAggregationMethod"/> provided.
+    /// </summary>
+    public sealed class StatsRecorder
+    {
+        /// <summary>
+        /// The side channel that is used to receive the new parameter values.
+        /// </summary>
+        readonly StatsSideChannel m_Channel;
+
+        /// <summary>
+        /// Constructor.
+        /// </summary>
+        internal StatsRecorder()
+        {
+            m_Channel = new StatsSideChannel();
+            SideChannelManager.RegisterSideChannel(m_Channel);
+        }
+
+        /// <summary>
+        /// Add a stat value for reporting.
+        /// </summary>
+        /// <param name="key">The stat name.</param>
+        /// <param name="value">
+        /// The stat value. You can nest stats in TensorBoard by using "/".
+        /// </param>
+        /// <param name="aggregationMethod">
+        /// How multiple values sent in the same summary window should be treated.
+        /// </param>
+        public void Add(
+            string key,
+            float value,
+            StatAggregationMethod aggregationMethod = StatAggregationMethod.Average)
+        {
+            m_Channel.AddStat(key, value, aggregationMethod);
+        }
+
+        internal void Dispose()
+        {
+            SideChannelManager.UnregisterSideChannel(m_Channel);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/StatsRecorder.cs.meta b/com.unity.ml-agents/Runtime/StatsRecorder.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bfc4addbb19fa2c4afcc73fc56cf673cb89af044
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/StatsRecorder.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d9add8900e8a746e6a4cb410cb27d664
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Timer.cs b/com.unity.ml-agents/Runtime/Timer.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fe0ba47211265969748c8cb81b75c632dbeebd97
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Timer.cs
@@ -0,0 +1,534 @@
+// Compile with: csc CRefTest.cs -doc:Results.xml
+#if UNITY_EDITOR || UNITY_STANDALONE
+#define MLA_SUPPORTED_TRAINING_PLATFORM
+#endif
+using System;
+using UnityEngine;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using UnityEngine.Profiling;
+using System.Runtime.Serialization;
+using System.Runtime.Serialization.Json;
+using UnityEngine.SceneManagement;
+
+namespace Unity.MLAgents
+{
+    [DataContract]
+    internal class TimerNode
+    {
+        static string s_Separator = ".";
+        static double s_TicksToSeconds = 1e-7; // 100 ns per tick
+
+        /// <summary>
+        /// Full name of the node. This is the node's parents full name concatenated with this
+        /// node's name.
+        /// </summary>
+        string m_FullName;
+
+        /// <summary>
+        /// Child nodes, indexed by name.
+        /// </summary>
+        [DataMember(Name = "children", Order = 999)]
+        Dictionary<string, TimerNode> m_Children;
+
+        /// <summary>
+        /// Custom sampler used to add timings to the profiler.
+        /// </summary>
+        CustomSampler m_Sampler;
+
+        /// <summary>
+        /// Number of total ticks elapsed for this node.
+        /// </summary>
+        long m_TotalTicks;
+
+        /// <summary>
+        /// If the node is currently running, the time (in ticks) when the node was started.
+        /// If the node is not running, is set to 0.
+        /// </summary>
+        long m_TickStart;
+
+        /// <summary>
+        /// Number of times the corresponding code block has been called.
+        /// </summary>
+        [DataMember(Name = "count")]
+        int m_NumCalls;
+
+        /// <summary>
+        /// The total recorded ticks for the timer node, plus the currently elapsed ticks
+        /// if the timer is still running (i.e. if m_TickStart is non-zero).
+        /// </summary>
+        public long CurrentTicks
+        {
+            get
+            {
+                var currentTicks = m_TotalTicks;
+                if (m_TickStart != 0)
+                {
+                    currentTicks += (DateTime.Now.Ticks - m_TickStart);
+                }
+
+                return currentTicks;
+            }
+        }
+
+        /// <summary>
+        /// Total elapsed seconds.
+        /// </summary>
+        [DataMember(Name = "total")]
+        public double TotalSeconds
+        {
+            get { return CurrentTicks * s_TicksToSeconds; }
+            set { }   // Serialization needs this, but unused.
+        }
+
+        /// <summary>
+        /// Total seconds spent in this block, excluding it's children.
+        /// </summary>
+        [DataMember(Name = "self")]
+        public double SelfSeconds
+        {
+            get
+            {
+                long totalChildTicks = 0;
+                if (m_Children != null)
+                {
+                    foreach (var child in m_Children.Values)
+                    {
+                        totalChildTicks += child.m_TotalTicks;
+                    }
+                }
+
+                var selfTicks = Mathf.Max(0, CurrentTicks - totalChildTicks);
+                return selfTicks * s_TicksToSeconds;
+            }
+            set { }   // Serialization needs this, but unused.
+        }
+
+        public IReadOnlyDictionary<string, TimerNode> Children
+        {
+            get { return m_Children; }
+        }
+
+        public int NumCalls
+        {
+            get { return m_NumCalls; }
+        }
+
+        public TimerNode(string name, bool isRoot = false)
+        {
+            m_FullName = name;
+            if (isRoot)
+            {
+                // The root node is considered always running. This means that when we output stats, it'll
+                // have a sensible value for total time (the running time since reset).
+                // The root node doesn't have a sampler since that could interfere with the profiler.
+                m_NumCalls = 1;
+                m_TickStart = DateTime.Now.Ticks;
+            }
+            else
+            {
+                m_Sampler = CustomSampler.Create(m_FullName);
+            }
+        }
+
+        /// <summary>
+        /// Start timing a block of code.
+        /// </summary>
+        public void Begin()
+        {
+            m_Sampler?.Begin();
+            m_TickStart = DateTime.Now.Ticks;
+        }
+
+        /// <summary>
+        /// Stop timing a block of code, and increment internal counts.
+        /// </summary>
+        public void End()
+        {
+            var elapsed = DateTime.Now.Ticks - m_TickStart;
+            m_TotalTicks += elapsed;
+            m_TickStart = 0;
+            m_NumCalls++;
+            m_Sampler?.End();
+        }
+
+        /// <summary>
+        /// Return a child node for the given name.
+        /// The children dictionary will be created if it does not already exist, and
+        /// a new Node will be created if it's not already in the dictionary.
+        /// Note that these allocations only happen once for a given timed block.
+        /// </summary>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public TimerNode GetChild(string name)
+        {
+            // Lazily create the children dictionary.
+            if (m_Children == null)
+            {
+                m_Children = new Dictionary<string, TimerNode>();
+            }
+
+            if (!m_Children.ContainsKey(name))
+            {
+                var childFullName = m_FullName + s_Separator + name;
+                var newChild = new TimerNode(childFullName);
+                m_Children[name] = newChild;
+                return newChild;
+            }
+
+            return m_Children[name];
+        }
+
+        /// <summary>
+        /// Recursively form a string representing the current timer information.
+        /// </summary>
+        /// <param name="parentName"></param>
+        /// <param name="level"></param>
+        /// <returns></returns>
+        public string DebugGetTimerString(string parentName = "", int level = 0)
+        {
+            var indent = new string(' ', 2 * level); // TODO generalize
+            var shortName = (level == 0) ? m_FullName : m_FullName.Replace(parentName + s_Separator, "");
+            string timerString;
+            if (level == 0)
+            {
+                timerString = $"{shortName}(root)\n";
+            }
+            else
+            {
+                timerString = $"{indent}{shortName}\t\traw={TotalSeconds}  rawCount={m_NumCalls}\n";
+            }
+
+            // TODO use StringBuilder? might be overkill since this is only debugging code?
+            if (m_Children != null)
+            {
+                foreach (var c in m_Children.Values)
+                {
+                    timerString += c.DebugGetTimerString(m_FullName, level + 1);
+                }
+            }
+            return timerString;
+        }
+    }
+
+    [DataContract]
+    internal class RootNode : TimerNode
+    {
+        // Timer output format version
+        internal const string k_TimerFormatVersion = "0.1.0";
+
+        [DataMember(Name = "metadata", Order = 0)]
+        Dictionary<string, string> m_Metadata = new Dictionary<string, string>();
+
+        /// <summary>
+        /// Gauge Nodes to measure arbitrary values.
+        /// </summary>
+        [DataMember(Name = "gauges", EmitDefaultValue = false)]
+        Dictionary<string, GaugeNode> m_Gauges = new Dictionary<string, GaugeNode>();
+
+        public RootNode(string name = "root") : base(name, true)
+        {
+            m_Metadata.Add("timer_format_version", k_TimerFormatVersion);
+            m_Metadata.Add("start_time_seconds", $"{DateTimeOffset.Now.ToUnixTimeSeconds()}");
+            m_Metadata.Add("unity_version", Application.unityVersion);
+            m_Metadata.Add("command_line_arguments", String.Join(" ", GetCleanedCommandLineArguments()));
+        }
+
+        /// <summary>
+        /// Cleans Environment CommandLine Argument from license infos
+        /// </summary>
+        /// <returns>cleaned string list of commandLine </returns>
+        private static List<string> GetCleanedCommandLineArguments()
+        {
+            List<string> commandLineArgs = Environment.GetCommandLineArgs().ToList();
+            List<int> toRemoveIndices = new List<int> { };
+            for (var i = 0; i < commandLineArgs.Count; i++)
+            {
+                if (commandLineArgs[i].Contains("accessToken") ||
+                    commandLineArgs[i].Contains("hubSessionId") ||
+                    commandLineArgs[i].Contains("licensingIpc"))
+                {
+                    toRemoveIndices.Add(i);
+                    toRemoveIndices.Add(i + 1);
+                }
+            }
+            // remove in reverse order
+            for (var i = toRemoveIndices.Count() - 1; i >= 0; i--)
+            {
+                commandLineArgs.RemoveAt(toRemoveIndices[i]);
+            }
+            return commandLineArgs;
+        }
+
+        public void AddMetadata(string key, string value)
+        {
+            m_Metadata[key] = value;
+        }
+
+        public Dictionary<string, GaugeNode> Gauges
+        {
+            get { return m_Gauges; }
+        }
+
+        public Dictionary<string, string> Metadata
+        {
+            get { return m_Metadata; }
+        }
+    }
+
+    /// <summary>
+    /// Tracks the most recent value of a metric. This is analogous to gauges in statsd and Prometheus.
+    /// </summary>
+    [DataContract]
+    internal class GaugeNode
+    {
+        const float k_SmoothingFactor = .25f; // weight for exponential moving average.
+
+        /// <summary>
+        /// The most recent value that the gauge was set to.
+        /// </summary>
+        [DataMember]
+        public float value;
+
+        /// <summary>
+        /// The smallest value that has been seen for the gauge since it was created.
+        /// </summary>
+        [DataMember(Name = "min")]
+        public float minValue;
+
+        /// <summary>
+        /// The largest value that has been seen for the gauge since it was created.
+        /// </summary>
+        [DataMember(Name = "max")]
+        public float maxValue;
+
+        /// <summary>
+        /// The exponential moving average of the gauge value. This will take all values into account,
+        /// but weights older values less as more values are added.
+        /// </summary>
+        [DataMember(Name = "weightedAverage")]
+        public float weightedAverage;
+
+        /// <summary>
+        /// The running average of all gauge values.
+        /// </summary>
+        [DataMember]
+        public float runningAverage;
+
+        /// <summary>
+        /// The number of times the gauge has been updated.
+        /// </summary>
+        [DataMember]
+        public uint count;
+
+        public GaugeNode(float value)
+        {
+            this.value = value;
+            weightedAverage = value;
+            runningAverage = value;
+            minValue = value;
+            maxValue = value;
+            count = 1;
+        }
+
+        public void Update(float newValue)
+        {
+            ++count;
+            minValue = Mathf.Min(minValue, newValue);
+            maxValue = Mathf.Max(maxValue, newValue);
+            // update exponential moving average
+            weightedAverage = (k_SmoothingFactor * newValue) + ((1f - k_SmoothingFactor) * weightedAverage);
+            value = newValue;
+
+            // Update running average - see https://www.johndcook.com/blog/standard_deviation/ for formula.
+            runningAverage = runningAverage + (newValue - runningAverage) / count;
+        }
+    }
+
+    /// <summary>
+    /// A "stack" of timers that allows for lightweight hierarchical profiling of long-running processes.
+    /// <example>
+    /// Example usage:
+    /// <code>
+    /// using(TimerStack.Instance.Scoped("foo"))
+    /// {
+    ///     doSomeWork();
+    ///     for (int i=0; i&lt;5; i++)
+    ///     {
+    ///         using(myTimer.Scoped("bar"))
+    ///         {
+    ///             doSomeMoreWork();
+    ///         }
+    ///     }
+    /// }
+    /// </code>
+    /// </example>
+    /// </summary>
+    /// <remarks>
+    /// This implements the Singleton pattern (solution 4) as described in
+    /// https://csharpindepth.com/articles/singleton
+    /// </remarks>
+    internal class TimerStack : IDisposable
+    {
+        static readonly TimerStack k_Instance = new TimerStack();
+
+        Stack<TimerNode> m_Stack;
+        RootNode m_RootNode;
+        Dictionary<string, string> m_Metadata;
+
+        // Explicit static constructor to tell C# compiler
+        // not to mark type as beforefieldinit
+        static TimerStack()
+        {
+        }
+
+        TimerStack()
+        {
+            Reset();
+        }
+
+        /// <summary>
+        /// Resets the timer stack and the root node.
+        /// </summary>
+        /// <param name="name">Name of the root node.</param>
+        public void Reset(string name = "root")
+        {
+            m_Stack = new Stack<TimerNode>();
+            m_RootNode = new RootNode(name);
+            m_Stack.Push(m_RootNode);
+        }
+
+        /// <summary>
+        /// The singleton <see cref="TimerStack"/> instance.
+        /// </summary>
+        public static TimerStack Instance
+        {
+            get { return k_Instance; }
+        }
+
+        internal RootNode RootNode
+        {
+            get { return m_RootNode; }
+        }
+
+        /// <summary>
+        /// Updates the referenced gauge in the root node with the provided value.
+        /// </summary>
+        /// <param name="name">The name of the Gauge to modify.</param>
+        /// <param name="value">The value to update the Gauge with.</param>
+        public void SetGauge(string name, float value)
+        {
+            if (!float.IsNaN(value))
+            {
+                GaugeNode gauge;
+                if (m_RootNode.Gauges.TryGetValue(name, out gauge))
+                {
+                    gauge.Update(value);
+                }
+                else
+                {
+                    m_RootNode.Gauges[name] = new GaugeNode(value);
+                }
+            }
+        }
+
+        public void AddMetadata(string key, string value)
+        {
+            m_RootNode.AddMetadata(key, value);
+        }
+
+        void Push(string name)
+        {
+            var current = m_Stack.Peek();
+            var next = current.GetChild(name);
+            m_Stack.Push(next);
+            next.Begin();
+        }
+
+        void Pop()
+        {
+            var node = m_Stack.Pop();
+            node.End();
+        }
+
+        /// <summary>
+        /// Start a scoped timer. This should be used with the "using" statement.
+        /// </summary>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public TimerStack Scoped(string name)
+        {
+            Push(name);
+            return this;
+        }
+
+        /// <summary>
+        /// Closes the current scoped timer. This should never be called directly, only
+        /// at the end of a "using" statement.
+        /// Note that the instance is not actually disposed of; this is just to allow it to be used
+        /// conveniently with "using".
+        /// </summary>
+        public void Dispose()
+        {
+            Pop();
+        }
+
+        /// <summary>
+        /// Get a string representation of the timers.
+        /// Potentially slow so call sparingly.
+        /// </summary>
+        /// <returns></returns>
+        internal string DebugGetTimerString()
+        {
+            return m_RootNode.DebugGetTimerString();
+        }
+
+        /// <summary>
+        /// Save the timers in JSON format to the provided filename.
+        /// If the filename is null, a default one will be used.
+        /// </summary>
+        /// <param name="filename"></param>
+        public void SaveJsonTimers(string filename = null)
+        {
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+            try
+            {
+                if (filename == null)
+                {
+                    var activeScene = SceneManager.GetActiveScene();
+                    var timerDir = Path.Combine(Application.dataPath, "ML-Agents", "Timers");
+                    Directory.CreateDirectory(timerDir);
+
+                    filename = Path.Combine(timerDir, $"{activeScene.name}_timers.json");
+                }
+
+                var fs = new FileStream(filename, FileMode.Create, FileAccess.Write);
+                SaveJsonTimers(fs);
+                fs.Close();
+            }
+            catch (SystemException)
+            {
+                // We may not have write access to the directory.
+                Debug.LogWarning($"Unable to save timers to file {filename}");
+            }
+#endif
+        }
+
+        /// <summary>
+        /// Write the timers in JSON format to the provided stream.
+        /// </summary>
+        /// <param name="stream"></param>
+        public void SaveJsonTimers(Stream stream)
+        {
+            // Add some final metadata info
+            AddMetadata("scene_name", SceneManager.GetActiveScene().name);
+            AddMetadata("end_time_seconds", $"{DateTimeOffset.Now.ToUnixTimeSeconds()}");
+
+            var jsonSettings = new DataContractJsonSerializerSettings();
+            jsonSettings.UseSimpleDictionaryFormat = true;
+            var ser = new DataContractJsonSerializer(typeof(RootNode), jsonSettings);
+            ser.WriteObject(stream, m_RootNode);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Timer.cs.meta b/com.unity.ml-agents/Runtime/Timer.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e28315908dbd87e67a80d51e5b54d9cc5e7a19c5
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Timer.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d268f7dfcc74c47939e1fc520adb8d81
+MonoImporter:
+  externalObjects: {}
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diff --git a/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef b/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..3dafd78f4210bbffbf91b97842ad63a2928c0fa0
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef
@@ -0,0 +1,38 @@
+{
+    "name": "Unity.ML-Agents",
+    "rootNamespace": "",
+    "references": [
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects",
+        "Unity.Mathematics"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": false,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "Google.Protobuf.dll",
+        "Grpc.Core.dll"
+    ],
+    "autoReferenced": true,
+    "defineConstraints": [],
+    "versionDefines": [
+        {
+            "name": "com.unity.modules.unityanalytics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_ANALYTICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics2d",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS2D_MODULE"
+        }
+    ],
+    "noEngineReferences": false
+}
diff --git a/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef.meta b/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..21cbeb979350d6b92953007e7275bc968e0cf703
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Unity.ML-Agents.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 85e0054f8e64b47309646d35f8851f81
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/UnityAgentsException.cs b/com.unity.ml-agents/Runtime/UnityAgentsException.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0abc1ace88d3f0fccd9a4d50b86842fad76b5f04
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/UnityAgentsException.cs
@@ -0,0 +1,32 @@
+using System;
+
+namespace Unity.MLAgents
+{
+    /// <summary>
+    /// Contains exceptions specific to ML-Agents.
+    /// </summary>
+    [Serializable]
+    public class UnityAgentsException : Exception
+    {
+        /// <summary>
+        /// When a UnityAgentsException is called, the timeScale is set to 0.
+        /// The simulation will end since no steps will be taken.
+        /// </summary>
+        /// <param name="message">The exception message</param>
+        public UnityAgentsException(string message) : base(message)
+        {
+        }
+
+        /// <summary>
+        /// A constructor is needed for serialization when an exception propagates
+        /// from a remoting server to the client.
+        /// </summary>
+        /// <param name="info">Data for serializing/de-serializing</param>
+        /// <param name="context">Describes the source and destination of the serialized stream</param>
+        protected UnityAgentsException(
+            System.Runtime.Serialization.SerializationInfo info,
+            System.Runtime.Serialization.StreamingContext context)
+        {
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/UnityAgentsException.cs.meta b/com.unity.ml-agents/Runtime/UnityAgentsException.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f72768b1df0e18fcd98649b609690d5f833f7e1f
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/UnityAgentsException.cs.meta
@@ -0,0 +1,12 @@
+fileFormatVersion: 2
+guid: e63e4a66d820245778f9a2abfa5b68e0
+timeCreated: 1504131359
+licenseType: Free
+MonoImporter:
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Runtime/Utilities.cs b/com.unity.ml-agents/Runtime/Utilities.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e9d442504889146c41f7ad9d7c94249014d20755
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Utilities.cs
@@ -0,0 +1,60 @@
+using System;
+using System.Diagnostics;
+using UnityEngine;
+
+namespace Unity.MLAgents
+{
+    internal static class Utilities
+    {
+        /// <summary>
+        /// Calculates the cumulative sum of an integer array. The result array will be one element
+        /// larger than the input array since it has a padded 0 at the beginning.
+        /// If the input is [a, b, c], the result will be [0, a, a+b, a+b+c]
+        /// </summary>
+        /// <param name="input">
+        /// Input array whose elements will be cumulatively added
+        /// </param>
+        /// <returns> The cumulative sum of the input array.</returns>
+        internal static int[] CumSum(int[] input)
+        {
+            var runningSum = 0;
+            var result = new int[input.Length + 1];
+            for (var actionIndex = 0; actionIndex < input.Length; actionIndex++)
+            {
+                runningSum += input[actionIndex];
+                result[actionIndex + 1] = runningSum;
+            }
+            return result;
+        }
+
+        /// <summary>
+        /// Safely destroy a texture. This has to be used differently in unit tests.
+        /// </summary>
+        /// <param name="texture"></param>
+        internal static void DestroyTexture(Texture2D texture)
+        {
+            if (Application.isEditor)
+            {
+                // Edit Mode tests complain if we use Destroy()
+                UnityEngine.Object.DestroyImmediate(texture);
+            }
+            else
+            {
+                UnityEngine.Object.Destroy(texture);
+            }
+        }
+
+        [Conditional("DEBUG")]
+        internal static void DebugCheckNanAndInfinity(float value, string valueCategory, string caller)
+        {
+            if (float.IsNaN(value))
+            {
+                throw new ArgumentException($"NaN {valueCategory} passed to {caller}.");
+            }
+            if (float.IsInfinity(value))
+            {
+                throw new ArgumentException($"Inifinity {valueCategory} passed to {caller}.");
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Runtime/Utilities.cs.meta b/com.unity.ml-agents/Runtime/Utilities.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1481e00c44551a8cba9e27bddbb1457fa2feeada
--- /dev/null
+++ b/com.unity.ml-agents/Runtime/Utilities.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 0e664c25f496478c9c26df6688379f7e
+timeCreated: 1537468595
\ No newline at end of file
diff --git a/com.unity.ml-agents/Samples.meta b/com.unity.ml-agents/Samples.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6bfe79069d9db4c48d3302d2e24565e6022a656a
--- /dev/null
+++ b/com.unity.ml-agents/Samples.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: df09abf155607462ebf9ac4697220357
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Samples/3DBall.meta b/com.unity.ml-agents/Samples/3DBall.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a52f058ba4395ca2f905facab7bde839cdeb249e
--- /dev/null
+++ b/com.unity.ml-agents/Samples/3DBall.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: e7c3ff420d48043aea9c820c1f87c5f7
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Samples/3DBall/.sample.json b/com.unity.ml-agents/Samples/3DBall/.sample.json
new file mode 100644
index 0000000000000000000000000000000000000000..7055a5b2201ce495337348d94152f417dec2af1a
--- /dev/null
+++ b/com.unity.ml-agents/Samples/3DBall/.sample.json
@@ -0,0 +1 @@
+{"displayName":"3D Ball","description":"The 3D Ball sample is a simple environment that is a great for jumping into ML-Agents to see how things work."}
diff --git a/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage b/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage
new file mode 100644
index 0000000000000000000000000000000000000000..eb4043d19d0de4c3410723fc30919efc348ac52d
Binary files /dev/null and b/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage differ
diff --git a/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage.meta b/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0301d45ba4159d0c6d93aec770dbfd7643dc8108
--- /dev/null
+++ b/com.unity.ml-agents/Samples/3DBall/3DBall.unitypackage.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 3eff7a05c1c4e459a90f7c9951f7d549
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests.meta b/com.unity.ml-agents/Tests.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ffec42d32e87bb0048998faa772078371e9ff6ed
--- /dev/null
+++ b/com.unity.ml-agents/Tests.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 2715dc4ceb2c345df9ba92d799ae72ff
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/.tests.json b/com.unity.ml-agents/Tests/.tests.json
new file mode 100644
index 0000000000000000000000000000000000000000..327abb29e523ac0bf5338190d4ca6c9a765838bc
--- /dev/null
+++ b/com.unity.ml-agents/Tests/.tests.json
@@ -0,0 +1,3 @@
+{
+	"createSeparatePackage": false
+}
diff --git a/com.unity.ml-agents/Tests/Editor.meta b/com.unity.ml-agents/Tests/Editor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9a45234ac07abbc40bc1ebef623d3411001edfbf
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: b779986bc10d84f2aa8601a3f1c763ff
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/AcademyTests.cs b/com.unity.ml-agents/Tests/Editor/AcademyTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0e1d08014d08e4a9e8984e6e2b0dd3c9cf21d086
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/AcademyTests.cs
@@ -0,0 +1,50 @@
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class AcademyTests
+    {
+        [Test]
+        public void TestPackageVersion()
+        {
+            var packageInfo = UnityEditor.PackageManager.PackageInfo.FindForAssembly(typeof(Agent).Assembly);
+            Assert.AreEqual("com.unity.ml-agents", packageInfo.name);
+            Assert.AreEqual(Academy.k_PackageVersion, packageInfo.version);
+        }
+
+        class RecursiveAgent : Agent
+        {
+            int m_collectObsCount;
+            public override void CollectObservations(VectorSensor sensor)
+            {
+                m_collectObsCount++;
+                if (m_collectObsCount == 1)
+                {
+                    // NEVER DO THIS IN REAL CODE!
+                    Academy.Instance.EnvironmentStep();
+                }
+            }
+        }
+
+        [Test]
+        public void TestRecursiveStepThrows()
+        {
+            var gameObj = new GameObject();
+            var agent = gameObj.AddComponent<RecursiveAgent>();
+            agent.Awake();
+            agent.LazyInitialize();
+            agent.RequestDecision();
+
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                Academy.Instance.EnvironmentStep();
+            });
+
+            // Make sure the Academy reset to a good state and is still steppable.
+            Academy.Instance.EnvironmentStep();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/AcademyTests.cs.meta b/com.unity.ml-agents/Tests/Editor/AcademyTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fa65ced67da8c59f2f0fb34e71ba01aeecd792d6
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/AcademyTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: f434773fe0f1b41c5b3f446fa0adece4
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators.meta b/com.unity.ml-agents/Tests/Editor/Actuators.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5c6399dc6c3d6aeb56c4272ee183fb12d0476285
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: c7e705f7d549e43c6be18ae809cd6f54
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6fec579a3450dc7b3fd0c29d0a57b15fb5995e4a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs
@@ -0,0 +1,62 @@
+using System;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+
+namespace Unity.MLAgents.Tests.Actuators
+{
+    [TestFixture]
+    public class ActionSegmentTests
+    {
+        [Test]
+        public void TestConstruction()
+        {
+            var floatArray = new[] { 1f, 2f, 3f, 4f, 5f, 6f, 7f };
+            Assert.Throws<ArgumentOutOfRangeException>(
+                () => new ActionSegment<float>(floatArray, 100, 1));
+
+            var segment = new ActionSegment<float>(Array.Empty<float>(), 0, 0);
+            Assert.AreEqual(segment, ActionSegment<float>.Empty);
+        }
+
+        [Test]
+        public void TestIndexing()
+        {
+            var floatArray = new[] { 1f, 2f, 3f, 4f, 5f, 6f, 7f };
+            for (var i = 0; i < floatArray.Length; i++)
+            {
+                var start = 0 + i;
+                var length = floatArray.Length - i;
+                var actionSegment = new ActionSegment<float>(floatArray, start, length);
+                for (var j = 0; j < actionSegment.Length; j++)
+                {
+                    Assert.AreEqual(actionSegment[j], floatArray[start + j]);
+                }
+            }
+        }
+
+        [Test]
+        public void TestEnumerator()
+        {
+            var floatArray = new[] { 1f, 2f, 3f, 4f, 5f, 6f, 7f };
+            for (var i = 0; i < floatArray.Length; i++)
+            {
+                var start = 0 + i;
+                var length = floatArray.Length - i;
+                var actionSegment = new ActionSegment<float>(floatArray, start, length);
+                var j = 0;
+                foreach (var item in actionSegment)
+                {
+                    Assert.AreEqual(item, floatArray[start + j++]);
+                }
+            }
+        }
+
+        [Test]
+        public void TestNullConstructor()
+        {
+            var actionSegment = new ActionSegment<float>(null);
+            Assert.IsTrue(actionSegment.Length == 0);
+            Assert.IsTrue(actionSegment.Array == Array.Empty<float>());
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2332580c178ce327e313cabaa807581f8050e243
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSegmentTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 18cb6d052fba43a2b7437d87c0d9abad
+timeCreated: 1596486604
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..09dfd336705b08c2ed2041a74fbcc36852fa1ebd
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs
@@ -0,0 +1,37 @@
+using System.Collections.Generic;
+using System.Linq;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+
+namespace Unity.MLAgents.Tests.Actuators
+{
+    [TestFixture]
+    public class ActionSpecTests
+    {
+        [Test]
+        public void ActionSpecCombineTest()
+        {
+            var as0 = new ActionSpec(3, new[] { 3, 2, 1 });
+            var as1 = new ActionSpec(1, new[] { 35, 122, 1, 3, 8, 3 });
+
+            var as0NumCon = 3;
+            var as0NumDis = as0.NumDiscreteActions;
+            var as1NumCon = 1;
+            var as1NumDis = as1.NumDiscreteActions;
+            var branchSizes = new List<int>();
+            branchSizes.AddRange(as0.BranchSizes);
+            branchSizes.AddRange(as1.BranchSizes);
+
+            var asc = ActionSpec.Combine(as0, as1);
+
+            Assert.AreEqual(as0NumCon + as1NumCon, asc.NumContinuousActions);
+            Assert.AreEqual(as0NumDis + as1NumDis, asc.NumDiscreteActions);
+            Assert.IsTrue(branchSizes.ToArray().SequenceEqual(asc.BranchSizes));
+
+            as0 = new ActionSpec(3);
+            as1 = new ActionSpec(1);
+            asc = ActionSpec.Combine(as0, as1);
+            Assert.IsEmpty(asc.BranchSizes);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..18ebcbb881521f2d42f3bdeb85a59f9f39196df5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 99d76ec04c944b75bc6b85abfff4ac4e
+timeCreated: 1613680505
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1c486af483693c7bbdf08a8a307afe3a2f933ee1
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs
@@ -0,0 +1,136 @@
+using System.Collections.Generic;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+
+namespace Unity.MLAgents.Tests.Actuators
+{
+    [TestFixture]
+    public class ActuatorDiscreteActionMaskTests
+    {
+        [Test]
+        public void Construction()
+        {
+            var masker = new ActuatorDiscreteActionMask(new List<IActuator>(), 0, 0);
+            Assert.IsNotNull(masker);
+        }
+
+        [Test]
+        public void NullMask()
+        {
+            var masker = new ActuatorDiscreteActionMask(new List<IActuator>(), 0, 0);
+            var mask = masker.GetMask();
+            Assert.IsNull(mask);
+        }
+
+        [Test]
+        public void FirstBranchMask()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new IActuator[] { actuator1 }, 15, 3);
+            var mask = masker.GetMask();
+            Assert.IsNull(mask);
+            masker.SetActionEnabled(0, 1, false);
+            masker.SetActionEnabled(0, 2, false);
+            masker.SetActionEnabled(0, 3, false);
+            mask = masker.GetMask();
+            Assert.IsFalse(mask[0]);
+            Assert.IsTrue(mask[1]);
+            Assert.IsTrue(mask[2]);
+            Assert.IsTrue(mask[3]);
+            Assert.IsFalse(mask[4]);
+            Assert.AreEqual(mask.Length, 15);
+        }
+
+        [Test]
+        public void CanOverwriteMask()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new IActuator[] { actuator1 }, 15, 3);
+            masker.SetActionEnabled(0, 1, false);
+            var mask = masker.GetMask();
+            Assert.IsTrue(mask[1]);
+
+            masker.SetActionEnabled(0, 1, true);
+            Assert.IsFalse(mask[1]);
+        }
+
+        [Test]
+        public void SecondBranchMask()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new[] { actuator1 }, 15, 3);
+            masker.SetActionEnabled(1, 1, false);
+            masker.SetActionEnabled(1, 2, false);
+            masker.SetActionEnabled(1, 3, false);
+            var mask = masker.GetMask();
+            Assert.IsFalse(mask[0]);
+            Assert.IsFalse(mask[4]);
+            Assert.IsTrue(mask[5]);
+            Assert.IsTrue(mask[6]);
+            Assert.IsTrue(mask[7]);
+            Assert.IsFalse(mask[8]);
+            Assert.IsFalse(mask[9]);
+        }
+
+        [Test]
+        public void MaskReset()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new IActuator[] { actuator1 }, 15, 3);
+            masker.SetActionEnabled(1, 1, false);
+            masker.SetActionEnabled(1, 2, false);
+            masker.SetActionEnabled(1, 3, false);
+            masker.ResetMask();
+            var mask = masker.GetMask();
+            for (var i = 0; i < 15; i++)
+            {
+                Assert.IsFalse(mask[i]);
+            }
+        }
+
+        [Test]
+        public void ThrowsError()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new IActuator[] { actuator1 }, 15, 3);
+            Assert.Catch<UnityAgentsException>(
+                () => masker.SetActionEnabled(0, 5, false));
+            Assert.Catch<UnityAgentsException>(
+                () => masker.SetActionEnabled(1, 5, false));
+            masker.SetActionEnabled(2, 5, false);
+            Assert.Catch<UnityAgentsException>(
+                () => masker.SetActionEnabled(3, 1, false));
+            masker.GetMask();
+            masker.ResetMask();
+            masker.SetActionEnabled(0, 0, false);
+            masker.SetActionEnabled(0, 1, false);
+            masker.SetActionEnabled(0, 2, false);
+            masker.SetActionEnabled(0, 3, false);
+            Assert.Catch<UnityAgentsException>(
+                () => masker.GetMask());
+        }
+
+        [Test]
+        public void MultipleMaskEdit()
+        {
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 4, 5, 6 }), "actuator1");
+            var masker = new ActuatorDiscreteActionMask(new IActuator[] { actuator1 }, 15, 3);
+            masker.SetActionEnabled(0, 0, false);
+            masker.SetActionEnabled(0, 1, false);
+            masker.SetActionEnabled(0, 3, false);
+            masker.SetActionEnabled(2, 1, false);
+            var mask = masker.GetMask();
+            for (var i = 0; i < 15; i++)
+            {
+                if ((i == 0) || (i == 1) || (i == 3) || (i == 10))
+                {
+                    Assert.IsTrue(mask[i]);
+                }
+                else
+                {
+                    Assert.IsFalse(mask[i]);
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a5dd1f3ad99ff8f4b5ea3a08e64de9f48d02d464
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorDiscreteActionMaskTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: b9f5f87049d04d8bba39d193a3ab2f5a
+timeCreated: 1596491682
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..63791b303cfd30b98d2d47ecf768cfb036f02ec8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs
@@ -0,0 +1,352 @@
+using System;
+using System.Collections.Generic;
+using System.Globalization;
+using System.Linq;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+using UnityEngine.TestTools;
+using Assert = UnityEngine.Assertions.Assert;
+
+namespace Unity.MLAgents.Tests.Actuators
+{
+    [TestFixture]
+    public class ActuatorManagerTests
+    {
+        [Test]
+        public void TestEnsureBufferSizeContinuous()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(10), "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(2), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            var actuator1ActionSpaceDef = actuator1.ActionSpec;
+            var actuator2ActionSpaceDef = actuator2.ActionSpec;
+            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 },
+                actuator1ActionSpaceDef.NumContinuousActions + actuator2ActionSpaceDef.NumContinuousActions,
+                actuator1ActionSpaceDef.SumOfDiscreteBranchSizes + actuator2ActionSpaceDef.SumOfDiscreteBranchSizes,
+                actuator1ActionSpaceDef.NumDiscreteActions + actuator2ActionSpaceDef.NumDiscreteActions);
+
+            manager.UpdateActions(new ActionBuffers(new[]
+                { 0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, 10f, 11f }, Array.Empty<int>()));
+
+            Assert.IsTrue(12 == manager.NumContinuousActions);
+            Assert.IsTrue(0 == manager.NumDiscreteActions);
+            Assert.IsTrue(0 == manager.SumOfDiscreteBranchSizes);
+            Assert.IsTrue(12 == manager.StoredActions.ContinuousActions.Length);
+            Assert.IsTrue(0 == manager.StoredActions.DiscreteActions.Length);
+        }
+
+        [Test]
+        public void TestEnsureBufferDiscrete()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }), "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 1, 1 }), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            var actuator1ActionSpaceDef = actuator1.ActionSpec;
+            var actuator2ActionSpaceDef = actuator2.ActionSpec;
+            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 },
+                actuator1ActionSpaceDef.NumContinuousActions + actuator2ActionSpaceDef.NumContinuousActions,
+                actuator1ActionSpaceDef.SumOfDiscreteBranchSizes + actuator2ActionSpaceDef.SumOfDiscreteBranchSizes,
+                actuator1ActionSpaceDef.NumDiscreteActions + actuator2ActionSpaceDef.NumDiscreteActions);
+
+            manager.UpdateActions(new ActionBuffers(Array.Empty<float>(),
+                new[] { 0, 1, 2, 3, 4, 5, 6 }));
+
+            Assert.IsTrue(0 == manager.NumContinuousActions);
+            Assert.IsTrue(7 == manager.NumDiscreteActions);
+            Assert.IsTrue(13 == manager.SumOfDiscreteBranchSizes);
+            Assert.IsTrue(0 == manager.StoredActions.ContinuousActions.Length);
+            Assert.IsTrue(7 == manager.StoredActions.DiscreteActions.Length);
+        }
+
+        [Test]
+        public void TestAllowMixedActions()
+        {
+            // Make sure discrete + continuous actuators are allowed.
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }), "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 }, 3, 10, 4);
+        }
+
+        [Test]
+        public void TestFailOnSameActuatorName()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator1");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 }, 3, 10, 4);
+            LogAssert.Expect(LogType.Assert, "Actuator names must be unique.");
+        }
+
+        [Test]
+        public void TestExecuteActionsDiscrete()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }), "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 1, 1 }), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+
+            var discreteActionBuffer = new[] { 0, 1, 2, 3, 4, 5, 6 };
+            manager.UpdateActions(new ActionBuffers(Array.Empty<float>(),
+                discreteActionBuffer));
+
+            manager.ExecuteActions();
+            var actuator1Actions = actuator1.LastActionBuffer.DiscreteActions;
+            var actuator2Actions = actuator2.LastActionBuffer.DiscreteActions;
+            TestSegmentEquality(actuator1Actions, discreteActionBuffer); TestSegmentEquality(actuator2Actions, discreteActionBuffer);
+        }
+
+        [Test]
+        public void TestExecuteActionsContinuous()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(3),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+
+            var continuousActionBuffer = new[] { 0f, 1f, 2f, 3f, 4f, 5f };
+            manager.UpdateActions(new ActionBuffers(continuousActionBuffer,
+                Array.Empty<int>()));
+
+            manager.ExecuteActions();
+            var actuator1Actions = actuator1.LastActionBuffer.ContinuousActions;
+            var actuator2Actions = actuator2.LastActionBuffer.ContinuousActions;
+            TestSegmentEquality(actuator1Actions, continuousActionBuffer);
+            TestSegmentEquality(actuator2Actions, continuousActionBuffer);
+        }
+
+        static void TestSegmentEquality<T>(ActionSegment<T> actionSegment, T[] actionBuffer)
+            where T : struct
+        {
+            Assert.IsFalse(actionSegment.Length == 0);
+            for (var i = 0; i < actionSegment.Length; i++)
+            {
+                var action = actionSegment[i];
+                Assert.AreEqual(action, actionBuffer[actionSegment.Offset + i]);
+            }
+        }
+
+        [Test]
+        public void TestUpdateActionsContinuous()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(3),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            var continuousActionBuffer = new[] { 0f, 1f, 2f, 3f, 4f, 5f };
+            manager.UpdateActions(new ActionBuffers(continuousActionBuffer,
+                Array.Empty<int>()));
+
+            Assert.IsTrue(manager.StoredActions.ContinuousActions.SequenceEqual(continuousActionBuffer));
+        }
+
+        [Test]
+        public void TestUpdateActionsDiscrete()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            var discreteActionBuffer = new[] { 0, 1, 2, 3, 4, 5 };
+            manager.UpdateActions(new ActionBuffers(Array.Empty<float>(),
+                discreteActionBuffer));
+
+            Debug.Log(manager.StoredActions.DiscreteActions);
+            Debug.Log(discreteActionBuffer);
+            Assert.IsTrue(manager.StoredActions.DiscreteActions.SequenceEqual(discreteActionBuffer));
+        }
+
+        [Test]
+        public void TestRemove()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "actuator2");
+
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            Assert.IsTrue(manager.NumDiscreteActions == 6);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 12);
+
+            manager.Remove(actuator2);
+
+            Assert.IsTrue(manager.NumDiscreteActions == 3);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 6);
+
+            manager.Remove(null);
+
+            Assert.IsTrue(manager.NumDiscreteActions == 3);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 6);
+
+            manager.RemoveAt(0);
+            Assert.IsTrue(manager.NumDiscreteActions == 0);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 0);
+        }
+
+        [Test]
+        public void TestClear()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+
+            Assert.IsTrue(manager.NumDiscreteActions == 6);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 12);
+
+            manager.Clear();
+
+            Assert.IsTrue(manager.NumDiscreteActions == 0);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 0);
+        }
+
+        [Test]
+        public void TestIndexSet()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "actuator2");
+            manager.Add(actuator1);
+            Assert.IsTrue(manager.NumDiscreteActions == 4);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 10);
+            manager[0] = actuator2;
+            Assert.IsTrue(manager.NumDiscreteActions == 3);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 6);
+        }
+
+        [Test]
+        public void TestInsert()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "actuator2");
+            manager.Add(actuator1);
+            Assert.IsTrue(manager.NumDiscreteActions == 4);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 10);
+            manager.Insert(0, actuator2);
+            Assert.IsTrue(manager.NumDiscreteActions == 7);
+            Assert.IsTrue(manager.SumOfDiscreteBranchSizes == 16);
+            Assert.IsTrue(manager.IndexOf(actuator2) == 0);
+        }
+
+        [Test]
+        public void TestResetData()
+        {
+            var manager = new ActuatorManager();
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(3),
+                "actuator1");
+            var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
+            manager.Add(actuator1);
+            manager.Add(actuator2);
+            var continuousActionBuffer = new[] { 0f, 1f, 2f, 3f, 4f, 5f };
+            manager.UpdateActions(new ActionBuffers(continuousActionBuffer,
+                Array.Empty<int>()));
+
+            Assert.IsTrue(manager.StoredActions.ContinuousActions.SequenceEqual(continuousActionBuffer));
+            Assert.IsTrue(manager.NumContinuousActions == 6);
+            manager.ResetData();
+
+            Assert.IsTrue(manager.StoredActions.ContinuousActions.SequenceEqual(new[] { 0f, 0f, 0f, 0f, 0f, 0f }));
+        }
+
+        [Test]
+        public void TestWriteDiscreteActionMask()
+        {
+            var manager = new ActuatorManager(2);
+            var va1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3 }), "name");
+            var va2 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 3, 2, 1 }), "name1");
+            manager.Add(va1);
+            manager.Add(va2);
+
+            var groundTruthMask = new[]
+            {
+                false,
+                true, false,
+                false, true, true,
+                true, false, true,
+                false, true,
+                false
+            };
+
+            va1.Masks = new[]
+            {
+                Array.Empty<int>(),
+                new[] { 0 },
+                new[] { 1, 2 }
+            };
+
+            va2.Masks = new[]
+            {
+                new[] {0, 2},
+                new[] {1},
+                Array.Empty<int>()
+            };
+            manager.WriteActionMask();
+            Assert.IsTrue(groundTruthMask.SequenceEqual(manager.DiscreteActionMask.GetMask()));
+        }
+
+        [Test]
+        public void TestHeuristic()
+        {
+            var manager = new ActuatorManager(2);
+            var va1 = new TestActuator(ActionSpec.MakeDiscrete(1, 2, 3), "name");
+            var va2 = new TestActuator(ActionSpec.MakeDiscrete(3, 2, 1, 8), "name1");
+            manager.Add(va1);
+            manager.Add(va2);
+
+            var actionBuf = new ActionBuffers(Array.Empty<float>(), new[] { 0, 0, 0, 0, 0, 0, 0 });
+            manager.ApplyHeuristic(actionBuf);
+
+            Assert.IsTrue(va1.m_HeuristicCalled);
+            Assert.AreEqual(va1.m_DiscreteBufferSize, 3);
+            Assert.IsTrue(va2.m_HeuristicCalled);
+            Assert.AreEqual(va2.m_DiscreteBufferSize, 4);
+        }
+
+        /// <summary>
+        /// Test that sensors sort by name consistently across culture settings.
+        /// Example strings and cultures taken from
+        /// https://docs.microsoft.com/en-us/globalization/locale/sorting-and-string-comparison
+        /// </summary>
+        /// <param name="culture"></param>
+        [TestCase("da-DK")]
+        [TestCase("en-US")]
+        public void TestSortActuators(string culture)
+        {
+            List<IActuator> actuators = new List<IActuator>();
+            var actuator0 = new TestActuator(ActionSpec.MakeContinuous(2), "Apple");
+            var actuator1 = new TestActuator(ActionSpec.MakeContinuous(2), "Æble");
+            actuators.Add(actuator0);
+            actuators.Add(actuator1);
+
+            var originalCulture = CultureInfo.CurrentCulture;
+            CultureInfo.CurrentCulture = new CultureInfo(culture);
+            ActuatorManager.SortActuators(actuators);
+            CultureInfo.CurrentCulture = originalCulture;
+
+            Assert.AreEqual(actuator1, actuators[0]);
+            Assert.AreEqual(actuator0, actuators[1]);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4946ff19fb5f374828dea3f11fc6a0bd0fe64415
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d48ba72f0ac64d7db0af22c9d82b11d8
+timeCreated: 1596494279
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs b/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..643e5131b13807f9bbcb4b3e3db0ce4da27da345
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs
@@ -0,0 +1,48 @@
+using Unity.MLAgents.Actuators;
+namespace Unity.MLAgents.Tests.Actuators
+{
+    internal class TestActuator : IActuator
+    {
+        public ActionBuffers LastActionBuffer;
+        public int[][] Masks;
+        public bool m_HeuristicCalled;
+        public int m_DiscreteBufferSize;
+
+        public TestActuator(ActionSpec actuatorSpace, string name)
+        {
+            ActionSpec = actuatorSpace;
+
+            Name = name;
+        }
+
+        public void OnActionReceived(ActionBuffers actionBuffers)
+        {
+            LastActionBuffer = actionBuffers;
+        }
+
+        public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+        {
+            for (var i = 0; i < Masks.Length; i++)
+            {
+                foreach (var actionIndex in Masks[i])
+                {
+                    actionMask.SetActionEnabled(i, actionIndex, false);
+                }
+            }
+        }
+
+        public ActionSpec ActionSpec { get; }
+
+        public string Name { get; }
+
+        public void ResetData()
+        {
+        }
+
+        public void Heuristic(in ActionBuffers actionBuffersOut)
+        {
+            m_HeuristicCalled = true;
+            m_DiscreteBufferSize = actionBuffersOut.DiscreteActions.Length;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..57e13a0e26c338b7b9f1c972351ecca373953544
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: fa950d7b175749bfa287fd8761dd831f
+timeCreated: 1596665978
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs b/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7fe52951c8337eb961d640751e2aa01950a569f8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs
@@ -0,0 +1,117 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Assert = UnityEngine.Assertions.Assert;
+
+namespace Unity.MLAgents.Tests.Actuators
+{
+    [TestFixture]
+    public class VectorActuatorTests
+    {
+        class TestActionReceiver : IActionReceiver, IHeuristicProvider
+        {
+            public ActionBuffers LastActionBuffers;
+            public int Branch;
+            public IList<int> Mask;
+            public ActionSpec ActionSpec { get; }
+            public bool HeuristicCalled;
+
+            public void OnActionReceived(ActionBuffers actionBuffers)
+            {
+                LastActionBuffers = actionBuffers;
+            }
+
+            public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+            {
+                foreach (var actionIndex in Mask)
+                {
+                    actionMask.SetActionEnabled(Branch, actionIndex, false);
+                }
+            }
+
+            public void Heuristic(in ActionBuffers actionBuffersOut)
+            {
+                HeuristicCalled = true;
+            }
+        }
+
+        [Test]
+        public void TestConstruct()
+        {
+            var ar = new TestActionReceiver();
+            var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
+
+            Assert.IsTrue(va.ActionSpec.NumDiscreteActions == 3);
+            Assert.IsTrue(va.ActionSpec.SumOfDiscreteBranchSizes == 6);
+            Assert.IsTrue(va.ActionSpec.NumContinuousActions == 0);
+
+            var va1 = new VectorActuator(ar, ActionSpec.MakeContinuous(4), "name");
+
+            Assert.IsTrue(va1.ActionSpec.NumContinuousActions == 4);
+            Assert.IsTrue(va1.ActionSpec.SumOfDiscreteBranchSizes == 0);
+            Assert.AreEqual(va1.Name, "name-Continuous");
+        }
+
+        [Test]
+        public void TestOnActionReceived()
+        {
+            var ar = new TestActionReceiver();
+            var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
+
+            var discreteActions = new[] { 0, 1, 1 };
+            var ab = new ActionBuffers(ActionSegment<float>.Empty,
+                new ActionSegment<int>(discreteActions, 0, 3));
+
+            va.OnActionReceived(ab);
+
+            Assert.AreEqual(ar.LastActionBuffers, ab);
+            va.ResetData();
+            Assert.AreEqual(va.ActionBuffers.ContinuousActions, ActionSegment<float>.Empty);
+            Assert.AreEqual(va.ActionBuffers.DiscreteActions, ActionSegment<int>.Empty);
+        }
+
+        [Test]
+        public void TestResetData()
+        {
+            var ar = new TestActionReceiver();
+            var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
+
+            var discreteActions = new[] { 0, 1, 1 };
+            var ab = new ActionBuffers(ActionSegment<float>.Empty,
+                new ActionSegment<int>(discreteActions, 0, 3));
+
+            va.OnActionReceived(ab);
+        }
+
+        [Test]
+        public void TestWriteDiscreteActionMask()
+        {
+            var ar = new TestActionReceiver();
+            var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
+            var bdam = new ActuatorDiscreteActionMask(new[] { va }, 6, 3);
+
+            var groundTruthMask = new[] { false, true, false, false, true, true };
+
+            ar.Branch = 1;
+            ar.Mask = new[] { 0 };
+            va.WriteDiscreteActionMask(bdam);
+            ar.Branch = 2;
+            ar.Mask = new[] { 1, 2 };
+            va.WriteDiscreteActionMask(bdam);
+
+            Assert.IsTrue(groundTruthMask.SequenceEqual(bdam.GetMask()));
+        }
+
+        [Test]
+        public void TestHeuristic()
+        {
+            var ar = new TestActionReceiver();
+            var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
+
+            va.Heuristic(new ActionBuffers(Array.Empty<float>(), va.ActionSpec.BranchSizes));
+            Assert.IsTrue(ar.HeuristicCalled);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2a5a86efd011175c182fcb114237fad8197eab50
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Actuators/VectorActuatorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c2b191d2929f49adab0769705d49d86a
+timeCreated: 1596580289
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Analytics.meta b/com.unity.ml-agents/Tests/Editor/Analytics.meta
new file mode 100644
index 0000000000000000000000000000000000000000..473f2be08fefdf920bffb21153e933af4b6f3ab6
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Analytics.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: adbf291ff40848a296523d69a5be65a5
+timeCreated: 1607379470
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs b/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7686517b6b4849a70b195832ab6d66a133791db3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs
@@ -0,0 +1,105 @@
+using System;
+using System.Collections.Generic;
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Analytics;
+using UnityEditor;
+
+
+namespace Unity.MLAgents.Tests.Analytics
+{
+    [TestFixture]
+    public class InferenceAnalyticsTests
+    {
+        const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_v1_0.onnx";
+        NNModel continuousONNXModel;
+        Test3DSensorComponent sensor_21_20_3;
+        Test3DSensorComponent sensor_20_22_3;
+
+        ActionSpec GetContinuous2vis8vec2actionActionSpec()
+        {
+            return ActionSpec.MakeContinuous(2);
+        }
+
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+
+            continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
+            var go = new GameObject("SensorA");
+            sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);
+            sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_20_22_3.Sensor = new Test3DSensor("SensorB", 20, 22, 3);
+        }
+
+        [Test]
+        public void TestModelEvent()
+        {
+            var sensors = new List<ISensor> { sensor_21_20_3.Sensor, sensor_20_22_3.Sensor };
+            var behaviorName = "continuousModel";
+            var actionSpec = GetContinuous2vis8vec2actionActionSpec();
+
+            var vectorActuator = new VectorActuator(null, actionSpec, "test'");
+            var actuators = new IActuator[] { vectorActuator };
+
+            var continuousEvent = InferenceAnalytics.GetEventForModel(
+                continuousONNXModel, behaviorName,
+                InferenceDevice.CPU, sensors, actionSpec,
+                actuators
+            );
+
+            // The behavior name should be hashed, not pass-through.
+            Assert.AreNotEqual(behaviorName, continuousEvent.BehaviorName);
+
+            Assert.AreEqual(2, continuousEvent.ActionSpec.NumContinuousActions);
+            Assert.AreEqual(0, continuousEvent.ActionSpec.NumDiscreteActions);
+            Assert.AreEqual(2, continuousEvent.ObservationSpecs.Count);
+            Assert.AreEqual(3, continuousEvent.ObservationSpecs[0].DimensionInfos.Length);
+            Assert.AreEqual(20, continuousEvent.ObservationSpecs[0].DimensionInfos[0].Size);
+            Assert.AreEqual(0, continuousEvent.ObservationSpecs[0].ObservationType);
+            Assert.AreEqual((int)DimensionProperty.TranslationalEquivariance, continuousEvent.ObservationSpecs[0].DimensionInfos[0].Flags);
+            Assert.AreEqual((int)DimensionProperty.None, continuousEvent.ObservationSpecs[0].DimensionInfos[2].Flags);
+            Assert.AreEqual("None", continuousEvent.ObservationSpecs[0].CompressionType);
+            Assert.AreEqual(Test3DSensor.k_BuiltInSensorType, continuousEvent.ObservationSpecs[0].BuiltInSensorType);
+            Assert.AreEqual((int)BuiltInActuatorType.VectorActuator, continuousEvent.ActuatorInfos[0].BuiltInActuatorType);
+            Assert.AreNotEqual(null, continuousEvent.ModelHash);
+
+            // Make sure nested fields get serialized
+            var jsonString = JsonUtility.ToJson(continuousEvent, true);
+            Assert.IsTrue(jsonString.Contains("ObservationSpecs"));
+            Assert.IsTrue(jsonString.Contains("ActionSpec"));
+            Assert.IsTrue(jsonString.Contains("NumDiscreteActions"));
+            Assert.IsTrue(jsonString.Contains("SensorName"));
+            Assert.IsTrue(jsonString.Contains("Flags"));
+            Assert.IsTrue(jsonString.Contains("ActuatorInfos"));
+        }
+
+        [Test]
+        public void TestBarracudaPolicy()
+        {
+            // Explicitly request decisions for a policy so we get code coverage on the event sending
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                var sensors = new List<ISensor> { sensor_21_20_3.Sensor, sensor_20_22_3.Sensor };
+                var policy = new BarracudaPolicy(
+                    GetContinuous2vis8vec2actionActionSpec(),
+                    Array.Empty<IActuator>(),
+                    continuousONNXModel,
+                    InferenceDevice.CPU,
+                    "testBehavior"
+                );
+                policy.RequestDecision(new AgentInfo(), sensors);
+            }
+            Academy.Instance.Dispose();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..20f024f03b11183f33ac49c5ace67c5a0ca3ef31
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 9f054f620b8b468bbd8ccf7d2cc14ccd
+timeCreated: 1607379491
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs b/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0487a7a524b5ebe2d3ede45476fad1e7f3f9814f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs
@@ -0,0 +1,96 @@
+using System;
+using System.Collections.Generic;
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Analytics;
+using Unity.MLAgents.Policies;
+using UnityEditor;
+
+namespace Unity.MLAgents.Tests.Analytics
+{
+    [TestFixture]
+    public class TrainingAnalyticsTests
+    {
+        [TestCase("foo?team=42", ExpectedResult = "foo")]
+        [TestCase("foo", ExpectedResult = "foo")]
+        [TestCase("foo?bar?team=1337", ExpectedResult = "foo?bar")]
+        public string TestParseBehaviorName(string fullyQualifiedBehaviorName)
+        {
+            return TrainingAnalytics.ParseBehaviorName(fullyQualifiedBehaviorName);
+        }
+
+        [Test]
+        public void TestRemotePolicyEvent()
+        {
+            var behaviorName = "testBehavior";
+            var sensor1 = new Test3DSensor("SensorA", 21, 20, 3);
+            var sensor2 = new Test3DSensor("SensorB", 20, 22, 3);
+            var sensors = new List<ISensor> { sensor1, sensor2 };
+
+            var actionSpec = ActionSpec.MakeContinuous(2);
+
+            var vectorActuator = new VectorActuator(null, actionSpec, "test'");
+            var actuators = new IActuator[] { vectorActuator };
+
+            var remotePolicyEvent = TrainingAnalytics.GetEventForRemotePolicy(behaviorName, sensors, actionSpec, actuators);
+
+            // The behavior name should be hashed, not pass-through.
+            Assert.AreNotEqual(behaviorName, remotePolicyEvent.BehaviorName);
+
+            Assert.AreEqual(2, remotePolicyEvent.ObservationSpecs.Count);
+            Assert.AreEqual(3, remotePolicyEvent.ObservationSpecs[0].DimensionInfos.Length);
+            Assert.AreEqual(20, remotePolicyEvent.ObservationSpecs[0].DimensionInfos[0].Size);
+            Assert.AreEqual(0, remotePolicyEvent.ObservationSpecs[0].ObservationType);
+            Assert.AreEqual("None", remotePolicyEvent.ObservationSpecs[0].CompressionType);
+            Assert.AreEqual(Test3DSensor.k_BuiltInSensorType, remotePolicyEvent.ObservationSpecs[0].BuiltInSensorType);
+
+            Assert.AreEqual(2, remotePolicyEvent.ActionSpec.NumContinuousActions);
+            Assert.AreEqual(0, remotePolicyEvent.ActionSpec.NumDiscreteActions);
+
+            Assert.AreEqual(2, remotePolicyEvent.ActuatorInfos[0].NumContinuousActions);
+            Assert.AreEqual(0, remotePolicyEvent.ActuatorInfos[0].NumDiscreteActions);
+        }
+
+        [Test]
+        public void TestRemotePolicy()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                var actionSpec = ActionSpec.MakeContinuous(3);
+                var policy = new RemotePolicy(actionSpec, Array.Empty<IActuator>(), "TestBehavior?team=42");
+                policy.RequestDecision(new AgentInfo(), new List<ISensor>());
+            }
+
+            Academy.Instance.Dispose();
+        }
+
+        [TestCase("a name we expect to hash", ExpectedResult = "d084a8b6da6a6a1c097cdc9ffea95e1546da4647352113ed77cbe7b4192e6d73")]
+        [TestCase("another_name", ExpectedResult = "0b74613c872e79aba11e06eda3538f2b646eb2b459e75087829ea500bd703d0b")]
+        [TestCase("0b74613c872e79aba11e06eda3538f2b646eb2b459e75087829ea500bd703d0b", ExpectedResult = "0b74613c872e79aba11e06eda3538f2b646eb2b459e75087829ea500bd703d0b")]
+        public string TestTrainingBehaviorInitialized(string stringToMaybeHash)
+        {
+            var tbiEvent = new TrainingBehaviorInitializedEvent();
+            tbiEvent.BehaviorName = stringToMaybeHash;
+            tbiEvent.Config = "{}";
+
+            var sanitizedEvent = TrainingAnalytics.SanitizeTrainingBehaviorInitializedEvent(tbiEvent);
+            return sanitizedEvent.BehaviorName;
+        }
+
+        [Test]
+        public void TestEnableAnalytics()
+        {
+#if UNITY_EDITOR && MLA_UNITY_ANALYTICS_MODULE
+            Assert.IsTrue(EditorAnalytics.enabled == TrainingAnalytics.EnableAnalytics());
+#else
+            Assert.IsFalse(TrainingAnalytics.EnableAnalytics());
+#endif
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..df394c157a66e0e2da47d64adb4cbb90d1424762
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Analytics/TrainingAnalyticsTest.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 70b8f1544bc34b4e8f1bc1068c64f01c
+timeCreated: 1610419546
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Areas.meta b/com.unity.ml-agents/Tests/Editor/Areas.meta
new file mode 100644
index 0000000000000000000000000000000000000000..42901a0e6bbff460f671542f11db5ea7f7f4bdef
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Areas.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d32a102dc1f004c33b05a30190a9d039
+timeCreated: 1632841906
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs b/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1daeb980ee980b8caecba0d75a894518a227c00a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs
@@ -0,0 +1,83 @@
+using System.Linq;
+using NUnit.Framework;
+using Unity.Mathematics;
+using Unity.MLAgents.Areas;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests.Areas
+{
+    [TestFixture]
+    public class TrainingAreaReplicatorTests
+    {
+        TrainingAreaReplicator m_Replicator;
+
+        [SetUp]
+        public void Setup()
+        {
+            var gameObject = new GameObject();
+            var trainingArea = new GameObject();
+            trainingArea.name = "MyTrainingArea";
+            m_Replicator = gameObject.AddComponent<TrainingAreaReplicator>();
+            m_Replicator.baseArea = trainingArea;
+        }
+
+        [TearDown]
+        public void TearDown()
+        {
+            var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName);
+            foreach (var trainingArea in trainingAreas)
+            {
+                Object.DestroyImmediate(trainingArea);
+            }
+            m_Replicator = null;
+        }
+
+        private static object[] NumAreasCases =
+        {
+            new object[] {1},
+            new object[] {2},
+            new object[] {5},
+            new object[] {7},
+            new object[] {8},
+            new object[] {64},
+            new object[] {63},
+        };
+
+        [TestCaseSource(nameof(NumAreasCases))]
+        public void TestComputeGridSize(int numAreas)
+        {
+            m_Replicator.numAreas = numAreas;
+            m_Replicator.Awake();
+            m_Replicator.OnEnable();
+            var m_CorrectGridSize = int3.zero;
+            var m_RootNumAreas = Mathf.Pow(numAreas, 1.0f / 3.0f);
+            m_CorrectGridSize.x = Mathf.CeilToInt(m_RootNumAreas);
+            m_CorrectGridSize.y = Mathf.CeilToInt(m_RootNumAreas);
+            m_CorrectGridSize.z = Mathf.CeilToInt((float)numAreas / (m_CorrectGridSize.x * m_CorrectGridSize.y));
+            Assert.GreaterOrEqual(m_Replicator.GridSize.x * m_Replicator.GridSize.y * m_Replicator.GridSize.z, m_Replicator.numAreas);
+            Assert.AreEqual(m_CorrectGridSize, m_Replicator.GridSize);
+        }
+
+        [Test]
+        public void TestAddEnvironments()
+        {
+            m_Replicator.numAreas = 10;
+            m_Replicator.buildOnly = false;
+            m_Replicator.Awake();
+            m_Replicator.OnEnable();
+            var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName);
+            Assert.AreEqual(10, trainingAreas.Count());
+        }
+
+        [Test]
+        public void TestAddEnvironmentsBuildOnly()
+        {
+            m_Replicator.numAreas = 10;
+            m_Replicator.buildOnly = true;
+            m_Replicator.Awake();
+            m_Replicator.OnEnable();
+            var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName);
+            Assert.AreEqual(1, trainingAreas.Count());
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4ebc4ba4d109cb07725de74739fdc04e210acc24
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 391a03d82068e44b5bba0ca55215b0c7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs b/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..78f5891785d09c6c1bb084429c440b1c519ed2cb
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs
@@ -0,0 +1,76 @@
+using NUnit.Framework;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+using Unity.MLAgents.Policies;
+using UnityEditor;
+using UnityEngine.TestTools;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class BehaviorParameterTests : IHeuristicProvider
+    {
+        const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_v1_0.onnx";
+        public void Heuristic(in ActionBuffers actionsOut)
+        {
+            // No-op
+        }
+
+        [Test]
+        public void TestNoModelInferenceOnlyThrows()
+        {
+            var gameObj = new GameObject();
+            var bp = gameObj.AddComponent<BehaviorParameters>();
+            bp.BehaviorType = BehaviorType.InferenceOnly;
+            var actionSpec = new ActionSpec();
+
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                bp.GeneratePolicy(actionSpec, new ActuatorManager());
+            });
+        }
+
+        [Test]
+        public void TestIsInHeuristicMode()
+        {
+            var gameObj = new GameObject();
+            var bp = gameObj.AddComponent<BehaviorParameters>();
+            bp.Model = null;
+            gameObj.AddComponent<Agent>();
+            bp.BehaviorType = BehaviorType.HeuristicOnly;
+            Assert.IsTrue(bp.IsInHeuristicMode());
+
+            bp.BehaviorType = BehaviorType.Default;
+            Assert.IsTrue(bp.IsInHeuristicMode());
+
+            bp.Model = ScriptableObject.CreateInstance<NNModel>();
+            Assert.IsFalse(bp.IsInHeuristicMode());
+        }
+
+        [Test]
+        public void TestPolicyUpdateEventFired()
+        {
+            var gameObj = new GameObject();
+            var bp = gameObj.AddComponent<BehaviorParameters>();
+            gameObj.AddComponent<Agent>().LazyInitialize();
+            bp.OnPolicyUpdated += delegate (bool isInHeuristicMode) { Debug.Log($"OnPolicyChanged:{isInHeuristicMode}"); };
+            bp.BehaviorType = BehaviorType.HeuristicOnly;
+            LogAssert.Expect(LogType.Log, $"OnPolicyChanged:{true}");
+
+            bp.BehaviorType = BehaviorType.Default;
+            LogAssert.Expect(LogType.Log, $"OnPolicyChanged:{true}");
+
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                bp.BehaviorType = BehaviorType.InferenceOnly;
+            });
+
+            bp.Model = AssetDatabase.LoadAssetAtPath<NNModel>(k_continuousONNXPath);
+            LogAssert.Expect(LogType.Log, $"OnPolicyChanged:{false}");
+
+            bp.BehaviorType = BehaviorType.HeuristicOnly;
+            LogAssert.Expect(LogType.Log, $"OnPolicyChanged:{true}");
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs.meta b/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0656104d8cba5cfe3fccfeca12b082d00c1c3bf2
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 877266b9e1bfe4330a68ab5f2da1836b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator.meta b/com.unity.ml-agents/Tests/Editor/Communicator.meta
new file mode 100644
index 0000000000000000000000000000000000000000..170d273a27c4c1ed34295947e03911dd1f1dbce1
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: da8f640243c749388a0329393c8fce64
+timeCreated: 1586386315
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs b/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..34bd3caaea78cefa5d551520bbe41bb3b28548c6
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs
@@ -0,0 +1,276 @@
+using System;
+using System.Text.RegularExpressions;
+using Google.Protobuf;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+
+using Unity.MLAgents.Analytics;
+using Unity.MLAgents.CommunicatorObjects;
+using UnityEngine;
+using UnityEngine.TestTools;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class GrpcExtensionsTests
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities();
+        }
+
+        [Test]
+        public void TestDefaultBrainParametersToProto()
+        {
+            // Should be able to convert a default instance to proto.
+            var brain = new BrainParameters();
+            brain.ToProto("foo", false);
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            brain.ToProto("foo", false);
+        }
+
+        [Test]
+        public void TestDefaultActionSpecToProto()
+        {
+            // Should be able to convert a default instance to proto.
+            var actionSpec = new ActionSpec();
+            actionSpec.ToBrainParametersProto("foo", false);
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false);
+
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities();
+            // Continuous
+            actionSpec = ActionSpec.MakeContinuous(3);
+            actionSpec.ToBrainParametersProto("foo", false);
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false);
+
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities();
+
+            // Discrete
+            actionSpec = ActionSpec.MakeDiscrete(1, 2, 3);
+            actionSpec.ToBrainParametersProto("foo", false);
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false);
+        }
+
+        [Test]
+        public void ToBrainParameters()
+        {
+            // Should be able to convert a default instance to proto.
+            var actionSpec = new ActionSpec();
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities();
+            // Continuous
+            actionSpec = ActionSpec.MakeContinuous(3);
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities();
+
+            // Discrete
+            actionSpec = ActionSpec.MakeDiscrete(1, 2, 3);
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                HybridActions = false
+            };
+            actionSpec.ToBrainParametersProto("foo", false).ToBrainParameters();
+        }
+
+        [Test]
+        public void TestDefaultAgentInfoToProto()
+        {
+            // Should be able to convert a default instance to proto.
+            var agentInfo = new AgentInfo();
+            var pairProto = agentInfo.ToInfoActionPairProto();
+            pairProto.AgentInfo.Observations.Add(new ObservationProto
+            {
+                CompressedData = ByteString.Empty,
+                CompressionType = CompressionTypeProto.None,
+                FloatData = new ObservationProto.Types.FloatData(),
+                ObservationType = ObservationTypeProto.Default,
+                Name = "Sensor"
+            });
+            pairProto.AgentInfo.Observations[0].Shape.Add(0);
+            pairProto.GetObservationSummaries();
+            agentInfo.ToAgentInfoProto();
+            agentInfo.groupId = 1;
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                MultiAgentGroups = false
+            };
+            agentInfo.ToAgentInfoProto();
+            LogAssert.Expect(LogType.Warning, new Regex(".+"));
+            Academy.Instance.TrainerCapabilities = new UnityRLCapabilities
+            {
+                BaseRLCapabilities = true,
+                MultiAgentGroups = true
+            };
+            agentInfo.ToAgentInfoProto();
+        }
+
+        [Test]
+        public void TestDefaultDemonstrationMetaDataToProto()
+        {
+            // Should be able to convert a default instance to proto.
+            var demoMetaData = new DemonstrationMetaData();
+            demoMetaData.ToProto();
+        }
+
+        class DummySensor : ISensor
+        {
+            public ObservationSpec ObservationSpec;
+            public SensorCompressionType CompressionType;
+
+            public ObservationSpec GetObservationSpec()
+            {
+                return ObservationSpec;
+            }
+
+            public int Write(ObservationWriter writer)
+            {
+                return 0;
+            }
+
+            public byte[] GetCompressedObservation()
+            {
+                return new byte[] { 13, 37 };
+            }
+
+            public void Update() { }
+
+            public void Reset() { }
+
+            public CompressionSpec GetCompressionSpec()
+            {
+                return new CompressionSpec(CompressionType);
+            }
+
+            public string GetName()
+            {
+                return "Dummy";
+            }
+        }
+
+        [Test]
+        public void TestGetObservationProtoCapabilities()
+        {
+            // Shape, compression type, concatenatedPngObservations, expect throw
+            var variants = new[]
+            {
+                // Vector observations
+                (new[] {3}, SensorCompressionType.None, false, false),
+                // Uncompressed floats
+                (new[] {4, 4, 3}, SensorCompressionType.None, false, false),
+                // Compressed floats, 3 channels
+                (new[] {4, 4, 3}, SensorCompressionType.PNG, false, true),
+
+                // Compressed floats, >3 channels
+                (new[] {4, 4, 4}, SensorCompressionType.PNG, false, false), // Unsupported - results in uncompressed
+                (new[] {4, 4, 4}, SensorCompressionType.PNG, true, true), // Supported compressed
+            };
+
+            foreach (var (shape, compressionType, supportsMultiPngObs, expectCompressed) in variants)
+            {
+                var inplaceShape = InplaceArray<int>.FromList(shape);
+                var dummySensor = new DummySensor();
+                var obsWriter = new ObservationWriter();
+
+                if (shape.Length == 1)
+                {
+                    dummySensor.ObservationSpec = ObservationSpec.Vector(shape[0]);
+                }
+                else if (shape.Length == 3)
+                {
+                    dummySensor.ObservationSpec = ObservationSpec.Visual(shape[0], shape[1], shape[2]);
+                }
+                else
+                {
+                    throw new ArgumentOutOfRangeException();
+                }
+                dummySensor.CompressionType = compressionType;
+                obsWriter.SetTarget(new float[128], inplaceShape, 0);
+
+                var caps = new UnityRLCapabilities
+                {
+                    ConcatenatedPngObservations = supportsMultiPngObs
+                };
+                Academy.Instance.TrainerCapabilities = caps;
+
+
+                var obsProto = dummySensor.GetObservationProto(obsWriter);
+                if (expectCompressed)
+                {
+                    Assert.Greater(obsProto.CompressedData.Length, 0);
+                    Assert.AreEqual(obsProto.FloatData, null);
+                }
+                else
+                {
+                    Assert.Greater(obsProto.FloatData.Data.Count, 0);
+                    Assert.AreEqual(obsProto.CompressedData.Length, 0);
+                }
+            }
+        }
+
+        [Test]
+        public void TestDefaultTrainingEvents()
+        {
+            var trainingEnvInit = new TrainingEnvironmentInitialized
+            {
+                PythonVersion = "test",
+            };
+            var trainingEnvInitEvent = trainingEnvInit.ToTrainingEnvironmentInitializedEvent();
+            Assert.AreEqual(trainingEnvInit.PythonVersion, trainingEnvInitEvent.TrainerPythonVersion);
+
+            var trainingBehavInit = new TrainingBehaviorInitialized
+            {
+                BehaviorName = "testBehavior",
+                ExtrinsicRewardEnabled = true,
+                CuriosityRewardEnabled = true,
+
+                RecurrentEnabled = true,
+                SelfPlayEnabled = true,
+            };
+            var trainingBehavInitEvent = trainingBehavInit.ToTrainingBehaviorInitializedEvent();
+            Assert.AreEqual(trainingBehavInit.BehaviorName, trainingBehavInitEvent.BehaviorName);
+
+            Assert.AreEqual(RewardSignals.Extrinsic | RewardSignals.Curiosity, trainingBehavInitEvent.RewardSignalFlags);
+            Assert.AreEqual(TrainingFeatures.Recurrent | TrainingFeatures.SelfPlay, trainingBehavInitEvent.TrainingFeatureFlags);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..411f1cd45ec165f8539030dbd355a5caabc54dad
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7aa28d0e370064c18bb8a913417ad21d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs b/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..937c8555fad4434504a21c565cfcdaa6831e4521
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs
@@ -0,0 +1,40 @@
+using NUnit.Framework;
+using UnityEngine.TestTools;
+
+namespace Unity.MLAgents.Tests.Communicator
+{
+    [TestFixture]
+    public class RpcCommunicatorTests
+    {
+        [Test]
+        public void TestCheckCommunicationVersionsAreCompatible()
+        {
+            var unityVerStr = "1.0.0";
+            var pythonVerStr = "1.0.0";
+
+            Assert.IsTrue(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+            LogAssert.NoUnexpectedReceived();
+
+            pythonVerStr = "1.1.0";
+            Assert.IsTrue(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+            LogAssert.NoUnexpectedReceived();
+
+            unityVerStr = "2.0.0";
+            Assert.IsFalse(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+
+            unityVerStr = "0.15.0";
+            pythonVerStr = "0.15.0";
+            Assert.IsTrue(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+            unityVerStr = "0.16.0";
+            Assert.IsFalse(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+            unityVerStr = "1.15.0";
+            Assert.IsFalse(RpcCommunicator.CheckCommunicationVersionsAreCompatible(unityVerStr,
+                pythonVerStr));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1d0689e5cb7e83378b85c80616d0b15c264a6b8e
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/RpcCommunicatorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 251fab8dff424abb95b2b381c7c924c3
+timeCreated: 1586386329
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs b/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..402ceda612f886b72f2be9bfcd2005d5b8e2d087
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs
@@ -0,0 +1,22 @@
+using System.Text.RegularExpressions;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEngine.TestTools;
+
+namespace Unity.MLAgents.Tests.Communicator
+{
+    [TestFixture]
+    public class UnityRLCapabilitiesTests
+    {
+        [Test]
+        public void TestWarnOnPythonMissingBaseRLCapabilities()
+        {
+            var caps = new UnityRLCapabilities();
+            Assert.False(caps.WarnOnPythonMissingBaseRLCapabilities());
+            LogAssert.NoUnexpectedReceived();
+            caps = new UnityRLCapabilities(false);
+            Assert.True(caps.WarnOnPythonMissingBaseRLCapabilities());
+            LogAssert.Expect(LogType.Warning, new Regex(".+"));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fc7b8cec7ad9bd6ab590872c2376558e0147a54e
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Communicator/UnityRLCapabilitiesTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e6a3e82911b84029a446dcfd2d8af520
+timeCreated: 1587695055
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs b/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..55060f5645d20ea4befd058c884785d52b783729
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs
@@ -0,0 +1,150 @@
+using NUnit.Framework;
+using UnityEngine;
+using System.IO.Abstractions.TestingHelpers;
+using System.Reflection;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.CommunicatorObjects;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Utils.Tests;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class DemonstrationTests
+    {
+        const string k_DemoDirectory = "Assets/Demonstrations/";
+        const string k_ExtensionType = ".demo";
+        const string k_DemoName = "Test";
+
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestSanitization()
+        {
+            const string dirtyString = "abc1234567&!@";
+            const string knownCleanString = "abc123";
+            var cleanString = DemonstrationRecorder.SanitizeName(dirtyString, 6);
+            Assert.AreNotEqual(dirtyString, cleanString);
+            Assert.AreEqual(cleanString, knownCleanString);
+        }
+
+        [Test]
+        public void TestStoreInitialize()
+        {
+            var fileSystem = new MockFileSystem();
+
+            var gameobj = new GameObject("gameObj");
+
+            var bp = gameobj.AddComponent<BehaviorParameters>();
+            bp.BrainParameters.VectorObservationSize = 3;
+            bp.BrainParameters.NumStackedVectorObservations = 2;
+            bp.BrainParameters.VectorActionDescriptions = new[] { "TestActionA", "TestActionB" };
+            bp.BrainParameters.ActionSpec = ActionSpec.MakeDiscrete(2, 2);
+
+            gameobj.AddComponent<TestAgent>();
+
+            Assert.IsFalse(fileSystem.Directory.Exists(k_DemoDirectory));
+
+            var demoRec = gameobj.AddComponent<DemonstrationRecorder>();
+            demoRec.Record = true;
+            demoRec.DemonstrationName = k_DemoName;
+            demoRec.DemonstrationDirectory = k_DemoDirectory;
+            var demoWriter = demoRec.LazyInitialize(fileSystem);
+
+            Assert.IsTrue(fileSystem.Directory.Exists(k_DemoDirectory));
+            Assert.IsTrue(fileSystem.FileExists(k_DemoDirectory + k_DemoName + k_ExtensionType));
+
+            var agentInfo = new AgentInfo
+            {
+                reward = 1f,
+                discreteActionMasks = new[] { false, true },
+                done = true,
+                episodeId = 5,
+                maxStepReached = true,
+                storedActions = new ActionBuffers(null, new[] { 0, 1 }),
+            };
+
+
+            demoWriter.Record(agentInfo, new System.Collections.Generic.List<ISensor>());
+            demoRec.Close();
+
+            // Make sure close can be called multiple times
+            demoWriter.Close();
+            demoRec.Close();
+
+            // Make sure trying to write after closing doesn't raise an error.
+            demoWriter.Record(agentInfo, new System.Collections.Generic.List<ISensor>());
+        }
+
+        public class ObservationAgent : TestAgent
+        {
+            public override void CollectObservations(VectorSensor sensor)
+            {
+                collectObservationsCalls += 1;
+                sensor.AddObservation(1f);
+                sensor.AddObservation(2f);
+                sensor.AddObservation(3f);
+            }
+        }
+
+        [Test]
+        public void TestAgentWrite()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bpA = agentGo1.AddComponent<BehaviorParameters>();
+            bpA.BrainParameters.VectorObservationSize = 3;
+            bpA.BrainParameters.NumStackedVectorObservations = 1;
+            bpA.BrainParameters.VectorActionDescriptions = new[] { "TestActionA", "TestActionB" };
+            bpA.BrainParameters.ActionSpec = ActionSpec.MakeDiscrete(2, 2);
+
+            agentGo1.AddComponent<ObservationAgent>();
+            var agent1 = agentGo1.GetComponent<ObservationAgent>();
+
+            agentGo1.AddComponent<DemonstrationRecorder>();
+            var demoRecorder = agentGo1.GetComponent<DemonstrationRecorder>();
+            var fileSystem = new MockFileSystem();
+            demoRecorder.DemonstrationDirectory = k_DemoDirectory;
+            demoRecorder.DemonstrationName = "TestBrain";
+            demoRecorder.Record = true;
+            demoRecorder.LazyInitialize(fileSystem);
+
+            var agentEnableMethod = typeof(Agent).GetMethod("OnEnable",
+                BindingFlags.Instance | BindingFlags.NonPublic);
+            var agentSendInfo = typeof(Agent).GetMethod("SendInfo",
+                BindingFlags.Instance | BindingFlags.NonPublic);
+
+            agentEnableMethod?.Invoke(agent1, new object[] { });
+
+            // Step the agent
+            agent1.RequestDecision();
+            agentSendInfo?.Invoke(agent1, new object[] { });
+
+            demoRecorder.Close();
+
+            // Read back the demo file and make sure observations were written
+            var reader = fileSystem.File.OpenRead("Assets/Demonstrations/TestBrain.demo");
+            reader.Seek(DemonstrationWriter.MetaDataBytes + 1, 0);
+            BrainParametersProto.Parser.ParseDelimitedFrom(reader);
+
+            var agentInfoProto = AgentInfoActionPairProto.Parser.ParseDelimitedFrom(reader).AgentInfo;
+            var obs = agentInfoProto.Observations[2]; // skip dummy sensors
+            {
+                var vecObs = obs.FloatData.Data;
+                Assert.AreEqual(bpA.BrainParameters.VectorObservationSize, vecObs.Count);
+                for (var i = 0; i < vecObs.Count; i++)
+                {
+                    Assert.AreEqual((float)i + 1, vecObs[i]);
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs.meta b/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..434861ff9b9625cff65d36e94a54d5c49d89bfa5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 4c5a970f5b6be4b57b3bd7a5f84c3623
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference.meta b/com.unity.ml-agents/Tests/Editor/Inference.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1427471ab066360cb0a8226fb0a4fe50749bab5c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 7b8fc3bc69d3a4cd9a66ad334f944fb2
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs b/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..aa3fc8c40d8b0113402dca7ddca76c8e977e8521
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs
@@ -0,0 +1,85 @@
+using System.Collections.Generic;
+using Unity.Barracuda;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+
+namespace Unity.MLAgents.Tests
+{
+    public class DiscreteActionOutputApplierTest
+    {
+        [Test]
+        public void TestDiscreteApply()
+        {
+            var actionSpec = ActionSpec.MakeDiscrete(3, 2);
+
+            var applier = new DiscreteActionOutputApplier(actionSpec, 2020, null);
+            var agentIds = new List<int> { 42, 1337 };
+            var actionBuffers = new Dictionary<int, ActionBuffers>();
+            actionBuffers[42] = new ActionBuffers(actionSpec);
+            actionBuffers[1337] = new ActionBuffers(actionSpec);
+
+            var actionTensor = new TensorProxy
+            {
+                data = new Tensor(
+                    2,
+                    2,
+                    new[]
+                    {
+                        2.0f, // Agent 0, branch 0
+                        1.0f, // Agent 0, branch 1
+                        0.0f, // Agent 1, branch 0
+                        0.0f  // Agent 1, branch 1
+                    }),
+                shape = new long[] { 2, 2 },
+                valueType = TensorProxy.TensorType.FloatingPoint
+            };
+
+            applier.Apply(actionTensor, agentIds, actionBuffers);
+            Assert.AreEqual(2, actionBuffers[42].DiscreteActions[0]);
+            Assert.AreEqual(1, actionBuffers[42].DiscreteActions[1]);
+
+            Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[0]);
+            Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[1]);
+        }
+    }
+
+    public class LegacyDiscreteActionOutputApplierTest
+    {
+        [Test]
+        public void TestDiscreteApply()
+        {
+            var actionSpec = ActionSpec.MakeDiscrete(3, 2);
+            const float smallLogProb = -1000.0f;
+            const float largeLogProb = -1.0f;
+
+            var logProbs = new TensorProxy
+            {
+                data = new Tensor(
+                    2,
+                    5,
+                    new[]
+                    {
+                        smallLogProb, smallLogProb, largeLogProb, // Agent 0, branch 0
+                        smallLogProb, largeLogProb,               // Agent 0, branch 1
+                        largeLogProb, smallLogProb, smallLogProb, // Agent 1, branch 0
+                        largeLogProb, smallLogProb,               // Agent 1, branch 1
+                    }),
+                valueType = TensorProxy.TensorType.FloatingPoint
+            };
+
+            var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 2020, null);
+            var agentIds = new List<int> { 42, 1337 };
+            var actionBuffers = new Dictionary<int, ActionBuffers>();
+            actionBuffers[42] = new ActionBuffers(actionSpec);
+            actionBuffers[1337] = new ActionBuffers(actionSpec);
+
+            applier.Apply(logProbs, agentIds, actionBuffers);
+            Assert.AreEqual(2, actionBuffers[42].DiscreteActions[0]);
+            Assert.AreEqual(1, actionBuffers[42].DiscreteActions[1]);
+
+            Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[0]);
+            Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[1]);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ac93a50aaba54c1924c30d7c96b2e53b58e226f5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/DiscreteActionOutputApplierTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: aa4c4ceac5f246a0b341958724ecd752
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e679254908c1ed20168ae274e7e826fd7c55855a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs
@@ -0,0 +1,200 @@
+using System.Collections.Generic;
+using NUnit.Framework;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+
+namespace Unity.MLAgents.Tests
+{
+    public class EditModeTestInternalBrainTensorApplier
+    {
+        class TestAgent : Agent
+        {
+        }
+
+        [Test]
+        public void Construction()
+        {
+            var actionSpec = new ActionSpec();
+            var alloc = new TensorCachingAllocator();
+            var mem = new Dictionary<int, List<float>>();
+            var tensorGenerator = new TensorApplier(actionSpec, 0, alloc, mem);
+            Assert.IsNotNull(tensorGenerator);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void ApplyContinuousActionOutput()
+        {
+            var actionSpec = ActionSpec.MakeContinuous(3);
+            var inputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 3 },
+                data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
+            };
+
+            var applier = new ContinuousActionOutputApplier(actionSpec);
+
+            var agentIds = new List<int>() { 0, 1 };
+            // Dictionary from AgentId to Action
+            var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
+
+            applier.Apply(inputTensor, agentIds, actionDict);
+
+
+            Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
+            Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
+            Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
+
+            Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
+            Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
+            Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
+        }
+
+        [Test]
+        public void ApplyDiscreteActionOutputLegacy()
+        {
+            var actionSpec = ActionSpec.MakeDiscrete(2, 3);
+            var inputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 5 },
+                data = new Tensor(
+                    2,
+                    5,
+                    new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
+            };
+            var alloc = new TensorCachingAllocator();
+            var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc);
+
+            var agentIds = new List<int>() { 0, 1 };
+            // Dictionary from AgentId to Action
+            var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
+
+
+            applier.Apply(inputTensor, agentIds, actionDict);
+
+            Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
+
+            Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void ApplyDiscreteActionOutput()
+        {
+            var actionSpec = ActionSpec.MakeDiscrete(2, 3);
+            var inputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 2 },
+                data = new Tensor(
+                    2,
+                    2,
+                    new[] { 1f, 1f, 1f, 2f }),
+            };
+            var alloc = new TensorCachingAllocator();
+            var applier = new DiscreteActionOutputApplier(actionSpec, 0, alloc);
+
+            var agentIds = new List<int>() { 0, 1 };
+            // Dictionary from AgentId to Action
+            var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
+
+
+            applier.Apply(inputTensor, agentIds, actionDict);
+
+            Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
+
+            Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void ApplyHybridActionOutputLegacy()
+        {
+            var actionSpec = new ActionSpec(3, new[] { 2, 3 });
+            var continuousInputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 3 },
+                data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
+            };
+            var discreteInputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 8 },
+                data = new Tensor(
+                    2,
+                    5,
+                    new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
+            };
+            var continuousApplier = new ContinuousActionOutputApplier(actionSpec);
+            var alloc = new TensorCachingAllocator();
+            var discreteApplier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc);
+
+            var agentIds = new List<int>() { 0, 1 };
+            // Dictionary from AgentId to Action
+            var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
+
+
+            continuousApplier.Apply(continuousInputTensor, agentIds, actionDict);
+            discreteApplier.Apply(discreteInputTensor, agentIds, actionDict);
+
+            Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
+            Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
+            Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
+            Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
+
+            Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
+            Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
+            Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
+            Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void ApplyHybridActionOutput()
+        {
+            var actionSpec = new ActionSpec(3, new[] { 2, 3 });
+            var continuousInputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 3 },
+                data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
+            };
+            var discreteInputTensor = new TensorProxy()
+            {
+                shape = new long[] { 2, 2 },
+                data = new Tensor(
+                    2,
+                    2,
+                    new[] { 1f, 1f, 1f, 2f }),
+            };
+            var continuousApplier = new ContinuousActionOutputApplier(actionSpec);
+            var alloc = new TensorCachingAllocator();
+            var discreteApplier = new DiscreteActionOutputApplier(actionSpec, 0, alloc);
+
+            var agentIds = new List<int>() { 0, 1 };
+            // Dictionary from AgentId to Action
+            var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
+
+
+            continuousApplier.Apply(continuousInputTensor, agentIds, actionDict);
+            discreteApplier.Apply(discreteInputTensor, agentIds, actionDict);
+
+            Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
+            Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
+            Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
+            Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
+
+            Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
+            Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
+            Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
+            Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
+            Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
+            alloc.Dispose();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5f80c46f6ded8f0adf5d1a112bdf12191fba7f22
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: be419f7ed5c24b24a6f2636d3b107535
+timeCreated: 1537915674
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs
new file mode 100644
index 0000000000000000000000000000000000000000..579a204c4bbb7f3e5ff11ed9a6edc27c690a75d7
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs
@@ -0,0 +1,191 @@
+using System.Collections.Generic;
+using Unity.Barracuda;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Utils.Tests;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class EditModeTestInternalBrainTensorGenerator
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        static List<TestAgent> GetFakeAgents(ObservableAttributeOptions observableAttributeOptions = ObservableAttributeOptions.Ignore)
+        {
+            var goA = new GameObject("goA");
+            var bpA = goA.AddComponent<BehaviorParameters>();
+            bpA.BrainParameters.VectorObservationSize = 3;
+            bpA.BrainParameters.NumStackedVectorObservations = 1;
+            bpA.ObservableAttributeHandling = observableAttributeOptions;
+            var agentA = goA.AddComponent<TestAgent>();
+
+            var goB = new GameObject("goB");
+            var bpB = goB.AddComponent<BehaviorParameters>();
+            bpB.BrainParameters.VectorObservationSize = 3;
+            bpB.BrainParameters.NumStackedVectorObservations = 1;
+            bpB.ObservableAttributeHandling = observableAttributeOptions;
+            var agentB = goB.AddComponent<TestAgent>();
+
+            var agents = new List<TestAgent> { agentA, agentB };
+            foreach (var agent in agents)
+            {
+                agent.LazyInitialize();
+            }
+            agentA.collectObservationsSensor.AddObservation(new Vector3(1, 2, 3));
+            agentB.collectObservationsSensor.AddObservation(new Vector3(4, 5, 6));
+
+            var infoA = new AgentInfo
+            {
+                storedActions = new ActionBuffers(null, new[] { 1, 2 }),
+                discreteActionMasks = null,
+            };
+
+            var infoB = new AgentInfo
+            {
+                storedActions = new ActionBuffers(null, new[] { 3, 4 }),
+                discreteActionMasks = new[] { true, false, false, false, false },
+            };
+
+
+            agentA._Info = infoA;
+            agentB._Info = infoB;
+            return agents;
+        }
+
+        [Test]
+        public void Construction()
+        {
+            var alloc = new TensorCachingAllocator();
+            var mem = new Dictionary<int, List<float>>();
+            var tensorGenerator = new TensorGenerator(0, alloc, mem);
+            Assert.IsNotNull(tensorGenerator);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void GenerateBatchSize()
+        {
+            var inputTensor = new TensorProxy();
+            var alloc = new TensorCachingAllocator();
+            const int batchSize = 4;
+            var generator = new BatchSizeGenerator(alloc);
+            generator.Generate(inputTensor, batchSize, null);
+            Assert.IsNotNull(inputTensor.data);
+            Assert.AreEqual(inputTensor.data[0], batchSize);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void GenerateSequenceLength()
+        {
+            var inputTensor = new TensorProxy();
+            var alloc = new TensorCachingAllocator();
+            const int batchSize = 4;
+            var generator = new SequenceLengthGenerator(alloc);
+            generator.Generate(inputTensor, batchSize, null);
+            Assert.IsNotNull(inputTensor.data);
+            Assert.AreEqual(inputTensor.data[0], 1);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void GenerateVectorObservation()
+        {
+            var inputTensor = new TensorProxy
+            {
+                shape = new long[] { 2, 4 }
+            };
+            const int batchSize = 4;
+            var agentInfos = GetFakeAgents(ObservableAttributeOptions.ExamineAll);
+            var alloc = new TensorCachingAllocator();
+            var generator = new ObservationGenerator(alloc);
+            generator.AddSensorIndex(0); // ObservableAttribute (size 1)
+            generator.AddSensorIndex(1); // TestSensor (size 0)
+            generator.AddSensorIndex(2); // TestSensor (size 0)
+            generator.AddSensorIndex(3); // VectorSensor (size 3)
+            var agent0 = agentInfos[0];
+            var agent1 = agentInfos[1];
+            var inputs = new List<AgentInfoSensorsPair>
+            {
+                new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors},
+                new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors},
+            };
+            generator.Generate(inputTensor, batchSize, inputs);
+            Assert.IsNotNull(inputTensor.data);
+            Assert.AreEqual(inputTensor.data[0, 1], 1);
+            Assert.AreEqual(inputTensor.data[0, 3], 3);
+            Assert.AreEqual(inputTensor.data[1, 1], 4);
+            Assert.AreEqual(inputTensor.data[1, 3], 6);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void GeneratePreviousActionInput()
+        {
+            var inputTensor = new TensorProxy
+            {
+                shape = new long[] { 2, 2 },
+                valueType = TensorProxy.TensorType.Integer
+            };
+            const int batchSize = 4;
+            var agentInfos = GetFakeAgents();
+            var alloc = new TensorCachingAllocator();
+            var generator = new PreviousActionInputGenerator(alloc);
+            var agent0 = agentInfos[0];
+            var agent1 = agentInfos[1];
+            var inputs = new List<AgentInfoSensorsPair>
+            {
+                new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors},
+                new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors},
+            };
+            generator.Generate(inputTensor, batchSize, inputs);
+            Assert.IsNotNull(inputTensor.data);
+            Assert.AreEqual(inputTensor.data[0, 0], 1);
+            Assert.AreEqual(inputTensor.data[0, 1], 2);
+            Assert.AreEqual(inputTensor.data[1, 0], 3);
+            Assert.AreEqual(inputTensor.data[1, 1], 4);
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void GenerateActionMaskInput()
+        {
+            var inputTensor = new TensorProxy
+            {
+                shape = new long[] { 2, 5 },
+                valueType = TensorProxy.TensorType.FloatingPoint
+            };
+            const int batchSize = 4;
+            var agentInfos = GetFakeAgents();
+            var alloc = new TensorCachingAllocator();
+            var generator = new ActionMaskInputGenerator(alloc);
+
+            var agent0 = agentInfos[0];
+            var agent1 = agentInfos[1];
+            var inputs = new List<AgentInfoSensorsPair>
+            {
+                new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors},
+                new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors},
+            };
+
+            generator.Generate(inputTensor, batchSize, inputs);
+            Assert.IsNotNull(inputTensor.data);
+            Assert.AreEqual(inputTensor.data[0, 0], 1);
+            Assert.AreEqual(inputTensor.data[0, 4], 1);
+            Assert.AreEqual(inputTensor.data[1, 0], 0);
+            Assert.AreEqual(inputTensor.data[1, 4], 1);
+            alloc.Dispose();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..bffd9e8144840dc85b279cf330a1f4bd3260cea4
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorGenerator.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d2d2076c51c414ac7a91f8fbf15d4f7c
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs b/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0fbc933f5084d94289c5851b3c469be1697489f3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs
@@ -0,0 +1,240 @@
+using System;
+using System.Linq;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEditor;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Policies;
+using System.Collections.Generic;
+
+namespace Unity.MLAgents.Tests
+{
+    public class FloatThresholdComparer : IEqualityComparer<float>
+    {
+        private readonly float _threshold;
+        public FloatThresholdComparer(float threshold)
+        {
+            _threshold = threshold;
+        }
+
+        public bool Equals(float x, float y)
+        {
+            return Math.Abs(x - y) < _threshold;
+        }
+
+        public int GetHashCode(float f)
+        {
+            throw new NotImplementedException("Unable to generate a hash code for threshold floats, do not use this method");
+        }
+    }
+
+    [TestFixture]
+    public class ModelRunnerTest
+    {
+        const string k_hybrid_ONNX_recurr_v2 = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis8vec_2c_2_3d_v2_0.onnx";
+
+        const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_v1_0.onnx";
+        const string k_discreteONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_obsolete_recurr_v1_0.onnx";
+        const string k_hybridONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction_v1_0.onnx";
+        const string k_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated_v1_0.nn";
+        const string k_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated_v1_0.nn";
+        // models with deterministic action tensors
+        private const string k_deterministic_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/deterDiscrete1obs3action_v2_0.onnx";
+        private const string k_deterministic_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/deterContinuous2vis8vec2action_v2_0.onnx";
+
+        NNModel hybridONNXModelV2;
+        NNModel continuousONNXModel;
+        NNModel discreteONNXModel;
+        NNModel hybridONNXModel;
+        NNModel continuousNNModel;
+        NNModel discreteNNModel;
+        NNModel deterministicDiscreteNNModel;
+        NNModel deterministicContinuousNNModel;
+        Test3DSensorComponent sensor_21_20_3;
+        Test3DSensorComponent sensor_20_22_3;
+
+
+        ActionSpec GetContinuous2vis8vec2actionActionSpec()
+        {
+            return ActionSpec.MakeContinuous(2);
+        }
+
+        ActionSpec GetDiscrete1vis0vec_2_3action_recurrModelActionSpec()
+        {
+            return ActionSpec.MakeDiscrete(2, 3);
+        }
+
+        ActionSpec GetHybrid0vis53vec_3c_2dActionSpec()
+        {
+            return new ActionSpec(3, new[] { 2 });
+        }
+
+        [SetUp]
+        public void SetUp()
+        {
+            hybridONNXModelV2 = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybrid_ONNX_recurr_v2, typeof(NNModel));
+
+            continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
+            discreteONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteONNXPath, typeof(NNModel));
+            hybridONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybridONNXPath, typeof(NNModel));
+            continuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousNNPath, typeof(NNModel));
+            discreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteNNPath, typeof(NNModel));
+            deterministicDiscreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_deterministic_discreteNNPath, typeof(NNModel));
+            deterministicContinuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_deterministic_continuousNNPath, typeof(NNModel));
+            var go = new GameObject("SensorA");
+            sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);
+            sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_20_22_3.Sensor = new Test3DSensor("SensorB", 20, 22, 3);
+        }
+
+        [Test]
+        public void TestModelExist()
+        {
+            Assert.IsNotNull(continuousONNXModel);
+            Assert.IsNotNull(discreteONNXModel);
+            Assert.IsNotNull(hybridONNXModel);
+            Assert.IsNotNull(continuousNNModel);
+            Assert.IsNotNull(discreteNNModel);
+            Assert.IsNotNull(hybridONNXModelV2);
+            Assert.IsNotNull(deterministicDiscreteNNModel);
+            Assert.IsNotNull(deterministicContinuousNNModel);
+        }
+
+        [Test]
+        public void TestCreation()
+        {
+            var inferenceDevice = InferenceDevice.Burst;
+            var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec(), inferenceDevice);
+            modelRunner.Dispose();
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                // Cannot load a model trained with 1.x that has an LSTM
+                modelRunner = new ModelRunner(discreteONNXModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec(), inferenceDevice);
+                modelRunner.Dispose();
+            });
+            modelRunner = new ModelRunner(hybridONNXModel, GetHybrid0vis53vec_3c_2dActionSpec(), inferenceDevice);
+            modelRunner.Dispose();
+            modelRunner = new ModelRunner(continuousNNModel, GetContinuous2vis8vec2actionActionSpec(), inferenceDevice);
+            modelRunner.Dispose();
+
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                // Cannot load a model trained with 1.x that has an LSTM
+                modelRunner = new ModelRunner(discreteNNModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec(), inferenceDevice);
+                modelRunner.Dispose();
+            });
+            // This one was trained with 2.0 so it should not raise an error:
+            modelRunner = new ModelRunner(hybridONNXModelV2, new ActionSpec(2, new[] { 2, 3 }), inferenceDevice);
+            modelRunner.Dispose();
+
+            // V2.0 Model that has serialized deterministic action tensors, discrete
+            modelRunner = new ModelRunner(deterministicDiscreteNNModel, new ActionSpec(0, new[] { 7 }), inferenceDevice);
+            modelRunner.Dispose();
+            // V2.0 Model that has serialized deterministic action tensors, continuous
+            modelRunner = new ModelRunner(deterministicContinuousNNModel,
+                GetContinuous2vis8vec2actionActionSpec(), inferenceDevice,
+                deterministicInference: true);
+            modelRunner.Dispose();
+        }
+
+        [Test]
+        public void TestHasModel()
+        {
+            var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec(), InferenceDevice.CPU);
+            Assert.True(modelRunner.HasModel(continuousONNXModel, InferenceDevice.CPU));
+            Assert.False(modelRunner.HasModel(continuousONNXModel, InferenceDevice.GPU));
+            Assert.False(modelRunner.HasModel(discreteONNXModel, InferenceDevice.CPU));
+            modelRunner.Dispose();
+        }
+
+        [Test]
+        public void TestRunModel()
+        {
+            var actionSpec = GetContinuous2vis8vec2actionActionSpec();
+            var modelRunner = new ModelRunner(continuousONNXModel, actionSpec, InferenceDevice.Burst);
+            var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
+            var info1 = new AgentInfo();
+            info1.episodeId = 1;
+            modelRunner.PutObservations(info1, new[]
+            {
+                sensor_8,
+                sensor_21_20_3.CreateSensors()[0],
+                sensor_20_22_3.CreateSensors()[0]
+            }.ToList());
+            var info2 = new AgentInfo();
+            info2.episodeId = 2;
+            modelRunner.PutObservations(info2, new[]
+            {
+                sensor_8,
+                sensor_21_20_3.CreateSensors()[0],
+                sensor_20_22_3.CreateSensors()[0]
+            }.ToList());
+
+            modelRunner.DecideBatch();
+
+            Assert.IsFalse(modelRunner.GetAction(1).Equals(ActionBuffers.Empty));
+            Assert.IsFalse(modelRunner.GetAction(2).Equals(ActionBuffers.Empty));
+            Assert.IsTrue(modelRunner.GetAction(3).Equals(ActionBuffers.Empty));
+            Assert.AreEqual(actionSpec.NumDiscreteActions, modelRunner.GetAction(1).DiscreteActions.Length);
+            modelRunner.Dispose();
+        }
+
+        [Test]
+        public void TestRunModel_stochastic()
+        {
+            var actionSpec = GetContinuous2vis8vec2actionActionSpec();
+            // deterministicInference = false by default
+            var modelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst);
+            var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
+            var info1 = new AgentInfo();
+            var obs = new[]
+            {
+                sensor_8,
+                sensor_21_20_3.CreateSensors()[0],
+                sensor_20_22_3.CreateSensors()[0]
+            }.ToList();
+            info1.episodeId = 1;
+            modelRunner.PutObservations(info1, obs);
+            modelRunner.DecideBatch();
+            var stochAction1 = (float[])modelRunner.GetAction(1).ContinuousActions.Array.Clone();
+
+            modelRunner.PutObservations(info1, obs);
+            modelRunner.DecideBatch();
+            var stochAction2 = (float[])modelRunner.GetAction(1).ContinuousActions.Array.Clone();
+            // Stochastic action selection should output randomly different action values with same obs
+            Assert.IsFalse(Enumerable.SequenceEqual(stochAction1, stochAction2, new FloatThresholdComparer(0.001f)));
+            modelRunner.Dispose();
+        }
+
+        [Test]
+        public void TestRunModel_deterministic()
+        {
+            var actionSpec = GetContinuous2vis8vec2actionActionSpec();
+            var modelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst);
+            var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
+            var info1 = new AgentInfo();
+            var obs = new[]
+            {
+                sensor_8,
+                sensor_21_20_3.CreateSensors()[0],
+                sensor_20_22_3.CreateSensors()[0]
+            }.ToList();
+            var deterministicModelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst,
+                deterministicInference: true);
+            info1.episodeId = 1;
+            deterministicModelRunner.PutObservations(info1, obs);
+            deterministicModelRunner.DecideBatch();
+            var deterministicAction1 = (float[])deterministicModelRunner.GetAction(1).ContinuousActions.Array.Clone();
+
+            deterministicModelRunner.PutObservations(info1, obs);
+            deterministicModelRunner.DecideBatch();
+            var deterministicAction2 = (float[])deterministicModelRunner.GetAction(1).ContinuousActions.Array.Clone();
+            // Deterministic action selection should output same action everytime
+            Assert.IsTrue(Enumerable.SequenceEqual(deterministicAction1, deterministicAction2, new FloatThresholdComparer(0.001f)));
+            modelRunner.Dispose();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..273d3bb579936f3734845e7ac62e5c133876e1ac
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/ModelRunnerTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a2b924bac7d86467183ad9dc1436e550
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs b/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ae9d52d74be7b803098f1cf9d16935e0a8e5cce5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs
@@ -0,0 +1,558 @@
+using System.Linq;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEditor;
+using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Tests
+{
+    public class Test3DSensorComponent : SensorComponent
+    {
+        public ISensor Sensor;
+
+        public override ISensor[] CreateSensors()
+        {
+            return new ISensor[] { Sensor };
+        }
+    }
+
+    public class Test3DSensor : ISensor, IBuiltInSensor
+    {
+        int m_Width;
+        int m_Height;
+        int m_Channels;
+        string m_Name;
+        // Dummy value for the IBuiltInSensor interface
+        public const int k_BuiltInSensorType = -42;
+
+        public Test3DSensor(string name, int width, int height, int channels)
+        {
+            m_Width = width;
+            m_Height = height;
+            m_Channels = channels;
+            m_Name = name;
+        }
+
+        public ObservationSpec GetObservationSpec()
+        {
+            return ObservationSpec.Visual(m_Height, m_Width, m_Channels);
+        }
+
+        public int Write(ObservationWriter writer)
+        {
+            for (int i = 0; i < m_Width * m_Height * m_Channels; i++)
+            {
+                writer[i] = 0.0f;
+            }
+            return m_Width * m_Height * m_Channels;
+        }
+
+        public byte[] GetCompressedObservation()
+        {
+            return new byte[0];
+        }
+
+        public void Update() { }
+        public void Reset() { }
+
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        public BuiltInSensorType GetBuiltInSensorType()
+        {
+            return (BuiltInSensorType)k_BuiltInSensorType;
+        }
+    }
+
+    [TestFixture]
+    public class ParameterLoaderTest
+    {
+        const string k_discrete_ONNX_v2 = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete_rank2_vector_v2_0.onnx";
+        const string k_hybrid_ONNX_recurr_v2 = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis8vec_2c_2_3d_v2_0.onnx";
+
+
+        // ONNX model with continuous/discrete action output (support hybrid action)
+        const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_v1_0.onnx";
+        const string k_discreteONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_obsolete_recurr_v1_0.onnx";
+        const string k_hybridONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction_v1_0.onnx";
+        // NN model with single action output (deprecated, does not support hybrid action).
+        // Same BrainParameters settings as the corresponding ONNX model.
+        const string k_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated_v1_0.nn";
+        const string k_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated_v1_0.nn";
+
+        NNModel rank2ONNXModel;
+        NNModel hybridRecurrV2Model;
+        NNModel continuousONNXModel;
+        NNModel discreteONNXModel;
+        NNModel hybridONNXModel;
+        NNModel continuousNNModel;
+        NNModel discreteNNModel;
+        Test3DSensorComponent sensor_21_20_3;
+        Test3DSensorComponent sensor_20_22_3;
+        BufferSensor sensor_23_20;
+        VectorSensor sensor_8;
+        VectorSensor sensor_10;
+
+        BrainParameters GetContinuous2vis8vec2actionBrainParameters()
+        {
+            var validBrainParameters = new BrainParameters();
+            validBrainParameters.VectorObservationSize = 8;
+            validBrainParameters.NumStackedVectorObservations = 1;
+            validBrainParameters.ActionSpec = ActionSpec.MakeContinuous(2);
+            return validBrainParameters;
+        }
+
+        BrainParameters GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters()
+        {
+            var validBrainParameters = new BrainParameters();
+            validBrainParameters.VectorObservationSize = 0;
+            validBrainParameters.NumStackedVectorObservations = 1;
+            validBrainParameters.ActionSpec = ActionSpec.MakeDiscrete(2, 3);
+            return validBrainParameters;
+        }
+
+        BrainParameters GetHybridBrainParameters()
+        {
+            var validBrainParameters = new BrainParameters();
+            validBrainParameters.VectorObservationSize = 53;
+            validBrainParameters.NumStackedVectorObservations = 1;
+            validBrainParameters.ActionSpec = new ActionSpec(3, new[] { 2 });
+            return validBrainParameters;
+        }
+
+        BrainParameters GetRank2BrainParameters()
+        {
+            var validBrainParameters = new BrainParameters();
+            validBrainParameters.VectorObservationSize = 4;
+            validBrainParameters.NumStackedVectorObservations = 2;
+            validBrainParameters.ActionSpec = ActionSpec.MakeDiscrete(3, 3, 3);
+            return validBrainParameters;
+        }
+
+        BrainParameters GetRecurrHybridBrainParameters()
+        {
+            var validBrainParameters = new BrainParameters();
+            validBrainParameters.VectorObservationSize = 8;
+            validBrainParameters.NumStackedVectorObservations = 1;
+            validBrainParameters.ActionSpec = new ActionSpec(2, new int[] { 2, 3 });
+            return validBrainParameters;
+        }
+
+        [SetUp]
+        public void SetUp()
+        {
+            continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
+            discreteONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteONNXPath, typeof(NNModel));
+            hybridONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybridONNXPath, typeof(NNModel));
+            continuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousNNPath, typeof(NNModel));
+            discreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteNNPath, typeof(NNModel));
+            rank2ONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discrete_ONNX_v2, typeof(NNModel));
+            hybridRecurrV2Model = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybrid_ONNX_recurr_v2, typeof(NNModel));
+            var go = new GameObject("SensorA");
+            sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);
+            sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>();
+            sensor_20_22_3.Sensor = new Test3DSensor("SensorA", 20, 22, 3);
+            sensor_23_20 = new BufferSensor(20, 23, "BufferSensor");
+            sensor_8 = new VectorSensor(8, "VectorSensor8");
+            sensor_10 = new VectorSensor(10, "VectorSensor10");
+        }
+
+        [Test]
+        public void TestModelExist()
+        {
+            Assert.IsNotNull(continuousONNXModel);
+            Assert.IsNotNull(discreteONNXModel);
+            Assert.IsNotNull(hybridONNXModel);
+            Assert.IsNotNull(continuousNNModel);
+            Assert.IsNotNull(discreteNNModel);
+            Assert.IsNotNull(rank2ONNXModel);
+            Assert.IsNotNull(hybridRecurrV2Model);
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestGetInputTensorsContinuous(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
+            var inputNames = model.GetInputNames();
+            // Model should contain 3 inputs : vector, visual 1 and visual 2
+            Assert.AreEqual(3, inputNames.Count());
+            Assert.Contains(TensorNames.VectorObservationPlaceholder, inputNames);
+            Assert.Contains(TensorNames.VisualObservationPlaceholderPrefix + "0", inputNames);
+            Assert.Contains(TensorNames.VisualObservationPlaceholderPrefix + "1", inputNames);
+
+            Assert.AreEqual(2, model.GetNumVisualInputs());
+
+            // Test if the model is null
+            model = null;
+            Assert.AreEqual(0, model.GetInputTensors().Count);
+            Assert.AreEqual(0, model.GetNumVisualInputs());
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestGetInputTensorsDiscrete(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
+            var inputNames = model.GetInputNames();
+            // Model should contain 2 inputs : recurrent and visual 1
+
+            Assert.Contains(TensorNames.VisualObservationPlaceholderPrefix + "0", inputNames);
+            // TODO :There are some memory tensors as well
+        }
+
+        [Test]
+        public void TestGetInputTensorsHybrid()
+        {
+            var model = ModelLoader.Load(hybridONNXModel);
+            var inputNames = model.GetInputNames();
+            Assert.Contains(TensorNames.VectorObservationPlaceholder, inputNames);
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestGetOutputTensorsContinuous(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
+            var outputNames = model.GetOutputNames();
+            var actionOutputName = useDeprecatedNNModel ? TensorNames.ActionOutputDeprecated : TensorNames.ContinuousActionOutput;
+            Assert.Contains(actionOutputName, outputNames);
+            Assert.AreEqual(1, outputNames.Count());
+
+            model = null;
+            Assert.AreEqual(0, model.GetOutputNames().Count());
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestGetOutputTensorsDiscrete(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
+            var outputNames = model.GetOutputNames();
+            var actionOutputName = useDeprecatedNNModel ? TensorNames.ActionOutputDeprecated : TensorNames.DiscreteActionOutput;
+            Assert.Contains(actionOutputName, outputNames);
+            // TODO : There are some memory tensors as well
+        }
+
+        [Test]
+        public void TestGetOutputTensorsHybrid()
+        {
+            var model = ModelLoader.Load(hybridONNXModel);
+            var outputNames = model.GetOutputNames();
+
+            Assert.AreEqual(2, outputNames.Count());
+            Assert.Contains(TensorNames.ContinuousActionOutput, outputNames);
+            Assert.Contains(TensorNames.DiscreteActionOutput, outputNames);
+
+            model = null;
+            Assert.AreEqual(0, model.GetOutputNames().Count());
+        }
+
+        [Test]
+        public void TestCheckModelRank2()
+        {
+            var model = ModelLoader.Load(rank2ONNXModel);
+            var validBrainParameters = GetRank2BrainParameters();
+
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { sensor_23_20, sensor_10, sensor_8 }, new ActuatorComponent[0]
+            );
+            Assert.AreEqual(0, errors.Count()); // There should not be any errors
+
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { sensor_23_20, sensor_10 }, new ActuatorComponent[0]
+            );
+            Assert.AreNotEqual(0, errors.Count()); // Wrong number of sensors
+
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { new BufferSensor(20, 40, "BufferSensor"), sensor_10, sensor_8 }, new ActuatorComponent[0]
+            );
+            Assert.AreNotEqual(0, errors.Count()); // Wrong buffer sensor size
+
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { sensor_23_20, sensor_10, sensor_10 }, new ActuatorComponent[0]
+            );
+            Assert.AreNotEqual(0, errors.Count()); // Wrong vector sensor size
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelValidContinuous(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
+            var validBrainParameters = GetContinuous2vis8vec2actionBrainParameters();
+
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[]
+                {
+                    new VectorSensor(8),
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.AreEqual(0, errors.Count()); // There should not be any errors
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelValidDiscrete(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
+            var validBrainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
+
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { sensor_21_20_3.CreateSensors()[0] }, new ActuatorComponent[0]
+            );
+            foreach (var e in errors)
+            {
+                Debug.Log(e.Message);
+            }
+            Assert.Greater(errors.Count(), 0); // There should be an error since LSTM v1.x is not supported
+        }
+
+        [Test]
+        public void TestCheckModelValidRecurrent()
+        {
+            var model = ModelLoader.Load(hybridRecurrV2Model);
+            var num_errors = 0; // A model trained with v2 should not raise errors
+            var validBrainParameters = GetRecurrHybridBrainParameters();
+
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[] { sensor_8 }, new ActuatorComponent[0]
+            );
+            Assert.AreEqual(num_errors, errors.Count()); // There should not be any errors
+
+            var invalidBrainParameters = GetRecurrHybridBrainParameters();
+            invalidBrainParameters.ActionSpec = new ActionSpec(1, new int[] { 2, 3 });
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, invalidBrainParameters,
+                new ISensor[] { sensor_8 }, new ActuatorComponent[0]
+            );
+            Assert.AreEqual(1, errors.Count()); // 1 continuous action instead of 2
+
+            invalidBrainParameters.ActionSpec = new ActionSpec(2, new int[] { 3, 2 });
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, invalidBrainParameters,
+                new ISensor[] { sensor_8 }, new ActuatorComponent[0]
+            );
+            Assert.AreEqual(1, errors.Count()); // Discrete action branches flipped
+        }
+
+        [Test]
+        public void TestCheckModelValidHybrid()
+        {
+            var model = ModelLoader.Load(hybridONNXModel);
+            var validBrainParameters = GetHybridBrainParameters();
+
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, validBrainParameters,
+                new ISensor[]
+                {
+                    new VectorSensor(validBrainParameters.VectorObservationSize)
+                }, new ActuatorComponent[0]
+            );
+            Assert.AreEqual(0, errors.Count()); // There should not be any errors
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelThrowsVectorObservationContinuous(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
+
+            var brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.VectorObservationSize = 9; // Invalid observation
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters,
+                new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+
+            brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.NumStackedVectorObservations = 2;// Invalid stacking
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters,
+                new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelThrowsVectorObservationDiscrete(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
+
+            var brainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
+            brainParameters.VectorObservationSize = 1; // Invalid observation
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters, new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [Test]
+        public void TestCheckModelThrowsVectorObservationHybrid()
+        {
+            var model = ModelLoader.Load(hybridONNXModel);
+
+            var brainParameters = GetHybridBrainParameters();
+            brainParameters.VectorObservationSize = 9; // Invalid observation
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters,
+                new ISensor[] { }, new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+
+            brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.NumStackedVectorObservations = 2;// Invalid stacking
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters,
+                new ISensor[] { }, new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelThrowsActionContinuous(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
+
+            var brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.ActionSpec = ActionSpec.MakeContinuous(3); // Invalid action
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters, new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+
+            brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.ActionSpec = ActionSpec.MakeDiscrete(3); // Invalid SpaceType
+            errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters, new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestCheckModelThrowsActionDiscrete(bool useDeprecatedNNModel)
+        {
+            var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
+
+            var brainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
+            brainParameters.ActionSpec = ActionSpec.MakeDiscrete(3, 3); // Invalid action
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model, brainParameters,
+                new ISensor[] { sensor_21_20_3.CreateSensors()[0] },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+
+            brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.ActionSpec = ActionSpec.MakeContinuous(2); // Invalid SpaceType
+            errors = BarracudaModelParamLoader.CheckModel(
+                model,
+                brainParameters,
+                new ISensor[] { sensor_21_20_3.CreateSensors()[0] },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [Test]
+        public void TestCheckModelThrowsActionHybrid()
+        {
+            var model = ModelLoader.Load(hybridONNXModel);
+
+            var brainParameters = GetHybridBrainParameters();
+            brainParameters.ActionSpec = new ActionSpec(3, new[] { 3 }); // Invalid discrete action size
+            var errors = BarracudaModelParamLoader.CheckModel(
+                model,
+                brainParameters,
+                new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+
+            brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            brainParameters.ActionSpec = ActionSpec.MakeDiscrete(2); // Missing continuous action
+            errors = BarracudaModelParamLoader.CheckModel(
+                model,
+                brainParameters,
+                new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+
+        [Test]
+        public void TestCheckModelThrowsNoModel()
+        {
+            var brainParameters = GetContinuous2vis8vec2actionBrainParameters();
+            var errors = BarracudaModelParamLoader.CheckModel(
+                null,
+                brainParameters,
+                new ISensor[]
+                {
+                    sensor_21_20_3.CreateSensors()[0],
+                    sensor_20_22_3.CreateSensors()[0]
+                },
+                new ActuatorComponent[0]
+            );
+            Assert.Greater(errors.Count(), 0);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..25bff938efabf5307993759207c074c680d97774
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/ParameterLoaderTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: edd38d6ad78c8456d80f0a90bcb2e1b7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs b/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5673b7ddef9f7f891e15cb6e696dbd09a717c2e9
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs
@@ -0,0 +1,135 @@
+using System;
+using NUnit.Framework;
+using Unity.Barracuda;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Inference.Utils;
+
+namespace Unity.MLAgents.Tests
+{
+    public class TensorUtilsTest
+    {
+        [TestCase(4, TestName = "TestResizeTensor_4D")]
+        [TestCase(8, TestName = "TestResizeTensor_8D")]
+        public void TestResizeTensor(int dimension)
+        {
+            var alloc = new TensorCachingAllocator();
+            var height = 64;
+            var width = 84;
+            var channels = 3;
+
+            // Set shape to {1, ..., height, width, channels}
+            // For 8D, the ... are all 1's
+            var shape = new long[dimension];
+            for (var i = 0; i < dimension; i++)
+            {
+                shape[i] = 1;
+            }
+
+            shape[dimension - 3] = height;
+            shape[dimension - 2] = width;
+            shape[dimension - 1] = channels;
+
+            var intShape = new int[dimension];
+            for (var i = 0; i < dimension; i++)
+            {
+                intShape[i] = (int)shape[i];
+            }
+
+            var tensorProxy = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.Integer,
+                data = new Tensor(intShape),
+                shape = shape,
+            };
+
+            // These should be invariant after the resize.
+            Assert.AreEqual(height, tensorProxy.data.shape.height);
+            Assert.AreEqual(width, tensorProxy.data.shape.width);
+            Assert.AreEqual(channels, tensorProxy.data.shape.channels);
+
+            TensorUtils.ResizeTensor(tensorProxy, 42, alloc);
+
+            Assert.AreEqual(height, tensorProxy.shape[dimension - 3]);
+            Assert.AreEqual(width, tensorProxy.shape[dimension - 2]);
+            Assert.AreEqual(channels, tensorProxy.shape[dimension - 1]);
+
+            Assert.AreEqual(height, tensorProxy.data.shape.height);
+            Assert.AreEqual(width, tensorProxy.data.shape.width);
+            Assert.AreEqual(channels, tensorProxy.data.shape.channels);
+
+            alloc.Dispose();
+        }
+
+        [Test]
+        public void RandomNormalTestTensorInt()
+        {
+            var rn = new RandomNormal(1982);
+            var t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.Integer
+            };
+
+            Assert.Throws<NotImplementedException>(
+                () => TensorUtils.FillTensorWithRandomNormal(t, rn));
+        }
+
+        [Test]
+        public void RandomNormalTestDataNull()
+        {
+            var rn = new RandomNormal(1982);
+            var t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.FloatingPoint
+            };
+
+            Assert.Throws<ArgumentNullException>(
+                () => TensorUtils.FillTensorWithRandomNormal(t, rn));
+        }
+
+        [Test]
+        public void RandomNormalTestTensor()
+        {
+            var rn = new RandomNormal(1982);
+            var t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.FloatingPoint,
+                data = new Tensor(1, 3, 4, 2)
+            };
+
+            TensorUtils.FillTensorWithRandomNormal(t, rn);
+
+            var reference = new[]
+            {
+                -0.4315872f,
+                -1.11074f,
+                0.3414804f,
+                -1.130287f,
+                0.1413168f,
+                -0.5105762f,
+                -0.3027347f,
+                -0.2645015f,
+                1.225356f,
+                -0.02921959f,
+                0.3716498f,
+                -1.092338f,
+                0.9561074f,
+                -0.5018106f,
+                1.167787f,
+                -0.7763879f,
+                -0.07491868f,
+                0.5396146f,
+                -0.1377991f,
+                0.3331701f,
+                0.06144788f,
+                0.9520947f,
+                1.088157f,
+                -1.177194f,
+            };
+
+            for (var i = 0; i < t.data.length; i++)
+            {
+                Assert.AreEqual(t.data[i], reference[i], 0.0001);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4141d495048ed8a176b8f7e36c35031f508017f8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Inference/TensorUtilsTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 0a700a7c6187a433ca44d60d243bb0cd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs b/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2e8f21cf289f51bc444cececa0b125770adc0775
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs
@@ -0,0 +1,190 @@
+using System;
+using System.Collections;
+using NUnit.Framework;
+
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class InplaceArrayTests
+    {
+        class LengthCases : IEnumerable
+        {
+            public IEnumerator GetEnumerator()
+            {
+                yield return 1;
+                yield return 2;
+                yield return 3;
+                yield return 4;
+            }
+        }
+
+        private InplaceArray<int> GetTestArray(int length)
+        {
+            switch (length)
+            {
+                case 1:
+                    return new InplaceArray<int>(11);
+                case 2:
+                    return new InplaceArray<int>(11, 22);
+                case 3:
+                    return new InplaceArray<int>(11, 22, 33);
+                case 4:
+                    return new InplaceArray<int>(11, 22, 33, 44);
+                default:
+                    throw new ArgumentException("bad test!");
+            }
+        }
+
+        private InplaceArray<int> GetZeroArray(int length)
+        {
+            switch (length)
+            {
+                case 1:
+                    return new InplaceArray<int>(0);
+                case 2:
+                    return new InplaceArray<int>(0, 0);
+                case 3:
+                    return new InplaceArray<int>(0, 0, 0);
+                case 4:
+                    return new InplaceArray<int>(0, 0, 0, 0);
+                default:
+                    throw new ArgumentException("bad test!");
+            }
+        }
+
+        [Test]
+        public void TestInplaceArrayCtor()
+        {
+            var a1 = new InplaceArray<int>(11);
+            Assert.AreEqual(1, a1.Length);
+            Assert.AreEqual(11, a1[0]);
+
+            var a2 = new InplaceArray<int>(11, 22);
+            Assert.AreEqual(2, a2.Length);
+            Assert.AreEqual(11, a2[0]);
+            Assert.AreEqual(22, a2[1]);
+
+            var a3 = new InplaceArray<int>(11, 22, 33);
+            Assert.AreEqual(3, a3.Length);
+            Assert.AreEqual(11, a3[0]);
+            Assert.AreEqual(22, a3[1]);
+            Assert.AreEqual(33, a3[2]);
+
+            var a4 = new InplaceArray<int>(11, 22, 33, 44);
+            Assert.AreEqual(4, a4.Length);
+            Assert.AreEqual(11, a4[0]);
+            Assert.AreEqual(22, a4[1]);
+            Assert.AreEqual(33, a4[2]);
+            Assert.AreEqual(44, a4[3]);
+        }
+
+        [TestCaseSource(typeof(LengthCases))]
+        public void TestInplaceGetSet(int length)
+        {
+            var original = GetTestArray(length);
+
+            for (var i = 0; i < original.Length; i++)
+            {
+                var modified = original;
+                modified[i] = 0;
+                for (var j = 0; j < original.Length; j++)
+                {
+                    if (i == j)
+                    {
+                        // This is the one we overwrote
+                        Assert.AreEqual(0, modified[j]);
+                    }
+                    else
+                    {
+                        // Other elements should be unchanged
+                        Assert.AreEqual(original[j], modified[j]);
+                    }
+                }
+            }
+        }
+
+        [TestCaseSource(typeof(LengthCases))]
+        public void TestInvalidAccess(int length)
+        {
+            var tmp = 0;
+            var a = GetTestArray(length);
+            // get
+            Assert.Throws<IndexOutOfRangeException>(() => { tmp += a[-1]; });
+            Assert.Throws<IndexOutOfRangeException>(() => { tmp += a[length]; });
+
+            // set
+            Assert.Throws<IndexOutOfRangeException>(() => { a[-1] = 0; });
+            Assert.Throws<IndexOutOfRangeException>(() => { a[length] = 0; });
+
+            // Make sure temp is used
+            Assert.AreEqual(0, tmp);
+        }
+
+        [Test]
+        public void TestOperatorEqualsDifferentLengths()
+        {
+            // Check arrays of different length are never equal (even if they have 0s in all elements)
+            for (var l1 = 1; l1 <= 4; l1++)
+            {
+                var a1 = GetZeroArray(l1);
+                for (var l2 = 1; l2 <= 4; l2++)
+                {
+                    var a2 = GetZeroArray(l2);
+                    if (l1 == l2)
+                    {
+                        Assert.AreEqual(a1, a2);
+                        Assert.IsTrue(a1 == a2);
+                    }
+                    else
+                    {
+                        Assert.AreNotEqual(a1, a2);
+                        Assert.IsTrue(a1 != a2);
+                    }
+                }
+            }
+        }
+
+        [TestCaseSource(typeof(LengthCases))]
+        public void TestOperatorEquals(int length)
+        {
+            for (var index = 0; index < length; index++)
+            {
+                var a1 = GetTestArray(length);
+                var a2 = GetTestArray(length);
+                Assert.AreEqual(a1, a2);
+                Assert.IsTrue(a1 == a2);
+
+                a1[index] = 42;
+                Assert.AreNotEqual(a1, a2);
+                Assert.IsTrue(a1 != a2);
+
+                a2[index] = 42;
+                Assert.AreEqual(a1, a2);
+                Assert.IsTrue(a1 == a2);
+            }
+        }
+
+        [Test]
+        public void TestToString()
+        {
+            Assert.AreEqual("[1]", new InplaceArray<int>(1).ToString());
+            Assert.AreEqual("[1, 2]", new InplaceArray<int>(1, 2).ToString());
+            Assert.AreEqual("[1, 2, 3]", new InplaceArray<int>(1, 2, 3).ToString());
+            Assert.AreEqual("[1, 2, 3, 4]", new InplaceArray<int>(1, 2, 3, 4).ToString());
+        }
+
+        [TestCaseSource(typeof(LengthCases))]
+        public void TestFromList(int length)
+        {
+            var intArray = new int[length];
+            for (var i = 0; i < length; i++)
+            {
+                intArray[i] = (i + 1) * 11; // 11, 22, etc.
+            }
+
+            var converted = InplaceArray<int>.FromList(intArray);
+            Assert.AreEqual(GetTestArray(length), converted);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs.meta b/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..227738d65fd363b10bee518e2a0170279c2a7997
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/InplaceArrayTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8e1cdc27e533749fabc04b3cdeb93501
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations.meta b/com.unity.ml-agents/Tests/Editor/Integrations.meta
new file mode 100644
index 0000000000000000000000000000000000000000..395f71ca51b20b3821c912d768c7d3be0d1e5e04
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 7e89e6f6ab7e4c9397958c0320bd5931
+timeCreated: 1618359633
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f710b9aa061d909b0ada63a04fc9bfe8d44e4e52
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 77b0212dde404f7c8ce9aac13bd550b8
+timeCreated: 1601332716
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3b46c7ec3e23dbb863b3dbf23a46ebece55f147a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs
@@ -0,0 +1,215 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Integrations.Match3;
+
+namespace Unity.MLAgents.Tests.Integrations.Match3
+{
+    internal class StringBoard : AbstractBoard
+    {
+        internal int MaxRows;
+        internal int MaxColumns;
+        internal int NumCellTypes;
+        internal int NumSpecialTypes;
+        public int CurrentRows;
+        public int CurrentColumns;
+
+        public override BoardSize GetMaxBoardSize()
+        {
+            return new BoardSize
+            {
+                Rows = MaxRows,
+                Columns = MaxColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        public override BoardSize GetCurrentBoardSize()
+        {
+            return new BoardSize
+            {
+                Rows = CurrentRows,
+                Columns = CurrentColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        private string[] m_Board;
+        private string[] m_Special;
+
+        /// <summary>
+        /// Convert a string like "000\n010\n000" to a board representation
+        /// Row 0 is considered the bottom row
+        /// </summary>
+        /// <param name="newBoard"></param>
+        public void SetBoard(string newBoard)
+        {
+            m_Board = newBoard.Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
+            MaxRows = m_Board.Length;
+            MaxColumns = m_Board[0].Length;
+            CurrentRows = MaxRows;
+            CurrentColumns = MaxColumns;
+            NumCellTypes = 0;
+            for (var r = 0; r < MaxRows; r++)
+            {
+                for (var c = 0; c < MaxColumns; c++)
+                {
+                    NumCellTypes = Mathf.Max(NumCellTypes, 1 + GetCellType(r, c));
+                }
+            }
+        }
+
+        public void SetSpecial(string newSpecial)
+        {
+            m_Special = newSpecial.Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
+            Debug.Assert(MaxRows == m_Special.Length);
+            Debug.Assert(MaxColumns == m_Special[0].Length);
+            NumSpecialTypes = 0;
+            for (var r = 0; r < MaxRows; r++)
+            {
+                for (var c = 0; c < MaxColumns; c++)
+                {
+                    NumSpecialTypes = Mathf.Max(NumSpecialTypes, GetSpecialType(r, c));
+                }
+            }
+        }
+
+        public override bool MakeMove(Move m)
+        {
+            return true;
+        }
+
+        public override bool IsMoveValid(Move m)
+        {
+            return SimpleIsMoveValid(m);
+        }
+
+        public override int GetCellType(int row, int col)
+        {
+            if (row >= CurrentRows || col >= CurrentColumns)
+            {
+                throw new IndexOutOfRangeException("Tried to get celltype out of bounds");
+            }
+
+            var character = m_Board[m_Board.Length - 1 - row][col];
+            return (character - '0');
+        }
+
+        public override int GetSpecialType(int row, int col)
+        {
+            if (row >= CurrentRows || col >= CurrentColumns)
+            {
+                throw new IndexOutOfRangeException("Tried to get specialtype out of bounds");
+            }
+
+            var character = m_Special[m_Board.Length - 1 - row][col];
+            return (character - '0');
+        }
+    }
+
+    public class AbstractBoardTests
+    {
+        [Test]
+        public void TestBoardInit()
+        {
+            var boardString =
+@"000
+                  000
+                  010";
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+
+            var boardSize = board.GetMaxBoardSize();
+
+            Assert.AreEqual(3, boardSize.Rows);
+            Assert.AreEqual(3, boardSize.Columns);
+            Assert.AreEqual(2, boardSize.NumCellTypes);
+            for (var r = 0; r < 3; r++)
+            {
+                for (var c = 0; c < 3; c++)
+                {
+                    var expected = (r == 0 && c == 1) ? 1 : 0;
+                    Assert.AreEqual(expected, board.GetCellType(r, c));
+                }
+            }
+        }
+
+        internal static List<Move> GetValidMoves4x4(bool fullBoard, BoardSize boardSize)
+        {
+            var validMoves = new List<Move>
+            {
+                Move.FromPositionAndDirection(2, 1, Direction.Down, boardSize), // equivalent to (1, 1, Up)
+                Move.FromPositionAndDirection(1, 1, Direction.Down, boardSize),
+                Move.FromPositionAndDirection(1, 1, Direction.Left, boardSize),
+                Move.FromPositionAndDirection(1, 1, Direction.Right, boardSize),
+                Move.FromPositionAndDirection(0, 1, Direction.Left, boardSize),
+            };
+
+            if (fullBoard)
+            {
+                // This would move out of range on the small board
+                // Equivalent to (3, 1, Down)
+                validMoves.Add(Move.FromPositionAndDirection(2, 1, Direction.Up, boardSize));
+
+                // These moves require matching with a cell that's off the small board, so they're invalid
+                // (even though the move itself doesn't go out of range).
+                validMoves.Add(Move.FromPositionAndDirection(2, 1, Direction.Left, boardSize)); // Equivalent to (2, 0, Right)
+                validMoves.Add(Move.FromPositionAndDirection(2, 1, Direction.Right, boardSize));
+            }
+
+            return validMoves;
+        }
+
+        [TestCase(true, TestName = "Full Board")]
+        [TestCase(false, TestName = "Small Board")]
+        public void TestCheckValidMoves(bool fullBoard)
+        {
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+
+            var boardString =
+@"0105
+                  1024
+                  0203
+                  2022";
+            board.SetBoard(boardString);
+            var boardSize = board.GetMaxBoardSize();
+            if (!fullBoard)
+            {
+                board.CurrentRows -= 1;
+            }
+
+            var validMoves = GetValidMoves4x4(fullBoard, boardSize);
+
+            foreach (var m in validMoves)
+            {
+                Assert.IsTrue(board.IsMoveValid(m));
+            }
+
+            // Run through all moves and make sure those are the only valid ones
+            HashSet<int> validIndices = new HashSet<int>();
+            foreach (var m in validMoves)
+            {
+                validIndices.Add(m.MoveIndex);
+            }
+
+            // Make sure iterating over AllMoves is OK with the smaller board
+            foreach (var move in board.AllMoves())
+            {
+                var expected = validIndices.Contains(move.MoveIndex);
+                Assert.AreEqual(expected, board.IsMoveValid(move), $"({move.Row}, {move.Column}, {move.Direction})");
+            }
+
+            HashSet<int> validIndicesFromIterator = new HashSet<int>();
+            foreach (var move in board.ValidMoves())
+            {
+                validIndicesFromIterator.Add(move.MoveIndex);
+            }
+            Assert.IsTrue(validIndices.SetEquals(validIndicesFromIterator));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..79da98cb7a5fedcb4cf3e40feb87ded218b35fb1
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/AbstractBoardTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: a6d0404471364cd5b0b86ef72e6fe653
+timeCreated: 1601332740
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6711ed3b12e70467bf9d140e7aa63563a56c53f8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs
@@ -0,0 +1,207 @@
+using System.Collections.Generic;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Integrations.Match3;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests.Integrations.Match3
+{
+    internal class SimpleBoard : AbstractBoard
+    {
+        public int Rows;
+        public int Columns;
+        public int NumCellTypes;
+        public int NumSpecialTypes;
+
+        public int LastMoveIndex;
+        public bool MovesAreValid = true;
+
+        public bool CallbackCalled;
+
+        public override BoardSize GetMaxBoardSize()
+        {
+            return new BoardSize
+            {
+                Rows = Rows,
+                Columns = Columns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        public override int GetCellType(int row, int col)
+        {
+            return 0;
+        }
+
+        public override int GetSpecialType(int row, int col)
+        {
+            return 0;
+        }
+
+        public override bool IsMoveValid(Move m)
+        {
+            return MovesAreValid;
+        }
+
+        public override bool MakeMove(Move m)
+        {
+            LastMoveIndex = m.MoveIndex;
+            return MovesAreValid;
+        }
+
+        public void Callback()
+        {
+            CallbackCalled = true;
+        }
+    }
+
+    public class Match3ActuatorTests
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [TestCase(true)]
+        [TestCase(false)]
+        public void TestValidMoves(bool movesAreValid)
+        {
+            // Check that a board with no valid moves doesn't raise an exception.
+            var gameObj = new GameObject();
+            var board = gameObj.AddComponent<SimpleBoard>();
+            var agent = gameObj.AddComponent<Agent>();
+            gameObj.AddComponent<Match3ActuatorComponent>();
+
+            board.Rows = 5;
+            board.Columns = 5;
+            board.NumCellTypes = 5;
+            board.NumSpecialTypes = 0;
+
+            board.MovesAreValid = movesAreValid;
+            board.OnNoValidMovesAction = board.Callback;
+            board.LastMoveIndex = -1;
+
+            agent.LazyInitialize();
+            agent.RequestDecision();
+            Academy.Instance.EnvironmentStep();
+
+            if (movesAreValid)
+            {
+                Assert.IsFalse(board.CallbackCalled);
+            }
+            else
+            {
+                Assert.IsTrue(board.CallbackCalled);
+            }
+            Assert.AreNotEqual(-1, board.LastMoveIndex);
+        }
+
+        [Test]
+        public void TestActionSpec()
+        {
+            var gameObj = new GameObject();
+            var board = gameObj.AddComponent<SimpleBoard>();
+            var actuator = gameObj.AddComponent<Match3ActuatorComponent>();
+
+            board.Rows = 5;
+            board.Columns = 5;
+            board.NumCellTypes = 5;
+            board.NumSpecialTypes = 0;
+
+            var actionSpec = actuator.ActionSpec;
+            Assert.AreEqual(1, actionSpec.NumDiscreteActions);
+            Assert.AreEqual(board.NumMoves(), actionSpec.BranchSizes[0]);
+        }
+
+        [Test]
+        public void TestActionSpecNullBoard()
+        {
+            var gameObj = new GameObject();
+            var actuator = gameObj.AddComponent<Match3ActuatorComponent>();
+
+            var actionSpec = actuator.ActionSpec;
+            Assert.AreEqual(0, actionSpec.NumDiscreteActions);
+            Assert.AreEqual(0, actionSpec.NumContinuousActions);
+        }
+
+        public class HashSetActionMask : IDiscreteActionMask
+        {
+            public HashSet<int>[] HashSets;
+            public HashSetActionMask(ActionSpec spec)
+            {
+                HashSets = new HashSet<int>[spec.NumDiscreteActions];
+                for (var i = 0; i < spec.NumDiscreteActions; i++)
+                {
+                    HashSets[i] = new HashSet<int>();
+                }
+            }
+
+            public void SetActionEnabled(int branch, int actionIndex, bool isEnabled)
+            {
+                var hashSet = HashSets[branch];
+                if (isEnabled)
+                {
+                    hashSet.Remove(actionIndex);
+                }
+                else
+                {
+                    hashSet.Add(actionIndex);
+                }
+            }
+        }
+
+        [TestCase(true, TestName = "Full Board")]
+        [TestCase(false, TestName = "Small Board")]
+        public void TestMasking(bool fullBoard)
+        {
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+
+            var boardString =
+@"0105
+                  1024
+                  0203
+                  2022";
+            board.SetBoard(boardString);
+            var boardSize = board.GetMaxBoardSize();
+            if (!fullBoard)
+            {
+                board.CurrentRows -= 1;
+            }
+
+            var validMoves = AbstractBoardTests.GetValidMoves4x4(fullBoard, boardSize);
+
+            var actuatorComponent = gameObj.AddComponent<Match3ActuatorComponent>();
+            var actuator = actuatorComponent.CreateActuators()[0];
+
+            var masks = new HashSetActionMask(actuator.ActionSpec);
+            actuator.WriteDiscreteActionMask(masks);
+
+            // Run through all moves and make sure those are the only valid ones
+            HashSet<int> validIndices = new HashSet<int>();
+            foreach (var m in validMoves)
+            {
+                validIndices.Add(m.MoveIndex);
+            }
+
+            // Valid moves and masked moves should be disjoint
+            Assert.IsFalse(validIndices.Overlaps(masks.HashSets[0]));
+            // And they should add up to all the potential moves
+            Assert.AreEqual(validIndices.Count + masks.HashSets[0].Count, board.NumMoves());
+        }
+
+        [Test]
+        public void TestNoBoardReturnsEmptyActuators()
+        {
+            var gameObj = new GameObject("board");
+            var actuatorComponent = gameObj.AddComponent<Match3ActuatorComponent>();
+            var actuators = actuatorComponent.CreateActuators();
+            Assert.AreEqual(0, actuators.Length);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..3731b4e758021712ed04bfb6d049156637e0b86b
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 2edf24df24ac426085cb31a94d063683
+timeCreated: 1603392289
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8528b18464c694a8aa48941bdfca10b12264b84d
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs
@@ -0,0 +1,407 @@
+using System.Collections.Generic;
+using System.IO;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Integrations.Match3;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests.Integrations.Match3
+{
+    public class Match3SensorTests
+    {
+        // Whether the expected PNG data should be written to a file.
+        // Only set this to true if the compressed observation format changes.
+        private bool WritePNGDataToFile = false;
+        private const string k_CellObservationPng = "match3obs_";
+        private const string k_SpecialObservationPng = "match3obs_special_";
+        private const string k_Suffix2x2 = "2x2_";
+
+        [TestCase(true, TestName = "Full Board")]
+        [TestCase(false, TestName = "Small Board")]
+        public void TestVectorObservations(bool fullBoard)
+        {
+            var boardString =
+@"000
+                  000
+                  010";
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+            if (!fullBoard)
+            {
+                board.CurrentRows = 2;
+                board.CurrentColumns = 2;
+            }
+
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            sensorComponent.ObservationType = Match3ObservationType.Vector;
+            var sensor = sensorComponent.CreateSensors()[0];
+
+            var expectedShape = new InplaceArray<int>(3 * 3 * 2);
+            Assert.AreEqual(expectedShape, sensor.GetObservationSpec().Shape);
+
+            float[] expectedObs;
+
+            if (fullBoard)
+            {
+                expectedObs = new float[]
+                {
+                    1, 0, /* 0 */ 0, 1, /* 1 */ 1, 0, /* 0 */
+                    1, 0, /* 0 */ 1, 0, /* 0 */ 1, 0, /* 0 */
+                    1, 0, /* 0 */ 1, 0, /* 0 */ 1, 0, /* 0 */
+                };
+            }
+            else
+            {
+                expectedObs = new float[]
+                {
+                    1, 0, /*   0   */ 0, 1, /*   1   */ 0, 0, /* empty */
+                    1, 0, /*   0   */ 1, 0, /*   0   */ 0, 0, /* empty */
+                    0, 0, /* empty */ 0, 0, /* empty */ 0, 0, /* empty */
+                };
+            }
+            SensorTestHelper.CompareObservation(sensor, expectedObs);
+        }
+
+        [Test]
+        public void TestVectorObservationsSpecial()
+        {
+            var boardString =
+@"000
+                  000
+                  010";
+            var specialString =
+@"010
+                  200
+                  000";
+
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+            board.SetSpecial(specialString);
+
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            sensorComponent.ObservationType = Match3ObservationType.Vector;
+            var sensors = sensorComponent.CreateSensors();
+            var cellSensor = sensors[0];
+            var specialSensor = sensors[1];
+
+            {
+                var expectedShape = new InplaceArray<int>(3 * 3 * 2);
+                Assert.AreEqual(expectedShape, cellSensor.GetObservationSpec().Shape);
+
+                var expectedObs = new float[]
+                {
+                    1, 0, /* (0) */ 0, 1, /* (1) */ 1, 0, /* (0) */
+                    1, 0, /* (0) */ 1, 0, /* (0) */ 1, 0, /* (0) */
+                    1, 0, /* (0) */ 1, 0, /* (0) */ 1, 0, /* (0) */
+                };
+                SensorTestHelper.CompareObservation(cellSensor, expectedObs);
+            }
+            {
+                var expectedShape = new InplaceArray<int>(3 * 3 * 3);
+                Assert.AreEqual(expectedShape, specialSensor.GetObservationSpec().Shape);
+
+                var expectedObs = new float[]
+                {
+                    1, 0, 0, /* (0) */ 1, 0, 0, /* (1) */ 1, 0, 0, /* (0) */
+                    0, 0, 1, /* (2) */ 1, 0, 0, /* (0) */ 1, 0, 0, /* (0) */
+                    1, 0, 0, /* (0) */ 0, 1, 0, /* (1) */ 1, 0, 0, /* (0) */
+                };
+                SensorTestHelper.CompareObservation(specialSensor, expectedObs);
+            }
+        }
+
+        [TestCase(true, TestName = "Full Board")]
+        [TestCase(false, TestName = "Small Board")]
+        public void TestVisualObservations(bool fullBoard)
+        {
+            var boardString =
+@"000
+                  000
+                  010";
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+            if (!fullBoard)
+            {
+                board.CurrentRows = 2;
+                board.CurrentColumns = 2;
+            }
+
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            sensorComponent.ObservationType = Match3ObservationType.UncompressedVisual;
+            var sensor = sensorComponent.CreateSensors()[0];
+
+            var expectedShape = new InplaceArray<int>(3, 3, 2);
+            Assert.AreEqual(expectedShape, sensor.GetObservationSpec().Shape);
+
+            Assert.AreEqual(SensorCompressionType.None, sensor.GetCompressionSpec().SensorCompressionType);
+
+            float[] expectedObs;
+            float[,,] expectedObs3D;
+
+            if (fullBoard)
+            {
+                expectedObs = new float[]
+                {
+                    1, 0, /**/ 0, 1, /**/ 1, 0,
+                    1, 0, /**/ 1, 0, /**/ 1, 0,
+                    1, 0, /**/ 1, 0, /**/ 1, 0,
+                };
+
+                expectedObs3D = new float[,,]
+                {
+                    {{1, 0}, {0, 1}, {1, 0}},
+                    {{1, 0}, {1, 0}, {1, 0}},
+                    {{1, 0}, {1, 0}, {1, 0}},
+                };
+            }
+            else
+            {
+                expectedObs = new float[]
+                {
+                    1, 0, /*   0   */ 0, 1, /*   1   */ 0, 0, /* empty */
+                    1, 0, /*   0   */ 1, 0, /*   0   */ 0, 0, /* empty */
+                    0, 0, /* empty */ 0, 0, /* empty */ 0, 0, /* empty */
+                };
+                expectedObs3D = new float[,,]
+                {
+                    {{1, 0}, {0, 1}, {0, 0}},
+                    {{1, 0}, {1, 0}, {0, 0}},
+                    {{0, 0}, {0, 0}, {0, 0}},
+                };
+            }
+            SensorTestHelper.CompareObservation(sensor, expectedObs);
+            SensorTestHelper.CompareObservation(sensor, expectedObs3D);
+        }
+
+        [Test]
+        public void TestVisualObservationsSpecial()
+        {
+            var boardString =
+@"000
+                  000
+                  010";
+            var specialString =
+@"010
+                  200
+                  000";
+
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+            board.SetSpecial(specialString);
+
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            sensorComponent.ObservationType = Match3ObservationType.UncompressedVisual;
+            var sensors = sensorComponent.CreateSensors();
+            var cellSensor = sensors[0];
+            var specialSensor = sensors[1];
+
+            {
+                var expectedShape = new InplaceArray<int>(3, 3, 2);
+                Assert.AreEqual(expectedShape, cellSensor.GetObservationSpec().Shape);
+
+                Assert.AreEqual(SensorCompressionType.None, cellSensor.GetCompressionSpec().SensorCompressionType);
+
+                var expectedObs = new float[]
+                {
+                    1, 0, /* (0) */ 0, 1, /* (1) */ 1, 0, /* (0) */
+                    1, 0, /* (0) */ 1, 0, /* (0) */ 1, 0, /* (0) */
+                    1, 0, /* (0) */ 1, 0, /* (0) */ 1, 0, /* (0) */
+                };
+                SensorTestHelper.CompareObservation(cellSensor, expectedObs);
+
+                var expectedObs3D = new float[,,]
+                {
+                    {{1, 0}, {0, 1}, {1, 0}},
+                    {{1, 0}, {1, 0}, {1, 0}},
+                    {{1, 0}, {1, 0}, {1, 0}},
+                };
+                SensorTestHelper.CompareObservation(cellSensor, expectedObs3D);
+            }
+            {
+                var expectedShape = new InplaceArray<int>(3, 3, 3);
+                Assert.AreEqual(expectedShape, specialSensor.GetObservationSpec().Shape);
+
+                Assert.AreEqual(SensorCompressionType.None, specialSensor.GetCompressionSpec().SensorCompressionType);
+
+                var expectedObs = new float[]
+                {
+                    1, 0, 0, /* (0) */ 1, 0, 0, /* (1) */ 1, 0, 0, /* (0) */
+                    0, 0, 1, /* (2) */ 1, 0, 0, /* (0) */ 1, 0, 0, /* (0) */
+                    1, 0, 0, /* (0) */ 0, 1, 0, /* (1) */ 1, 0, 0, /* (0) */
+                };
+                SensorTestHelper.CompareObservation(specialSensor, expectedObs);
+
+                var expectedObs3D = new float[,,]
+                {
+                    {{1, 0, 0}, {1, 0, 0}, {1, 0, 0}},
+                    {{0, 0, 1}, {1, 0, 0}, {1, 0, 0}},
+                    {{1, 0, 0}, {0, 1, 0}, {1, 0, 0}},
+                };
+                SensorTestHelper.CompareObservation(specialSensor, expectedObs3D);
+            }
+
+            // Test that Dispose() cleans up the component and its sensors
+            sensorComponent.Dispose();
+
+            var flags = BindingFlags.Instance | BindingFlags.NonPublic;
+            var componentSensors = (ISensor[])typeof(Match3SensorComponent).GetField("m_Sensors", flags).GetValue(sensorComponent);
+            Assert.IsNull(componentSensors);
+            var cellTexture = (Texture2D)typeof(Match3Sensor).GetField("m_ObservationTexture", flags).GetValue(cellSensor);
+            Assert.IsNull(cellTexture);
+            var specialTexture = (Texture2D)typeof(Match3Sensor).GetField("m_ObservationTexture", flags).GetValue(cellSensor);
+            Assert.IsNull(specialTexture);
+        }
+
+        [TestCase(true, false, TestName = "Full Board, No Special")]
+        [TestCase(false, false, TestName = "Small Board, No Special")]
+        [TestCase(true, true, TestName = "Full Board, Special")]
+        [TestCase(false, true, TestName = "Small Board, Special")]
+        public void TestCompressedVisualObservationsSpecial(bool fullBoard, bool useSpecial)
+        {
+            var boardString =
+@"003
+                  000
+                  010";
+            var specialString =
+@"014
+                  200
+                  000";
+
+            var gameObj = new GameObject("board");
+            var board = gameObj.AddComponent<StringBoard>();
+            board.SetBoard(boardString);
+            var paths = new List<string> { k_CellObservationPng };
+            if (useSpecial)
+            {
+                board.SetSpecial(specialString);
+                paths.Add(k_SpecialObservationPng);
+            }
+
+            if (!fullBoard)
+            {
+                // Shrink the board, and change the paths we're using for the ground truth PNGs
+                board.CurrentRows = 2;
+                board.CurrentColumns = 2;
+                for (var i = 0; i < paths.Count; i++)
+                {
+                    paths[i] = paths[i] + k_Suffix2x2;
+                }
+            }
+
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            sensorComponent.ObservationType = Match3ObservationType.CompressedVisual;
+            var sensors = sensorComponent.CreateSensors();
+
+            var expectedNumChannels = new[] { 4, 5 };
+
+            for (var i = 0; i < paths.Count; i++)
+            {
+                var sensor = sensors[i];
+                var expectedShape = new InplaceArray<int>(3, 3, expectedNumChannels[i]);
+                Assert.AreEqual(expectedShape, sensor.GetObservationSpec().Shape);
+
+                Assert.AreEqual(SensorCompressionType.PNG, sensor.GetCompressionSpec().SensorCompressionType);
+
+                var pngData = sensor.GetCompressedObservation();
+                if (WritePNGDataToFile)
+                {
+                    // Enable this if the format of the observation changes
+                    SavePNGs(pngData, paths[i]);
+                }
+
+                var expectedPng = LoadPNGs(paths[i], 2);
+                Assert.AreEqual(expectedPng, pngData);
+            }
+        }
+
+        /// <summary>
+        /// Helper method for un-concatenating PNG observations.
+        /// </summary>
+        /// <param name="concatenated"></param>
+        /// <returns></returns>
+        List<byte[]> SplitPNGs(byte[] concatenated)
+        {
+            var pngsOut = new List<byte[]>();
+            var pngHeader = new byte[] { 137, 80, 78, 71, 13, 10, 26, 10 };
+
+            var current = new List<byte>();
+            for (var i = 0; i < concatenated.Length; i++)
+            {
+                current.Add(concatenated[i]);
+
+                // Check if the header starts at the next position
+                // If so, we'll start a new output array.
+                var headerIsNext = false;
+                if (i + 1 < concatenated.Length - pngHeader.Length)
+                {
+                    for (var j = 0; j < pngHeader.Length; j++)
+                    {
+                        if (concatenated[i + 1 + j] != pngHeader[j])
+                        {
+                            break;
+                        }
+
+                        if (j == pngHeader.Length - 1)
+                        {
+                            headerIsNext = true;
+                        }
+                    }
+                }
+
+                if (headerIsNext)
+                {
+                    pngsOut.Add(current.ToArray());
+                    current = new List<byte>();
+                }
+            }
+            pngsOut.Add(current.ToArray());
+
+            return pngsOut;
+        }
+
+        void SavePNGs(byte[] concatenatedPngData, string pathPrefix)
+        {
+            var splitPngs = SplitPNGs(concatenatedPngData);
+
+            for (var i = 0; i < splitPngs.Count; i++)
+            {
+                var pngData = splitPngs[i];
+                var path = $"Packages/com.unity.ml-agents/Tests/Editor/Integrations/Match3/{pathPrefix}{i}.png";
+                using (var sw = File.Create(path))
+                {
+                    foreach (var b in pngData)
+                    {
+                        sw.WriteByte(b);
+                    }
+                }
+            }
+        }
+
+        byte[] LoadPNGs(string pathPrefix, int numExpected)
+        {
+            var bytesOut = new List<byte>();
+            for (var i = 0; i < numExpected; i++)
+            {
+                var path = $"Packages/com.unity.ml-agents/Tests/Editor/Integrations/Match3/{pathPrefix}{i}.png";
+                var res = File.ReadAllBytes(path);
+                bytesOut.AddRange(res);
+            }
+
+            return bytesOut.ToArray();
+        }
+
+        [Test]
+        public void TestNoBoardReturnsEmptySensors()
+        {
+            var gameObj = new GameObject("board");
+            var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
+            var sensors = sensorComponent.CreateSensors();
+            Assert.AreEqual(0, sensors.Length);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..38a1a4d010697ae074e69bc91b2e9ce99439f978
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3SensorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: dfe94a9d6e994f408cb97d07dd44c994
+timeCreated: 1603493723
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f4755b4860b7fc74cf5d14881cabc669faf1311f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs
@@ -0,0 +1,65 @@
+using System;
+using NUnit.Framework;
+using Unity.MLAgents.Integrations.Match3;
+
+namespace Unity.MLAgents.Tests.Integrations.Match3
+{
+    public class MoveTests
+    {
+        [Test]
+        public void TestMoveEquivalence()
+        {
+            var board10x10 = new BoardSize { Rows = 10, Columns = 10 };
+            var moveUp = Move.FromPositionAndDirection(1, 1, Direction.Up, board10x10);
+            var moveDown = Move.FromPositionAndDirection(2, 1, Direction.Down, board10x10);
+            Assert.AreEqual(moveUp.MoveIndex, moveDown.MoveIndex);
+
+            var moveRight = Move.FromPositionAndDirection(1, 1, Direction.Right, board10x10);
+            var moveLeft = Move.FromPositionAndDirection(1, 2, Direction.Left, board10x10);
+            Assert.AreEqual(moveRight.MoveIndex, moveLeft.MoveIndex);
+        }
+
+        [Test]
+        public void TestNext()
+        {
+            var maxRows = 8;
+            var maxCols = 13;
+            var boardSize = new BoardSize
+            {
+                Rows = maxRows,
+                Columns = maxCols
+            };
+            // make sure using Next agrees with FromMoveIndex.
+            var advanceMove = Move.FromMoveIndex(0, boardSize);
+            for (var moveIndex = 0; moveIndex < Move.NumPotentialMoves(boardSize); moveIndex++)
+            {
+                var moveFromIndex = Move.FromMoveIndex(moveIndex, boardSize);
+                Assert.AreEqual(advanceMove.MoveIndex, moveFromIndex.MoveIndex);
+                Assert.AreEqual(advanceMove.Row, moveFromIndex.Row);
+                Assert.AreEqual(advanceMove.Column, moveFromIndex.Column);
+                Assert.AreEqual(advanceMove.Direction, moveFromIndex.Direction);
+
+                advanceMove.Next(boardSize);
+            }
+        }
+
+        // These are off the board
+        [TestCase(-1, 5, Direction.Up)]
+        [TestCase(10, 5, Direction.Up)]
+        [TestCase(5, -1, Direction.Up)]
+        [TestCase(5, 10, Direction.Up)]
+        // These are on the board but would move off
+        [TestCase(0, 5, Direction.Down)]
+        [TestCase(9, 5, Direction.Up)]
+        [TestCase(5, 0, Direction.Left)]
+        [TestCase(5, 9, Direction.Right)]
+        public void TestInvalidMove(int row, int col, Direction dir)
+        {
+            var board10x10 = new BoardSize { Rows = 10, Columns = 10 };
+            Assert.Throws<IndexOutOfRangeException>(() =>
+            {
+                Move.FromPositionAndDirection(row, col, dir, board10x10);
+            });
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e016865fcb566aeaea501afdccf99cf105c9b70c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/MoveTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 42981032af6f4241ae20fe24e898f60b
+timeCreated: 1601336681
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..0743d0bc134ec0cc0a2ad89b3355f4443046cd01
Binary files /dev/null and b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png differ
diff --git a/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png.meta b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9faafa2e61b6bca6b4b3c7ec69e11df090b6e7fd
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_0.png.meta
@@ -0,0 +1,92 @@
+fileFormatVersion: 2
+guid: d91bf3f1eb13c4361bfb8bb61b94a71a
+TextureImporter:
+  internalIDToNameTable: []
+  externalObjects: {}
+  serializedVersion: 11
+  mipmaps:
+    mipMapMode: 0
+    enableMipMap: 1
+    sRGBTexture: 1
+    linearTexture: 0
+    fadeOut: 0
+    borderMipMap: 0
+    mipMapsPreserveCoverage: 0
+    alphaTestReferenceValue: 0.5
+    mipMapFadeDistanceStart: 1
+    mipMapFadeDistanceEnd: 3
+  bumpmap:
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new file mode 100644
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--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Integrations/Match3/match3obs_special_2x2_1.png.meta
@@ -0,0 +1,92 @@
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diff --git a/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs b/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..221fe71cf7e4cd8abff4ed5c493e5ee923e48bff
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs
@@ -0,0 +1,742 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using NUnit.Framework;
+using System.Reflection;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.SideChannels;
+using Unity.MLAgents.Utils.Tests;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class EditModeTestGeneration
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestAcademy()
+        {
+            var aca = Academy.Instance;
+            Assert.AreNotEqual(null, aca);
+            Assert.AreEqual(0, aca.EpisodeCount);
+            Assert.AreEqual(0, aca.StepCount);
+            Assert.AreEqual(0, aca.TotalStepCount);
+        }
+
+        [Test]
+        public void TestAgent()
+        {
+            var agentGo = new GameObject("TestAgent");
+            agentGo.AddComponent<TestAgent>();
+            var agent = agentGo.GetComponent<TestAgent>();
+            Assert.AreNotEqual(null, agent);
+            Assert.AreEqual(0, agent.initializeAgentCalls);
+        }
+    }
+
+    [TestFixture]
+    public class EditModeTestInitialization
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestAcademy()
+        {
+            Assert.AreEqual(false, Academy.IsInitialized);
+            var aca = Academy.Instance;
+            Assert.AreEqual(true, Academy.IsInitialized);
+
+            // Check that init is idempotent
+            aca.LazyInitialize();
+            aca.LazyInitialize();
+
+            Assert.AreEqual(0, aca.EpisodeCount);
+            Assert.AreEqual(0, aca.StepCount);
+            Assert.AreEqual(0, aca.TotalStepCount);
+            Assert.AreNotEqual(null, SideChannelManager.GetSideChannel<EnvironmentParametersChannel>());
+            Assert.AreNotEqual(null, SideChannelManager.GetSideChannel<EngineConfigurationChannel>());
+            Assert.AreNotEqual(null, SideChannelManager.GetSideChannel<StatsSideChannel>());
+
+            // Check that Dispose is idempotent
+            aca.Dispose();
+            Assert.AreEqual(false, Academy.IsInitialized);
+            aca.Dispose();
+        }
+
+        [Test]
+        public void TestAcademyDispose()
+        {
+            var envParams1 = SideChannelManager.GetSideChannel<EnvironmentParametersChannel>();
+            var engineParams1 = SideChannelManager.GetSideChannel<EngineConfigurationChannel>();
+            var statsParams1 = SideChannelManager.GetSideChannel<StatsSideChannel>();
+            Academy.Instance.Dispose();
+
+            Academy.Instance.LazyInitialize();
+            var envParams2 = SideChannelManager.GetSideChannel<EnvironmentParametersChannel>();
+            var engineParams2 = SideChannelManager.GetSideChannel<EngineConfigurationChannel>();
+            var statsParams2 = SideChannelManager.GetSideChannel<StatsSideChannel>();
+            Academy.Instance.Dispose();
+
+            Assert.AreNotEqual(envParams1, envParams2);
+            Assert.AreNotEqual(engineParams1, engineParams2);
+            Assert.AreNotEqual(statsParams1, statsParams2);
+        }
+
+        [Test]
+        public void TestAgent()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var bp1 = agentGo1.GetComponent<BehaviorParameters>();
+            bp1.ObservableAttributeHandling = ObservableAttributeOptions.ExcludeInherited;
+
+            var agentGo2 = new GameObject("TestAgent");
+            agentGo2.AddComponent<TestAgent>();
+            var agent2 = agentGo2.GetComponent<TestAgent>();
+
+            Assert.AreEqual(0, agent1.agentOnEpisodeBeginCalls);
+            Assert.AreEqual(0, agent2.agentOnEpisodeBeginCalls);
+            Assert.AreEqual(0, agent1.initializeAgentCalls);
+            Assert.AreEqual(0, agent2.initializeAgentCalls);
+            Assert.AreEqual(0, agent1.agentActionCalls);
+            Assert.AreEqual(0, agent2.agentActionCalls);
+
+
+            agent2.LazyInitialize();
+            agent1.LazyInitialize();
+
+            // agent1 was not enabled when the academy started
+            // The agents have been initialized
+            Assert.AreEqual(0, agent1.agentOnEpisodeBeginCalls);
+            Assert.AreEqual(0, agent2.agentOnEpisodeBeginCalls);
+            Assert.AreEqual(1, agent1.initializeAgentCalls);
+            Assert.AreEqual(1, agent2.initializeAgentCalls);
+            Assert.AreEqual(0, agent1.agentActionCalls);
+            Assert.AreEqual(0, agent2.agentActionCalls);
+
+            // Make sure the Sensors were sorted
+            Assert.AreEqual(agent1.sensors[0].GetName(), "observableFloat");
+            Assert.AreEqual(agent1.sensors[1].GetName(), "testsensor1");
+            Assert.AreEqual(agent1.sensors[2].GetName(), "testsensor2");
+
+            // agent2 should only have two sensors (no observableFloat)
+            Assert.AreEqual(agent2.sensors[0].GetName(), "testsensor1");
+            Assert.AreEqual(agent2.sensors[1].GetName(), "testsensor2");
+        }
+    }
+
+    [TestFixture]
+    public class EditModeTestStep
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestAcademy()
+        {
+            var aca = Academy.Instance;
+
+            var numberReset = 0;
+            for (var i = 0; i < 10; i++)
+            {
+                Assert.AreEqual(numberReset, aca.EpisodeCount);
+                Assert.AreEqual(i, aca.StepCount);
+
+                // The reset happens at the beginning of the first step
+                if (i == 0)
+                {
+                    numberReset += 1;
+                }
+                Academy.Instance.EnvironmentStep();
+            }
+        }
+
+        [Test]
+        public void TestAcademyAutostep()
+        {
+            var aca = Academy.Instance;
+            Assert.IsTrue(aca.AutomaticSteppingEnabled);
+            aca.AutomaticSteppingEnabled = false;
+            Assert.IsFalse(aca.AutomaticSteppingEnabled);
+            aca.AutomaticSteppingEnabled = true;
+            Assert.IsTrue(aca.AutomaticSteppingEnabled);
+        }
+
+        [Test]
+        public void TestAgent()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var agentGo2 = new GameObject("TestAgent");
+            var bp2 = agentGo2.AddComponent<BehaviorParameters>();
+            bp2.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo2.AddComponent<TestAgent>();
+            var agent2 = agentGo2.GetComponent<TestAgent>();
+
+            var aca = Academy.Instance;
+
+            var decisionRequester = agent1.gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 2;
+            decisionRequester.Awake();
+            // agent1 will take an action at every step and request a decision every 2 steps
+            // agent2 will request decisions only when RequestDecision is called
+
+            agent1.LazyInitialize();
+
+            var numberAgent1Episodes = 0;
+            var numberAgent2Episodes = 0;
+            var numberAgent2Initialization = 0;
+            var requestDecision = 0;
+            var requestAction = 0;
+            for (var i = 0; i < 50; i++)
+            {
+                Assert.AreEqual(numberAgent1Episodes, agent1.agentOnEpisodeBeginCalls);
+                Assert.AreEqual(numberAgent2Episodes, agent2.agentOnEpisodeBeginCalls);
+                Assert.AreEqual(1, agent1.initializeAgentCalls);
+                Assert.AreEqual(numberAgent2Initialization, agent2.initializeAgentCalls);
+                Assert.AreEqual(i, agent1.agentActionCalls);
+                Assert.AreEqual(requestAction, agent2.agentActionCalls);
+                Assert.AreEqual((i + 1) / 2, agent1.collectObservationsCalls);
+                Assert.AreEqual(requestDecision, agent2.collectObservationsCalls);
+                // Agent 1 starts a new episode at the first step
+                if (i == 0)
+                {
+                    numberAgent1Episodes += 1;
+                }
+                //Agent 2 is only initialized at step 2
+                if (i == 2)
+                {
+                    // Since Agent2 is initialized after the Academy has stepped, its OnEpisodeBegin should be called now.
+                    Assert.AreEqual(0, agent2.agentOnEpisodeBeginCalls);
+                    agent2.LazyInitialize();
+                    Assert.AreEqual(1, agent2.agentOnEpisodeBeginCalls);
+                    numberAgent2Initialization += 1;
+                    numberAgent2Episodes += 1;
+                }
+
+                // We are testing request decision and request actions when called
+                // at different intervals
+                if ((i % 3 == 0) && (i > 2))
+                {
+                    //Every 3 steps after agent 2 is initialized, request decision
+                    requestDecision += 1;
+                    requestAction += 1;
+                    agent2.RequestDecision();
+                }
+                else if ((i % 5 == 0) && (i > 2))
+                {
+                    // Every 5 steps after agent 2 is initialized, request action
+                    requestAction += 1;
+                    agent2.RequestAction();
+                }
+                aca.EnvironmentStep();
+            }
+        }
+    }
+
+    [TestFixture]
+    public class EditModeTestReset
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestAcademy()
+        {
+            var aca = Academy.Instance;
+
+            var numberReset = 0;
+            var stepsSinceReset = 0;
+            for (var i = 0; i < 50; i++)
+            {
+                Assert.AreEqual(stepsSinceReset, aca.StepCount);
+                Assert.AreEqual(numberReset, aca.EpisodeCount);
+                Assert.AreEqual(i, aca.TotalStepCount);
+
+                // Academy resets at the first step
+                if (i == 0)
+                {
+                    numberReset += 1;
+                }
+
+                stepsSinceReset += 1;
+                aca.EnvironmentStep();
+            }
+        }
+
+        [Test]
+        public void TestAgent()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var agentGo2 = new GameObject("TestAgent");
+            var bp2 = agentGo2.AddComponent<BehaviorParameters>();
+            bp2.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo2.AddComponent<TestAgent>();
+            var agent2 = agentGo2.GetComponent<TestAgent>();
+
+            var aca = Academy.Instance;
+
+            var decisionRequester = agent1.gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 2;
+
+            agent2.LazyInitialize();
+
+            var numberAgent1Episodes = 0;
+            var numberAgent2Episodes = 0;
+            var numberAcaReset = 0;
+            var acaStepsSinceReset = 0;
+            var agent2StepForEpisode = 0;
+            for (var i = 0; i < 5000; i++)
+            {
+                Assert.AreEqual(acaStepsSinceReset, aca.StepCount);
+                Assert.AreEqual(numberAcaReset, aca.EpisodeCount);
+
+                Assert.AreEqual(i, aca.TotalStepCount);
+                Assert.AreEqual(numberAgent2Episodes, agent2.agentOnEpisodeBeginCalls);
+                Assert.AreEqual(agent2StepForEpisode, agent2.StepCount);
+
+                // Agent 2 and academy reset at the first step
+                if (i == 0)
+                {
+                    Assert.AreEqual(numberAgent2Episodes, agent2.agentOnEpisodeBeginCalls);
+                    numberAcaReset += 1;
+                    numberAgent2Episodes += 1;
+                }
+
+                //Agent 1 is only initialized at step 2
+                if (i == 2)
+                {
+                    Assert.AreEqual(numberAgent1Episodes, agent1.agentOnEpisodeBeginCalls);
+                    agent1.LazyInitialize();
+                    numberAgent1Episodes += 1;
+                    Assert.AreEqual(numberAgent1Episodes, agent1.agentOnEpisodeBeginCalls);
+                }
+
+                // Set agent 1 to done every 11 steps to test behavior
+                if (i % 11 == 5)
+                {
+                    Assert.AreEqual(numberAgent1Episodes, agent1.agentOnEpisodeBeginCalls);
+                    agent1.EndEpisode();
+                    numberAgent1Episodes += 1;
+                    Assert.AreEqual(numberAgent1Episodes, agent1.agentOnEpisodeBeginCalls);
+                }
+
+                // Ending the episode for agent 2 regularly
+                if (i % 13 == 3)
+                {
+                    Assert.AreEqual(numberAgent2Episodes, agent2.agentOnEpisodeBeginCalls);
+                    agent2.EndEpisode();
+                    numberAgent2Episodes += 1;
+                    agent2StepForEpisode = 0;
+                    Assert.AreEqual(numberAgent2Episodes, agent2.agentOnEpisodeBeginCalls);
+                }
+
+                // Request a decision for agent 2 regularly
+                if (i % 3 == 2)
+                {
+                    agent2.RequestDecision();
+                }
+                else if (i % 5 == 1)
+                {
+                    // Request an action without decision regularly
+                    agent2.RequestAction();
+                }
+
+                acaStepsSinceReset += 1;
+                agent2StepForEpisode += 1;
+                aca.EnvironmentStep();
+            }
+        }
+    }
+
+    [TestFixture]
+    public class EditModeTestMiscellaneous
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        [Test]
+        public void TestCumulativeReward()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            var agent1 = agentGo1.AddComponent<TestAgent>();
+            var agentGo2 = new GameObject("TestAgent");
+            var bp2 = agentGo2.AddComponent<BehaviorParameters>();
+            bp2.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            var agent2 = agentGo2.AddComponent<TestAgent>();
+            var aca = Academy.Instance;
+
+            var decisionRequester = agent1.gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 2;
+            decisionRequester.Awake();
+
+
+            agent1.MaxStep = 20;
+
+            agent2.LazyInitialize();
+            agent1.LazyInitialize();
+            agent2.SetPolicy(new TestPolicy());
+
+            var expectedAgent1ActionForEpisode = 0;
+
+            for (var i = 0; i < 50; i++)
+            {
+                expectedAgent1ActionForEpisode += 1;
+                if (expectedAgent1ActionForEpisode == agent1.MaxStep || i == 0)
+                {
+                    expectedAgent1ActionForEpisode = 0;
+                }
+                agent2.RequestAction();
+                Assert.LessOrEqual(Mathf.Abs(expectedAgent1ActionForEpisode * 10.1f - agent1.GetCumulativeReward()), 0.05f);
+                Assert.LessOrEqual(Mathf.Abs(i * 0.1f - agent2.GetCumulativeReward()), 0.05f);
+
+                agent1.AddReward(10f);
+                aca.EnvironmentStep();
+            }
+        }
+
+        [Test]
+        public void TestMaxStepsReset()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var aca = Academy.Instance;
+
+            var decisionRequester = agent1.gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 1;
+            decisionRequester.Awake();
+
+            const int maxStep = 6;
+            agent1.MaxStep = maxStep;
+            agent1.LazyInitialize();
+
+            var expectedAgentStepCount = 0;
+            var expectedEpisodes = 0;
+            var expectedAgentAction = 0;
+            var expectedAgentActionForEpisode = 0;
+            var expectedCollectObsCalls = 0;
+            var expectedCollectObsCallsForEpisode = 0;
+            var expectedCompletedEpisodes = 0;
+            var expectedSensorResetCalls = 0;
+
+            for (var i = 0; i < 15; i++)
+            {
+                // Agent should observe and act on each Academy step
+                expectedAgentAction += 1;
+                expectedAgentActionForEpisode += 1;
+                expectedCollectObsCalls += 1;
+                expectedCollectObsCallsForEpisode += 1;
+                expectedAgentStepCount += 1;
+
+                // If the next step will put the agent at maxSteps, we expect it to reset
+                if (agent1.StepCount == maxStep - 1 || (i == 0))
+                {
+                    expectedEpisodes += 1;
+                }
+
+                if (agent1.StepCount == maxStep - 1)
+                {
+                    expectedAgentActionForEpisode = 0;
+                    expectedCollectObsCallsForEpisode = 0;
+                    expectedAgentStepCount = 0;
+                    expectedCompletedEpisodes++;
+                    expectedSensorResetCalls++;
+                    expectedCollectObsCalls += 1;
+                }
+                aca.EnvironmentStep();
+
+                Assert.AreEqual(expectedAgentStepCount, agent1.StepCount);
+                Assert.AreEqual(expectedEpisodes, agent1.agentOnEpisodeBeginCalls);
+                Assert.AreEqual(expectedAgentAction, agent1.agentActionCalls);
+                Assert.AreEqual(expectedAgentActionForEpisode, agent1.agentActionCallsForEpisode);
+                Assert.AreEqual(expectedCollectObsCalls, agent1.collectObservationsCalls);
+                Assert.AreEqual(expectedCollectObsCallsForEpisode, agent1.collectObservationsCallsForEpisode);
+                Assert.AreEqual(expectedCompletedEpisodes, agent1.CompletedEpisodes);
+                Assert.AreEqual(expectedSensorResetCalls, agent1.sensor1.numResetCalls);
+            }
+        }
+
+        [Test]
+        public void TestHeuristicPolicyStepsSensors()
+        {
+            // Make sure that Agents with HeuristicPolicies step their sensors each Academy step.
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var aca = Academy.Instance;
+
+            var decisionRequester = agent1.gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 1;
+            decisionRequester.Awake();
+
+            agent1.LazyInitialize();
+            Assert.AreEqual(agent1.GetPolicy().GetType(), typeof(HeuristicPolicy));
+
+            var numSteps = 10;
+            for (var i = 0; i < numSteps; i++)
+            {
+                aca.EnvironmentStep();
+            }
+            Assert.AreEqual(numSteps, agent1.heuristicCalls);
+            Assert.AreEqual(numSteps, agent1.sensor1.numWriteCalls);
+            Assert.AreEqual(numSteps, agent1.sensor2.numCompressedCalls);
+
+            Assert.AreEqual(
+                agent1.collectObservationsCallsForEpisode,
+                agent1.GetStoredActionBuffers().ContinuousActions[0]
+            );
+        }
+
+        [Test]
+        public void TestNullList()
+        {
+            var nullList = new HeuristicPolicy.NullList();
+            Assert.Throws<NotImplementedException>(() =>
+            {
+                _ = ((IEnumerable<float>)nullList).GetEnumerator();
+            });
+
+            Assert.Throws<NotImplementedException>(() =>
+            {
+                _ = ((IEnumerable)nullList).GetEnumerator();
+            });
+
+            Assert.Throws<NotImplementedException>(() =>
+            {
+                nullList.CopyTo(new[] { 0f }, 0);
+            });
+
+            nullList.Add(0);
+            Assert.IsTrue(nullList.Count == 0);
+
+            nullList.Clear();
+            Assert.IsTrue(nullList.Count == 0);
+
+            nullList.Add(0);
+            Assert.IsFalse(nullList.Contains(0));
+            Assert.IsFalse(nullList.Remove(0));
+            Assert.IsFalse(nullList.IsReadOnly);
+            Assert.IsTrue(nullList.IndexOf(0) == -1);
+            nullList.Insert(0, 0);
+            Assert.IsFalse(nullList.Count > 0);
+            nullList.RemoveAt(0);
+            Assert.IsTrue(nullList.Count == 0);
+            Assert.IsTrue(Mathf.Approximately(0f, nullList[0]));
+            Assert.IsTrue(Mathf.Approximately(0f, nullList[1]));
+        }
+    }
+
+    [TestFixture]
+    public class TestOnEnableOverride
+    {
+        public class OnEnableAgent : Agent
+        {
+            public bool callBase;
+
+            protected override void OnEnable()
+            {
+                if (callBase)
+                    base.OnEnable();
+            }
+        }
+
+        static void _InnerAgentTestOnEnableOverride(bool callBase = false)
+        {
+            var go = new GameObject();
+            var agent = go.AddComponent<OnEnableAgent>();
+            agent.callBase = callBase;
+            var onEnable = typeof(OnEnableAgent).GetMethod("OnEnable", BindingFlags.NonPublic | BindingFlags.Instance);
+            var sendInfo = typeof(Agent).GetMethod("SendInfoToBrain", BindingFlags.NonPublic | BindingFlags.Instance);
+            Assert.NotNull(onEnable);
+            onEnable.Invoke(agent, null);
+            Assert.NotNull(sendInfo);
+            if (agent.callBase)
+            {
+                Assert.DoesNotThrow(() => sendInfo.Invoke(agent, null));
+            }
+            else
+            {
+                Assert.Throws<UnityAgentsException>(() =>
+                {
+                    try
+                    {
+                        sendInfo.Invoke(agent, null);
+                    }
+                    catch (TargetInvocationException e)
+                    {
+                        throw e.GetBaseException();
+                    }
+                });
+            }
+        }
+
+        [Test]
+        public void TestAgentCallBaseOnEnable()
+        {
+            _InnerAgentTestOnEnableOverride(true);
+        }
+
+        [Test]
+        public void TestAgentDontCallBaseOnEnable()
+        {
+            _InnerAgentTestOnEnableOverride();
+        }
+    }
+
+    [TestFixture]
+    public class ObservableAttributeBehaviorTests
+    {
+        public class BaseObservableAgent : Agent
+        {
+            [Observable]
+            public float BaseField;
+        }
+
+        public class DerivedObservableAgent : BaseObservableAgent
+        {
+            [Observable]
+            public float DerivedField;
+        }
+
+
+        [Test]
+        public void TestObservableAttributeBehaviorIgnore()
+        {
+            var variants = new[]
+            {
+                // No observables found
+                (ObservableAttributeOptions.Ignore, 0),
+                // Only DerivedField found
+                (ObservableAttributeOptions.ExcludeInherited, 1),
+                // DerivedField and BaseField found
+                (ObservableAttributeOptions.ExamineAll, 2)
+            };
+
+            foreach (var (behavior, expectedNumSensors) in variants)
+            {
+                var go = new GameObject();
+                var agent = go.AddComponent<DerivedObservableAgent>();
+                var bp = go.GetComponent<BehaviorParameters>();
+                bp.ObservableAttributeHandling = behavior;
+                agent.LazyInitialize();
+                int numAttributeSensors = 0;
+                foreach (var sensor in agent.sensors)
+                {
+                    if (sensor.GetType() != typeof(VectorSensor))
+                    {
+                        numAttributeSensors++;
+                    }
+                }
+                Assert.AreEqual(expectedNumSensors, numAttributeSensors);
+            }
+        }
+    }
+
+    [TestFixture]
+    public class AgentRecursionTests
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        class CollectObsEndEpisodeAgent : Agent
+        {
+            public override void CollectObservations(VectorSensor sensor)
+            {
+                // NEVER DO THIS IN REAL CODE!
+                EndEpisode();
+            }
+        }
+
+        class OnEpisodeBeginEndEpisodeAgent : Agent
+        {
+            public override void OnEpisodeBegin()
+            {
+                // NEVER DO THIS IN REAL CODE!
+                EndEpisode();
+            }
+        }
+
+        void TestRecursiveThrows<T>() where T : Agent
+        {
+            var gameObj = new GameObject();
+            var agent = gameObj.AddComponent<T>();
+            agent.LazyInitialize();
+            agent.RequestDecision();
+
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                Academy.Instance.EnvironmentStep();
+            });
+        }
+
+        [Test]
+        public void TestRecursiveCollectObsEndEpisodeThrows()
+        {
+            TestRecursiveThrows<CollectObsEndEpisodeAgent>();
+        }
+
+        [Test]
+        public void TestRecursiveOnEpisodeBeginEndEpisodeThrows()
+        {
+            TestRecursiveThrows<OnEpisodeBeginEndEpisodeAgent>();
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs.meta b/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2823ab05a5945c7abaff0cc3a6d2e591541900e0
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MLAgentsEditModeTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 3170fcbfa5f4d4a8ca82c50c750e9083
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs b/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5d987d55dfc98e743b924b71cc0f3f8b91634e37
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs
@@ -0,0 +1,118 @@
+using System;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests
+{
+    public class MultiAgentGroupTests
+    {
+        class TestAgent : Agent
+        {
+            internal int _GroupId
+            {
+                get
+                {
+                    return (int)typeof(Agent).GetField("m_GroupId", BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+                }
+            }
+
+            internal float _GroupReward
+            {
+                get
+                {
+                    return (float)typeof(Agent).GetField("m_GroupReward", BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+                }
+            }
+
+            internal Action<Agent> _OnAgentDisabledActions
+            {
+                get
+                {
+                    return (Action<Agent>)typeof(Agent).GetField("OnAgentDisabled", BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+                }
+            }
+        }
+
+        [Test]
+        public void TestRegisteredAgentGroupId()
+        {
+            var agentGo = new GameObject("TestAgent");
+            agentGo.AddComponent<TestAgent>();
+            var agent = agentGo.GetComponent<TestAgent>();
+
+            // test register
+            SimpleMultiAgentGroup agentGroup1 = new SimpleMultiAgentGroup();
+            agentGroup1.RegisterAgent(agent);
+            Assert.AreEqual(agentGroup1.GetId(), agent._GroupId);
+            Assert.IsNotNull(agent._OnAgentDisabledActions);
+
+            // should not be able to registered to multiple groups
+            SimpleMultiAgentGroup agentGroup2 = new SimpleMultiAgentGroup();
+            Assert.Throws<UnityAgentsException>(
+                () => agentGroup2.RegisterAgent(agent));
+            Assert.AreEqual(agentGroup1.GetId(), agent._GroupId);
+
+            // test unregister
+            agentGroup1.UnregisterAgent(agent);
+            Assert.AreEqual(0, agent._GroupId);
+            Assert.IsNull(agent._OnAgentDisabledActions);
+
+            // test register to another group after unregister
+            agentGroup2.RegisterAgent(agent);
+            Assert.AreEqual(agentGroup2.GetId(), agent._GroupId);
+            Assert.IsNotNull(agent._OnAgentDisabledActions);
+        }
+
+        [Test]
+        public void TestRegisterMultipleAgent()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            agentGo1.AddComponent<TestAgent>();
+            var agent1 = agentGo1.GetComponent<TestAgent>();
+            var agentGo2 = new GameObject("TestAgent");
+            agentGo2.AddComponent<TestAgent>();
+            var agent2 = agentGo2.GetComponent<TestAgent>();
+
+            SimpleMultiAgentGroup agentGroup = new SimpleMultiAgentGroup();
+            agentGroup.RegisterAgent(agent1); // register
+            Assert.AreEqual(agentGroup.GetRegisteredAgents().Count, 1);
+            agentGroup.UnregisterAgent(agent2); // unregister non-member agent
+            Assert.AreEqual(agentGroup.GetRegisteredAgents().Count, 1);
+            agentGroup.UnregisterAgent(agent1); // unregister
+            Assert.AreEqual(agentGroup.GetRegisteredAgents().Count, 0);
+            agentGroup.RegisterAgent(agent1);
+            agentGroup.RegisterAgent(agent1); // duplicated register
+            Assert.AreEqual(agentGroup.GetRegisteredAgents().Count, 1);
+            agentGroup.RegisterAgent(agent2); // register another
+            Assert.AreEqual(agentGroup.GetRegisteredAgents().Count, 2);
+
+            // test add/set group rewards
+            agentGroup.AddGroupReward(0.1f);
+            Assert.AreEqual(0.1f, agent1._GroupReward);
+            agentGroup.AddGroupReward(0.5f);
+            Assert.AreEqual(0.6f, agent1._GroupReward);
+            agentGroup.SetGroupReward(0.3f);
+            Assert.AreEqual(0.3f, agent1._GroupReward);
+            // unregistered agent should not receive group reward
+            agentGroup.UnregisterAgent(agent1);
+            agentGroup.AddGroupReward(0.2f);
+            Assert.AreEqual(0.3f, agent1._GroupReward);
+            Assert.AreEqual(0.5f, agent2._GroupReward);
+
+            // dispose group should automatically unregister all
+            agentGroup.Dispose();
+            Assert.AreEqual(0, agent1._GroupId);
+            Assert.AreEqual(0, agent2._GroupId);
+        }
+
+        [Test]
+        public void TestGroupIdCounter()
+        {
+            SimpleMultiAgentGroup group1 = new SimpleMultiAgentGroup();
+            SimpleMultiAgentGroup group2 = new SimpleMultiAgentGroup();
+            // id should be unique
+            Assert.AreNotEqual(group1.GetId(), group2.GetId());
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs.meta b/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7edd5022788dd0f9dc9cea8b823966f9c8d23c6d
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MultiAgentGroupTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ef0158fde748d478ca5ee3bbe22a4c9e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs b/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..28e3b1db6e69d41ba77a0e618ebaec9943dec795
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs
@@ -0,0 +1,54 @@
+using NUnit.Framework;
+using Unity.MLAgents.Inference.Utils;
+
+namespace Unity.MLAgents.Tests
+{
+    public class MultinomialTest
+    {
+        [Test]
+        public void TestDim1()
+        {
+            var m = new Multinomial(2018);
+            var cdf = new[] { 1f };
+
+            Assert.AreEqual(0, m.Sample(cdf));
+            Assert.AreEqual(0, m.Sample(cdf));
+            Assert.AreEqual(0, m.Sample(cdf));
+        }
+
+        [Test]
+        public void TestDim1Unscaled()
+        {
+            var m = new Multinomial(2018);
+            var cdf = new[] { 0.1f };
+
+            Assert.AreEqual(0, m.Sample(cdf));
+            Assert.AreEqual(0, m.Sample(cdf));
+            Assert.AreEqual(0, m.Sample(cdf));
+        }
+
+        [Test]
+        public void TestDim3()
+        {
+            var m = new Multinomial(2018);
+            var cdf = new[] { 0.1f, 0.3f, 1.0f };
+
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(1, m.Sample(cdf));
+        }
+
+        [Test]
+        public void TestDim3Unscaled()
+        {
+            var m = new Multinomial(2018);
+            var cdf = new[] { 0.05f, 0.15f, 0.5f };
+
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(2, m.Sample(cdf));
+            Assert.AreEqual(1, m.Sample(cdf));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs.meta b/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..44fd9bbe91f9cbf23542f0f8f1c62cecf945a013
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/MultinomialTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 668f4ac2d83814df5a8883722633e4e5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs b/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3dd0c911567ead739381746499bb67feac6dcfc7
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs
@@ -0,0 +1,75 @@
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class ObservationSpecTests
+    {
+        [Test]
+        public void TestVectorObsSpec()
+        {
+            var obsSpec = ObservationSpec.Vector(5);
+            Assert.AreEqual(1, obsSpec.Rank);
+
+            var shape = obsSpec.Shape;
+            Assert.AreEqual(1, shape.Length);
+            Assert.AreEqual(5, shape[0]);
+
+            var dimensionProps = obsSpec.DimensionProperties;
+            Assert.AreEqual(1, dimensionProps.Length);
+            Assert.AreEqual(DimensionProperty.None, dimensionProps[0]);
+
+            Assert.AreEqual(ObservationType.Default, obsSpec.ObservationType);
+        }
+
+        [Test]
+        public void TestVariableLengthObsSpec()
+        {
+            var obsSpec = ObservationSpec.VariableLength(5, 6);
+            Assert.AreEqual(2, obsSpec.Rank);
+
+            var shape = obsSpec.Shape;
+            Assert.AreEqual(2, shape.Length);
+            Assert.AreEqual(5, shape[0]);
+            Assert.AreEqual(6, shape[1]);
+
+            var dimensionProps = obsSpec.DimensionProperties;
+            Assert.AreEqual(2, dimensionProps.Length);
+            Assert.AreEqual(DimensionProperty.VariableSize, dimensionProps[0]);
+            Assert.AreEqual(DimensionProperty.None, dimensionProps[1]);
+
+            Assert.AreEqual(ObservationType.Default, obsSpec.ObservationType);
+        }
+
+        [Test]
+        public void TestVisualObsSpec()
+        {
+            var obsSpec = ObservationSpec.Visual(5, 6, 7);
+            Assert.AreEqual(3, obsSpec.Rank);
+
+            var shape = obsSpec.Shape;
+            Assert.AreEqual(3, shape.Length);
+            Assert.AreEqual(5, shape[0]);
+            Assert.AreEqual(6, shape[1]);
+            Assert.AreEqual(7, shape[2]);
+
+            var dimensionProps = obsSpec.DimensionProperties;
+            Assert.AreEqual(3, dimensionProps.Length);
+            Assert.AreEqual(DimensionProperty.TranslationalEquivariance, dimensionProps[0]);
+            Assert.AreEqual(DimensionProperty.TranslationalEquivariance, dimensionProps[1]);
+            Assert.AreEqual(DimensionProperty.None, dimensionProps[2]);
+
+            Assert.AreEqual(ObservationType.Default, obsSpec.ObservationType);
+        }
+
+        [Test]
+        public void TestMismatchShapeDimensionPropThrows()
+        {
+            var shape = new InplaceArray<int>(1, 2);
+            var dimProps = new InplaceArray<DimensionProperty>(DimensionProperty.TranslationalEquivariance);
+            Assert.Throws<UnityAgentsException>(() => new ObservationSpec(shape, dimProps));
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs.meta b/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2ea6756e5031646da242b5aaaad9a734db765dbf
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/ObservationSpecTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 27ff1979bd5e4b8ebeb4d98f414a5090
+timeCreated: 1615863866
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Policies.meta b/com.unity.ml-agents/Tests/Editor/Policies.meta
new file mode 100644
index 0000000000000000000000000000000000000000..be3f189b91e57f05fa55749b9c829bef0e8e649b
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Policies.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: df271cac120e4d6893b14599fa8eb64d
+timeCreated: 1617813392
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs b/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..22b48ea9e74e39a7a0a711a682907f5633b2d2f4
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs
@@ -0,0 +1,123 @@
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests.Policies
+{
+    [TestFixture]
+    public class HeuristicPolicyTest
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+        }
+
+        /// <summary>
+        /// Assert that the action buffers are initialized to zero, and then set them to non-zero values.
+        /// </summary>
+        /// <param name="actionsOut"></param>
+        static void CheckAndSetBuffer(in ActionBuffers actionsOut)
+        {
+            var continuousActions = actionsOut.ContinuousActions;
+            for (var continuousIndex = 0; continuousIndex < continuousActions.Length; continuousIndex++)
+            {
+                Assert.AreEqual(continuousActions[continuousIndex], 0.0f);
+                continuousActions[continuousIndex] = 1.0f;
+            }
+
+            var discreteActions = actionsOut.DiscreteActions;
+            for (var discreteIndex = 0; discreteIndex < discreteActions.Length; discreteIndex++)
+            {
+                Assert.AreEqual(discreteActions[discreteIndex], 0);
+                discreteActions[discreteIndex] = 1;
+            }
+        }
+
+        class ActionClearedAgent : Agent
+        {
+            public int HeuristicCalls;
+            public override void Heuristic(in ActionBuffers actionsOut)
+            {
+                CheckAndSetBuffer(actionsOut);
+                HeuristicCalls++;
+            }
+        }
+
+        class ActionClearedActuator : IActuator
+        {
+            public int HeuristicCalls;
+            public ActionClearedActuator(ActionSpec actionSpec)
+            {
+                ActionSpec = actionSpec;
+                Name = GetType().Name;
+            }
+
+            public void OnActionReceived(ActionBuffers actionBuffers)
+            {
+            }
+
+            public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+            {
+            }
+
+            public void Heuristic(in ActionBuffers actionBuffersOut)
+            {
+                CheckAndSetBuffer(actionBuffersOut);
+                HeuristicCalls++;
+            }
+
+            public ActionSpec ActionSpec { get; }
+            public string Name { get; }
+
+            public void ResetData()
+            {
+            }
+        }
+
+        class ActionClearedActuatorComponent : ActuatorComponent
+        {
+            public ActionClearedActuator ActionClearedActuator;
+            public ActionClearedActuatorComponent()
+            {
+                ActionSpec = new ActionSpec(2, new[] { 3, 3 });
+            }
+
+            public override IActuator[] CreateActuators()
+            {
+                ActionClearedActuator = new ActionClearedActuator(ActionSpec);
+                return new IActuator[] { ActionClearedActuator };
+            }
+
+            public override ActionSpec ActionSpec { get; }
+        }
+
+        [Test]
+        public void TestActionsCleared()
+        {
+            var gameObj = new GameObject();
+            var agent = gameObj.AddComponent<ActionClearedAgent>();
+            var behaviorParameters = agent.GetComponent<BehaviorParameters>();
+            behaviorParameters.BrainParameters.ActionSpec = new ActionSpec(1, new[] { 4 });
+            behaviorParameters.BrainParameters.VectorObservationSize = 0;
+            behaviorParameters.BehaviorType = BehaviorType.HeuristicOnly;
+
+            var actuatorComponent = gameObj.AddComponent<ActionClearedActuatorComponent>();
+            agent.LazyInitialize();
+
+            const int k_NumSteps = 5;
+            for (var i = 0; i < k_NumSteps; i++)
+            {
+                agent.RequestDecision();
+                Academy.Instance.EnvironmentStep();
+            }
+
+            Assert.AreEqual(agent.HeuristicCalls, k_NumSteps);
+            Assert.AreEqual(actuatorComponent.ActionClearedActuator.HeuristicCalls, k_NumSteps);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs.meta b/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..682a64b746a26d34e3b7c651941dc901462798de
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 5108e92f91a04ddab9d628c9bc57cadb
+timeCreated: 1617813411
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/PublicAPI.meta b/com.unity.ml-agents/Tests/Editor/PublicAPI.meta
new file mode 100644
index 0000000000000000000000000000000000000000..96673482aba2c9cfc87838707816a33e43248c0b
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/PublicAPI.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: f5aa894d83ebc411581c8475cd2f9ae0
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs b/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fd45bfefc508ceda26f9dcea33a1ec5829aa5282
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs
@@ -0,0 +1,101 @@
+using Unity.MLAgents.Sensors;
+using NUnit.Framework;
+using Unity.MLAgents;
+using UnityEngine;
+
+namespace Unity.MLAgentsExamples
+{
+    /// <summary>
+    /// The purpose of these tests is to make sure that we can do basic operations like creating
+    /// an Agent and adding components from code without requiring access to internal methods.
+    /// The tests aren't intended to add extra test coverage (although they might) and might
+    /// not check any conditions.
+    /// </summary>
+    [TestFixture]
+    public class PublicApiValidation
+    {
+        [Test]
+        public void CheckSetupCameraSensorComponent()
+        {
+            var gameObject = new GameObject();
+            var width = 24;
+            var height = 16;
+
+            var sensorComponent = gameObject.AddComponent<CameraSensorComponent>();
+            sensorComponent.Camera = Camera.main;
+            sensorComponent.SensorName = "camera1";
+            sensorComponent.Width = width;
+            sensorComponent.Height = height;
+            sensorComponent.Grayscale = true;
+
+            // Make sure the sets actually applied
+            Assert.AreEqual("camera1", sensorComponent.SensorName);
+            Assert.AreEqual(width, sensorComponent.Width);
+            Assert.AreEqual(height, sensorComponent.Height);
+            Assert.IsTrue(sensorComponent.Grayscale);
+        }
+
+        [Test]
+        public void CheckSetupRenderTextureSensorComponent()
+        {
+            var gameObject = new GameObject();
+
+            var sensorComponent = gameObject.AddComponent<RenderTextureSensorComponent>();
+            var width = 24;
+            var height = 16;
+            var texture = new RenderTexture(width, height, 0);
+            sensorComponent.RenderTexture = texture;
+            sensorComponent.SensorName = "rtx1";
+            sensorComponent.Grayscale = true;
+
+            // Make sure the sets actually applied
+            Assert.AreEqual("rtx1", sensorComponent.SensorName);
+            Assert.IsTrue(sensorComponent.Grayscale);
+        }
+
+#if MLA_UNITY_PHYSICS_MODULE
+        [Test]
+        public void CheckSetupRayPerceptionSensorComponent()
+        {
+            var gameObject = new GameObject();
+
+            var sensorComponent = gameObject.AddComponent<RayPerceptionSensorComponent3D>();
+            sensorComponent.SensorName = "ray3d";
+            sensorComponent.DetectableTags = new List<string> { "Player", "Respawn" };
+            sensorComponent.RaysPerDirection = 3;
+            sensorComponent.MaxRayDegrees = 30;
+            sensorComponent.SphereCastRadius = .1f;
+            sensorComponent.RayLayerMask = 0;
+            sensorComponent.ObservationStacks = 2;
+
+            sensorComponent.CreateSensors();
+
+            var sensor = sensorComponent.RaySensor;
+            sensor.Update();
+            var outputs = sensor.RayPerceptionOutput;
+            Assert.AreEqual(outputs.RayOutputs.Length, 2 * sensorComponent.RaysPerDirection + 1);
+        }
+
+#endif
+
+        /// <summary>
+        /// Make sure we can inherit from DecisionRequester and override some logic.
+        /// </summary>
+        class CustomDecisionRequester : DecisionRequester
+        {
+            /// <summary>
+            /// Example logic. If the killswitch flag is set, the Agent never requests a decision.
+            /// </summary>
+            public bool KillswitchEnabled;
+
+            public CustomDecisionRequester()
+            {
+            }
+
+            protected override bool ShouldRequestDecision(DecisionRequestContext context)
+            {
+                return !KillswitchEnabled && base.ShouldRequestDecision(context);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs.meta b/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9f344cf15c327c10a487f846b80717443c89ad24
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 016a3ac45b0345e3ab95f14ecaabdb11
+timeCreated: 1583858370
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef b/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..c96b91cd52ad55ddee980f5243986b5513bc61ca
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef
@@ -0,0 +1,26 @@
+{
+    "name": "Unity.ML-Agents.Editor.Tests.PublicAPI",
+    "references": [
+        "Unity.ML-Agents.Editor",
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ]
+}
diff --git a/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef.meta b/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e1329a95fc7a3ea7f57e73f102354ae5a40df72c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/PublicAPI/Unity.ML-Agents.Editor.Tests.PublicAPI.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 38254a2538c8f42fb9f2057d17fd0e70
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs b/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..520e3e656f7425ad5226bdfc3991e4933d2881df
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs
@@ -0,0 +1,91 @@
+using System;
+using NUnit.Framework;
+using Unity.MLAgents.Inference.Utils;
+
+namespace Unity.MLAgents.Tests
+{
+    public class RandomNormalTest
+    {
+        const float k_FirstValue = -1.19580f;
+        const float k_SecondValue = -0.97345f;
+        const double k_Epsilon = 0.0001;
+
+        [Test]
+        public void RandomNormalTestTwoDouble()
+        {
+            var rn = new RandomNormal(2018);
+
+            Assert.AreEqual(k_FirstValue, rn.NextDouble(), k_Epsilon);
+            Assert.AreEqual(k_SecondValue, rn.NextDouble(), k_Epsilon);
+        }
+
+        [Test]
+        public void RandomNormalTestWithMean()
+        {
+            var rn = new RandomNormal(2018, 5.0f);
+
+            Assert.AreEqual(k_FirstValue + 5.0, rn.NextDouble(), k_Epsilon);
+            Assert.AreEqual(k_SecondValue + 5.0, rn.NextDouble(), k_Epsilon);
+        }
+
+        [Test]
+        public void RandomNormalTestWithStddev()
+        {
+            var rn = new RandomNormal(2018, 0.0f, 4.2f);
+
+            Assert.AreEqual(k_FirstValue * 4.2, rn.NextDouble(), k_Epsilon);
+            Assert.AreEqual(k_SecondValue * 4.2, rn.NextDouble(), k_Epsilon);
+        }
+
+        [Test]
+        public void RandomNormalTestWithMeanStddev()
+        {
+            const float mean = -3.2f;
+            const float stddev = 2.2f;
+            var rn = new RandomNormal(2018, mean, stddev);
+
+            Assert.AreEqual(k_FirstValue * stddev + mean, rn.NextDouble(), k_Epsilon);
+            Assert.AreEqual(k_SecondValue * stddev + mean, rn.NextDouble(), k_Epsilon);
+        }
+
+        [Test]
+        public void RandomNormalTestDistribution()
+        {
+            const float mean = -3.2f;
+            const float stddev = 2.2f;
+            var rn = new RandomNormal(2018, mean, stddev);
+
+            const int numSamples = 100000;
+            // Adapted from https://www.johndcook.com/blog/standard_deviation/
+            // Computes stddev and mean without losing precision
+            double oldM = 0.0, newM = 0.0, oldS = 0.0, newS = 0.0;
+
+            for (var i = 0; i < numSamples; i++)
+            {
+                var x = rn.NextDouble();
+                if (i == 0)
+                {
+                    oldM = newM = x;
+                    oldS = 0.0;
+                }
+                else
+                {
+                    newM = oldM + (x - oldM) / i;
+                    newS = oldS + (x - oldM) * (x - newM);
+
+                    // set up for next iteration
+                    oldM = newM;
+                    oldS = newS;
+                }
+            }
+
+            var sampleMean = newM;
+            var sampleVariance = newS / (numSamples - 1);
+            var sampleStddev = Math.Sqrt(sampleVariance);
+
+            // Note a larger epsilon here. We could get closer to the true values with more samples.
+            Assert.AreEqual(mean, sampleMean, 0.01);
+            Assert.AreEqual(stddev, sampleStddev, 0.01);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs.meta b/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f1d5c0833bd4dc13cff89f288c224721d2e897d3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/RandomNormalTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 518c8e6e10fd94059a064ffbe65557af
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs b/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a06e914dbddf7bb71e057003ba94c2612638dd61
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs
@@ -0,0 +1,72 @@
+using System;
+using NUnit.Framework;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class RecursionCheckerTests
+    {
+        class InfiniteRecurser
+        {
+            RecursionChecker m_checker = new RecursionChecker("InfiniteRecurser");
+            public int NumCalls;
+
+            public void Implode()
+            {
+                NumCalls++;
+                using (m_checker.Start())
+                {
+                    Implode();
+                }
+            }
+        }
+
+        [Test]
+        public void TestRecursionCheck()
+        {
+            var rc = new InfiniteRecurser();
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                rc.Implode();
+            });
+
+            // Should increment twice before bailing out.
+            Assert.AreEqual(2, rc.NumCalls);
+        }
+
+        class OneTimeThrower
+        {
+            RecursionChecker m_checker = new RecursionChecker("OneTimeThrower");
+            public int NumCalls;
+
+            public void DoStuff()
+            {
+                // This method throws from inside the checker the first time.
+                // Later calls do nothing.
+                NumCalls++;
+                using (m_checker.Start())
+                {
+                    if (NumCalls == 1)
+                    {
+                        throw new ArgumentException("oops");
+                    }
+                }
+            }
+        }
+
+        [Test]
+        public void TestThrowResetsFlag()
+        {
+            var ott = new OneTimeThrower();
+            Assert.Throws<ArgumentException>(() =>
+            {
+                ott.DoStuff();
+            });
+
+            // Make sure the flag is cleared if we throw in the "using". Should be able to step subsequently.
+            ott.DoStuff();
+            ott.DoStuff();
+            Assert.AreEqual(3, ott.NumCalls);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs.meta b/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7240ff8a0b2a881e4bc93aba1a9e72ff1c4b05fe
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/RecursionCheckerTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 5a7183e11dd5434684a4225c80169173
+timeCreated: 1602781778
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/SamplerTests.cs b/com.unity.ml-agents/Tests/Editor/SamplerTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..3f8dfa6df23506a858a9123fd6c856d7ee2c11c9
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SamplerTests.cs
@@ -0,0 +1,107 @@
+using NUnit.Framework;
+using System.IO;
+using Unity.MLAgents.SideChannels;
+
+namespace Unity.MLAgents.Tests
+{
+    public class SamplerTests
+    {
+        const int k_Seed = 1337;
+        const double k_Epsilon = 0.0001;
+        EnvironmentParametersChannel m_Channel;
+
+        public SamplerTests()
+        {
+            m_Channel = SideChannelManager.GetSideChannel<EnvironmentParametersChannel>();
+            // if running test on its own
+            if (m_Channel == null)
+            {
+                m_Channel = new EnvironmentParametersChannel();
+                SideChannelManager.RegisterSideChannel(m_Channel);
+            }
+        }
+
+        [Test]
+        public void UniformSamplerTest()
+        {
+            float min_value = 1.0f;
+            float max_value = 2.0f;
+            string parameter = "parameter1";
+            using (var outgoingMsg = new OutgoingMessage())
+            {
+                outgoingMsg.WriteString(parameter);
+                // 1 indicates this meessage is a Sampler
+                outgoingMsg.WriteInt32(1);
+                outgoingMsg.WriteInt32(k_Seed);
+                outgoingMsg.WriteInt32((int)SamplerType.Uniform);
+                outgoingMsg.WriteFloat32(min_value);
+                outgoingMsg.WriteFloat32(max_value);
+                byte[] message = GetByteMessage(m_Channel, outgoingMsg);
+                SideChannelManager.ProcessSideChannelData(message);
+            }
+            Assert.AreEqual(1.208888f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+            Assert.AreEqual(1.118017f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+        }
+
+        [Test]
+        public void GaussianSamplerTest()
+        {
+            float mean = 3.0f;
+            float stddev = 0.2f;
+            string parameter = "parameter2";
+            using (var outgoingMsg = new OutgoingMessage())
+            {
+                outgoingMsg.WriteString(parameter);
+                // 1 indicates this meessage is a Sampler
+                outgoingMsg.WriteInt32(1);
+                outgoingMsg.WriteInt32(k_Seed);
+                outgoingMsg.WriteInt32((int)SamplerType.Gaussian);
+                outgoingMsg.WriteFloat32(mean);
+                outgoingMsg.WriteFloat32(stddev);
+                byte[] message = GetByteMessage(m_Channel, outgoingMsg);
+                SideChannelManager.ProcessSideChannelData(message);
+            }
+            Assert.AreEqual(2.936162f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+            Assert.AreEqual(2.951348f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+        }
+
+        [Test]
+        public void MultiRangeUniformSamplerTest()
+        {
+            float[] intervals = new float[4];
+            intervals[0] = 1.2f;
+            intervals[1] = 2f;
+            intervals[2] = 3.2f;
+            intervals[3] = 4.1f;
+            string parameter = "parameter3";
+            using (var outgoingMsg = new OutgoingMessage())
+            {
+                outgoingMsg.WriteString(parameter);
+                // 1 indicates this meessage is a Sampler
+                outgoingMsg.WriteInt32(1);
+                outgoingMsg.WriteInt32(k_Seed);
+                outgoingMsg.WriteInt32((int)SamplerType.MultiRangeUniform);
+                outgoingMsg.WriteFloatList(intervals);
+                byte[] message = GetByteMessage(m_Channel, outgoingMsg);
+                SideChannelManager.ProcessSideChannelData(message);
+            }
+            Assert.AreEqual(3.387999f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+            Assert.AreEqual(1.294413f, m_Channel.GetWithDefault(parameter, 1.0f), k_Epsilon);
+        }
+
+        internal static byte[] GetByteMessage(SideChannel sideChannel, OutgoingMessage msg)
+        {
+            byte[] message = msg.ToByteArray();
+            using (var memStream = new MemoryStream())
+            {
+                using (var binaryWriter = new BinaryWriter(memStream))
+                {
+                    binaryWriter.Write(sideChannel.ChannelId.ToByteArray());
+                    binaryWriter.Write(message.Length);
+                    binaryWriter.Write(message);
+                }
+                return memStream.ToArray();
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/SamplerTests.cs.meta b/com.unity.ml-agents/Tests/Editor/SamplerTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ef0d54e72a0ba586d34c38dc2fee6b52297bbc5f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SamplerTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 7e6609c51018d4132beda8ddedd46d91
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Serialization.meta b/com.unity.ml-agents/Tests/Editor/Serialization.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1386f3eca687ba4ed1a18f9a53dc8c08a53d0315
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Serialization.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: e76819646e3e4424f9c2802edbd2e41b
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs b/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs
new file mode 100644
index 0000000000000000000000000000000000000000..945ee199c1032da2bd7cb190aa7624cd554a7b5a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs
@@ -0,0 +1,25 @@
+using NUnit.Framework;
+using UnityEngine;
+using UnityEditor;
+using Unity.MLAgents.Policies;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class TestSerialization
+    {
+        const string k_oldPrefabPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/old_serialized_agent.prefab";
+        const int k_numVecObs = 212;
+        const int k_numContinuousActions = 39;
+
+        [Test]
+        public void TestDeserialization()
+        {
+            var prefab = AssetDatabase.LoadAssetAtPath<GameObject>(k_oldPrefabPath);
+            var agent = GameObject.Instantiate(prefab);
+            var bp = agent.GetComponent<BehaviorParameters>();
+            Assert.AreEqual(bp.BrainParameters.ActionSpec.NumContinuousActions, k_numContinuousActions);
+            Assert.AreEqual(bp.BrainParameters.VectorObservationSize, k_numVecObs);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs.meta b/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..11479dd124d0bfffa5e627b083595a6f4cb00986
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Serialization/TestLoadOldPrefab.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a3070d063598144268171a468db17ddd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/SideChannels.meta b/com.unity.ml-agents/Tests/Editor/SideChannels.meta
new file mode 100644
index 0000000000000000000000000000000000000000..67d4d62cd3b3432785ceaf79cfe915c4655418f2
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SideChannels.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 1228f198ceee45a38c7d9ff50425b65d
+timeCreated: 1610760867
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs b/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..50be2de09a019756896ee9c85124b3776a759336
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs
@@ -0,0 +1,42 @@
+using NUnit.Framework;
+using Unity.MLAgents.SideChannels;
+using UnityEngine;
+
+namespace Unity.MLAgents.Tests
+{
+    public class EngineConfigurationChannelTests
+    {
+        float m_OldTimeScale = 1.0f;
+
+        [SetUp]
+        public void Setup()
+        {
+            m_OldTimeScale = Time.timeScale;
+        }
+
+        [TearDown]
+        public void TearDown()
+        {
+            Time.timeScale = m_OldTimeScale;
+        }
+
+        [Test]
+        public void TestTimeScaleClamping()
+        {
+            OutgoingMessage pythonMsg = new OutgoingMessage();
+            pythonMsg.WriteInt32((int)EngineConfigurationChannel.ConfigurationType.TimeScale);
+            pythonMsg.WriteFloat32(1000f);
+
+            var sideChannel = new EngineConfigurationChannel();
+            sideChannel.ProcessMessage(pythonMsg.ToByteArray());
+
+#if UNITY_EDITOR
+            // Should be clamped
+            Assert.AreEqual(100.0f, Time.timeScale);
+#else
+            // Not sure we can run this test from a player, but just in case, shouldn't clamp.
+            Assert.AreEqual(1000.0f, Time.timeScale);
+#endif
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs.meta b/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..68ff93bb9e44eaa3d59d2bd0ee50f2bce9b0b323
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 71aa620295f74ca5875e8e4782f08768
+timeCreated: 1610760906
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/SideChannels/SideChannelTests.cs b/com.unity.ml-agents/Tests/Editor/SideChannels/SideChannelTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..6ca25f2eda25005df90c5f4e63a3be7480c9cad3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/SideChannels/SideChannelTests.cs
@@ -0,0 +1,193 @@
+using System;
+using NUnit.Framework;
+using System.Collections.Generic;
+using System.Text;
+using Unity.MLAgents.SideChannels;
+
+namespace Unity.MLAgents.Tests
+{
+    public class SideChannelTests
+    {
+        // This test side channel only deals in integers
+        public class TestSideChannel : SideChannel
+        {
+            public List<int> messagesReceived = new List<int>();
+
+            public TestSideChannel()
+            {
+                ChannelId = new Guid("6afa2c06-4f82-11ea-b238-784f4387d1f7");
+            }
+
+            protected override void OnMessageReceived(IncomingMessage msg)
+            {
+                messagesReceived.Add(msg.ReadInt32());
+            }
+
+            public void SendInt(int value)
+            {
+                using (var msg = new OutgoingMessage())
+                {
+                    msg.WriteInt32(value);
+                    QueueMessageToSend(msg);
+                }
+            }
+        }
+
+        [Test]
+        public void TestIntegerSideChannel()
+        {
+            var intSender = new TestSideChannel();
+            var intReceiver = new TestSideChannel();
+            var dictSender = new Dictionary<Guid, SideChannel> { { intSender.ChannelId, intSender } };
+            var dictReceiver = new Dictionary<Guid, SideChannel> { { intReceiver.ChannelId, intReceiver } };
+
+            intSender.SendInt(4);
+            intSender.SendInt(5);
+            intSender.SendInt(6);
+
+            byte[] fakeData = SideChannelManager.GetSideChannelMessage(dictSender);
+            SideChannelManager.ProcessSideChannelData(dictReceiver, fakeData);
+
+            Assert.AreEqual(intReceiver.messagesReceived[0], 4);
+            Assert.AreEqual(intReceiver.messagesReceived[1], 5);
+            Assert.AreEqual(intReceiver.messagesReceived[2], 6);
+        }
+
+        [Test]
+        public void TestRawBytesSideChannel()
+        {
+            var str1 = "Test string";
+            var str2 = "Test string, second";
+
+            var strSender = new RawBytesChannel(new Guid("9a5b8954-4f82-11ea-b238-784f4387d1f7"));
+            var strReceiver = new RawBytesChannel(new Guid("9a5b8954-4f82-11ea-b238-784f4387d1f7"));
+            var dictSender = new Dictionary<Guid, SideChannel> { { strSender.ChannelId, strSender } };
+            var dictReceiver = new Dictionary<Guid, SideChannel> { { strReceiver.ChannelId, strReceiver } };
+
+            strSender.SendRawBytes(Encoding.ASCII.GetBytes(str1));
+            strSender.SendRawBytes(Encoding.ASCII.GetBytes(str2));
+
+            byte[] fakeData = SideChannelManager.GetSideChannelMessage(dictSender);
+            SideChannelManager.ProcessSideChannelData(dictReceiver, fakeData);
+
+            var messages = strReceiver.GetAndClearReceivedMessages();
+
+            Assert.AreEqual(messages.Count, 2);
+            Assert.AreEqual(Encoding.ASCII.GetString(messages[0]), str1);
+            Assert.AreEqual(Encoding.ASCII.GetString(messages[1]), str2);
+        }
+
+        [Test]
+        public void TestFloatPropertiesSideChannel()
+        {
+            var k1 = "gravity";
+            var k2 = "length";
+            int wasCalled = 0;
+
+            var propA = new FloatPropertiesChannel();
+            var propB = new FloatPropertiesChannel();
+            var dictReceiver = new Dictionary<Guid, SideChannel> { { propA.ChannelId, propA } };
+            var dictSender = new Dictionary<Guid, SideChannel> { { propB.ChannelId, propB } };
+
+            propA.RegisterCallback(k1, f => { wasCalled++; });
+            var tmp = propB.GetWithDefault(k2, 3.0f);
+            Assert.AreEqual(tmp, 3.0f);
+            propB.Set(k2, 1.0f);
+            tmp = propB.GetWithDefault(k2, 3.0f);
+            Assert.AreEqual(tmp, 1.0f);
+
+            byte[] fakeData = SideChannelManager.GetSideChannelMessage(dictSender);
+            SideChannelManager.ProcessSideChannelData(dictReceiver, fakeData);
+
+            tmp = propA.GetWithDefault(k2, 3.0f);
+            Assert.AreEqual(tmp, 1.0f);
+
+            Assert.AreEqual(wasCalled, 0);
+            propB.Set(k1, 1.0f);
+            Assert.AreEqual(wasCalled, 0);
+            fakeData = SideChannelManager.GetSideChannelMessage(dictSender);
+            SideChannelManager.ProcessSideChannelData(dictReceiver, fakeData);
+            Assert.AreEqual(wasCalled, 1);
+
+            var keysA = propA.Keys();
+            Assert.AreEqual(2, keysA.Count);
+            Assert.IsTrue(keysA.Contains(k1));
+            Assert.IsTrue(keysA.Contains(k2));
+
+            var keysB = propA.Keys();
+            Assert.AreEqual(2, keysB.Count);
+            Assert.IsTrue(keysB.Contains(k1));
+            Assert.IsTrue(keysB.Contains(k2));
+        }
+
+        [Test]
+        public void TestOutgoingMessageRawBytes()
+        {
+            // Make sure that SetRawBytes resets the buffer correctly.
+            // Write 8 bytes (an int and float) then call SetRawBytes with 4 bytes
+            var msg = new OutgoingMessage();
+            msg.WriteInt32(42);
+            msg.WriteFloat32(1.0f);
+
+            var data = new byte[] { 1, 2, 3, 4 };
+            msg.SetRawBytes(data);
+
+            var result = msg.ToByteArray();
+            Assert.AreEqual(data, result);
+        }
+
+        [Test]
+        public void TestMessageReadWrites()
+        {
+            var boolVal = true;
+            var intVal = 1337;
+            var floatVal = 4.2f;
+            var floatListVal = new float[] { 1001, 1002 };
+            var stringVal = "mlagents!";
+
+            IncomingMessage incomingMsg;
+            using (var outgoingMsg = new OutgoingMessage())
+            {
+                outgoingMsg.WriteBoolean(boolVal);
+                outgoingMsg.WriteInt32(intVal);
+                outgoingMsg.WriteFloat32(floatVal);
+                outgoingMsg.WriteString(stringVal);
+                outgoingMsg.WriteFloatList(floatListVal);
+
+                incomingMsg = new IncomingMessage(outgoingMsg.ToByteArray());
+            }
+
+            Assert.AreEqual(boolVal, incomingMsg.ReadBoolean());
+            Assert.AreEqual(intVal, incomingMsg.ReadInt32());
+            Assert.AreEqual(floatVal, incomingMsg.ReadFloat32());
+            Assert.AreEqual(stringVal, incomingMsg.ReadString());
+            Assert.AreEqual(floatListVal, incomingMsg.ReadFloatList());
+        }
+
+        [Test]
+        public void TestMessageReadDefaults()
+        {
+            // Make sure reading past the end of a message will apply defaults.
+            IncomingMessage incomingMsg;
+            using (var outgoingMsg = new OutgoingMessage())
+            {
+                incomingMsg = new IncomingMessage(outgoingMsg.ToByteArray());
+            }
+
+            Assert.AreEqual(false, incomingMsg.ReadBoolean());
+            Assert.AreEqual(true, incomingMsg.ReadBoolean(defaultValue: true));
+
+            Assert.AreEqual(0, incomingMsg.ReadInt32());
+            Assert.AreEqual(42, incomingMsg.ReadInt32(defaultValue: 42));
+
+            Assert.AreEqual(0.0f, incomingMsg.ReadFloat32());
+            Assert.AreEqual(1337.0f, incomingMsg.ReadFloat32(defaultValue: 1337.0f));
+
+            Assert.AreEqual(default(string), incomingMsg.ReadString());
+            Assert.AreEqual("foo", incomingMsg.ReadString(defaultValue: "foo"));
+
+            Assert.AreEqual(default(float[]), incomingMsg.ReadFloatList());
+            Assert.AreEqual(new float[] { 1001, 1002 }, incomingMsg.ReadFloatList(new float[] { 1001, 1002 }));
+        }
+    }
+}
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+  m_Children: []
+  m_Father: {fileID: 0}
+  m_RootOrder: 0
+  m_LocalEulerAnglesHint: {x: 0, y: 90, z: 0}
+--- !u!114 &5414230854946179998
+MonoBehaviour:
+  m_ObjectHideFlags: 0
+  m_CorrespondingSourceObject: {fileID: 0}
+  m_PrefabInstance: {fileID: 0}
+  m_PrefabAsset: {fileID: 0}
+  m_GameObject: {fileID: 5381908961062339374}
+  m_Enabled: 1
+  m_EditorHideFlags: 0
+  m_Script: {fileID: 11500000, guid: 5d1c4e0b1822b495aa52bc52839ecb30, type: 3}
+  m_Name: 
+  m_EditorClassIdentifier: 
+  m_BrainParameters:
+    vectorObservationSize: 212
+    numStackedVectorObservations: 1
+    vectorActionSize: 27000000
+    vectorActionDescriptions: []
+    vectorActionSpaceType: 1
+  m_Model: {fileID: 11400000, guid: 4e86a19e012da43bfa5ab97ae8089b98, type: 3}
+  m_InferenceDevice: 0
+  m_BehaviorType: 0
+  m_BehaviorName: Walker
+  TeamId: 0
+  m_UseChildSensors: 1
+  m_UseChildActuators: 1
+  m_ObservableAttributeHandling: 0
+--- !u!114 &8153975132613398210
+MonoBehaviour:
+  m_ObjectHideFlags: 0
+  m_CorrespondingSourceObject: {fileID: 0}
+  m_PrefabInstance: {fileID: 0}
+  m_PrefabAsset: {fileID: 0}
+  m_GameObject: {fileID: 5381908961062339374}
+  m_Enabled: 1
+  m_EditorHideFlags: 0
+  m_Script: {fileID: 11500000, guid: 88b6042bc9a5d4aa58d931eae49442e5, type: 3}
+  m_Name: 
+  m_EditorClassIdentifier: 
+  agentParameters:
+    maxStep: 0
+  hasUpgradedFromAgentParameters: 1
+  MaxStep: 0
diff --git a/com.unity.ml-agents/Tests/Editor/TestModels/old_serialized_agent.prefab.meta b/com.unity.ml-agents/Tests/Editor/TestModels/old_serialized_agent.prefab.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2d444fbbd264d2f0ac830f364a92005a96ce8f2f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/TestModels/old_serialized_agent.prefab.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 07e2de94fa37b49c3bdf7a21857c0f73
+PrefabImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/TimerTest.cs b/com.unity.ml-agents/Tests/Editor/TimerTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..cc4ba18e8d30e9fa82b12f9fa5cb7b2e8977a8a3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/TimerTest.cs
@@ -0,0 +1,84 @@
+using NUnit.Framework;
+
+namespace Unity.MLAgents.Tests
+{
+    public class TimerTests
+    {
+        [Test]
+        public void TestNested()
+        {
+            TimerStack myTimer = TimerStack.Instance;
+            myTimer.Reset();
+            using (myTimer.Scoped("foo"))
+            {
+                for (int i = 0; i < 5; i++)
+                {
+                    using (myTimer.Scoped("bar"))
+                    {
+                        myTimer.SetGauge("my_gauge", i);
+                        myTimer.AddMetadata("i", $"{i}");
+                    }
+                }
+            }
+
+            var rootChildren = myTimer.RootNode.Children;
+            Assert.That(rootChildren, Contains.Key("foo"));
+            Assert.AreEqual(rootChildren["foo"].NumCalls, 1);
+            var gauge = myTimer.RootNode.Gauges["my_gauge"];
+            Assert.NotNull(gauge);
+            Assert.AreEqual(5, gauge.count);
+            Assert.AreEqual(0, gauge.minValue);
+            Assert.AreEqual(4, gauge.maxValue);
+            Assert.AreEqual(4, gauge.value);
+            Assert.AreEqual("4", myTimer.RootNode.Metadata["i"]);
+
+            var fooChildren = rootChildren["foo"].Children;
+            Assert.That(fooChildren, Contains.Key("bar"));
+            Assert.AreEqual(fooChildren["bar"].NumCalls, 5);
+
+            myTimer.Reset();
+            Assert.AreEqual(myTimer.RootNode.Children, null);
+        }
+
+        [Test]
+        public void TestGauges()
+        {
+            TimerStack myTimer = TimerStack.Instance;
+            myTimer.Reset();
+
+            // Simple test - adding 1's should keep that for the weighted and running averages.
+            myTimer.SetGauge("one", 1.0f);
+            var oneNode = myTimer.RootNode.Gauges["one"];
+            Assert.AreEqual(oneNode.weightedAverage, 1.0f);
+            Assert.AreEqual(oneNode.runningAverage, 1.0f);
+
+            for (int i = 0; i < 10; i++)
+            {
+                myTimer.SetGauge("one", 1.0f);
+            }
+
+            Assert.AreEqual(oneNode.weightedAverage, 1.0f);
+            Assert.AreEqual(oneNode.runningAverage, 1.0f);
+
+            // Try some more interesting values
+            myTimer.SetGauge("increasing", 1.0f);
+            myTimer.SetGauge("increasing", 2.0f);
+            myTimer.SetGauge("increasing", 3.0f);
+
+            myTimer.SetGauge("decreasing", 3.0f);
+            myTimer.SetGauge("decreasing", 2.0f);
+            myTimer.SetGauge("decreasing", 1.0f);
+            var increasingNode = myTimer.RootNode.Gauges["increasing"];
+            var decreasingNode = myTimer.RootNode.Gauges["decreasing"];
+
+            // Expect the running average to be (roughly) the same,
+            // but weighted averages will be biased differently.
+            Assert.AreEqual(increasingNode.runningAverage, 2.0f);
+            Assert.AreEqual(decreasingNode.runningAverage, 2.0f);
+
+            // The older values are actually weighted more heavily, so we expect the
+            // increasing series to have a lower moving average.
+            Assert.Less(increasingNode.weightedAverage, decreasingNode.weightedAverage);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/TimerTest.cs.meta b/com.unity.ml-agents/Tests/Editor/TimerTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..86429e2106fc11ff758ded4f982a3f7154eebcf0
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/TimerTest.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 506de2f6a1c74967a6f16ebf494c01d5
+timeCreated: 1569370981
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs b/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d71abf6555e4f224d75f75c10c8e77f08e942ea5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs
@@ -0,0 +1,65 @@
+using System;
+using System.Linq;
+using System.Text;
+using NUnit.Framework;
+using Google.Protobuf;
+using Unity.MLAgents.Analytics;
+using Unity.MLAgents.SideChannels;
+using Unity.MLAgents.CommunicatorObjects;
+
+
+namespace Unity.MLAgents.Tests
+{
+    /// <summary>
+    /// These tests send messages through the event handling code.
+    /// There's no output to test, so just make sure there are no exceptions
+    /// (and get the code coverage above the minimum).
+    /// </summary>
+    public class TrainingAnalyticsSideChannelTests
+    {
+        [Test]
+        public void TestTrainingEnvironmentReceived()
+        {
+            var anyMsg = Google.Protobuf.WellKnownTypes.Any.Pack(new TrainingEnvironmentInitialized());
+            var anyMsgBytes = anyMsg.ToByteArray();
+            var sideChannel = new TrainingAnalyticsSideChannel();
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                sideChannel.ProcessMessage(anyMsgBytes);
+            }
+        }
+
+        [Test]
+        public void TestTrainingBehaviorReceived()
+        {
+            var anyMsg = Google.Protobuf.WellKnownTypes.Any.Pack(new TrainingBehaviorInitialized());
+            var anyMsgBytes = anyMsg.ToByteArray();
+            var sideChannel = new TrainingAnalyticsSideChannel();
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                sideChannel.ProcessMessage(anyMsgBytes);
+            }
+        }
+
+        [Test]
+        public void TestInvalidProtobufMessage()
+        {
+            // Test an invalid (non-protobuf) message. This should silently ignore the data.
+            var badBytes = Encoding.ASCII.GetBytes("Lorem ipsum");
+            var sideChannel = new TrainingAnalyticsSideChannel();
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                sideChannel.ProcessMessage(badBytes);
+            }
+
+            // Test an almost-valid message. This should silently ignore the data.
+            var anyMsg = Google.Protobuf.WellKnownTypes.Any.Pack(new TrainingBehaviorInitialized());
+            var anyMsgBytes = anyMsg.ToByteArray();
+            var truncatedMessage = new ArraySegment<byte>(anyMsgBytes, 0, anyMsgBytes.Length - 1).ToArray();
+            using (new AnalyticsUtils.DisableAnalyticsSending())
+            {
+                sideChannel.ProcessMessage(truncatedMessage);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs.meta b/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ebb5915235a510bfc21e704b89bba7cceec91795
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/TrainingAnalyticsSideChannelTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: c2a71036ddec4ba4bf83c5e8ba1b8daa
+timeCreated: 1610574895
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef b/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..128105b400190035f77d145dac93464196fd9adc
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef
@@ -0,0 +1,47 @@
+{
+    "name": "Unity.ML-Agents.Editor.Tests",
+    "references": [
+        "Unity.ML-Agents.Editor",
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.Mathematics",
+        "Unity.ML-Agents.CommunicatorObjects",
+        "Unity.ML-Agents.Runtime.Utils.Tests",
+        "Unity.ML-Agents.Runtime.Sensor.Tests"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [
+        "Editor"
+    ],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll",
+        "Google.Protobuf.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ],
+    "versionDefines": [
+        {
+            "name": "com.unity.modules.unityanalytics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_ANALYTICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics2d",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS2D_MODULE"
+        }
+    ]
+}
diff --git a/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef.meta b/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..db461686388ffa5d0bc2d914ca41faaf8c734396
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/Unity.ML-Agents.Editor.Tests.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 57f6004a925b546cd94e94ed518e275d
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs b/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..de90a6797a1fa00724d40e3c0cb5811c9833e28c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs
@@ -0,0 +1,23 @@
+using NUnit.Framework;
+
+namespace Unity.MLAgents.Tests
+{
+    public class UtilitiesTests
+    {
+        [Test]
+        public void TestCumSum()
+        {
+            var output = Utilities.CumSum(new[] { 1, 2, 3, 10 });
+            CollectionAssert.AreEqual(output, new[] { 0, 1, 3, 6, 16 });
+
+            output = Utilities.CumSum(new int[0]);
+            CollectionAssert.AreEqual(output, new[] { 0 });
+
+            output = Utilities.CumSum(new[] { 100 });
+            CollectionAssert.AreEqual(output, new[] { 0, 100 });
+
+            output = Utilities.CumSum(new[] { -1, 10 });
+            CollectionAssert.AreEqual(output, new[] { 0, -1, 9 });
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs.meta b/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..66d7be1a61348af0a71a0c0098f0fb1108465dc3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Editor/UtilitiesTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 45ab7fc6851444d8ba622b4f63b8290b
+timeCreated: 1538775063
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime.meta b/com.unity.ml-agents/Tests/Runtime.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1260b3a158c2bbd0867aaaee050a04af3729139a
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: fec0f6b603d3046f086e09ea2a44ed1f
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs b/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..743212a69beaecf8fdc5eb60c1d421e8d5af73f0
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs
@@ -0,0 +1,135 @@
+using System.Collections;
+using System.Collections.Generic;
+using Unity.MLAgents;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+using NUnit.Framework;
+using Unity.MLAgents.Actuators;
+using UnityEngine;
+using UnityEngine.TestTools;
+
+namespace Tests
+{
+    public class PublicApiAgent : Agent
+    {
+        public int numHeuristicCalls;
+
+        [Observable]
+        public float ObservableFloat;
+
+        public override void Heuristic(in ActionBuffers actionsOut)
+        {
+            numHeuristicCalls++;
+            base.Heuristic(actionsOut);
+        }
+    }
+
+    // Simple SensorComponent that sets up a StackingSensor
+    public class StackingComponent : SensorComponent
+    {
+        public SensorComponent wrappedComponent;
+        public int numStacks;
+
+        public override ISensor[] CreateSensors()
+        {
+            var wrappedSensors = wrappedComponent.CreateSensors();
+            var sensorsOut = new ISensor[wrappedSensors.Length];
+            for (var i = 0; i < wrappedSensors.Length; i++)
+            {
+                sensorsOut[i] = new StackingSensor(wrappedSensors[i], numStacks);
+            }
+
+            return sensorsOut;
+        }
+    }
+
+    public class RuntimeApiTest
+    {
+        [SetUp]
+        public static void Setup()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+            Academy.Instance.AutomaticSteppingEnabled = false;
+        }
+
+        [UnityTest]
+        public IEnumerator RuntimeApiTestWithEnumeratorPasses()
+        {
+            Academy.Instance.InferenceSeed = 1337;
+            var gameObject = new GameObject();
+
+            var behaviorParams = gameObject.AddComponent<BehaviorParameters>();
+            behaviorParams.BrainParameters.VectorObservationSize = 3;
+            behaviorParams.BrainParameters.NumStackedVectorObservations = 2;
+            behaviorParams.BrainParameters.VectorActionDescriptions = new[] { "Continuous1", "TestActionA", "TestActionB" };
+            behaviorParams.BrainParameters.ActionSpec = new ActionSpec(1, new[] { 2, 2 });
+            behaviorParams.BehaviorName = "TestBehavior";
+            behaviorParams.TeamId = 42;
+            behaviorParams.UseChildSensors = true;
+            behaviorParams.DeterministicInference = false;
+            behaviorParams.ObservableAttributeHandling = ObservableAttributeOptions.ExamineAll;
+
+
+            // Can't actually create an Agent with InferenceOnly and no model, so change back
+            behaviorParams.BehaviorType = BehaviorType.Default;
+#if MLA_UNITY_PHYSICS_MODULE
+            var sensorComponent = gameObject.AddComponent<RayPerceptionSensorComponent3D>();
+            sensorComponent.SensorName = "ray3d";
+            sensorComponent.DetectableTags = new List<string> { "Player", "Respawn" };
+            sensorComponent.RaysPerDirection = 3;
+
+            // Make a StackingSensor that wraps the RayPerceptionSensorComponent3D
+            // This isn't necessarily practical, just to ensure that it can be done
+            var wrappingSensorComponent = gameObject.AddComponent<StackingComponent>();
+            wrappingSensorComponent.wrappedComponent = sensorComponent;
+            wrappingSensorComponent.numStacks = 3;
+
+            // ISensor isn't set up yet.
+            Assert.IsNull(sensorComponent.RaySensor);
+#endif
+
+
+            // Make sure we can set the behavior type correctly after the agent is initialized
+            // (this creates a new policy).
+            behaviorParams.BehaviorType = BehaviorType.HeuristicOnly;
+
+            // Agent needs to be added after everything else is setup.
+            var agent = gameObject.AddComponent<PublicApiAgent>();
+
+            // DecisionRequester has to be added after Agent.
+            var decisionRequester = gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 2;
+            decisionRequester.TakeActionsBetweenDecisions = true;
+
+#if MLA_UNITY_PHYSICS_MODULE
+            // Initialization should set up the sensors
+            Assert.IsNotNull(sensorComponent.RaySensor);
+#endif
+            // Let's change the inference device
+            var otherDevice = behaviorParams.InferenceDevice == InferenceDevice.CPU ? InferenceDevice.GPU : InferenceDevice.CPU;
+            agent.SetModel(behaviorParams.BehaviorName, behaviorParams.Model, otherDevice);
+
+            agent.AddReward(1.0f);
+
+            // skip a frame.
+            yield return null;
+
+            Academy.Instance.EnvironmentStep();
+
+            var actions = agent.GetStoredActionBuffers().DiscreteActions;
+            // default Heuristic implementation should return zero actions.
+            Assert.AreEqual(new ActionSegment<int>(new[] { 0, 0 }), actions);
+            Assert.AreEqual(1, agent.numHeuristicCalls);
+
+            Academy.Instance.EnvironmentStep();
+            Assert.AreEqual(1, agent.numHeuristicCalls);
+
+            Academy.Instance.EnvironmentStep();
+            Assert.AreEqual(2, agent.numHeuristicCalls);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs.meta b/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..5f7821402b7663fbdd58085c3238a36624859401
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 17878576e4ed14b09875e37394e5ad90
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor.meta b/com.unity.ml-agents/Tests/Runtime/Sensor.meta
new file mode 100644
index 0000000000000000000000000000000000000000..67c65573c644929ef2530b4b18b2006663637509
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 782dd744bcb0744b8a880aa70c8c7421
+folderAsset: yes
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..902a48836d6010db6ad35f081395bd7b55903320
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs
@@ -0,0 +1,305 @@
+#if MLA_UNITY_PHYSICS_MODULE
+using System.Collections.Generic;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    internal class TestBoxOverlapChecker : BoxOverlapChecker
+    {
+        public TestBoxOverlapChecker(
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            bool rotateWithAgent,
+            LayerMask colliderMask,
+            GameObject centerObject,
+            GameObject agentGameObject,
+            string[] detectableTags,
+            int initialColliderBufferSize,
+            int maxColliderBufferSize
+        ) : base(
+            cellScale,
+            gridSize,
+            rotateWithAgent,
+            colliderMask,
+            centerObject,
+            agentGameObject,
+            detectableTags,
+            initialColliderBufferSize,
+            maxColliderBufferSize)
+        {}
+
+        public Vector3[] CellLocalPositions
+        {
+            get
+            {
+                return (Vector3[])typeof(BoxOverlapChecker).GetField("m_CellLocalPositions",
+                    BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+            }
+        }
+
+        public Collider[] ColliderBuffer
+        {
+            get
+            {
+                return (Collider[])typeof(BoxOverlapChecker).GetField("m_ColliderBuffer",
+                    BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+            }
+        }
+
+        public static TestBoxOverlapChecker CreateChecker(
+            float cellScaleX = 1f,
+            float cellScaleZ = 1f,
+            int gridSizeX = 10,
+            int gridSizeZ = 10,
+            bool rotateWithAgent = true,
+            GameObject centerObject = null,
+            GameObject agentGameObject = null,
+            string[] detectableTags = null,
+            int initialColliderBufferSize = 4,
+            int maxColliderBufferSize = 500)
+        {
+            return new TestBoxOverlapChecker(
+                new Vector3(cellScaleX, 0.01f, cellScaleZ),
+                new Vector3Int(gridSizeX, 1, gridSizeZ),
+                rotateWithAgent,
+                LayerMask.GetMask("Default"),
+                centerObject,
+                agentGameObject,
+                detectableTags,
+                initialColliderBufferSize,
+                maxColliderBufferSize);
+        }
+    }
+
+    public class BoxOverlapCheckerTests
+    {
+        [Test]
+        public void TestCellLocalPosition()
+        {
+            var testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            var boxOverlapSquare = TestBoxOverlapChecker.CreateChecker(gridSizeX: 10, gridSizeZ: 10, rotateWithAgent: false, agentGameObject: testGo);
+
+            var localPos = boxOverlapSquare.CellLocalPositions;
+            Assert.AreEqual(new Vector3(-4.5f, 0, -4.5f), localPos[0]);
+            Assert.AreEqual(new Vector3(-4.5f, 0, 4.5f), localPos[9]);
+            Assert.AreEqual(new Vector3(4.5f, 0, -4.5f), localPos[90]);
+            Assert.AreEqual(new Vector3(4.5f, 0, 4.5f), localPos[99]);
+            Object.DestroyImmediate(testGo);
+
+            var testGo2 = new GameObject("test");
+            testGo2.transform.position = new Vector3(3.5f, 8f, 17f); // random, should have no effect on local positions
+            var boxOverlapRect = TestBoxOverlapChecker.CreateChecker(gridSizeX: 5, gridSizeZ: 15, rotateWithAgent: true, agentGameObject: testGo);
+
+            localPos = boxOverlapRect.CellLocalPositions;
+            Assert.AreEqual(new Vector3(-2f, 0, -7f), localPos[0]);
+            Assert.AreEqual(new Vector3(-2f, 0, 7f), localPos[14]);
+            Assert.AreEqual(new Vector3(2f, 0, -7f), localPos[60]);
+            Assert.AreEqual(new Vector3(2f, 0, 7f), localPos[74]);
+            Object.DestroyImmediate(testGo2);
+        }
+
+        [Test]
+        public void TestCellGlobalPositionNoRotate()
+        {
+            var testGo = new GameObject("test");
+            var position = new Vector3(3.5f, 8f, 17f);
+            testGo.transform.position = position;
+            var boxOverlap = TestBoxOverlapChecker.CreateChecker(gridSizeX: 10, gridSizeZ: 10, rotateWithAgent: false, agentGameObject: testGo, centerObject: testGo);
+
+            Assert.AreEqual(new Vector3(-4.5f, 0, -4.5f) + position, boxOverlap.GetCellGlobalPosition(0));
+            Assert.AreEqual(new Vector3(-4.5f, 0, 4.5f) + position, boxOverlap.GetCellGlobalPosition(9));
+            Assert.AreEqual(new Vector3(4.5f, 0, -4.5f) + position, boxOverlap.GetCellGlobalPosition(90));
+            Assert.AreEqual(new Vector3(4.5f, 0, 4.5f) + position, boxOverlap.GetCellGlobalPosition(99));
+
+            testGo.transform.Rotate(0, 90, 0); // should have no effect on positions
+            Assert.AreEqual(new Vector3(-4.5f, 0, -4.5f) + position, boxOverlap.GetCellGlobalPosition(0));
+            Assert.AreEqual(new Vector3(-4.5f, 0, 4.5f) + position, boxOverlap.GetCellGlobalPosition(9));
+            Assert.AreEqual(new Vector3(4.5f, 0, -4.5f) + position, boxOverlap.GetCellGlobalPosition(90));
+            Assert.AreEqual(new Vector3(4.5f, 0, 4.5f) + position, boxOverlap.GetCellGlobalPosition(99));
+
+            Object.DestroyImmediate(testGo);
+        }
+
+        [Test]
+        public void TestCellGlobalPositionRotate()
+        {
+            var testGo = new GameObject("test");
+            var position = new Vector3(15f, 6f, 13f);
+            testGo.transform.position = position;
+            var boxOverlap = TestBoxOverlapChecker.CreateChecker(gridSizeX: 5, gridSizeZ: 15, rotateWithAgent: true, agentGameObject: testGo, centerObject: testGo);
+
+            Assert.AreEqual(new Vector3(-2f, 0, -7f) + position, boxOverlap.GetCellGlobalPosition(0));
+            Assert.AreEqual(new Vector3(-2f, 0, 7f) + position, boxOverlap.GetCellGlobalPosition(14));
+            Assert.AreEqual(new Vector3(2f, 0, -7f) + position, boxOverlap.GetCellGlobalPosition(60));
+            Assert.AreEqual(new Vector3(2f, 0, 7f) + position, boxOverlap.GetCellGlobalPosition(74));
+
+            testGo.transform.Rotate(0, 90, 0);
+            // round to int to ignore numeric errors
+            Assert.AreEqual(Vector3Int.RoundToInt(new Vector3(-7f, 0, 2f) + position), Vector3Int.RoundToInt(boxOverlap.GetCellGlobalPosition(0)));
+            Assert.AreEqual(Vector3Int.RoundToInt(new Vector3(7f, 0, 2f) + position), Vector3Int.RoundToInt(boxOverlap.GetCellGlobalPosition(14)));
+            Assert.AreEqual(Vector3Int.RoundToInt(new Vector3(-7f, 0, -2f) + position), Vector3Int.RoundToInt(boxOverlap.GetCellGlobalPosition(60)));
+            Assert.AreEqual(Vector3Int.RoundToInt(new Vector3(7f, 0, -2f) + position), Vector3Int.RoundToInt(boxOverlap.GetCellGlobalPosition(74)));
+
+            Object.DestroyImmediate(testGo);
+        }
+
+        [Test]
+        public void TestBufferResize()
+        {
+            List<GameObject> testObjects = new List<GameObject>();
+            var testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            testObjects.Add(testGo);
+            var boxOverlap = TestBoxOverlapChecker.CreateChecker(agentGameObject: testGo, centerObject: testGo, initialColliderBufferSize: 2, maxColliderBufferSize: 5);
+            boxOverlap.Perceive();
+            Assert.AreEqual(2, boxOverlap.ColliderBuffer.Length);
+
+            for (var i = 0; i < 3; i++)
+            {
+                var boxGo = new GameObject("test");
+                boxGo.transform.position = Vector3.zero;
+                boxGo.AddComponent<BoxCollider>();
+                testObjects.Add(boxGo);
+            }
+            boxOverlap.Perceive();
+            Assert.AreEqual(4, boxOverlap.ColliderBuffer.Length);
+
+            for (var i = 0; i < 2; i++)
+            {
+                var boxGo = new GameObject("test");
+                boxGo.transform.position = Vector3.zero;
+                boxGo.AddComponent<BoxCollider>();
+                testObjects.Add(boxGo);
+            }
+            boxOverlap.Perceive();
+            Assert.AreEqual(5, boxOverlap.ColliderBuffer.Length);
+
+            Object.DestroyImmediate(testGo);
+            foreach (var go in testObjects)
+            {
+                Object.DestroyImmediate(go);
+            }
+        }
+
+        [Test]
+        public void TestParseCollidersClosest()
+        {
+            var tag1 = "Player";
+            List<GameObject> testObjects = new List<GameObject>();
+            var testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            var boxOverlap = TestBoxOverlapChecker.CreateChecker(
+                cellScaleX: 10f,
+                cellScaleZ: 10f,
+                gridSizeX: 2,
+                gridSizeZ: 2,
+                agentGameObject: testGo,
+                centerObject: testGo,
+                detectableTags: new[] { tag1 });
+            var helper = new VerifyParseCollidersHelper();
+            boxOverlap.GridOverlapDetectedClosest += helper.DetectedAction;
+
+            for (var i = 0; i < 3; i++)
+            {
+                var boxGo = new GameObject("test");
+                boxGo.transform.position = new Vector3(i + 1, 0, 1);
+                boxGo.AddComponent<BoxCollider>();
+                boxGo.tag = tag1;
+                testObjects.Add(boxGo);
+            }
+
+            boxOverlap.Perceive();
+            helper.Verify(1, new List<GameObject> { testObjects[0] });
+
+            Object.DestroyImmediate(testGo);
+            foreach (var go in testObjects)
+            {
+                Object.DestroyImmediate(go);
+            }
+        }
+
+        [Test]
+        public void TestParseCollidersAll()
+        {
+            var tag1 = "Player";
+            List<GameObject> testObjects = new List<GameObject>();
+            var testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            var boxOverlap = TestBoxOverlapChecker.CreateChecker(
+                cellScaleX: 10f,
+                cellScaleZ: 10f,
+                gridSizeX: 2,
+                gridSizeZ: 2,
+                agentGameObject: testGo,
+                centerObject: testGo,
+                detectableTags: new[] { tag1 });
+            var helper = new VerifyParseCollidersHelper();
+            boxOverlap.GridOverlapDetectedAll += helper.DetectedAction;
+
+            for (var i = 0; i < 3; i++)
+            {
+                var boxGo = new GameObject("test");
+                boxGo.transform.position = new Vector3(i + 1, 0, 1);
+                boxGo.AddComponent<BoxCollider>();
+                boxGo.tag = tag1;
+                testObjects.Add(boxGo);
+            }
+
+            boxOverlap.Perceive();
+            helper.Verify(3, testObjects);
+
+            Object.DestroyImmediate(testGo);
+            foreach (var go in testObjects)
+            {
+                Object.DestroyImmediate(go);
+            }
+        }
+
+        public class VerifyParseCollidersHelper
+        {
+            int m_NumInvoked;
+            List<GameObject> m_ParsedObjects = new List<GameObject>();
+
+            public void DetectedAction(GameObject go, int cellIndex)
+            {
+                m_NumInvoked += 1;
+                m_ParsedObjects.Add(go);
+            }
+
+            public void Verify(int expectNumInvoke, List<GameObject> expectedObjects)
+            {
+                Assert.AreEqual(expectNumInvoke, m_NumInvoked);
+                Assert.AreEqual(expectedObjects.Count, m_ParsedObjects.Count);
+                foreach (var obj in expectedObjects)
+                {
+                    Assert.Contains(obj, m_ParsedObjects);
+                }
+            }
+        }
+
+        [Test]
+        public void TestOnlyOneChecker()
+        {
+            var testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            var gridSensorComponent = testGo.AddComponent<SimpleTestGridSensorComponent>();
+            gridSensorComponent.SetComponentParameters(useGridSensorBase: true, useTestingGridSensor: true);
+            var sensors = gridSensorComponent.CreateSensors();
+            int numChecker = 0;
+            foreach (var sensor in sensors)
+            {
+                var gridsensor = (GridSensorBase)sensor;
+                if (gridsensor.m_GridPerception != null)
+                {
+                    numChecker += 1;
+                }
+            }
+            Assert.AreEqual(1, numChecker);
+        }
+    }
+}
+#endif
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d0b075e343e21fb6176136616173bd77d7e9e9b1
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/BoxOverlapCheckerTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 087f04a0f817c45f4a709ed36fe5ba1a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a1ec30dd16851e8aa29c41b1cd33af636df68dab
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs
@@ -0,0 +1,80 @@
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class BufferSensorTest
+    {
+        [Test]
+        public void TestBufferSensor()
+        {
+            var bufferSensor = new BufferSensor(20, 4, "testName");
+            var shape = bufferSensor.GetObservationSpec().Shape;
+            var dimProp = bufferSensor.GetObservationSpec().DimensionProperties;
+            Assert.AreEqual(shape[0], 20);
+            Assert.AreEqual(shape[1], 4);
+            Assert.AreEqual(shape.Length, 2);
+            Assert.AreEqual(dimProp[0], DimensionProperty.VariableSize);
+            Assert.AreEqual(dimProp[1], DimensionProperty.None);
+            Assert.AreEqual(dimProp.Length, 2);
+
+            bufferSensor.AppendObservation(new float[] { 1, 2, 3, 4 });
+            bufferSensor.AppendObservation(new float[] { 5, 6, 7, 8 });
+            var obsWriter = new ObservationWriter();
+            var obs = bufferSensor.GetObservationProto(obsWriter);
+
+            Assert.AreEqual(shape, InplaceArray<int>.FromList(obs.Shape));
+            Assert.AreEqual(obs.DimensionProperties.Count, 2);
+            Assert.AreEqual((int)dimProp[0], obs.DimensionProperties[0]);
+            Assert.AreEqual((int)dimProp[1], obs.DimensionProperties[1]);
+
+            for (int i = 0; i < 8; i++)
+            {
+                Assert.AreEqual(obs.FloatData.Data[i], i + 1);
+            }
+            for (int i = 8; i < 80; i++)
+            {
+                Assert.AreEqual(obs.FloatData.Data[i], 0);
+            }
+        }
+
+        [Test]
+        public void TestBufferSensorComponent()
+        {
+            var agentGameObj = new GameObject("agent");
+            var bufferComponent = agentGameObj.AddComponent<BufferSensorComponent>();
+            bufferComponent.MaxNumObservables = 20;
+            bufferComponent.ObservableSize = 4;
+            bufferComponent.SensorName = "TestName";
+
+            var sensor = bufferComponent.CreateSensors()[0];
+            var shape = sensor.GetObservationSpec().Shape;
+
+            Assert.AreEqual(shape[0], 20);
+            Assert.AreEqual(shape[1], 4);
+            Assert.AreEqual(shape.Length, 2);
+
+            bufferComponent.AppendObservation(new float[] { 1, 2, 3, 4 });
+            bufferComponent.AppendObservation(new float[] { 5, 6, 7, 8 });
+
+            var obsWriter = new ObservationWriter();
+            var obs = sensor.GetObservationProto(obsWriter);
+
+            Assert.AreEqual(shape, InplaceArray<int>.FromList(obs.Shape));
+            Assert.AreEqual(obs.DimensionProperties.Count, 2);
+
+            Assert.AreEqual(sensor.GetName(), "TestName");
+
+            for (int i = 0; i < 8; i++)
+            {
+                Assert.AreEqual(obs.FloatData.Data[i], i + 1);
+            }
+            for (int i = 8; i < 80; i++)
+            {
+                Assert.AreEqual(obs.FloatData.Data[i], 0);
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..be1021246e7f6eb469141c7462c71a5a78d537e1
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/BufferSensorTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 5267572aa66d34b49bbc65940674b2a6
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..bad7fefc4f869716a1fc4de904eb61347f91761c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs
@@ -0,0 +1,52 @@
+using System;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class CameraSensorComponentTest
+    {
+        [Test]
+        public void TestCameraSensorComponent()
+        {
+            foreach (var grayscale in new[] { true, false })
+            {
+                foreach (SensorCompressionType compression in Enum.GetValues(typeof(SensorCompressionType)))
+                {
+                    var width = 24;
+                    var height = 16;
+                    var camera = Camera.main;
+
+                    var agentGameObj = new GameObject("agent");
+
+                    var cameraComponent = agentGameObj.AddComponent<CameraSensorComponent>();
+                    cameraComponent.Camera = camera;
+                    cameraComponent.Height = height;
+                    cameraComponent.Width = width;
+                    cameraComponent.Grayscale = grayscale;
+                    cameraComponent.CompressionType = compression;
+                    cameraComponent.RuntimeCameraEnable = true;
+
+                    var sensor = cameraComponent.CreateSensors()[0];
+                    var expectedShape = new InplaceArray<int>(height, width, grayscale ? 1 : 3);
+                    Assert.AreEqual(expectedShape, sensor.GetObservationSpec().Shape);
+                    Assert.AreEqual(typeof(CameraSensor), sensor.GetType());
+
+                    var flags = BindingFlags.Instance | BindingFlags.NonPublic;
+                    var runtimeCameraEnabled = (bool)typeof(CameraSensorComponent).GetField("m_RuntimeCameraEnable", flags).GetValue(cameraComponent);
+                    Assert.True(runtimeCameraEnabled);
+
+                    // Make sure cleaning up the component cleans up the sensor too
+                    cameraComponent.Dispose();
+                    var cameraComponentSensor = (CameraSensor)typeof(CameraSensorComponent).GetField("m_Sensor", flags).GetValue(cameraComponent);
+                    Assert.IsNull(cameraComponentSensor);
+                    var cameraTexture = (Texture2D)typeof(CameraSensor).GetField("m_Texture", flags).GetValue(sensor);
+                    Assert.IsNull(cameraTexture);
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..f66b94c6b7b304ebbea646f9b65d128d45b2589c
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorComponentTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 4c0b188faef38407e82223854fc8eaf5
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f075d1941d4a4a46f9a2fdfc8d57062e822ffeff
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs
@@ -0,0 +1,56 @@
+using System;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class CameraSensorTest
+    {
+        [Test]
+        public void TestCameraSensor()
+        {
+            foreach (var grayscale in new[] { true, false })
+            {
+                foreach (SensorCompressionType compression in Enum.GetValues(typeof(SensorCompressionType)))
+                {
+                    var width = 24;
+                    var height = 16;
+                    var camera = Camera.main;
+                    var c = new GameObject();
+                    if (ReferenceEquals(null, camera))
+                    {
+                        camera = c.AddComponent<Camera>();
+                    }
+                    var sensor = new CameraSensor(camera, width, height, grayscale, "TestCameraSensor", compression);
+
+                    var obsWriter = new ObservationWriter();
+                    var obs = sensor.GetObservationProto(obsWriter);
+
+                    Assert.AreEqual((int)compression, (int)obs.CompressionType);
+                    var expectedShape = new[] { height, width, grayscale ? 1 : 3 };
+                    Assert.AreEqual(expectedShape, obs.Shape);
+                    UnityEngine.Object.DestroyImmediate(c);
+                }
+            }
+        }
+
+        [Test]
+        public void TestObservationType()
+        {
+            var width = 24;
+            var height = 16;
+            var camera = Camera.main;
+            var sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None);
+            var spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.Default);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.GoalSignal);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.GoalSignal);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..6f505b3aa3ce971880d2769876c12df037b3274f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ccdfc5b4015c9465cb1e811375be971c
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..95740a5c7c3fda532325d4d0d2eec92bd293ac7e
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs
@@ -0,0 +1,30 @@
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class CompressionSpecTests
+    {
+        [Test]
+        public void TestIsTrivialMapping()
+        {
+            Assert.IsTrue(CompressionSpec.Default().IsTrivialMapping());
+
+            var spec = new CompressionSpec(SensorCompressionType.PNG, null);
+            Assert.AreEqual(spec.IsTrivialMapping(), true);
+
+            spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 0, 0 });
+            Assert.AreEqual(spec.IsTrivialMapping(), true);
+
+            spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 1, 2, 3, 4 });
+            Assert.AreEqual(spec.IsTrivialMapping(), true);
+
+            spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 1, 2, 3, 4, -1, -1 });
+            Assert.AreEqual(spec.IsTrivialMapping(), false);
+
+            spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 0, 0, 1, 1, 1 });
+            Assert.AreEqual(spec.IsTrivialMapping(), false);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d9df7ce5b267e984fdb8630ffe35b866b766bcd7
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/CompressionSpecTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: cd0990de0eb646b0b0531b91c840c9da
+timeCreated: 1616030728
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..0f4a7ae1de5d9ec2a29e0b6c75ab2aead65d1fec
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs
@@ -0,0 +1,106 @@
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public class Float2DSensor : ISensor
+    {
+        public int Width { get; }
+        public int Height { get; }
+        string m_Name;
+        private ObservationSpec m_ObservationSpec;
+        public float[,] floatData;
+
+        public Float2DSensor(int width, int height, string name)
+        {
+            Width = width;
+            Height = height;
+            m_Name = name;
+
+            m_ObservationSpec = ObservationSpec.Visual(height, width, 1);
+            floatData = new float[Height, Width];
+        }
+
+        public Float2DSensor(float[,] floatData, string name)
+        {
+            this.floatData = floatData;
+            Height = floatData.GetLength(0);
+            Width = floatData.GetLength(1);
+            m_Name = name;
+            m_ObservationSpec = ObservationSpec.Visual(Height, Width, 1);
+        }
+
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        public byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        public int Write(ObservationWriter writer)
+        {
+            using (TimerStack.Instance.Scoped("Float2DSensor.Write"))
+            {
+                for (var h = 0; h < Height; h++)
+                {
+                    for (var w = 0; w < Width; w++)
+                    {
+                        writer[h, w, 0] = floatData[h, w];
+                    }
+                }
+                var numWritten = Height * Width;
+                return numWritten;
+            }
+        }
+
+        public void Update() { }
+        public void Reset() { }
+
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+    }
+
+    public class FloatVisualSensorTests
+    {
+        [Test]
+        public void TestFloat2DSensorWrite()
+        {
+            var sensor = new Float2DSensor(3, 4, "floatsensor");
+            for (var h = 0; h < 4; h++)
+            {
+                for (var w = 0; w < 3; w++)
+                {
+                    sensor.floatData[h, w] = 3 * h + w;
+                }
+            }
+
+            var output = new float[12];
+            var writer = new ObservationWriter();
+            writer.SetTarget(output, sensor.GetObservationSpec(), 0);
+            sensor.Write(writer);
+            for (var i = 0; i < 9; i++)
+            {
+                Assert.AreEqual(i, output[i]);
+            }
+        }
+
+        [Test]
+        public void TestFloat2DSensorExternalData()
+        {
+            var data = new float[4, 3];
+            var sensor = new Float2DSensor(data, "floatsensor");
+            Assert.AreEqual(sensor.Height, 4);
+            Assert.AreEqual(sensor.Width, 3);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..340841bc1fcecf4a6681471565eeb56d736ee9a2
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/FloatVisualSensorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 49b7da14949a486b803e28ed32d91a09
+timeCreated: 1578093005
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8fd5a3b28b150cb34e81501b5c373fae1ff8b764
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs
@@ -0,0 +1,87 @@
+using NUnit.Framework;
+using System;
+using System.Linq;
+
+namespace Unity.MLAgents.Tests
+{
+    public static class GridObsTestUtils
+    {
+        /// <summary>
+        /// Utility function to duplicate an array into an array of arrays
+        /// </summary>
+        /// <param name="array">array to duplicate</param>
+        /// <param name="numCopies">number of times to duplicate</param>
+        /// <returns>array of duplicated arrays</returns>
+        public static float[][] DuplicateArray(float[] array, int numCopies)
+        {
+            float[][] duplicated = new float[numCopies][];
+            for (int i = 0; i < numCopies; i++)
+            {
+                duplicated[i] = array;
+            }
+            return duplicated;
+        }
+
+        /// <summary>
+        /// Asserts that the sub-arrays of the total array are equal to specific subarrays at specific subarray indicies and equal to a default everywhere else.
+        /// </summary>
+        /// <param name="total">Array containing all data of the grid observation. Is a concatenation of N subarrays all of the same length</param>
+        /// <param name="indicies">The indicies to verify that differ from the default array</param>
+        /// <param name="expectedArrays">The sub arrays values that differ from the default array</param>
+        /// <param name="expectedDefaultArray">The default value of a sub array</param>
+        /// <example>
+        /// If the total array is data from a 4x4x2 grid observation, total will be an array of size 32 and each sub array will have a size of 2.
+        /// Let 3 cells at indicies (0, 1), (2, 2), and (3, 0) with values ([.1, .5]), ([.9, .7]), ([0, .2]), respectively.
+        /// If the default values of cells are ([0, 0]) then the grid observation will be as follows:
+        /// [ [0,  0], [.1, .5], [ 0, 0 ], [0, 0],
+        ///   [0,  0], [ 0, 0 ], [ 0, 0 ], [0, 0],
+        ///   [0,  0], [ 0, 0 ], [.9, .7], [0, 0],
+        ///   [0, .2], [ 0, 0 ], [ 0, 0 ],  [0, 0] ]
+        ///
+        /// Which will make the total array will be the flattened array
+        /// total = [0, 0, .1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, .9, .7, 0, 0, 0, .2, 0, 0, 0, 0, 0]
+        ///
+        /// The indicies of the activated cells in the flattened array will be 1, 10, and 12
+        ///
+        /// So to verify that the total array is as expected, AssertSubarraysAtIndex should be called as
+        /// AssertSubarraysAtIndex(
+        ///             total,
+        ///             indicies = new int[] {1, 10, 12},
+        ///             expectedArrays = new float[][] { new float[] {.1, .5}, new float[] {.9, .7}, new float[] {0, .2}},
+        ///             expecedDefaultArray = new float[] {0, 0}
+        ///             )
+        /// </example>
+        public static void AssertSubarraysAtIndex(float[] total, int[] indicies, float[][] expectedArrays, float[] expectedDefaultArray)
+        {
+            int totalIndex = 0;
+            int subIndex = 0;
+            int subarrayIndex = 0;
+            int lenOfData = expectedDefaultArray.Length;
+            int numArrays = total.Length / lenOfData;
+            for (int i = 0; i < numArrays; i++)
+            {
+                totalIndex = i * lenOfData;
+
+                if (indicies.Contains(i))
+                {
+                    subarrayIndex = Array.IndexOf(indicies, i);
+                    for (subIndex = 0; subIndex < lenOfData; subIndex++)
+                    {
+                        Assert.AreEqual(expectedArrays[subarrayIndex][subIndex], total[totalIndex],
+                            "Expected " + expectedArrays[subarrayIndex][subIndex] + " at subarray index " + totalIndex + ", index = " + subIndex + " but was " + total[totalIndex]);
+                        totalIndex++;
+                    }
+                }
+                else
+                {
+                    for (subIndex = 0; subIndex < lenOfData; subIndex++)
+                    {
+                        Assert.AreEqual(expectedDefaultArray[subIndex], total[totalIndex],
+                            "Expected default value " + expectedDefaultArray[subIndex] + " at subarray index " + totalIndex + ", index = " + subIndex + " but was " + total[totalIndex]);
+                        totalIndex++;
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..216e38aa9d778d6b8bcc36b9989c36afd37517b0
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTestUtils.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 53aa6ab552bca5d49a6d298f1e633717
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f568b74952c230fa29b7b3c7197efc90ef2355ed
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs
@@ -0,0 +1,207 @@
+#if MLA_UNITY_PHYSICS_MODULE
+using System.Collections.Generic;
+using System.Collections;
+using System.Reflection;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEngine.TestTools;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public class GridSensorTests
+    {
+        GameObject testGo;
+        GameObject boxGo;
+        SimpleTestGridSensorComponent gridSensorComponent;
+
+        // Use built-in tags
+        const string k_Tag1 = "Player";
+        const string k_Tag2 = "Respawn";
+
+        [UnitySetUp]
+        public IEnumerator SetupScene()
+        {
+            testGo = new GameObject("test");
+            testGo.transform.position = Vector3.zero;
+            gridSensorComponent = testGo.AddComponent<SimpleTestGridSensorComponent>();
+
+            boxGo = new GameObject("block");
+            boxGo.tag = k_Tag1;
+            boxGo.transform.position = new Vector3(3f, 0f, 3f);
+            boxGo.AddComponent<BoxCollider>();
+
+            TestGridSensorConfig.Reset();
+            yield return null;
+        }
+
+        [TearDown]
+        public void ClearScene()
+        {
+            Object.DestroyImmediate(boxGo);
+            Object.DestroyImmediate(testGo);
+        }
+
+        [Test]
+        public void TestBufferSize()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, gridSizeX: 3, gridSizeZ: 4, useTestingGridSensor: true);
+            TestGridSensorConfig.SetParameters(5, true, false);
+            var gridSensor = (SimpleTestGridSensor)gridSensorComponent.CreateSensors()[0];
+            Assert.AreEqual(gridSensor.PerceptionBuffer.Length, 3 * 4 * 5);
+        }
+
+        [Test]
+        public void TestInvalidSizeConfiguration()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, gridSizeY: 10, useTestingGridSensor: true);
+            gridSensorComponent.CreateSensors(); // expect no exception
+
+            gridSensorComponent.m_GridSize.y = 10;
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                gridSensorComponent.CreateSensors();
+            });
+        }
+
+        [Test]
+        public void TestInvalidCompressionConfiguration()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, compression: SensorCompressionType.PNG, useTestingGridSensor: true);
+
+            var gridSensor = (GridSensorBase)gridSensorComponent.CreateSensors()[0];
+            LogAssert.Expect(LogType.Warning, $"Compression type {SensorCompressionType.PNG} is only supported with normalized data. " +
+                "The sensor will not compress the data.");
+            Assert.AreEqual(gridSensor.CompressionType, SensorCompressionType.None);
+        }
+
+        [Test]
+        public void TestCreateSensor()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useGridSensorBase: true);
+
+            gridSensorComponent.CreateSensors();
+            var componentSensor = (List<GridSensorBase>) typeof(GridSensorComponent).GetField("m_Sensors",
+                BindingFlags.Instance | BindingFlags.NonPublic).GetValue(gridSensorComponent);
+            Assert.AreEqual(componentSensor.Count, 1);
+        }
+
+        [Test]
+        public void PerceiveNotSelf()
+        {
+            testGo.tag = k_Tag2;
+
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useGridSensorBase: true);
+            var gridSensor = (GridSensorBase)gridSensorComponent.CreateSensors()[0];
+
+            gridSensor.Update();
+
+            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 1 }, 4);
+            float[] expectedDefault = new float[] { 0 };
+            GridObsTestUtils.AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+        }
+
+        [Test]
+        public void TestReset()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useGridSensorBase: true);
+            TestGridSensorConfig.SetParameters(3, false, false);
+            var gridSensor = (GridSensorBase)gridSensorComponent.CreateSensors()[0];
+
+            gridSensor.Update();
+
+            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 1 }, 4);
+            float[] expectedDefault = new float[] { 0 };
+            GridObsTestUtils.AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+            Object.DestroyImmediate(boxGo);
+
+            gridSensor.Update();
+
+            subarrayIndicies = new int[0];
+            expectedSubarrays = new float[0][];
+            GridObsTestUtils.AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+        }
+
+        [Test]
+        public void TestOneHotSensor()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useOneHotTag: true);
+            var gridSensor = (OneHotGridSensor)gridSensorComponent.CreateSensors()[0];
+            Assert.AreEqual(gridSensor.PerceptionBuffer.Length, 10 * 10 * 2);
+
+            gridSensor.Update();
+
+            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 1, 0 }, 4);
+            float[] expectedDefault = new float[] { 0, 0 };
+            GridObsTestUtils.AssertSubarraysAtIndex(gridSensor.PerceptionBuffer, subarrayIndicies, expectedSubarrays, expectedDefault);
+        }
+
+        [Test]
+        public void TestCustomSensorInvalidData()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, compression: SensorCompressionType.PNG, useTestingGridSensor: true);
+            TestGridSensorConfig.SetParameters(5, true, false);
+            var gridSensor = (SimpleTestGridSensor)gridSensorComponent.CreateSensors()[0];
+
+            gridSensor.DummyData = new float[] { 1, 2, 3, 4, 5 };
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                gridSensor.Update();
+            });
+        }
+
+        [Test]
+        public void TestMultipleSensors()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useOneHotTag: true, useGridSensorBase: true, useTestingGridSensor: true);
+            var gridSensors = gridSensorComponent.CreateSensors();
+            Assert.IsNotNull(((GridSensorBase)gridSensors[0]).m_GridPerception);
+            Assert.IsNull(((GridSensorBase)gridSensors[1]).m_GridPerception);
+            Assert.IsNull(((GridSensorBase)gridSensors[2]).m_GridPerception);
+        }
+
+        [Test]
+        public void TestNoSensors()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags);
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                gridSensorComponent.CreateSensors();
+            });
+        }
+
+        [Test]
+        public void TestStackedSensors()
+        {
+            testGo.tag = k_Tag2;
+            string[] tags = { k_Tag1, k_Tag2 };
+            gridSensorComponent.SetComponentParameters(tags, useGridSensorBase: true);
+            gridSensorComponent.ObservationStacks = 3;
+            var sensors = gridSensorComponent.CreateSensors();
+            Assert.IsInstanceOf(typeof(StackingSensor), sensors[0]);
+        }
+    }
+}
+#endif
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e477ba84051e2a160f5e3a4e686dfcccd09213ee
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/GridSensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2a8626afd640a4edd942dac3d8d6bc85
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f71162ef530bfc3db195cb405e7c3e5dda9347c8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs
@@ -0,0 +1,360 @@
+using System;
+using System.Collections.Generic;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class ObservableAttributeTests
+    {
+        public enum TestEnum
+        {
+            ValueA = -100,
+            ValueB = 1,
+            ValueC = 42,
+        }
+
+        [Flags]
+        public enum TestFlags
+        {
+            FlagA = 1,
+            FlagB = 2,
+            FlagC = 4
+        }
+
+        class TestClass
+        {
+            // Non-observables
+            int m_NonObservableInt;
+            float m_NonObservableFloat;
+
+            //
+            // Int
+            //
+            [Observable]
+            public int m_IntMember;
+
+            int m_IntProperty;
+
+            [Observable]
+            public int IntProperty
+            {
+                get => m_IntProperty;
+                set => m_IntProperty = value;
+            }
+
+            //
+            // Float
+            //
+            [Observable("floatMember")]
+            public float m_FloatMember;
+
+            float m_FloatProperty;
+            [Observable("floatProperty")]
+            public float FloatProperty
+            {
+                get => m_FloatProperty;
+                set => m_FloatProperty = value;
+            }
+
+            //
+            // Bool
+            //
+            [Observable("boolMember")]
+            public bool m_BoolMember;
+
+            bool m_BoolProperty;
+            [Observable("boolProperty")]
+            public bool BoolProperty
+            {
+                get => m_BoolProperty;
+                set => m_BoolProperty = value;
+            }
+
+            //
+            // Vector2
+            //
+
+            [Observable("vector2Member")]
+            public Vector2 m_Vector2Member;
+
+            Vector2 m_Vector2Property;
+
+            [Observable("vector2Property")]
+            public Vector2 Vector2Property
+            {
+                get => m_Vector2Property;
+                set => m_Vector2Property = value;
+            }
+
+            //
+            // Vector3
+            //
+            [Observable("vector3Member")]
+            public Vector3 m_Vector3Member;
+
+            Vector3 m_Vector3Property;
+
+            [Observable("vector3Property")]
+            public Vector3 Vector3Property
+            {
+                get => m_Vector3Property;
+                set => m_Vector3Property = value;
+            }
+
+            //
+            // Vector4
+            //
+
+            [Observable("vector4Member")]
+            public Vector4 m_Vector4Member;
+
+            Vector4 m_Vector4Property;
+
+            [Observable("vector4Property")]
+            public Vector4 Vector4Property
+            {
+                get => m_Vector4Property;
+                set => m_Vector4Property = value;
+            }
+
+            //
+            // Quaternion
+            //
+            [Observable("quaternionMember")]
+            public Quaternion m_QuaternionMember;
+
+            Quaternion m_QuaternionProperty;
+
+            [Observable("quaternionProperty")]
+            public Quaternion QuaternionProperty
+            {
+                get => m_QuaternionProperty;
+                set => m_QuaternionProperty = value;
+            }
+
+            //
+            // Enum
+            //
+
+            [Observable("enumMember")]
+            public TestEnum m_EnumMember = TestEnum.ValueA;
+
+            TestEnum m_EnumProperty = TestEnum.ValueC;
+
+            [Observable("enumProperty")]
+            public TestEnum EnumProperty
+            {
+                get => m_EnumProperty;
+                set => m_EnumProperty = value;
+            }
+
+            [Observable("badEnumMember")]
+            public TestEnum m_BadEnumMember = (TestEnum)1337;
+
+            //
+            // Flags
+            //
+            [Observable("flagMember")]
+            public TestFlags m_FlagMember = TestFlags.FlagA;
+
+            TestFlags m_FlagProperty = TestFlags.FlagB | TestFlags.FlagC;
+
+            [Observable("flagProperty")]
+            public TestFlags FlagProperty
+            {
+                get => m_FlagProperty;
+                set => m_FlagProperty = value;
+            }
+        }
+
+        [Test]
+        public void TestGetObservableSensors()
+        {
+            var testClass = new TestClass();
+            testClass.m_IntMember = 1;
+            testClass.IntProperty = 2;
+
+            testClass.m_FloatMember = 1.1f;
+            testClass.FloatProperty = 1.2f;
+
+            testClass.m_BoolMember = true;
+            testClass.BoolProperty = true;
+
+            testClass.m_Vector2Member = new Vector2(2.0f, 2.1f);
+            testClass.Vector2Property = new Vector2(2.2f, 2.3f);
+
+            testClass.m_Vector3Member = new Vector3(3.0f, 3.1f, 3.2f);
+            testClass.Vector3Property = new Vector3(3.3f, 3.4f, 3.5f);
+
+            testClass.m_Vector4Member = new Vector4(4.0f, 4.1f, 4.2f, 4.3f);
+            testClass.Vector4Property = new Vector4(4.4f, 4.5f, 4.5f, 4.7f);
+
+            testClass.m_Vector4Member = new Vector4(4.0f, 4.1f, 4.2f, 4.3f);
+            testClass.Vector4Property = new Vector4(4.4f, 4.5f, 4.5f, 4.7f);
+
+            testClass.m_QuaternionMember = new Quaternion(5.0f, 5.1f, 5.2f, 5.3f);
+            testClass.QuaternionProperty = new Quaternion(5.4f, 5.5f, 5.5f, 5.7f);
+
+            var sensors = ObservableAttribute.CreateObservableSensors(testClass, false);
+
+            var sensorsByName = new Dictionary<string, ISensor>();
+            foreach (var sensor in sensors)
+            {
+                sensorsByName[sensor.GetName()] = sensor;
+            }
+
+            SensorTestHelper.CompareObservation(sensorsByName["ObservableAttribute:TestClass.m_IntMember"], new[] { 1.0f });
+            SensorTestHelper.CompareObservation(sensorsByName["ObservableAttribute:TestClass.IntProperty"], new[] { 2.0f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["floatMember"], new[] { 1.1f });
+            SensorTestHelper.CompareObservation(sensorsByName["floatProperty"], new[] { 1.2f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["boolMember"], new[] { 1.0f });
+            SensorTestHelper.CompareObservation(sensorsByName["boolProperty"], new[] { 1.0f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["vector2Member"], new[] { 2.0f, 2.1f });
+            SensorTestHelper.CompareObservation(sensorsByName["vector2Property"], new[] { 2.2f, 2.3f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["vector3Member"], new[] { 3.0f, 3.1f, 3.2f });
+            SensorTestHelper.CompareObservation(sensorsByName["vector3Property"], new[] { 3.3f, 3.4f, 3.5f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["vector4Member"], new[] { 4.0f, 4.1f, 4.2f, 4.3f });
+            SensorTestHelper.CompareObservation(sensorsByName["vector4Property"], new[] { 4.4f, 4.5f, 4.5f, 4.7f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["quaternionMember"], new[] { 5.0f, 5.1f, 5.2f, 5.3f });
+            SensorTestHelper.CompareObservation(sensorsByName["quaternionProperty"], new[] { 5.4f, 5.5f, 5.5f, 5.7f });
+
+            // Actual ordering is B, C, A
+            SensorTestHelper.CompareObservation(sensorsByName["enumMember"], new[] { 0.0f, 0.0f, 1.0f });
+            SensorTestHelper.CompareObservation(sensorsByName["enumProperty"], new[] { 0.0f, 1.0f, 0.0f });
+            SensorTestHelper.CompareObservation(sensorsByName["badEnumMember"], new[] { 0.0f, 0.0f, 0.0f });
+
+            SensorTestHelper.CompareObservation(sensorsByName["flagMember"], new[] { 1.0f, 0.0f, 0.0f });
+            SensorTestHelper.CompareObservation(sensorsByName["flagProperty"], new[] { 0.0f, 1.0f, 1.0f });
+        }
+
+        [Test]
+        public void TestGetTotalObservationSize()
+        {
+            var testClass = new TestClass();
+            var errors = new List<string>();
+            var expectedObsSize = 2 * ( // two fields each of these
+                1     // int
+                + 1     // float
+                + 1     // bool
+                + 2     // vector2
+                + 3     // vector3
+                + 4     // vector4
+                + 4     // quaternion
+                + 3     // TestEnum - 3 values
+                + 3     // TestFlags - 3 values
+            )
+                + 3; // TestEnum with bad value
+            Assert.AreEqual(expectedObsSize, ObservableAttribute.GetTotalObservationSize(testClass, false, errors));
+            Assert.AreEqual(0, errors.Count);
+        }
+
+        class BadClass
+        {
+            [Observable]
+            double m_Double;
+
+            [Observable]
+            double DoubleProperty
+            {
+                get => m_Double;
+                set => m_Double = value;
+            }
+
+            float m_WriteOnlyProperty;
+
+            [Observable]
+            // No get property, so we shouldn't be able to make a sensor out of this.
+            public float WriteOnlyProperty
+            {
+                set => m_WriteOnlyProperty = value;
+            }
+        }
+
+        [Test]
+        public void TestInvalidObservables()
+        {
+            var bad = new BadClass();
+            bad.WriteOnlyProperty = 1.0f;
+            var errors = new List<string>();
+            Assert.AreEqual(0, ObservableAttribute.GetTotalObservationSize(bad, false, errors));
+            Assert.AreEqual(3, errors.Count);
+
+            // Should be able to safely generate sensors (and get nothing back)
+            var sensors = ObservableAttribute.CreateObservableSensors(bad, false);
+            Assert.AreEqual(0, sensors.Count);
+        }
+
+        class StackingClass
+        {
+            [Observable(numStackedObservations: 2)]
+            public float FloatVal;
+        }
+
+        [Test]
+        public void TestObservableAttributeStacking()
+        {
+            var c = new StackingClass();
+            c.FloatVal = 1.0f;
+            var sensors = ObservableAttribute.CreateObservableSensors(c, false);
+            var sensor = sensors[0];
+            Assert.AreEqual(typeof(StackingSensor), sensor.GetType());
+            SensorTestHelper.CompareObservation(sensor, new[] { 0.0f, 1.0f });
+
+            sensor.Update();
+            c.FloatVal = 3.0f;
+            SensorTestHelper.CompareObservation(sensor, new[] { 1.0f, 3.0f });
+
+            var errors = new List<string>();
+            Assert.AreEqual(2, ObservableAttribute.GetTotalObservationSize(c, false, errors));
+            Assert.AreEqual(0, errors.Count);
+        }
+
+        class BaseClass
+        {
+            [Observable("base")]
+            public float m_BaseField;
+
+            [Observable("private")]
+            float m_PrivateField;
+        }
+
+        class DerivedClass : BaseClass
+        {
+            [Observable("derived")]
+            float m_DerivedField;
+        }
+
+        [Test]
+        public void TestObservableAttributeExcludeInherited()
+        {
+            var d = new DerivedClass();
+            d.m_BaseField = 1.0f;
+
+            // excludeInherited=false will get fields in the derived class, plus public and protected inherited fields
+            var sensorAll = ObservableAttribute.CreateObservableSensors(d, false);
+            Assert.AreEqual(2, sensorAll.Count);
+            // Note - actual order doesn't matter here, we can change this to use a HashSet if neeed.
+            Assert.AreEqual("derived", sensorAll[0].GetName());
+            Assert.AreEqual("base", sensorAll[1].GetName());
+
+            // excludeInherited=true will only get fields in the derived class
+            var sensorsDerivedOnly = ObservableAttribute.CreateObservableSensors(d, true);
+            Assert.AreEqual(1, sensorsDerivedOnly.Count);
+            Assert.AreEqual("derived", sensorsDerivedOnly[0].GetName());
+
+            var b = new BaseClass();
+            var baseSensors = ObservableAttribute.CreateObservableSensors(b, false);
+            Assert.AreEqual(2, baseSensors.Count);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..611fdcfa124be8b96d4f17c8690f4cfd9d3c6ac6
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservableAttributeTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 33d7912e6b3504412bd261b40e46df32
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..bc805f954e389d123eca6ea76b40734440493eeb
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs
@@ -0,0 +1,99 @@
+using NUnit.Framework;
+using Unity.Barracuda;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Inference;
+
+
+namespace Unity.MLAgents.Tests
+{
+    public class ObservationWriterTests
+    {
+        [Test]
+        public void TestWritesToIList()
+        {
+            ObservationWriter writer = new ObservationWriter();
+            var buffer = new[] { 0f, 0f, 0f };
+            var shape = new InplaceArray<int>(3);
+
+            writer.SetTarget(buffer, shape, 0);
+            // Elementwise writes
+            writer[0] = 1f;
+            writer[2] = 2f;
+            Assert.AreEqual(new[] { 1f, 0f, 2f }, buffer);
+
+            // Elementwise writes with offset
+            writer.SetTarget(buffer, shape, 1);
+            writer[0] = 3f;
+            Assert.AreEqual(new[] { 1f, 3f, 2f }, buffer);
+
+            // AddList
+            writer.SetTarget(buffer, shape, 0);
+            writer.AddList(new[] { 4f, 5f });
+            Assert.AreEqual(new[] { 4f, 5f, 2f }, buffer);
+
+            // AddList with offset
+            writer.SetTarget(buffer, shape, 1);
+            writer.AddList(new[] { 6f, 7f });
+            Assert.AreEqual(new[] { 4f, 6f, 7f }, buffer);
+        }
+
+        [Test]
+        public void TestWritesToTensor()
+        {
+            ObservationWriter writer = new ObservationWriter();
+            var t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.FloatingPoint,
+                data = new Tensor(2, 3)
+            };
+
+            writer.SetTarget(t, 0, 0);
+            Assert.AreEqual(0f, t.data[0, 0]);
+            writer[0] = 1f;
+            Assert.AreEqual(1f, t.data[0, 0]);
+
+            writer.SetTarget(t, 1, 1);
+            writer[0] = 2f;
+            writer[1] = 3f;
+            // [0, 0] shouldn't change
+            Assert.AreEqual(1f, t.data[0, 0]);
+            Assert.AreEqual(2f, t.data[1, 1]);
+            Assert.AreEqual(3f, t.data[1, 2]);
+
+            // AddList
+            t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.FloatingPoint,
+                data = new Tensor(2, 3)
+            };
+
+            writer.SetTarget(t, 1, 1);
+            writer.AddList(new[] { -1f, -2f });
+            Assert.AreEqual(0f, t.data[0, 0]);
+            Assert.AreEqual(0f, t.data[0, 1]);
+            Assert.AreEqual(0f, t.data[0, 2]);
+            Assert.AreEqual(0f, t.data[1, 0]);
+            Assert.AreEqual(-1f, t.data[1, 1]);
+            Assert.AreEqual(-2f, t.data[1, 2]);
+        }
+
+        [Test]
+        public void TestWritesToTensor3D()
+        {
+            ObservationWriter writer = new ObservationWriter();
+            var t = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.FloatingPoint,
+                data = new Tensor(2, 2, 2, 3)
+            };
+
+            writer.SetTarget(t, 0, 0);
+            writer[1, 0, 1] = 1f;
+            Assert.AreEqual(1f, t.data[0, 1, 0, 1]);
+
+            writer.SetTarget(t, 0, 1);
+            writer[1, 0, 0] = 2f;
+            Assert.AreEqual(2f, t.data[0, 1, 0, 1]);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b03f609fdc3ceb903e360436d98e3f4c502a68e7
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/ObservationWriterTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 3de9cbda816e4d7b907e765577dd54f7
+timeCreated: 1572568337
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..763dec4964445fd843523d2de2fc00f15288ab3e
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs
@@ -0,0 +1,537 @@
+using System.Collections.Generic;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+using UnityEngine.TestTools;
+
+namespace Unity.MLAgents.Tests
+{
+    public class RayPerceptionSensorTests
+    {
+        [Test]
+        public void TestGetRayAngles()
+        {
+            var anglesAlternating = RayPerceptionSensorComponentBase.GetRayAnglesAlternating(3, 90f);
+            var expectedAnglesAlternating = new[] { 90f, 60f, 120f, 30f, 150f, 0f, 180f };
+            Assert.AreEqual(expectedAnglesAlternating.Length, anglesAlternating.Length);
+            for (var i = 0; i < anglesAlternating.Length; i++)
+            {
+                Assert.AreEqual(expectedAnglesAlternating[i], anglesAlternating[i], .01);
+            }
+
+            var angles = RayPerceptionSensorComponentBase.GetRayAngles(3, 90f);
+            var expectedAngles = new[] { 0f, 30f, 60f, 90f, 120f, 150f, 180f };
+            Assert.AreEqual(expectedAngles.Length, angles.Length);
+            for (var i = 0; i < angles.Length; i++)
+            {
+                Assert.AreEqual(expectedAngles[i], angles[i], .01);
+            }
+        }
+    }
+
+    public class RayPerception3DTests
+    {
+        [Test]
+        public void TestDefaultLayersAreNegativeFive()
+        {
+#if MLA_UNITY_PHYSICS_MODULE
+            Assert.IsTrue(Physics.DefaultRaycastLayers == -5);
+#endif
+#if MLA_UNITY_PHYSICS2D_MODULE
+            Assert.IsTrue(Physics2D.DefaultRaycastLayers == -5);
+#endif
+        }
+
+#if MLA_UNITY_PHYSICS_MODULE
+        // Use built-in tags
+        const string k_CubeTag = "Player";
+        const string k_SphereTag = "Respawn";
+
+        [TearDown]
+        public void RemoveGameObjects()
+        {
+            var objects = GameObject.FindObjectsOfType<GameObject>();
+            foreach (var o in objects)
+            {
+                UnityEngine.Object.DestroyImmediate(o);
+            }
+        }
+
+        void SetupScene()
+        {
+            /* Creates game objects in the world for testing.
+             *   C is a cube
+             *   S are spheres
+             *   @ is the agent (at the origin)
+             * Each space or line is 5 world units, +x is right, +z is up
+             *
+             *      C
+             *    S   S
+             *      @
+             *
+             *      S
+             */
+            var cube = GameObject.CreatePrimitive(PrimitiveType.Cube);
+            cube.transform.position = new Vector3(0, 0, 10);
+            cube.tag = k_CubeTag;
+            cube.name = "cube";
+
+            var sphere1 = GameObject.CreatePrimitive(PrimitiveType.Sphere);
+            sphere1.transform.position = new Vector3(-5, 0, 5);
+            sphere1.tag = k_SphereTag;
+            sphere1.name = "sphere1";
+
+            var sphere2 = GameObject.CreatePrimitive(PrimitiveType.Sphere);
+            sphere2.transform.position = new Vector3(5, 0, 5);
+            // No tag for sphere2
+            sphere2.name = "sphere2";
+
+            var sphere3 = GameObject.CreatePrimitive(PrimitiveType.Sphere);
+            sphere3.transform.position = new Vector3(0, 0, -10);
+            sphere3.tag = k_SphereTag;
+            sphere3.name = "sphere3";
+
+
+            Physics.SyncTransforms();
+        }
+
+        [Test]
+        public void TestRaycasts()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 1;
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+            perception.DetectableTags.Add(k_SphereTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var sensor = perception.CreateSensors()[0];
+                sensor.Update();
+
+                var expectedObs = (2 * perception.RaysPerDirection + 1) * (perception.DetectableTags.Count + 2);
+                Assert.AreEqual(sensor.GetObservationSpec().Shape[0], expectedObs);
+                var outputBuffer = new float[expectedObs];
+
+                ObservationWriter writer = new ObservationWriter();
+                writer.SetTarget(outputBuffer, sensor.GetObservationSpec(), 0);
+
+                var numWritten = sensor.Write(writer);
+                Assert.AreEqual(numWritten, expectedObs);
+
+                // Expected hits:
+                // ray 0 should hit the cube at roughly halfway
+                // ray 1 should hit a sphere but no tag
+                // ray 2 should hit a sphere with the k_SphereTag tag
+                // The hit fraction should be the same for rays 1 and
+                //
+                Assert.AreEqual(1.0f, outputBuffer[0]); // hit cube
+                Assert.AreEqual(0.0f, outputBuffer[1]); // missed sphere
+                Assert.AreEqual(0.0f, outputBuffer[2]); // missed unknown tag
+
+                // Hit is at z=9.0 in world space, ray length is 20
+                Assert.That(
+                    outputBuffer[3], Is.EqualTo((9.5f - castRadius) / perception.RayLength).Within(.0005f)
+                );
+
+                // Spheres are at 5,0,5 and 5,0,-5, so 5*sqrt(2) units from origin
+                // Minus 1.0 for the sphere radius to get the length of the hit.
+                var expectedHitLengthWorldSpace = 5.0f * Mathf.Sqrt(2.0f) - 0.5f - castRadius;
+                Assert.AreEqual(0.0f, outputBuffer[4]); // missed cube
+                Assert.AreEqual(0.0f, outputBuffer[5]); // missed sphere
+                Assert.AreEqual(0.0f, outputBuffer[6]); // hit unknown tag -> all 0
+                Assert.That(
+                    outputBuffer[7], Is.EqualTo(expectedHitLengthWorldSpace / perception.RayLength).Within(.0005f)
+                );
+
+                Assert.AreEqual(0.0f, outputBuffer[8]); // missed cube
+                Assert.AreEqual(1.0f, outputBuffer[9]); // hit sphere
+                Assert.AreEqual(0.0f, outputBuffer[10]); // missed unknown tag
+                Assert.That(
+                    outputBuffer[11], Is.EqualTo(expectedHitLengthWorldSpace / perception.RayLength).Within(.0005f)
+                );
+            }
+        }
+
+        [Test]
+        public void TestRaycastMiss()
+        {
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 0;
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+            perception.DetectableTags.Add(k_SphereTag);
+
+            var sensor = perception.CreateSensors()[0];
+            sensor.Update();
+            var expectedObs = (2 * perception.RaysPerDirection + 1) * (perception.DetectableTags.Count + 2);
+            Assert.AreEqual(sensor.GetObservationSpec().Shape[0], expectedObs);
+            var outputBuffer = new float[expectedObs];
+
+            ObservationWriter writer = new ObservationWriter();
+            writer.SetTarget(outputBuffer, sensor.GetObservationSpec(), 0);
+
+            var numWritten = sensor.Write(writer);
+            Assert.AreEqual(numWritten, expectedObs);
+
+            // Everything missed
+            Assert.AreEqual(new float[] { 0, 0, 1, 1 }, outputBuffer);
+        }
+
+        [Test]
+        public void TestRayFilter()
+        {
+            var cube = GameObject.CreatePrimitive(PrimitiveType.Cube);
+            cube.transform.position = new Vector3(0, 0, 10);
+            cube.tag = k_CubeTag;
+            cube.name = "cubeFar";
+
+            var cubeFiltered = GameObject.CreatePrimitive(PrimitiveType.Cube);
+            cubeFiltered.transform.position = new Vector3(0, 0, 5);
+            cubeFiltered.tag = k_CubeTag;
+            cubeFiltered.name = "cubeNear";
+            cubeFiltered.layer = 7;
+
+            Physics.SyncTransforms();
+
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+            perception.RaysPerDirection = 0;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+
+            var filterCubeLayers = new[] { false, true };
+            foreach (var filterCubeLayer in filterCubeLayers)
+            {
+                // Set the layer mask to either the default, or one that ignores the close cube's layer
+                var layerMask = Physics.DefaultRaycastLayers;
+                if (filterCubeLayer)
+                {
+                    layerMask &= ~(1 << cubeFiltered.layer);
+                }
+                perception.RayLayerMask = layerMask;
+
+                var sensor = perception.CreateSensors()[0];
+                sensor.Update();
+                var expectedObs = (2 * perception.RaysPerDirection + 1) * (perception.DetectableTags.Count + 2);
+                Assert.AreEqual(sensor.GetObservationSpec().Shape[0], expectedObs);
+                var outputBuffer = new float[expectedObs];
+
+                ObservationWriter writer = new ObservationWriter();
+                writer.SetTarget(outputBuffer, sensor.GetObservationSpec(), 0);
+
+                var numWritten = sensor.Write(writer);
+                Assert.AreEqual(numWritten, expectedObs);
+
+                if (filterCubeLayer)
+                {
+                    // Hit the far cube because close was filtered.
+                    Assert.That(outputBuffer[outputBuffer.Length - 1],
+                        Is.EqualTo((9.5f - perception.SphereCastRadius) / perception.RayLength).Within(.0005f)
+                    );
+                }
+                else
+                {
+                    // Hit the close cube because not filtered.
+                    Assert.That(outputBuffer[outputBuffer.Length - 1],
+                        Is.EqualTo((4.5f - perception.SphereCastRadius) / perception.RayLength).Within(.0005f)
+                    );
+                }
+            }
+        }
+
+        [Test]
+        public void TestRaycastsScaled()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+            obj.transform.localScale = new Vector3(2, 2, 2);
+
+            perception.RaysPerDirection = 0;
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var sensor = perception.CreateSensors()[0];
+                sensor.Update();
+
+                var expectedObs = (2 * perception.RaysPerDirection + 1) * (perception.DetectableTags.Count + 2);
+                Assert.AreEqual(sensor.GetObservationSpec().Shape[0], expectedObs);
+                var outputBuffer = new float[expectedObs];
+
+                ObservationWriter writer = new ObservationWriter();
+                writer.SetTarget(outputBuffer, sensor.GetObservationSpec(), 0);
+
+                var numWritten = sensor.Write(writer);
+                Assert.AreEqual(numWritten, expectedObs);
+
+                // Expected hits:
+                // ray 0 should hit the cube at roughly 1/4 way
+                //
+                Assert.AreEqual(1.0f, outputBuffer[0]); // hit cube
+                Assert.AreEqual(0.0f, outputBuffer[1]); // missed unknown tag
+
+                // Hit is at z=9.0 in world space, ray length was 20
+                // But scale increases the cast size and the ray length
+                var scaledRayLength = 2 * perception.RayLength;
+                var scaledCastRadius = 2 * castRadius;
+                Assert.That(
+                    outputBuffer[2], Is.EqualTo((9.5f - scaledCastRadius) / scaledRayLength).Within(.0005f)
+                );
+            }
+        }
+
+        [Test]
+        public void TestRayZeroLength()
+        {
+            // Place the cube touching the origin
+            var cube = GameObject.CreatePrimitive(PrimitiveType.Cube);
+            cube.transform.position = new Vector3(0, 0, .5f);
+            cube.tag = k_CubeTag;
+
+            Physics.SyncTransforms();
+
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+            perception.RaysPerDirection = 0;
+            perception.RayLength = 0.0f;
+            perception.SphereCastRadius = .5f;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+
+            {
+                // Set the layer mask to either the default, or one that ignores the close cube's layer
+
+                var sensor = perception.CreateSensors()[0];
+                sensor.Update();
+                var expectedObs = (2 * perception.RaysPerDirection + 1) * (perception.DetectableTags.Count + 2);
+                Assert.AreEqual(sensor.GetObservationSpec().Shape[0], expectedObs);
+                var outputBuffer = new float[expectedObs];
+
+                ObservationWriter writer = new ObservationWriter();
+                writer.SetTarget(outputBuffer, sensor.GetObservationSpec(), 0);
+
+                var numWritten = sensor.Write(writer);
+                Assert.AreEqual(numWritten, expectedObs);
+
+                // hit fraction is arbitrary but should be finite in [0,1]
+                Assert.GreaterOrEqual(outputBuffer[2], 0.0f);
+                Assert.LessOrEqual(outputBuffer[2], 1.0f);
+            }
+        }
+
+        [Test]
+        public void TestStaticPerceive()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 0; // single ray
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+            perception.DetectableTags.Add(k_SphereTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+                var castOutput = RayPerceptionSensor.Perceive(castInput, false);
+
+                Assert.AreEqual(1, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(0, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+        [Test]
+        public void TestStaticPerceiveInvalidTags()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 0; // single ray
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add("Bad tag");
+            perception.DetectableTags.Add(null);
+            perception.DetectableTags.Add("");
+            perception.DetectableTags.Add(k_CubeTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+
+                // There's no clean way that I can find to check for a defined tag without
+                // logging an error.
+                LogAssert.Expect(LogType.Error, "Tag: Bad tag is not defined.");
+                var castOutput = RayPerceptionSensor.Perceive(castInput, false);
+
+                Assert.AreEqual(1, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(3, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+        [Test]
+        public void TestStaticPerceiveNoTags()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 0; // single ray
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = null;
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+                var castOutput = RayPerceptionSensor.Perceive(castInput, false);
+
+                Assert.AreEqual(1, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(-1, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+                [Test]
+        public void TestStaticBatchedPerceive()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 1; // three rays
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add(k_CubeTag);
+            perception.DetectableTags.Add(k_SphereTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+                var castOutput = RayPerceptionSensor.Perceive(castInput, true);
+
+                Assert.AreEqual(3, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(0, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+        [Test]
+        public void TestStaticPerceiveBatchedInvalidTags()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 0; // three rays
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = new List<string>();
+            perception.DetectableTags.Add("Bad tag");
+            perception.DetectableTags.Add(null);
+            perception.DetectableTags.Add("");
+            perception.DetectableTags.Add(k_CubeTag);
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+
+                // There's no clean way that I can find to check for a defined tag without
+                // logging an error.
+                LogAssert.Expect(LogType.Error, "Tag: Bad tag is not defined.");
+                var castOutput = RayPerceptionSensor.Perceive(castInput, true);
+
+                Assert.AreEqual(1, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(3, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+        [Test]
+        public void TestStaticPerceiveBatchedNoTags()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            perception.RaysPerDirection = 1; // single ray
+            perception.MaxRayDegrees = 45;
+            perception.RayLength = 20;
+            perception.DetectableTags = null;
+
+            var radii = new[] { 0f, .5f };
+            foreach (var castRadius in radii)
+            {
+                perception.SphereCastRadius = castRadius;
+                var castInput = perception.GetRayPerceptionInput();
+                var castOutput = RayPerceptionSensor.Perceive(castInput, true);
+
+                Assert.AreEqual(3, castOutput.RayOutputs.Length);
+
+                // Expected to hit the cube
+                Assert.AreEqual("cube", castOutput.RayOutputs[0].HitGameObject.name);
+                Assert.AreEqual(-1, castOutput.RayOutputs[0].HitTagIndex);
+            }
+        }
+
+        [Test]
+        public void TestCreateDefault()
+        {
+            SetupScene();
+            var obj = new GameObject("agent");
+            var perception = obj.AddComponent<RayPerceptionSensorComponent3D>();
+
+            Assert.DoesNotThrow(() =>
+            {
+                perception.CreateSensors();
+            });
+        }
+
+#endif
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..af64f98bfa778cee034105f8e6687da5f4824240
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RayPerceptionSensorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: d2983e2bca9a40398f287727dc0472a5
+timeCreated: 1573242741
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c4dcc93ef572efb2e4ffd1e258e4fccd8606617b
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs
@@ -0,0 +1,38 @@
+using System;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class RenderTextureSensorComponentTest
+    {
+        [Test]
+        public void TestRenderTextureSensorComponent()
+        {
+            foreach (var grayscale in new[] { true, false })
+            {
+                foreach (SensorCompressionType compression in Enum.GetValues(typeof(SensorCompressionType)))
+                {
+                    var width = 24;
+                    var height = 16;
+                    var texture = new RenderTexture(width, height, 0);
+
+                    var agentGameObj = new GameObject("agent");
+
+                    var renderTexComponent = agentGameObj.AddComponent<RenderTextureSensorComponent>();
+                    renderTexComponent.RenderTexture = texture;
+                    renderTexComponent.Grayscale = grayscale;
+                    renderTexComponent.CompressionType = compression;
+
+                    var expectedShape = new InplaceArray<int>(height, width, grayscale ? 1 : 3);
+
+                    var sensor = renderTexComponent.CreateSensors()[0];
+                    Assert.AreEqual(expectedShape, sensor.GetObservationSpec().Shape);
+                    Assert.AreEqual(typeof(RenderTextureSensor), sensor.GetType());
+                }
+            }
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..0e4c37fa29ac53a0e6195e1c2684ac379f7e223f
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorComponentTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 6be53c3cd01244f179a58c96560c54cf
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..767dd50de562ab85eae2e58c3631f53a4a9f4d9d
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs
@@ -0,0 +1,50 @@
+using System;
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class RenderTextureSensorTests
+    {
+        [Test]
+        public void TestRenderTextureSensor()
+        {
+            foreach (var grayscale in new[] { true, false })
+            {
+                foreach (SensorCompressionType compression in Enum.GetValues(typeof(SensorCompressionType)))
+                {
+                    var width = 24;
+                    var height = 16;
+                    var texture = new RenderTexture(width, height, 0);
+                    var sensor = new RenderTextureSensor(texture, grayscale, "TestCameraSensor", compression);
+
+                    var obsWriter = new ObservationWriter();
+                    var obs = sensor.GetObservationProto(obsWriter);
+
+                    Assert.AreEqual((int)compression, (int)obs.CompressionType);
+                    var expectedShape = new[] { height, width, grayscale ? 1 : 3 };
+                    Assert.AreEqual(expectedShape, obs.Shape);
+                }
+            }
+        }
+
+        [Test]
+        public void TestObservationType()
+        {
+            var width = 24;
+            var height = 16;
+            var camera = Camera.main;
+            var sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None);
+            var spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.Default);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.GoalSignal);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.GoalSignal);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e73904f94fdd3a237c9adb9b1b34b22539bfea62
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/RenderTextureSensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: be9f7d8ce17d8407e92d46fbee2ab809
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..84b69b6172b97e0cb4a41f093c38c27cfbbcc802
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs
@@ -0,0 +1,146 @@
+using System.Collections.Generic;
+using System.Text.RegularExpressions;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEngine.TestTools;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public class DummySensor : ISensor
+    {
+        string m_Name = "DummySensor";
+        ObservationSpec m_ObservationSpec;
+
+        public DummySensor(int dim1)
+        {
+            m_ObservationSpec = ObservationSpec.Vector(dim1);
+        }
+
+        public DummySensor(int dim1, int dim2)
+        {
+            m_ObservationSpec = ObservationSpec.VariableLength(dim1, dim2);
+        }
+
+        public DummySensor(int dim1, int dim2, int dim3)
+        {
+            m_ObservationSpec = ObservationSpec.Visual(dim1, dim2, dim3);
+        }
+
+        public string GetName()
+        {
+            return m_Name;
+        }
+
+        public ObservationSpec GetObservationSpec()
+        {
+            return m_ObservationSpec;
+        }
+
+        public byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        public int Write(ObservationWriter writer)
+        {
+            return this.ObservationSize();
+        }
+
+        public void Update() { }
+        public void Reset() { }
+
+        public CompressionSpec GetCompressionSpec()
+        {
+            return CompressionSpec.Default();
+        }
+    }
+
+    public class SensorShapeValidatorTests
+    {
+        [Test]
+        public void TestShapesAgree()
+        {
+            var validator = new SensorShapeValidator();
+            var sensorList1 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList1);
+
+            var sensorList2 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList2);
+        }
+
+        [Test]
+        public void TestNumSensorMismatch()
+        {
+            var validator = new SensorShapeValidator();
+            var sensorList1 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList1);
+
+            var sensorList2 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), };
+            LogAssert.Expect(LogType.Assert, "Number of Sensors must match. 3 != 2");
+            validator.ValidateSensors(sensorList2);
+
+            // Add the sensors in the other order
+            validator = new SensorShapeValidator();
+            validator.ValidateSensors(sensorList2);
+            LogAssert.Expect(LogType.Assert, "Number of Sensors must match. 2 != 3");
+            validator.ValidateSensors(sensorList1);
+        }
+
+        [Test]
+        public void TestDimensionMismatch()
+        {
+            var validator = new SensorShapeValidator();
+            var sensorList1 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList1);
+
+            var sensorList2 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5) };
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList2);
+
+            // Add the sensors in the other order
+            validator = new SensorShapeValidator();
+            validator.ValidateSensors(sensorList2);
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList1);
+        }
+
+        [Test]
+        public void TestSizeMismatch()
+        {
+            var validator = new SensorShapeValidator();
+            var sensorList1 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList1);
+
+            var sensorList2 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 7) };
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList2);
+
+            // Add the sensors in the other order
+            validator = new SensorShapeValidator();
+            validator.ValidateSensors(sensorList2);
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList1);
+        }
+
+        [Test]
+        public void TestEverythingMismatch()
+        {
+            var validator = new SensorShapeValidator();
+            var sensorList1 = new List<ISensor>() { new DummySensor(1), new DummySensor(2, 3), new DummySensor(4, 5, 6) };
+            validator.ValidateSensors(sensorList1);
+
+            var sensorList2 = new List<ISensor>() { new DummySensor(1), new DummySensor(9) };
+            LogAssert.Expect(LogType.Assert, "Number of Sensors must match. 3 != 2");
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList2);
+
+            // Add the sensors in the other order
+            validator = new SensorShapeValidator();
+            validator.ValidateSensors(sensorList2);
+            LogAssert.Expect(LogType.Assert, "Number of Sensors must match. 2 != 3");
+            LogAssert.Expect(LogType.Assert, new Regex("Sensor shapes must match.*"));
+            validator.ValidateSensors(sensorList1);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c538a8a74306feba7d0b8fdcdaccf34045753dfd
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorShapeValidatorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: bbfcd7a9de490454cbc37b8d7d900e7e
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs
new file mode 100644
index 0000000000000000000000000000000000000000..208e0ade25b7c1d1221c7bf7ae18e4fa628745ca
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs
@@ -0,0 +1,22 @@
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public static class SensorTestHelper
+    {
+        public static void CompareObservation(ISensor sensor, float[] expected)
+        {
+            string errorMessage;
+            bool isOK = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
+            Assert.IsTrue(isOK, errorMessage);
+        }
+
+        public static void CompareObservation(ISensor sensor, float[,,] expected)
+        {
+            string errorMessage;
+            bool isOK = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
+            Assert.IsTrue(isOK, errorMessage);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..487ace557e93eb0967d1d7416d3bbb1399b79374
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorTestHelper.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e769354f8bd404ca180d7cd7302a5d61
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..ae1e190035d38b0248a7fff1a1814ded5615cf20
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs
@@ -0,0 +1,54 @@
+using System;
+using System.Collections.Generic;
+using System.Globalization;
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Utils.Tests;
+
+namespace Unity.MLAgents.Tests
+{
+    [TestFixture]
+    public class SensorUtilTests
+    {
+        internal class TempCulture : IDisposable
+        {
+            private CultureInfo m_OriginalCulture;
+
+            internal TempCulture(CultureInfo newCulture)
+            {
+                m_OriginalCulture = CultureInfo.CurrentCulture;
+                CultureInfo.CurrentCulture = newCulture;
+            }
+
+            public void Dispose()
+            {
+                CultureInfo.CurrentCulture = m_OriginalCulture;
+            }
+        }
+
+        /// <summary>
+        /// Test that sensors sort by name consistently across culture settings.
+        /// Example strings and cultures taken from
+        /// https://docs.microsoft.com/en-us/globalization/locale/sorting-and-string-comparison
+        /// </summary>
+        /// <param name="culture"></param>
+        [TestCase("da-DK")]
+        [TestCase("en-US")]
+        public void TestSortCulture(string culture)
+        {
+            List<ISensor> sensors = new List<ISensor>();
+            var sensor0 = new TestSensor("Apple");
+            var sensor1 = new TestSensor("Æble");
+            sensors.Add(sensor0);
+            sensors.Add(sensor1);
+
+            var originalCulture = CultureInfo.CurrentCulture;
+            CultureInfo.CurrentCulture = new CultureInfo(culture);
+            SensorUtils.SortSensors(sensors);
+            CultureInfo.CurrentCulture = originalCulture;
+
+            Assert.AreEqual(sensor1, sensors[0]);
+            Assert.AreEqual(sensor0, sensors[1]);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c9e661ce9c687a3b402cf719a587f34feb97895b
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SensorUtilTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 929b34a718bc42c8aa75a3e1c8c11103
+timeCreated: 1617049947
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..eb576d8f78ad4d19a30c3668835e2ba2418376f8
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs
@@ -0,0 +1,141 @@
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public static class TestGridSensorConfig
+    {
+        public static int ObservationSize;
+        public static bool IsNormalized;
+        public static bool ParseAllColliders;
+
+        public static void SetParameters(int observationSize, bool isNormalized, bool parseAllColliders)
+        {
+            ObservationSize = observationSize;
+            IsNormalized = isNormalized;
+            ParseAllColliders = parseAllColliders;
+        }
+
+        public static void Reset()
+        {
+            ObservationSize = 0;
+            IsNormalized = false;
+            ParseAllColliders = false;
+        }
+    }
+
+    public class SimpleTestGridSensor : GridSensorBase
+    {
+        public float[] DummyData;
+
+        public SimpleTestGridSensor(
+            string name,
+            Vector3 cellScale,
+            Vector3Int gridSize,
+            string[] detectableTags,
+            SensorCompressionType compression
+        ) : base(
+            name,
+            cellScale,
+            gridSize,
+            detectableTags,
+            compression)
+        { }
+
+        protected override int GetCellObservationSize()
+        {
+            return TestGridSensorConfig.ObservationSize;
+        }
+
+        protected override bool IsDataNormalized()
+        {
+            return TestGridSensorConfig.IsNormalized;
+        }
+
+        protected internal override ProcessCollidersMethod GetProcessCollidersMethod()
+        {
+            return TestGridSensorConfig.ParseAllColliders ? ProcessCollidersMethod.ProcessAllColliders : ProcessCollidersMethod.ProcessClosestColliders;
+        }
+
+        protected override void GetObjectData(GameObject detectedObject, int typeIndex, float[] dataBuffer)
+        {
+            for (var i = 0; i < DummyData.Length; i++)
+            {
+                dataBuffer[i] = DummyData[i];
+            }
+        }
+    }
+
+    public class SimpleTestGridSensorComponent : GridSensorComponent
+    {
+        bool m_UseOneHotTag;
+        bool m_UseTestingGridSensor;
+        bool m_UseGridSensorBase;
+
+        protected override GridSensorBase[] GetGridSensors()
+        {
+            List<GridSensorBase> sensorList = new List<GridSensorBase>();
+            if (m_UseOneHotTag)
+            {
+                var testSensor = new OneHotGridSensor(
+                    SensorName,
+                    CellScale,
+                    GridSize,
+                    DetectableTags,
+                    CompressionType
+                );
+                sensorList.Add(testSensor);
+            }
+            if (m_UseGridSensorBase)
+            {
+                var testSensor = new GridSensorBase(
+                    SensorName,
+                    CellScale,
+                    GridSize,
+                    DetectableTags,
+                    CompressionType
+                );
+                sensorList.Add(testSensor);
+            }
+            if (m_UseTestingGridSensor)
+            {
+                var testSensor = new SimpleTestGridSensor(
+                    SensorName,
+                    CellScale,
+                    GridSize,
+                    DetectableTags,
+                    CompressionType
+                );
+                sensorList.Add(testSensor);
+            }
+            return sensorList.ToArray();
+        }
+
+        public void SetComponentParameters(
+            string[] detectableTags = null,
+            float cellScaleX = 1f,
+            float cellScaleZ = 1f,
+            int gridSizeX = 10,
+            int gridSizeY = 1,
+            int gridSizeZ = 10,
+            int colliderMaskInt = -1,
+            SensorCompressionType compression = SensorCompressionType.None,
+            bool rotateWithAgent = false,
+            bool useOneHotTag = false,
+            bool useTestingGridSensor = false,
+            bool useGridSensorBase = false
+        )
+        {
+            DetectableTags = detectableTags;
+            CellScale = new Vector3(cellScaleX, 0.01f, cellScaleZ);
+            GridSize = new Vector3Int(gridSizeX, gridSizeY, gridSizeZ);
+            ColliderMask = colliderMaskInt < 0 ? LayerMask.GetMask("Default") : colliderMaskInt;
+            RotateWithAgent = rotateWithAgent;
+            CompressionType = compression;
+            m_UseOneHotTag = useOneHotTag;
+            m_UseGridSensorBase = useGridSensorBase;
+            m_UseTestingGridSensor = useTestingGridSensor;
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..233b23c8d51e4f404217aab37f3e37050a373687
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/SimpleTestGridSensor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 72f121528c63749688e53aa926f2cb0a
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..5cd81ac2b00e2246a51ec4e421e319445e51b6a5
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs
@@ -0,0 +1,294 @@
+using NUnit.Framework;
+using System;
+using System.Linq;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Utils.Tests;
+
+namespace Unity.MLAgents.Tests
+{
+    public class StackingSensorTests
+    {
+        [SetUp]
+        public void SetUp()
+        {
+            if (Academy.IsInitialized)
+            {
+                Academy.Instance.Dispose();
+            }
+
+            Academy.Instance.AutomaticSteppingEnabled = false;
+        }
+
+        [TearDown]
+        public void TearDown()
+        {
+            CommunicatorFactory.ClearCreator();
+        }
+
+        [Test]
+        public void TestCtor()
+        {
+            ISensor wrapped = new VectorSensor(4);
+            ISensor sensor = new StackingSensor(wrapped, 4);
+            Assert.AreEqual("StackingSensor_size4_VectorSensor_size4", sensor.GetName());
+            Assert.AreEqual(sensor.GetObservationSpec().Shape, new InplaceArray<int>(16));
+        }
+
+        [Test]
+        public void AssertStackingReset()
+        {
+            var agentGo1 = new GameObject("TestAgent");
+            var bp1 = agentGo1.AddComponent<BehaviorParameters>();
+            bp1.BrainParameters.NumStackedVectorObservations = 3;
+            bp1.BrainParameters.ActionSpec = ActionSpec.MakeContinuous(1);
+            var aca = Academy.Instance;
+            var agent1 = agentGo1.AddComponent<TestAgent>();
+            var policy = new TestPolicy();
+            agent1.SetPolicy(policy);
+
+            StackingSensor sensor = null;
+            foreach (ISensor s in agent1.sensors)
+            {
+                if (s is StackingSensor)
+                {
+                    sensor = s as StackingSensor;
+                }
+            }
+
+            Assert.NotNull(sensor);
+
+            for (int i = 0; i < 20; i++)
+            {
+                agent1.RequestDecision();
+                aca.EnvironmentStep();
+            }
+            SensorTestHelper.CompareObservation(sensor, new[] { 18f, 19f, 20f });
+            policy.OnRequestDecision = () => SensorTestHelper.CompareObservation(sensor, new[] { 19f, 20f, 21f });
+            agent1.EndEpisode();
+            policy.OnRequestDecision = () => { };
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f });
+            for (int i = 0; i < 20; i++)
+            {
+                agent1.RequestDecision();
+                aca.EnvironmentStep();
+                SensorTestHelper.CompareObservation(sensor, new[] { Math.Max(0, i - 1f), i, i + 1 });
+            }
+        }
+
+        [Test]
+        public void TestVectorStacking()
+        {
+            VectorSensor wrapped = new VectorSensor(2);
+            StackingSensor sensor = new StackingSensor(wrapped, 3);
+
+            wrapped.AddObservation(new[] { 1f, 2f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f, 0f, 1f, 2f });
+            var data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 0f, 0f, 0f, 0f, 1f, 2f }));
+
+            sensor.Update();
+            wrapped.AddObservation(new[] { 3f, 4f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 1f, 2f, 3f, 4f });
+            data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 0f, 0f, 1f, 2f, 3f, 4f }));
+
+            sensor.Update();
+            wrapped.AddObservation(new[] { 5f, 6f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 3f, 4f, 5f, 6f });
+            data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 1f, 2f, 3f, 4f, 5f, 6f }));
+
+            sensor.Update();
+            wrapped.AddObservation(new[] { 7f, 8f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 3f, 4f, 5f, 6f, 7f, 8f });
+            data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 3f, 4f, 5f, 6f, 7f, 8f }));
+
+            sensor.Update();
+            wrapped.AddObservation(new[] { 9f, 10f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 5f, 6f, 7f, 8f, 9f, 10f });
+            data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 5f, 6f, 7f, 8f, 9f, 10f }));
+
+            // Check that if we don't call Update(), the same observations are produced
+            SensorTestHelper.CompareObservation(sensor, new[] { 5f, 6f, 7f, 8f, 9f, 10f });
+            data = sensor.GetStackedObservations();
+            Assert.IsTrue(data.ToArray().SequenceEqual(new[] { 5f, 6f, 7f, 8f, 9f, 10f }));
+        }
+
+        [Test]
+        public void TestVectorStackingReset()
+        {
+            VectorSensor wrapped = new VectorSensor(2);
+            ISensor sensor = new StackingSensor(wrapped, 3);
+
+            wrapped.AddObservation(new[] { 1f, 2f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f, 0f, 1f, 2f });
+
+            sensor.Update();
+            wrapped.AddObservation(new[] { 3f, 4f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 1f, 2f, 3f, 4f });
+
+            sensor.Reset();
+            wrapped.AddObservation(new[] { 5f, 6f });
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f, 0f, 5f, 6f });
+        }
+
+        class Dummy3DSensor : ISensor
+        {
+            public SensorCompressionType CompressionType = SensorCompressionType.PNG;
+            public int[] Mapping;
+            public ObservationSpec ObservationSpec;
+            public float[,,] CurrentObservation;
+
+            public ObservationSpec GetObservationSpec()
+            {
+                return ObservationSpec;
+            }
+
+            public int Write(ObservationWriter writer)
+            {
+                for (var h = 0; h < ObservationSpec.Shape[0]; h++)
+                {
+                    for (var w = 0; w < ObservationSpec.Shape[1]; w++)
+                    {
+                        for (var c = 0; c < ObservationSpec.Shape[2]; c++)
+                        {
+                            writer[h, w, c] = CurrentObservation[h, w, c];
+                        }
+                    }
+                }
+                return ObservationSpec.Shape[0] * ObservationSpec.Shape[1] * ObservationSpec.Shape[2];
+            }
+
+            public byte[] GetCompressedObservation()
+            {
+                var writer = new ObservationWriter();
+                var flattenedObservation = new float[ObservationSpec.Shape[0] * ObservationSpec.Shape[1] * ObservationSpec.Shape[2]];
+                writer.SetTarget(flattenedObservation, ObservationSpec.Shape, 0);
+                Write(writer);
+                byte[] bytes = Array.ConvertAll(flattenedObservation, (z) => (byte)z);
+                return bytes;
+            }
+
+            public void Update() { }
+
+            public void Reset() { }
+
+            public CompressionSpec GetCompressionSpec()
+            {
+                return new CompressionSpec(CompressionType, Mapping);
+            }
+
+            public string GetName()
+            {
+                return "Dummy";
+            }
+        }
+
+        [Test]
+        public void TestStackingMapping()
+        {
+            // Test grayscale stacked mapping with CameraSensor
+            var cameraSensor = new CameraSensor(new Camera(), 64, 64,
+                true, "grayscaleCamera", SensorCompressionType.PNG);
+            var stackedCameraSensor = new StackingSensor(cameraSensor, 2);
+            Assert.AreEqual(stackedCameraSensor.GetCompressionSpec().CompressedChannelMapping, new[] { 0, 0, 0, 1, 1, 1 });
+
+            // Test RGB stacked mapping with RenderTextureSensor
+            var renderTextureSensor = new RenderTextureSensor(new RenderTexture(24, 16, 0),
+                false, "renderTexture", SensorCompressionType.PNG);
+            var stackedRenderTextureSensor = new StackingSensor(renderTextureSensor, 2);
+            Assert.AreEqual(stackedRenderTextureSensor.GetCompressionSpec().CompressedChannelMapping, new[] { 0, 1, 2, 3, 4, 5 });
+
+            // Test mapping with number of layers not being multiple of 3
+            var dummySensor = new Dummy3DSensor();
+            dummySensor.ObservationSpec = ObservationSpec.Visual(2, 2, 4);
+            dummySensor.Mapping = new[] { 0, 1, 2, 3 };
+            var stackedDummySensor = new StackingSensor(dummySensor, 2);
+            Assert.AreEqual(stackedDummySensor.GetCompressionSpec().CompressedChannelMapping, new[] { 0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1 });
+
+            // Test mapping with dummy layers that should be dropped
+            var paddedDummySensor = new Dummy3DSensor();
+            paddedDummySensor.ObservationSpec = ObservationSpec.Visual(2, 2, 4);
+            paddedDummySensor.Mapping = new[] { 0, 1, 2, 3, -1, -1 };
+            var stackedPaddedDummySensor = new StackingSensor(paddedDummySensor, 2);
+            Assert.AreEqual(stackedPaddedDummySensor.GetCompressionSpec().CompressedChannelMapping, new[] { 0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1 });
+        }
+
+        [Test]
+        public void Test3DStacking()
+        {
+            var wrapped = new Dummy3DSensor();
+            wrapped.ObservationSpec = ObservationSpec.Visual(2, 1, 2);
+            var sensor = new StackingSensor(wrapped, 2);
+
+            // Check the stacking is on the last dimension
+            wrapped.CurrentObservation = new[, ,] { { { 1f, 2f } }, { { 3f, 4f } } };
+            SensorTestHelper.CompareObservation(sensor, new[, ,] { { { 0f, 0f, 1f, 2f } }, { { 0f, 0f, 3f, 4f } } });
+
+            sensor.Update();
+            wrapped.CurrentObservation = new[, ,] { { { 5f, 6f } }, { { 7f, 8f } } };
+            SensorTestHelper.CompareObservation(sensor, new[, ,] { { { 1f, 2f, 5f, 6f } }, { { 3f, 4f, 7f, 8f } } });
+
+            sensor.Update();
+            wrapped.CurrentObservation = new[, ,] { { { 9f, 10f } }, { { 11f, 12f } } };
+            SensorTestHelper.CompareObservation(sensor, new[, ,] { { { 5f, 6f, 9f, 10f } }, { { 7f, 8f, 11f, 12f } } });
+
+            // Check that if we don't call Update(), the same observations are produced
+            SensorTestHelper.CompareObservation(sensor, new[, ,] { { { 5f, 6f, 9f, 10f } }, { { 7f, 8f, 11f, 12f } } });
+
+            // Test reset
+            sensor.Reset();
+            wrapped.CurrentObservation = new[, ,] { { { 13f, 14f } }, { { 15f, 16f } } };
+            SensorTestHelper.CompareObservation(sensor, new[, ,] { { { 0f, 0f, 13f, 14f } }, { { 0f, 0f, 15f, 16f } } });
+        }
+
+        [Test]
+        public void TestStackedGetCompressedObservation()
+        {
+            var wrapped = new Dummy3DSensor();
+            wrapped.ObservationSpec = ObservationSpec.Visual(1, 1, 3);
+            var sensor = new StackingSensor(wrapped, 2);
+
+            wrapped.CurrentObservation = new[, ,] { { { 1f, 2f, 3f } } };
+            var expected1 = sensor.CreateEmptyPNG();
+            expected1 = expected1.Concat(Array.ConvertAll(new[] { 1f, 2f, 3f }, (z) => (byte)z)).ToArray();
+            Assert.AreEqual(sensor.GetCompressedObservation(), expected1);
+
+            sensor.Update();
+            wrapped.CurrentObservation = new[, ,] { { { 4f, 5f, 6f } } };
+            var expected2 = Array.ConvertAll(new[] { 1f, 2f, 3f, 4f, 5f, 6f }, (z) => (byte)z);
+            Assert.AreEqual(sensor.GetCompressedObservation(), expected2);
+
+            sensor.Update();
+            wrapped.CurrentObservation = new[, ,] { { { 7f, 8f, 9f } } };
+            var expected3 = Array.ConvertAll(new[] { 4f, 5f, 6f, 7f, 8f, 9f }, (z) => (byte)z);
+            Assert.AreEqual(sensor.GetCompressedObservation(), expected3);
+
+            // Test reset
+            sensor.Reset();
+            wrapped.CurrentObservation = new[, ,] { { { 10f, 11f, 12f } } };
+            var expected4 = sensor.CreateEmptyPNG();
+            expected4 = expected4.Concat(Array.ConvertAll(new[] { 10f, 11f, 12f }, (z) => (byte)z)).ToArray();
+            Assert.AreEqual(sensor.GetCompressedObservation(), expected4);
+        }
+
+        [Test]
+        public void TestStackingSensorBuiltInSensorType()
+        {
+            var dummySensor = new Dummy3DSensor();
+            dummySensor.ObservationSpec = ObservationSpec.Visual(2, 2, 4);
+            dummySensor.Mapping = new[] { 0, 1, 2, 3 };
+            var stackedDummySensor = new StackingSensor(dummySensor, 2);
+            Assert.AreEqual(stackedDummySensor.GetBuiltInSensorType(), BuiltInSensorType.Unknown);
+
+            var vectorSensor = new VectorSensor(4);
+            var stackedVectorSensor = new StackingSensor(vectorSensor, 4);
+            Assert.AreEqual(stackedVectorSensor.GetBuiltInSensorType(), BuiltInSensorType.VectorSensor);
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b275e379277eb954280454fb7631b9d1ef1c6799
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/StackingSensorTests.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 7b071fdf91474d18a05ea20175c6b3bd
+timeCreated: 1572564843
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef b/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..d779451c40d38467d370ced501d48d4d5fbc6721
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef
@@ -0,0 +1,37 @@
+{
+    "name": "Unity.ML-Agents.Runtime.Sensor.Tests",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects",
+        "Unity.ML-Agents.Runtime.Utils.Tests"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll",
+        "Google.Protobuf.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ],
+    "versionDefines": [
+        {
+            "name": "com.unity.modules.physics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics2d",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS2D_MODULE"
+        }
+    ]
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2b92b0bbb24658f3ef2d9fd45f7f7eb4f3e3dfc2
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/Unity.ML-Agents.Runtime.Sensor.Tests.asmdef.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 917f332e7ad944428f0246683188de8f
+timeCreated: 1616137285
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs b/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f58606e99c268041977730cbbc82885d7901cd10
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs
@@ -0,0 +1,131 @@
+using NUnit.Framework;
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Tests
+{
+    public class VectorSensorTests
+    {
+        [Test]
+        public void TestCtor()
+        {
+            ISensor sensor = new VectorSensor(4);
+            Assert.AreEqual("VectorSensor_size4", sensor.GetName());
+
+            sensor = new VectorSensor(3, "test_sensor");
+            Assert.AreEqual("test_sensor", sensor.GetName());
+        }
+
+        [Test]
+        public void TestWrite()
+        {
+            var sensor = new VectorSensor(4);
+            sensor.AddObservation(1f);
+            sensor.AddObservation(2f);
+            sensor.AddObservation(3f);
+            sensor.AddObservation(4f);
+
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 3f, 4f });
+            // Check that if we don't call Update(), the same observations are produced
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 3f, 4f });
+
+            // Check that Update() clears the data
+            sensor.Update();
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f, 0f });
+        }
+
+        [Test]
+        public void TestAddObservationFloat()
+        {
+            var sensor = new VectorSensor(1);
+            sensor.AddObservation(1.2f);
+            SensorTestHelper.CompareObservation(sensor, new[] { 1.2f });
+        }
+
+        [Test]
+        public void TestObservationType()
+        {
+            var sensor = new VectorSensor(1);
+            var spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new VectorSensor(1, observationType: ObservationType.Default);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new VectorSensor(1, observationType: ObservationType.GoalSignal);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.GoalSignal);
+        }
+
+        [Test]
+        public void TestAddObservationInt()
+        {
+            var sensor = new VectorSensor(1);
+            sensor.AddObservation(42);
+            SensorTestHelper.CompareObservation(sensor, new[] { 42f });
+        }
+
+        [Test]
+        public void TestAddObservationVec()
+        {
+            var sensor = new VectorSensor(3);
+            sensor.AddObservation(new Vector3(1, 2, 3));
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 3f });
+
+            sensor = new VectorSensor(2);
+            sensor.AddObservation(new Vector2(4, 5));
+            SensorTestHelper.CompareObservation(sensor, new[] { 4f, 5f });
+        }
+
+        [Test]
+        public void TestAddObservationQuaternion()
+        {
+            var sensor = new VectorSensor(4);
+            sensor.AddObservation(Quaternion.identity);
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 0f, 1f });
+        }
+
+        [Test]
+        public void TestWriteEnumerable()
+        {
+            var sensor = new VectorSensor(4);
+            sensor.AddObservation(new[] { 1f, 2f, 3f, 4f });
+
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 3f, 4f });
+        }
+
+        [Test]
+        public void TestAddObservationBool()
+        {
+            var sensor = new VectorSensor(1);
+            sensor.AddObservation(true);
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f });
+        }
+
+        [Test]
+        public void TestAddObservationOneHot()
+        {
+            var sensor = new VectorSensor(4);
+            sensor.AddOneHotObservation(2, 4);
+            SensorTestHelper.CompareObservation(sensor, new[] { 0f, 0f, 1f, 0f });
+        }
+
+        [Test]
+        public void TestWriteTooMany()
+        {
+            var sensor = new VectorSensor(2);
+            sensor.AddObservation(new[] { 1f, 2f, 3f, 4f });
+
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f });
+        }
+
+        [Test]
+        public void TestWriteNotEnough()
+        {
+            var sensor = new VectorSensor(4);
+            sensor.AddObservation(new[] { 1f, 2f });
+
+            // Make sure extra zeros are added
+            SensorTestHelper.CompareObservation(sensor, new[] { 1f, 2f, 0f, 0f });
+        }
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs.meta b/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..05c14f9206f9eeb8494d9216710fc91dc3011839
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 18c0d390ce4c5464ab48b96db0392eb0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef b/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..b740695c2addd907a778fb268daa82b27d1a2087
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef
@@ -0,0 +1,37 @@
+{
+    "name": "Unity.ML-Agents.Runtime.Tests",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects",
+        "Unity.ML-Agents.Editor"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll",
+        "Google.Protobuf.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ],
+    "versionDefines": [
+        {
+            "name": "com.unity.modules.physics",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS_MODULE"
+        },
+        {
+            "name": "com.unity.modules.physics2d",
+            "expression": "1.0.0",
+            "define": "MLA_UNITY_PHYSICS2D_MODULE"
+        }
+    ]
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef.meta b/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4fa9a793f6f01f7e3b1ae805efb6beb932becd57
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Unity.ML-Agents.Runtime.Tests.asmdef.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: d29014db7ebcd4cf4a14f537fbf02110
+AssemblyDefinitionImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/Tests/Runtime/Utils.meta b/com.unity.ml-agents/Tests/Runtime/Utils.meta
new file mode 100644
index 0000000000000000000000000000000000000000..412c47bbf60dfd168ae10544fc6da83f0750e8f6
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Utils.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e4c1564ff83a4d51855e5b5b461f6a59
+timeCreated: 1616137685
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs b/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f17b88279e3da3d4351d10485ecc1d22ed3ed4b3
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs
@@ -0,0 +1,169 @@
+using System;
+using System.Collections.Generic;
+using System.Reflection;
+using System.Runtime.CompilerServices;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Sensors.Reflection;
+
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Runtime.Sensor.Tests")]
+[assembly: InternalsVisibleTo("Unity.ML-Agents.Extensions.EditorTests")]
+
+namespace Unity.MLAgents.Utils.Tests
+{
+    internal class TestPolicy : IPolicy
+    {
+        public Action OnRequestDecision;
+        ObservationWriter m_ObsWriter = new ObservationWriter();
+        static ActionSpec s_ActionSpec = ActionSpec.MakeContinuous(1);
+        static ActionBuffers s_EmptyActionBuffers = new ActionBuffers(new float[1], Array.Empty<int>());
+
+        public void RequestDecision(AgentInfo info, List<ISensor> sensors)
+        {
+            foreach (var sensor in sensors)
+            {
+                sensor.GetObservationProto(m_ObsWriter);
+            }
+            OnRequestDecision?.Invoke();
+        }
+
+        public ref readonly ActionBuffers DecideAction() { return ref s_EmptyActionBuffers; }
+
+        public void Dispose() { }
+    }
+
+    public class TestAgent : Agent
+    {
+        internal AgentInfo _Info
+        {
+            get
+            {
+                return (AgentInfo)typeof(Agent).GetField("m_Info", BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+            }
+            set
+            {
+                typeof(Agent).GetField("m_Info", BindingFlags.Instance | BindingFlags.NonPublic).SetValue(this, value);
+            }
+        }
+
+        internal void SetPolicy(IPolicy policy)
+        {
+            typeof(Agent).GetField("m_Brain", BindingFlags.Instance | BindingFlags.NonPublic).SetValue(this, policy);
+        }
+
+        internal IPolicy GetPolicy()
+        {
+            return (IPolicy)typeof(Agent).GetField("m_Brain", BindingFlags.Instance | BindingFlags.NonPublic).GetValue(this);
+        }
+
+        public int initializeAgentCalls;
+        public int collectObservationsCalls;
+        public int collectObservationsCallsForEpisode;
+        public int agentActionCalls;
+        public int agentActionCallsForEpisode;
+        public int agentOnEpisodeBeginCalls;
+        public int heuristicCalls;
+        public TestSensor sensor1;
+        public TestSensor sensor2;
+
+        [Observable("observableFloat")]
+        public float observableFloat;
+
+        public override void Initialize()
+        {
+            initializeAgentCalls += 1;
+
+            // Add in some custom Sensors so we can confirm they get sorted as expected.
+            sensor1 = new TestSensor("testsensor1");
+            sensor2 = new TestSensor("testsensor2");
+            sensor2.compressionType = SensorCompressionType.PNG;
+
+            sensors.Add(sensor2);
+            sensors.Add(sensor1);
+        }
+
+        public override void CollectObservations(VectorSensor sensor)
+        {
+            collectObservationsCalls += 1;
+            collectObservationsCallsForEpisode += 1;
+            sensor.AddObservation(collectObservationsCallsForEpisode);
+        }
+
+        public override void OnActionReceived(ActionBuffers buffers)
+        {
+            agentActionCalls += 1;
+            agentActionCallsForEpisode += 1;
+            AddReward(0.1f);
+        }
+
+        public override void OnEpisodeBegin()
+        {
+            agentOnEpisodeBeginCalls += 1;
+            collectObservationsCallsForEpisode = 0;
+            agentActionCallsForEpisode = 0;
+        }
+
+        public override void Heuristic(in ActionBuffers actionsOut)
+        {
+            var obs = GetObservations();
+            var continuousActions = actionsOut.ContinuousActions;
+            continuousActions[0] = (int)obs[0];
+            heuristicCalls++;
+        }
+    }
+
+    public class TestSensor : ISensor
+    {
+        public string sensorName;
+        public int numWriteCalls;
+        public int numCompressedCalls;
+        public int numResetCalls;
+        public SensorCompressionType compressionType = SensorCompressionType.None;
+
+        public TestSensor(string n)
+        {
+            sensorName = n;
+        }
+
+        public ObservationSpec GetObservationSpec()
+        {
+            return ObservationSpec.Vector(0);
+        }
+
+        public int Write(ObservationWriter writer)
+        {
+            numWriteCalls++;
+            // No-op
+            return 0;
+        }
+
+        public byte[] GetCompressedObservation()
+        {
+            numCompressedCalls++;
+            return new byte[] { 0 };
+        }
+
+        public CompressionSpec GetCompressionSpec()
+        {
+            return new CompressionSpec(compressionType);
+        }
+
+        public string GetName()
+        {
+            return sensorName;
+        }
+
+        public void Update() { }
+
+        public void Reset()
+        {
+            numResetCalls++;
+        }
+    }
+
+    public class TestClasses
+    {
+    }
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs.meta b/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..a7cb7f2276cbd0d703c3d6137b1af97a8dd00995
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Utils/TestClasses.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 329ca71a721948a9a64a7b3b48604058
+timeCreated: 1616137640
\ No newline at end of file
diff --git a/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef b/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef
new file mode 100644
index 0000000000000000000000000000000000000000..0827203bcc8c2ad423a11adfdc96888acb4416de
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef
@@ -0,0 +1,24 @@
+{
+    "name": "Unity.ML-Agents.Runtime.Utils.Tests",
+    "references": [
+        "Unity.ML-Agents",
+        "Unity.Barracuda",
+        "Unity.ML-Agents.CommunicatorObjects"
+    ],
+    "optionalUnityReferences": [
+        "TestAssemblies"
+    ],
+    "includePlatforms": [],
+    "excludePlatforms": [],
+    "allowUnsafeCode": false,
+    "overrideReferences": true,
+    "precompiledReferences": [
+        "System.IO.Abstractions.dll",
+        "System.IO.Abstractions.TestingHelpers.dll",
+        "Google.Protobuf.dll"
+    ],
+    "autoReferenced": false,
+    "defineConstraints": [
+        "UNITY_INCLUDE_TESTS"
+    ]
+}
diff --git a/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef.meta b/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cf380bec780e8448fb35d86d87d663678f6bf187
--- /dev/null
+++ b/com.unity.ml-agents/Tests/Runtime/Utils/Unity.ML-Agents.Runtime.Utils.Tests.asmdef.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: daaa01096d9848a38185ee08fa7321c0
+timeCreated: 1616137694
\ No newline at end of file
diff --git a/com.unity.ml-agents/Third Party Notices.md b/com.unity.ml-agents/Third Party Notices.md
new file mode 100644
index 0000000000000000000000000000000000000000..fc124fc4686e6281445b560da7d9d80af2956bb2
--- /dev/null
+++ b/com.unity.ml-agents/Third Party Notices.md	
@@ -0,0 +1,381 @@
+This package contains third-party software components governed by the license(s) indicated below:
+ ---------
+
+ Component Name: System.Buffers.dll
+
+ License Type: MIT
+
+ The MIT License (MIT)
+
+ Copyright (c) .NET Foundation and Contributors
+
+ All rights reserved.
+
+ Permission is hereby granted, free of charge, to any person obtaining a copy
+ of this software and associated documentation files (the "Software"), to deal
+ in the Software without restriction, including without limitation the rights
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ copies of the Software, and to permit persons to whom the Software is
+ furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in all
+ copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ SOFTWARE.
+
+
+ ---------
+
+Component Name: System.Numerics.Vectors.dll
+
+License Type: MIT
+
+The MIT License (MIT)
+
+Copyright (c) .NET Foundation and Contributors
+
+All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ ---------
+
+Component Name: System.Runtime.CompilerServices.Unsafe
+
+License Type: MIT
+
+The MIT License (MIT)
+
+Copyright (c) .NET Foundation and Contributors
+
+All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ ---------
+
+Component Name: System.Memory.dll
+
+License Type: MIT
+
+The MIT License (MIT)
+
+Copyright (c) .NET Foundation and Contributors
+
+All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ ---------
+
+Component Name: System.IO.Abstractions
+
+License Type: MIT
+
+The MIT License (MIT)
+
+Copyright (c) Tatham Oddie and Contributors
+
+All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ ---------
+
+Component Name: System.Interactive.Async.dll
+
+License Type: Apache-2.0
+
+Copyright (c) .NET Foundation and Contributors
+All Rights Reserved
+
+Licensed under the Apache License, Version 2.0 (the "License"); you
+may not use this file except in compliance with the License. You may
+obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied. See the License for the specific language governing permissions
+and limitations under the License.
+
+ ---------
+
+Component Name: Grpc
+
+License Type: Apache-2.0
+
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
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+      transformation or translation of a Source form, including but
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+      "Work" shall mean the work of authorship, whether in Source or
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+      copyright notice that is included in or attached to the work
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+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
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+      "Contribution" shall mean any work of authorship, including
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+   2. Grant of Copyright License. Subject to the terms and conditions of
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+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
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+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      implied, including, without limitation, any warranties or conditions
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
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+      risks associated with Your exercise of permissions under this License.
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
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+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/com.unity.ml-agents/Third Party Notices.md.meta b/com.unity.ml-agents/Third Party Notices.md.meta
new file mode 100644
index 0000000000000000000000000000000000000000..00901a06669262e4882035a342e9ce557e75b084
--- /dev/null
+++ b/com.unity.ml-agents/Third Party Notices.md.meta	
@@ -0,0 +1,7 @@
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+TextScriptImporter:
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+  userData: 
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diff --git a/com.unity.ml-agents/catalog-info.yaml b/com.unity.ml-agents/catalog-info.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67d98bb424adb34120c7c201ea7c984177dac8f4
--- /dev/null
+++ b/com.unity.ml-agents/catalog-info.yaml
@@ -0,0 +1,21 @@
+# For more information about the available options please visit: http://go/backstage (VPN required)
+apiVersion: backstage.io/v1alpha1
+kind: Component
+metadata:
+  annotations:
+    github.com/project-slug: unity/com.unity.ml-agents
+  name: com.unity.ml-agents
+  description: "Unity ML-Agents Package"
+  labels:
+    costcenter: "5160"
+  tags:
+    - planned-public
+    - enterprise
+  links:
+    - url: https://unity.slack.com/messages/C8FECS6L9/
+      title: "#devs-ml-agents"
+      icon: chat
+spec:
+  type: unity-package
+  lifecycle: production
+  owner: unity/behavior-authoring
diff --git a/com.unity.ml-agents/catalog-info.yaml.meta b/com.unity.ml-agents/catalog-info.yaml.meta
new file mode 100644
index 0000000000000000000000000000000000000000..15b7d888e506fb67fd36e6dbaafb87c8773d10f6
--- /dev/null
+++ b/com.unity.ml-agents/catalog-info.yaml.meta
@@ -0,0 +1,7 @@
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+guid: 7d543dec1acb6455fb97a799ca89315c
+TextScriptImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings b/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings
new file mode 100644
index 0000000000000000000000000000000000000000..76282484bb531419a543e79e75c71368868dce5d
--- /dev/null
+++ b/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings
@@ -0,0 +1,21 @@
+<wpf:ResourceDictionary xml:space="preserve" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" xmlns:s="clr-namespace:System;assembly=mscorlib" xmlns:ss="urn:shemas-jetbrains-com:settings-storage-xaml" xmlns:wpf="http://schemas.microsoft.com/winfx/2006/xaml/presentation">
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=BLAS/@EntryIndexedValue">BLAS</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=CPU/@EntryIndexedValue">CPU</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=GPU/@EntryIndexedValue">GPU</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=NN/@EntryIndexedValue">NN</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=PNG/@EntryIndexedValue">PNG</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=RL/@EntryIndexedValue">RL</s:String>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=BLAS/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Gizmos/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Logits/@EntryIndexedValue">True</s:Boolean>
+	
+	
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Marsaglia/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=multinomial/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Probs/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=protobuf/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Scaler/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=Scriptable/@EntryIndexedValue">True</s:Boolean>
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+	<s:Boolean x:Key="/Default/UserDictionary/Words/=stddev/@EntryIndexedValue">True</s:Boolean>
+	<s:Boolean x:Key="/Default/UserDictionary/Words/=vals/@EntryIndexedValue">True</s:Boolean></wpf:ResourceDictionary>
diff --git a/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings.meta b/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ee052ef017083076258de6d073b46ba517476b76
--- /dev/null
+++ b/com.unity.ml-agents/com.unity.ml-agents.sln.DotSettings.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: c8f6f0111d3fb4d71af892263a7a614d
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
diff --git a/com.unity.ml-agents/package.json b/com.unity.ml-agents/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..2bfab776d9f99b2c1d000bd122a71fe60b753a78
--- /dev/null
+++ b/com.unity.ml-agents/package.json
@@ -0,0 +1,13 @@
+{
+  "name": "com.unity.ml-agents",
+  "displayName": "ML Agents",
+  "version": "2.3.0-exp.4",
+  "unity": "2022.3",
+  "description": "Use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).",
+  "dependencies": {
+    "com.unity.barracuda": "3.0.0",
+    "com.unity.modules.imageconversion": "1.0.0",
+    "com.unity.modules.jsonserialize": "1.0.0",
+    "com.unity.modules.physics": "1.0.0"
+  }
+}
diff --git a/com.unity.ml-agents/package.json.meta b/com.unity.ml-agents/package.json.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d76c84a5faffd8f5c34ca71b704bdfb2ef49239d
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+++ b/com.unity.ml-agents/package.json.meta
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diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..412634e4c38e3f06dc7a230670f0d6445be25aa9
--- /dev/null
+++ b/config.json
@@ -0,0 +1 @@
+{"behaviors": {"Pyramids": {"trainer_type": "ppo", "hyperparameters": {"batch_size": 128, "buffer_size": 2048, "learning_rate": 0.0003, "beta": 0.01, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "learning_rate_schedule": "linear"}, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 2, "vis_encode_type": "simple"}, "reward_signals": {"extrinsic": {"gamma": 0.99, "strength": 1.0}, "rnd": {"gamma": 0.99, "strength": 0.01, "network_settings": {"hidden_units": 64, "num_layers": 3}, "learning_rate": 0.0001}}, "keep_checkpoints": 5, "max_steps": 100000, "time_horizon": 128, "summary_freq": 30000}}}
\ No newline at end of file
diff --git a/config/imitation/Crawler.yaml b/config/imitation/Crawler.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..88a38b3efb89a0b260d37511cb1d80029028cfcb
--- /dev/null
+++ b/config/imitation/Crawler.yaml
@@ -0,0 +1,39 @@
+behaviors:
+  Crawler:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2024
+      buffer_size: 20240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      gail:
+        gamma: 0.99
+        strength: 1.0
+        network_settings:
+          normalize: true
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 1000
+    summary_freq: 30000
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+      steps: 50000
+      strength: 0.5
+      samples_per_update: 0
diff --git a/config/imitation/Hallway.yaml b/config/imitation/Hallway.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..60c45877fcbe0df220e2ed0df6ae58039892008d
--- /dev/null
+++ b/config/imitation/Hallway.yaml
@@ -0,0 +1,35 @@
+behaviors:
+  Hallway:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 1024
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 256
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Hallway/Demos/ExpertHallway.demo
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 64
+    summary_freq: 10000
diff --git a/config/imitation/PushBlock.yaml b/config/imitation/PushBlock.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..42f39d1f8a57f6edcf9bc35e7b9c3f12c9f4602e
--- /dev/null
+++ b/config/imitation/PushBlock.yaml
@@ -0,0 +1,42 @@
+behaviors:
+  PushBlock:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          normalize: false
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo
+    keep_checkpoints: 5
+    max_steps: 100000
+    time_horizon: 64
+    summary_freq: 60000
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo
+      steps: 50000
+      strength: 1.0
+      samples_per_update: 0
diff --git a/config/imitation/Pyramids.yaml b/config/imitation/Pyramids.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..813a4e54a7c76f91dbdb40f8d990d73d96e45f1a
--- /dev/null
+++ b/config/imitation/Pyramids.yaml
@@ -0,0 +1,34 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    time_horizon: 128
+    max_steps: 1.0e7
+    hyperparameters:
+      batch_size: 128
+      beta: 0.01
+      buffer_size: 2048
+      epsilon: 0.2
+      lambd: 0.95
+      learning_rate: 0.0003
+      num_epoch: 3
+    network_settings:
+      num_layers: 2
+      normalize: false
+      hidden_units: 512
+    reward_signals:
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        network_settings:
+          hidden_units: 256
+      gail:
+        strength: 0.01
+        gamma: 0.99
+        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+      strength: 0.5
+      steps: 150000
diff --git a/config/poca/DungeonEscape.yaml b/config/poca/DungeonEscape.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..49542ebc367d5027aadb4d50dff172b7bee7c50d
--- /dev/null
+++ b/config/poca/DungeonEscape.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  DungeonEscape:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 64
+    summary_freq: 60000
diff --git a/config/poca/PushBlockCollab.yaml b/config/poca/PushBlockCollab.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..df77d1c6c0f2b7368ac93e06cacc685e9ae6a2d7
--- /dev/null
+++ b/config/poca/PushBlockCollab.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  PushBlockCollab:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 64
+    summary_freq: 60000
diff --git a/config/poca/SoccerTwos.yaml b/config/poca/SoccerTwos.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cf658aa22565e52a06f87d264fd534f638d7ed87
--- /dev/null
+++ b/config/poca/SoccerTwos.yaml
@@ -0,0 +1,32 @@
+behaviors:
+  SoccerTwos:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 50000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 2000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0
diff --git a/config/poca/StrikersVsGoalie.yaml b/config/poca/StrikersVsGoalie.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..56facaee0ed4e611701ebf9250aca0c9deeb7a65
--- /dev/null
+++ b/config/poca/StrikersVsGoalie.yaml
@@ -0,0 +1,63 @@
+behaviors:
+  Goalie:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 1000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0
+  Striker:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 4000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0
diff --git a/config/ppo/3DBall.yaml b/config/ppo/3DBall.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..031806270b4f545575c4ab8126fbcfc7a91991a6
--- /dev/null
+++ b/config/ppo/3DBall.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 64
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
diff --git a/config/ppo/3DBallHard.yaml b/config/ppo/3DBallHard.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e9a5a62620dd9bc9ba02a546477cbc3f68caeb7
--- /dev/null
+++ b/config/ppo/3DBallHard.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  3DBallHard:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 120
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
diff --git a/config/ppo/3DBall_randomize.yaml b/config/ppo/3DBall_randomize.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ec76b0aec17c9e968e1d82d39455f79a76116c69
--- /dev/null
+++ b/config/ppo/3DBall_randomize.yaml
@@ -0,0 +1,36 @@
+behaviors:
+  3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 64
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
+environment_parameters:
+  mass:
+    sampler_type: uniform
+    sampler_parameters:
+      min_value: 0.5
+      max_value: 10
+  scale:
+    sampler_type: uniform
+    sampler_parameters:
+      min_value: 0.75
+      max_value: 3
diff --git a/config/ppo/Basic.yaml b/config/ppo/Basic.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ece4f5e185ac083e6aadf62c97363fdb837913f3
--- /dev/null
+++ b/config/ppo/Basic.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  Basic:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 32
+      buffer_size: 256
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 20
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 3
+    summary_freq: 2000
diff --git a/config/ppo/Crawler.yaml b/config/ppo/Crawler.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69256c1d0c6c2067ad9dd78cb26703c9788b6c9
--- /dev/null
+++ b/config/ppo/Crawler.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  Crawler:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/config/ppo/FoodCollector.yaml b/config/ppo/FoodCollector.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..36b018e900fd5609f2b0bd3849ac564e84f26706
--- /dev/null
+++ b/config/ppo/FoodCollector.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  GridFoodCollector:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 10000
diff --git a/config/ppo/GridWorld.yaml b/config/ppo/GridWorld.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ebe80cf7a2d2b793153a082e42477185447bfbe9
--- /dev/null
+++ b/config/ppo/GridWorld.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  GridWorld:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 32
+      buffer_size: 256
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 5
+    summary_freq: 20000
diff --git a/config/ppo/Hallway.yaml b/config/ppo/Hallway.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3dae7cfe7c93ff0bf9905c4845953564833945bf
--- /dev/null
+++ b/config/ppo/Hallway.yaml
@@ -0,0 +1,28 @@
+behaviors:
+  Hallway:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 1024
+      learning_rate: 0.0003
+      beta: 0.03
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 128
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 64
+    summary_freq: 10000
diff --git a/config/ppo/Match3.yaml b/config/ppo/Match3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..379e8bf3e87120035ac23072c91149484db02ed4
--- /dev/null
+++ b/config/ppo/Match3.yaml
@@ -0,0 +1,48 @@
+default_settings:
+  trainer_type: ppo
+  hyperparameters:
+    batch_size: 16
+    buffer_size: 120
+    learning_rate: 0.0003
+    beta: 0.005
+    epsilon: 0.2
+    lambd: 0.99
+    num_epoch: 3
+    learning_rate_schedule: constant
+  network_settings:
+    normalize: true
+    hidden_units: 256
+    num_layers: 4
+    vis_encode_type: match3
+  reward_signals:
+    extrinsic:
+      gamma: 0.99
+      strength: 1.0
+  keep_checkpoints: 5
+  max_steps: 5000000
+  time_horizon: 128
+  summary_freq: 10000
+
+behaviors:
+  Match3SimpleHeuristic:
+    # Settings can be very simple since we don't care about actually training the model
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 16
+      buffer_size: 120
+    network_settings:
+      hidden_units: 4
+      num_layers: 1
+    max_steps: 5000000
+    summary_freq: 10000
+  Match3SmartHeuristic:
+    # Settings can be very simple since we don't care about actually training the model
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 16
+      buffer_size: 120
+    network_settings:
+      hidden_units: 4
+      num_layers: 1
+    max_steps: 5000000
+    summary_freq: 10000
diff --git a/config/ppo/PushBlock.yaml b/config/ppo/PushBlock.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3f648d5be75f9838dba301a4b5e452486dbf8b82
--- /dev/null
+++ b/config/ppo/PushBlock.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  PushBlock:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 60000
diff --git a/config/ppo/Pyramids.yaml b/config/ppo/Pyramids.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8aedc9c8e84b335b22b4f3c7ad790b5813a69d96
--- /dev/null
+++ b/config/ppo/Pyramids.yaml
@@ -0,0 +1,31 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      curiosity:
+        gamma: 0.99
+        strength: 0.02
+        network_settings:
+          hidden_units: 256
+        learning_rate: 0.0003
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 128
+    summary_freq: 30000
diff --git a/config/ppo/PyramidsRND.yaml b/config/ppo/PyramidsRND.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..622413ac6534d7786e0d899b9a47b361949d75d8
--- /dev/null
+++ b/config/ppo/PyramidsRND.yaml
@@ -0,0 +1,32 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      rnd:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          hidden_units: 64
+          num_layers: 3
+        learning_rate: 0.0001
+    keep_checkpoints: 5
+    max_steps: 100000
+    time_horizon: 128
+    summary_freq: 30000
diff --git a/config/ppo/Sorter_curriculum.yaml b/config/ppo/Sorter_curriculum.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..131d950d6a4125aa88e14660ee5897d42318c835
--- /dev/null
+++ b/config/ppo/Sorter_curriculum.yaml
@@ -0,0 +1,102 @@
+behaviors:
+  Sorter:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 512
+      buffer_size: 40960
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: False
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 256
+    summary_freq: 10000
+environment_parameters:
+  num_tiles:
+    curriculum:
+      - name: Lesson0 # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value: 2.0
+      - name: Lesson1
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.4
+        value: 4.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.45
+        value: 6.0
+      - name: Lesson3
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value: 8.0
+      - name: Lesson4
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.55
+        value: 10.0
+      - name: Lesson5
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.6
+        value: 12.0
+      - name: Lesson6
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.65
+        value: 14.0
+      - name: Lesson7
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.7
+        value: 16.0
+      - name: Lesson8
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.75
+        value: 18.0
+      - name: Lesson9
+        value: 20.0
diff --git a/config/ppo/Visual3DBall.yaml b/config/ppo/Visual3DBall.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cc72c57aa4ecd87d567baf7d880e24140e0fa7f8
--- /dev/null
+++ b/config/ppo/Visual3DBall.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  Visual3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 256
+      buffer_size: 2560
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 400000
+    time_horizon: 64
+    summary_freq: 20000
diff --git a/config/ppo/VisualFoodCollector.yaml b/config/ppo/VisualFoodCollector.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..be7af9a5033a3e198837f29e7a9319a08c5fc148
--- /dev/null
+++ b/config/ppo/VisualFoodCollector.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  VisualFoodCollector:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 100
+    summary_freq: 40000
diff --git a/config/ppo/Walker.yaml b/config/ppo/Walker.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0ba1ecae54bd3ad1c7b55c4be9569c21195bcb0e
--- /dev/null
+++ b/config/ppo/Walker.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  Walker:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 256
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/config/ppo/WallJump.yaml b/config/ppo/WallJump.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5d853d8b49585d16249d6ec1dc28426ee5bc9dd3
--- /dev/null
+++ b/config/ppo/WallJump.yaml
@@ -0,0 +1,49 @@
+behaviors:
+  BigWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000
diff --git a/config/ppo/WallJump_curriculum.yaml b/config/ppo/WallJump_curriculum.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..441d7a76df90f5193384d343b92d4c627c08a794
--- /dev/null
+++ b/config/ppo/WallJump_curriculum.yaml
@@ -0,0 +1,118 @@
+behaviors:
+  BigWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000
+environment_parameters:
+  big_wall_height:
+    curriculum:
+      - name: Lesson0 # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.1
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 0.0
+            max_value: 4.0
+      - name: Lesson1 # This is the start of the second lesson
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 4.0
+            max_value: 7.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 6.0
+            max_value: 8.0
+      - name: Lesson3
+        value: 8.0
+  small_wall_height:
+    curriculum:
+      - name: Lesson0
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.1
+        value: 1.5
+      - name: Lesson1
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value: 2.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value: 2.5
+      - name: Lesson3
+        value: 4.0
diff --git a/config/ppo/Worm.yaml b/config/ppo/Worm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0fcebcd09fb836cc331270b302cd91ff831d1700
--- /dev/null
+++ b/config/ppo/Worm.yaml
@@ -0,0 +1,25 @@
+behaviors:
+  Worm:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2024
+      buffer_size: 20240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 7000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/config/sac/3DBall.yaml b/config/sac/3DBall.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9d0f3cd51d37c1c88aac5f69eda4047a1030b8c6
--- /dev/null
+++ b/config/sac/3DBall.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  3DBall:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 64
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.5
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: true
+      hidden_units: 64
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 200000
+    time_horizon: 1000
+    summary_freq: 12000
diff --git a/config/sac/3DBallHard.yaml b/config/sac/3DBallHard.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c0b75fbfee73ee960d93c9c32f74fc2c2fe19dc9
--- /dev/null
+++ b/config/sac/3DBallHard.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  3DBallHard:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
diff --git a/config/sac/Basic.yaml b/config/sac/Basic.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f80abc280b16d5d8bfac5cf112e76ead690f3bb
--- /dev/null
+++ b/config/sac/Basic.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  Basic:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 64
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.01
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 20
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 10
+    summary_freq: 2000
diff --git a/config/sac/Crawler.yaml b/config/sac/Crawler.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fbf37919883aec50ccf7997554d1ca1401720e49
--- /dev/null
+++ b/config/sac/Crawler.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  Crawler:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 20.0
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/config/sac/FoodCollector.yaml b/config/sac/FoodCollector.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..de02369f0e0f24a7c2628596c81e8bf8b379ef51
--- /dev/null
+++ b/config/sac/FoodCollector.yaml
@@ -0,0 +1,28 @@
+behaviors:
+  GridFoodCollector:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 2048
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.05
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 60000
+    threaded: false
diff --git a/config/sac/GridWorld.yaml b/config/sac/GridWorld.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..80c27c0c4b2d3b4cdf941415430d5f6862fe1853
--- /dev/null
+++ b/config/sac/GridWorld.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  GridWorld:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 1000
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.5
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 5
+    summary_freq: 20000
diff --git a/config/sac/Hallway.yaml b/config/sac/Hallway.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3952f4667b00be68bc6824b20418bd3b76f80fd5
--- /dev/null
+++ b/config/sac/Hallway.yaml
@@ -0,0 +1,30 @@
+behaviors:
+  Hallway:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 512
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 128
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 4000000
+    time_horizon: 64
+    summary_freq: 10000
diff --git a/config/sac/PushBlock.yaml b/config/sac/PushBlock.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..617cd0e95508ca4559653813ec3952e11a7b6d3e
--- /dev/null
+++ b/config/sac/PushBlock.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  PushBlock:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.05
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 100000
diff --git a/config/sac/Pyramids.yaml b/config/sac/Pyramids.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ab6566f753d25e664c42f0258f482d94abe33dd8
--- /dev/null
+++ b/config/sac/Pyramids.yaml
@@ -0,0 +1,34 @@
+behaviors:
+  Pyramids:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 2000000
+      buffer_init_steps: 1000
+      tau: 0.01
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.01
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 2.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        learning_rate: 0.0003
+        use_actions: true
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 128
+    summary_freq: 30000
diff --git a/config/sac/Walker.yaml b/config/sac/Walker.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..55990f43b1b46b924bb2f511e6cded9adca2a2a0
--- /dev/null
+++ b/config/sac/Walker.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  Walker:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 1024
+      buffer_size: 2000000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 30.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 30.0
+    network_settings:
+      normalize: true
+      hidden_units: 256
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/config/sac/WallJump.yaml b/config/sac/WallJump.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..590fe611825ef6d111ba19b482204cba98c4c465
--- /dev/null
+++ b/config/sac/WallJump.yaml
@@ -0,0 +1,53 @@
+behaviors:
+  BigWallJump:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000
diff --git a/config/sac/Worm.yaml b/config/sac/Worm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..879c95d8c415b67d655aa3af76663f4385cee697
--- /dev/null
+++ b/config/sac/Worm.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  Worm:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 20.0
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 1000
+    summary_freq: 30000
diff --git a/configuration.yaml b/configuration.yaml
index cff3bc25a6adad89a250d52fdf404dc23d2cdd96..622413ac6534d7786e0d899b9a47b361949d75d8 100644
--- a/configuration.yaml
+++ b/configuration.yaml
@@ -27,6 +27,6 @@ behaviors:
           num_layers: 3
         learning_rate: 0.0001
     keep_checkpoints: 5
-    max_steps: 1000000
+    max_steps: 100000
     time_horizon: 128
     summary_freq: 30000
diff --git a/conftest.py b/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..846ff868c7202e9a031316c525c6e8c284acbce8
--- /dev/null
+++ b/conftest.py
@@ -0,0 +1,84 @@
+"""
+This module provides the 'base_port' pytest fixture for mlagents tests.
+
+This is useful because each mlagents environment requires a unique port to communicate over and will fail on collisions.
+Normally this would prevent tests from being run in parallel but with the help of this fixture we can guarantee every
+test gets the ports it needs.
+
+See the base_port function for usage details.
+"""
+import tempfile
+from pathlib import Path
+
+import pytest
+from filelock import FileLock
+
+# TODO: Use this in all ml-agents tests so they can all run in parallel.
+import mlagents.plugins.trainer_type
+
+_BASE_PORT = 6005
+
+
+# Hook for xdist
+# https://github.com/ohmu/pytest-xdist/blob/master/xdist/newhooks.py
+def pytest_testnodeready():
+    PortAllocator().setup_once_per_node()
+
+
+class PortAllocator:
+    """
+    WARNING: Should only be used within this file.
+    Handles handing out unique ports to tests that need ports to test.
+    Shares state between parallel tests on the same node via a text file and lockfile.
+    Should only be used through the base_port test fixture.
+    """
+
+    def __init__(self):
+        self._port_alloc_file_path: Path = (
+            Path(tempfile.gettempdir()) / "next_mla_test_port.txt"
+        )
+        self._port_alloc_lock_path: Path = self._port_alloc_file_path.with_suffix(
+            ".lock"
+        )
+        self.lock = FileLock(str(self._port_alloc_lock_path))
+
+    def reserve_n_ports(self, n: int) -> int:
+        with self.lock:
+            if self._port_alloc_file_path.is_file():
+                base_port = int(self._port_alloc_file_path.read_text())
+            else:
+                base_port = 6005
+            self._port_alloc_file_path.write_text(str(base_port + n))
+        return base_port
+
+    def setup_once_per_node(self) -> None:
+        """
+        Clean up state files from previous runs, shoud only be called once per node.
+        Intended to only be called via xdist hooks.
+        """
+        # TODO: Use .unlink(missing_ok=True) once python 3.8 is min version.
+        if self._port_alloc_lock_path.exists():
+            self._port_alloc_lock_path.unlink()
+        if self._port_alloc_file_path.exists():
+            self._port_alloc_file_path.unlink()
+
+
+@pytest.fixture
+def base_port(n_ports: int) -> int:
+    """
+    Reserve a range of ports for testing (allows parallel testing even with envs).
+    Usage:
+        @pytest.mark.parametrize("n_ports", [2])
+        def test_something(base_port: int) -> None:
+            do_something(base_port)
+            do_something(base_port + 1)
+    :param _port_allocator: The global port allocator (custom pytest fixture).
+    :param n_ports: The number of ports needed.
+    :return: The base port number.
+    """
+    return PortAllocator().reserve_n_ports(n_ports)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_plugin_trainers():
+    _, _ = mlagents.plugins.trainer_type.register_trainer_plugins()
diff --git a/docs/API-Reference.md b/docs/API-Reference.md
new file mode 100644
index 0000000000000000000000000000000000000000..556fbc8b5d6f41e208476d3b5aad306d1a7f88ed
--- /dev/null
+++ b/docs/API-Reference.md
@@ -0,0 +1,20 @@
+# API Reference
+
+Our developer-facing C# classes have been documented to be compatible with
+Doxygen for auto-generating HTML documentation.
+
+To generate the API reference, download Doxygen and run the following command
+within the `docs/` directory:
+
+```sh
+doxygen dox-ml-agents.conf
+```
+
+`dox-ml-agents.conf` is a Doxygen configuration file for the ML-Agents Toolkit
+that includes the classes that have been properly formatted. The generated HTML
+files will be placed in the `html/` subdirectory. Open `index.html` within that
+subdirectory to navigate to the API reference home. Note that `html/` is already
+included in the repository's `.gitignore` file.
+
+In the near future, we aim to expand our documentation to include the Python
+classes.
diff --git a/docs/Background-Machine-Learning.md b/docs/Background-Machine-Learning.md
new file mode 100644
index 0000000000000000000000000000000000000000..58c8b2f838addb8741249b492e81c3a8b9e0d82c
--- /dev/null
+++ b/docs/Background-Machine-Learning.md
@@ -0,0 +1,195 @@
+# Background: Machine Learning
+
+Given that a number of users of the ML-Agents Toolkit might not have a formal
+machine learning background, this page provides an overview to facilitate the
+understanding of the ML-Agents Toolkit. However, we will not attempt to provide
+a thorough treatment of machine learning as there are fantastic resources
+online.
+
+Machine learning, a branch of artificial intelligence, focuses on learning
+patterns from data. The three main classes of machine learning algorithms
+include: unsupervised learning, supervised learning and reinforcement learning.
+Each class of algorithm learns from a different type of data. The following
+paragraphs provide an overview for each of these classes of machine learning, as
+well as introductory examples.
+
+## Unsupervised Learning
+
+The goal of
+[unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning) is
+to group or cluster similar items in a data set. For example, consider the
+players of a game. We may want to group the players depending on how engaged
+they are with the game. This would enable us to target different groups (e.g.
+for highly-engaged players we might invite them to be beta testers for new
+features, while for unengaged players we might email them helpful tutorials).
+Say that we wish to split our players into two groups. We would first define
+basic attributes of the players, such as the number of hours played, total money
+spent on in-app purchases and number of levels completed. We can then feed this
+data set (three attributes for every player) to an unsupervised learning
+algorithm where we specify the number of groups to be two. The algorithm would
+then split the data set of players into two groups where the players within each
+group would be similar to each other. Given the attributes we used to describe
+each player, in this case, the output would be a split of all the players into
+two groups, where one group would semantically represent the engaged players and
+the second group would semantically represent the unengaged players.
+
+With unsupervised learning, we did not provide specific examples of which
+players are considered engaged and which are considered unengaged. We just
+defined the appropriate attributes and relied on the algorithm to uncover the
+two groups on its own. This type of data set is typically called an unlabeled
+data set as it is lacking these direct labels. Consequently, unsupervised
+learning can be helpful in situations where these labels can be expensive or
+hard to produce. In the next paragraph, we overview supervised learning
+algorithms which accept input labels in addition to attributes.
+
+## Supervised Learning
+
+In [supervised learning](https://en.wikipedia.org/wiki/Supervised_learning), we
+do not want to just group similar items but directly learn a mapping from each
+item to the group (or class) that it belongs to. Returning to our earlier
+example of clustering players, let's say we now wish to predict which of our
+players are about to churn (that is stop playing the game for the next 30 days).
+We can look into our historical records and create a data set that contains
+attributes of our players in addition to a label indicating whether they have
+churned or not. Note that the player attributes we use for this churn prediction
+task may be different from the ones we used for our earlier clustering task. We
+can then feed this data set (attributes **and** label for each player) into a
+supervised learning algorithm which would learn a mapping from the player
+attributes to a label indicating whether that player will churn or not. The
+intuition is that the supervised learning algorithm will learn which values of
+these attributes typically correspond to players who have churned and not
+churned (for example, it may learn that players who spend very little and play
+for very short periods will most likely churn). Now given this learned model, we
+can provide it the attributes of a new player (one that recently started playing
+the game) and it would output a _predicted_ label for that player. This
+prediction is the algorithms expectation of whether the player will churn or
+not. We can now use these predictions to target the players who are expected to
+churn and entice them to continue playing the game.
+
+As you may have noticed, for both supervised and unsupervised learning, there
+are two tasks that need to be performed: attribute selection and model
+selection. Attribute selection (also called feature selection) pertains to
+selecting how we wish to represent the entity of interest, in this case, the
+player. Model selection, on the other hand, pertains to selecting the algorithm
+(and its parameters) that perform the task well. Both of these tasks are active
+areas of machine learning research and, in practice, require several iterations
+to achieve good performance.
+
+We now switch to reinforcement learning, the third class of machine learning
+algorithms, and arguably the one most relevant for the ML-Agents Toolkit.
+
+## Reinforcement Learning
+
+[Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning)
+can be viewed as a form of learning for sequential decision making that is
+commonly associated with controlling robots (but is, in fact, much more
+general). Consider an autonomous firefighting robot that is tasked with
+navigating into an area, finding the fire and neutralizing it. At any given
+moment, the robot perceives the environment through its sensors (e.g. camera,
+heat, touch), processes this information and produces an action (e.g. move to
+the left, rotate the water hose, turn on the water). In other words, it is
+continuously making decisions about how to interact in this environment given
+its view of the world (i.e. sensors input) and objective (i.e. neutralizing the
+fire). Teaching a robot to be a successful firefighting machine is precisely
+what reinforcement learning is designed to do.
+
+More specifically, the goal of reinforcement learning is to learn a **policy**,
+which is essentially a mapping from **observations** to **actions**. An
+observation is what the robot can measure from its **environment** (in this
+case, all its sensory inputs) and an action, in its most raw form, is a change
+to the configuration of the robot (e.g. position of its base, position of its
+water hose and whether the hose is on or off).
+
+The last remaining piece of the reinforcement learning task is the **reward
+signal**. The robot is trained to learn a policy that maximizes its overall rewards. When training a robot to be a mean firefighting machine, we provide it
+with rewards (positive and negative) indicating how well it is doing on
+completing the task. Note that the robot does not _know_ how to put out fires
+before it is trained. It learns the objective because it receives a large
+positive reward when it puts out the fire and a small negative reward for every
+passing second. The fact that rewards are sparse (i.e. may not be provided at
+every step, but only when a robot arrives at a success or failure situation), is
+a defining characteristic of reinforcement learning and precisely why learning
+good policies can be difficult (and/or time-consuming) for complex environments.
+
+<div style="text-align: center"><img src="../images/rl_cycle.png" alt="The reinforcement learning lifecycle."></div>
+
+[Learning a policy](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/)
+usually requires many trials and iterative policy updates. More specifically,
+the robot is placed in several fire situations and over time learns an optimal
+policy which allows it to put out fires more effectively. Obviously, we cannot
+expect to train a robot repeatedly in the real world, particularly when fires
+are involved. This is precisely why the use of
+[Unity as a simulator](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
+serves as the perfect training grounds for learning such behaviors. While our
+discussion of reinforcement learning has centered around robots, there are
+strong parallels between robots and characters in a game. In fact, in many ways,
+one can view a non-playable character (NPC) as a virtual robot, with its own
+observations about the environment, its own set of actions and a specific
+objective. Thus it is natural to explore how we can train behaviors within Unity
+using reinforcement learning. This is precisely what the ML-Agents Toolkit
+offers. The video linked below includes a reinforcement learning demo showcasing
+training character behaviors using the ML-Agents Toolkit.
+
+<p align="center">
+  <a href="http://www.youtube.com/watch?feature=player_embedded&v=fiQsmdwEGT8" target="_blank">
+    <img src="http://img.youtube.com/vi/fiQsmdwEGT8/0.jpg" alt="RL Demo" width="400" border="10" />
+  </a>
+</p>
+
+Similar to both unsupervised and supervised learning, reinforcement learning
+also involves two tasks: attribute selection and model selection. Attribute
+selection is defining the set of observations for the robot that best help it
+complete its objective, while model selection is defining the form of the policy
+(mapping from observations to actions) and its parameters. In practice, training
+behaviors is an iterative process that may require changing the attribute and
+model choices.
+
+## Training and Inference
+
+One common aspect of all three branches of machine learning is that they all
+involve a **training phase** and an **inference phase**. While the details of
+the training and inference phases are different for each of the three, at a
+high-level, the training phase involves building a model using the provided
+data, while the inference phase involves applying this model to new, previously
+unseen, data. More specifically:
+
+- For our unsupervised learning example, the training phase learns the optimal
+  two clusters based on the data describing existing players, while the
+  inference phase assigns a new player to one of these two clusters.
+- For our supervised learning example, the training phase learns the mapping
+  from player attributes to player label (whether they churned or not), and the
+  inference phase predicts whether a new player will churn or not based on that
+  learned mapping.
+- For our reinforcement learning example, the training phase learns the optimal
+  policy through guided trials, and in the inference phase, the agent observes
+  and takes actions in the wild using its learned policy.
+
+To briefly summarize: all three classes of algorithms involve training and
+inference phases in addition to attribute and model selections. What ultimately
+separates them is the type of data available to learn from. In unsupervised
+learning our data set was a collection of attributes, in supervised learning our
+data set was a collection of attribute-label pairs, and, lastly, in
+reinforcement learning our data set was a collection of
+observation-action-reward tuples.
+
+## Deep Learning
+
+[Deep learning](https://en.wikipedia.org/wiki/Deep_learning) is a family of
+algorithms that can be used to address any of the problems introduced above.
+More specifically, they can be used to solve both attribute and model selection
+tasks. Deep learning has gained popularity in recent years due to its
+outstanding performance on several challenging machine learning tasks. One
+example is [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo), a
+[computer Go](https://en.wikipedia.org/wiki/Computer_Go) program, that leverages
+deep learning, that was able to beat Lee Sedol (a Go world champion).
+
+A key characteristic of deep learning algorithms is their ability to learn very
+complex functions from large amounts of training data. This makes them a natural
+choice for reinforcement learning tasks when a large amount of data can be
+generated, say through the use of a simulator or engine such as Unity. By
+generating hundreds of thousands of simulations of the environment within Unity,
+we can learn policies for very complex environments (a complex environment is
+one where the number of observations an agent perceives and the number of
+actions they can take are large). Many of the algorithms we provide in ML-Agents
+use some form of deep learning, built on top of the open-source library,
+[PyTorch](Background-PyTorch.md).
diff --git a/docs/Background-PyTorch.md b/docs/Background-PyTorch.md
new file mode 100644
index 0000000000000000000000000000000000000000..f63a066745ee14e53c2b4d2861d4d81ef1364645
--- /dev/null
+++ b/docs/Background-PyTorch.md
@@ -0,0 +1,35 @@
+# Background: PyTorch
+
+As discussed in our
+[machine learning background page](Background-Machine-Learning.md), many of the
+algorithms we provide in the ML-Agents Toolkit leverage some form of deep
+learning. More specifically, our implementations are built on top of the
+open-source library [PyTorch](https://pytorch.org/). In this page we
+provide a brief overview of PyTorch and TensorBoard
+that we leverage within the ML-Agents Toolkit.
+
+## PyTorch
+
+[PyTorch](https://pytorch.org/) is an open source library for
+performing computations using data flow graphs, the underlying representation of
+deep learning models. It facilitates training and inference on CPUs and GPUs in
+a desktop, server, or mobile device. Within the ML-Agents Toolkit, when you
+train the behavior of an agent, the output is a model (.onnx) file that you can
+then associate with an Agent. Unless you implement a new algorithm, the use of
+PyTorch is mostly abstracted away and behind the scenes.
+
+## TensorBoard
+
+One component of training models with PyTorch is setting the values of
+certain model attributes (called _hyperparameters_). Finding the right values of
+these hyperparameters can require a few iterations. Consequently, we leverage a
+visualization tool called
+[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
+It allows the visualization of certain agent attributes (e.g. reward) throughout
+training which can be helpful in both building intuitions for the different
+hyperparameters and setting the optimal values for your Unity environment. We
+provide more details on setting the hyperparameters in the
+[Training ML-Agents](Training-ML-Agents.md) page. If you are unfamiliar with
+TensorBoard we recommend our guide on
+[using TensorBoard with ML-Agents](Using-Tensorboard.md) or this
+[tutorial](https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial).
diff --git a/docs/Background-Unity.md b/docs/Background-Unity.md
new file mode 100644
index 0000000000000000000000000000000000000000..7d144e53dac68199d1b81540200145075361d565
--- /dev/null
+++ b/docs/Background-Unity.md
@@ -0,0 +1,19 @@
+# Background: Unity
+
+If you are not familiar with the [Unity Engine](https://unity3d.com/unity), we
+highly recommend the [Unity Manual](https://docs.unity3d.com/Manual/index.html)
+and [Tutorials page](https://unity3d.com/learn/tutorials). The
+[Roll-a-ball tutorial](https://learn.unity.com/project/roll-a-ball)
+is a fantastic resource to learn all the basic concepts of Unity to get started
+with the ML-Agents Toolkit:
+
+- [Editor](https://docs.unity3d.com/Manual/UsingTheEditor.html)
+- [Scene](https://docs.unity3d.com/Manual/CreatingScenes.html)
+- [GameObject](https://docs.unity3d.com/Manual/GameObjects.html)
+- [Rigidbody](https://docs.unity3d.com/ScriptReference/Rigidbody.html)
+- [Camera](https://docs.unity3d.com/Manual/Cameras.html)
+- [Scripting](https://docs.unity3d.com/Manual/ScriptingSection.html)
+- [Physics](https://docs.unity3d.com/Manual/PhysicsSection.html)
+- [Ordering of event functions](https://docs.unity3d.com/Manual/ExecutionOrder.html)
+  (e.g. FixedUpdate, Update)
+- [Prefabs](https://docs.unity3d.com/Manual/Prefabs.html)
diff --git a/docs/CODE_OF_CONDUCT.md b/docs/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..14a6a4a839a0c29b9d6a3193e8a97dcf58d923c1
--- /dev/null
+++ b/docs/CODE_OF_CONDUCT.md
@@ -0,0 +1 @@
+{!../CODE_OF_CONDUCT.md!}
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..6629acde5fac5edac2928755ed2e318a62d4dd88
--- /dev/null
+++ b/docs/CONTRIBUTING.md
@@ -0,0 +1,36 @@
+# How to Contribute to ML-Agents
+
+## 1.Fork the repository
+Fork the ML-Agents repository by clicking on the "Fork" button in the top right corner of the GitHub page. This creates a copy of the repository under your GitHub account.
+
+## 2. Set up your development environment
+Clone the forked repository to your local machine using Git. Install the necessary dependencies and follow the instructions provided in the project's documentation to set up your development environment properly.
+
+## 3. Choose an issue or feature
+Browse the project's issue tracker or discussions to find an open issue or feature that you would like to contribute to. Read the guidelines and comments associated with the issue to understand the requirements and constraints.
+
+## 4. Make your changes
+Create a new branch for your changes based on the main branch of the ML-Agents repository. Implement your code changes or add new features as necessary. Ensure that your code follows the project's coding style and conventions.
+
+* Example: Let's say you want to add support for a new type of reward function in the ML-Agents framework. You can create a new branch named feature/reward-function to implement this feature.
+
+## 5. Test your changes
+Run the appropriate tests to ensure your changes work as intended. If necessary, add new tests to cover your code and verify that it doesn't introduce regressions.
+
+* Example: For the reward function feature, you would write tests to check different scenarios and expected outcomes of the new reward function.
+
+## 6. Submit a pull request
+Push your branch to your forked repository and submit a pull request (PR) to the ML-Agents main repository. Provide a clear and concise description of your changes, explaining the problem you solved or the feature you added.
+
+* Example: In the pull request description, you would explain how the new reward function works, its benefits, and any relevant implementation details.
+
+## 7. Respond to feedback
+Be responsive to any feedback or comments provided by the project maintainers. Address the feedback by making necessary revisions to your code and continue the discussion if required.
+
+## 8. Continuous integration and code review
+The ML-Agents project utilizes automated continuous integration (CI) systems to run tests on pull requests. Address any issues flagged by the CI system and actively participate in the code review process by addressing comments from reviewers.
+
+## 9. Merge your changes
+Once your pull request has been approved and meets all the project's requirements, a project maintainer will merge your changes into the main repository. Congratulations, your contribution has been successfully integrated!
+
+**Remember to always adhere to the project's code of conduct, be respectful, and follow any specific contribution guidelines provided by the ML-Agents project. Happy contributing!**
diff --git a/docs/Custom-SideChannels.md b/docs/Custom-SideChannels.md
new file mode 100644
index 0000000000000000000000000000000000000000..dab58c3d79b10727cbb00af033573838e3744f03
--- /dev/null
+++ b/docs/Custom-SideChannels.md
@@ -0,0 +1,229 @@
+# Custom Side Channels
+
+You can create your own side channel in C# and Python and use it to communicate
+custom data structures between the two. This can be useful for situations in
+which the data to be sent is too complex or structured for the built-in
+`EnvironmentParameters`, or is not related to any specific agent, and therefore
+inappropriate as an agent observation.
+
+## Overview
+
+In order to use a side channel, it must be implemented as both Unity and Python
+classes.
+
+### Unity side
+
+The side channel will have to implement the `SideChannel` abstract class and the
+following method.
+
+- `OnMessageReceived(IncomingMessage msg)` : You must implement this method and
+  read the data from IncomingMessage. The data must be read in the order that it
+  was written.
+
+The side channel must also assign a `ChannelId` property in the constructor. The
+`ChannelId` is a Guid (or UUID in Python) used to uniquely identify a side
+channel. This Guid must be the same on C# and Python. There can only be one side
+channel of a certain id during communication.
+
+To send data from C# to Python, create an `OutgoingMessage` instance, add data
+to it, call the `base.QueueMessageToSend(msg)` method inside the side channel,
+and call the `OutgoingMessage.Dispose()` method.
+
+To register a side channel on the Unity side, call
+`SideChannelManager.RegisterSideChannel` with the side channel as only argument.
+
+### Python side
+
+The side channel will have to implement the `SideChannel` abstract class. You
+must implement :
+
+- `on_message_received(self, msg: "IncomingMessage") -> None` : You must
+  implement this method and read the data from IncomingMessage. The data must be
+  read in the order that it was written.
+
+The side channel must also assign a `channel_id` property in the constructor.
+The `channel_id` is a UUID (referred in C# as Guid) used to uniquely identify a
+side channel. This number must be the same on C# and Python. There can only be
+one side channel of a certain id during communication.
+
+To assign the `channel_id` call the abstract class constructor with the
+appropriate `channel_id` as follows:
+
+```python
+super().__init__(my_channel_id)
+```
+
+To send a byte array from Python to C#, create an `OutgoingMessage` instance,
+add data to it, and call the `super().queue_message_to_send(msg)` method inside
+the side channel.
+
+To register a side channel on the Python side, pass the side channel as argument
+when creating the `UnityEnvironment` object. One of the arguments of the
+constructor (`side_channels`) is a list of side channels.
+
+## Example implementation
+
+Below is a simple implementation of a side channel that will exchange ASCII
+encoded strings between a Unity environment and Python.
+
+### Example Unity C# code
+
+The first step is to create the `StringLogSideChannel` class within the Unity
+project. Here is an implementation of a `StringLogSideChannel` that will listen
+for messages from python and print them to the Unity debug log, as well as send
+error messages from Unity to python.
+
+```csharp
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.SideChannels;
+using System.Text;
+using System;
+
+public class StringLogSideChannel : SideChannel
+{
+    public StringLogSideChannel()
+    {
+        ChannelId = new Guid("621f0a70-4f87-11ea-a6bf-784f4387d1f7");
+    }
+
+    protected override void OnMessageReceived(IncomingMessage msg)
+    {
+        var receivedString = msg.ReadString();
+        Debug.Log("From Python : " + receivedString);
+    }
+
+    public void SendDebugStatementToPython(string logString, string stackTrace, LogType type)
+    {
+        if (type == LogType.Error)
+        {
+            var stringToSend = type.ToString() + ": " + logString + "\n" + stackTrace;
+            using (var msgOut = new OutgoingMessage())
+            {
+                msgOut.WriteString(stringToSend);
+                QueueMessageToSend(msgOut);
+            }
+        }
+    }
+}
+```
+
+Once we have defined our custom side channel class, we need to ensure that it is
+instantiated and registered. This can typically be done wherever the logic of
+the side channel makes sense to be associated, for example on a MonoBehaviour
+object that might need to access data from the side channel. Here we show a
+simple MonoBehaviour object which instantiates and registers the new side
+channel. If you have not done it already, make sure that the MonoBehaviour which
+registers the side channel is attached to a GameObject which will be live in
+your Unity scene.
+
+```csharp
+using UnityEngine;
+using Unity.MLAgents;
+
+
+public class RegisterStringLogSideChannel : MonoBehaviour
+{
+
+    StringLogSideChannel stringChannel;
+    public void Awake()
+    {
+        // We create the Side Channel
+        stringChannel = new StringLogSideChannel();
+
+        // When a Debug.Log message is created, we send it to the stringChannel
+        Application.logMessageReceived += stringChannel.SendDebugStatementToPython;
+
+        // The channel must be registered with the SideChannelManager class
+        SideChannelManager.RegisterSideChannel(stringChannel);
+    }
+
+    public void OnDestroy()
+    {
+        // De-register the Debug.Log callback
+        Application.logMessageReceived -= stringChannel.SendDebugStatementToPython;
+        if (Academy.IsInitialized){
+            SideChannelManager.UnregisterSideChannel(stringChannel);
+        }
+    }
+
+    public void Update()
+    {
+        // Optional : If the space bar is pressed, raise an error !
+        if (Input.GetKeyDown(KeyCode.Space))
+        {
+            Debug.LogError("This is a fake error. Space bar was pressed in Unity.");
+        }
+    }
+}
+```
+
+### Example Python code
+
+Now that we have created the necessary Unity C# classes, we can create their
+Python counterparts.
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.side_channel import (
+    SideChannel,
+    IncomingMessage,
+    OutgoingMessage,
+)
+import numpy as np
+import uuid
+
+
+# Create the StringLogChannel class
+class StringLogChannel(SideChannel):
+
+    def __init__(self) -> None:
+        super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7"))
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Note: We must implement this method of the SideChannel interface to
+        receive messages from Unity
+        """
+        # We simply read a string from the message and print it.
+        print(msg.read_string())
+
+    def send_string(self, data: str) -> None:
+        # Add the string to an OutgoingMessage
+        msg = OutgoingMessage()
+        msg.write_string(data)
+        # We call this method to queue the data we want to send
+        super().queue_message_to_send(msg)
+```
+
+We can then instantiate the new side channel, launch a `UnityEnvironment` with
+that side channel active, and send a series of messages to the Unity environment
+from Python using it.
+
+```python
+# Create the channel
+string_log = StringLogChannel()
+
+# We start the communication with the Unity Editor and pass the string_log side channel as input
+env = UnityEnvironment(side_channels=[string_log])
+env.reset()
+string_log.send_string("The environment was reset")
+
+group_name = list(env.behavior_specs.keys())[0]  # Get the first group_name
+group_spec = env.behavior_specs[group_name]
+for i in range(1000):
+    decision_steps, terminal_steps = env.get_steps(group_name)
+    # We send data to Unity : A string with the number of Agent at each
+    string_log.send_string(
+        f"Step {i} occurred with {len(decision_steps)} deciding agents and "
+        f"{len(terminal_steps)} terminal agents"
+    )
+    env.step()  # Move the simulation forward
+
+env.close()
+```
+
+Now, if you run this script and press `Play` the Unity Editor when prompted, the
+console in the Unity Editor will display a message at every Python step.
+Additionally, if you press the Space Bar in the Unity Engine, a message will
+appear in the terminal.
diff --git a/docs/ELO-Rating-System.md b/docs/ELO-Rating-System.md
new file mode 100644
index 0000000000000000000000000000000000000000..df22dc53fe81180416dac050c4079333a5875564
--- /dev/null
+++ b/docs/ELO-Rating-System.md
@@ -0,0 +1,60 @@
+# ELO Rating System
+In adversarial games, the cumulative environment reward may **not be a meaningful metric** by which to track
+learning progress.
+
+This is because the cumulative reward is **entirely dependent on the skill of the opponent**.
+
+An agent at a particular skill level will get more or less reward against a worse or better agent,
+respectively.
+
+Instead, it's better to use ELO rating system, a method to calculate **the relative skill level between two players in a zero-sum game**.
+
+If the training performs correctly, **this value should steadily increase**.
+
+## What is a zero-sum game?
+A zero-sum game is a game where **each player's gain or loss of utility is exactly balanced by the gain or loss of the utility of the opponent**.
+
+Simply explained, we face a zero-sum game **when one agent gets +1.0, its opponent gets -1.0 reward**.
+
+For instance, Tennis is a zero-sum game: if you win the point you get +1.0 and your opponent gets -1.0 reward.
+
+## How works the ELO Rating System
+- Each player **has an initial ELO score**. It's defined in the `initial_elo` trainer config hyperparameter.
+
+- The **difference in rating between the two players** serves as the predictor of the outcomes of a match.
+
+![Example Elo](images/elo_example.png)
+*For instance, if player A has an Elo score of 2100 and player B has an ELO score of 1800 the chance that player A wins is 85% against 15% for player b.*
+
+- We calculate the **expected score of each player** using this formula:
+
+![Elo Expected Score Formula](images/elo_expected_score_formula.png)
+
+- At the end of the game, based on the outcome **we update the player’s actual Elo score**, we use a linear adjustment proportional to the amount by which the player over-performed or under-performed.
+The winning player takes points from the losing one:
+  - If the *higher-rated player wins* → **a few points** will be taken from the lower-rated player.
+  - If the *lower-rated player wins* → **a lot of points** will be taken from the high-rated player.
+  - If it’s *a draw* → the lower-rated player gains **a few points** from higher.
+
+- We update players rating using this formula:
+
+![Elo Score Update Formula](images/elo_score_update_formula.png)
+
+### The Tennis example
+
+- We start to train our agents.
+- Both of them have the same skills. So ELO score for each of them that we defined using parameter `initial_elo = 1200.0`.
+
+We calculate the expected score E:
+Ea = 0.5
+Eb = 0.5
+
+So it means that each player has 50% chances of winning the point.
+
+If A wins, the new rating R would be:
+
+Ra = 1200 + 16 * (1 - 0.5) → 1208
+
+Rb = 1200 + 16 * (0 - 0.5) → 1192
+
+Player A has now an ELO score of 1208 and Player B an ELO score of 1192. Therefore, Player A is now a little bit **better than Player B**.
diff --git a/docs/FAQ.md b/docs/FAQ.md
new file mode 100644
index 0000000000000000000000000000000000000000..ee9135b3089ae51562e0dfcb82a93c6435624bd5
--- /dev/null
+++ b/docs/FAQ.md
@@ -0,0 +1,79 @@
+# Frequently Asked Questions
+
+## Installation problems
+
+## Environment Permission Error
+
+If you directly import your Unity environment without building it in the editor,
+you might need to give it additional permissions to execute it.
+
+If you receive such a permission error on macOS, run:
+
+```sh
+chmod -R 755 *.app
+```
+
+or on Linux:
+
+```sh
+chmod -R 755 *.x86_64
+```
+
+On Windows, you can find
+[instructions](https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2008-R2-and-2008/cc754344(v=ws.11)).
+
+## Environment Connection Timeout
+
+If you are able to launch the environment from `UnityEnvironment` but then
+receive a timeout error like this:
+
+```
+UnityAgentsException: The Communicator was unable to connect. Please make sure the External process is ready to accept communication with Unity.
+```
+
+There may be a number of possible causes:
+
+- _Cause_: There may be no agent in the scene
+- _Cause_: On OSX, the firewall may be preventing communication with the
+  environment. _Solution_: Add the built environment binary to the list of
+  exceptions on the firewall by following
+  [instructions](https://support.apple.com/en-us/HT201642).
+- _Cause_: An error happened in the Unity Environment preventing communication.
+  _Solution_: Look into the
+  [log files](https://docs.unity3d.com/Manual/LogFiles.html) generated by the
+  Unity Environment to figure what error happened.
+- _Cause_: You have assigned `HTTP_PROXY` and `HTTPS_PROXY` values in your
+  environment variables. _Solution_: Remove these values and try again.
+- _Cause_: You are running in a headless environment (e.g. remotely connected
+  to a server). _Solution_: Pass `--no-graphics` to `mlagents-learn`, or
+  `no_graphics=True` to `RemoteRegistryEntry.make()` or the `UnityEnvironment`
+  initializer. If you need graphics for visual observations, you will need to
+  set up `xvfb` (or equivalent).
+
+## Communication port {} still in use
+
+If you receive an exception
+`"Couldn't launch new environment because communication port {} is still in use. "`,
+you can change the worker number in the Python script when calling
+
+```python
+UnityEnvironment(file_name=filename, worker_id=X)
+```
+
+## Mean reward : nan
+
+If you receive a message `Mean reward : nan` when attempting to train a model
+using PPO, this is due to the episodes of the Learning Environment not
+terminating. In order to address this, set `Max Steps` for the Agents within the
+Scene Inspector to a value greater than 0. Alternatively, it is possible to
+manually set `done` conditions for episodes from within scripts for custom
+episode-terminating events.
+
+## "File name" cannot be opened because the developer cannot be verified.
+
+If you have downloaded the repository using the github website on macOS 10.15 (Catalina)
+or later, you may see this error when attempting to play scenes in the Unity project.
+Workarounds include installing the package using the Unity Package Manager (this is
+the officially supported approach - see [here](Installation.md)), or following the
+instructions [here](https://support.apple.com/en-us/HT202491) to verify the relevant
+files on your machine on a file-by-file basis.
diff --git a/docs/Getting-Started.md b/docs/Getting-Started.md
new file mode 100644
index 0000000000000000000000000000000000000000..c4ec5332adc8508407437b8541f050b5a7dc7e97
--- /dev/null
+++ b/docs/Getting-Started.md
@@ -0,0 +1,265 @@
+# Getting Started Guide
+
+This guide walks through the end-to-end process of opening one of our
+[example environments](Learning-Environment-Examples.md) in Unity, training an
+Agent in it, and embedding the trained model into the Unity environment. After
+reading this tutorial, you should be able to train any of the example
+environments. If you are not familiar with the
+[Unity Engine](https://unity3d.com/unity), view our
+[Background: Unity](Background-Unity.md) page for helpful pointers.
+Additionally, if you're not familiar with machine learning, view our
+[Background: Machine Learning](Background-Machine-Learning.md) page for a brief
+overview and helpful pointers.
+
+![3D Balance Ball](images/balance.png)
+
+For this guide, we'll use the **3D Balance Ball** environment which contains a
+number of agent cubes and balls (which are all copies of each other). Each agent
+cube tries to keep its ball from falling by rotating either horizontally or
+vertically. In this environment, an agent cube is an **Agent** that receives a
+reward for every step that it balances the ball. An agent is also penalized with
+a negative reward for dropping the ball. The goal of the training process is to
+have the agents learn to balance the ball on their head.
+
+Let's get started!
+
+## Installation
+
+If you haven't already, follow the [installation instructions](Installation.md).
+Afterwards, open the Unity Project that contains all the example environments:
+
+1. Open the Package Manager Window by navigating to `Window -> Package Manager`
+   in the menu.
+1. Navigate to the ML-Agents Package and click on it.
+1. Find the `3D Ball` sample and click `Import`.
+1. In the **Project** window, go to the
+   `Assets/ML-Agents/Examples/3DBall/Scenes` folder and open the `3DBall` scene
+   file.
+
+## Understanding a Unity Environment
+
+An agent is an autonomous actor that observes and interacts with an
+_environment_. In the context of Unity, an environment is a scene containing one
+or more Agent objects, and, of course, the other entities that an agent
+interacts with.
+
+![Unity Editor](images/mlagents-3DBallHierarchy.png)
+
+**Note:** In Unity, the base object of everything in a scene is the
+_GameObject_. The GameObject is essentially a container for everything else,
+including behaviors, graphics, physics, etc. To see the components that make up
+a GameObject, select the GameObject in the Scene window, and open the Inspector
+window. The Inspector shows every component on a GameObject.
+
+The first thing you may notice after opening the 3D Balance Ball scene is that
+it contains not one, but several agent cubes. Each agent cube in the scene is an
+independent agent, but they all share the same Behavior. 3D Balance Ball does
+this to speed up training since all twelve agents contribute to training in
+parallel.
+
+### Agent
+
+The Agent is the actor that observes and takes actions in the environment. In
+the 3D Balance Ball environment, the Agent components are placed on the twelve
+"Agent" GameObjects. The base Agent object has a few properties that affect its
+behavior:
+
+- **Behavior Parameters** — Every Agent must have a Behavior. The Behavior
+  determines how an Agent makes decisions.
+- **Max Step** — Defines how many simulation steps can occur before the Agent's
+  episode ends. In 3D Balance Ball, an Agent restarts after 5000 steps.
+
+#### Behavior Parameters : Vector Observation Space
+
+Before making a decision, an agent collects its observation about its state in
+the world. The vector observation is a vector of floating point numbers which
+contain relevant information for the agent to make decisions.
+
+The Behavior Parameters of the 3D Balance Ball example uses a `Space Size` of 8.
+This means that the feature vector containing the Agent's observations contains
+eight elements: the `x` and `z` components of the agent cube's rotation and the
+`x`, `y`, and `z` components of the ball's relative position and velocity.
+
+#### Behavior Parameters : Actions
+
+An Agent is given instructions in the form of actions.
+ML-Agents Toolkit classifies actions into two types: continuous and discrete.
+The 3D Balance Ball example is programmed to use continuous actions, which
+are a vector of floating-point numbers that can vary continuously. More specifically,
+it uses a `Space Size` of 2 to control the amount of `x` and `z` rotations to apply to
+itself to keep the ball balanced on its head.
+
+## Running a pre-trained model
+
+We include pre-trained models for our agents (`.onnx` files) and we use the
+[Unity Inference Engine](Unity-Inference-Engine.md) to run these models inside
+Unity. In this section, we will use the pre-trained model for the 3D Ball
+example.
+
+1. In the **Project** window, go to the
+   `Assets/ML-Agents/Examples/3DBall/Prefabs` folder. Expand `3DBall` and click
+   on the `Agent` prefab. You should see the `Agent` prefab in the **Inspector**
+   window.
+
+   **Note**: The platforms in the `3DBall` scene were created using the `3DBall`
+   prefab. Instead of updating all 12 platforms individually, you can update the
+   `3DBall` prefab instead.
+
+   ![Platform Prefab](images/platform_prefab.png)
+
+1. In the **Project** window, drag the **3DBall** Model located in
+   `Assets/ML-Agents/Examples/3DBall/TFModels` into the `Model` property under
+   `Behavior Parameters (Script)` component in the Agent GameObject
+   **Inspector** window.
+
+   ![3dball learning brain](images/3dball_learning_brain.png)
+
+1. You should notice that each `Agent` under each `3DBall` in the **Hierarchy**
+   windows now contains **3DBall** as `Model` on the `Behavior Parameters`.
+   **Note** : You can modify multiple game objects in a scene by selecting them
+   all at once using the search bar in the Scene Hierarchy.
+1. Set the **Inference Device** to use for this model as `CPU`.
+1. Click the **Play** button in the Unity Editor and you will see the platforms
+   balance the balls using the pre-trained model.
+
+## Training a new model with Reinforcement Learning
+
+While we provide pre-trained models for the agents in this environment, any
+environment you make yourself will require training agents from scratch to
+generate a new model file. In this section we will demonstrate how to use the
+reinforcement learning algorithms that are part of the ML-Agents Python package
+to accomplish this. We have provided a convenient command `mlagents-learn` which
+accepts arguments used to configure both training and inference phases.
+
+### Training the environment
+
+1. Open a command or terminal window.
+1. Navigate to the folder where you cloned the `ml-agents` repository. **Note**:
+   If you followed the default [installation](Installation.md), then you should
+   be able to run `mlagents-learn` from any directory.
+1. Run `mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun`.
+   - `config/ppo/3DBall.yaml` is the path to a default training
+     configuration file that we provide. The `config/ppo` folder includes training configuration
+     files for all our example environments, including 3DBall.
+   - `run-id` is a unique name for this training session.
+1. When the message _"Start training by pressing the Play button in the Unity
+   Editor"_ is displayed on the screen, you can press the **Play** button in
+   Unity to start training in the Editor.
+
+If `mlagents-learn` runs correctly and starts training, you should see something
+like this:
+
+```console
+INFO:mlagents_envs:
+'Ball3DAcademy' started successfully!
+Unity Academy name: Ball3DAcademy
+
+INFO:mlagents_envs:Connected new brain:
+Unity brain name: 3DBallLearning
+        Number of Visual Observations (per agent): 0
+        Vector Observation space size (per agent): 8
+        Number of stacked Vector Observation: 1
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
+        batch_size:          64
+        beta:                0.001
+        buffer_size:         12000
+        epsilon:             0.2
+        gamma:               0.995
+        hidden_units:        128
+        lambd:               0.99
+        learning_rate:       0.0003
+        max_steps:           5.0e4
+        normalize:           True
+        num_epoch:           3
+        num_layers:          2
+        time_horizon:        1000
+        sequence_length:     64
+        summary_freq:        1000
+        use_recurrent:       False
+        memory_size:         256
+        use_curiosity:       False
+        curiosity_strength:  0.01
+        curiosity_enc_size:  128
+        output_path: ./results/first3DBallRun/3DBallLearning
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
+```
+
+Note how the `Mean Reward` value printed to the screen increases as training
+progresses. This is a positive sign that training is succeeding.
+
+**Note**: You can train using an executable rather than the Editor. To do so,
+follow the instructions in
+[Using an Executable](Learning-Environment-Executable.md).
+
+### Observing Training Progress
+
+Once you start training using `mlagents-learn` in the way described in the
+previous section, the `ml-agents` directory will contain a `results`
+directory. In order to observe the training process in more detail, you can use
+TensorBoard. From the command line run:
+
+```sh
+tensorboard --logdir results
+```
+
+Then navigate to `localhost:6006` in your browser to view the TensorBoard
+summary statistics as shown below. For the purposes of this section, the most
+important statistic is `Environment/Cumulative Reward` which should increase
+throughout training, eventually converging close to `100` which is the maximum
+reward the agent can accumulate.
+
+![Example TensorBoard Run](images/mlagents-TensorBoard.png)
+
+## Embedding the model into the Unity Environment
+
+Once the training process completes, and the training process saves the model
+(denoted by the `Saved Model` message) you can add it to the Unity project and
+use it with compatible Agents (the Agents that generated the model). **Note:**
+Do not just close the Unity Window once the `Saved Model` message appears.
+Either wait for the training process to close the window or press `Ctrl+C` at
+the command-line prompt. If you close the window manually, the `.onnx` file
+containing the trained model is not exported into the ml-agents folder.
+
+If you've quit the training early using `Ctrl+C` and want to resume training,
+run the same command again, appending the `--resume` flag:
+
+```sh
+mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume
+```
+
+Your trained model will be at `results/<run-identifier>/<behavior_name>.onnx` where
+`<behavior_name>` is the name of the `Behavior Name` of the agents corresponding
+to the model. This file corresponds to your model's latest checkpoint. You can
+now embed this trained model into your Agents by following the steps below,
+which is similar to the steps described [above](#running-a-pre-trained-model).
+
+1. Move your model file into
+   `Project/Assets/ML-Agents/Examples/3DBall/TFModels/`.
+1. Open the Unity Editor, and select the **3DBall** scene as described above.
+1. Select the **3DBall** prefab Agent object.
+1. Drag the `<behavior_name>.onnx` file from the Project window of the Editor to
+   the **Model** placeholder in the **Ball3DAgent** inspector window.
+1. Press the **Play** button at the top of the Editor.
+
+## Next Steps
+
+- For more information on the ML-Agents Toolkit, in addition to helpful
+  background, check out the [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
+  page.
+- For a "Hello World" introduction to creating your own Learning Environment,
+  check out the
+  [Making a New Learning Environment](Learning-Environment-Create-New.md) page.
+- For an overview on the more complex example environments that are provided in
+  this toolkit, check out the
+  [Example Environments](Learning-Environment-Examples.md) page.
+- For more information on the various training options available, check out the
+  [Training ML-Agents](Training-ML-Agents.md) page.
diff --git a/docs/Glossary.md b/docs/Glossary.md
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+# ML-Agents Toolkit Glossary
+
+- **Academy** - Singleton object which controls timing, reset, and
+  training/inference settings of the environment.
+- **Action** - The carrying-out of a decision on the part of an agent within the
+  environment.
+- **Agent** - Unity Component which produces observations and takes actions in
+  the environment. Agents actions are determined by decisions produced by a
+  Policy.
+- **Decision** - The specification produced by a Policy for an action to be
+  carried out given an observation.
+- **Editor** - The Unity Editor, which may include any pane (e.g. Hierarchy,
+  Scene, Inspector).
+- **Environment** - The Unity scene which contains Agents.
+- **Experience** - Corresponds to a tuple of [Agent observations, actions,
+  rewards] of a single Agent obtained after a Step.
+- **External Coordinator** - ML-Agents class responsible for communication with
+  outside processes (in this case, the Python API).
+- **FixedUpdate** - Unity method called each time the game engine is stepped.
+  ML-Agents logic should be placed here.
+- **Frame** - An instance of rendering the main camera for the display.
+  Corresponds to each `Update` call of the game engine.
+- **Observation** - Partial information describing the state of the environment
+  available to a given agent. (e.g. Vector, Visual)
+- **Policy** - The decision making mechanism for producing decisions from
+  observations, typically a neural network model.
+- **Reward** - Signal provided at every step used to indicate desirability of an
+  agent’s action within the current state of the environment.
+- **State** - The underlying properties of the environment (including all agents
+  within it) at a given time.
+- **Step** - Corresponds to an atomic change of the engine that happens between
+  Agent decisions.
+- **Trainer** - Python class which is responsible for training a given group of
+  Agents.
+- **Update** - Unity function called each time a frame is rendered. ML-Agents
+  logic should not be placed here.
diff --git a/docs/Hugging-Face-Integration.md b/docs/Hugging-Face-Integration.md
new file mode 100644
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@@ -0,0 +1,56 @@
+# The Hugging Face Integration
+
+The [Hugging Face Hub 🤗](https://huggingface.co/models?pipeline_tag=reinforcement-learning) is a central place **where anyone can share and download models**.
+
+It allows you to:
+- **Host** your trained models.
+- **Download** trained models from the community.
+- Visualize your agents **playing directly on your browser**.
+
+You can see the list of ml-agents models [here](https://huggingface.co/models?library=ml-agents).
+
+We wrote a **complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub**:
+
+- A short tutorial where you [teach **Huggy the Dog to fetch the stick** and then play with him directly in your browser](https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction)
+- A [more in-depth tutorial](https://huggingface.co/learn/deep-rl-course/unit5/introduction)
+
+## Download a model from the Hub
+
+You can simply download a model from the Hub using `mlagents-load-from-hf`.
+
+You need to define two parameters:
+
+- `--repo-id`: the name of the Hugging Face repo you want to download.
+- `--local-dir`: the path to download the model.
+
+For instance, I want to load the model with model-id "ThomasSimonini/MLAgents-Pyramids" and put it in the downloads directory:
+
+```sh
+mlagents-load-from-hf --repo-id="ThomasSimonini/MLAgents-Pyramids" --local-dir="./downloads"
+```
+
+## Upload a model to the Hub
+
+You can simply upload a model to the Hub using `mlagents-push-to-hf`
+
+You need to define four parameters:
+
+- `--run-id`: the name of the training run id.
+- `--local-dir`: where the model was saved
+- `--repo-id`: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name> If the repo does not exist it will be created automatically
+- `--commit-message`: since HF repos are git repositories you need to give a commit message.
+
+For instance, I want to upload my model trained with run-id "SnowballTarget1" to the repo-id: ThomasSimonini/ppo-SnowballTarget:
+
+```sh
+  mlagents-push-to-hf --run-id="SnowballTarget1" --local-dir="./results/SnowballTarget1" --repo-id="ThomasSimonini/ppo-SnowballTarget" --commit-message="First Push"
+```
+
+## Visualize an agent playing
+
+You can watch your agent playing directly in your browser (if the environment is from the [ML-Agents official environments](Learning-Environment-Examples.md))
+
+- Step 1: Go to https://huggingface.co/unity and select the environment demo.
+- Step 2: Find your model_id in the list.
+- Step 3: Select your .nn /.onnx file.
+- Step 4: Click on Watch the agent play
diff --git a/docs/Installation-Anaconda-Windows.md b/docs/Installation-Anaconda-Windows.md
new file mode 100644
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+++ b/docs/Installation-Anaconda-Windows.md
@@ -0,0 +1,362 @@
+# Installing ML-Agents Toolkit for Windows (Deprecated)
+
+:warning: **Note:** We no longer use this guide ourselves and so it may not work
+correctly. We've decided to keep it up just in case it is helpful to you.
+
+The ML-Agents Toolkit supports Windows 10. While it might be possible to run the
+ML-Agents Toolkit using other versions of Windows, it has not been tested on
+other versions. Furthermore, the ML-Agents Toolkit has not been tested on a
+Windows VM such as Bootcamp or Parallels.
+
+To use the ML-Agents Toolkit, you install Python and the required Python
+packages as outlined below. This guide also covers how set up GPU-based training
+(for advanced users). GPU-based training is not currently required for the
+ML-Agents Toolkit. However, training on a GPU might be required by future
+versions and features.
+
+## Step 1: Install Python via Anaconda
+
+[Download](https://www.anaconda.com/download/#windows) and install Anaconda for
+Windows. By using Anaconda, you can manage separate environments for different
+distributions of Python. Python 3.7.2 or higher is required as we no longer
+support Python 2. In this guide, we are using Python version 3.7 and Anaconda
+version 5.1
+([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe)
+or [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe)
+direct links).
+
+<p align="center">
+  <img src="images/anaconda_install.PNG"
+       alt="Anaconda Install"
+       width="500" border="10" />
+</p>
+
+We recommend the default _advanced installation options_. However, select the
+options appropriate for your specific situation.
+
+<p align="center">
+  <img src="images/anaconda_default.PNG" alt="Anaconda Install" width="500" border="10" />
+</p>
+
+After installation, you must open **Anaconda Navigator** to finish the setup.
+From the Windows search bar, type _anaconda navigator_. You can close Anaconda
+Navigator after it opens.
+
+If environment variables were not created, you will see error "conda is not
+recognized as internal or external command" when you type `conda` into the
+command line. To solve this you will need to set the environment variable
+correctly.
+
+Type `environment variables` in the search bar (this can be reached by hitting
+the Windows key or the bottom left Windows button). You should see an option
+called **Edit the system environment variables**.
+
+<p align="center">
+  <img src="images/edit_env_var.png"
+       alt="edit env variables"
+       width="250" border="10" />
+</p>
+
+From here, click the **Environment Variables** button. Double click "Path" under
+**System variable** to edit the "Path" variable, click **New** to add the
+following new paths.
+
+```console
+%UserProfile%\Anaconda3\Scripts
+%UserProfile%\Anaconda3\Scripts\conda.exe
+%UserProfile%\Anaconda3
+%UserProfile%\Anaconda3\python.exe
+```
+
+## Step 2: Setup and Activate a New Conda Environment
+
+You will create a new [Conda environment](https://conda.io/docs/) to be used
+with the ML-Agents Toolkit. This means that all the packages that you install
+are localized to just this environment. It will not affect any other
+installation of Python or other environments. Whenever you want to run
+ML-Agents, you will need activate this Conda environment.
+
+To create a new Conda environment, open a new Anaconda Prompt (_Anaconda Prompt_
+in the search bar) and type in the following command:
+
+```sh
+conda create -n ml-agents python=3.7
+```
+
+You may be asked to install new packages. Type `y` and press enter _(make sure
+you are connected to the Internet)_. You must install these required packages.
+The new Conda environment is called ml-agents and uses Python version 3.7.
+
+<p align="center">
+  <img src="images/conda_new.PNG" alt="Anaconda Install" width="500" border="10" />
+</p>
+
+To use this environment, you must activate it. _(To use this environment In the
+future, you can run the same command)_. In the same Anaconda Prompt, type in the
+following command:
+
+```sh
+activate ml-agents
+```
+
+You should see `(ml-agents)` prepended on the last line.
+
+Next, install `tensorflow`. Install this package using `pip` - which is a
+package management system used to install Python packages. Latest versions of
+TensorFlow won't work, so you will need to make sure that you install version
+1.7.1. In the same Anaconda Prompt, type in the following command _(make sure
+you are connected to the Internet)_:
+
+```sh
+pip install tensorflow==1.7.1
+```
+
+## Step 3: Install Required Python Packages
+
+The ML-Agents Toolkit depends on a number of Python packages. Use `pip` to
+install these Python dependencies.
+
+If you haven't already, clone the ML-Agents Toolkit Github repository to your
+local computer. You can do this using Git
+([download here](https://git-scm.com/download/win)) and running the following
+commands in an Anaconda Prompt _(if you open a new prompt, be sure to activate
+the ml-agents Conda environment by typing `activate ml-agents`)_:
+
+```sh
+git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git
+```
+
+The `--branch release_19` option will switch to the tag of the latest stable
+release. Omitting that will get the `main` branch which is potentially
+unstable.
+
+If you don't want to use Git, you can find download links on the
+[releases page](https://github.com/Unity-Technologies/ml-agents/releases).
+
+The `com.unity.ml-agents` subdirectory contains the core code to add to your
+projects. The `Project` subdirectory contains many
+[example environments](Learning-Environment-Examples.md) to help you get
+started.
+
+The `ml-agents` subdirectory contains a Python package which provides deep
+reinforcement learning trainers to use with Unity environments.
+
+The `ml-agents-envs` subdirectory contains a Python API to interface with Unity,
+which the `ml-agents` package depends on.
+
+The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.
+
+Keep in mind where the files were downloaded, as you will need the trainer
+config files in this directory when running `mlagents-learn`. Make sure you are
+connected to the Internet and then type in the Anaconda Prompt:
+
+```console
+python -m pip install mlagents==0.30.0
+```
+
+This will complete the installation of all the required Python packages to run
+the ML-Agents Toolkit.
+
+Sometimes on Windows, when you use pip to install certain Python packages, the
+pip will get stuck when trying to read the cache of the package. If you see
+this, you can try:
+
+```console
+python -m pip install mlagents==0.30.0 --no-cache-dir
+```
+
+This `--no-cache-dir` tells the pip to disable the cache.
+
+### Installing for Development
+
+If you intend to make modifications to `ml-agents` or `ml-agents-envs`, you
+should install the packages from the cloned repo rather than from PyPi. To do
+this, you will need to install `ml-agents` and `ml-agents-envs` separately.
+
+In our example, the files are located in `C:\Downloads`. After you have either
+cloned or downloaded the files, from the Anaconda Prompt, change to the
+ml-agents subdirectory inside the ml-agents directory:
+
+```console
+cd C:\Downloads\ml-agents
+```
+
+From the repo's main directory, now run:
+
+```console
+cd ml-agents-envs
+pip install -e .
+cd ..
+cd ml-agents
+pip install -e .
+```
+
+Running pip with the `-e` flag will let you make changes to the Python files
+directly and have those reflected when you run `mlagents-learn`. It is important
+to install these packages in this order as the `mlagents` package depends on
+`mlagents_envs`, and installing it in the other order will download
+`mlagents_envs` from PyPi.
+
+## (Optional) Step 4: GPU Training using The ML-Agents Toolkit
+
+GPU is not required for the ML-Agents Toolkit and won't speed up the PPO
+algorithm a lot during training(but something in the future will benefit from
+GPU). This is a guide for advanced users who want to train using GPUs.
+Additionally, you will need to check if your GPU is CUDA compatible. Please
+check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
+
+Currently for the ML-Agents Toolkit, only CUDA v9.0 and cuDNN v7.0.5 is
+supported.
+
+### Install Nvidia CUDA toolkit
+
+[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the
+CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated
+libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017)
+compiler and a runtime library and is needed to run the ML-Agents Toolkit. In
+this guide, we are using version
+[9.0.176](https://developer.nvidia.com/compute/cuda/9.0/Prod/network_installers/cuda_9.0.176_win10_network-exe)).
+
+Before installing, please make sure you **close any running instances of Unity
+or Visual Studio**.
+
+Run the installer and select the Express option. Note the directory where you
+installed the CUDA toolkit. In this guide, we installed in the directory
+`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`
+
+### Install Nvidia cuDNN library
+
+[Download](https://developer.nvidia.com/cudnn) and install the cuDNN library
+from Nvidia. cuDNN is a GPU-accelerated library of primitives for deep neural
+networks. Before you can download, you will need to sign up for free to the
+Nvidia Developer Program.
+
+<p align="center">
+  <img src="images/cuDNN_membership_required.png"
+       alt="cuDNN membership required"
+       width="500" border="10" />
+</p>
+
+Once you've signed up, go back to the cuDNN
+[downloads page](https://developer.nvidia.com/cudnn). You may or may not be
+asked to fill out a short survey. When you get to the list cuDNN releases,
+**make sure you are downloading the right version for the CUDA toolkit you
+installed in Step 1.** In this guide, we are using version 7.0.5 for CUDA
+toolkit version 9.0
+([direct link](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/9.0_20171129/cudnn-9.0-windows10-x64-v7)).
+
+After you have downloaded the cuDNN files, you will need to extract the files
+into the CUDA toolkit directory. In the cuDNN zip file, there are three folders
+called `bin`, `include`, and `lib`.
+
+<p align="center">
+  <img src="images/cudnn_zip_files.PNG"
+       alt="cuDNN zip files"
+       width="500" border="10" />
+</p>
+
+Copy these three folders into the CUDA toolkit directory. The CUDA toolkit
+directory is located at
+`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`
+
+<p align="center">
+  <img src="images/cuda_toolkit_directory.PNG"
+       alt="cuda toolkit directory"
+       width="500" border="10" />
+</p>
+
+### Set Environment Variables
+
+You will need to add one environment variable and two path variables.
+
+To set the environment variable, type `environment variables` in the search bar
+(this can be reached by hitting the Windows key or the bottom left Windows
+button). You should see an option called **Edit the system environment
+variables**.
+
+<p align="center">
+  <img src="images/edit_env_var.png"
+       alt="edit env variables"
+       width="250" border="10" />
+</p>
+
+From here, click the **Environment Variables** button. Click **New** to add a
+new system variable _(make sure you do this under **System variables** and not
+User variables_.
+
+<p align="center">
+  <img src="images/new_system_variable.PNG"
+       alt="new system variable"
+       width="500" border="10" />
+</p>
+
+For **Variable Name**, enter `CUDA_HOME`. For the variable value, put the
+directory location for the CUDA toolkit. In this guide, the directory location
+is `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`. Press **OK** once.
+
+<p align="center">
+  <img src="images/system_variable_name_value.PNG"
+       alt="system variable names and values"
+       width="500" border="10" />
+</p>
+
+To set the two path variables, inside the same **Environment Variables** window
+and under the second box called **System Variables**, find a variable called
+`Path` and click **Edit**. You will add two directories to the list. For this
+guide, the two entries would look like:
+
+```console
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64
+```
+
+Make sure to replace the relevant directory location with the one you have
+installed. _Please note that case sensitivity matters_.
+
+<p align="center">
+    <img src="images/path_variables.PNG"
+        alt="Path variables"
+        width="500" border="10" />
+</p>
+
+### Install TensorFlow GPU
+
+Next, install `tensorflow-gpu` using `pip`. You'll need version 1.7.1. In an
+Anaconda Prompt with the Conda environment ml-agents activated, type in the
+following command to uninstall TensorFlow for cpu and install TensorFlow for gpu
+_(make sure you are connected to the Internet)_:
+
+```sh
+pip uninstall tensorflow
+pip install tensorflow-gpu==1.7.1
+```
+
+Lastly, you should test to see if everything installed properly and that
+TensorFlow can identify your GPU. In the same Anaconda Prompt, open Python in
+the Prompt by calling:
+
+```sh
+python
+```
+
+And then type the following commands:
+
+```python
+import tensorflow as tf
+
+sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
+```
+
+You should see something similar to:
+
+```console
+Found device 0 with properties ...
+```
+
+## Acknowledgments
+
+We would like to thank
+[Jason Weimann](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/)
+and
+[Nitish S. Mutha](http://blog.nitishmutha.com/tensorflow/2017/01/22/TensorFlow-with-gpu-for-windows.html)
+for writing the original articles which were used to create this guide.
diff --git a/docs/Installation.md b/docs/Installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..f4bd381581cf080d247f5a6db856d8880a85f8c7
--- /dev/null
+++ b/docs/Installation.md
@@ -0,0 +1,199 @@
+# Installation
+
+The ML-Agents Toolkit contains several components:
+
+- Unity package ([`com.unity.ml-agents`](../com.unity.ml-agents/)) contains the
+  Unity C# SDK that will be integrated into your Unity project.  This package contains
+  a sample to help you get started with ML-Agents.
+- Unity package
+  ([`com.unity.ml-agents.extensions`](../com.unity.ml-agents.extensions/))
+  contains experimental C#/Unity components that are not yet ready to be part
+  of the base `com.unity.ml-agents` package. `com.unity.ml-agents.extensions`
+  has a direct dependency on `com.unity.ml-agents`.
+- Two Python packages:
+  - [`mlagents`](../ml-agents/) contains the machine learning algorithms that
+    enables you to train behaviors in your Unity scene. Most users of ML-Agents
+    will only need to directly install `mlagents`.
+  - [`mlagents_envs`](../ml-agents-envs/) contains a set of Python APIs to interact with
+    a Unity scene. It is a foundational layer that facilitates data messaging
+    between Unity scene and the Python machine learning algorithms.
+    Consequently, `mlagents` depends on `mlagents_envs`.
+- Unity [Project](https://github.com/Unity-Technologies/ml-agents/tree/main/Project/) that contains several
+  [example environments](Learning-Environment-Examples.md) that highlight the
+  various features of the toolkit to help you get started.
+
+Consequently, to install and use the ML-Agents Toolkit you will need to:
+
+- Install Unity (2022.3 or later)
+- Install Python (3.8.13 or higher)
+- Clone this repository (Optional)
+  - __Note:__ If you do not clone the repository, then you will not be
+  able to access the example environments and training configurations or the
+  `com.unity.ml-agents.extensions` package. Additionally, the
+  [Getting Started Guide](Getting-Started.md) assumes that you have cloned the
+  repository.
+- Install the `com.unity.ml-agents` Unity package
+- Install the `com.unity.ml-agents.extensions` Unity package (Optional)
+- Install the `mlagents` Python package
+
+### Install **Unity 2022.3** or Later
+
+[Download](https://unity3d.com/get-unity/download) and install Unity. We
+strongly recommend that you install Unity through the Unity Hub as it will
+enable you to manage multiple Unity versions.
+
+### Install **Python 3.7.2** or Higher
+
+We recommend [installing](https://www.python.org/downloads/) Python 3.7.
+If you are using Windows, please install the x86-64 version and not x86.
+If your Python environment doesn't include `pip3`, see these
+[instructions](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
+on installing it.
+
+### Clone the ML-Agents Toolkit Repository (Optional)
+
+Now that you have installed Unity and Python, you can now install the Unity and
+Python packages. You do not need to clone the repository to install those
+packages, but you may choose to clone the repository if you'd like download our
+example environments and training configurations to experiment with them (some
+of our tutorials / guides assume you have access to our example environments).
+
+**NOTE:** There are samples shipped with the Unity Package.  You only need to clone
+the repository if you would like to explore more examples.
+
+```sh
+git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git
+```
+
+The `--branch release_20` option will switch to the tag of the latest stable
+release. Omitting that will get the `main` branch which is potentially unstable.
+
+#### Advanced: Local Installation for Development
+
+You will need to clone the repository if you plan to modify or extend the
+ML-Agents Toolkit for your purposes. If you plan to contribute those changes
+back, make sure to clone the `main` branch (by omitting `--branch release_20`
+from the command above). See our
+[Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
+information on contributing to the ML-Agents Toolkit.
+
+### Install the `com.unity.ml-agents` Unity package
+
+The Unity ML-Agents C# SDK is a Unity Package. You can install the
+`com.unity.ml-agents` package
+[directly from the Package Manager registry](https://docs.unity3d.com/Manual/upm-ui-install.html).
+Please make sure you enable 'Preview Packages' in the 'Advanced' dropdown in
+order to find the latest Preview release of the package.
+
+**NOTE:** If you do not see the ML-Agents package listed in the Package Manager
+please follow the [advanced installation instructions](#advanced-local-installation-for-development) below.
+
+#### Advanced: Local Installation for Development
+
+You can [add the local](https://docs.unity3d.com/Manual/upm-ui-local.html)
+`com.unity.ml-agents` package (from the repository that you just cloned) to your
+project by:
+
+1. navigating to the menu `Window` -> `Package Manager`.
+1. In the package manager window click on the `+` button on the top left of the packages list).
+1. Select `Add package from disk...`
+1. Navigate into the `com.unity.ml-agents` folder.
+1. Select the `package.json` file.
+
+<p align="center">
+  <img src="../images/unity_package_manager_window.png"
+       alt="Unity Package Manager Window"
+       height="150"
+       border="10" />
+  <img src="../images/unity_package_json.png"
+     alt="package.json"
+     height="150"
+     border="10" />
+</p>
+
+If you are going to follow the examples from our documentation, you can open the
+`Project` folder in Unity and start tinkering immediately.
+
+### Install the `com.unity.ml-agents.extensions` Unity package (Optional)
+
+To install the `com.unity.ml-agents.extensions` package, you need to first
+clone the repo and then complete a local installation similar to what was
+outlined in the previous
+[Advanced: Local Installation for Development](#advanced-local-installation-for-development-1)
+section. Complete installation steps can be found in the
+[package documentation](../com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md#installation).
+
+### Install the `mlagents` Python package
+
+Installing the `mlagents` Python package involves installing other Python
+packages that `mlagents` depends on. So you may run into installation issues if
+your machine has older versions of any of those dependencies already installed.
+Consequently, our supported path for installing `mlagents` is to leverage Python
+Virtual Environments. Virtual Environments provide a mechanism for isolating the
+dependencies for each project and are supported on Mac / Windows / Linux. We
+offer a dedicated [guide on Virtual Environments](Using-Virtual-Environment.md).
+
+#### (Windows) Installing PyTorch
+
+On Windows, you'll have to install the PyTorch package separately prior to
+installing ML-Agents. Activate your virtual environment and run from the command line:
+
+```sh
+pip3 install torch~=1.7.1 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+Note that on Windows, you may also need Microsoft's
+[Visual C++ Redistributable](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads)
+if you don't have it already. See the [PyTorch installation guide](https://pytorch.org/get-started/locally/)
+for more installation options and versions.
+
+#### Installing `mlagents`
+
+To install the `mlagents` Python package, activate your virtual environment and
+run from the command line:
+
+```sh
+python -m pip install mlagents==0.30.0
+```
+
+Note that this will install `mlagents` from PyPi, _not_ from the cloned
+repository. If you installed this correctly, you should be able to run
+`mlagents-learn --help`, after which you will see the command
+line parameters you can use with `mlagents-learn`.
+
+By installing the `mlagents` package, the dependencies listed in the
+[setup.py file](../ml-agents/setup.py) are also installed. These include
+[PyTorch](Background-PyTorch.md) (Requires a CPU w/ AVX support).
+
+#### Advanced: Local Installation for Development
+
+If you intend to make modifications to `mlagents` or `mlagents_envs`, you should
+install the packages from the cloned repository rather than from PyPi. To do
+this, you will need to install `mlagents` and `mlagents_envs` separately. From
+the repository's root directory, run:
+
+```sh
+pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html
+pip3 install -e ./ml-agents-envs
+pip3 install -e ./ml-agents
+```
+
+Running pip with the `-e` flag will let you make changes to the Python files
+directly and have those reflected when you run `mlagents-learn`. It is important
+to install these packages in this order as the `mlagents` package depends on
+`mlagents_envs`, and installing it in the other order will download
+`mlagents_envs` from PyPi.
+
+## Next Steps
+
+The [Getting Started](Getting-Started.md) guide contains several short tutorials
+on setting up the ML-Agents Toolkit within Unity, running a pre-trained model,
+in addition to building and training environments.
+
+## Help
+
+If you run into any problems regarding ML-Agents, refer to our [FAQ](FAQ.md) and
+our [Limitations](Limitations.md) pages. If you can't find anything please
+[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
+make sure to cite relevant information on OS, Python version, and exact error
+message (whenever possible).
diff --git a/docs/Integrations-Match3.md b/docs/Integrations-Match3.md
new file mode 100644
index 0000000000000000000000000000000000000000..6b462501779a47ea5936f7410ca62863d1d3420e
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+++ b/docs/Integrations-Match3.md
@@ -0,0 +1,109 @@
+# Match-3 with ML-Agents
+
+<img src="images/match3.png" align="center" width="3000"/>
+
+## Getting started
+The C# code for Match-3 exists inside of the Unity package (`com.unity.ml-agents`).
+The good first step would be to take a look at how we have implemented the C# code in the example Match-3 scene (located
+under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to
+implement the C# code for Match-3 from the extensions package.
+
+Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game
+isn't human playable as implemented and can be only played via training.
+
+## Technical specifications for Match-3 with ML-Agents
+
+### AbstractBoard class
+The `AbstractBoard` is the bridge between ML-Agents and your game. It allows ML-Agents to
+* ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are
+* ask your game what the "color" of a cell is
+* ask whether the cell is a "special" piece type or not
+* ask your game whether a move is allowed
+* request that your game make a move
+
+These are handled by implementing the abstract methods of `AbstractBoard`.
+
+##### `public abstract BoardSize GetMaxBoardSize()`
+Returns the largest `BoardSize` that the game can use. This is used to determine the sizes of observations and sensors,
+so don't make it larger than necessary.
+
+##### `public virtual BoardSize GetCurrentBoardSize()`
+Returns the current size of the board. Each field on this BoardSize must be less than or equal to the corresponding
+field returned by `GetMaxBoardSize()`. This method is optional; if your always use the same size board, you don't
+need to override it.
+
+If the current board size is smaller than the maximum board size, `GetCellType()` and `GetSpecialType()` will not be
+called for cells outside the current board size, and `IsValidMove` won't be called for moves that would go outside of
+the current board size.
+
+##### `public abstract int GetCellType(int row, int col)`
+Returns the "color" of piece at the given row and column.
+This should be between 0 and BoardSize.NumCellTypes-1 (inclusive).
+The actual order of the values doesn't matter.
+
+##### `public abstract int GetSpecialType(int row, int col)`
+Returns the special type of the piece at the given row and column.
+This should be between 0 and BoardSize.NumSpecialTypes (inclusive).
+The actual order of the values doesn't matter.
+
+##### `public abstract bool IsMoveValid(Move m)`
+Check whether the particular `Move` is valid for the game.
+The actual results will depend on the rules of the game, but we provide the `SimpleIsMoveValid()` method
+that handles basic match3 rules with no special or immovable pieces.
+
+##### `public abstract bool MakeMove(Move m)`
+Instruct the game to make the given move. Returns true if the move was made.
+Note that during training, a move that was marked as invalid may occasionally still be
+requested. If this happens, it is safe to do nothing and request another move.
+
+### `Move` struct
+The Move struct encapsulates a swap of two adjacent cells. You can get the number of potential moves
+for a board of a given size with. `Move.NumPotentialMoves(maxBoardSize)`. There are two helper
+functions to create a new `Move`:
+* `public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize)` can be used to
+iterate over all potential moves for the board by looping from 0 to `Move.NumPotentialMoves()`
+* `public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize)` creates
+a `Move` from a row, column, and direction (and board size).
+
+### `BoardSize` struct
+Describes the "size" of the board, including the number of potential piece types that the board can have.
+This is returned by the AbstractBoard.GetMaxBoardSize() and GetCurrentBoardSize() methods.
+
+#### `Match3Sensor` and `Match3SensorComponent` classes
+The `Match3Sensor` generates observations about the state using the `AbstractBoard` interface. You can
+choose whether to use vector or "visual" observations; in theory, visual observations should perform
+better because they are 2-dimensional like the board, but we need to experiment more on this.
+
+A `Match3SensorComponent` generates `Match3Sensor`s (the exact number of sensors depends on your configuration)
+at runtime, and should be added to the same GameObject as your `Agent` implementation. You do not need to write any
+additional code to use them.
+
+#### `Match3Actuator` and `Match3ActuatorComponent` classes
+The `Match3Actuator` converts actions from training or inference into a `Move` that is sent to` AbstractBoard.MakeMove()`
+It also checks `AbstractBoard.IsMoveValid` for each potential move and uses this to set the action mask for Agent.
+
+A `Match3ActuatorComponent` generates a `Match3Actuator` at runtime, and should be added to the same GameObject
+as your `Agent` implementation.  You do not need to write any additional code to use them.
+
+### Setting up Match-3 simulation
+* Implement the `AbstractBoard` methods to integrate with your game.
+* Give the `Agent` rewards when it does what you want it to (match multiple pieces in a row, clears pieces of a certain
+type, etc).
+* Add the `Agent`, `AbstractBoard` implementation, `Match3SensorComponent`, and `Match3ActuatorComponent` to the same
+`GameObject`.
+* Call `Agent.RequestDecision()` when you're ready for the `Agent` to make a move on the next `Academy` step. During
+the next `Academy` step, the `MakeMove()` method on the board will be called.
+
+## Implementation Details
+
+### Action Space
+The indexing for actions is the same as described in
+[Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
+(for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones.
+<img src="images/match3-moves.png" align="center"/>
+
+## Feedback
+If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario,
+[we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested
+Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the
+[form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.
diff --git a/docs/Integrations.md b/docs/Integrations.md
new file mode 100644
index 0000000000000000000000000000000000000000..4a007c18666071ad13cc114845536121875cfd67
--- /dev/null
+++ b/docs/Integrations.md
@@ -0,0 +1,10 @@
+# Game Integrations
+ML-Agents provides some utilities to make it easier to integrate with some common genres of games.
+
+## Match-3
+The [Match-3 integration](Integrations-Match3.md) provides an abstraction of a match-3 game board and moves, along with
+a sensor to observe the game state, and an actuator to translate the ML-Agent actions into game moves.
+
+## Interested in more game templates?
+Do you have a type of game you are interested for ML-Agents? If so, please post a
+[forum issue](https://forum.unity.com/forums/ml-agents.453/) with `[GAME TEMPLATE]` in the title.
diff --git a/docs/LICENSE.md b/docs/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..b5bebb551dc782c8e46498a3a34fe7a5d05c4e21
--- /dev/null
+++ b/docs/LICENSE.md
@@ -0,0 +1 @@
+{!../LICENSE.md!}
diff --git a/docs/Learning-Environment-Create-New.md b/docs/Learning-Environment-Create-New.md
new file mode 100644
index 0000000000000000000000000000000000000000..adec58080a712768549ff8505f5a5ac48c3a0a92
--- /dev/null
+++ b/docs/Learning-Environment-Create-New.md
@@ -0,0 +1,498 @@
+# Making a New Learning Environment
+
+This tutorial walks through the process of creating a Unity Environment from
+scratch. We recommend first reading the [Getting Started](Getting-Started.md)
+guide to understand the concepts presented here first in an already-built
+environment.
+
+![A simple ML-Agents environment](images/mlagents-NewTutSplash.png)
+
+In this example, we will create an agent capable of controlling a ball on a
+platform. We will then train the agent to roll the ball toward the cube while
+avoiding falling off the platform.
+
+## Overview
+
+Using the ML-Agents Toolkit in a Unity project involves the following basic
+steps:
+
+1. Create an environment for your agents to live in. An environment can range
+   from a simple physical simulation containing a few objects to an entire game
+   or ecosystem.
+1. Implement your Agent subclasses. An Agent subclass defines the code an Agent
+   uses to observe its environment, to carry out assigned actions, and to
+   calculate the rewards used for reinforcement training. You can also implement
+   optional methods to reset the Agent when it has finished or failed its task.
+1. Add your Agent subclasses to appropriate GameObjects, typically, the object
+   in the scene that represents the Agent in the simulation.
+
+**Note:** If you are unfamiliar with Unity, refer to the
+[Unity manual](https://docs.unity3d.com/Manual/index.html)
+if an Editor task isn't explained sufficiently in this tutorial.
+
+If you haven't already, follow the [installation instructions](Installation.md).
+
+## Set Up the Unity Project
+
+The first task to accomplish is simply creating a new Unity project and
+importing the ML-Agents assets into it:
+
+1. Launch Unity Hub and create a new 3D project named "RollerBall".
+1. [Add the ML-Agents Unity package](Installation.md#install-the-comunityml-agents-unity-package)
+   to your project.
+
+Your Unity **Project** window should contain the following assets:
+
+<p align="left">
+  <img src="images/roller-ball-projects.png"
+       alt="Project window"
+       width="250" border="10" />
+</p>
+
+## Create the Environment
+
+Next, we will create a very simple scene to act as our learning environment. The
+"physical" components of the environment include a Plane to act as the floor for
+the Agent to move around on, a Cube to act as the goal or target for the agent
+to seek, and a Sphere to represent the Agent itself.
+
+### Create the Floor Plane
+
+1. Right click in Hierarchy window, select 3D Object > Plane.
+1. Name the GameObject "Floor".
+1. Select the Floor Plane to view its properties in the Inspector window.
+1. Set Transform to Position = `(0, 0, 0)`, Rotation = `(0, 0, 0)`, Scale =
+   `(1, 1, 1)`.
+
+<p align="left">
+  <img src="images/roller-ball-floor.png"
+       alt="The Floor in the Inspector window"
+       width="400" border="10" />
+</p>
+
+### Add the Target Cube
+
+1. Right click in Hierarchy window, select 3D Object > Cube.
+1. Name the GameObject "Target".
+1. Select the Target Cube to view its properties in the Inspector window.
+1. Set Transform to Position = `(3, 0.5, 3)`, Rotation = `(0, 0, 0)`, Scale =
+   `(1, 1, 1)`.
+
+<p align="left">
+  <img src="images/roller-ball-target.png"
+       alt="The Target Cube in the Inspector window"
+       width="400" border="10" />
+</p>
+
+### Add the Agent Sphere
+
+1. Right click in Hierarchy window, select 3D Object > Sphere.
+1. Name the GameObject "RollerAgent".
+1. Select the RollerAgent Sphere to view its properties in the Inspector window.
+1. Set Transform to Position = `(0, 0.5, 0)`, Rotation = `(0, 0, 0)`, Scale =
+   `(1, 1, 1)`.
+1. Click **Add Component**.
+1. Add the `Rigidbody` component to the Sphere.
+
+### Group into Training Area
+
+Group the floor, target and agent under a single, empty, GameObject. This will simplify
+some of our subsequent steps.
+
+To do so:
+
+1. Right-click on your Project Hierarchy and create a new empty GameObject. Name
+   it TrainingArea.
+1. Reset the TrainingArea’s Transform so that it is at `(0,0,0)` with Rotation
+   `(0,0,0)` and Scale `(1,1,1)`.
+1. Drag the Floor, Target, and RollerAgent GameObjects in the Hierarchy into the
+   TrainingArea GameObject.
+
+<p align="left">
+  <img src="images/roller-ball-hierarchy.png"
+       alt="The Hierarchy window"
+       width="250" border="10" />
+</p>
+
+## Implement an Agent
+
+To create the Agent Script:
+
+1. Select the RollerAgent GameObject to view it in the Inspector window.
+1. Click **Add Component**.
+1. Click **New Script** in the list of components (at the bottom).
+1. Name the script "RollerAgent".
+1. Click **Create and Add**.
+
+Then, edit the new `RollerAgent` script:
+
+1. In the Unity Project window, double-click the `RollerAgent` script to open it
+   in your code editor.
+1. Import ML-Agent package by adding
+
+   ```csharp
+   using Unity.MLAgents;
+   using Unity.MLAgents.Sensors;
+   using Unity.MLAgents.Actuators;
+   ```
+   then change the base class from `MonoBehaviour` to `Agent`.
+1. Delete `Update()` since we are not using it, but keep `Start()`.
+
+So far, these are the basic steps that you would use to add ML-Agents to any
+Unity project. Next, we will add the logic that will let our Agent learn to roll
+to the cube using reinforcement learning. More specifically, we will need to
+extend three methods from the `Agent` base class:
+
+- `OnEpisodeBegin()`
+- `CollectObservations(VectorSensor sensor)`
+- `OnActionReceived(ActionBuffers actionBuffers)`
+
+We overview each of these in more detail in the dedicated subsections below.
+
+### Initialization and Resetting the Agent
+
+The process of training in the ML-Agents Toolkit involves running episodes where
+the Agent (Sphere) attempts to solve the task. Each episode lasts until the
+Agents solves the task (i.e. reaches the cube), fails (rolls off the platform)
+or times out (takes too long to solve or fail at the task). At the start of each
+episode, `OnEpisodeBegin()` is called to set-up the environment for a
+new episode. Typically the scene is initialized in a random manner to enable the
+agent to learn to solve the task under a variety of conditions.
+
+In this example, each time the Agent (Sphere) reaches its target (Cube), the
+episode ends and the target (Cube) is moved to a new random location; and if
+the Agent rolls off the platform, it will be put back onto the floor.
+These are all handled in `OnEpisodeBegin()`.
+
+To move the target (Cube), we need a reference to its Transform (which stores a
+GameObject's position, orientation and scale in the 3D world). To get this
+reference, add a public field of type `Transform` to the RollerAgent class.
+Public fields of a component in Unity get displayed in the Inspector window,
+allowing you to choose which GameObject to use as the target in the Unity
+Editor.
+
+To reset the Agent's velocity (and later to apply force to move the agent) we
+need a reference to the Rigidbody component. A
+[Rigidbody](https://docs.unity3d.com/ScriptReference/Rigidbody.html) is Unity's
+primary element for physics simulation. (See
+[Physics](https://docs.unity3d.com/Manual/PhysicsSection.html) for full
+documentation of Unity physics.) Since the Rigidbody component is on the same
+GameObject as our Agent script, the best way to get this reference is using
+`GameObject.GetComponent<T>()`, which we can call in our script's `Start()`
+method.
+
+So far, our RollerAgent script looks like:
+
+```csharp
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
+
+public class RollerAgent : Agent
+{
+    Rigidbody rBody;
+    void Start () {
+        rBody = GetComponent<Rigidbody>();
+    }
+
+    public Transform Target;
+    public override void OnEpisodeBegin()
+    {
+       // If the Agent fell, zero its momentum
+        if (this.transform.localPosition.y < 0)
+        {
+            this.rBody.angularVelocity = Vector3.zero;
+            this.rBody.velocity = Vector3.zero;
+            this.transform.localPosition = new Vector3( 0, 0.5f, 0);
+        }
+
+        // Move the target to a new spot
+        Target.localPosition = new Vector3(Random.value * 8 - 4,
+                                           0.5f,
+                                           Random.value * 8 - 4);
+    }
+}
+```
+
+Next, let's implement the `Agent.CollectObservations(VectorSensor sensor)`
+method.
+
+### Observing the Environment
+
+The Agent sends the information we collect to the Brain, which uses it to make a
+decision. When you train the Agent (or use a trained model), the data is fed
+into a neural network as a feature vector. For an Agent to successfully learn a
+task, we need to provide the correct information. A good rule of thumb for
+deciding what information to collect is to consider what you would need to
+calculate an analytical solution to the problem.
+
+In our case, the information our Agent collects includes the position of the
+target, the position of the agent itself, and the velocity of the agent. This
+helps the Agent learn to control its speed so it doesn't overshoot the target
+and roll off the platform. In total, the agent observation contains 8 values as
+implemented below:
+
+```csharp
+public override void CollectObservations(VectorSensor sensor)
+{
+    // Target and Agent positions
+    sensor.AddObservation(Target.localPosition);
+    sensor.AddObservation(this.transform.localPosition);
+
+    // Agent velocity
+    sensor.AddObservation(rBody.velocity.x);
+    sensor.AddObservation(rBody.velocity.z);
+}
+```
+
+### Taking Actions and Assigning Rewards
+
+The final part of the Agent code is the `Agent.OnActionReceived()` method, which
+receives actions and assigns the reward.
+
+#### Actions
+
+To solve the task of moving towards the target, the Agent (Sphere) needs to be
+able to move in the `x` and `z` directions. As such, the agent needs 2 actions:
+the first determines the force applied along the x-axis; and the
+second determines the force applied along the z-axis. (If we allowed the Agent
+to move in three dimensions, then we would need a third action.)
+
+The RollerAgent applies the values from the `action[]` array to its Rigidbody
+component `rBody`, using `Rigidbody.AddForce()`:
+
+```csharp
+Vector3 controlSignal = Vector3.zero;
+controlSignal.x = action[0];
+controlSignal.z = action[1];
+rBody.AddForce(controlSignal * forceMultiplier);
+```
+
+#### Rewards
+
+Reinforcement learning requires rewards to signal which decisions are good and
+which are bad. The learning algorithm uses the rewards to determine whether it
+is giving the Agent the optimal actions. You want to reward an Agent for
+completing the assigned task. In this case, the Agent is given a reward of 1.0
+for reaching the Target cube.
+
+Rewards are assigned in `OnActionReceived()`. The RollerAgent
+calculates the distance to detect when it reaches the target.
+When it does, the code calls `Agent.SetReward()` to assign a reward
+of 1.0 and marks the agent as finished by calling `EndEpisode()` on
+the Agent.
+
+```csharp
+float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition);
+// Reached target
+if (distanceToTarget < 1.42f)
+{
+    SetReward(1.0f);
+    EndEpisode();
+}
+```
+
+Finally, if the Agent falls off the platform, end the episode so that it can
+reset itself:
+
+```csharp
+// Fell off platform
+if (this.transform.localPosition.y < 0)
+{
+    EndEpisode();
+}
+```
+
+#### OnActionReceived()
+
+With the action and reward logic outlined above, the final version of
+`OnActionReceived()` looks like:
+
+```csharp
+public float forceMultiplier = 10;
+public override void OnActionReceived(ActionBuffers actionBuffers)
+{
+    // Actions, size = 2
+    Vector3 controlSignal = Vector3.zero;
+    controlSignal.x = actionBuffers.ContinuousActions[0];
+    controlSignal.z = actionBuffers.ContinuousActions[1];
+    rBody.AddForce(controlSignal * forceMultiplier);
+
+    // Rewards
+    float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition);
+
+    // Reached target
+    if (distanceToTarget < 1.42f)
+    {
+        SetReward(1.0f);
+        EndEpisode();
+    }
+
+    // Fell off platform
+    else if (this.transform.localPosition.y < 0)
+    {
+        EndEpisode();
+    }
+}
+```
+
+Note the `forceMultiplier` class variable is defined before the method definition.
+Since `forceMultiplier` is public, you can set the value from the Inspector window.
+
+## Final Agent Setup in Editor
+
+Now that all the GameObjects and ML-Agent components are in place, it is time
+to connect everything together in the Unity Editor. This involves adding and
+setting some of the Agent Component's properties so that they are compatible
+with our Agent script.
+
+1. Select the **RollerAgent** GameObject to show its properties in the Inspector
+   window.
+1. Drag the Target GameObject in the Hierarchy into the `Target` field in RollerAgent Script.
+1. Add a `Decision Requester` script with the **Add Component** button.
+   Set the **Decision Period** to `10`. For more information on decisions,
+   see [the Agent documentation](Learning-Environment-Design-Agents.md#decisions)
+1. Add a `Behavior Parameters` script with the **Add Component** button.
+   Set the Behavior Parameters of the Agent to the following:
+   - `Behavior Name`: _RollerBall_
+   - `Vector Observation` > `Space Size` = 8
+   - `Actions` > `Continuous Actions` = 2
+
+In the inspector, the `RollerAgent` should look like this now:
+<p align="left">
+  <img src="images/roller-ball-agent.png"
+       alt="The Agent GameObject in the Inspector window"
+       width="400" border="5" />
+</p>
+
+Now you are ready to test the environment before training.
+
+## Testing the Environment
+
+It is always a good idea to first test your environment by controlling the Agent
+using the keyboard. To do so, you will need to extend the `Heuristic()` method
+in the `RollerAgent` class. For our example, the heuristic will generate an
+action corresponding to the values of the "Horizontal" and "Vertical" input axis
+(which correspond to the keyboard arrow keys):
+
+```csharp
+public override void Heuristic(in ActionBuffers actionsOut)
+{
+    var continuousActionsOut = actionsOut.ContinuousActions;
+    continuousActionsOut[0] = Input.GetAxis("Horizontal");
+    continuousActionsOut[1] = Input.GetAxis("Vertical");
+}
+```
+
+In order for the Agent to use the Heuristic, You will need to set the
+`Behavior Type` to `Heuristic Only` in the `Behavior Parameters` of the
+RollerAgent.
+
+Press **Play** to run the scene and use the arrows keys to move the Agent around
+the platform. Make sure that there are no errors displayed in the Unity Editor
+Console window and that the Agent resets when it reaches its target or falls
+from the platform.
+
+## Training the Environment
+
+The process is the same as described in the
+[Getting Started Guide](Getting-Started.md).
+
+The hyperparameters for training are specified in a configuration file that you
+pass to the `mlagents-learn` program. Create a new `rollerball_config.yaml` file
+under `config/` and include the following hyperparameter values:
+
+```yml
+behaviors:
+  RollerBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 10
+      buffer_size: 100
+      learning_rate: 3.0e-4
+      beta: 5.0e-4
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+      beta_schedule: constant
+      epsilon_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    max_steps: 500000
+    time_horizon: 64
+    summary_freq: 10000
+```
+
+Hyperparameters are explained in [the training configuration file documentation](Training-Configuration-File.md)
+
+Since this example creates a very simple training environment with only a few
+inputs and outputs, using small batch and buffer sizes speeds up the training
+considerably. However, if you add more complexity to the environment or change
+the reward or observation functions, you might also find that training performs
+better with different hyperparameter values. In addition to setting these
+hyperparameter values, the Agent **DecisionFrequency** parameter has a large
+effect on training time and success. A larger value reduces the number of
+decisions the training algorithm has to consider and, in this simple
+environment, speeds up training.
+
+To train your agent, run the following command before pressing **Play** in the
+Editor:
+
+    mlagents-learn config/rollerball_config.yaml --run-id=RollerBall
+
+To monitor the statistics of Agent performance during training, use
+[TensorBoard](Using-Tensorboard.md).
+
+![TensorBoard statistics display](images/mlagents-RollerAgentStats.png)
+
+In particular, the _cumulative_reward_ and _value_estimate_ statistics show how
+well the Agent is achieving the task. In this example, the maximum reward an
+Agent can earn is 1.0, so these statistics approach that value when the Agent
+has successfully _solved_ the problem.
+
+## Optional: Multiple Training Areas within the Same Scene
+
+In many of the [example environments](Learning-Environment-Examples.md), many
+copies of the training area are instantiated in the scene. This generally speeds
+up training, allowing the environment to gather many experiences in parallel.
+This can be achieved simply by instantiating many Agents with the same
+`Behavior Name`. Note that we've already simplified our transition to using
+multiple areas by creating the `TrainingArea` GameObject and relying on local
+positions in `RollerAgent.cs`. Use the following steps to parallelize your
+RollerBall environment:
+
+1. Drag the TrainingArea GameObject, along with its attached GameObjects, into
+   your Assets browser, turning it into a prefab.
+1. You can now instantiate copies of the TrainingArea prefab. Drag them into
+   your scene, positioning them so that they do not overlap.
+
+Alternatively, you can use the `TrainingAreaReplicator` to replicate training areas. Use the following steps:
+
+1. Create a new empty Game Object in the scene.
+2. Click on the new object and add a TrainingAreaReplicator component to the empty Game Object through the inspector.
+3. Drag the training area to `Base Area` in the Training Area Replicator.
+4. Specify the number of areas to replicate and the separation between areas.
+5. Hit play and the areas will be replicated automatically!
+
+## Optional: Training Using Concurrent Unity Instances
+Another level of parallelization comes by training using
+[concurrent Unity instances](ML-Agents-Overview.md#additional-features).
+For example,
+
+```
+mlagents-learn config/rollerball_config.yaml --run-id=RollerBall --num-envs=2
+```
+
+will start ML Agents training with two environment instances. Combining multiple
+training areas within the same scene, with concurrent Unity instances, effectively
+gives you two levels of parallelism to speed up training. The command line option
+`--num-envs=<n>` controls the number of concurrent Unity instances that are
+executed in parallel during training.
diff --git a/docs/Learning-Environment-Design-Agents.md b/docs/Learning-Environment-Design-Agents.md
new file mode 100644
index 0000000000000000000000000000000000000000..b9a65a88bddfdd7d1acd6c81ddc99872a4ffcb03
--- /dev/null
+++ b/docs/Learning-Environment-Design-Agents.md
@@ -0,0 +1,1181 @@
+# Agents
+
+**Table of Contents:**
+
+- [Decisions](#decisions)
+- [Observations and Sensors](#observations-and-sensors)
+  - [Generating Observations](#generating-observations)
+    - [Agent.CollectObservations()](#agentcollectobservations)
+    - [Observable Fields and Properties](#observable-fields-and-properties)
+    - [ISensor interface and SensorComponents](#isensor-interface-and-sensorcomponents)
+  - [Vector Observations](#vector-observations)
+    - [One-hot encoding categorical information](#one-hot-encoding-categorical-information)
+    - [Normalization](#normalization)
+    - [Stacking](#stacking)
+    - [Vector Observation Summary & Best Practices](#vector-observation-summary--best-practices)
+  - [Visual Observations](#visual-observations)
+    - [Visual Observation Summary & Best Practices](#visual-observation-summary--best-practices)
+  - [Raycast Observations](#raycast-observations)
+    - [RayCast Observation Summary & Best Practices](#raycast-observation-summary--best-practices)
+  - [Variable Length Observations](#variable-length-observations)
+    - [Variable Length Observation Summary & Best Practices](#variable-length-observation-summary--best-practices)
+  - [Goal Signal](#goal-signal)
+    - [Goal Signal Summary & Best Practices](#goal-signal-summary--best-practices)
+- [Actions and Actuators](#actions-and-actuators)
+  - [Continuous Actions](#continuous-actions)
+  - [Discrete Actions](#discrete-actions)
+    - [Masking Discrete Actions](#masking-discrete-actions)
+  - [Actions Summary & Best Practices](#actions-summary--best-practices)
+- [Rewards](#rewards)
+  - [Examples](#examples)
+  - [Rewards Summary & Best Practices](#rewards-summary--best-practices)
+- [Agent Properties](#agent-properties)
+- [Destroying an Agent](#destroying-an-agent)
+- [Defining Multi-agent Scenarios](#defining-multi-agent-scenarios)
+  - [Teams for Adversarial Scenarios](#teams-for-adversarial-scenarios)
+  - [Groups for Cooperative Scenarios](#groups-for-cooperative-scenarios)
+- [Recording Demonstrations](#recording-demonstrations)
+
+An agent is an entity that can observe its environment, decide on the best
+course of action using those observations, and execute those actions within its
+environment. Agents can be created in Unity by extending the `Agent` class. The
+most important aspects of creating agents that can successfully learn are the
+observations the agent collects, and the reward you assign to estimate the value
+of the agent's current state toward accomplishing its tasks.
+
+An Agent passes its observations to its Policy. The Policy then makes a decision
+and passes the chosen action back to the agent. Your agent code must execute the
+action, for example, move the agent in one direction or another. In order to
+[train an agent using reinforcement learning](Learning-Environment-Design.md),
+your agent must calculate a reward value at each action. The reward is used to
+discover the optimal decision-making policy.
+
+The `Policy` class abstracts out the decision making logic from the Agent itself
+so that you can use the same Policy in multiple Agents. How a Policy makes its
+decisions depends on the `Behavior Parameters` associated with the agent. If you
+set `Behavior Type` to `Heuristic Only`, the Agent will use its `Heuristic()`
+method to make decisions which can allow you to control the Agent manually or
+write your own Policy. If the Agent has a `Model` file, its Policy will use the
+neural network `Model` to take decisions.
+
+When you create an Agent, you should usually extend the base Agent class. This
+includes implementing the following methods:
+
+- `Agent.OnEpisodeBegin()` — Called at the beginning of an Agent's episode,
+  including at the beginning of the simulation.
+- `Agent.CollectObservations(VectorSensor sensor)` — Called every step that the Agent
+  requests a decision. This is one possible way for collecting the Agent's
+  observations of the environment; see [Generating Observations](#generating-observations)
+  below for more options.
+- `Agent.OnActionReceived()` — Called every time the Agent receives an action to
+  take. Receives the action chosen by the Agent. It is also common to assign a
+  reward in this method.
+- `Agent.Heuristic()` - When the `Behavior Type` is set to `Heuristic Only` in
+  the Behavior Parameters of the Agent, the Agent will use the `Heuristic()`
+  method to generate the actions of the Agent. As such, the `Heuristic()` method
+  writes to the array of floats provided to the Heuristic method as argument.
+  __Note__: Do not create a new float array of action in the `Heuristic()` method,
+  as this will prevent writing floats to the original action array.
+
+As a concrete example, here is how the Ball3DAgent class implements these methods:
+
+- `Agent.OnEpisodeBegin()` — Resets the agent cube and ball to their starting
+  positions. The function randomizes the reset values so that the training
+  generalizes to more than a specific starting position and agent cube
+  orientation.
+- `Agent.CollectObservations(VectorSensor sensor)` — Adds information about the
+  orientation of the agent cube, the ball velocity, and the relative position
+  between the ball and the cube. Since the  `CollectObservations()`
+  method calls `VectorSensor.AddObservation()` such that vector size adds up to 8,
+  the Behavior Parameters of the Agent are set with vector observation space
+  with a state size of 8.
+- `Agent.OnActionReceived()` — The action results
+  in a small change in the agent cube's rotation at each step. In this example,
+  an Agent receives a small positive reward for each step it keeps the ball on the
+  agent cube's head and a larger, negative reward for dropping the ball. An
+  Agent's episode is also ended when it drops the ball so that it will reset
+  with a new ball for the next simulation step.
+- `Agent.Heuristic()` - Converts the keyboard inputs into actions.
+
+## Decisions
+
+The observation-decision-action-reward cycle repeats each time the Agent request
+a decision. Agents will request a decision when `Agent.RequestDecision()` is
+called. If you need the Agent to request decisions on its own at regular
+intervals, add a `Decision Requester` component to the Agent's GameObject.
+Making decisions at regular step intervals is generally most appropriate for
+physics-based simulations. For example, an agent in a robotic simulator that
+must provide fine-control of joint torques should make its decisions every step
+of the simulation. In games such as real-time strategy, where many agents make
+their decisions at regular intervals, the decision timing for each agent can be
+staggered by setting the `DecisionStep` parameter in the `Decision Requester`
+component for each agent. On the other hand, an agent that only needs to make
+decisions when certain game or simulation events occur, such as in a turn-based
+game, should call `Agent.RequestDecision()` manually.
+
+## Observations and Sensors
+In order for an agent to learn, the observations should include all the
+information an agent needs to accomplish its task. Without sufficient and
+relevant information, an agent may learn poorly or may not learn at all. A
+reasonable approach for determining what information should be included is to
+consider what you would need to calculate an analytical solution to the problem,
+or what you would expect a human to be able to use to solve the problem.
+
+### Generating Observations
+ML-Agents provides multiple ways for an Agent to make observations:
+  1. Overriding the `Agent.CollectObservations()` method and passing the
+    observations to the provided `VectorSensor`.
+  1. Adding the `[Observable]` attribute to fields and properties on the Agent.
+  1. Implementing the `ISensor` interface, using a `SensorComponent` attached to
+  the Agent to create the `ISensor`.
+
+#### Agent.CollectObservations()
+Agent.CollectObservations() is best used for aspects of the environment which are
+numerical and non-visual. The Policy class calls the
+`CollectObservations(VectorSensor sensor)` method of each Agent. Your
+implementation of this function must call `VectorSensor.AddObservation` to add
+vector observations.
+
+The `VectorSensor.AddObservation` method provides a number of overloads for
+adding common types of data to your observation vector. You can add Integers and
+booleans directly to the observation vector, as well as some common Unity data
+types such as `Vector2`, `Vector3`, and `Quaternion`.
+
+For examples of various state observation functions, you can look at the
+[example environments](Learning-Environment-Examples.md) included in the
+ML-Agents SDK. For instance, the 3DBall example uses the rotation of the
+platform, the relative position of the ball, and the velocity of the ball as its
+state observation.
+
+```csharp
+public GameObject ball;
+
+public override void CollectObservations(VectorSensor sensor)
+{
+    // Orientation of the cube (2 floats)
+    sensor.AddObservation(gameObject.transform.rotation.z);
+    sensor.AddObservation(gameObject.transform.rotation.x);
+    // Relative position of the ball to the cube (3 floats)
+    sensor.AddObservation(ball.transform.position - gameObject.transform.position);
+    // Velocity of the ball (3 floats)
+    sensor.AddObservation(m_BallRb.velocity);
+    // 8 floats total
+}
+```
+
+As an experiment, you can remove the velocity components from
+the observation and retrain the 3DBall agent. While it will learn to balance the
+ball reasonably well, the performance of the agent without using velocity is
+noticeably worse.
+
+The observations passed to `VectorSensor.AddObservation()` must always contain
+the same number of elements must always be in the same order. If the number
+of observed entities in an environment can vary, you can pad the calls
+with zeros for any missing entities in a specific observation, or you can limit
+an agent's observations to a fixed subset. For example, instead of observing
+every enemy in an environment, you could only observe the closest five.
+
+Additionally, when you set up an Agent's `Behavior Parameters` in the Unity
+Editor, you must set the **Vector Observations > Space Size**
+to equal the number of floats that are written by `CollectObservations()`.
+
+#### Observable Fields and Properties
+Another approach is to define the relevant observations as fields or properties
+on your Agent class, and annotate them with an `ObservableAttribute`. For
+example, in the Ball3DHardAgent, the difference between positions could be observed
+by adding a property to the Agent:
+```csharp
+using Unity.MLAgents.Sensors.Reflection;
+
+public class Ball3DHardAgent : Agent {
+
+    [Observable(numStackedObservations: 9)]
+    Vector3 PositionDelta
+    {
+        get
+        {
+            return ball.transform.position - gameObject.transform.position;
+        }
+    }
+}
+```
+`ObservableAttribute` currently supports most basic types (e.g. floats, ints,
+bools), as well as `Vector2`, `Vector3`, `Vector4`, `Quaternion`, and enums.
+
+The behavior of `ObservableAttribute`s are controlled by the "Observable Attribute
+Handling" in the Agent's `Behavior Parameters`. The possible values for this are:
+ * **Ignore** (default) - All ObservableAttributes on the Agent will be ignored.
+  If there are no ObservableAttributes on the Agent, this will result in the
+  fastest  initialization time.
+ * **Exclude Inherited** - Only members on the declared class will be examined;
+  members that are inherited are ignored. This is a reasonable tradeoff between
+  performance and flexibility.
+ * **Examine All** All members on the class will be examined. This can lead to
+  slower startup times.
+
+"Exclude Inherited" is generally sufficient, but if your Agent inherits from
+another Agent implementation that has Observable members, you will need to use
+"Examine All".
+
+Internally, ObservableAttribute uses reflection to determine which members of
+the Agent have ObservableAttributes, and also uses reflection to access the
+fields or invoke the properties at runtime. This may be slower than using
+CollectObservations or an ISensor, although this might not be enough to
+noticeably affect performance.
+
+**NOTE**: you do not need to adjust the Space Size in the Agent's
+`Behavior Parameters` when you add `[Observable]` fields or properties to an
+Agent, since their size can be computed before they are used.
+
+#### ISensor interface and SensorComponents
+The `ISensor` interface is generally intended for advanced users. The `Write()`
+method is used to actually generate the observation, but some other methods
+such as returning the shape of the observations must also be implemented.
+
+The `SensorComponent` abstract class is used to create the actual `ISensor` at
+runtime. It must be attached to the same `GameObject` as the `Agent`, or to a
+child `GameObject`.
+
+There are several SensorComponents provided in the API, including:
+- `CameraSensorComponent` - Uses images from a `Camera` as observations.
+- `RenderTextureSensorComponent` - Uses the content of a `RenderTexture` as
+observations.
+- `RayPerceptionSensorComponent` - Uses the information from set of ray casts
+as observations.
+- `Match3SensorComponent` - Uses the board of a [Match-3 game](Integrations-Match3.md)
+as observations.
+- `GridSensorComponent` - Uses a set of box queries in a grid shape as
+observations.
+
+**NOTE**: you do not need to adjust the Space Size in the Agent's
+`Behavior Parameters` when using `SensorComponents`s.
+
+Internally, both `Agent.CollectObservations` and `[Observable]` attribute use an
+ISensors to write observations, although this is mostly abstracted from the user.
+
+### Vector Observations
+Both `Agent.CollectObservations()` and `ObservableAttribute`s produce vector
+observations, which are represented at lists of `float`s. `ISensor`s can
+produce both vector observations and visual observations, which are
+multi-dimensional arrays of floats.
+
+Below are some additional considerations when dealing with vector observations:
+
+#### One-hot encoding categorical information
+
+Type enumerations should be encoded in the _one-hot_ style. That is, add an
+element to the feature vector for each element of enumeration, setting the
+element representing the observed member to one and set the rest to zero. For
+example, if your enumeration contains \[Sword, Shield, Bow\] and the agent
+observes that the current item is a Bow, you would add the elements: 0, 0, 1 to
+the feature vector. The following code example illustrates how to add.
+
+```csharp
+enum ItemType { Sword, Shield, Bow, LastItem }
+public override void CollectObservations(VectorSensor sensor)
+{
+    for (int ci = 0; ci < (int)ItemType.LastItem; ci++)
+    {
+        sensor.AddObservation((int)currentItem == ci ? 1.0f : 0.0f);
+    }
+}
+```
+
+`VectorSensor` also provides a two-argument function `AddOneHotObservation()` as
+a shortcut for _one-hot_ style observations. The following example is identical
+to the previous one.
+
+```csharp
+enum ItemType { Sword, Shield, Bow, LastItem }
+const int NUM_ITEM_TYPES = (int)ItemType.LastItem + 1;
+
+public override void CollectObservations(VectorSensor sensor)
+{
+    // The first argument is the selection index; the second is the
+    // number of possibilities
+    sensor.AddOneHotObservation((int)currentItem, NUM_ITEM_TYPES);
+}
+```
+
+`ObservableAttribute` has built-in support for enums. Note that you don't need
+the `LastItem` placeholder in this case:
+```csharp
+enum ItemType { Sword, Shield, Bow }
+
+public class HeroAgent : Agent
+{
+    [Observable]
+    ItemType m_CurrentItem;
+}
+```
+
+#### Normalization
+
+For the best results when training, you should normalize the components of your
+feature vector to the range [-1, +1] or [0, 1]. When you normalize the values,
+the PPO neural network can often converge to a solution faster. Note that it
+isn't always necessary to normalize to these recommended ranges, but it is
+considered a best practice when using neural networks. The greater the variation
+in ranges between the components of your observation, the more likely that
+training will be affected.
+
+To normalize a value to [0, 1], you can use the following formula:
+
+```csharp
+normalizedValue = (currentValue - minValue)/(maxValue - minValue)
+```
+
+:warning: For vectors, you should apply the above formula to each component (x,
+y, and z). Note that this is _not_ the same as using the `Vector3.normalized`
+property or `Vector3.Normalize()` method in Unity (and similar for `Vector2`).
+
+Rotations and angles should also be normalized. For angles between 0 and 360
+degrees, you can use the following formulas:
+
+```csharp
+Quaternion rotation = transform.rotation;
+Vector3 normalized = rotation.eulerAngles / 180.0f - Vector3.one;  // [-1,1]
+Vector3 normalized = rotation.eulerAngles / 360.0f;  // [0,1]
+```
+
+For angles that can be outside the range [0,360], you can either reduce the
+angle, or, if the number of turns is significant, increase the maximum value
+used in your normalization formula.
+
+#### Stacking
+Stacking refers to repeating observations from previous steps as part of a
+larger observation. For example, consider an Agent that generates these
+observations in four steps
+```
+step 1: [0.1]
+step 2: [0.2]
+step 3: [0.3]
+step 4: [0.4]
+```
+
+If we use a stack size of 3, the observations would instead be:
+```csharp
+step 1: [0.1, 0.0, 0.0]
+step 2: [0.2, 0.1, 0.0]
+step 3: [0.3, 0.2, 0.1]
+step 4: [0.4, 0.3, 0.2]
+```
+(The observations are padded with zeroes for the first `stackSize-1` steps).
+This is a simple way to give an Agent limited "memory" without the complexity
+of adding a recurrent neural network (RNN).
+
+The steps for enabling stacking depends on how you generate observations:
+* For Agent.CollectObservations(), set "Stacked Vectors" on the Agent's
+  `Behavior Parameters` to a value greater than 1.
+* For ObservableAttribute, set the `numStackedObservations` parameter in the
+  constructor, e.g. `[Observable(numStackedObservations: 2)]`.
+* For `ISensor`s, wrap them in a `StackingSensor` (which is also an `ISensor`).
+  Generally, this should happen in the `CreateSensor()` method of your
+  `SensorComponent`.
+
+#### Vector Observation Summary & Best Practices
+
+- Vector Observations should include all variables relevant for allowing the
+  agent to take the optimally informed decision, and ideally no extraneous
+  information.
+- In cases where Vector Observations need to be remembered or compared over
+  time, either an RNN should be used in the model, or the `Stacked Vectors`
+  value in the agent GameObject's `Behavior Parameters` should be changed.
+- Categorical variables such as type of object (Sword, Shield, Bow) should be
+  encoded in one-hot fashion (i.e. `3` -> `0, 0, 1`). This can be done
+  automatically using the `AddOneHotObservation()` method of the `VectorSensor`,
+  or using `[Observable]` on an enum field or property of the Agent.
+- In general, all inputs should be normalized to be in the range 0 to +1 (or -1
+  to 1). For example, the `x` position information of an agent where the maximum
+  possible value is `maxValue` should be recorded as
+  `VectorSensor.AddObservation(transform.position.x / maxValue);` rather than
+  `VectorSensor.AddObservation(transform.position.x);`.
+- Positional information of relevant GameObjects should be encoded in relative
+  coordinates wherever possible. This is often relative to the agent position.
+
+### Visual Observations
+
+Visual observations are generally provided to agent via either a `CameraSensor`
+or `RenderTextureSensor`. These collect image information and transforms it into
+a 3D Tensor which can be fed into the convolutional neural network (CNN) of the
+agent policy. For more information on CNNs, see
+[this guide](http://cs231n.github.io/convolutional-networks/). This allows
+agents to learn from spatial regularities in the observation images. It is
+possible to use visual and vector observations with the same agent.
+
+Agents using visual observations can capture state of arbitrary complexity and
+are useful when the state is difficult to describe numerically. However, they
+are also typically less efficient and slower to train, and sometimes don't
+succeed at all as compared to vector observations. As such, they should only be
+used when it is not possible to properly define the problem using vector or
+ray-cast observations.
+
+Visual observations can be derived from Cameras or RenderTextures within your
+scene. To add a visual observation to an Agent, add either a Camera Sensor
+Component or RenderTextures Sensor Component to the Agent. Then drag the camera
+or render texture you want to add to the `Camera` or `RenderTexture` field. You
+can have more than one camera or render texture and even use a combination of
+both attached to an Agent. For each visual observation, set the width and height
+of the image (in pixels) and whether or not the observation is color or
+grayscale.
+
+![Agent Camera](images/visual-observation.png)
+
+or
+
+![Agent RenderTexture](images/visual-observation-rendertexture.png)
+
+Each Agent that uses the same Policy must have the same number of visual
+observations, and they must all have the same resolutions (including whether or
+not they are grayscale). Additionally, each Sensor Component on an Agent must
+have a unique name so that they can be sorted deterministically (the name must
+be unique for that Agent, but multiple Agents can have a Sensor Component with
+the same name).
+
+Visual observations also support stacking, by specifying `Observation Stacks`
+to a value greater than 1. The visual observations from the last `stackSize`
+steps will be stacked on the last dimension (channel dimension).
+
+When using `RenderTexture` visual observations, a handy feature for debugging is
+adding a `Canvas`, then adding a `Raw Image` with it's texture set to the
+Agent's `RenderTexture`. This will render the agent observation on the game
+screen.
+
+![RenderTexture with Raw Image](images/visual-observation-rawimage.png)
+
+The [GridWorld environment](Learning-Environment-Examples.md#gridworld) is an
+example on how to use a RenderTexture for both debugging and observation. Note
+that in this example, a Camera is rendered to a RenderTexture, which is then
+used for observations and debugging. To update the RenderTexture, the Camera
+must be asked to render every time a decision is requested within the game code.
+When using Cameras as observations directly, this is done automatically by the
+Agent.
+
+![Agent RenderTexture Debug](images/gridworld.png)
+
+#### Visual Observation Summary & Best Practices
+
+- To collect visual observations, attach `CameraSensor` or `RenderTextureSensor`
+  components to the agent GameObject.
+- Visual observations should generally only be used when vector observations are
+  not sufficient.
+- Image size should be kept as small as possible, without the loss of needed
+  details for decision making.
+- Images should be made grayscale in situations where color information is not
+  needed for making informed decisions.
+
+### Raycast Observations
+
+Raycasts are another possible method for providing observations to an agent.
+This can be easily implemented by adding a `RayPerceptionSensorComponent3D` (or
+`RayPerceptionSensorComponent2D`) to the Agent GameObject.
+
+During observations, several rays (or spheres, depending on settings) are cast
+into the physics world, and the objects that are hit determine the observation
+vector that is produced.
+
+![Agent with two RayPerceptionSensorComponent3Ds](images/ray_perception.png)
+
+Both sensor components have several settings:
+
+- _Detectable Tags_ A list of strings corresponding to the types of objects that
+  the Agent should be able to distinguish between. For example, in the WallJump
+  example, we use "wall", "goal", and "block" as the list of objects to detect.
+- _Rays Per Direction_ Determines the number of rays that are cast. One ray is
+  always cast forward, and this many rays are cast to the left and right.
+- _Max Ray Degrees_ The angle (in degrees) for the outermost rays. 90 degrees
+  corresponds to the left and right of the agent.
+- _Sphere Cast Radius_ The size of the sphere used for sphere casting. If set to
+  0, rays will be used instead of spheres. Rays may be more efficient,
+  especially in complex scenes.
+- _Ray Length_ The length of the casts
+- _Ray Layer Mask_ The [LayerMask](https://docs.unity3d.com/ScriptReference/LayerMask.html)
+  passed to the raycast or spherecast. This can be used to ignore certain types
+  of objects when casting.
+- _Observation Stacks_ The number of previous results to "stack" with the cast
+  results. Note that this can be independent of the "Stacked Vectors" setting in
+  `Behavior Parameters`.
+- _Start Vertical Offset_ (3D only) The vertical offset of the ray start point.
+- _End Vertical Offset_ (3D only) The vertical offset of the ray end point.
+- _Alternating Ray Order_ Alternating is the default, it gives an order of (0,
+  -delta, delta, -2*delta, 2*delta, ..., -n*delta, n*delta). If alternating is
+  disabled the order is left to right (-n*delta, -(n-1)*delta, ..., -delta, 0,
+  delta, ..., (n-1)*delta, n*delta). For general usage there is no difference
+  but if using custom models the left-to-right layout that matches the spatial
+  structuring can be preferred (e.g. for processing with conv nets).
+- _Use Batched Raycasts_ (3D only) Whether to use batched raycasts. Enable to use batched raycasts and the jobs system.
+
+In the example image above, the Agent has two `RayPerceptionSensorComponent3D`s.
+Both use 3 Rays Per Direction and 90 Max Ray Degrees. One of the components had
+a vertical offset, so the Agent can tell whether it's clear to jump over the
+wall.
+
+The total size of the created observations is
+
+```
+(Observation Stacks) * (1 + 2 * Rays Per Direction) * (Num Detectable Tags + 2)
+```
+
+so the number of rays and tags should be kept as small as possible to reduce the
+amount of data used. Note that this is separate from the State Size defined in
+`Behavior Parameters`, so you don't need to worry about the formula above when
+setting the State Size.
+
+#### RayCast Observation Summary & Best Practices
+
+- Attach `RayPerceptionSensorComponent3D` or `RayPerceptionSensorComponent2D` to
+  use.
+- This observation type is best used when there is relevant spatial information
+  for the agent that doesn't require a fully rendered image to convey.
+- Use as few rays and tags as necessary to solve the problem in order to improve
+  learning stability and agent performance.
+- If you run into performance issues, try using batched raycasts by enabling the _Use Batched Raycast_ setting.
+  (Only available for 3D ray perception sensors.)
+
+### Grid Observations
+Grid-base observations combine the advantages of 2D spatial representation in
+visual observations, and the flexibility of defining detectable objects in
+RayCast observations. The sensor uses a set of box queries in a grid shape and
+gives a top-down 2D view around the agent. This can be implemented by adding a
+`GridSensorComponent` to the Agent GameObject.
+
+During observations, the sensor detects the presence of detectable objects in
+each cell and encode that into one-hot representation. The collected information
+from each cell forms a 3D tensor observation and will be fed into the
+convolutional neural network (CNN) of the agent policy just like visual
+observations.
+
+![Agent with GridSensorComponent](images/grid_sensor.png)
+
+The sensor component has the following settings:
+- _Cell Scale_ The scale of each cell in the grid.
+- _Grid Size_ Number of cells on each side of the grid.
+- _Agent Game Object_ The Agent that holds the grid sensor. This is used to
+  disambiguate objects with the same tag as the agent so that the agent doesn't
+  detect itself.
+- _Rotate With Agent_ Whether the grid rotates with the Agent.
+- _Detectable Tags_ A list of strings corresponding to the types of objects that
+  the Agent should be able to distinguish between.
+- _Collider Mask_ The [LayerMask](https://docs.unity3d.com/ScriptReference/LayerMask.html)
+  passed to the collider detection. This can be used to ignore certain types
+  of objects.
+- _Initial Collider Buffer Size_ The initial size of the Collider buffer used
+  in the non-allocating Physics calls for each cell.
+- _Max Collider Buffer Size_ The max size of the Collider buffer used in the
+  non-allocating Physics calls for each cell.
+
+The observation for each grid cell is a one-hot encoding of the detected object.
+The total size of the created observations is
+
+```
+GridSize.x * GridSize.z * Num Detectable Tags
+```
+
+so the number of detectable tags and size of the grid should be kept as small as
+possible to reduce the amount of data used. This makes a trade-off between the
+granularity of the observation and training speed.
+
+To allow more variety of observations that grid sensor can capture, the
+`GridSensorComponent` and the underlying `GridSensorBase` also provides interfaces
+that can be overridden to collect customized observation from detected objects.
+See the doc on
+[extending grid Sensors](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/com.unity.ml-agents.extensions/Documentation~/CustomGridSensors.md)
+for more details on custom grid sensors.
+
+__Note__: The `GridSensor` only works in 3D environments and will not behave
+properly in 2D environments.
+
+#### Grid Observation Summary & Best Practices
+
+- Attach `GridSensorComponent` to use.
+- This observation type is best used when there is relevant non-visual spatial information that
+  can be best captured in 2D representations.
+- Use as small grid size and as few tags as necessary to solve the problem in order to improve
+  learning stability and agent performance.
+- Do not use `GridSensor` in a 2D game.
+
+### Variable Length Observations
+
+It is possible for agents to collect observations from a varying number of
+GameObjects by using a `BufferSensor`.
+You can add a `BufferSensor` to your Agent by adding a `BufferSensorComponent` to
+its GameObject.
+The `BufferSensor` can be useful in situations in which the Agent must pay
+attention to a varying number of entities (for example, a varying number of
+enemies or projectiles).
+On the trainer side, the `BufferSensor`
+is processed using an attention module. More information about attention
+mechanisms can be found [here](https://arxiv.org/abs/1706.03762). Training or
+doing inference with variable length observations can be slower than using
+a flat vector observation. However, attention mechanisms enable solving
+problems that require comparative reasoning between entities in a scene
+such as our [Sorter environment](Learning-Environment-Examples.md#sorter).
+Note that even though the `BufferSensor` can process a variable number of
+entities, you still need to define a maximum number of entities. This is
+because our network architecture requires to know what the shape of the
+observations will be. If fewer entities are observed than the maximum, the
+observation will be padded with zeros and the trainer will ignore
+the padded observations. Note that attention layers are invariant to
+the order of the entities, so there is no need to properly "order" the
+entities before feeding them into the `BufferSensor`.
+
+The `BufferSensorComponent` Editor inspector has two arguments:
+ - `Observation Size` : This is how many floats each entities will be
+ represented with. This number is fixed and all entities must
+ have the same representation. For example, if the entities you want to
+ put into the `BufferSensor` have for relevant information position and
+ speed, then the `Observation Size` should be 6 floats.
+ - `Maximum Number of Entities` : This is the maximum number of entities
+ the `BufferSensor` will be able to collect.
+
+To add an entity's observations to a `BufferSensorComponent`, you need
+to call `BufferSensorComponent.AppendObservation()` in the
+Agent.CollectObservations() method
+with a float array of size `Observation Size` as argument.
+
+__Note__: Currently, the observations put into the `BufferSensor` are
+not normalized, you will need to normalize your observations manually
+between -1 and 1.
+
+#### Variable Length Observation Summary & Best Practices
+ - Attach `BufferSensorComponent` to use.
+ - Call `BufferSensorComponent.AppendObservation()` in the
+ Agent.CollectObservations() methodto add the observations
+ of an entity to the `BufferSensor`.
+ - Normalize the entities observations before feeding them into the `BufferSensor`.
+
+### Goal Signal
+
+It is possible for agents to collect observations that will be treated as "goal signal".
+A goal signal is used to condition the policy of the agent, meaning that if the goal
+changes, the policy (i.e. the mapping from observations to actions) will change
+as well. Note that this is true
+for any observation since all observations influence the policy of the Agent to
+some degree. But by specifying a goal signal explicitly, we can make this conditioning
+more important to the agent. This feature can be used in settings where an agent
+must learn to solve different tasks that are similar by some aspects because the
+agent will learn to reuse learnings from different tasks to generalize better.
+In Unity, you can specify that a `VectorSensor` or
+a `CameraSensor` is a goal by attaching a `VectorSensorComponent` or a
+`CameraSensorComponent` to the Agent and selecting `Goal Signal` as `Observation Type`.
+On the trainer side, there are two different ways to condition the policy. This
+setting is determined by the
+[conditioning_type parameter](Training-Configuration-File.md#common-trainer-configurations).
+If set to `hyper` (default) a [HyperNetwork](https://arxiv.org/pdf/1609.09106.pdf)
+will be used to generate some of the
+weights of the policy using the goal observations as input. Note that using a
+HyperNetwork requires a lot of computations, it is recommended to use a smaller
+number of hidden units in the policy to alleviate this.
+If set to `none` the goal signal will be considered as regular observations.
+For an example on how to use a goal signal, see the
+[GridWorld example](Learning-Environment-Examples.md#gridworld).
+
+#### Goal Signal Summary & Best Practices
+ - Attach a `VectorSensorComponent` or `CameraSensorComponent` to an agent and
+ set the observation type to goal to use the feature.
+ - Set the conditioning_type parameter in the training configuration.
+ - Reduce the number of hidden units in the network when using the HyperNetwork
+ conditioning type.
+
+## Actions and Actuators
+
+An action is an instruction from the Policy that the agent carries out. The
+action is passed to the an `IActionReceiver` (either an `Agent` or an `IActuator`)
+as the `ActionBuffers` parameter when the Academy invokes the
+`IActionReciever.OnActionReceived()` function.
+There are two types of actions supported: **Continuous** and **Discrete**.
+
+Neither the Policy nor the training algorithm know anything about what the
+action values themselves mean. The training algorithm simply tries different
+values for the action list and observes the affect on the accumulated rewards
+over time and many training episodes. Thus, the only place actions are defined
+for an Agent is in the `OnActionReceived()` function.
+
+For example, if you designed an agent to move in two dimensions, you could use
+either continuous or the discrete actions. In the continuous case, you
+would set the action size to two (one for each dimension), and the
+agent's Policy would output an action with two floating point values. In the
+discrete case, you would use one Branch with a size of four (one for each
+direction), and the Policy would create an action array containing a single
+element with a value ranging from zero to three. Alternatively, you could create
+two branches of size two (one for horizontal movement and one for vertical
+movement), and the Policy would output an action array containing two elements
+with values ranging from zero to one. You could alternatively use a combination of continuous
+and discrete actions e.g., using one continuous action for horizontal movement
+and a discrete branch of size two for the vertical movement.
+
+Note that when you are programming actions for an agent, it is often helpful to
+test your action logic using the `Heuristic()` method of the Agent, which lets
+you map keyboard commands to actions.
+
+### Continuous Actions
+
+When an Agent's Policy has **Continuous** actions, the
+`ActionBuffers.ContinuousActions` passed to the Agent's `OnActionReceived()` function
+is an array with length equal to the `Continuous Action Size` property value. The
+individual values in the array have whatever meanings that you ascribe to them.
+If you assign an element in the array as the speed of an Agent, for example, the
+training process learns to control the speed of the Agent through this
+parameter.
+
+The [3DBall example](Learning-Environment-Examples.md#3dball-3d-balance-ball) uses
+continuous actions with two control values.
+
+![3DBall](images/balance.png)
+
+These control values are applied as rotation to the cube:
+
+```csharp
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);
+
+        gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
+    }
+```
+
+By default the output from our provided PPO algorithm pre-clamps the values of
+`ActionBuffers.ContinuousActions` into the [-1, 1] range. It is a best practice to manually clip
+these as well, if you plan to use a 3rd party algorithm with your environment.
+As shown above, you can scale the control values as needed after clamping them.
+
+### Discrete Actions
+
+When an Agent's Policy uses **discrete** actions, the
+`ActionBuffers.DiscreteActions` passed to the Agent's `OnActionReceived()` function
+is an array of integers with length equal to `Discrete Branch Size`. When defining the discrete actions, `Branches`
+is an array of integers, each value corresponds to the number of possibilities for each branch.
+
+For example, if we wanted an Agent that can move in a plane and jump, we could
+define two branches (one for motion and one for jumping) because we want our
+agent be able to move **and** jump concurrently. We define the first branch to
+have 5 possible actions (don't move, go left, go right, go backward, go forward)
+and the second one to have 2 possible actions (don't jump, jump). The
+`OnActionReceived()` method would look something like:
+
+```csharp
+// Get the action index for movement
+int movement = actionBuffers.DiscreteActions[0];
+// Get the action index for jumping
+int jump = actionBuffers.DiscreteActions[1];
+
+// Look up the index in the movement action list:
+if (movement == 1) { directionX = -1; }
+if (movement == 2) { directionX = 1; }
+if (movement == 3) { directionZ = -1; }
+if (movement == 4) { directionZ = 1; }
+// Look up the index in the jump action list:
+if (jump == 1 && IsGrounded()) { directionY = 1; }
+
+// Apply the action results to move the Agent
+gameObject.GetComponent<Rigidbody>().AddForce(
+    new Vector3(
+        directionX * 40f, directionY * 300f, directionZ * 40f));
+```
+
+#### Masking Discrete Actions
+
+When using Discrete Actions, it is possible to specify that some actions are
+impossible for the next decision. When the Agent is controlled by a neural
+network, the Agent will be unable to perform the specified action. Note that
+when the Agent is controlled by its Heuristic, the Agent will still be able to
+decide to perform the masked action. In order to disallow an action, override
+the `Agent.WriteDiscreteActionMask()` virtual method, and call
+`SetActionEnabled()` on the provided `IDiscreteActionMask`:
+
+```csharp
+public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+{
+    actionMask.SetActionEnabled(branch, actionIndex, isEnabled);
+}
+```
+
+Where:
+
+- `branch` is the index (starting at 0) of the branch on which you want to
+allow or disallow the action
+- `actionIndex` is the index of the action that you want to allow or disallow.
+- `isEnabled` is a bool indicating whether the action should be allowed or now.
+
+For example, if you have an Agent with 2 branches and on the first branch
+(branch 0) there are 4 possible actions : _"do nothing"_, _"jump"_, _"shoot"_
+and _"change weapon"_. Then with the code bellow, the Agent will either _"do
+nothing"_ or _"change weapon"_ for their next decision (since action index 1 and 2
+are masked)
+
+```csharp
+actionMask.SetActionEnabled(0, 1, false);
+actionMask.SetActionEnabled(0, 2, false);
+```
+
+Notes:
+
+- You can call `SetActionEnabled` multiple times if you want to put masks on multiple
+  branches.
+- At each step, the state of an action is reset and enabled by default.
+- You cannot mask all the actions of a branch.
+- You cannot mask actions in continuous control.
+
+
+### IActuator interface and ActuatorComponents
+The Actuator API allows users to abstract behavior out of Agents and in to
+components (similar to the ISensor API).  The `IActuator` interface and `Agent`
+class both implement the `IActionReceiver` interface to allow for backward compatibility
+with the current `Agent.OnActionReceived`.
+This means you will not have to change your code until you decide to use the `IActuator` API.
+
+Like the `ISensor` interface, the `IActuator` interface is intended for advanced users.
+
+The `ActuatorComponent` abstract class is used to create the actual `IActuator` at
+runtime. It must be attached to the same `GameObject` as the `Agent`, or to a
+child `GameObject`.  Actuators and all of their data structures are initialized
+during `Agent.Initialize`.  This was done to prevent an unexpected allocations at runtime.
+
+You can find an example of an `IActuator` implementation in the `Basic` example scene.
+**NOTE**: you do not need to adjust the Actions in the Agent's
+`Behavior Parameters` when using an `IActuator` and `ActuatorComponents`.
+
+Internally, `Agent.OnActionReceived` uses an `IActuator` to send actions to the Agent,
+although this is mostly abstracted from the user.
+
+
+### Actions Summary & Best Practices
+
+- Agents can use `Discrete` and/or `Continuous` actions.
+- Discrete actions can have multiple action branches, and it's possible to mask
+  certain actions so that they won't be taken.
+- In general, fewer actions will make for easier learning.
+- Be sure to set the Continuous Action Size and Discrete Branch Size to the desired
+  number for each type of action, and not greater, as doing the latter can interfere with the
+  efficiency of the training process.
+- Continuous action values should be clipped to an
+  appropriate range. The provided PPO model automatically clips these values
+  between -1 and 1, but third party training systems may not do so.
+
+## Rewards
+
+In reinforcement learning, the reward is a signal that the agent has done
+something right. The PPO reinforcement learning algorithm works by optimizing
+the choices an agent makes such that the agent earns the highest cumulative
+reward over time. The better your reward mechanism, the better your agent will
+learn.
+
+**Note:** Rewards are not used during inference by an Agent using a trained
+model and is also not used during imitation learning.
+
+Perhaps the best advice is to start simple and only add complexity as needed. In
+general, you should reward results rather than actions you think will lead to
+the desired results. You can even use the Agent's Heuristic to control the Agent
+while watching how it accumulates rewards.
+
+Allocate rewards to an Agent by calling the `AddReward()` or `SetReward()`
+methods on the agent. The reward assigned between each decision should be in the
+range [-1,1]. Values outside this range can lead to unstable training. The
+`reward` value is reset to zero when the agent receives a new decision. If there
+are multiple calls to `AddReward()` for a single agent decision, the rewards
+will be summed together to evaluate how good the previous decision was. The
+`SetReward()` will override all previous rewards given to an agent since the
+previous decision.
+
+### Examples
+
+You can examine the `OnActionReceived()` functions defined in the
+[example environments](Learning-Environment-Examples.md) to see how those
+projects allocate rewards.
+
+The `GridAgent` class in the
+[GridWorld example](Learning-Environment-Examples.md#gridworld) uses a very
+simple reward system:
+
+```csharp
+Collider[] hitObjects = Physics.OverlapBox(trueAgent.transform.position,
+                                           new Vector3(0.3f, 0.3f, 0.3f));
+if (hitObjects.Where(col => col.gameObject.tag == "goal").ToArray().Length == 1)
+{
+    AddReward(1.0f);
+    EndEpisode();
+}
+else if (hitObjects.Where(col => col.gameObject.tag == "pit").ToArray().Length == 1)
+{
+    AddReward(-1f);
+    EndEpisode();
+}
+```
+
+The agent receives a positive reward when it reaches the goal and a negative
+reward when it falls into the pit. Otherwise, it gets no rewards. This is an
+example of a _sparse_ reward system. The agent must explore a lot to find the
+infrequent reward.
+
+In contrast, the `AreaAgent` in the
+[Area example](Learning-Environment-Examples.md#push-block) gets a small
+negative reward every step. In order to get the maximum reward, the agent must
+finish its task of reaching the goal square as quickly as possible:
+
+```csharp
+AddReward( -0.005f);
+MoveAgent(act);
+
+if (gameObject.transform.position.y < 0.0f ||
+    Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f ||
+    Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8)
+{
+    AddReward(-1f);
+    EndEpisode();
+}
+```
+
+The agent also gets a larger negative penalty if it falls off the playing
+surface.
+
+The `Ball3DAgent` in the
+[3DBall](Learning-Environment-Examples.md#3dball-3d-balance-ball) takes a
+similar approach, but allocates a small positive reward as long as the agent
+balances the ball. The agent can maximize its rewards by keeping the ball on the
+platform:
+
+```csharp
+
+SetReward(0.1f);
+
+// When ball falls mark Agent as finished and give a negative penalty
+if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
+    Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
+    Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f)
+{
+    SetReward(-1f);
+    EndEpisode();
+
+}
+```
+
+The `Ball3DAgent` also assigns a negative penalty when the ball falls off the
+platform.
+
+Note that all of these environments make use of the `EndEpisode()` method, which
+manually terminates an episode when a termination condition is reached. This can
+be called independently of the `Max Step` property.
+
+### Rewards Summary & Best Practices
+
+- Use `AddReward()` to accumulate rewards between decisions. Use `SetReward()`
+  to overwrite any previous rewards accumulate between decisions.
+- The magnitude of any given reward should typically not be greater than 1.0 in
+  order to ensure a more stable learning process.
+- Positive rewards are often more helpful to shaping the desired behavior of an
+  agent than negative rewards. Excessive negative rewards can result in the
+  agent failing to learn any meaningful behavior.
+- For locomotion tasks, a small positive reward (+0.1) for forward velocity is
+  typically used.
+- If you want the agent to finish a task quickly, it is often helpful to provide
+  a small penalty every step (-0.05) that the agent does not complete the task.
+  In this case completion of the task should also coincide with the end of the
+  episode by calling `EndEpisode()` on the agent when it has accomplished its
+  goal.
+
+## Agent Properties
+
+![Agent Inspector](images/3dball_learning_brain.png)
+
+- `Behavior Parameters` - The parameters dictating what Policy the Agent will
+  receive.
+  - `Behavior Name` - The identifier for the behavior. Agents with the same
+    behavior name will learn the same policy.
+  - `Vector Observation`
+    - `Space Size` - Length of vector observation for the Agent.
+    - `Stacked Vectors` - The number of previous vector observations that will
+      be stacked and used collectively for decision making. This results in the
+      effective size of the vector observation being passed to the Policy being:
+      _Space Size_ x _Stacked Vectors_.
+  - `Actions`
+    - `Continuous Actions` - The number of concurrent continuous actions that
+     the Agent can take.
+    - `Discrete Branches` - An array of integers, defines multiple concurrent
+      discrete actions. The values in the `Discrete Branches` array correspond
+      to the number of possible discrete values for each action branch.
+  - `Model` - The neural network model used for inference (obtained after
+    training)
+  - `Inference Device` - Whether to use CPU or GPU to run the model during
+    inference
+  - `Behavior Type` - Determines whether the Agent will do training, inference,
+    or use its Heuristic() method:
+    - `Default` - the Agent will train if they connect to a python trainer,
+      otherwise they will perform inference.
+    - `Heuristic Only` - the Agent will always use the `Heuristic()` method.
+    - `Inference Only` - the Agent will always perform inference.
+  - `Team ID` - Used to define the team for self-play
+  - `Use Child Sensors` - Whether to use all Sensor components attached to child
+    GameObjects of this Agent.
+- `Max Step` - The per-agent maximum number of steps. Once this number is
+  reached, the Agent will be reset.
+
+## Destroying an Agent
+
+You can destroy an Agent GameObject during the simulation. Make sure that there
+is always at least one Agent training at all times by either spawning a new
+Agent every time one is destroyed or by re-spawning new Agents when the whole
+environment resets.
+
+## Defining Multi-agent Scenarios
+
+### Teams for Adversarial Scenarios
+
+Self-play is triggered by including the self-play hyperparameter hierarchy in
+the [trainer configuration](Training-ML-Agents.md#training-configurations). To
+distinguish opposing agents, set the team ID to different integer values in the
+behavior parameters script on the agent prefab.
+
+<p align="center">
+  <img src="images/team_id.png"
+       alt="Team ID"
+       width="375" border="10" />
+</p>
+
+**_Team ID must be 0 or an integer greater than 0._**
+
+In symmetric games, since all agents (even on opposing teams) will share the
+same policy, they should have the same 'Behavior Name' in their Behavior
+Parameters Script. In asymmetric games, they should have a different Behavior
+Name in their Behavior Parameters script. Note, in asymmetric games, the agents
+must have both different Behavior Names _and_ different team IDs!
+
+For examples of how to use this feature, you can see the trainer configurations
+and agent prefabs for our Tennis and Soccer environments. Tennis and Soccer
+provide examples of symmetric games. To train an asymmetric game, specify
+trainer configurations for each of your behavior names and include the self-play
+hyperparameter hierarchy in both.
+
+### Groups for Cooperative Scenarios
+
+Cooperative behavior in ML-Agents can be enabled by instantiating a `SimpleMultiAgentGroup`,
+typically in an environment controller or similar script, and adding agents to it
+using the `RegisterAgent(Agent agent)` method. Note that all agents added to the same `SimpleMultiAgentGroup`
+must have the same behavior name and Behavior Parameters. Using `SimpleMultiAgentGroup` enables the
+agents within a group to learn how to work together to achieve a common goal (i.e.,
+maximize a group-given reward), even if one or more of the group members are removed
+before the episode ends. You can then use this group to add/set rewards, end or interrupt episodes
+at a group level using the `AddGroupReward()`, `SetGroupReward()`, `EndGroupEpisode()`, and
+`GroupEpisodeInterrupted()` methods. For example:
+
+```csharp
+// Create a Multi Agent Group in Start() or Initialize()
+m_AgentGroup = new SimpleMultiAgentGroup();
+
+// Register agents in group at the beginning of an episode
+for (var agent in AgentList)
+{
+  m_AgentGroup.RegisterAgent(agent);
+}
+
+// if the team scores a goal
+m_AgentGroup.AddGroupReward(rewardForGoal);
+
+// If the goal is reached and the episode is over
+m_AgentGroup.EndGroupEpisode();
+ResetScene();
+
+// If time ran out and we need to interrupt the episode
+m_AgentGroup.GroupEpisodeInterrupted();
+ResetScene();
+```
+
+Multi Agent Groups should be used with the MA-POCA trainer, which is explicitly designed to train
+cooperative environments. This can be enabled by using the `poca` trainer - see the
+[training configurations](Training-Configuration-File.md) doc for more information on
+configuring MA-POCA. When using MA-POCA, agents which are deactivated or removed from the Scene
+during the episode will still learn to contribute to the group's long term rewards, even
+if they are not active in the scene to experience them.
+
+See the [Cooperative Push Block](Learning-Environment-Examples.md#cooperative-push-block) environment
+for an example of how to use Multi Agent Groups, and the
+[Dungeon Escape](Learning-Environment-Examples.md#dungeon-escape) environment for an example of
+how the Multi Agent Group can be used with agents that are removed from the scene mid-episode.
+
+**NOTE**: Groups differ from Teams (for competitive settings) in the following way - Agents
+working together should be added to the same Group, while agents playing against each other
+should be given different Team Ids. If in the Scene there is one playing field and two teams,
+there should be two Groups, one for each team, and each team should be assigned a different
+Team Id. If this playing field is duplicated many times in the Scene (e.g. for training
+speedup), there should be two Groups _per playing field_, and two unique Team Ids
+_for the entire Scene_. In environments with both Groups and Team Ids configured, MA-POCA and
+self-play can be used together for training. In the diagram below, there are two agents on each team,
+and two playing fields where teams are pitted against each other. All the blue agents should share a Team Id
+(and the orange ones a different ID), and there should be four group managers, one per pair of agents.
+
+<p align="center">
+  <img src="images/groupmanager_teamid.png"
+       alt="Group Manager vs Team Id"
+       width="650" border="10" />
+</p>
+
+Please see the [SoccerTwos](Learning-Environment-Examples.md#soccer-twos) environment for an example.
+
+#### Cooperative Behaviors Notes and Best Practices
+* An agent can only be registered to one MultiAgentGroup at a time. If you want to re-assign an
+agent from one group to another, you have to unregister it from the current group first.
+
+* Agents with different behavior names in the same group are not supported.
+
+* Agents within groups should always set the `Max Steps` parameter in the Agent script to 0.
+Instead, handle Max Steps using the MultiAgentGroup by ending the episode for the entire
+Group using `GroupEpisodeInterrupted()`.
+
+* `EndGroupEpisode` and `GroupEpisodeInterrupted` do the same job in the game, but has
+slightly different effect on the training. If the episode is completed, you would want to call
+`EndGroupEpisode`. But if the episode is not over but it has been running for enough steps, i.e.
+reaching max step, you would call `GroupEpisodeInterrupted`.
+
+* If an agent finished earlier, e.g. completed tasks/be removed/be killed in the game, do not call
+`EndEpisode()` on the Agent. Instead, disable the agent and re-enable it when the next episode starts,
+or destroy the agent entirely. This is because calling `EndEpisode()` will call `OnEpisodeBegin()`, which
+will reset the agent immediately. While it is possible to call `EndEpisode()` in this way, it is usually not the
+desired behavior when training groups of agents.
+
+* If an agent that was disabled in a scene needs to be re-enabled, it must be re-registered to the MultiAgentGroup.
+
+* Group rewards are meant to reinforce agents to act in the group's best interest instead of
+individual ones, and are treated differently than individual agent rewards during
+training. So calling `AddGroupReward()` is not equivalent to calling agent.AddReward() on each agent
+in the group.
+
+* You can still add incremental rewards to agents using `Agent.AddReward()` if they are
+in a Group. These rewards will only be given to those agents and are received when the
+Agent is active.
+
+* Environments which use Multi Agent Groups can be trained using PPO or SAC, but agents will
+not be able to learn from group rewards after deactivation/removal, nor will they behave as cooperatively.
+
+## Recording Demonstrations
+
+In order to record demonstrations from an agent, add the
+`Demonstration Recorder` component to a GameObject in the scene which contains
+an `Agent` component. Once added, it is possible to name the demonstration that
+will be recorded from the agent.
+
+<p align="center">
+  <img src="images/demo_component.png"
+       alt="Demonstration Recorder"
+       width="650" border="10" />
+</p>
+
+When `Record` is checked, a demonstration will be created whenever the scene is
+played from the Editor. Depending on the complexity of the task, anywhere from a
+few minutes or a few hours of demonstration data may be necessary to be useful
+for imitation learning. To specify an exact number of steps you want to record
+use the `Num Steps To Record` field and the editor will end your play session
+automatically once that many steps are recorded. If you set `Num Steps To Record`
+to `0` then recording will continue until you manually end the play session. Once
+the play session ends a `.demo` file will be created in the `Assets/Demonstrations`
+folder (by default). This file contains the demonstrations. Clicking on the file will
+provide metadata about the demonstration in the inspector.
+
+<p align="center">
+  <img src="images/demo_inspector.png"
+       alt="Demonstration Inspector"
+       width="375" border="10" />
+</p>
+
+You can then specify the path to this file in your
+[training configurations](Training-Configuration-File.md#behavioral-cloning).
diff --git a/docs/Learning-Environment-Design.md b/docs/Learning-Environment-Design.md
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+++ b/docs/Learning-Environment-Design.md
@@ -0,0 +1,167 @@
+# Designing a Learning Environment
+
+This page contains general advice on how to design your learning environment, in
+addition to overviewing aspects of the ML-Agents Unity SDK that pertain to
+setting up your scene and simulation as opposed to designing your agents within
+the scene. We have a dedicated page on
+[Designing Agents](Learning-Environment-Design-Agents.md) which includes how to
+instrument observations, actions and rewards, define teams for multi-agent
+scenarios and record agent demonstrations for imitation learning.
+
+To help on-board to the entire set of functionality provided by the ML-Agents
+Toolkit, we recommend exploring our [API documentation](API-Reference.md).
+Additionally, our [example environments](Learning-Environment-Examples.md) are a
+great resource as they provide sample usage of almost all of our features.
+
+## The Simulation and Training Process
+
+Training and simulation proceed in steps orchestrated by the ML-Agents Academy
+class. The Academy works with Agent objects in the scene to step through the
+simulation.
+
+During training, the external Python training process communicates with the
+Academy to run a series of episodes while it collects data and optimizes its
+neural network model. When training is completed successfully, you can add the
+trained model file to your Unity project for later use.
+
+The ML-Agents Academy class orchestrates the agent simulation loop as follows:
+
+1. Calls your Academy's `OnEnvironmentReset` delegate.
+1. Calls the `OnEpisodeBegin()` function for each Agent in the scene.
+1. Gathers information about the scene. This is done by calling the
+  `CollectObservations(VectorSensor sensor)` function for each Agent in the
+  scene, as well as updating their sensor and collecting the resulting
+  observations.
+1. Uses each Agent's Policy to decide on the Agent's next action.
+1. Calls the `OnActionReceived()` function for each Agent in the scene, passing
+   in the action chosen by the Agent's Policy.
+1. Calls the Agent's `OnEpisodeBegin()` function if the Agent has reached its
+   `Max Step` count or has otherwise marked itself as `EndEpisode()`.
+
+To create a training environment, extend the Agent class to implement the above
+methods whether you need to implement them or not depends on your specific
+scenario.
+
+## Organizing the Unity Scene
+
+To train and use the ML-Agents Toolkit in a Unity scene, the scene as many Agent
+subclasses as you need. Agent instances should be attached to the GameObject
+representing that Agent.
+
+### Academy
+
+The Academy is a singleton which orchestrates Agents and their decision making
+processes. Only a single Academy exists at a time.
+
+#### Academy resetting
+
+To alter the environment at the start of each episode, add your method to the
+Academy's OnEnvironmentReset action.
+
+```csharp
+public class MySceneBehavior : MonoBehaviour
+{
+    public void Awake()
+    {
+        Academy.Instance.OnEnvironmentReset += EnvironmentReset;
+    }
+
+    void EnvironmentReset()
+    {
+        // Reset the scene here
+    }
+}
+```
+
+For example, you might want to reset an Agent to its starting position or move a
+goal to a random position. An environment resets when the `reset()` method is
+called on the Python `UnityEnvironment`.
+
+When you reset an environment, consider the factors that should change so that
+training is generalizable to different conditions. For example, if you were
+training a maze-solving agent, you would probably want to change the maze itself
+for each training episode. Otherwise, the agent would probably on learn to solve
+one, particular maze, not mazes in general.
+
+### Multiple Areas
+
+In many of the example environments, many copies of the training area are
+instantiated in the scene. This generally speeds up training, allowing the
+environment to gather many experiences in parallel. This can be achieved simply
+by instantiating many Agents with the same Behavior Name. If possible, consider
+designing your scene to support multiple areas.
+
+Check out our example environments to see examples of multiple areas.
+Additionally, the
+[Making a New Learning Environment](Learning-Environment-Create-New.md#optional-multiple-training-areas-within-the-same-scene)
+guide demonstrates this option.
+
+## Environments
+
+When you create a training environment in Unity, you must set up the scene so
+that it can be controlled by the external training process. Considerations
+include:
+
+- The training scene must start automatically when your Unity application is
+  launched by the training process.
+- The Academy must reset the scene to a valid starting point for each episode of
+  training.
+- A training episode must have a definite end — either using `Max Steps` or by
+  each Agent ending its episode manually with `EndEpisode()`.
+
+## Environment Parameters
+
+Curriculum learning and environment parameter randomization are two training
+methods that control specific parameters in your environment. As such, it is
+important to ensure that your environment parameters are updated at each step to
+the correct values. To enable this, we expose a `EnvironmentParameters` C# class
+that you can use to retrieve the values of the parameters defined in the
+training configurations for both of those features. Please see our
+[documentation](Training-ML-Agents.md#environment-parameters)
+for curriculum learning and environment parameter randomization for details.
+
+We recommend modifying the environment from the Agent's `OnEpisodeBegin()`
+function by leveraging `Academy.Instance.EnvironmentParameters`. See the
+WallJump example environment for a sample usage (specifically,
+[WallJumpAgent.cs](../Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
+).
+
+## Agent
+
+The Agent class represents an actor in the scene that collects observations and
+carries out actions. The Agent class is typically attached to the GameObject in
+the scene that otherwise represents the actor — for example, to a player object
+in a football game or a car object in a vehicle simulation. Every Agent must
+have appropriate `Behavior Parameters`.
+
+Generally, when creating an Agent, you should extend the Agent class and implement
+the `CollectObservations(VectorSensor sensor)` and `OnActionReceived()` methods:
+
+- `CollectObservations(VectorSensor sensor)` — Collects the Agent's observation
+  of its environment.
+- `OnActionReceived()` — Carries out the action chosen by the Agent's Policy and
+  assigns a reward to the current state.
+
+Your implementations of these functions determine how the Behavior Parameters
+assigned to this Agent must be set.
+
+You must also determine how an Agent finishes its task or times out. You can
+manually terminate an Agent episode in your `OnActionReceived()` function when
+the Agent has finished (or irrevocably failed) its task by calling the
+`EndEpisode()` function. You can also set the Agent's `Max Steps` property to a
+positive value and the Agent will consider the episode over after it has taken
+that many steps. You can use the `Agent.OnEpisodeBegin()` function to prepare
+the Agent to start again.
+
+See [Agents](Learning-Environment-Design-Agents.md) for detailed information
+about programming your own Agents.
+
+## Recording Statistics
+
+We offer developers a mechanism to record statistics from within their Unity
+environments. These statistics are aggregated and generated during the training
+process. To record statistics, see the `StatsRecorder` C# class.
+
+See the FoodCollector example environment for a sample usage (specifically,
+[FoodCollectorSettings.cs](../Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs)
+).
diff --git a/docs/Learning-Environment-Examples.md b/docs/Learning-Environment-Examples.md
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index 0000000000000000000000000000000000000000..22fb7fcd37e4d522a10105895ab66776e839d0ad
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+++ b/docs/Learning-Environment-Examples.md
@@ -0,0 +1,500 @@
+# Example Learning Environments
+
+<img src="../images/example-envs.png" align="middle" width="3000"/>
+
+The Unity ML-Agents Toolkit includes an expanding set of example environments
+that highlight the various features of the toolkit. These environments can also
+serve as templates for new environments or as ways to test new ML algorithms.
+Environments are located in `Project/Assets/ML-Agents/Examples` and summarized
+below.
+
+For the environments that highlight specific features of the toolkit, we provide
+the pre-trained model files and the training config file that enables you to
+train the scene yourself. The environments that are designed to serve as
+challenges for researchers do not have accompanying pre-trained model files or
+training configs and are marked as _Optional_ below.
+
+This page only overviews the example environments we provide. To learn more on
+how to design and build your own environments see our
+[Making a New Learning Environment](Learning-Environment-Create-New.md) page. If
+you would like to contribute environments, please see our
+[contribution guidelines](CONTRIBUTING.md) page.
+
+## Basic
+
+![Basic](images/basic.png)
+
+- Set-up: A linear movement task where the agent must move left or right to
+  rewarding states.
+- Goal: Move to the most reward state.
+- Agents: The environment contains one agent.
+- Agent Reward Function:
+  - -0.01 at each step
+  - +0.1 for arriving at suboptimal state.
+  - +1.0 for arriving at optimal state.
+- Behavior Parameters:
+  - Vector Observation space: One variable corresponding to current state.
+  - Actions: 1 discrete action branch with 3 actions (Move left, do nothing, move
+    right).
+  - Visual Observations: None
+- Float Properties: None
+- Benchmark Mean Reward: 0.93
+
+## 3DBall: 3D Balance Ball
+
+![3D Balance Ball](images/balance.png)
+
+- Set-up: A balance-ball task, where the agent balances the ball on it's head.
+- Goal: The agent must balance the ball on it's head for as long as possible.
+- Agents: The environment contains 12 agents of the same kind, all using the
+  same Behavior Parameters.
+- Agent Reward Function:
+  - +0.1 for every step the ball remains on it's head.
+  - -1.0 if the ball falls off.
+- Behavior Parameters:
+  - Vector Observation space: 8 variables corresponding to rotation of the agent
+    cube, and position and velocity of ball.
+  - Vector Observation space (Hard Version): 5 variables corresponding to
+    rotation of the agent cube and position of ball.
+  - Actions: 2 continuous actions, with one value corresponding to
+    X-rotation, and the other to Z-rotation.
+  - Visual Observations: Third-person view from the upper-front of the agent. Use
+    `Visual3DBall` scene.
+- Float Properties: Three
+  - scale: Specifies the scale of the ball in the 3 dimensions (equal across the
+    three dimensions)
+    - Default: 1
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 5
+  - gravity: Magnitude of gravity
+    - Default: 9.81
+    - Recommended Minimum: 4
+    - Recommended Maximum: 105
+  - mass: Specifies mass of the ball
+    - Default: 1
+    - Recommended Minimum: 0.1
+    - Recommended Maximum: 20
+- Benchmark Mean Reward: 100
+
+## GridWorld
+
+![GridWorld](images/gridworld.png)
+
+- Set-up: A multi-goal version of the grid-world task. Scene contains agent, goal,
+  and obstacles.
+- Goal: The agent must navigate the grid to the appropriate goal while
+  avoiding the obstacles.
+- Agents: The environment contains nine agents with the same Behavior
+  Parameters.
+- Agent Reward Function:
+  - -0.01 for every step.
+  - +1.0 if the agent navigates to the correct goal (episode ends).
+  - -1.0 if the agent navigates to an incorrect goal (episode ends).
+- Behavior Parameters:
+  - Vector Observation space: None
+  - Actions: 1 discrete action branch with 5 actions, corresponding to movement in
+    cardinal directions or not moving. Note that for this environment,
+    [action masking](Learning-Environment-Design-Agents.md#masking-discrete-actions)
+    is turned on by default (this option can be toggled using the `Mask Actions`
+    checkbox within the `trueAgent` GameObject). The trained model file provided
+    was generated with action masking turned on.
+  - Visual Observations: One corresponding to top-down view of GridWorld.
+  - Goal Signal : A one hot vector corresponding to which color is the correct goal
+  for the Agent
+- Float Properties: Three, corresponding to grid size, number of green goals, and
+  number of red goals.
+- Benchmark Mean Reward: 0.8
+
+## Push Block
+
+![Push](images/push.png)
+
+- Set-up: A platforming environment where the agent can push a block around.
+- Goal: The agent must push the block to the goal.
+- Agents: The environment contains one agent.
+- Agent Reward Function:
+  - -0.0025 for every step.
+  - +1.0 if the block touches the goal.
+- Behavior Parameters:
+  - Vector Observation space: (Continuous) 70 variables corresponding to 14
+    ray-casts each detecting one of three possible objects (wall, goal, or
+    block).
+  - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
+    and counterclockwise, move along four different face directions, or do nothing.
+- Float Properties: Four
+  - block_scale: Scale of the block along the x and z dimensions
+    - Default: 2
+    - Recommended Minimum: 0.5
+    - Recommended Maximum: 4
+  - dynamic_friction: Coefficient of friction for the ground material acting on
+    moving objects
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 1
+  - static_friction: Coefficient of friction for the ground material acting on
+    stationary objects
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 1
+  - block_drag: Effect of air resistance on block
+    - Default: 0.5
+    - Recommended Minimum: 0
+    - Recommended Maximum: 2000
+- Benchmark Mean Reward: 4.5
+
+## Wall Jump
+
+![Wall](images/wall.png)
+
+- Set-up: A platforming environment where the agent can jump over a wall.
+- Goal: The agent must use the block to scale the wall and reach the goal.
+- Agents: The environment contains one agent linked to two different Models. The
+  Policy the agent is linked to changes depending on the height of the wall. The
+  change of Policy is done in the WallJumpAgent class.
+- Agent Reward Function:
+  - -0.0005 for every step.
+  - +1.0 if the agent touches the goal.
+  - -1.0 if the agent falls off the platform.
+- Behavior Parameters:
+  - Vector Observation space: Size of 74, corresponding to 14 ray casts each
+    detecting 4 possible objects. plus the global position of the agent and
+    whether or not the agent is grounded.
+  - Actions: 4 discrete action branches:
+    - Forward Motion (3 possible actions: Forward, Backwards, No Action)
+    - Rotation (3 possible actions: Rotate Left, Rotate Right, No Action)
+    - Side Motion (3 possible actions: Left, Right, No Action)
+    - Jump (2 possible actions: Jump, No Action)
+  - Visual Observations: None
+- Float Properties: Four
+- Benchmark Mean Reward (Big & Small Wall): 0.8
+
+## Crawler
+
+![Crawler](images/crawler.png)
+
+- Set-up: A creature with 4 arms and 4 forearms.
+- Goal: The agents must move its body toward the goal direction without falling.
+- Agents: The environment contains 10 agents with same Behavior Parameters.
+- Agent Reward Function (independent):
+  The reward function is now geometric meaning the reward each step is a product
+  of all the rewards instead of a sum, this helps the agent try to maximize all
+  rewards instead of the easiest rewards.
+  - Body velocity matches goal velocity. (normalized between (0,1))
+  - Head direction alignment with goal direction. (normalized between (0,1))
+- Behavior Parameters:
+  - Vector Observation space: 172 variables corresponding to position, rotation,
+    velocity, and angular velocities of each limb plus the acceleration and
+    angular acceleration of the body.
+  - Actions: 20 continuous actions, corresponding to target
+    rotations for joints.
+  - Visual Observations: None
+- Float Properties: None
+- Benchmark Mean Reward: 3000
+
+## Worm
+
+![Worm](images/worm.png)
+
+- Set-up: A worm with a head and 3 body segments.
+- Goal: The agents must move its body toward the goal direction.
+- Agents: The environment contains 10 agents with same Behavior Parameters.
+- Agent Reward Function (independent):
+  The reward function is now geometric meaning the reward each step is a product
+  of all the rewards instead of a sum, this helps the agent try to maximize all
+  rewards instead of the easiest rewards.
+  - Body velocity matches goal velocity. (normalized between (0,1))
+  - Body direction alignment with goal direction. (normalized between (0,1))
+- Behavior Parameters:
+  - Vector Observation space: 64 variables corresponding to position, rotation,
+    velocity, and angular velocities of each limb plus the acceleration and
+    angular acceleration of the body.
+  - Actions: 9 continuous actions, corresponding to target
+    rotations for joints.
+  - Visual Observations: None
+- Float Properties: None
+- Benchmark Mean Reward: 800
+
+## Food Collector
+
+![Collector](images/foodCollector.png)
+
+- Set-up: A multi-agent environment where agents compete to collect food.
+- Goal: The agents must learn to collect as many green food spheres as possible
+  while avoiding red spheres.
+- Agents: The environment contains 5 agents with same Behavior Parameters.
+- Agent Reward Function (independent):
+  - +1 for interaction with green spheres
+  - -1 for interaction with red spheres
+- Behavior Parameters:
+  - Vector Observation space: 53 corresponding to velocity of agent (2), whether
+    agent is frozen and/or shot its laser (2), plus grid based perception of
+    objects around agent's forward direction (40 by 40 with 6 different categories).
+  - Actions:
+    - 3 continuous actions correspond to Forward Motion, Side Motion and Rotation
+    - 1 discrete acion branch for Laser with 2 possible actions corresponding to
+      Shoot Laser or No Action
+  - Visual Observations (Optional): First-person camera per-agent, plus one vector
+    flag representing the frozen state of the agent. This scene uses a combination
+    of vector and visual observations and the training will not succeed without
+    the frozen vector flag. Use `VisualFoodCollector` scene.
+- Float Properties: Two
+  - laser_length: Length of the laser used by the agent
+    - Default: 1
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 7
+  - agent_scale: Specifies the scale of the agent in the 3 dimensions (equal
+    across the three dimensions)
+    - Default: 1
+    - Recommended Minimum: 0.5
+    - Recommended Maximum: 5
+- Benchmark Mean Reward: 10
+
+## Hallway
+
+![Hallway](images/hallway.png)
+
+- Set-up: Environment where the agent needs to find information in a room,
+  remember it, and use it to move to the correct goal.
+- Goal: Move to the goal which corresponds to the color of the block in the
+  room.
+- Agents: The environment contains one agent.
+- Agent Reward Function (independent):
+  - +1 For moving to correct goal.
+  - -0.1 For moving to incorrect goal.
+  - -0.0003 Existential penalty.
+- Behavior Parameters:
+  - Vector Observation space: 30 corresponding to local ray-casts detecting
+    objects, goals, and walls.
+  - Actions: 1 discrete action Branch, with 4 actions corresponding to agent
+    rotation and forward/backward movement.
+- Float Properties: None
+- Benchmark Mean Reward: 0.7
+  - To train this environment, you can enable curiosity by adding the `curiosity` reward signal
+    in `config/ppo/Hallway.yaml`
+
+## Soccer Twos
+
+![SoccerTwos](images/soccer.png)
+
+- Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game.
+- Goal:
+  - Get the ball into the opponent's goal while preventing the ball from
+    entering own goal.
+- Agents: The environment contains two different Multi Agent Groups with two agents in each.
+  Parameters : SoccerTwos.
+- Agent Reward Function (dependent):
+  - (1 - `accumulated time penalty`) When ball enters opponent's goal
+    `accumulated time penalty` is incremented by (1 / `MaxStep`) every fixed
+    update and is reset to 0 at the beginning of an episode.
+  - -1 When ball enters team's goal.
+- Behavior Parameters:
+  - Vector Observation space: 336 corresponding to 11 ray-casts forward
+    distributed over 120 degrees and 3 ray-casts backward distributed over 90
+    degrees each detecting 6 possible object types, along with the object's
+    distance. The forward ray-casts contribute 264 state dimensions and backward
+    72 state dimensions over three observation stacks.
+  - Actions: 3 discrete branched actions corresponding to
+    forward, backward, sideways movement, as well as rotation.
+  - Visual Observations: None
+- Float Properties: Two
+  - ball_scale: Specifies the scale of the ball in the 3 dimensions (equal
+    across the three dimensions)
+    - Default: 7.5
+    - Recommended minimum: 4
+    - Recommended maximum: 10
+  - gravity: Magnitude of the gravity
+    - Default: 9.81
+    - Recommended minimum: 6
+    - Recommended maximum: 20
+
+## Strikers Vs. Goalie
+
+![StrikersVsGoalie](images/strikersvsgoalie.png)
+
+- Set-up: Environment where two agents compete in a 2 vs 1 soccer variant.
+- Goal:
+  - Striker: Get the ball into the opponent's goal.
+  - Goalie: Keep the ball out of the goal.
+- Agents: The environment contains two different Multi Agent Groups. One with two Strikers and the other one Goalie.
+  Behavior Parameters : Striker, Goalie.
+- Striker Agent Reward Function (dependent):
+  - +1 When ball enters opponent's goal.
+  - -0.001 Existential penalty.
+- Goalie Agent Reward Function (dependent):
+  - -1 When ball enters goal.
+  - 0.001 Existential bonus.
+- Behavior Parameters:
+  - Striker Vector Observation space: 294 corresponding to 11 ray-casts forward
+    distributed over 120 degrees and 3 ray-casts backward distributed over 90
+    degrees each detecting 5 possible object types, along with the object's
+    distance. The forward ray-casts contribute 231 state dimensions and backward
+    63 state dimensions over three observation stacks.
+  - Striker Actions: 3 discrete branched actions corresponding
+    to forward, backward, sideways movement, as well as rotation.
+  - Goalie Vector Observation space: 738 corresponding to 41 ray-casts
+    distributed over 360 degrees each detecting 4 possible object types, along
+    with the object's distance and 3 observation stacks.
+  - Goalie Actions: 3 discrete branched actions corresponding
+    to forward, backward, sideways movement, as well as rotation.
+  - Visual Observations: None
+- Float Properties: Two
+  - ball_scale: Specifies the scale of the ball in the 3 dimensions (equal
+    across the three dimensions)
+    - Default: 7.5
+    - Recommended minimum: 4
+    - Recommended maximum: 10
+  - gravity: Magnitude of the gravity
+    - Default: 9.81
+    - Recommended minimum: 6
+    - Recommended maximum: 20
+
+## Walker
+
+![Walker](images/walker.png)
+
+- Set-up: Physics-based Humanoid agents with 26 degrees of freedom. These DOFs
+  correspond to articulation of the following body-parts: hips, chest, spine,
+  head, thighs, shins, feet, arms, forearms and hands.
+- Goal: The agents must move its body toward the goal direction without falling.
+- Agents: The environment contains 10 independent agents with same Behavior
+  Parameters.
+- Agent Reward Function (independent):
+  The reward function is now geometric meaning the reward each step is a product
+  of all the rewards instead of a sum, this helps the agent try to maximize all
+  rewards instead of the easiest rewards.
+  - Body velocity matches goal velocity. (normalized between (0,1))
+  - Head direction alignment with goal direction. (normalized between (0,1))
+- Behavior Parameters:
+  - Vector Observation space: 243 variables corresponding to position, rotation,
+    velocity, and angular velocities of each limb, along with goal direction.
+  - Actions: 39 continuous actions, corresponding to target
+    rotations and strength applicable to the joints.
+  - Visual Observations: None
+- Float Properties: Four
+  - gravity: Magnitude of gravity
+    - Default: 9.81
+    - Recommended Minimum:
+    - Recommended Maximum:
+  - hip_mass: Mass of the hip component of the walker
+    - Default: 8
+    - Recommended Minimum: 7
+    - Recommended Maximum: 28
+  - chest_mass: Mass of the chest component of the walker
+    - Default: 8
+    - Recommended Minimum: 3
+    - Recommended Maximum: 20
+  - spine_mass: Mass of the spine component of the walker
+    - Default: 8
+    - Recommended Minimum: 3
+    - Recommended Maximum: 20
+- Benchmark Mean Reward : 2500
+
+
+## Pyramids
+
+![Pyramids](images/pyramids.png)
+
+- Set-up: Environment where the agent needs to press a button to spawn a
+  pyramid, then navigate to the pyramid, knock it over, and move to the gold
+  brick at the top.
+- Goal: Move to the golden brick on top of the spawned pyramid.
+- Agents: The environment contains one agent.
+- Agent Reward Function (independent):
+  - +2 For moving to golden brick (minus 0.001 per step).
+- Behavior Parameters:
+  - Vector Observation space: 148 corresponding to local ray-casts detecting
+    switch, bricks, golden brick, and walls, plus variable indicating switch
+    state.
+  - Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and
+    forward/backward movement.
+- Float Properties: None
+- Benchmark Mean Reward: 1.75
+
+## Match 3
+![Match 3](images/match3.png)
+
+- Set-up: Simple match-3 game. Matched pieces are removed, and remaining pieces
+drop down. New pieces are spawned randomly at the top, with a chance of being
+"special".
+- Goal: Maximize score from matching pieces.
+- Agents: The environment contains several independent Agents.
+- Agent Reward Function (independent):
+  - .01 for each normal piece cleared. Special pieces are worth 2x or 3x.
+- Behavior Parameters:
+  - None
+  - Observations and actions are defined with a sensor and actuator respectively.
+- Float Properties: None
+- Benchmark Mean Reward:
+  - 39.5 for visual observations
+  - 38.5 for vector observations
+  - 34.2 for simple heuristic (pick a random valid move)
+  - 37.0 for greedy heuristic (pick the highest-scoring valid move)
+
+## Sorter
+![Sorter](images/sorter.png)
+
+ - Set-up: The Agent is in a circular room with numbered tiles. The values of the
+ tiles are random between 1 and 20. The tiles present in the room are randomized
+ at each episode. When the Agent visits a tile, it turns green.
+ - Goal: Visit all the tiles in ascending order.
+ - Agents: The environment contains a single Agent
+ - Agent Reward Function:
+  - -.0002 Existential penalty.
+  - +1 For visiting the right tile
+  - -1 For visiting the wrong tile
+ - BehaviorParameters:
+  - Vector Observations : 4 : 2 floats for Position and 2 floats for orientation
+  - Variable Length Observations : Between 1 and 20 entities (one for each tile)
+  each with 22 observations, the first 20 are one hot encoding of the value of the tile,
+  the 21st and 22nd represent the position of the tile relative to the Agent and the 23rd
+  is `1` if the tile was visited and `0` otherwise.
+  - Actions: 3 discrete branched actions corresponding to forward, backward,
+  sideways movement, as well as rotation.
+  - Float Properties: One
+    - num_tiles: The maximum number of tiles to sample.
+      - Default: 2
+      - Recommended Minimum: 1
+      - Recommended Maximum: 20
+  - Benchmark Mean Reward: Depends on the number of tiles.
+
+## Cooperative Push Block
+![CoopPushBlock](images/cooperative_pushblock.png)
+
+- Set-up: Similar to Push Block, the agents are in an area with blocks that need
+to be pushed into a goal. Small blocks can be pushed by one agents and are worth
++1 value, medium blocks require two agents to push in and are worth +2, and large
+blocks require all 3 agents to push and are worth +3.
+- Goal: Push all blocks into the goal.
+- Agents: The environment contains three Agents in a Multi Agent Group.
+- Agent Reward Function:
+  - -0.0001 Existential penalty, as a group reward.
+  - +1, +2, or +3 for pushing in a block, added as a group reward.
+- Behavior Parameters:
+  - Observation space: A single Grid Sensor with separate tags for each block size,
+    the goal, the walls, and other agents.
+  - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
+    and counterclockwise, move along four different face directions, or do nothing.
+- Float Properties: None
+- Benchmark Mean Reward: 11 (Group Reward)
+
+## Dungeon Escape
+![DungeonEscape](images/dungeon_escape.png)
+
+- Set-up: Agents are trapped in a dungeon with a dragon, and must work together to escape.
+  To retrieve the key, one of the agents must find and slay the dragon, sacrificing itself
+  to do so. The dragon will drop a key for the others to use. The other agents can then pick
+  up this key and unlock the dungeon door. If the agents take too long, the dragon will escape
+  through a portal and the environment resets.
+- Goal: Unlock the dungeon door and leave.
+- Agents: The environment contains three Agents in a Multi Agent Group and one Dragon, which
+  moves in a predetermined pattern.
+- Agent Reward Function:
+  - +1 group reward if any agent successfully unlocks the door and leaves the dungeon.
+- Behavior Parameters:
+  - Observation space: A Ray Perception Sensor with separate tags for the walls, other agents,
+    the door, key, the dragon, and the dragon's portal. A single Vector Observation which indicates
+    whether the agent is holding a key.
+  - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
+    and counterclockwise, move along four different face directions, or do nothing.
+- Float Properties: None
+- Benchmark Mean Reward: 1.0 (Group Reward)
diff --git a/docs/Learning-Environment-Executable.md b/docs/Learning-Environment-Executable.md
new file mode 100644
index 0000000000000000000000000000000000000000..f5ea6936cc9c2a93180bf560ecefce77362da085
--- /dev/null
+++ b/docs/Learning-Environment-Executable.md
@@ -0,0 +1,198 @@
+# Using an Environment Executable
+
+This section will help you create and use built environments rather than the
+Editor to interact with an environment. Using an executable has some advantages
+over using the Editor:
+
+- You can exchange executable with other people without having to share your
+  entire repository.
+- You can put your executable on a remote machine for faster training.
+- You can use `Server Build` (`Headless`) mode for faster training (as long as the executable does not need rendering).
+- You can keep using the Unity Editor for other tasks while the agents are
+  training.
+
+## Building the 3DBall environment
+
+The first step is to open the Unity scene containing the 3D Balance Ball
+environment:
+
+1. Launch Unity.
+1. On the Projects dialog, choose the **Open** option at the top of the window.
+1. Using the file dialog that opens, locate the `Project` folder within the
+   ML-Agents project and click **Open**.
+1. In the **Project** window, navigate to the folder
+   `Assets/ML-Agents/Examples/3DBall/Scenes/`.
+1. Double-click the `3DBall` file to load the scene containing the Balance Ball
+   environment.
+
+![3DBall Scene](images/mlagents-Open3DBall.png)
+
+Next, we want the set up scene to play correctly when the training process
+launches our environment executable. This means:
+
+- The environment application runs in the background.
+- No dialogs require interaction.
+- The correct scene loads automatically.
+
+1. Open Player Settings (menu: **Edit** > **Project Settings** > **Player**).
+1. Under **Resolution and Presentation**:
+   - Ensure that **Run in Background** is Checked.
+   - Ensure that **Display Resolution Dialog** is set to Disabled. (Note: this
+    setting may not be available in newer versions of the editor.)
+1. Open the Build Settings window (menu:**File** > **Build Settings**).
+1. Choose your target platform.
+   - (optional) Select “Development Build� to
+     [log debug messages](https://docs.unity3d.com/Manual/LogFiles.html).
+1. If any scenes are shown in the **Scenes in Build** list, make sure that the
+   3DBall Scene is the only one checked. (If the list is empty, then only the
+   current scene is included in the build).
+1. Click **Build**:
+   - In the File dialog, navigate to your ML-Agents directory.
+   - Assign a file name and click **Save**.
+   - (For Windows）With Unity 2018.1, it will ask you to select a folder instead
+     of a file name. Create a subfolder within the root directory and select
+     that folder to build. In the following steps you will refer to this
+     subfolder's name as `env_name`. You cannot create builds in the Assets
+     folder
+
+![Build Window](images/mlagents-BuildWindow.png)
+
+Now that we have a Unity executable containing the simulation environment, we
+can interact with it.
+
+## Interacting with the Environment
+
+If you want to use the [Python API](Python-LLAPI.md) to interact with your
+executable, you can pass the name of the executable with the argument
+'file_name' of the `UnityEnvironment`. For instance:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+env = UnityEnvironment(file_name=<env_name>)
+```
+
+## Training the Environment
+
+1. Open a command or terminal window.
+1. Navigate to the folder where you installed the ML-Agents Toolkit. If you
+   followed the default [installation](Installation.md), then navigate to the
+   `ml-agents/` folder.
+1. Run
+   `mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier>`
+   Where:
+   - `<trainer-config-file>` is the file path of the trainer configuration yaml
+   - `<env_name>` is the name and path to the executable you exported from Unity
+     (without extension)
+   - `<run-identifier>` is a string used to separate the results of different
+     training runs
+
+For example, if you are training with a 3DBall executable, and you saved it to
+the directory where you installed the ML-Agents Toolkit, run:
+
+```sh
+mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=firstRun
+```
+
+And you should see something like
+
+```console
+ml-agents$ mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=first-run
+
+
+                        â–„â–„â–„â–“â–“â–“â–“
+                   ╓▓▓▓▓▓▓█▓▓▓▓▓
+              ,▄▄▄m▀▀▀'  ,▓▓▓▀▓▓▄                           ▓▓▓  ▓▓▌
+            ▄▓▓▓▀'      ▄▓▓▀  ▓▓▓      ▄▄     ▄▄ ,▄▄ ▄▄▄▄   ,▄▄ ▄▓▓▌▄ ▄▄▄    ,▄▄
+          ▄▓▓▓▀        ▄▓▓▀   �▓▓▌     ▓▓▌   �▓▓ �▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌  ╒▓▓▌
+        ▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓      ▓▀      ▓▓▌   �▓▓ �▓▓    ▓▓▓ ▓▓▓  ▓▓▌   �▓▓▄ ▓▓▌
+        ▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄     ▓▓      ▓▓▌   �▓▓ �▓▓    ▓▓▓ ▓▓▓  ▓▓▌    �▓▓�▓▓
+          ^█▓▓▓        ▀▓▓▄   �▓▓▌     ▓▓▓▓▄▓▓▓▓ �▓▓    ▓▓▓ ▓▓▓  ▓▓▓▄    ▓▓▓▓`
+            '▀▓▓▓▄      ^▓▓▓  ▓▓▓       └▀▀▀▀ ▀▀ ^▀▀    `▀▀ `▀▀   '▀▀    �▓▓▌
+               ▀▀▀▀▓▄▄▄   ▓▓▓▓▓▓,                                      ▓▓▓▓▀
+                   `▀█▓▓▓▓▓▓▓▓▓▌
+                        ¬`▀▀▀█▓
+
+```
+
+**Note**: If you're using Anaconda, don't forget to activate the ml-agents
+environment first.
+
+If `mlagents-learn` runs correctly and starts training, you should see something
+like this:
+
+```console
+CrashReporter: initialized
+Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed'
+Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc'
+INFO:mlagents_envs:
+'Ball3DAcademy' started successfully!
+Unity Academy name: Ball3DAcademy
+
+INFO:mlagents_envs:Connected new brain:
+Unity brain name: Ball3DLearning
+        Number of Visual Observations (per agent): 0
+        Vector Observation space size (per agent): 8
+        Number of stacked Vector Observation: 1
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning:
+        batch_size:          64
+        beta:                0.001
+        buffer_size:         12000
+        epsilon:             0.2
+        gamma:               0.995
+        hidden_units:        128
+        lambd:               0.99
+        learning_rate:       0.0003
+        max_steps:           5.0e4
+        normalize:           True
+        num_epoch:           3
+        num_layers:          2
+        time_horizon:        1000
+        sequence_length:     64
+        summary_freq:        1000
+        use_recurrent:       False
+        memory_size:         256
+        use_curiosity:       False
+        curiosity_strength:  0.01
+        curiosity_enc_size:  128
+        output_path: ./results/first-run-0/Ball3DLearning
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
+INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
+```
+
+You can press Ctrl+C to stop the training, and your trained model will be at
+`results/<run-identifier>/<behavior_name>.onnx`, which corresponds to your model's
+latest checkpoint. (**Note:** There is a known bug on Windows that causes the
+saving of the model to fail when you early terminate the training, it's
+recommended to wait until Step has reached the max_steps parameter you set in
+your config YAML.) You can now embed this trained model into your Agent by
+following the steps below:
+
+1. Move your model file into
+   `Project/Assets/ML-Agents/Examples/3DBall/TFModels/`.
+1. Open the Unity Editor, and select the **3DBall** scene as described above.
+1. Select the **3DBall** prefab from the Project window and select **Agent**.
+1. Drag the `<behavior_name>.onnx` file from the Project window of the Editor to
+   the **Model** placeholder in the **Ball3DAgent** inspector window.
+1. Press the **Play** button at the top of the Editor.
+
+## Training on Headless Server
+
+To run training on headless server with no graphics rendering support, you need to turn off
+graphics display in the Unity executable. There are two ways to achieve this:
+1. Pass `--no-graphics` option to mlagents-learn training command. This is equivalent to
+   adding `-nographics -batchmode` to the Unity executable's commandline.
+2. Build your Unity executable with **Server Build**. You can find this setting in Build Settings
+   in the Unity Editor.
+
+If you want to train with graphics (for example, using camera and visual observations), you'll
+need to set up display rendering support (e.g. xvfb) on you server machine. In our
+[Colab Notebook Tutorials](ML-Agents-Toolkit-Documentation.md#python-tutorial-with-google-colab), the Setup section has
+examples of setting up xvfb on servers.
diff --git a/docs/Limitations.md b/docs/Limitations.md
new file mode 100644
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--- /dev/null
+++ b/docs/Limitations.md
@@ -0,0 +1,7 @@
+# Limitations
+
+See the package-specific Limitations pages:
+
+- [`com.unity.mlagents` Unity package](https://docs.unity3d.com/Packages/com.unity.ml-agents@2.3/manual/index.html#known-limitations)
+- [`mlagents` Python package](../ml-agents/)
+- [`mlagents_envs` Python package](../ml-agents-envs/)
diff --git a/docs/ML-Agents-Envs-README.md b/docs/ML-Agents-Envs-README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9e382027ea4c24d5a2defddf8739e164291947af
--- /dev/null
+++ b/docs/ML-Agents-Envs-README.md
@@ -0,0 +1 @@
+{!../ml-agents-envs/README.md!}
diff --git a/docs/ML-Agents-Overview.md b/docs/ML-Agents-Overview.md
new file mode 100644
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--- /dev/null
+++ b/docs/ML-Agents-Overview.md
@@ -0,0 +1,819 @@
+# ML-Agents Toolkit Overview
+
+**Table of Contents**
+
+- [Running Example: Training NPC Behaviors](#running-example-training-npc-behaviors)
+- [Key Components](#key-components)
+- [Training Modes](#training-modes)
+  - [Built-in Training and Inference](#built-in-training-and-inference)
+    - [Cross-Platform Inference](#cross-platform-inference)
+  - [Custom Training and Inference](#custom-training-and-inference)
+- [Flexible Training Scenarios](#flexible-training-scenarios)
+- [Training Methods: Environment-agnostic](#training-methods-environment-agnostic)
+  - [A Quick Note on Reward Signals](#a-quick-note-on-reward-signals)
+  - [Deep Reinforcement Learning](#deep-reinforcement-learning)
+    - [Curiosity for Sparse-reward Environments](#curiosity-for-sparse-reward-environments)
+    - [RND for Sparse-reward Environments](#rnd-for-sparse-reward-environments)
+  - [Imitation Learning](#imitation-learning)
+    - [GAIL (Generative Adversarial Imitation Learning)](#gail-generative-adversarial-imitation-learning)
+    - [Behavioral Cloning (BC)](#behavioral-cloning-bc)
+    - [Recording Demonstrations](#recording-demonstrations)
+  - [Summary](#summary)
+- [Training Methods: Environment-specific](#training-methods-environment-specific)
+  - [Training in Competitive Multi-Agent Environments with Self-Play](#training-in-competitive-multi-agent-environments-with-self-play)
+  - [Training in Cooperative Multi-Agent Environments with MA-POCA](#training-in-cooperative-multi-agent-environments-with-ma-poca)
+  - [Solving Complex Tasks using Curriculum Learning](#solving-complex-tasks-using-curriculum-learning)
+  - [Training Robust Agents using Environment Parameter Randomization](#training-robust-agents-using-environment-parameter-randomization)
+- [Model Types](#model-types)
+  - [Learning from Vector Observations](#learning-from-vector-observations)
+  - [Learning from Cameras using Convolutional Neural Networks](#learning-from-cameras-using-convolutional-neural-networks)
+  - [Learning from Variable Length Observations using Attention](#learning-from-variable-length-observations-using-attention)
+  - [Memory-enhanced Agents using Recurrent Neural Networks](#memory-enhanced-agents-using-recurrent-neural-networks)
+- [Additional Features](#additional-features)
+- [Summary and Next Steps](#summary-and-next-steps)
+
+**The Unity Machine Learning Agents Toolkit** (ML-Agents Toolkit) is an
+open-source project that enables games and simulations to serve as environments
+for training intelligent agents. Agents can be trained using reinforcement
+learning, imitation learning, neuroevolution, or other machine learning methods
+through a simple-to-use Python API. We also provide implementations (based on
+PyTorch) of state-of-the-art algorithms to enable game developers and
+hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These
+trained agents can be used for multiple purposes, including controlling NPC
+behavior (in a variety of settings such as multi-agent and adversarial),
+automated testing of game builds and evaluating different game design decisions
+pre-release. The ML-Agents Toolkit is mutually beneficial for both game
+developers and AI researchers as it provides a central platform where advances
+in AI can be evaluated on Unity’s rich environments and then made accessible to
+the wider research and game developer communities.
+
+Depending on your background (i.e. researcher, game developer, hobbyist), you
+may have very different questions on your mind at the moment. To make your
+transition to the ML-Agents Toolkit easier, we provide several background pages
+that include overviews and helpful resources on the
+[Unity Engine](Background-Unity.md),
+[machine learning](Background-Machine-Learning.md) and
+[PyTorch](Background-PyTorch.md). We **strongly** recommend browsing the
+relevant background pages if you're not familiar with a Unity scene, basic
+machine learning concepts or have not previously heard of PyTorch.
+
+The remainder of this page contains a deep dive into ML-Agents, its key
+components, different training modes and scenarios. By the end of it, you should
+have a good sense of _what_ the ML-Agents Toolkit allows you to do. The
+subsequent documentation pages provide examples of _how_ to use ML-Agents. To
+get started, watch this
+[demo video of ML-Agents in action](https://www.youtube.com/watch?v=fiQsmdwEGT8&feature=youtu.be).
+
+## Running Example: Training NPC Behaviors
+
+To help explain the material and terminology in this page, we'll use a
+hypothetical, running example throughout. We will explore the problem of
+training the behavior of a non-playable character (NPC) in a game. (An NPC is a
+game character that is never controlled by a human player and its behavior is
+pre-defined by the game developer.) More specifically, let's assume we're
+building a multi-player, war-themed game in which players control the soldiers.
+In this game, we have a single NPC who serves as a medic, finding and reviving
+wounded players. Lastly, let us assume that there are two teams, each with five
+players and one NPC medic.
+
+The behavior of a medic is quite complex. It first needs to avoid getting
+injured, which requires detecting when it is in danger and moving to a safe
+location. Second, it needs to be aware of which of its team members are injured
+and require assistance. In the case of multiple injuries, it needs to assess the
+degree of injury and decide who to help first. Lastly, a good medic will always
+place itself in a position where it can quickly help its team members. Factoring
+in all of these traits means that at every instance, the medic needs to measure
+several attributes of the environment (e.g. position of team members, position
+of enemies, which of its team members are injured and to what degree) and then
+decide on an action (e.g. hide from enemy fire, move to help one of its
+members). Given the large number of settings of the environment and the large
+number of actions that the medic can take, defining and implementing such
+complex behaviors by hand is challenging and prone to errors.
+
+With ML-Agents, it is possible to _train_ the behaviors of such NPCs (called
+**Agents**) using a variety of methods. The basic idea is quite simple. We need
+to define three entities at every moment of the game (called **environment**):
+
+- **Observations** - what the medic perceives about the environment.
+  Observations can be numeric and/or visual. Numeric observations measure
+  attributes of the environment from the point of view of the agent. For our
+  medic this would be attributes of the battlefield that are visible to it. For
+  most interesting environments, an agent will require several continuous
+  numeric observations. Visual observations, on the other hand, are images
+  generated from the cameras attached to the agent and represent what the agent
+  is seeing at that point in time. It is common to confuse an agent's
+  observation with the environment (or game) **state**. The environment state
+  represents information about the entire scene containing all the game
+  characters. The agents observation, however, only contains information that
+  the agent is aware of and is typically a subset of the environment state. For
+  example, the medic observation cannot include information about an enemy in
+  hiding that the medic is unaware of.
+- **Actions** - what actions the medic can take. Similar to observations,
+  actions can either be continuous or discrete depending on the complexity of
+  the environment and agent. In the case of the medic, if the environment is a
+  simple grid world where only their location matters, then a discrete action
+  taking on one of four values (north, south, east, west) suffices. However, if
+  the environment is more complex and the medic can move freely then using two
+  continuous actions (one for direction and another for speed) is more
+  appropriate.
+- **Reward signals** - a scalar value indicating how well the medic is doing.
+  Note that the reward signal need not be provided at every moment, but only
+  when the medic performs an action that is good or bad. For example, it can
+  receive a large negative reward if it dies, a modest positive reward whenever
+  it revives a wounded team member, and a modest negative reward when a wounded
+  team member dies due to lack of assistance. Note that the reward signal is how
+  the objectives of the task are communicated to the agent, so they need to be
+  set up in a manner where maximizing reward generates the desired optimal
+  behavior.
+
+After defining these three entities (the building blocks of a **reinforcement
+learning task**), we can now _train_ the medic's behavior. This is achieved by
+simulating the environment for many trials where the medic, over time, learns
+what is the optimal action to take for every observation it measures by
+maximizing its future reward. The key is that by learning the actions that
+maximize its reward, the medic is learning the behaviors that make it a good
+medic (i.e. one who saves the most number of lives). In **reinforcement
+learning** terminology, the behavior that is learned is called a **policy**,
+which is essentially a (optimal) mapping from observations to actions. Note that
+the process of learning a policy through running simulations is called the
+**training phase**, while playing the game with an NPC that is using its learned
+policy is called the **inference phase**.
+
+The ML-Agents Toolkit provides all the necessary tools for using Unity as the
+simulation engine for learning the policies of different objects in a Unity
+environment. In the next few sections, we discuss how the ML-Agents Toolkit
+achieves this and what features it provides.
+
+## Key Components
+
+The ML-Agents Toolkit contains five high-level components:
+
+- **Learning Environment** - which contains the Unity scene and all the game
+  characters. The Unity scene provides the environment in which agents observe,
+  act, and learn. How you set up the Unity scene to serve as a learning
+  environment really depends on your goal. You may be trying to solve a specific
+  reinforcement learning problem of limited scope, in which case you can use the
+  same scene for both training and for testing trained agents. Or, you may be
+  training agents to operate in a complex game or simulation. In this case, it
+  might be more efficient and practical to create a purpose-built training
+  scene. The ML-Agents Toolkit includes an ML-Agents Unity SDK
+  (`com.unity.ml-agents` package) that enables you to transform any Unity scene
+  into a learning environment by defining the agents and their behaviors.
+- **Python Low-Level API** - which contains a low-level Python interface for
+  interacting and manipulating a learning environment. Note that, unlike the
+  Learning Environment, the Python API is not part of Unity, but lives outside
+  and communicates with Unity through the Communicator. This API is contained in
+  a dedicated `mlagents_envs` Python package and is used by the Python training
+  process to communicate with and control the Academy during training. However,
+  it can be used for other purposes as well. For example, you could use the API
+  to use Unity as the simulation engine for your own machine learning
+  algorithms. See [Python API](Python-LLAPI.md) for more information.
+- **External Communicator** - which connects the Learning Environment with the
+  Python Low-Level API. It lives within the Learning Environment.
+- **Python Trainers** which contains all the machine learning algorithms that
+  enable training agents. The algorithms are implemented in Python and are part
+  of their own `mlagents` Python package. The package exposes a single
+  command-line utility `mlagents-learn` that supports all the training methods
+  and options outlined in this document. The Python Trainers interface solely
+  with the Python Low-Level API.
+- **Gym Wrapper** (not pictured). A common way in which machine learning
+  researchers interact with simulation environments is via a wrapper provided by
+  OpenAI called [gym](https://github.com/openai/gym). We provide a gym wrapper
+  in the `ml-agents-envs` package and [instructions](Python-Gym-API.md) for using
+  it with existing machine learning algorithms which utilize gym.
+- **PettingZoo Wrapper** (not pictured) PettingZoo is python API for
+  interacting with multi-agent simulation environments that provides a
+  gym-like interface. We provide a PettingZoo wrapper for Unity ML-Agents
+  environments in the `ml-agents-envs` package and
+  [instructions](Python-PettingZoo-API.md) for using it with machine learning
+  algorithms.
+
+<p align="center">
+  <img src="../images/learning_environment_basic.png"
+       alt="Simplified ML-Agents Scene Block Diagram"
+       width="600"
+       border="10" />
+</p>
+
+_Simplified block diagram of ML-Agents._
+
+The Learning Environment contains two Unity Components that help organize the
+Unity scene:
+
+- **Agents** - which is attached to a Unity GameObject (any character within a
+  scene) and handles generating its observations, performing the actions it
+  receives and assigning a reward (positive / negative) when appropriate. Each
+  Agent is linked to a Behavior.
+- **Behavior** - defines specific attributes of the agent such as the number of
+  actions that agent can take. Each Behavior is uniquely identified by a
+  `Behavior Name` field. A Behavior can be thought as a function that receives
+  observations and rewards from the Agent and returns actions. A Behavior can be
+  of one of three types: Learning, Heuristic or Inference. A Learning Behavior
+  is one that is not, yet, defined but about to be trained. A Heuristic Behavior
+  is one that is defined by a hard-coded set of rules implemented in code. An
+  Inference Behavior is one that includes a trained Neural Network file. In
+  essence, after a Learning Behavior is trained, it becomes an Inference
+  Behavior.
+
+Every Learning Environment will always have one Agent for every character in the
+scene. While each Agent must be linked to a Behavior, it is possible for Agents
+that have similar observations and actions to have the same Behavior. In our
+sample game, we have two teams each with their own medic. Thus we will have two
+Agents in our Learning Environment, one for each medic, but both of these medics
+can have the same Behavior. This does not mean that at each instance they will
+have identical observation and action _values_.
+
+<p align="center">
+  <img src="../images/learning_environment_example.png"
+       alt="Example ML-Agents Scene Block Diagram"
+       width="700"
+       border="10" />
+</p>
+
+_Example block diagram of ML-Agents Toolkit for our sample game._
+
+Note that in a single environment, there can be multiple Agents and multiple
+Behaviors at the same time. For example, if we expanded our game to include tank
+driver NPCs, then the Agent attached to those characters cannot share its
+Behavior with the Agent linked to the medics (medics and drivers have different
+actions). The Learning Environment through the Academy (not represented in the
+diagram) ensures that all the Agents are in sync in addition to controlling
+environment-wide settings.
+
+Lastly, it is possible to exchange data between Unity and Python outside of the
+machine learning loop through _Side Channels_. One example of using _Side
+Channels_ is to exchange data with Python about _Environment Parameters_. The
+following diagram illustrates the above.
+
+<p align="center">
+  <img src="../images/learning_environment_full.png"
+       alt="More Complete Example ML-Agents Scene Block Diagram"
+       border="10" />
+</p>
+
+## Training Modes
+
+Given the flexibility of ML-Agents, there are a few ways in which training and
+inference can proceed.
+
+### Built-in Training and Inference
+
+As mentioned previously, the ML-Agents Toolkit ships with several
+implementations of state-of-the-art algorithms for training intelligent agents.
+More specifically, during training, all the medics in the scene send their
+observations to the Python API through the External Communicator. The Python API
+processes these observations and sends back actions for each medic to take.
+During training these actions are mostly exploratory to help the Python API
+learn the best policy for each medic. Once training concludes, the learned
+policy for each medic can be exported as a model file. Then during the inference
+phase, the medics still continue to generate their observations, but instead of
+being sent to the Python API, they will be fed into their (internal, embedded)
+model to generate the _optimal_ action for each medic to take at every point in
+time.
+
+The [Getting Started Guide](Getting-Started.md) tutorial covers this training
+mode with the **3D Balance Ball** sample environment.
+
+#### Cross-Platform Inference
+
+It is important to note that the ML-Agents Toolkit leverages the
+[Unity Inference Engine](Unity-Inference-Engine.md) to run the models within a
+Unity scene such that an agent can take the _optimal_ action at each step. Given
+that the Unity Inference Engine support most platforms that Unity does, this
+means that any model you train with the ML-Agents Toolkit can be embedded into
+your Unity application that runs on any platform. See our
+[dedicated blog post](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
+for additional information.
+
+### Custom Training and Inference
+
+In the previous mode, the Agents were used for training to generate a PyTorch
+model that the Agents can later use. However, any user of the ML-Agents Toolkit
+can leverage their own algorithms for training. In this case, the behaviors of
+all the Agents in the scene will be controlled within Python. You can even turn
+your environment into a [gym.](Python-Gym-API.md)
+
+We do not currently have a tutorial highlighting this mode, but you can learn
+more about the Python API [here](Python-LLAPI.md).
+
+## Flexible Training Scenarios
+
+While the discussion so-far has mostly focused on training a single agent, with
+ML-Agents, several training scenarios are possible. We are excited to see what
+kinds of novel and fun environments the community creates. For those new to
+training intelligent agents, below are a few examples that can serve as
+inspiration:
+
+- Single-Agent. A single agent, with its own reward signal. The traditional way
+  of training an agent. An example is any single-player game, such as Chicken.
+- Simultaneous Single-Agent. Multiple independent agents with independent reward
+  signals with same `Behavior Parameters`. A parallelized version of the
+  traditional training scenario, which can speed-up and stabilize the training
+  process. Helpful when you have multiple versions of the same character in an
+  environment who should learn similar behaviors. An example might be training a
+  dozen robot-arms to each open a door simultaneously.
+- Adversarial Self-Play. Two interacting agents with inverse reward signals. In
+  two-player games, adversarial self-play can allow an agent to become
+  increasingly more skilled, while always having the perfectly matched opponent:
+  itself. This was the strategy employed when training AlphaGo, and more
+  recently used by OpenAI to train a human-beating 1-vs-1 Dota 2 agent.
+- Cooperative Multi-Agent. Multiple interacting agents with a shared reward
+  signal with same or different `Behavior Parameters`. In this scenario, all
+  agents must work together to accomplish a task that cannot be done alone.
+  Examples include environments where each agent only has access to partial
+  information, which needs to be shared in order to accomplish the task or
+  collaboratively solve a puzzle.
+- Competitive Multi-Agent. Multiple interacting agents with inverse reward
+  signals with same or different `Behavior Parameters`. In this scenario, agents
+  must compete with one another to either win a competition, or obtain some
+  limited set of resources. All team sports fall into this scenario.
+- Ecosystem. Multiple interacting agents with independent reward signals with
+  same or different `Behavior Parameters`. This scenario can be thought of as
+  creating a small world in which animals with different goals all interact,
+  such as a savanna in which there might be zebras, elephants and giraffes, or
+  an autonomous driving simulation within an urban environment.
+
+## Training Methods: Environment-agnostic
+
+The remaining sections overview the various state-of-the-art machine learning
+algorithms that are part of the ML-Agents Toolkit. If you aren't studying
+machine and reinforcement learning as a subject and just want to train agents to
+accomplish tasks, you can treat these algorithms as _black boxes_. There are a
+few training-related parameters to adjust inside Unity as well as on the Python
+training side, but you do not need in-depth knowledge of the algorithms
+themselves to successfully create and train agents. Step-by-step procedures for
+running the training process are provided in the
+[Training ML-Agents](Training-ML-Agents.md) page.
+
+This section specifically focuses on the training methods that are available
+regardless of the specifics of your learning environment.
+
+#### A Quick Note on Reward Signals
+
+In this section we introduce the concepts of _intrinsic_ and _extrinsic_
+rewards, which helps explain some of the training methods.
+
+In reinforcement learning, the end goal for the Agent is to discover a behavior
+(a Policy) that maximizes a reward. You will need to provide the agent one or
+more reward signals to use during training. Typically, a reward is defined by
+your environment, and corresponds to reaching some goal. These are what we refer
+to as _extrinsic_ rewards, as they are defined external of the learning
+algorithm.
+
+Rewards, however, can be defined outside of the environment as well, to
+encourage the agent to behave in certain ways, or to aid the learning of the
+true extrinsic reward. We refer to these rewards as _intrinsic_ reward signals.
+The total reward that the agent will learn to maximize can be a mix of extrinsic
+and intrinsic reward signals.
+
+The ML-Agents Toolkit allows reward signals to be defined in a modular way, and
+we provide four reward signals that can the mixed and matched to help shape
+your agent's behavior:
+
+- `extrinsic`: represents the rewards defined in your environment, and is
+  enabled by default
+- `gail`: represents an intrinsic reward signal that is defined by GAIL (see
+  below)
+- `curiosity`: represents an intrinsic reward signal that encourages exploration
+  in sparse-reward environments that is defined by the Curiosity module (see
+  below).
+- `rnd`: represents an intrinsic reward signal that encourages exploration
+  in sparse-reward environments that is defined by the Curiosity module (see
+  below).
+
+### Deep Reinforcement Learning
+
+ML-Agents provide an implementation of two reinforcement learning algorithms:
+
+- [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/)
+- [Soft Actor-Critic (SAC)](https://bair.berkeley.edu/blog/2018/12/14/sac/)
+
+The default algorithm is PPO. This is a method that has been shown to be more
+general purpose and stable than many other RL algorithms.
+
+In contrast with PPO, SAC is _off-policy_, which means it can learn from
+experiences collected at any time during the past. As experiences are collected,
+they are placed in an experience replay buffer and randomly drawn during
+training. This makes SAC significantly more sample-efficient, often requiring
+5-10 times less samples to learn the same task as PPO. However, SAC tends to
+require more model updates. SAC is a good choice for heavier or slower
+environments (about 0.1 seconds per step or more). SAC is also a "maximum
+entropy" algorithm, and enables exploration in an intrinsic way. Read more about
+maximum entropy RL
+[here](https://bair.berkeley.edu/blog/2017/10/06/soft-q-learning/).
+
+#### Curiosity for Sparse-reward Environments
+
+In environments where the agent receives rare or infrequent rewards (i.e.
+sparse-reward), an agent may never receive a reward signal on which to bootstrap
+its training process. This is a scenario where the use of an intrinsic reward
+signals can be valuable. Curiosity is one such signal which can help the agent
+explore when extrinsic rewards are sparse.
+
+The `curiosity` Reward Signal enables the Intrinsic Curiosity Module. This is an
+implementation of the approach described in
+[Curiosity-driven Exploration by Self-supervised Prediction](https://pathak22.github.io/noreward-rl/)
+by Pathak, et al. It trains two networks:
+
+- an inverse model, which takes the current and next observation of the agent,
+  encodes them, and uses the encoding to predict the action that was taken
+  between the observations
+- a forward model, which takes the encoded current observation and action, and
+  predicts the next encoded observation.
+
+The loss of the forward model (the difference between the predicted and actual
+encoded observations) is used as the intrinsic reward, so the more surprised the
+model is, the larger the reward will be.
+
+For more information, see our dedicated
+[blog post on the Curiosity module](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/).
+
+#### RND for Sparse-reward Environments
+
+Similarly to Curiosity, Random Network Distillation (RND) is useful in sparse or rare
+reward environments as it helps the Agent explore. The RND Module is implemented following
+the paper [Exploration by Random Network Distillation](https://arxiv.org/abs/1810.12894).
+RND uses two networks:
+ - The first is a network with fixed random weights that takes observations as inputs and
+ generates an encoding
+ - The second is a network with similar architecture that is trained to predict the
+ outputs of the first network and uses the observations the Agent collects as training data.
+
+The loss (the squared difference between the predicted and actual encoded observations)
+of the trained model is used as intrinsic reward. The more an Agent visits a state, the
+more accurate the predictions and the lower the rewards which encourages the Agent to
+explore new states with higher prediction errors.
+
+### Imitation Learning
+
+It is often more intuitive to simply demonstrate the behavior we want an agent
+to perform, rather than attempting to have it learn via trial-and-error methods.
+For example, instead of indirectly training a medic with the help of a reward
+function, we can give the medic real world examples of observations from the
+game and actions from a game controller to guide the medic's behavior. Imitation
+Learning uses pairs of observations and actions from a demonstration to learn a
+policy. See this [video demo](https://youtu.be/kpb8ZkMBFYs) of imitation
+learning .
+
+Imitation learning can either be used alone or in conjunction with reinforcement
+learning. If used alone it can provide a mechanism for learning a specific type
+of behavior (i.e. a specific style of solving the task). If used in conjunction
+with reinforcement learning it can dramatically reduce the time the agent takes
+to solve the environment. This can be especially pronounced in sparse-reward
+environments. For instance, on the
+[Pyramids environment](Learning-Environment-Examples.md#pyramids), using 6
+episodes of demonstrations can reduce training steps by more than 4 times. See
+Behavioral Cloning + GAIL + Curiosity + RL below.
+
+<p align="center">
+  <img src="../images/mlagents-ImitationAndRL.png"
+       alt="Using Demonstrations with Reinforcement Learning"
+       width="700" border="0" />
+</p>
+
+The ML-Agents Toolkit provides a way to learn directly from demonstrations, as
+well as use them to help speed up reward-based training (RL). We include two
+algorithms called Behavioral Cloning (BC) and Generative Adversarial Imitation
+Learning (GAIL). In most scenarios, you can combine these two features:
+
+- If you want to help your agents learn (especially with environments that have
+  sparse rewards) using pre-recorded demonstrations, you can generally enable
+  both GAIL and Behavioral Cloning at low strengths in addition to having an
+  extrinsic reward. An example of this is provided for the PushBlock example
+  environment in `config/imitation/PushBlock.yaml`.
+- If you want to train purely from demonstrations with GAIL and BC _without_ an
+  extrinsic reward signal, please see the CrawlerStatic example environment under
+  in `config/imitation/CrawlerStatic.yaml`.
+
+***Note:*** GAIL introduces a [_survivor bias_](https://arxiv.org/pdf/1809.02925.pdf)
+to the learning process. That is, by giving positive rewards based on similarity
+to the expert, the agent is incentivized to remain alive for as long as possible.
+This can directly conflict with goal-oriented tasks like our PushBlock or Pyramids
+example environments where an agent must reach a goal state thus ending the
+episode as quickly as possible. In these cases, we strongly recommend that you
+use a low strength GAIL reward signal and a sparse extrinisic signal when
+the agent achieves the task. This way, the GAIL reward signal will guide the
+agent until it discovers the extrnisic signal and will not overpower it. If the
+agent appears to be ignoring the extrinsic reward signal, you should reduce
+the strength of GAIL.
+
+#### GAIL (Generative Adversarial Imitation Learning)
+
+GAIL, or
+[Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476),
+uses an adversarial approach to reward your Agent for behaving similar to a set
+of demonstrations. GAIL can be used with or without environment rewards, and
+works well when there are a limited number of demonstrations. In this framework,
+a second neural network, the discriminator, is taught to distinguish whether an
+observation/action is from a demonstration or produced by the agent. This
+discriminator can then examine a new observation/action and provide it a reward
+based on how close it believes this new observation/action is to the provided
+demonstrations.
+
+At each training step, the agent tries to learn how to maximize this reward.
+Then, the discriminator is trained to better distinguish between demonstrations
+and agent state/actions. In this way, while the agent gets better and better at
+mimicking the demonstrations, the discriminator keeps getting stricter and
+stricter and the agent must try harder to "fool" it.
+
+This approach learns a _policy_ that produces states and actions similar to the
+demonstrations, requiring fewer demonstrations than direct cloning of the
+actions. In addition to learning purely from demonstrations, the GAIL reward
+signal can be mixed with an extrinsic reward signal to guide the learning
+process.
+
+#### Behavioral Cloning (BC)
+
+BC trains the Agent's policy to exactly mimic the actions shown in a set of
+demonstrations. The BC feature can be enabled on the PPO or SAC trainers. As BC
+cannot generalize past the examples shown in the demonstrations, BC tends to
+work best when there exists demonstrations for nearly all of the states that the
+agent can experience, or in conjunction with GAIL and/or an extrinsic reward.
+
+#### Recording Demonstrations
+
+Demonstrations of agent behavior can be recorded from the Unity Editor or build,
+and saved as assets. These demonstrations contain information on the
+observations, actions, and rewards for a given agent during the recording
+session. They can be managed in the Editor, as well as used for training with BC
+and GAIL. See the
+[Designing Agents](Learning-Environment-Design-Agents.md#recording-demonstrations)
+page for more information on how to record demonstrations for your agent.
+
+### Summary
+
+To summarize, we provide 3 training methods: BC, GAIL and RL (PPO or SAC) that
+can be used independently or together:
+
+- BC can be used on its own or as a pre-training step before GAIL and/or RL
+- GAIL can be used with or without extrinsic rewards
+- RL can be used on its own (either PPO or SAC) or in conjunction with BC and/or
+  GAIL.
+
+Leveraging either BC or GAIL requires recording demonstrations to be provided as
+input to the training algorithms.
+
+## Training Methods: Environment-specific
+
+In addition to the three environment-agnostic training methods introduced in the
+previous section, the ML-Agents Toolkit provides additional methods that can aid
+in training behaviors for specific types of environments.
+
+### Training in Competitive Multi-Agent Environments with Self-Play
+
+ML-Agents provides the functionality to train both symmetric and asymmetric
+adversarial games with
+[Self-Play](https://openai.com/blog/competitive-self-play/). A symmetric game is
+one in which opposing agents are equal in form, function and objective. Examples
+of symmetric games are our Tennis and Soccer example environments. In
+reinforcement learning, this means both agents have the same observation and
+actions and learn from the same reward function and so _they can share the
+same policy_. In asymmetric games, this is not the case. An example of an
+asymmetric games are Hide and Seek. Agents in these types of games do not always
+have the same observation or actions and so sharing policy networks is not
+necessarily ideal.
+
+With self-play, an agent learns in adversarial games by competing against fixed,
+past versions of its opponent (which could be itself as in symmetric games) to
+provide a more stable, stationary learning environment. This is compared to
+competing against the current, best opponent in every episode, which is
+constantly changing (because it's learning).
+
+Self-play can be used with our implementations of both Proximal Policy
+Optimization (PPO) and Soft Actor-Critic (SAC). However, from the perspective of
+an individual agent, these scenarios appear to have non-stationary dynamics
+because the opponent is often changing. This can cause significant issues in the
+experience replay mechanism used by SAC. Thus, we recommend that users use PPO.
+For further reading on this issue in particular, see the paper
+[Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/1702.08887.pdf).
+
+See our
+[Designing Agents](Learning-Environment-Design-Agents.md#defining-teams-for-multi-agent-scenarios)
+page for more information on setting up teams in your Unity scene. Also, read
+our
+[blog post on self-play](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
+for additional information. Additionally, check [ELO Rating System](ELO-Rating-System.md) the method we use to calculate
+the relative skill level between two players.
+
+### Training In Cooperative Multi-Agent Environments with MA-POCA
+
+![PushBlock with Agents Working Together](images/cooperative_pushblock.png)
+
+ML-Agents provides the functionality for training cooperative behaviors - i.e.,
+groups of agents working towards a common goal, where the success of the individual
+is linked to the success of the whole group. In such a scenario, agents typically receive
+rewards as a group. For instance, if a team of agents wins a game against an opposing
+team, everyone is rewarded - even agents who did not directly contribute to the win. This
+makes learning what to do as an individual difficult - you may get a win
+for doing nothing, and a loss for doing your best.
+
+In ML-Agents, we provide MA-POCA (MultiAgent POsthumous Credit Assignment), which
+is a novel multi-agent trainer that trains a _centralized critic_, a neural network
+that acts as a "coach" for a whole group of agents. You can then give rewards to the team
+as a whole, and the agents will learn how best to contribute to achieving that reward.
+Agents can _also_ be given rewards individually, and the team will work together to help the
+individual achieve those goals. During an episode, agents can be added or removed from the group,
+such as when agents spawn or die in a game. If agents are removed mid-episode (e.g., if teammates die
+or are removed from the game), they will still learn whether their actions contributed
+to the team winning later, enabling agents to take group-beneficial actions even if
+they result in the individual being removed from the game (i.e., self-sacrifice).
+MA-POCA can also be combined with self-play to train teams of agents to play against each other.
+
+To learn more about enabling cooperative behaviors for agents in an ML-Agents environment,
+check out [this page](Learning-Environment-Design-Agents.md#groups-for-cooperative-scenarios).
+
+To learn more about MA-POCA, please see our paper
+[On the Use and Misuse of Absorbing States in Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/2111.05992.pdf).
+For further reading, MA-POCA builds on previous work in multi-agent cooperative learning
+([Lowe et al.](https://arxiv.org/abs/1706.02275), [Foerster et al.](https://arxiv.org/pdf/1705.08926.pdf),
+among others) to enable the above use-cases.
+
+### Solving Complex Tasks using Curriculum Learning
+
+Curriculum learning is a way of training a machine learning model where more
+difficult aspects of a problem are gradually introduced in such a way that the
+model is always optimally challenged. This idea has been around for a long time,
+and it is how we humans typically learn. If you imagine any childhood primary
+school education, there is an ordering of classes and topics. Arithmetic is
+taught before algebra, for example. Likewise, algebra is taught before calculus.
+The skills and knowledge learned in the earlier subjects provide a scaffolding
+for later lessons. The same principle can be applied to machine learning, where
+training on easier tasks can provide a scaffolding for harder tasks in the
+future.
+
+Imagine training the medic to scale a wall to arrive at a wounded team
+member. The starting point when training a medic to accomplish this task will be
+a random policy. That starting policy will have the medic running in circles,
+and will likely never, or very rarely scale the wall properly to revive their
+team member (and achieve the reward). If we start with a simpler task, such as
+moving toward an unobstructed team member, then the medic can easily learn to
+accomplish the task. From there, we can slowly add to the difficulty of the task
+by increasing the size of the wall until the medic can complete the initially
+near-impossible task of scaling the wall. We have included an environment to
+demonstrate this with ML-Agents, called
+[Wall Jump](Learning-Environment-Examples.md#wall-jump).
+
+![Wall](images/curriculum.png)
+
+_Demonstration of a hypothetical curriculum training scenario in which a
+progressively taller wall obstructs the path to the goal._
+
+_[**Note**: The example provided above is for instructional purposes, and was
+based on an early version of the
+[Wall Jump example environment](Learning-Environment-Examples.md). As such, it
+is not possible to directly replicate the results here using that environment.]_
+
+The ML-Agents Toolkit supports modifying custom environment parameters during
+the training process to aid in learning. This allows elements of the environment
+related to difficulty or complexity to be dynamically adjusted based on training
+progress. The [Training ML-Agents](Training-ML-Agents.md#curriculum-learning)
+page has more information on defining training curriculums.
+
+### Training Robust Agents using Environment Parameter Randomization
+
+An agent trained on a specific environment, may be unable to generalize to any
+tweaks or variations in the environment (in machine learning this is referred to
+as overfitting). This becomes problematic in cases where environments are
+instantiated with varying objects or properties. One mechanism to alleviate this
+and train more robust agents that can generalize to unseen variations of the
+environment is to expose them to these variations during training. Similar to
+Curriculum Learning, where environments become more difficult as the agent
+learns, the ML-Agents Toolkit provides a way to randomly sample parameters of
+the environment during training. We refer to this approach as **Environment
+Parameter Randomization**. For those familiar with Reinforcement Learning
+research, this approach is based on the concept of
+[Domain Randomization](https://arxiv.org/abs/1703.06907). By using
+[parameter randomization during training](Training-ML-Agents.md#environment-parameter-randomization),
+the agent can be better suited to adapt (with higher performance) to future
+unseen variations of the environment.
+
+|      Ball scale of 0.5       |      Ball scale of 4       |
+| :--------------------------: | :------------------------: |
+| ![](images/3dball_small.png) | ![](images/3dball_big.png) |
+
+_Example of variations of the 3D Ball environment. The environment parameters
+are `gravity`, `ball_mass` and `ball_scale`._
+
+## Model Types
+
+Regardless of the training method deployed, there are a few model types that
+users can train using the ML-Agents Toolkit. This is due to the flexibility in
+defining agent observations, which include vector, ray cast and visual
+observations. You can learn more about how to instrument an agent's observation
+in the [Designing Agents](Learning-Environment-Design-Agents.md) guide.
+
+### Learning from Vector Observations
+
+Whether an agent's observations are ray cast or vector, the ML-Agents Toolkit
+provides a fully connected neural network model to learn from those
+observations. At training time you can configure different aspects of this model
+such as the number of hidden units and number of layers.
+
+### Learning from Cameras using Convolutional Neural Networks
+
+Unlike other platforms, where the agent’s observation might be limited to a
+single vector or image, the ML-Agents Toolkit allows multiple cameras to be used
+for observations per agent. This enables agents to learn to integrate
+information from multiple visual streams. This can be helpful in several
+scenarios such as training a self-driving car which requires multiple cameras
+with different viewpoints, or a navigational agent which might need to integrate
+aerial and first-person visuals. You can learn more about adding visual
+observations to an agent
+[here](Learning-Environment-Design-Agents.md#multiple-visual-observations).
+
+When visual observations are utilized, the ML-Agents Toolkit leverages
+convolutional neural networks (CNN) to learn from the input images. We offer
+three network architectures:
+
+- a simple encoder which consists of two convolutional layers
+- the implementation proposed by
+  [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of
+  three convolutional layers,
+- the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three
+  stacked layers, each with two residual blocks, making a much larger network
+  than the other two.
+
+The choice of the architecture depends on the visual complexity of the scene and
+the available computational resources.
+
+### Learning from Variable Length Observations using Attention
+
+Using the ML-Agents Toolkit, it is possible to have agents learn from a
+varying number of inputs. To do so, each agent can keep track of a buffer
+of vector observations. At each step, the agent will go through all the
+elements in the buffer and extract information but the elements
+in the buffer can change at every step.
+This can be useful in scenarios in which the agents must keep track of
+a varying number of elements throughout the episode. For example in a game
+where an agent must learn to avoid projectiles, but the projectiles can vary in
+numbers.
+
+![Variable Length Observations Illustrated](images/variable-length-observation-illustrated.png)
+
+You can learn more about variable length observations
+[here](Learning-Environment-Design-Agents.md#variable-length-observations).
+When variable length observations are utilized, the ML-Agents Toolkit
+leverages attention networks to learn from a varying number of entities.
+Agents using attention will ignore entities that are deemed not relevant
+and pay special attention to entities relevant to the current situation
+based on context.
+
+### Memory-enhanced Agents using Recurrent Neural Networks
+
+Have you ever entered a room to get something and immediately forgot what you
+were looking for? Don't let that happen to your agents.
+
+![Inspector](images/ml-agents-LSTM.png)
+
+In some scenarios, agents must learn to remember the past in order to take the
+best decision. When an agent only has partial observability of the environment,
+keeping track of past observations can help the agent learn. Deciding what the
+agents should remember in order to solve a task is not easy to do by hand, but
+our training algorithms can learn to keep track of what is important to remember
+with [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory).
+
+## Additional Features
+
+Beyond the flexible training scenarios available, the ML-Agents Toolkit includes
+additional features which improve the flexibility and interpretability of the
+training process.
+
+- **Concurrent Unity Instances** - We enable developers to run concurrent,
+  parallel instances of the Unity executable during training. For certain
+  scenarios, this should speed up training. Check out our dedicated page on
+  [creating a Unity executable](Learning-Environment-Executable.md) and the
+  [Training ML-Agents](Training-ML-Agents.md#training-using-concurrent-unity-instances)
+  page for instructions on how to set the number of concurrent instances.
+- **Recording Statistics from Unity** - We enable developers to
+  [record statistics](Learning-Environment-Design.md#recording-statistics) from
+  within their Unity environments. These statistics are aggregated and generated
+  during the training process.
+- **Custom Side Channels** - We enable developers to
+  [create custom side channels](Custom-SideChannels.md) to manage data transfer
+  between Unity and Python that is unique to their training workflow and/or
+  environment.
+- **Custom Samplers** - We enable developers to
+  [create custom sampling methods](Training-ML-Agents.md#defining-a-new-sampler-type)
+  for Environment Parameter Randomization. This enables users to customize this
+  training method for their particular environment.
+
+## Summary and Next Steps
+
+To briefly summarize: The ML-Agents Toolkit enables games and simulations built
+in Unity to serve as the platform for training intelligent agents. It is
+designed to enable a large variety of training modes and scenarios and comes
+packed with several features to enable researchers and developers to leverage
+(and enhance) machine learning within Unity.
+
+In terms of next steps:
+
+- For a walkthrough of running ML-Agents with a simple scene, check out the
+  [Getting Started](Getting-Started.md) guide.
+- For a "Hello World" introduction to creating your own Learning Environment,
+  check out the
+  [Making a New Learning Environment](Learning-Environment-Create-New.md) page.
+- For an overview on the more complex example environments that are provided in
+  this toolkit, check out the
+  [Example Environments](Learning-Environment-Examples.md) page.
+- For more information on the various training options available, check out the
+  [Training ML-Agents](Training-ML-Agents.md) page.
diff --git a/docs/ML-Agents-README.md b/docs/ML-Agents-README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5e2c7107a8c86a5b554b8645a63d65011adfd568
--- /dev/null
+++ b/docs/ML-Agents-README.md
@@ -0,0 +1 @@
+{!../ml-agents/README.md!}
diff --git a/docs/ML-Agents-Toolkit-Documentation.md b/docs/ML-Agents-Toolkit-Documentation.md
new file mode 100644
index 0000000000000000000000000000000000000000..a4389dc38a976825993beac6325223fc305f69b8
--- /dev/null
+++ b/docs/ML-Agents-Toolkit-Documentation.md
@@ -0,0 +1,84 @@
+# Unity ML-Agents Toolkit Documentation
+
+## Installation & Set-up
+
+- [Installation](Installation.md)
+  - [Using Virtual Environment](Using-Virtual-Environment.md)
+
+## Getting Started
+
+- [Getting Started Guide](Getting-Started.md)
+- [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
+  - [Background: Unity](Background-Unity.md)
+  - [Background: Machine Learning](Background-Machine-Learning.md)
+  - [Background: PyTorch](Background-PyTorch.md)
+- [Example Environments](Learning-Environment-Examples.md)
+
+## Creating Learning Environments
+
+- [Making a New Learning Environment](Learning-Environment-Create-New.md)
+- [Designing a Learning Environment](Learning-Environment-Design.md)
+  - [Designing Agents](Learning-Environment-Design-Agents.md)
+- [Using an Executable Environment](Learning-Environment-Executable.md)
+- [ML-Agents Package Settings](Package-Settings.md)
+
+## Training & Inference
+
+- [Training ML-Agents](Training-ML-Agents.md)
+  - [Training Configuration File](Training-Configuration-File.md)
+  - [Using TensorBoard to Observe Training](Using-Tensorboard.md)
+  - [Profiling Trainers](Profiling-Python.md)
+- [Unity Inference Engine](Unity-Inference-Engine.md)
+
+## Extending ML-Agents
+
+- [Creating Custom Side Channels](Custom-SideChannels.md)
+- [Creating Custom Samplers for Environment Parameter Randomization](Training-ML-Agents.md#defining-a-new-sampler-type)
+
+## Hugging Face Integration
+
+- [Using Hugging Face to download and upload trained models](Hugging-Face-Integration.md)
+
+## Python Tutorial with Google Colab
+
+- [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_20_docs/colab/Colab_UnityEnvironment_1_Run.ipynb)
+- [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_20_docs/colab/Colab_UnityEnvironment_2_Train.ipynb)
+- [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_20_docs/colab/Colab_UnityEnvironment_3_SideChannel.ipynb)
+
+## Help
+
+- [Migrating from earlier versions of ML-Agents](Migrating.md)
+- [Frequently Asked Questions](FAQ.md)
+- [ML-Agents Glossary](Glossary.md)
+- [Limitations](Limitations.md)
+
+## API Docs
+
+- [API Reference](API-Reference.md)
+- [Python API Documentation](Python-LLAPI-Documentation.md)
+- [How to use the Python API](Python-LLAPI.md)
+- [How to use the Unity Environment Registry](Unity-Environment-Registry.md)
+- [Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)](Python-Gym-API.md)
+
+## Translations
+
+To make the Unity ML-Agents Toolkit accessible to the global research and Unity
+developer communities, we're attempting to create and maintain translations of
+our documentation. We've started with translating a subset of the documentation
+to one language (Chinese), but we hope to continue translating more pages and to
+other languages. Consequently, we welcome any enhancements and improvements from
+the community.
+
+- [Chinese](../localized_docs/zh-CN/)
+- [Korean](../localized_docs/KR/)
+
+## Deprecated Docs
+
+We no longer use them ourselves and so they may not be up-to-date. We've decided
+to keep them up just in case they are helpful to you.
+
+- [Windows Anaconda Installation](Installation-Anaconda-Windows.md)
+- [Using Docker](Using-Docker.md)
+- [Training on the Cloud with Amazon Web Services](Training-on-Amazon-Web-Service.md)
+- [Training on the Cloud with Microsoft Azure](Training-on-Microsoft-Azure.md)
+- [Using the Video Recorder](https://github.com/Unity-Technologies/video-recorder)
diff --git a/docs/Migrating.md b/docs/Migrating.md
new file mode 100644
index 0000000000000000000000000000000000000000..dc294447496c34c9fd53c28b28cbe200990e318e
--- /dev/null
+++ b/docs/Migrating.md
@@ -0,0 +1,882 @@
+# Upgrading
+
+# Migrating
+<!---
+TODO: update ml-agents-env package version before release
+--->
+## Migrating to the ml-agents-envs 0.29.0 package
+- Python 3.8 is now the minimum version of python supported due to [python3.6 EOL](https://endoflife.date/python).
+  Please update your python installation to 3.8.13 or higher.
+- The `gym-unity` package has been refactored into the `ml-agents-envs` package. Please update your imports accordingly.
+- Example:
+  - Before
+```python
+from gym_unity.unity_gym_env import UnityToGymWrapper
+```
+  - After:
+```python
+from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
+```
+
+
+## Migrating the package to version 2.0
+- The official version of Unity ML-Agents supports is now 2022.3 LTS. If you run
+  into issues, please consider deleting your project's Library folder and reponening your
+  project.
+- If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These
+deprecated APIs have been removed. See the migration steps bellow for specific API replacements.
+
+### Deprecated methods removed
+| **Deprecated API** | **Suggested Replacement** |
+|:-------:|:------:|
+| `IActuator ActuatorComponent.CreateActuator()` | `IActuator[] ActuatorComponent.CreateActuators()` |
+| `IActionReceiver.PackActions(in float[] destination)` | none |
+| `Agent.CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)` | `Agent.WriteDiscreteActionMask(IDiscreteActionMask actionMask)` |
+| `Agent.Heuristic(float[] actionsOut)` | `Agent.Heuristic(in ActionBuffers actionsOut)` |
+| `Agent.OnActionReceived(float[] vectorAction)` | `Agent.OnActionReceived(ActionBuffers actions)` |
+| `Agent.GetAction()` | `Agent.GetStoredActionBuffers()` |
+| `BrainParameters.SpaceType`, `VectorActionSize`, `VectorActionSpaceType`, and `NumActions` | `BrainParameters.ActionSpec` |
+| `ObservationWriter.AddRange(IEnumerable<float> data, int writeOffset = 0)` | `ObservationWriter. AddList(IList<float> data, int writeOffset = 0` |
+| `SensorComponent.IsVisual()` and `IsVector()` | none |
+| `VectorSensor.AddObservation(IEnumerable<float> observation)` | `VectorSensor.AddObservation(IList<float> observation)` |
+| `SideChannelsManager` | `SideChannelManager` |
+
+### IDiscreteActionMask changes
+- The interface for disabling specific discrete actions has changed. `IDiscreteActionMask.WriteMask()` was removed,
+and replaced with `SetActionEnabled()`. Instead of returning an IEnumerable with indices to disable, you can
+now call `SetActionEnabled` for each index to disable (or enable). As an example, if you overrode
+`Agent.WriteDiscreteActionMask()` with something that looked like:
+
+```csharp
+public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+{
+    var branch = 2;
+    var actionsToDisable = new[] {1, 3};
+    actionMask.WriteMask(branch, actionsToDisable);
+}
+```
+
+the equivalent code would now be
+
+```csharp
+public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
+{
+    var branch = 2;
+    actionMask.SetActionEnabled(branch, 1, false);
+    actionMask.SetActionEnabled(branch, 3, false);
+}
+```
+### IActuator changes
+- The `IActuator` interface now implements `IHeuristicProvider`.  Please add the corresponding `Heuristic(in ActionBuffers)`
+method to your custom Actuator classes.
+
+### ISensor and SensorComponent changes
+- The `ISensor.GetObservationShape()` method and `ITypedSensor`
+and `IDimensionPropertiesSensor` interfaces were removed, and `GetObservationSpec()` was added. You can use
+`ObservationSpec.Vector()` or `ObservationSpec.Visual()` to generate `ObservationSpec`s that are equivalent to
+the previous shape. For example, if your old ISensor looked like:
+
+```csharp
+public override int[] GetObservationShape()
+{
+    return new[] { m_Height, m_Width, m_NumChannels };
+}
+```
+
+the equivalent code would now be
+
+```csharp
+public override ObservationSpec GetObservationSpec()
+{
+    return ObservationSpec.Visual(m_Height, m_Width, m_NumChannels);
+}
+```
+
+- The `ISensor.GetCompressionType()` method and `ISparseChannelSensor` interface was removed,
+and `GetCompressionSpec()` was added. You can use `CompressionSpec.Default()` or
+`CompressionSpec.Compressed()` to generate `CompressionSpec`s that are  equivalent to
+ the previous values. For example, if your old ISensor looked like:
+ ```csharp
+public virtual SensorCompressionType GetCompressionType()
+{
+    return SensorCompressionType.None;
+}
+```
+
+the equivalent code would now be
+
+```csharp
+public CompressionSpec GetCompressionSpec()
+{
+    return CompressionSpec.Default();
+}
+```
+
+- The abstract method `SensorComponent.GetObservationShape()` was removed.
+- The abstract method `SensorComponent.CreateSensor()` was replaced with `CreateSensors()`, which returns an `ISensor[]`.
+
+### Match3 integration changes
+The Match-3 integration utilities were moved from `com.unity.ml-agents.extensions` to `com.unity.ml-agents`.
+
+The `AbstractBoard` interface was changed:
+* `AbstractBoard` no longer contains `Rows`, `Columns`, `NumCellTypes`, and `NumSpecialTypes` fields.
+* `public abstract BoardSize GetMaxBoardSize()` was added as an abstract method. `BoardSize` is a new struct that
+contains `Rows`, `Columns`, `NumCellTypes`, and `NumSpecialTypes` fields, with the same meanings as the old
+`AbstractBoard` fields.
+* `public virtual BoardSize GetCurrentBoardSize()` is an optional method; by default it returns `GetMaxBoardSize()`. If
+you wish to use a single behavior to work with multiple board sizes, override `GetCurrentBoardSize()` to return the
+current `BoardSize`. The values returned by `GetCurrentBoardSize()` must be less than or equal to the corresponding
+values from `GetMaxBoardSize()`.
+
+### GridSensor changes
+The sensor configuration has changed:
+* The sensor implementation has been refactored and exsisting GridSensor created from extension package
+will not work in newer version. Some errors might show up when loading the old sensor in the scene.
+You'll need to remove the old sensor and create a new GridSensor.
+* These parameters names have changed but still refer to the same concept in the sensor: `GridNumSide` -> `GridSize`,
+`RotateToAgent` -> `RotateWithAgent`, `ObserveMask` -> `ColliderMask`, `DetectableObjects` -> `DetectableTags`
+* `DepthType` (`ChanelBase`/`ChannelHot`) option and `ChannelDepth` are removed. Now the default is
+one-hot encoding for detected tag. If you were using original GridSensor without overriding any method,
+switching to new GridSensor will produce similar effect for training although the actual observations
+will be slightly different.
+
+For creating your GridSensor implementation with custom data:
+* To create custom GridSensor, derive from `GridSensorBase` instead of `GridSensor`. Besides overriding
+`GetObjectData()`, you will also need to consider override `GetCellObservationSize()`, `IsDataNormalized()`
+and `GetProcessCollidersMethod()` according to the data you collect. Also you'll need to override
+`GridSensorComponent.GetGridSensors()` and return your custom GridSensor.
+* The input argument `tagIndex` in `GetObjectData()` has changed from 1-indexed to 0-indexed and the
+data type changed from `float` to `int`. The index of first detectable tag will be 0 instead of 1.
+`normalizedDistance` was removed from input.
+* The observation data should be written to the input `dataBuffer` instead of creating and returning a new array.
+* Removed the constraint of all data required to be normalized. You should specify it in `IsDataNormalized()`.
+Sensors with non-normalized data cannot use PNG compression type.
+* The sensor will not further encode the data recieved from `GetObjectData()` anymore. The values
+recieved from `GetObjectData()` will be the observation sent to the trainer.
+
+### LSTM models from previous releases no longer supported
+The way the Unity Inference Engine processes LSTM (recurrent neural networks) has changed. As a result, models
+trained with previous versions of ML-Agents will not be usable at inference if they were trained with a `memory`
+setting in the `.yaml` config file.
+If you want to use a model that has a recurrent neural network in this release of ML-Agents, you need to train
+the model using the python trainer from this release.
+
+
+## Migrating to Release 13
+### Implementing IHeuristic in your IActuator implementations
+ - If you have any custom actuators, you can now implement the `IHeuristicProvider` interface to have your actuator
+  handle the generation of actions when an Agent is running in heuristic mode.
+- `VectorSensor.AddObservation(IEnumerable<float>)` is deprecated. Use `VectorSensor.AddObservation(IList<float>)`
+  instead.
+- `ObservationWriter.AddRange()` is deprecated. Use `ObservationWriter.AddList()` instead.
+- `ActuatorComponent.CreateAcuator()` is deprecated.  Please use override `ActuatorComponent.CreateActuators`
+  instead.  Since `ActuatorComponent.CreateActuator()` is abstract, you will still need to override it in your
+  class until it is removed.  It is only ever called if you don't override `ActuatorComponent.CreateActuators`.
+  You can suppress the warnings by surrounding the method with the following pragma:
+    ```c#
+    #pragma warning disable 672
+    public IActuator CreateActuator() { ... }
+    #pragma warning restore 672
+    ```
+
+
+# Migrating
+## Migrating to Release 11
+### Agent virtual method deprecation
+ - `Agent.CollectDiscreteActionMasks()` was deprecated and should be replaced with `Agent.WriteDiscreteActionMask()`
+ - `Agent.Heuristic(float[])` was deprecated and should be replaced with `Agent.Heuristic(ActionBuffers)`.
+ - `Agent.OnActionReceived(float[])` was deprecated and should be replaced with `Agent.OnActionReceived(ActionBuffers)`.
+ - `Agent.GetAction()` was deprecated and should be replaced with `Agent.GetStoredActionBuffers()`.
+
+The default implementation of these will continue to call the deprecated versions where appropriate. However, the
+deprecated versions may not be compatible with continuous and discrete actions on the same Agent.
+
+### BrainParameters field and method deprecation
+ - `BrainParameters.VectorActionSize` was deprecated; you can now set `BrainParameters.ActionSpec.NumContinuousActions`
+ or `BrainParameters.ActionSpec.BranchSizes` instead.
+ - `BrainParameters.VectorActionSpaceType` was deprecated, since both continuous and discrete actions can now be used.
+ - `BrainParameters.NumActions()` was deprecated. Use  `BrainParameters.ActionSpec.NumContinuousActions` and
+ `BrainParameters.ActionSpec.NumDiscreteActions` instead.
+
+## Migrating from Release 7 to latest
+
+### Important changes
+- Some trainer files were moved. If you were using the `TrainerFactory` class, it was moved to
+the `trainers/trainer` folder.
+- The `components` folder containing `bc` and `reward_signals` code was moved to the `trainers/tf`
+folder
+
+### Steps to Migrate
+- Replace calls to `from mlagents.trainers.trainer_util import TrainerFactory` to `from mlagents.trainers.trainer import TrainerFactory`
+- Replace calls to `from mlagents.trainers.trainer_util import handle_existing_directories` to `from mlagents.trainers.directory_utils import validate_existing_directories`
+- Replace `mlagents.trainers.components` with `mlagents.trainers.tf.components` in your import statements.
+
+
+## Migrating from Release 3 to Release 7
+
+### Important changes
+- The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler
+in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the
+behavior. More information
+[here](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Training-ML-Agents.md).(#4160)
+
+### Steps to Migrate
+- The configuration format for curriculum and parameter randomization has changed. To upgrade your configuration files,
+an upgrade script has been provided. Run `python -m mlagents.trainers.upgrade_config -h` to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To update manually:
+  - If your config file used a `parameter_randomization` section, rename that section to `environment_parameters`
+  - If your config file used a `curriculum` section, you will need to rewrite your curriculum with this [format](Training-ML-Agents.md#curriculum).
+
+## Migrating from Release 1 to Release 3
+
+### Important changes
+- Training artifacts (trained models, summaries) are now found under `results/`
+  instead of `summaries/` and `models/`.
+- Trainer configuration, curriculum configuration, and parameter randomization
+  configuration have all been moved to a single YAML file. (#3791)
+- Trainer configuration format has changed, and using a "default" behavior name has
+  been deprecated. (#3936)
+- `max_step` in the `TerminalStep` and `TerminalSteps` objects was renamed `interrupted`.
+- On the UnityEnvironment API, `get_behavior_names()` and `get_behavior_specs()` methods were combined into the property `behavior_specs` that contains a mapping from behavior names to behavior spec.
+- `use_visual` and `allow_multiple_visual_obs` in the `UnityToGymWrapper` constructor
+were replaced by `allow_multiple_obs` which allows one or more visual observations and
+vector observations to be used simultaneously.
+- `--save-freq` has been removed from the CLI and is now configurable in the trainer configuration
+  file.
+- `--lesson` has been removed from the CLI. Lessons will resume when using `--resume`.
+  To start at a different lesson, modify your Curriculum configuration.
+
+### Steps to Migrate
+- To upgrade your configuration files, an upgrade script has been provided. Run
+  `python -m mlagents.trainers.upgrade_config -h` to see the script usage. Note that you will have
+  had to upgrade to/install the current version of ML-Agents before running the script.
+
+  To do it manually, copy your `<BehaviorName>` sections from `trainer_config.yaml` into a separate trainer configuration file, under a `behaviors` section.
+  The `default` section is no longer needed. This new file should be specific to your environment, and not contain
+  configurations for multiple environments (unless they have the same Behavior Names).
+  - You will need to reformat your trainer settings as per the [example](Training-ML-Agents.md).
+  - If your training uses [curriculum](Training-ML-Agents.md#curriculum-learning), move those configurations under a `curriculum` section.
+  - If your training uses [parameter randomization](Training-ML-Agents.md#environment-parameter-randomization), move
+  the contents of the sampler config to `parameter_randomization` in the main trainer configuration.
+- If you are using `UnityEnvironment` directly, replace `max_step` with `interrupted`
+ in the `TerminalStep` and `TerminalSteps` objects.
+ - Replace usage of `get_behavior_names()` and `get_behavior_specs()` in UnityEnvironment with `behavior_specs`.
+ - If you use the `UnityToGymWrapper`, remove `use_visual` and `allow_multiple_visual_obs`
+ from the constructor and add `allow_multiple_obs = True` if the environment contains either
+ both visual and vector observations or multiple visual observations.
+ - If you were setting `--save-freq` in the CLI, add a `checkpoint_interval` value in your
+  trainer configuration, and set it equal to `save-freq * n_agents_in_scene`.
+
+## Migrating from 0.15 to Release 1
+
+### Important changes
+
+- The `MLAgents` C# namespace was renamed to `Unity.MLAgents`, and other nested
+  namespaces were similarly renamed (#3843).
+- The `--load` and `--train` command-line flags have been deprecated and
+  replaced with `--resume` and `--inference`.
+- Running with the same `--run-id` twice will now throw an error.
+- The `play_against_current_self_ratio` self-play trainer hyperparameter has
+  been renamed to `play_against_latest_model_ratio`
+- Removed the multi-agent gym option from the gym wrapper. For multi-agent
+  scenarios, use the [Low Level Python API](Python-LLAPI.md).
+- The low level Python API has changed. You can look at the document
+  [Low Level Python API documentation](Python-LLAPI.md) for more information. If
+  you use `mlagents-learn` for training, this should be a transparent change.
+- The obsolete `Agent` methods `GiveModel`, `Done`, `InitializeAgent`,
+  `AgentAction` and `AgentReset` have been removed.
+- The signature of `Agent.Heuristic()` was changed to take a `float[]` as a
+  parameter, instead of returning the array. This was done to prevent a common
+  source of error where users would return arrays of the wrong size.
+- The SideChannel API has changed (#3833, #3660) :
+  - Introduced the `SideChannelManager` to register, unregister and access side
+    channels.
+  - `EnvironmentParameters` replaces the default `FloatProperties`. You can
+    access the `EnvironmentParameters` with
+    `Academy.Instance.EnvironmentParameters` on C#. If you were previously
+    creating a `UnityEnvironment` in python and passing it a
+    `FloatPropertiesChannel`, create an `EnvironmentParametersChannel` instead.
+  - `SideChannel.OnMessageReceived` is now a protected method (was public)
+  - SideChannel IncomingMessages methods now take an optional default argument,
+    which is used when trying to read more data than the message contains.
+  - Added a feature to allow sending stats from C# environments to TensorBoard
+    (and other python StatsWriters). To do this from your code, use
+    `Academy.Instance.StatsRecorder.Add(key, value)`(#3660)
+- `num_updates` and `train_interval` for SAC have been replaced with
+  `steps_per_update`.
+- The `UnityEnv` class from the `gym-unity` package was renamed
+  `UnityToGymWrapper` and no longer creates the `UnityEnvironment`. Instead, the
+  `UnityEnvironment` must be passed as input to the constructor of
+  `UnityToGymWrapper`
+- Public fields and properties on several classes were renamed to follow Unity's
+  C# style conventions. All public fields and properties now use "PascalCase"
+  instead of "camelCase"; for example, `Agent.maxStep` was renamed to
+  `Agent.MaxStep`. For a full list of changes, see the pull request. (#3828)
+- `WriteAdapter` was renamed to `ObservationWriter`. (#3834)
+
+### Steps to Migrate
+
+- In C# code, replace `using MLAgents` with `using Unity.MLAgents`. Replace
+  other nested namespaces such as `using MLAgents.Sensors` with
+  `using Unity.MLAgents.Sensors`
+- Replace the `--load` flag with `--resume` when calling `mlagents-learn`, and
+  don't use the `--train` flag as training will happen by default. To run
+  without training, use `--inference`.
+- To force-overwrite files from a pre-existing run, add the `--force`
+  command-line flag.
+- The Jupyter notebooks have been removed from the repository.
+- If your Agent class overrides `Heuristic()`, change the signature to
+  `public override void Heuristic(float[] actionsOut)` and assign values to
+  `actionsOut` instead of returning an array.
+- If you used `SideChannels` you must:
+  - Replace `Academy.FloatProperties` with
+    `Academy.Instance.EnvironmentParameters`.
+  - `Academy.RegisterSideChannel` and `Academy.UnregisterSideChannel` were
+    removed. Use `SideChannelManager.RegisterSideChannel` and
+    `SideChannelManager.UnregisterSideChannel` instead.
+- Set `steps_per_update` to be around equal to the number of agents in your
+  environment, times `num_updates` and divided by `train_interval`.
+- Replace `UnityEnv` with `UnityToGymWrapper` in your code. The constructor no
+  longer takes a file name as input but a fully constructed `UnityEnvironment`
+  instead.
+- Update uses of "camelCase" fields and properties to "PascalCase".
+
+## Migrating from 0.14 to 0.15
+
+### Important changes
+
+- The `Agent.CollectObservations()` virtual method now takes as input a
+  `VectorSensor` sensor as argument. The `Agent.AddVectorObs()` methods were
+  removed.
+- The `SetMask` was renamed to `SetMask` method must now be called on the
+  `DiscreteActionMasker` argument of the `CollectDiscreteActionMasks` virtual
+  method.
+- We consolidated our API for `DiscreteActionMasker`. `SetMask` takes two
+  arguments : the branch index and the list of masked actions for that branch.
+- The `Monitor` class has been moved to the Examples Project. (It was prone to
+  errors during testing)
+- The `MLAgents.Sensors` namespace has been introduced. All sensors classes are
+  part of the `MLAgents.Sensors` namespace.
+- The `MLAgents.SideChannels` namespace has been introduced. All side channel
+  classes are part of the `MLAgents.SideChannels` namespace.
+- The interface for `RayPerceptionSensor.PerceiveStatic()` was changed to take
+  an input class and write to an output class, and the method was renamed to
+  `Perceive()`.
+- The `SetMask` method must now be called on the `DiscreteActionMasker` argument
+  of the `CollectDiscreteActionMasks` method.
+- The method `GetStepCount()` on the Agent class has been replaced with the
+  property getter `StepCount`
+- The `--multi-gpu` option has been removed temporarily.
+- `AgentInfo.actionMasks` has been renamed to `AgentInfo.discreteActionMasks`.
+- `BrainParameters` and `SpaceType` have been removed from the public API
+- `BehaviorParameters` have been removed from the public API.
+- `DecisionRequester` has been made internal (you can still use the
+  DecisionRequesterComponent from the inspector). `RepeatAction` was renamed
+  `TakeActionsBetweenDecisions` for clarity.
+- The following methods in the `Agent` class have been renamed. The original
+  method names will be removed in a later release:
+  - `InitializeAgent()` was renamed to `Initialize()`
+  - `AgentAction()` was renamed to `OnActionReceived()`
+  - `AgentReset()` was renamed to `OnEpsiodeBegin()`
+  - `Done()` was renamed to `EndEpisode()`
+  - `GiveModel()` was renamed to `SetModel()`
+- The `IFloatProperties` interface has been removed.
+- The interface for SideChannels was changed:
+  - In C#, `OnMessageReceived` now takes a `IncomingMessage` argument, and
+    `QueueMessageToSend` takes an `OutgoingMessage` argument.
+  - In python, `on_message_received` now takes a `IncomingMessage` argument, and
+    `queue_message_to_send` takes an `OutgoingMessage` argument.
+  - Automatic stepping for Academy is now controlled from the
+    AutomaticSteppingEnabled property.
+
+### Steps to Migrate
+
+- Add the `using MLAgents.Sensors;` in addition to `using MLAgents;` on top of
+  your Agent's script.
+- Replace your Agent's implementation of `CollectObservations()` with
+  `CollectObservations(VectorSensor sensor)`. In addition, replace all calls to
+  `AddVectorObs()` with `sensor.AddObservation()` or
+  `sensor.AddOneHotObservation()` on the `VectorSensor` passed as argument.
+- Replace your calls to `SetActionMask` on your Agent to
+  `DiscreteActionMasker.SetActionMask` in `CollectDiscreteActionMasks`.
+- If you call `RayPerceptionSensor.PerceiveStatic()` manually, add your inputs
+  to a `RayPerceptionInput`. To get the previous float array output, iterate
+  through `RayPerceptionOutput.rayOutputs` and call
+  `RayPerceptionOutput.RayOutput.ToFloatArray()`.
+- Replace all calls to `Agent.GetStepCount()` with `Agent.StepCount`
+- We strongly recommend replacing the following methods with their new
+  equivalent as they will be removed in a later release:
+  - `InitializeAgent()` to `Initialize()`
+  - `AgentAction()` to `OnActionReceived()`
+  - `AgentReset()` to `OnEpisodeBegin()`
+  - `Done()` to `EndEpisode()`
+  - `GiveModel()` to `SetModel()`
+- Replace `IFloatProperties` variables with `FloatPropertiesChannel` variables.
+- If you implemented custom `SideChannels`, update the signatures of your
+  methods, and add your data to the `OutgoingMessage` or read it from the
+  `IncomingMessage`.
+- Replace calls to Academy.EnableAutomaticStepping()/DisableAutomaticStepping()
+  with Academy.AutomaticSteppingEnabled = true/false.
+
+## Migrating from 0.13 to 0.14
+
+### Important changes
+
+- The `UnitySDK` folder has been split into a Unity Package
+  (`com.unity.ml-agents`) and an examples project (`Project`). Please follow the
+  [Installation Guide](Installation.md) to get up and running with this new repo
+  structure.
+- Several changes were made to how agents are reset and marked as done:
+  - Calling `Done()` on the Agent will now reset it immediately and call the
+    `AgentReset` virtual method. (This is to simplify the previous logic in
+    which the Agent had to wait for the next `EnvironmentStep` to reset)
+  - The "Reset on Done" setting in AgentParameters was removed; this is now
+    effectively always true. `AgentOnDone` virtual method on the Agent has been
+    removed.
+- The `Decision Period` and `On Demand decision` checkbox have been removed from
+  the Agent. On demand decision is now the default (calling `RequestDecision` on
+  the Agent manually.)
+- The Academy class was changed to a singleton, and its virtual methods were
+  removed.
+- Trainer steps are now counted per-Agent, not per-environment as in previous
+  versions. For instance, if you have 10 Agents in the scene, 20 environment
+  steps now corresponds to 200 steps as printed in the terminal and in
+  Tensorboard.
+- Curriculum config files are now YAML formatted and all curricula for a
+  training run are combined into a single file.
+- The `--num-runs` command-line option has been removed from `mlagents-learn`.
+- Several fields on the Agent were removed or made private in order to simplify
+  the interface.
+  - The `agentParameters` field of the Agent has been removed. (Contained only
+    `maxStep` information)
+  - `maxStep` is now a public field on the Agent. (Was moved from
+    `agentParameters`)
+  - The `Info` field of the Agent has been made private. (Was only used
+    internally and not meant to be modified outside of the Agent)
+  - The `GetReward()` method on the Agent has been removed. (It was being
+    confused with `GetCumulativeReward()`)
+  - The `AgentAction` struct no longer contains a `value` field. (Value
+    estimates were not set during inference)
+  - The `GetValueEstimate()` method on the Agent has been removed.
+  - The `UpdateValueAction()` method on the Agent has been removed.
+- The deprecated `RayPerception3D` and `RayPerception2D` classes were removed,
+  and the `legacyHitFractionBehavior` argument was removed from
+  `RayPerceptionSensor.PerceiveStatic()`.
+- RayPerceptionSensor was inconsistent in how it handle scale on the Agent's
+  transform. It now scales the ray length and sphere size for casting as the
+  transform's scale changes.
+
+### Steps to Migrate
+
+- Follow the instructions on how to install the `com.unity.ml-agents` package
+  into your project in the [Installation Guide](Installation.md).
+- If your Agent implemented `AgentOnDone` and did not have the checkbox
+  `Reset On Done` checked in the inspector, you must call the code that was in
+  `AgentOnDone` manually.
+- If you give your Agent a reward or penalty at the end of an episode (e.g. for
+  reaching a goal or falling off of a platform), make sure you call
+  `AddReward()` or `SetReward()` _before_ calling `Done()`. Previously, the
+  order didn't matter.
+- If you were not using `On Demand Decision` for your Agent, you **must** add a
+  `DecisionRequester` component to your Agent GameObject and set its
+  `Decision Period` field to the old `Decision Period` of the Agent.
+- If you have a class that inherits from Academy:
+  - If the class didn't override any of the virtual methods and didn't store any
+    additional data, you can just remove the old script from the scene.
+  - If the class had additional data, create a new MonoBehaviour and store the
+    data in the new MonoBehaviour instead.
+  - If the class overrode the virtual methods, create a new MonoBehaviour and
+    move the logic to it:
+    - Move the InitializeAcademy code to MonoBehaviour.Awake
+    - Move the AcademyStep code to MonoBehaviour.FixedUpdate
+    - Move the OnDestroy code to MonoBehaviour.OnDestroy.
+    - Move the AcademyReset code to a new method and add it to the
+      Academy.OnEnvironmentReset action.
+- Multiply `max_steps` and `summary_freq` in your `trainer_config.yaml` by the
+  number of Agents in the scene.
+- Combine curriculum configs into a single file. See
+  [the WallJump curricula](https://github.com/Unity-Technologies/ml-agents/blob/0.14.1/config/curricula/wall_jump.yaml) for an example of
+  the new curriculum config format. A tool like https://www.json2yaml.com may be
+  useful to help with the conversion.
+- If you have a model trained which uses RayPerceptionSensor and has non-1.0
+  scale in the Agent's transform, it must be retrained.
+
+## Migrating from ML-Agents Toolkit v0.12.0 to v0.13.0
+
+### Important changes
+
+- The low level Python API has changed. You can look at the document
+  [Low Level Python API documentation](Python-LLAPI.md) for more information. This
+  should only affect you if you're writing a custom trainer; if you use
+  `mlagents-learn` for training, this should be a transparent change.
+  - `reset()` on the Low-Level Python API no longer takes a `train_mode`
+    argument. To modify the performance/speed of the engine, you must use an
+    `EngineConfigurationChannel`
+  - `reset()` on the Low-Level Python API no longer takes a `config` argument.
+    `UnityEnvironment` no longer has a `reset_parameters` field. To modify float
+    properties in the environment, you must use a `FloatPropertiesChannel`. For
+    more information, refer to the
+    [Low Level Python API documentation](Python-LLAPI.md)
+- `CustomResetParameters` are now removed.
+- The Academy no longer has a `Training Configuration` nor
+  `Inference Configuration` field in the inspector. To modify the configuration
+  from the Low-Level Python API, use an `EngineConfigurationChannel`. To modify
+  it during training, use the new command line arguments `--width`, `--height`,
+  `--quality-level`, `--time-scale` and `--target-frame-rate` in
+  `mlagents-learn`.
+- The Academy no longer has a `Default Reset Parameters` field in the inspector.
+  The Academy class no longer has a `ResetParameters`. To access shared float
+  properties with Python, use the new `FloatProperties` field on the Academy.
+- Offline Behavioral Cloning has been removed. To learn from demonstrations, use
+  the GAIL and Behavioral Cloning features with either PPO or SAC.
+- `mlagents.envs` was renamed to `mlagents_envs`. The previous repo layout
+  depended on [PEP420](https://www.python.org/dev/peps/pep-0420/), which caused
+  problems with some of our tooling such as mypy and pylint.
+- The official version of Unity ML-Agents supports is now 2018.4 LTS. If you run
+  into issues, please consider deleting your library folder and reponening your
+  projects. You will need to install the Barracuda package into your project in
+  order to ML-Agents to compile correctly.
+
+### Steps to Migrate
+
+- If you had a custom `Training Configuration` in the Academy inspector, you
+  will need to pass your custom configuration at every training run using the
+  new command line arguments `--width`, `--height`, `--quality-level`,
+  `--time-scale` and `--target-frame-rate`.
+- If you were using `--slow` in `mlagents-learn`, you will need to pass your old
+  `Inference Configuration` of the Academy inspector with the new command line
+  arguments `--width`, `--height`, `--quality-level`, `--time-scale` and
+  `--target-frame-rate` instead.
+- Any imports from `mlagents.envs` should be replaced with `mlagents_envs`.
+
+## Migrating from ML-Agents Toolkit v0.11.0 to v0.12.0
+
+### Important Changes
+
+- Text actions and observations, and custom action and observation protos have
+  been removed.
+- RayPerception3D and RayPerception2D are marked deprecated, and will be removed
+  in a future release. They can be replaced by RayPerceptionSensorComponent3D
+  and RayPerceptionSensorComponent2D.
+- The `Use Heuristic` checkbox in Behavior Parameters has been replaced with a
+  `Behavior Type` dropdown menu. This has the following options:
+  - `Default` corresponds to the previous unchecked behavior, meaning that
+    Agents will train if they connect to a python trainer, otherwise they will
+    perform inference.
+  - `Heuristic Only` means the Agent will always use the `Heuristic()` method.
+    This corresponds to having "Use Heuristic" selected in 0.11.0.
+  - `Inference Only` means the Agent will always perform inference.
+- Barracuda was upgraded to 0.3.2, and it is now installed via the Unity Package
+  Manager.
+
+### Steps to Migrate
+
+- We [fixed a bug](https://github.com/Unity-Technologies/ml-agents/pull/2823) in
+  `RayPerception3d.Perceive()` that was causing the `endOffset` to be used
+  incorrectly. However this may produce different behavior from previous
+  versions if you use a non-zero `startOffset`. To reproduce the old behavior,
+  you should increase the value of `endOffset` by `startOffset`. You can
+  verify your raycasts are performing as expected in scene view using the debug
+  rays.
+- If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D
+  (and similarly for 2D). The settings, such as ray angles and detectable tags,
+  are configured on the component now. RayPerception3D would contribute
+  `(# of rays) * (# of tags + 2)` to the State Size in Behavior Parameters, but
+  this is no longer necessary, so you should reduce the State Size by this
+  amount. Making this change will require retraining your model, since the
+  observations that RayPerceptionSensorComponent3D produces are different from
+  the old behavior.
+- If you see messages such as
+  `The type or namespace 'Barracuda' could not be found` or
+  `The type or namespace 'Google' could not be found`, you will need to
+  [install the Barracuda preview package](Installation.md#package-installation).
+
+## Migrating from ML-Agents Toolkit v0.10 to v0.11.0
+
+### Important Changes
+
+- The definition of the gRPC service has changed.
+- The online BC training feature has been removed.
+- The BroadcastHub has been deprecated. If there is a training Python process,
+  all LearningBrains in the scene will automatically be trained. If there is no
+  Python process, inference will be used.
+- The Brain ScriptableObjects have been deprecated. The Brain Parameters are now
+  on the Agent and are referred to as Behavior Parameters. Make sure the
+  Behavior Parameters is attached to the Agent GameObject.
+- To use a heuristic behavior, implement the `Heuristic()` method in the Agent
+  class and check the `use heuristic` checkbox in the Behavior Parameters.
+- Several changes were made to the setup for visual observations (i.e. using
+  Cameras or RenderTextures):
+  - Camera resolutions are no longer stored in the Brain Parameters.
+  - AgentParameters no longer stores lists of Cameras and RenderTextures
+  - To add visual observations to an Agent, you must now attach a
+    CameraSensorComponent or RenderTextureComponent to the agent. The
+    corresponding Camera or RenderTexture can be added to these in the editor,
+    and the resolution and color/grayscale is configured on the component
+    itself.
+
+#### Steps to Migrate
+
+- In order to be able to train, make sure both your ML-Agents Python package and
+  UnitySDK code come from the v0.11 release. Training will not work, for
+  example, if you update the ML-Agents Python package, and only update the API
+  Version in UnitySDK.
+- If your Agents used visual observations, you must add a CameraSensorComponent
+  corresponding to each old Camera in the Agent's camera list (and similarly for
+  RenderTextures).
+- Since Brain ScriptableObjects have been removed, you will need to delete all
+  the Brain ScriptableObjects from your `Assets` folder. Then, add a
+  `Behavior Parameters` component to each `Agent` GameObject. You will then need
+  to complete the fields on the new `Behavior Parameters` component with the
+  BrainParameters of the old Brain.
+
+## Migrating from ML-Agents Toolkit v0.9 to v0.10
+
+### Important Changes
+
+- We have updated the C# code in our repository to be in line with Unity Coding
+  Conventions. This has changed the name of some public facing classes and
+  enums.
+- The example environments have been updated. If you were using these
+  environments to benchmark your training, please note that the resulting
+  rewards may be slightly different in v0.10.
+
+#### Steps to Migrate
+
+- `UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs` and its class
+  `Communicator` have been renamed to
+  `UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs` and `ICommunicator`
+  respectively.
+- The `SpaceType` Enums `discrete`, and `continuous` have been renamed to
+  `Discrete` and `Continuous`.
+- We have removed the `Done` call as well as the capacity to set `Max Steps` on
+  the Academy. Therefore an AcademyReset will never be triggered from C# (only
+  from Python). If you want to reset the simulation after a fixed number of
+  steps, or when an event in the simulation occurs, we recommend looking at our
+  multi-agent example environments (such as FoodCollector). In our examples,
+  groups of Agents can be reset through an "Area" that can reset groups of
+  Agents.
+- The import for `mlagents.envs.UnityEnvironment` was removed. If you are using
+  the Python API, change `from mlagents_envs import UnityEnvironment` to
+  `from mlagents_envs.environment import UnityEnvironment`.
+
+## Migrating from ML-Agents Toolkit v0.8 to v0.9
+
+### Important Changes
+
+- We have changed the way reward signals (including Curiosity) are defined in
+  the `trainer_config.yaml`.
+- When using multiple environments, every "step" is recorded in TensorBoard.
+- The steps in the command line console corresponds to a single step of a single
+  environment. Previously, each step corresponded to one step for all
+  environments (i.e., `num_envs` steps).
+
+#### Steps to Migrate
+
+- If you were overriding any of these following parameters in your config file,
+  remove them from the top-level config and follow the steps below:
+  - `gamma`: Define a new `extrinsic` reward signal and set it's `gamma` to your
+    new gamma.
+  - `use_curiosity`, `curiosity_strength`, `curiosity_enc_size`: Define a
+    `curiosity` reward signal and set its `strength` to `curiosity_strength`,
+    and `encoding_size` to `curiosity_enc_size`. Give it the same `gamma` as
+    your `extrinsic` signal to mimic previous behavior.
+- TensorBoards generated when running multiple environments in v0.8 are not
+  comparable to those generated in v0.9 in terms of step count. Multiply your
+  v0.8 step count by `num_envs` for an approximate comparison. You may need to
+  change `max_steps` in your config as appropriate as well.
+
+## Migrating from ML-Agents Toolkit v0.7 to v0.8
+
+### Important Changes
+
+- We have split the Python packages into two separate packages `ml-agents` and
+  `ml-agents-envs`.
+- `--worker-id` option of `learn.py` has been removed, use `--base-port` instead
+  if you'd like to run multiple instances of `learn.py`.
+
+#### Steps to Migrate
+
+- If you are installing via PyPI, there is no change.
+- If you intend to make modifications to `ml-agents` or `ml-agents-envs` please
+  check the Installing for Development in the
+  [Installation documentation](Installation.md).
+
+## Migrating from ML-Agents Toolkit v0.6 to v0.7
+
+### Important Changes
+
+- We no longer support TFS and are now using the
+  [Unity Inference Engine](Unity-Inference-Engine.md)
+
+#### Steps to Migrate
+
+- Make sure to remove the `ENABLE_TENSORFLOW` flag in your Unity Project
+  settings
+
+## Migrating from ML-Agents Toolkit v0.5 to v0.6
+
+### Important Changes
+
+- Brains are now Scriptable Objects instead of MonoBehaviors.
+- You can no longer modify the type of a Brain. If you want to switch between
+  `PlayerBrain` and `LearningBrain` for multiple agents, you will need to assign
+  a new Brain to each agent separately. **Note:** You can pass the same Brain to
+  multiple agents in a scene by leveraging Unity's prefab system or look for all
+  the agents in a scene using the search bar of the `Hierarchy` window with the
+  word `Agent`.
+
+- We replaced the **Internal** and **External** Brain with **Learning Brain**.
+  When you need to train a model, you need to drag it into the `Broadcast Hub`
+  inside the `Academy` and check the `Control` checkbox.
+- We removed the `Broadcast` checkbox of the Brain, to use the broadcast
+  functionality, you need to drag the Brain into the `Broadcast Hub`.
+- When training multiple Brains at the same time, each model is now stored into
+  a separate model file rather than in the same file under different graph
+  scopes.
+- The **Learning Brain** graph scope, placeholder names, output names and custom
+  placeholders can no longer be modified.
+
+#### Steps to Migrate
+
+- To update a scene from v0.5 to v0.6, you must:
+  - Remove the `Brain` GameObjects in the scene. (Delete all of the Brain
+    GameObjects under Academy in the scene.)
+  - Create new `Brain` Scriptable Objects using `Assets -> Create -> ML-Agents`
+    for each type of the Brain you plan to use, and put the created files under
+    a folder called Brains within your project.
+  - Edit their `Brain Parameters` to be the same as the parameters used in the
+    `Brain` GameObjects.
+  - Agents have a `Brain` field in the Inspector, you need to drag the
+    appropriate Brain ScriptableObject in it.
+  - The Academy has a `Broadcast Hub` field in the inspector, which is list of
+    brains used in the scene. To train or control your Brain from the
+    `mlagents-learn` Python script, you need to drag the relevant
+    `LearningBrain` ScriptableObjects used in your scene into entries into this
+    list.
+
+## Migrating from ML-Agents Toolkit v0.4 to v0.5
+
+### Important
+
+- The Unity project `unity-environment` has been renamed `UnitySDK`.
+- The `python` folder has been renamed to `ml-agents`. It now contains two
+  packages, `mlagents.env` and `mlagents.trainers`. `mlagents.env` can be used
+  to interact directly with a Unity environment, while `mlagents.trainers`
+  contains the classes for training agents.
+- The supported Unity version has changed from `2017.1 or later` to
+  `2017.4 or later`. 2017.4 is an LTS (Long Term Support) version that helps us
+  maintain good quality and support. Earlier versions of Unity might still work,
+  but you may encounter an
+  [error](FAQ.md#instance-of-corebraininternal-couldnt-be-created) listed here.
+
+### Unity API
+
+- Discrete Actions now use [branches](https://arxiv.org/abs/1711.08946). You can
+  now specify concurrent discrete actions. You will need to update the Brain
+  Parameters in the Brain Inspector in all your environments that use discrete
+  actions. Refer to the
+  [discrete action documentation](Learning-Environment-Design-Agents.md#discrete-action-space)
+  for more information.
+
+### Python API
+
+- In order to run a training session, you can now use the command
+  `mlagents-learn` instead of `python3 learn.py` after installing the `mlagents`
+  packages. This change is documented
+  [here](Training-ML-Agents.md#training-with-mlagents-learn). For example, if we
+  previously ran
+
+  ```sh
+  python3 learn.py 3DBall --train
+  ```
+
+  from the `python` subdirectory (which is changed to `ml-agents` subdirectory
+  in v0.5), we now run
+
+  ```sh
+  mlagents-learn config/trainer_config.yaml --env=3DBall --train
+  ```
+
+  from the root directory where we installed the ML-Agents Toolkit.
+
+- It is now required to specify the path to the yaml trainer configuration file
+  when running `mlagents-learn`. For an example trainer configuration file, see
+  [trainer_config.yaml](https://github.com/Unity-Technologies/ml-agents/blob/0.5.0a/config/trainer_config.yaml). An example of passing a
+  trainer configuration to `mlagents-learn` is shown above.
+- The environment name is now passed through the `--env` option.
+- Curriculum learning has been changed. In summary:
+  - Curriculum files for the same environment must now be placed into a folder.
+    Each curriculum file should be named after the Brain whose curriculum it
+    specifies.
+  - `min_lesson_length` now specifies the minimum number of episodes in a lesson
+    and affects reward thresholding.
+  - It is no longer necessary to specify the `Max Steps` of the Academy to use
+    curriculum learning.
+
+## Migrating from ML-Agents Toolkit v0.3 to v0.4
+
+### Unity API
+
+- `using MLAgents;` needs to be added in all of the C# scripts that use
+  ML-Agents.
+
+### Python API
+
+- We've changed some of the Python packages dependencies in requirement.txt
+  file. Make sure to run `pip3 install -e .` within your `ml-agents/python`
+  folder to update your Python packages.
+
+## Migrating from ML-Agents Toolkit v0.2 to v0.3
+
+There are a large number of new features and improvements in the ML-Agents
+toolkit v0.3 which change both the training process and Unity API in ways which
+will cause incompatibilities with environments made using older versions. This
+page is designed to highlight those changes for users familiar with v0.1 or v0.2
+in order to ensure a smooth transition.
+
+### Important
+
+- The ML-Agents Toolkit is no longer compatible with Python 2.
+
+### Python Training
+
+- The training script `ppo.py` and `PPO.ipynb` Python notebook have been
+  replaced with a single `learn.py` script as the launching point for training
+  with ML-Agents. For more information on using `learn.py`, see
+  [here](Training-ML-Agents.md#training-with-mlagents-learn).
+- Hyperparameters for training Brains are now stored in the
+  `trainer_config.yaml` file. For more information on using this file, see
+  [here](Training-ML-Agents.md#training-configurations).
+
+### Unity API
+
+- Modifications to an Agent's rewards must now be done using either
+  `AddReward()` or `SetReward()`.
+- Setting an Agent to done now requires the use of the `Done()` method.
+- `CollectStates()` has been replaced by `CollectObservations()`, which now no
+  longer returns a list of floats.
+- To collect observations, call `AddVectorObs()` within `CollectObservations()`.
+  Note that you can call `AddVectorObs()` with floats, integers, lists and
+  arrays of floats, Vector3 and Quaternions.
+- `AgentStep()` has been replaced by `AgentAction()`.
+- `WaitTime()` has been removed.
+- The `Frame Skip` field of the Academy is replaced by the Agent's
+  `Decision Frequency` field, enabling the Agent to make decisions at different
+  frequencies.
+- The names of the inputs in the Internal Brain have been changed. You must
+  replace `state` with `vector_observation` and `observation` with
+  `visual_observation`. In addition, you must remove the `epsilon` placeholder.
+
+### Semantics
+
+In order to more closely align with the terminology used in the Reinforcement
+Learning field, and to be more descriptive, we have changed the names of some of
+the concepts used in ML-Agents. The changes are highlighted in the table below.
+
+| Old - v0.2 and earlier | New - v0.3 and later |
+| ---------------------- | -------------------- |
+| State                  | Vector Observation   |
+| Observation            | Visual Observation   |
+| Action                 | Vector Action        |
+| N/A                    | Text Observation     |
+| N/A                    | Text Action          |
diff --git a/docs/Package-Settings.md b/docs/Package-Settings.md
new file mode 100644
index 0000000000000000000000000000000000000000..4bc00973c42efa0bc4217fc61e471dc78dc1e208
--- /dev/null
+++ b/docs/Package-Settings.md
@@ -0,0 +1,33 @@
+# ML-Agents Package Settings
+
+ML-Agents Package Settings contains settings that apply to the whole project.
+It allows you to configure ML-Agents-specific settings in the Editor.  These settings are available for use in both the Editor and Player.
+
+You can find them at `Edit` > `Project Settings...` > `ML-Agents`. It lists out all the available settings and their default values.
+
+
+## Create Custom Settings
+In order to to use your own settings for your project, you'll need to create a settings asset.
+
+You can do this by clicking the `Create Settings Asset` buttom or clicking the gear on the top right and select `New Settings Asset...`.
+The asset file can be placed anywhere in the `Asset/` folder in your project.
+After Creating the settings asset, you'll be able to modify the settings for your project and your settings will be saved in the asset.
+
+![Package Settings](images/package-settings.png)
+
+
+## Multiple Custom Settings for Different Scenarios
+You can create multiple settings assets in one project.
+
+By clicking the gear on the top right you'll see all available settings listed in the drop-down menu to choose from.
+
+This allows you to create different settings for different scenatios. For example, you can create two
+separate settings for training and inference, and specify which one you want to use according to what you're currently running.
+
+![Multiple Settings](images/multiple-settings.png)
+
+
+
+
+
+
diff --git a/docs/Profiling-Python.md b/docs/Profiling-Python.md
new file mode 100644
index 0000000000000000000000000000000000000000..0e69aefcd05dad2eff7476949baf6c5782dbbc15
--- /dev/null
+++ b/docs/Profiling-Python.md
@@ -0,0 +1,72 @@
+# Profiling in Python
+
+As part of the ML-Agents Tookit, we provide a lightweight profiling system, in
+order to identity hotspots in the training process and help spot regressions
+from changes.
+
+Timers are hierarchical, meaning that the time tracked in a block of code can be
+further split into other blocks if desired. This also means that a function that
+is called from multiple places in the code will appear in multiple places in the
+timing output.
+
+All timers operate using a "global" instance by default, but this can be
+overridden if necessary (mainly for testing).
+
+## Adding Profiling
+
+There are two ways to indicate code should be included in profiling. The
+simplest way is to add the `@timed` decorator to a function or method of
+interested.
+
+```python
+class TrainerController:
+    # ....
+    @timed
+    def advance(self, env: EnvManager) -> int:
+        # do stuff
+```
+
+You can also used the `hierarchical_timer` context manager.
+
+```python
+with hierarchical_timer("communicator.exchange"):
+    outputs = self.communicator.exchange(step_input)
+```
+
+The context manager may be easier than the `@timed` decorator for profiling
+different parts of a large function, or profiling calls to abstract methods that
+might not use decorator.
+
+## Output
+
+By default, at the end of training, timers are collected and written in json
+format to `{summaries_dir}/{run_id}_timers.json`. The output consists of node
+objects with the following keys:
+
+- total (float): The total time in seconds spent in the block, including child
+  calls.
+- count (int): The number of times the block was called.
+- self (float): The total time in seconds spent in the block, excluding child
+  calls.
+- children (dictionary): A dictionary of child nodes, keyed by the node name.
+- is_parallel (bool): Indicates that the block of code was executed in multiple
+  threads or processes (see below). This is optional and defaults to false.
+
+### Parallel execution
+
+#### Subprocesses
+
+For code that executes in multiple processes (for example,
+SubprocessEnvManager), we periodically send the timer information back to the
+"main" process, aggregate the timers there, and flush them in the subprocess.
+Note that (depending on the number of processes) this can result in timers where
+the total time may exceed the parent's total time. This is analogous to the
+difference between "real" and "user" values reported from the unix `time`
+command. In the timer output, blocks that were run in parallel are indicated by
+the `is_parallel` flag.
+
+#### Threads
+
+Timers currently use `time.perf_counter()` to track time spent, which may not
+give accurate results for multiple threads. If this is problematic, set
+`threaded: false` in your trainer configuration.
diff --git a/docs/Python-Custom-Trainer-Plugin.md b/docs/Python-Custom-Trainer-Plugin.md
new file mode 100644
index 0000000000000000000000000000000000000000..2881d7d506a445945df2898f59e7a1289d819ed5
--- /dev/null
+++ b/docs/Python-Custom-Trainer-Plugin.md
@@ -0,0 +1,51 @@
+# Unity Ml-Agents Custom trainers Plugin
+
+As an attempt to bring a wider variety of reinforcement learning algorithms to our users, we have added custom trainers
+capabilities. we introduce an extensible plugin system to define new trainers based on the High level trainer API
+in `Ml-agents` Package. This will allow rerouting `mlagents-learn` CLI to custom trainers and extending the config files
+with hyper-parameters specific to your new trainers. We will expose a high-level extensible trainer (both on-policy,
+and off-policy trainers) optimizer and hyperparameter classes with documentation for the use of this plugin. For more
+infromation on how python plugin system works see [Plugin interfaces](Training-Plugins.md).
+## Overview
+Model-free RL algorithms generally fall into two broad categories: on-policy and off-policy. On-policy algorithms perform updates based on data gathered from the current policy. Off-policy algorithms learn a Q function from a buffer of previous data, then use this Q function to make decisions. Off-policy algorithms have three key benefits in the context of ML-Agents: They tend to use fewer samples than on-policy as they can pull and re-use data from the buffer many times. They allow player demonstrations to be inserted in-line with RL data into the buffer, enabling new ways of doing imitation learning by streaming player data.
+
+To add new custom trainers to ML-agents, you would need to create a new python package.
+To give you an idea of how to structure your package, we have created a [mlagents_trainer_plugin](../ml-agents-trainer-plugin) package ourselves as an
+example, with implementation of `A2c` and `DQN` algorithms. You would need a `setup.py` file to list extra requirements and
+register the new RL algorithm in ml-agents ecosystem and be able to call `mlagents-learn` CLI with your customized
+configuration.
+
+
+```shell
+├── mlagents_trainer_plugin
+│    ├── __init__.py
+│    ├── a2c
+│    │    ├── __init__.py
+│    │    ├── a2c_3DBall.yaml
+│    │    ├── a2c_optimizer.py
+│    │    └── a2c_trainer.py
+│    └── dqn
+│        ├── __init__.py
+│        ├── dqn_basic.yaml
+│        ├── dqn_optimizer.py
+│        └── dqn_trainer.py
+└── setup.py
+```
+## Installation and Execution
+If you haven't already, follow the [installation instructions](Installation.md). Once you have the `ml-agents-env` and `ml-agents` packages you can install the plugin package. From the repository's root directory install `ml-agents-trainer-plugin` (or replace with the name of your plugin folder).
+
+```sh
+pip3 install -e <./ml-agents-trainer-plugin>
+```
+
+Following the previous installations your package is added as an entrypoint and you can use a config file with new
+trainers:
+```sh
+mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name>
+--env <env-executable>
+```
+
+## Tutorial
+Here’s a step-by-step [tutorial](Tutorial-Custom-Trainer-Plugin.md) on how to write a setup file and extend ml-agents trainers, optimizers, and
+hyperparameter settings.To extend ML-agents classes see references on
+[trainers](Python-On-Off-Policy-Trainer-Documentation.md) and [Optimizer](Python-Optimizer-Documentation.md).
diff --git a/docs/Python-Gym-API-Documentation.md b/docs/Python-Gym-API-Documentation.md
new file mode 100644
index 0000000000000000000000000000000000000000..b35771fc46c0e870e86a946edb9a2e327ed34291
--- /dev/null
+++ b/docs/Python-Gym-API-Documentation.md
@@ -0,0 +1,161 @@
+# Table of Contents
+
+* [mlagents\_envs.envs.unity\_gym\_env](#mlagents_envs.envs.unity_gym_env)
+  * [UnityGymException](#mlagents_envs.envs.unity_gym_env.UnityGymException)
+  * [UnityToGymWrapper](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper)
+    * [\_\_init\_\_](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__)
+    * [reset](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.reset)
+    * [step](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step)
+    * [render](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render)
+    * [close](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.close)
+    * [seed](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed)
+  * [ActionFlattener](#mlagents_envs.envs.unity_gym_env.ActionFlattener)
+    * [\_\_init\_\_](#mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__)
+    * [lookup\_action](#mlagents_envs.envs.unity_gym_env.ActionFlattener.lookup_action)
+
+<a name="mlagents_envs.envs.unity_gym_env"></a>
+# mlagents\_envs.envs.unity\_gym\_env
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityGymException"></a>
+## UnityGymException Objects
+
+```python
+class UnityGymException(error.Error)
+```
+
+Any error related to the gym wrapper of ml-agents.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper"></a>
+## UnityToGymWrapper Objects
+
+```python
+class UnityToGymWrapper(gym.Env)
+```
+
+Provides Gym wrapper for Unity Learning Environments.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None)
+```
+
+Environment initialization
+
+**Arguments**:
+
+- `unity_env`: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes.
+- `uint8_visual`: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).
+- `flatten_branched`: If True, turn branched discrete action spaces into a Discrete space rather than
+    MultiDiscrete.
+- `allow_multiple_obs`: If True, return a list of np.ndarrays as observations with the first elements
+    containing the visual observations and the last element containing the array of vector observations.
+    If False, returns a single np.ndarray containing either only a single visual observation or the array of
+    vector observations.
+- `action_space_seed`: If non-None, will be used to set the random seed on created gym.Space instances.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.reset"></a>
+#### reset
+
+```python
+ | reset() -> Union[List[np.ndarray], np.ndarray]
+```
+
+Resets the state of the environment and returns an initial observation.
+Returns: observation (object/list): the initial observation of the
+space.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step"></a>
+#### step
+
+```python
+ | step(action: List[Any]) -> GymStepResult
+```
+
+Run one timestep of the environment's dynamics. When end of
+episode is reached, you are responsible for calling `reset()`
+to reset this environment's state.
+Accepts an action and returns a tuple (observation, reward, done, info).
+
+**Arguments**:
+
+- `action` _object/list_ - an action provided by the environment
+
+**Returns**:
+
+- `observation` _object/list_ - agent's observation of the current environment
+  reward (float/list) : amount of reward returned after previous action
+- `done` _boolean/list_ - whether the episode has ended.
+- `info` _dict_ - contains auxiliary diagnostic information.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render"></a>
+#### render
+
+```python
+ | render(mode="rgb_array")
+```
+
+Return the latest visual observations.
+Note that it will not render a new frame of the environment.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.close"></a>
+#### close
+
+```python
+ | close() -> None
+```
+
+Override _close in your subclass to perform any necessary cleanup.
+Environments will automatically close() themselves when
+garbage collected or when the program exits.
+
+<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed"></a>
+#### seed
+
+```python
+ | seed(seed: Any = None) -> None
+```
+
+Sets the seed for this env's random number generator(s).
+Currently not implemented.
+
+<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener"></a>
+## ActionFlattener Objects
+
+```python
+class ActionFlattener()
+```
+
+Flattens branched discrete action spaces into single-branch discrete action spaces.
+
+<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(branched_action_space)
+```
+
+Initialize the flattener.
+
+**Arguments**:
+
+- `branched_action_space`: A List containing the sizes of each branch of the action
+space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.
+
+<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.lookup_action"></a>
+#### lookup\_action
+
+```python
+ | lookup_action(action)
+```
+
+Convert a scalar discrete action into a unique set of branched actions.
+
+**Arguments**:
+
+- `action`: A scalar value representing one of the discrete actions.
+
+**Returns**:
+
+The List containing the branched actions.
diff --git a/docs/Python-Gym-API.md b/docs/Python-Gym-API.md
new file mode 100644
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+++ b/docs/Python-Gym-API.md
@@ -0,0 +1,357 @@
+# Unity ML-Agents Gym Wrapper
+
+A common way in which machine learning researchers interact with simulation
+environments is via a wrapper provided by OpenAI called `gym`. For more
+information on the gym interface, see [here](https://github.com/openai/gym).
+
+We provide a gym wrapper and instructions for using it with existing machine
+learning algorithms which utilize gym. Our wrapper provides interfaces on top of
+our `UnityEnvironment` class, which is the default way of interfacing with a
+Unity environment via Python.
+
+## Installation
+
+The gym wrapper is part of the `mlgents_envs` package. Please refer to the
+[mlagents_envs installation instructions](ML-Agents-Envs-README.md).
+
+
+## Using the Gym Wrapper
+
+The gym interface is available from `gym_unity.envs`. To launch an environment
+from the root of the project repository use:
+
+```python
+from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
+
+env = UnityToGymWrapper(unity_env, uint8_visual, flatten_branched, allow_multiple_obs)
+```
+
+- `unity_env` refers to the Unity environment to be wrapped.
+
+- `uint8_visual` refers to whether to output visual observations as `uint8`
+  values (0-255). Many common Gym environments (e.g. Atari) do this. By default
+  they will be floats (0.0-1.0). Defaults to `False`.
+
+- `flatten_branched` will flatten a branched discrete action space into a Gym
+  Discrete. Otherwise, it will be converted into a MultiDiscrete. Defaults to
+  `False`.
+
+- `allow_multiple_obs` will return a list of observations. The first elements
+  contain the visual observations and the last element contains the array of
+  vector observations. If False the environment returns a single array (containing
+  a single visual observations, if present, otherwise the vector observation).
+  Defaults to `False`.
+
+- `action_space_seed` is the optional seed for action sampling. If non-None, will
+  be used to set the random seed on created gym.Space instances.
+
+The returned environment `env` will function as a gym.
+
+## Limitations
+
+- It is only possible to use an environment with a **single** Agent.
+- By default, the first visual observation is provided as the `observation`, if
+  present. Otherwise, vector observations are provided. You can receive all
+  visual and vector observations by using the `allow_multiple_obs=True` option in
+  the gym parameters. If set to `True`, you will receive a list of `observation`
+  instead of only one.
+- The `TerminalSteps` or `DecisionSteps` output from the environment can still
+  be accessed from the `info` provided by `env.step(action)`.
+- Stacked vector observations are not supported.
+- Environment registration for use with `gym.make()` is currently not supported.
+- Calling env.render() will not render a new frame of the environment. It will
+  return the latest visual observation if using visual observations.
+
+## Running OpenAI Baselines Algorithms
+
+OpenAI provides a set of open-source maintained and tested Reinforcement
+Learning algorithms called the [Baselines](https://github.com/openai/baselines).
+
+Using the provided Gym wrapper, it is possible to train ML-Agents environments
+using these algorithms. This requires the creation of custom training scripts to
+launch each algorithm. In most cases these scripts can be created by making
+slight modifications to the ones provided for Atari and Mujoco environments.
+
+These examples were tested with baselines version 0.1.6.
+
+### Example - DQN Baseline
+
+In order to train an agent to play the `GridWorld` environment using the
+Baselines DQN algorithm, you first need to install the baselines package using
+pip:
+
+```
+pip install git+git://github.com/openai/baselines
+```
+
+Next, create a file called `train_unity.py`. Then create an `/envs/` directory
+and build the environment to that directory. For more information on
+building Unity environments, see
+[here](../docs/Learning-Environment-Executable.md). Note that because of
+limitations of the DQN baseline, the environment must have a single visual
+observation, a single discrete action and a single Agent in the scene.
+Add the following code to the `train_unity.py` file:
+
+```python
+import gym
+
+from baselines import deepq
+from baselines import logger
+
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
+
+
+def main():
+  unity_env = UnityEnvironment( < path - to - environment >)
+  env = UnityToGymWrapper(unity_env, uint8_visual=True)
+  logger.configure('./logs')  # Change to log in a different directory
+  act = deepq.learn(
+    env,
+    "cnn",  # For visual inputs
+    lr=2.5e-4,
+    total_timesteps=1000000,
+    buffer_size=50000,
+    exploration_fraction=0.05,
+    exploration_final_eps=0.1,
+    print_freq=20,
+    train_freq=5,
+    learning_starts=20000,
+    target_network_update_freq=50,
+    gamma=0.99,
+    prioritized_replay=False,
+    checkpoint_freq=1000,
+    checkpoint_path='./logs',  # Change to save model in a different directory
+    dueling=True
+  )
+  print("Saving model to unity_model.pkl")
+  act.save("unity_model.pkl")
+
+
+if __name__ == '__main__':
+  main()
+```
+
+To start the training process, run the following from the directory containing
+`train_unity.py`:
+
+```sh
+python -m train_unity
+```
+
+### Other Algorithms
+
+Other algorithms in the Baselines repository can be run using scripts similar to
+the examples from the baselines package. In most cases, the primary changes
+needed to use a Unity environment are to import `UnityToGymWrapper`, and to
+replace the environment creation code, typically `gym.make()`, with a call to
+`UnityToGymWrapper(unity_environment)` passing the environment as input.
+
+A typical rule of thumb is that for vision-based environments, modification
+should be done to Atari training scripts, and for vector observation
+environments, modification should be done to Mujoco scripts.
+
+Some algorithms will make use of `make_env()` or `make_mujoco_env()` functions.
+You can define a similar function for Unity environments. An example of such a
+method using the PPO2 baseline:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.envs import UnityToGymWrapper
+from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.bench import Monitor
+from baselines import logger
+import baselines.ppo2.ppo2 as ppo2
+
+import os
+
+try:
+  from mpi4py import MPI
+except ImportError:
+  MPI = None
+
+
+def make_unity_env(env_directory, num_env, visual, start_index=0):
+  """
+  Create a wrapped, monitored Unity environment.
+  """
+
+  def make_env(rank, use_visual=True):  # pylint: disable=C0111
+    def _thunk():
+      unity_env = UnityEnvironment(env_directory, base_port=5000 + rank)
+      env = UnityToGymWrapper(unity_env, uint8_visual=True)
+      env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
+      return env
+
+    return _thunk
+
+  if visual:
+    return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
+  else:
+    rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+    return DummyVecEnv([make_env(rank, use_visual=False)])
+
+
+def main():
+  env = make_unity_env( < path - to - environment >, 4, True)
+  ppo2.learn(
+    network="mlp",
+    env=env,
+    total_timesteps=100000,
+    lr=1e-3,
+  )
+
+
+if __name__ == '__main__':
+  main()
+```
+
+## Run Google Dopamine Algorithms
+
+Google provides a framework [Dopamine](https://github.com/google/dopamine), and
+implementations of algorithms, e.g. DQN, Rainbow, and the C51 variant of
+Rainbow. Using the Gym wrapper, we can run Unity environments using Dopamine.
+
+First, after installing the Gym wrapper, clone the Dopamine repository.
+
+```
+git clone https://github.com/google/dopamine
+```
+
+Then, follow the appropriate install instructions as specified on
+[Dopamine's homepage](https://github.com/google/dopamine). Note that the
+Dopamine guide specifies using a virtualenv. If you choose to do so, make sure
+your unity_env package is also installed within the same virtualenv as Dopamine.
+
+### Adapting Dopamine's Scripts
+
+First, open `dopamine/atari/run_experiment.py`. Alternatively, copy the entire
+`atari` folder, and name it something else (e.g. `unity`). If you choose the
+copy approach, be sure to change the package names in the import statements in
+`train.py` to your new directory.
+
+Within `run_experiment.py`, we will need to make changes to which environment is
+instantiated, just as in the Baselines example. At the top of the file, insert
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.envs import UnityToGymWrapper
+```
+
+to import the Gym Wrapper. Navigate to the `create_atari_environment` method in
+the same file, and switch to instantiating a Unity environment by replacing the
+method with the following code.
+
+```python
+    game_version = 'v0' if sticky_actions else 'v4'
+    full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version)
+    unity_env = UnityEnvironment(<path-to-environment>)
+    env = UnityToGymWrapper(unity_env, uint8_visual=True)
+    return env
+```
+
+`<path-to-environment>` is the path to your built Unity executable. For more
+information on building Unity environments, see
+[here](../docs/Learning-Environment-Executable.md), and note the Limitations
+section below.
+
+Note that we are not using the preprocessor from Dopamine, as it uses many
+Atari-specific calls. Furthermore, frame-skipping can be done from within Unity,
+rather than on the Python side.
+
+### Limitations
+
+Since Dopamine is designed around variants of DQN, it is only compatible with
+discrete action spaces, and specifically the Discrete Gym space. For
+environments that use branched discrete action spaces, you can enable the
+`flatten_branched` parameter in `UnityToGymWrapper`, which treats each
+combination of branched actions as separate actions.
+
+Furthermore, when building your environments, ensure that your Agent is using
+visual observations with greyscale enabled, and that the dimensions of the
+visual observations is 84 by 84 (matches the parameter found in `dqn_agent.py`
+and `rainbow_agent.py`). Dopamine's agents currently do not automatically adapt
+to the observation dimensions or number of channels.
+
+### Hyperparameters
+
+The hyperparameters provided by Dopamine are tailored to the Atari games, and
+you will likely need to adjust them for ML-Agents environments. Here is a sample
+`dopamine/agents/rainbow/configs/rainbow.gin` file that is known to work with
+a simple GridWorld.
+
+```python
+import dopamine.agents.rainbow.rainbow_agent
+import dopamine.unity.run_experiment
+import dopamine.replay_memory.prioritized_replay_buffer
+import gin.tf.external_configurables
+
+RainbowAgent.num_atoms = 51
+RainbowAgent.stack_size = 1
+RainbowAgent.vmax = 10.
+RainbowAgent.gamma = 0.99
+RainbowAgent.update_horizon = 3
+RainbowAgent.min_replay_history = 20000  # agent steps
+RainbowAgent.update_period = 5
+RainbowAgent.target_update_period = 50  # agent steps
+RainbowAgent.epsilon_train = 0.1
+RainbowAgent.epsilon_eval = 0.01
+RainbowAgent.epsilon_decay_period = 50000  # agent steps
+RainbowAgent.replay_scheme = 'prioritized'
+RainbowAgent.tf_device = '/cpu:0'  # use '/cpu:*' for non-GPU version
+RainbowAgent.optimizer = @tf.train.AdamOptimizer()
+
+tf.train.AdamOptimizer.learning_rate = 0.00025
+tf.train.AdamOptimizer.epsilon = 0.0003125
+
+Runner.game_name = "Unity" # any name can be used here
+Runner.sticky_actions = False
+Runner.num_iterations = 200
+Runner.training_steps = 10000  # agent steps
+Runner.evaluation_steps = 500  # agent steps
+Runner.max_steps_per_episode = 27000  # agent steps
+
+WrappedPrioritizedReplayBuffer.replay_capacity = 1000000
+WrappedPrioritizedReplayBuffer.batch_size = 32
+```
+
+This example assumed you copied `atari` to a separate folder named `unity`.
+Replace `unity` in `import dopamine.unity.run_experiment` with the folder you
+copied your `run_experiment.py` and `trainer.py` files to. If you directly
+modified the existing files, then use `atari` here.
+
+### Starting a Run
+
+You can now run Dopamine as you would normally:
+
+```
+python -um dopamine.unity.train \
+  --agent_name=rainbow \
+  --base_dir=/tmp/dopamine \
+  --gin_files='dopamine/agents/rainbow/configs/rainbow.gin'
+```
+
+Again, we assume that you've copied `atari` into a separate folder. Remember to
+replace `unity` with the directory you copied your files into. If you edited the
+Atari files directly, this should be `atari`.
+
+### Example: GridWorld
+
+As a baseline, here are rewards over time for the three algorithms provided with
+Dopamine as run on the GridWorld example environment. All Dopamine (DQN,
+Rainbow, C51) runs were done with the same epsilon, epsilon decay, replay
+history, training steps, and buffer settings as specified above. Note that the
+first 20000 steps are used to pre-fill the training buffer, and no learning
+happens.
+
+We provide results from our PPO implementation and the DQN from Baselines as
+reference. Note that all runs used the same greyscale GridWorld as Dopamine. For
+PPO, `num_layers` was set to 2, and all other hyperparameters are the default
+for GridWorld in `config/ppo/GridWorld.yaml`. For Baselines DQN, the provided
+hyperparameters in the previous section are used. Note that Baselines implements
+certain features (e.g. dueling-Q) that are not enabled in Dopamine DQN.
+
+
+![Dopamine on GridWorld](images/dopamine_gridworld_plot.png)
+
diff --git a/docs/Python-LLAPI-Documentation.md b/docs/Python-LLAPI-Documentation.md
new file mode 100644
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@@ -0,0 +1,1352 @@
+# Table of Contents
+
+* [mlagents\_envs.base\_env](#mlagents_envs.base_env)
+  * [DecisionStep](#mlagents_envs.base_env.DecisionStep)
+  * [DecisionSteps](#mlagents_envs.base_env.DecisionSteps)
+    * [agent\_id\_to\_index](#mlagents_envs.base_env.DecisionSteps.agent_id_to_index)
+    * [\_\_getitem\_\_](#mlagents_envs.base_env.DecisionSteps.__getitem__)
+    * [empty](#mlagents_envs.base_env.DecisionSteps.empty)
+  * [TerminalStep](#mlagents_envs.base_env.TerminalStep)
+  * [TerminalSteps](#mlagents_envs.base_env.TerminalSteps)
+    * [agent\_id\_to\_index](#mlagents_envs.base_env.TerminalSteps.agent_id_to_index)
+    * [\_\_getitem\_\_](#mlagents_envs.base_env.TerminalSteps.__getitem__)
+    * [empty](#mlagents_envs.base_env.TerminalSteps.empty)
+  * [ActionTuple](#mlagents_envs.base_env.ActionTuple)
+    * [discrete\_dtype](#mlagents_envs.base_env.ActionTuple.discrete_dtype)
+  * [ActionSpec](#mlagents_envs.base_env.ActionSpec)
+    * [is\_discrete](#mlagents_envs.base_env.ActionSpec.is_discrete)
+    * [is\_continuous](#mlagents_envs.base_env.ActionSpec.is_continuous)
+    * [discrete\_size](#mlagents_envs.base_env.ActionSpec.discrete_size)
+    * [empty\_action](#mlagents_envs.base_env.ActionSpec.empty_action)
+    * [random\_action](#mlagents_envs.base_env.ActionSpec.random_action)
+    * [create\_continuous](#mlagents_envs.base_env.ActionSpec.create_continuous)
+    * [create\_discrete](#mlagents_envs.base_env.ActionSpec.create_discrete)
+  * [DimensionProperty](#mlagents_envs.base_env.DimensionProperty)
+    * [UNSPECIFIED](#mlagents_envs.base_env.DimensionProperty.UNSPECIFIED)
+    * [NONE](#mlagents_envs.base_env.DimensionProperty.NONE)
+    * [TRANSLATIONAL\_EQUIVARIANCE](#mlagents_envs.base_env.DimensionProperty.TRANSLATIONAL_EQUIVARIANCE)
+    * [VARIABLE\_SIZE](#mlagents_envs.base_env.DimensionProperty.VARIABLE_SIZE)
+  * [ObservationType](#mlagents_envs.base_env.ObservationType)
+    * [DEFAULT](#mlagents_envs.base_env.ObservationType.DEFAULT)
+    * [GOAL\_SIGNAL](#mlagents_envs.base_env.ObservationType.GOAL_SIGNAL)
+  * [ObservationSpec](#mlagents_envs.base_env.ObservationSpec)
+  * [BehaviorSpec](#mlagents_envs.base_env.BehaviorSpec)
+  * [BaseEnv](#mlagents_envs.base_env.BaseEnv)
+    * [step](#mlagents_envs.base_env.BaseEnv.step)
+    * [reset](#mlagents_envs.base_env.BaseEnv.reset)
+    * [close](#mlagents_envs.base_env.BaseEnv.close)
+    * [behavior\_specs](#mlagents_envs.base_env.BaseEnv.behavior_specs)
+    * [set\_actions](#mlagents_envs.base_env.BaseEnv.set_actions)
+    * [set\_action\_for\_agent](#mlagents_envs.base_env.BaseEnv.set_action_for_agent)
+    * [get\_steps](#mlagents_envs.base_env.BaseEnv.get_steps)
+* [mlagents\_envs.environment](#mlagents_envs.environment)
+  * [UnityEnvironment](#mlagents_envs.environment.UnityEnvironment)
+    * [\_\_init\_\_](#mlagents_envs.environment.UnityEnvironment.__init__)
+    * [close](#mlagents_envs.environment.UnityEnvironment.close)
+* [mlagents\_envs.registry](#mlagents_envs.registry)
+* [mlagents\_envs.registry.unity\_env\_registry](#mlagents_envs.registry.unity_env_registry)
+  * [UnityEnvRegistry](#mlagents_envs.registry.unity_env_registry.UnityEnvRegistry)
+    * [register](#mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.register)
+    * [register\_from\_yaml](#mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.register_from_yaml)
+    * [clear](#mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.clear)
+    * [\_\_getitem\_\_](#mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.__getitem__)
+* [mlagents\_envs.side\_channel](#mlagents_envs.side_channel)
+* [mlagents\_envs.side\_channel.raw\_bytes\_channel](#mlagents_envs.side_channel.raw_bytes_channel)
+  * [RawBytesChannel](#mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel)
+    * [on\_message\_received](#mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.on_message_received)
+    * [get\_and\_clear\_received\_messages](#mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.get_and_clear_received_messages)
+    * [send\_raw\_data](#mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.send_raw_data)
+* [mlagents\_envs.side\_channel.outgoing\_message](#mlagents_envs.side_channel.outgoing_message)
+  * [OutgoingMessage](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage)
+    * [\_\_init\_\_](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.__init__)
+    * [write\_bool](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_bool)
+    * [write\_int32](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_int32)
+    * [write\_float32](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_float32)
+    * [write\_float32\_list](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_float32_list)
+    * [write\_string](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_string)
+    * [set\_raw\_bytes](#mlagents_envs.side_channel.outgoing_message.OutgoingMessage.set_raw_bytes)
+* [mlagents\_envs.side\_channel.engine\_configuration\_channel](#mlagents_envs.side_channel.engine_configuration_channel)
+  * [EngineConfigurationChannel](#mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel)
+    * [on\_message\_received](#mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.on_message_received)
+    * [set\_configuration\_parameters](#mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.set_configuration_parameters)
+    * [set\_configuration](#mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.set_configuration)
+* [mlagents\_envs.side\_channel.side\_channel\_manager](#mlagents_envs.side_channel.side_channel_manager)
+  * [SideChannelManager](#mlagents_envs.side_channel.side_channel_manager.SideChannelManager)
+    * [process\_side\_channel\_message](#mlagents_envs.side_channel.side_channel_manager.SideChannelManager.process_side_channel_message)
+    * [generate\_side\_channel\_messages](#mlagents_envs.side_channel.side_channel_manager.SideChannelManager.generate_side_channel_messages)
+* [mlagents\_envs.side\_channel.stats\_side\_channel](#mlagents_envs.side_channel.stats_side_channel)
+  * [StatsSideChannel](#mlagents_envs.side_channel.stats_side_channel.StatsSideChannel)
+    * [on\_message\_received](#mlagents_envs.side_channel.stats_side_channel.StatsSideChannel.on_message_received)
+    * [get\_and\_reset\_stats](#mlagents_envs.side_channel.stats_side_channel.StatsSideChannel.get_and_reset_stats)
+* [mlagents\_envs.side\_channel.incoming\_message](#mlagents_envs.side_channel.incoming_message)
+  * [IncomingMessage](#mlagents_envs.side_channel.incoming_message.IncomingMessage)
+    * [\_\_init\_\_](#mlagents_envs.side_channel.incoming_message.IncomingMessage.__init__)
+    * [read\_bool](#mlagents_envs.side_channel.incoming_message.IncomingMessage.read_bool)
+    * [read\_int32](#mlagents_envs.side_channel.incoming_message.IncomingMessage.read_int32)
+    * [read\_float32](#mlagents_envs.side_channel.incoming_message.IncomingMessage.read_float32)
+    * [read\_float32\_list](#mlagents_envs.side_channel.incoming_message.IncomingMessage.read_float32_list)
+    * [read\_string](#mlagents_envs.side_channel.incoming_message.IncomingMessage.read_string)
+    * [get\_raw\_bytes](#mlagents_envs.side_channel.incoming_message.IncomingMessage.get_raw_bytes)
+* [mlagents\_envs.side\_channel.float\_properties\_channel](#mlagents_envs.side_channel.float_properties_channel)
+  * [FloatPropertiesChannel](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel)
+    * [on\_message\_received](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.on_message_received)
+    * [set\_property](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.set_property)
+    * [get\_property](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.get_property)
+    * [list\_properties](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.list_properties)
+    * [get\_property\_dict\_copy](#mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.get_property_dict_copy)
+* [mlagents\_envs.side\_channel.environment\_parameters\_channel](#mlagents_envs.side_channel.environment_parameters_channel)
+  * [EnvironmentParametersChannel](#mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel)
+    * [set\_float\_parameter](#mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_float_parameter)
+    * [set\_uniform\_sampler\_parameters](#mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_uniform_sampler_parameters)
+    * [set\_gaussian\_sampler\_parameters](#mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_gaussian_sampler_parameters)
+    * [set\_multirangeuniform\_sampler\_parameters](#mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_multirangeuniform_sampler_parameters)
+* [mlagents\_envs.side\_channel.side\_channel](#mlagents_envs.side_channel.side_channel)
+  * [SideChannel](#mlagents_envs.side_channel.side_channel.SideChannel)
+    * [queue\_message\_to\_send](#mlagents_envs.side_channel.side_channel.SideChannel.queue_message_to_send)
+    * [on\_message\_received](#mlagents_envs.side_channel.side_channel.SideChannel.on_message_received)
+    * [channel\_id](#mlagents_envs.side_channel.side_channel.SideChannel.channel_id)
+
+<a name="mlagents_envs.base_env"></a>
+# mlagents\_envs.base\_env
+
+Python Environment API for the ML-Agents Toolkit
+The aim of this API is to expose Agents evolving in a simulation
+to perform reinforcement learning on.
+This API supports multi-agent scenarios and groups similar Agents (same
+observations, actions spaces and behavior) together. These groups of Agents are
+identified by their BehaviorName.
+For performance reasons, the data of each group of agents is processed in a
+batched manner. Agents are identified by a unique AgentId identifier that
+allows tracking of Agents across simulation steps. Note that there is no
+guarantee that the number or order of the Agents in the state will be
+consistent across simulation steps.
+A simulation steps corresponds to moving the simulation forward until at least
+one agent in the simulation sends its observations to Python again. Since
+Agents can request decisions at different frequencies, a simulation step does
+not necessarily correspond to a fixed simulation time increment.
+
+<a name="mlagents_envs.base_env.DecisionStep"></a>
+## DecisionStep Objects
+
+```python
+class DecisionStep(NamedTuple)
+```
+
+Contains the data a single Agent collected since the last
+simulation step.
+ - obs is a list of numpy arrays observations collected by the agent.
+ - reward is a float. Corresponds to the rewards collected by the agent
+ since the last simulation step.
+ - agent_id is an int and an unique identifier for the corresponding Agent.
+ - action_mask is an optional list of one dimensional array of booleans.
+ Only available when using multi-discrete actions.
+ Each array corresponds to an action branch. Each array contains a mask
+ for each action of the branch. If true, the action is not available for
+ the agent during this simulation step.
+
+<a name="mlagents_envs.base_env.DecisionSteps"></a>
+## DecisionSteps Objects
+
+```python
+class DecisionSteps(Mapping)
+```
+
+Contains the data a batch of similar Agents collected since the last
+simulation step. Note that all Agents do not necessarily have new
+information to send at each simulation step. Therefore, the ordering of
+agents and the batch size of the DecisionSteps are not fixed across
+simulation steps.
+ - obs is a list of numpy arrays observations collected by the batch of
+ agent. Each obs has one extra dimension compared to DecisionStep: the
+ first dimension of the array corresponds to the batch size of the batch.
+ - reward is a float vector of length batch size. Corresponds to the
+ rewards collected by each agent since the last simulation step.
+ - agent_id is an int vector of length batch size containing unique
+ identifier for the corresponding Agent. This is used to track Agents
+ across simulation steps.
+ - action_mask is an optional list of two dimensional array of booleans.
+ Only available when using multi-discrete actions.
+ Each array corresponds to an action branch. The first dimension of each
+ array is the batch size and the second contains a mask for each action of
+ the branch. If true, the action is not available for the agent during
+ this simulation step.
+
+<a name="mlagents_envs.base_env.DecisionSteps.agent_id_to_index"></a>
+#### agent\_id\_to\_index
+
+```python
+ | @property
+ | agent_id_to_index() -> Dict[AgentId, int]
+```
+
+**Returns**:
+
+A Dict that maps agent_id to the index of those agents in
+this DecisionSteps.
+
+<a name="mlagents_envs.base_env.DecisionSteps.__getitem__"></a>
+#### \_\_getitem\_\_
+
+```python
+ | __getitem__(agent_id: AgentId) -> DecisionStep
+```
+
+returns the DecisionStep for a specific agent.
+
+**Arguments**:
+
+- `agent_id`: The id of the agent
+
+**Returns**:
+
+The DecisionStep
+
+<a name="mlagents_envs.base_env.DecisionSteps.empty"></a>
+#### empty
+
+```python
+ | @staticmethod
+ | empty(spec: "BehaviorSpec") -> "DecisionSteps"
+```
+
+Returns an empty DecisionSteps.
+
+**Arguments**:
+
+- `spec`: The BehaviorSpec for the DecisionSteps
+
+<a name="mlagents_envs.base_env.TerminalStep"></a>
+## TerminalStep Objects
+
+```python
+class TerminalStep(NamedTuple)
+```
+
+Contains the data a single Agent collected when its episode ended.
+ - obs is a list of numpy arrays observations collected by the agent.
+ - reward is a float. Corresponds to the rewards collected by the agent
+ since the last simulation step.
+ - interrupted is a bool. Is true if the Agent was interrupted since the last
+ decision step. For example, if the Agent reached the maximum number of steps for
+ the episode.
+ - agent_id is an int and an unique identifier for the corresponding Agent.
+
+<a name="mlagents_envs.base_env.TerminalSteps"></a>
+## TerminalSteps Objects
+
+```python
+class TerminalSteps(Mapping)
+```
+
+Contains the data a batch of Agents collected when their episode
+terminated. All Agents present in the TerminalSteps have ended their
+episode.
+ - obs is a list of numpy arrays observations collected by the batch of
+ agent. Each obs has one extra dimension compared to DecisionStep: the
+ first dimension of the array corresponds to the batch size of the batch.
+ - reward is a float vector of length batch size. Corresponds to the
+ rewards collected by each agent since the last simulation step.
+ - interrupted is an array of booleans of length batch size. Is true if the
+ associated Agent was interrupted since the last decision step. For example, if the
+ Agent reached the maximum number of steps for the episode.
+ - agent_id is an int vector of length batch size containing unique
+ identifier for the corresponding Agent. This is used to track Agents
+ across simulation steps.
+
+<a name="mlagents_envs.base_env.TerminalSteps.agent_id_to_index"></a>
+#### agent\_id\_to\_index
+
+```python
+ | @property
+ | agent_id_to_index() -> Dict[AgentId, int]
+```
+
+**Returns**:
+
+A Dict that maps agent_id to the index of those agents in
+this TerminalSteps.
+
+<a name="mlagents_envs.base_env.TerminalSteps.__getitem__"></a>
+#### \_\_getitem\_\_
+
+```python
+ | __getitem__(agent_id: AgentId) -> TerminalStep
+```
+
+returns the TerminalStep for a specific agent.
+
+**Arguments**:
+
+- `agent_id`: The id of the agent
+
+**Returns**:
+
+obs, reward, done, agent_id and optional action mask for a
+specific agent
+
+<a name="mlagents_envs.base_env.TerminalSteps.empty"></a>
+#### empty
+
+```python
+ | @staticmethod
+ | empty(spec: "BehaviorSpec") -> "TerminalSteps"
+```
+
+Returns an empty TerminalSteps.
+
+**Arguments**:
+
+- `spec`: The BehaviorSpec for the TerminalSteps
+
+<a name="mlagents_envs.base_env.ActionTuple"></a>
+## ActionTuple Objects
+
+```python
+class ActionTuple(_ActionTupleBase)
+```
+
+An object whose fields correspond to actions of different types.
+Continuous and discrete actions are numpy arrays of type float32 and
+int32, respectively and are type checked on construction.
+Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size),
+respectively. Note, this also holds when continuous or discrete size is
+zero.
+
+<a name="mlagents_envs.base_env.ActionTuple.discrete_dtype"></a>
+#### discrete\_dtype
+
+```python
+ | @property
+ | discrete_dtype() -> np.dtype
+```
+
+The dtype of a discrete action.
+
+<a name="mlagents_envs.base_env.ActionSpec"></a>
+## ActionSpec Objects
+
+```python
+class ActionSpec(NamedTuple)
+```
+
+A NamedTuple containing utility functions and information about the action spaces
+for a group of Agents under the same behavior.
+- num_continuous_actions is an int corresponding to the number of floats which
+constitute the action.
+- discrete_branch_sizes is a Tuple of int where each int corresponds to
+the number of discrete actions available to the agent on an independent action branch.
+
+<a name="mlagents_envs.base_env.ActionSpec.is_discrete"></a>
+#### is\_discrete
+
+```python
+ | is_discrete() -> bool
+```
+
+Returns true if this Behavior uses discrete actions
+
+<a name="mlagents_envs.base_env.ActionSpec.is_continuous"></a>
+#### is\_continuous
+
+```python
+ | is_continuous() -> bool
+```
+
+Returns true if this Behavior uses continuous actions
+
+<a name="mlagents_envs.base_env.ActionSpec.discrete_size"></a>
+#### discrete\_size
+
+```python
+ | @property
+ | discrete_size() -> int
+```
+
+Returns a an int corresponding to the number of discrete branches.
+
+<a name="mlagents_envs.base_env.ActionSpec.empty_action"></a>
+#### empty\_action
+
+```python
+ | empty_action(n_agents: int) -> ActionTuple
+```
+
+Generates ActionTuple corresponding to an empty action (all zeros)
+for a number of agents.
+
+**Arguments**:
+
+- `n_agents`: The number of agents that will have actions generated
+
+<a name="mlagents_envs.base_env.ActionSpec.random_action"></a>
+#### random\_action
+
+```python
+ | random_action(n_agents: int) -> ActionTuple
+```
+
+Generates ActionTuple corresponding to a random action (either discrete
+or continuous) for a number of agents.
+
+**Arguments**:
+
+- `n_agents`: The number of agents that will have actions generated
+
+<a name="mlagents_envs.base_env.ActionSpec.create_continuous"></a>
+#### create\_continuous
+
+```python
+ | @staticmethod
+ | create_continuous(continuous_size: int) -> "ActionSpec"
+```
+
+Creates an ActionSpec that is homogenously continuous
+
+<a name="mlagents_envs.base_env.ActionSpec.create_discrete"></a>
+#### create\_discrete
+
+```python
+ | @staticmethod
+ | create_discrete(discrete_branches: Tuple[int]) -> "ActionSpec"
+```
+
+Creates an ActionSpec that is homogenously discrete
+
+<a name="mlagents_envs.base_env.DimensionProperty"></a>
+## DimensionProperty Objects
+
+```python
+class DimensionProperty(IntFlag)
+```
+
+The dimension property of a dimension of an observation.
+
+<a name="mlagents_envs.base_env.DimensionProperty.UNSPECIFIED"></a>
+#### UNSPECIFIED
+
+No properties specified.
+
+<a name="mlagents_envs.base_env.DimensionProperty.NONE"></a>
+#### NONE
+
+No Property of the observation in that dimension. Observation can be processed with
+Fully connected networks.
+
+<a name="mlagents_envs.base_env.DimensionProperty.TRANSLATIONAL_EQUIVARIANCE"></a>
+#### TRANSLATIONAL\_EQUIVARIANCE
+
+Means it is suitable to do a convolution in this dimension.
+
+<a name="mlagents_envs.base_env.DimensionProperty.VARIABLE_SIZE"></a>
+#### VARIABLE\_SIZE
+
+Means that there can be a variable number of observations in this dimension.
+The observations are unordered.
+
+<a name="mlagents_envs.base_env.ObservationType"></a>
+## ObservationType Objects
+
+```python
+class ObservationType(Enum)
+```
+
+An Enum which defines the type of information carried in the observation
+of the agent.
+
+<a name="mlagents_envs.base_env.ObservationType.DEFAULT"></a>
+#### DEFAULT
+
+Observation information is generic.
+
+<a name="mlagents_envs.base_env.ObservationType.GOAL_SIGNAL"></a>
+#### GOAL\_SIGNAL
+
+Observation contains goal information for current task.
+
+<a name="mlagents_envs.base_env.ObservationSpec"></a>
+## ObservationSpec Objects
+
+```python
+class ObservationSpec(NamedTuple)
+```
+
+A NamedTuple containing information about the observation of Agents.
+- shape is a Tuple of int : It corresponds to the shape of
+an observation's dimensions.
+- dimension_property is a Tuple of DimensionProperties flag, one flag for each
+dimension.
+- observation_type is an enum of ObservationType.
+
+<a name="mlagents_envs.base_env.BehaviorSpec"></a>
+## BehaviorSpec Objects
+
+```python
+class BehaviorSpec(NamedTuple)
+```
+
+A NamedTuple containing information about the observation and action
+spaces for a group of Agents under the same behavior.
+- observation_specs is a List of ObservationSpec NamedTuple containing
+information about the information of the Agent's observations such as their shapes.
+The order of the ObservationSpec is the same as the order of the observations of an
+agent.
+- action_spec is an ActionSpec NamedTuple.
+
+<a name="mlagents_envs.base_env.BaseEnv"></a>
+## BaseEnv Objects
+
+```python
+class BaseEnv(ABC)
+```
+
+<a name="mlagents_envs.base_env.BaseEnv.step"></a>
+#### step
+
+```python
+ | @abstractmethod
+ | step() -> None
+```
+
+Signals the environment that it must move the simulation forward
+by one step.
+
+<a name="mlagents_envs.base_env.BaseEnv.reset"></a>
+#### reset
+
+```python
+ | @abstractmethod
+ | reset() -> None
+```
+
+Signals the environment that it must reset the simulation.
+
+<a name="mlagents_envs.base_env.BaseEnv.close"></a>
+#### close
+
+```python
+ | @abstractmethod
+ | close() -> None
+```
+
+Signals the environment that it must close.
+
+<a name="mlagents_envs.base_env.BaseEnv.behavior_specs"></a>
+#### behavior\_specs
+
+```python
+ | @property
+ | @abstractmethod
+ | behavior_specs() -> MappingType[str, BehaviorSpec]
+```
+
+Returns a Mapping from behavior names to behavior specs.
+Agents grouped under the same behavior name have the same action and
+observation specs, and are expected to behave similarly in the
+environment.
+Note that new keys can be added to this mapping as new policies are instantiated.
+
+<a name="mlagents_envs.base_env.BaseEnv.set_actions"></a>
+#### set\_actions
+
+```python
+ | @abstractmethod
+ | set_actions(behavior_name: BehaviorName, action: ActionTuple) -> None
+```
+
+Sets the action for all of the agents in the simulation for the next
+step. The Actions must be in the same order as the order received in
+the DecisionSteps.
+
+**Arguments**:
+
+- `behavior_name`: The name of the behavior the agents are part of
+- `action`: ActionTuple tuple of continuous and/or discrete action.
+Actions are np.arrays with dimensions  (n_agents, continuous_size) and
+(n_agents, discrete_size), respectively.
+
+<a name="mlagents_envs.base_env.BaseEnv.set_action_for_agent"></a>
+#### set\_action\_for\_agent
+
+```python
+ | @abstractmethod
+ | set_action_for_agent(behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple) -> None
+```
+
+Sets the action for one of the agents in the simulation for the next
+step.
+
+**Arguments**:
+
+- `behavior_name`: The name of the behavior the agent is part of
+- `agent_id`: The id of the agent the action is set for
+- `action`: ActionTuple tuple of continuous and/or discrete action
+Actions are np.arrays with dimensions  (1, continuous_size) and
+(1, discrete_size), respectively. Note, this initial dimensions of 1 is because
+this action is meant for a single agent.
+
+<a name="mlagents_envs.base_env.BaseEnv.get_steps"></a>
+#### get\_steps
+
+```python
+ | @abstractmethod
+ | get_steps(behavior_name: BehaviorName) -> Tuple[DecisionSteps, TerminalSteps]
+```
+
+Retrieves the steps of the agents that requested a step in the
+simulation.
+
+**Arguments**:
+
+- `behavior_name`: The name of the behavior the agents are part of
+
+**Returns**:
+
+A tuple containing :
+- A DecisionSteps NamedTuple containing the observations,
+the rewards, the agent ids and the action masks for the Agents
+of the specified behavior. These Agents need an action this step.
+- A TerminalSteps NamedTuple containing the observations,
+rewards, agent ids and interrupted flags of the agents that had their
+episode terminated last step.
+
+<a name="mlagents_envs.environment"></a>
+# mlagents\_envs.environment
+
+<a name="mlagents_envs.environment.UnityEnvironment"></a>
+## UnityEnvironment Objects
+
+```python
+class UnityEnvironment(BaseEnv)
+```
+
+<a name="mlagents_envs.environment.UnityEnvironment.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(file_name: Optional[str] = None, worker_id: int = 0, base_port: Optional[int] = None, seed: int = 0, no_graphics: bool = False, timeout_wait: int = 60, additional_args: Optional[List[str]] = None, side_channels: Optional[List[SideChannel]] = None, log_folder: Optional[str] = None, num_areas: int = 1)
+```
+
+Starts a new unity environment and establishes a connection with the environment.
+Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
+Ensure that the network where training takes place is secure.
+
+:string file_name: Name of Unity environment binary.
+:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
+If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used.
+:int worker_id: Offset from base_port. Used for training multiple environments simultaneously.
+:bool no_graphics: Whether to run the Unity simulator in no-graphics mode
+:int timeout_wait: Time (in seconds) to wait for connection from environment.
+:list args: Addition Unity command line arguments
+:list side_channels: Additional side channel for no-rl communication with Unity
+:str log_folder: Optional folder to write the Unity Player log file into.  Requires absolute path.
+
+<a name="mlagents_envs.environment.UnityEnvironment.close"></a>
+#### close
+
+```python
+ | close()
+```
+
+Sends a shutdown signal to the unity environment, and closes the socket connection.
+
+<a name="mlagents_envs.registry"></a>
+# mlagents\_envs.registry
+
+<a name="mlagents_envs.registry.unity_env_registry"></a>
+# mlagents\_envs.registry.unity\_env\_registry
+
+<a name="mlagents_envs.registry.unity_env_registry.UnityEnvRegistry"></a>
+## UnityEnvRegistry Objects
+
+```python
+class UnityEnvRegistry(Mapping)
+```
+
+### UnityEnvRegistry
+Provides a library of Unity environments that can be launched without the need
+of downloading the Unity Editor.
+The UnityEnvRegistry implements a Map, to access an entry of the Registry, use:
+```python
+registry = UnityEnvRegistry()
+entry = registry[<environment_identifyier>]
+```
+An entry has the following properties :
+ * `identifier` : Uniquely identifies this environment
+ * `expected_reward` : Corresponds to the reward an agent must obtained for the task
+ to be considered completed.
+ * `description` : A human readable description of the environment.
+
+To launch a Unity environment from a registry entry, use the `make` method:
+```python
+registry = UnityEnvRegistry()
+env = registry[<environment_identifyier>].make()
+```
+
+<a name="mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.register"></a>
+#### register
+
+```python
+ | register(new_entry: BaseRegistryEntry) -> None
+```
+
+Registers a new BaseRegistryEntry to the registry. The
+BaseRegistryEntry.identifier value will be used as indexing key.
+If two are more environments are registered under the same key, the most
+recentry added will replace the others.
+
+<a name="mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.register_from_yaml"></a>
+#### register\_from\_yaml
+
+```python
+ | register_from_yaml(path_to_yaml: str) -> None
+```
+
+Registers the environments listed in a yaml file (either local or remote). Note
+that the entries are registered lazily: the registration will only happen when
+an environment is accessed.
+The yaml file must have the following format :
+```yaml
+environments:
+- <identifier of the first environment>:
+    expected_reward: <expected reward of the environment>
+    description: | <a multi line description of the environment>
+      <continued multi line description>
+    linux_url: <The url for the Linux executable zip file>
+    darwin_url: <The url for the OSX executable zip file>
+    win_url: <The url for the Windows executable zip file>
+
+- <identifier of the second environment>:
+    expected_reward: <expected reward of the environment>
+    description: | <a multi line description of the environment>
+      <continued multi line description>
+    linux_url: <The url for the Linux executable zip file>
+    darwin_url: <The url for the OSX executable zip file>
+    win_url: <The url for the Windows executable zip file>
+
+- ...
+```
+
+**Arguments**:
+
+- `path_to_yaml`: A local path or url to the yaml file
+
+<a name="mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.clear"></a>
+#### clear
+
+```python
+ | clear() -> None
+```
+
+Deletes all entries in the registry.
+
+<a name="mlagents_envs.registry.unity_env_registry.UnityEnvRegistry.__getitem__"></a>
+#### \_\_getitem\_\_
+
+```python
+ | __getitem__(identifier: str) -> BaseRegistryEntry
+```
+
+Returns the BaseRegistryEntry with the provided identifier. BaseRegistryEntry
+can then be used to make a Unity Environment.
+
+**Arguments**:
+
+- `identifier`: The identifier of the BaseRegistryEntry
+
+**Returns**:
+
+The associated BaseRegistryEntry
+
+<a name="mlagents_envs.side_channel"></a>
+# mlagents\_envs.side\_channel
+
+<a name="mlagents_envs.side_channel.raw_bytes_channel"></a>
+# mlagents\_envs.side\_channel.raw\_bytes\_channel
+
+<a name="mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel"></a>
+## RawBytesChannel Objects
+
+```python
+class RawBytesChannel(SideChannel)
+```
+
+This is an example of what the SideChannel for raw bytes exchange would
+look like. Is meant to be used for general research purpose.
+
+<a name="mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.on_message_received"></a>
+#### on\_message\_received
+
+```python
+ | on_message_received(msg: IncomingMessage) -> None
+```
+
+Is called by the environment to the side channel. Can be called
+multiple times per step if multiple messages are meant for that
+SideChannel.
+
+<a name="mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.get_and_clear_received_messages"></a>
+#### get\_and\_clear\_received\_messages
+
+```python
+ | get_and_clear_received_messages() -> List[bytes]
+```
+
+returns a list of bytearray received from the environment.
+
+<a name="mlagents_envs.side_channel.raw_bytes_channel.RawBytesChannel.send_raw_data"></a>
+#### send\_raw\_data
+
+```python
+ | send_raw_data(data: bytearray) -> None
+```
+
+Queues a message to be sent by the environment at the next call to
+step.
+
+<a name="mlagents_envs.side_channel.outgoing_message"></a>
+# mlagents\_envs.side\_channel.outgoing\_message
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage"></a>
+## OutgoingMessage Objects
+
+```python
+class OutgoingMessage()
+```
+
+Utility class for forming the message that is written to a SideChannel.
+All data is written in little-endian format using the struct module.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__()
+```
+
+Create an OutgoingMessage with an empty buffer.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_bool"></a>
+#### write\_bool
+
+```python
+ | write_bool(b: bool) -> None
+```
+
+Append a boolean value.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_int32"></a>
+#### write\_int32
+
+```python
+ | write_int32(i: int) -> None
+```
+
+Append an integer value.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_float32"></a>
+#### write\_float32
+
+```python
+ | write_float32(f: float) -> None
+```
+
+Append a float value. It will be truncated to 32-bit precision.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_float32_list"></a>
+#### write\_float32\_list
+
+```python
+ | write_float32_list(float_list: List[float]) -> None
+```
+
+Append a list of float values. They will be truncated to 32-bit precision.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.write_string"></a>
+#### write\_string
+
+```python
+ | write_string(s: str) -> None
+```
+
+Append a string value. Internally, it will be encoded to ascii, and the
+encoded length will also be written to the message.
+
+<a name="mlagents_envs.side_channel.outgoing_message.OutgoingMessage.set_raw_bytes"></a>
+#### set\_raw\_bytes
+
+```python
+ | set_raw_bytes(buffer: bytearray) -> None
+```
+
+Set the internal buffer to a new bytearray. This will overwrite any existing data.
+
+**Arguments**:
+
+- `buffer`:
+
+**Returns**:
+
+
+
+<a name="mlagents_envs.side_channel.engine_configuration_channel"></a>
+# mlagents\_envs.side\_channel.engine\_configuration\_channel
+
+<a name="mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel"></a>
+## EngineConfigurationChannel Objects
+
+```python
+class EngineConfigurationChannel(SideChannel)
+```
+
+This is the SideChannel for engine configuration exchange. The data in the
+engine configuration is as follows :
+ - int width;
+ - int height;
+ - int qualityLevel;
+ - float timeScale;
+ - int targetFrameRate;
+ - int captureFrameRate;
+
+<a name="mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.on_message_received"></a>
+#### on\_message\_received
+
+```python
+ | on_message_received(msg: IncomingMessage) -> None
+```
+
+Is called by the environment to the side channel. Can be called
+multiple times per step if multiple messages are meant for that
+SideChannel.
+Note that Python should never receive an engine configuration from
+Unity
+
+<a name="mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.set_configuration_parameters"></a>
+#### set\_configuration\_parameters
+
+```python
+ | set_configuration_parameters(width: Optional[int] = None, height: Optional[int] = None, quality_level: Optional[int] = None, time_scale: Optional[float] = None, target_frame_rate: Optional[int] = None, capture_frame_rate: Optional[int] = None) -> None
+```
+
+Sets the engine configuration. Takes as input the configurations of the
+engine.
+
+**Arguments**:
+
+- `width`: Defines the width of the display. (Must be set alongside height)
+- `height`: Defines the height of the display. (Must be set alongside width)
+- `quality_level`: Defines the quality level of the simulation.
+- `time_scale`: Defines the multiplier for the deltatime in the
+simulation. If set to a higher value, time will pass faster in the
+simulation but the physics might break.
+- `target_frame_rate`: Instructs simulation to try to render at a
+specified frame rate.
+- `capture_frame_rate`: Instructs the simulation to consider time between
+updates to always be constant, regardless of the actual frame rate.
+
+<a name="mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel.set_configuration"></a>
+#### set\_configuration
+
+```python
+ | set_configuration(config: EngineConfig) -> None
+```
+
+Sets the engine configuration. Takes as input an EngineConfig.
+
+<a name="mlagents_envs.side_channel.side_channel_manager"></a>
+# mlagents\_envs.side\_channel.side\_channel\_manager
+
+<a name="mlagents_envs.side_channel.side_channel_manager.SideChannelManager"></a>
+## SideChannelManager Objects
+
+```python
+class SideChannelManager()
+```
+
+<a name="mlagents_envs.side_channel.side_channel_manager.SideChannelManager.process_side_channel_message"></a>
+#### process\_side\_channel\_message
+
+```python
+ | process_side_channel_message(data: bytes) -> None
+```
+
+Separates the data received from Python into individual messages for each
+registered side channel and calls on_message_received on them.
+
+**Arguments**:
+
+- `data`: The packed message sent by Unity
+
+<a name="mlagents_envs.side_channel.side_channel_manager.SideChannelManager.generate_side_channel_messages"></a>
+#### generate\_side\_channel\_messages
+
+```python
+ | generate_side_channel_messages() -> bytearray
+```
+
+Gathers the messages that the registered side channels will send to Unity
+and combines them into a single message ready to be sent.
+
+<a name="mlagents_envs.side_channel.stats_side_channel"></a>
+# mlagents\_envs.side\_channel.stats\_side\_channel
+
+<a name="mlagents_envs.side_channel.stats_side_channel.StatsSideChannel"></a>
+## StatsSideChannel Objects
+
+```python
+class StatsSideChannel(SideChannel)
+```
+
+Side channel that receives (string, float) pairs from the environment, so that they can eventually
+be passed to a StatsReporter.
+
+<a name="mlagents_envs.side_channel.stats_side_channel.StatsSideChannel.on_message_received"></a>
+#### on\_message\_received
+
+```python
+ | on_message_received(msg: IncomingMessage) -> None
+```
+
+Receive the message from the environment, and save it for later retrieval.
+
+**Arguments**:
+
+- `msg`:
+
+**Returns**:
+
+
+
+<a name="mlagents_envs.side_channel.stats_side_channel.StatsSideChannel.get_and_reset_stats"></a>
+#### get\_and\_reset\_stats
+
+```python
+ | get_and_reset_stats() -> EnvironmentStats
+```
+
+Returns the current stats, and resets the internal storage of the stats.
+
+**Returns**:
+
+
+
+<a name="mlagents_envs.side_channel.incoming_message"></a>
+# mlagents\_envs.side\_channel.incoming\_message
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage"></a>
+## IncomingMessage Objects
+
+```python
+class IncomingMessage()
+```
+
+Utility class for reading the message written to a SideChannel.
+Values must be read in the order they were written.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(buffer: bytes, offset: int = 0)
+```
+
+Create a new IncomingMessage from the bytes.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.read_bool"></a>
+#### read\_bool
+
+```python
+ | read_bool(default_value: bool = False) -> bool
+```
+
+Read a boolean value from the message buffer.
+
+**Arguments**:
+
+- `default_value`: Default value to use if the end of the message is reached.
+
+**Returns**:
+
+The value read from the message, or the default value if the end was reached.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.read_int32"></a>
+#### read\_int32
+
+```python
+ | read_int32(default_value: int = 0) -> int
+```
+
+Read an integer value from the message buffer.
+
+**Arguments**:
+
+- `default_value`: Default value to use if the end of the message is reached.
+
+**Returns**:
+
+The value read from the message, or the default value if the end was reached.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.read_float32"></a>
+#### read\_float32
+
+```python
+ | read_float32(default_value: float = 0.0) -> float
+```
+
+Read a float value from the message buffer.
+
+**Arguments**:
+
+- `default_value`: Default value to use if the end of the message is reached.
+
+**Returns**:
+
+The value read from the message, or the default value if the end was reached.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.read_float32_list"></a>
+#### read\_float32\_list
+
+```python
+ | read_float32_list(default_value: List[float] = None) -> List[float]
+```
+
+Read a list of float values from the message buffer.
+
+**Arguments**:
+
+- `default_value`: Default value to use if the end of the message is reached.
+
+**Returns**:
+
+The value read from the message, or the default value if the end was reached.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.read_string"></a>
+#### read\_string
+
+```python
+ | read_string(default_value: str = "") -> str
+```
+
+Read a string value from the message buffer.
+
+**Arguments**:
+
+- `default_value`: Default value to use if the end of the message is reached.
+
+**Returns**:
+
+The value read from the message, or the default value if the end was reached.
+
+<a name="mlagents_envs.side_channel.incoming_message.IncomingMessage.get_raw_bytes"></a>
+#### get\_raw\_bytes
+
+```python
+ | get_raw_bytes() -> bytes
+```
+
+Get a copy of the internal bytes used by the message.
+
+<a name="mlagents_envs.side_channel.float_properties_channel"></a>
+# mlagents\_envs.side\_channel.float\_properties\_channel
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel"></a>
+## FloatPropertiesChannel Objects
+
+```python
+class FloatPropertiesChannel(SideChannel)
+```
+
+This is the SideChannel for float properties shared with Unity.
+You can modify the float properties of an environment with the commands
+set_property, get_property and list_properties.
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.on_message_received"></a>
+#### on\_message\_received
+
+```python
+ | on_message_received(msg: IncomingMessage) -> None
+```
+
+Is called by the environment to the side channel. Can be called
+multiple times per step if multiple messages are meant for that
+SideChannel.
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.set_property"></a>
+#### set\_property
+
+```python
+ | set_property(key: str, value: float) -> None
+```
+
+Sets a property in the Unity Environment.
+
+**Arguments**:
+
+- `key`: The string identifier of the property.
+- `value`: The float value of the property.
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.get_property"></a>
+#### get\_property
+
+```python
+ | get_property(key: str) -> Optional[float]
+```
+
+Gets a property in the Unity Environment. If the property was not
+found, will return None.
+
+**Arguments**:
+
+- `key`: The string identifier of the property.
+
+**Returns**:
+
+The float value of the property or None.
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.list_properties"></a>
+#### list\_properties
+
+```python
+ | list_properties() -> List[str]
+```
+
+Returns a list of all the string identifiers of the properties
+currently present in the Unity Environment.
+
+<a name="mlagents_envs.side_channel.float_properties_channel.FloatPropertiesChannel.get_property_dict_copy"></a>
+#### get\_property\_dict\_copy
+
+```python
+ | get_property_dict_copy() -> Dict[str, float]
+```
+
+Returns a copy of the float properties.
+
+**Returns**:
+
+
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel"></a>
+# mlagents\_envs.side\_channel.environment\_parameters\_channel
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel"></a>
+## EnvironmentParametersChannel Objects
+
+```python
+class EnvironmentParametersChannel(SideChannel)
+```
+
+This is the SideChannel for sending environment parameters to Unity.
+You can send parameters to an environment with the command
+set_float_parameter.
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_float_parameter"></a>
+#### set\_float\_parameter
+
+```python
+ | set_float_parameter(key: str, value: float) -> None
+```
+
+Sets a float environment parameter in the Unity Environment.
+
+**Arguments**:
+
+- `key`: The string identifier of the parameter.
+- `value`: The float value of the parameter.
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_uniform_sampler_parameters"></a>
+#### set\_uniform\_sampler\_parameters
+
+```python
+ | set_uniform_sampler_parameters(key: str, min_value: float, max_value: float, seed: int) -> None
+```
+
+Sets a uniform environment parameter sampler.
+
+**Arguments**:
+
+- `key`: The string identifier of the parameter.
+- `min_value`: The minimum of the sampling distribution.
+- `max_value`: The maximum of the sampling distribution.
+- `seed`: The random seed to initialize the sampler.
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_gaussian_sampler_parameters"></a>
+#### set\_gaussian\_sampler\_parameters
+
+```python
+ | set_gaussian_sampler_parameters(key: str, mean: float, st_dev: float, seed: int) -> None
+```
+
+Sets a gaussian environment parameter sampler.
+
+**Arguments**:
+
+- `key`: The string identifier of the parameter.
+- `mean`: The mean of the sampling distribution.
+- `st_dev`: The standard deviation of the sampling distribution.
+- `seed`: The random seed to initialize the sampler.
+
+<a name="mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel.set_multirangeuniform_sampler_parameters"></a>
+#### set\_multirangeuniform\_sampler\_parameters
+
+```python
+ | set_multirangeuniform_sampler_parameters(key: str, intervals: List[Tuple[float, float]], seed: int) -> None
+```
+
+Sets a multirangeuniform environment parameter sampler.
+
+**Arguments**:
+
+- `key`: The string identifier of the parameter.
+- `intervals`: The lists of min and max that define each uniform distribution.
+- `seed`: The random seed to initialize the sampler.
+
+<a name="mlagents_envs.side_channel.side_channel"></a>
+# mlagents\_envs.side\_channel.side\_channel
+
+<a name="mlagents_envs.side_channel.side_channel.SideChannel"></a>
+## SideChannel Objects
+
+```python
+class SideChannel(ABC)
+```
+
+The side channel just get access to a bytes buffer that will be shared
+between C# and Python. For example, We will create a specific side channel
+for properties that will be a list of string (fixed size) to float number,
+that can be modified by both C# and Python. All side channels are passed
+to the Env object at construction.
+
+<a name="mlagents_envs.side_channel.side_channel.SideChannel.queue_message_to_send"></a>
+#### queue\_message\_to\_send
+
+```python
+ | queue_message_to_send(msg: OutgoingMessage) -> None
+```
+
+Queues a message to be sent by the environment at the next call to
+step.
+
+<a name="mlagents_envs.side_channel.side_channel.SideChannel.on_message_received"></a>
+#### on\_message\_received
+
+```python
+ | @abstractmethod
+ | on_message_received(msg: IncomingMessage) -> None
+```
+
+Is called by the environment to the side channel. Can be called
+multiple times per step if multiple messages are meant for that
+SideChannel.
+
+<a name="mlagents_envs.side_channel.side_channel.SideChannel.channel_id"></a>
+#### channel\_id
+
+```python
+ | @property
+ | channel_id() -> uuid.UUID
+```
+
+**Returns**:
+
+The type of side channel used. Will influence how the data is
+processed in the environment.
diff --git a/docs/Python-LLAPI.md b/docs/Python-LLAPI.md
new file mode 100644
index 0000000000000000000000000000000000000000..ace4a9a15d005a0c578f9e08c623c1575a759bee
--- /dev/null
+++ b/docs/Python-LLAPI.md
@@ -0,0 +1,354 @@
+# Unity ML-Agents Python Low Level API
+
+The `mlagents` Python package contains two components: a low level API which
+allows you to interact directly with a Unity Environment (`mlagents_envs`) and
+an entry point to train (`mlagents-learn`) which allows you to train agents in
+Unity Environments using our implementations of reinforcement learning or
+imitation learning. This document describes how to use the `mlagents_envs` API.
+For information on using `mlagents-learn`, see [here](Training-ML-Agents.md).
+For Python Low Level API documentation, see [here](Python-LLAPI-Documentation.md).
+
+The Python Low Level API can be used to interact directly with your Unity
+learning environment. As such, it can serve as the basis for developing and
+evaluating new learning algorithms.
+
+## mlagents_envs
+
+The ML-Agents Toolkit Low Level API is a Python API for controlling the
+simulation loop of an environment or game built with Unity. This API is used by
+the training algorithms inside the ML-Agent Toolkit, but you can also write your
+own Python programs using this API.
+
+The key objects in the Python API include:
+
+- **UnityEnvironment** — the main interface between the Unity application and
+  your code. Use UnityEnvironment to start and control a simulation or training
+  session.
+- **BehaviorName** - is a string that identifies a behavior in the simulation.
+- **AgentId** - is an `int` that serves as unique identifier for Agents in the
+  simulation.
+- **DecisionSteps** — contains the data from Agents belonging to the same
+  "Behavior" in the simulation, such as observations and rewards. Only Agents
+  that requested a decision since the last call to `env.step()` are in the
+  DecisionSteps object.
+- **TerminalSteps** — contains the data from Agents belonging to the same
+  "Behavior" in the simulation, such as observations and rewards. Only Agents
+  whose episode ended since the last call to `env.step()` are in the
+  TerminalSteps object.
+- **BehaviorSpec** — describes the shape of the observation data inside
+  DecisionSteps and TerminalSteps as well as the expected action shapes.
+
+These classes are all defined in the
+[base_env](../ml-agents-envs/mlagents_envs/base_env.py) script.
+
+An Agent "Behavior" is a group of Agents identified by a `BehaviorName` that
+share the same observations and action types (described in their
+`BehaviorSpec`). You can think about Agent Behavior as a group of agents that
+will share the same policy. All Agents with the same behavior have the same goal
+and reward signals.
+
+To communicate with an Agent in a Unity environment from a Python program, the
+Agent in the simulation must have `Behavior Parameters` set to communicate. You
+must set the `Behavior Type` to `Default` and give it a `Behavior Name`.
+
+_Notice: Currently communication between Unity and Python takes place over an
+open socket without authentication. As such, please make sure that the network
+where training takes place is secure. This will be addressed in a future
+release._
+
+## Loading a Unity Environment
+
+Python-side communication happens through `UnityEnvironment` which is located in
+[`environment.py`](../ml-agents-envs/mlagents_envs/environment.py). To load a
+Unity environment from a built binary file, put the file in the same directory
+as `envs`. For example, if the filename of your Unity environment is `3DBall`,
+in python, run:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+# This is a non-blocking call that only loads the environment.
+env = UnityEnvironment(file_name="3DBall", seed=1, side_channels=[])
+# Start interacting with the environment.
+env.reset()
+behavior_names = env.behavior_specs.keys()
+...
+```
+**NOTE:** Please read [Interacting with a Unity Environment](#interacting-with-a-unity-environment)
+to read more about how you can interact with the Unity environment from Python.
+
+- `file_name` is the name of the environment binary (located in the root
+  directory of the python project).
+- `worker_id` indicates which port to use for communication with the
+  environment. For use in parallel training regimes such as A3C.
+- `seed` indicates the seed to use when generating random numbers during the
+  training process. In environments which are stochastic, setting the seed
+  enables reproducible experimentation by ensuring that the environment and
+  trainers utilize the same random seed.
+- `side_channels` provides a way to exchange data with the Unity simulation that
+  is not related to the reinforcement learning loop. For example: configurations
+  or properties. More on them in the [Side Channels](Custom-SideChannels.md) doc.
+
+If you want to directly interact with the Editor, you need to use
+`file_name=None`, then press the **Play** button in the Editor when the message
+_"Start training by pressing the Play button in the Unity Editor"_ is displayed
+on the screen
+
+### Interacting with a Unity Environment
+
+#### The BaseEnv interface
+
+A `BaseEnv` has the following methods:
+
+- **Reset : `env.reset()`** Sends a signal to reset the environment. Returns
+  None.
+- **Step : `env.step()`** Sends a signal to step the environment. Returns None.
+  Note that a "step" for Python does not correspond to either Unity `Update` nor
+  `FixedUpdate`. When `step()` or `reset()` is called, the Unity simulation will
+  move forward until an Agent in the simulation needs a input from Python to
+  act.
+- **Close : `env.close()`** Sends a shutdown signal to the environment and
+  terminates the communication.
+- **Behavior Specs : `env.behavior_specs`** Returns a Mapping of
+  `BehaviorName` to `BehaviorSpec` objects (read only).
+  A `BehaviorSpec` contains the observation shapes and the
+  `ActionSpec` (which defines the action shape). Note that
+  the `BehaviorSpec` for a specific group is fixed throughout the simulation.
+  The number of entries in the Mapping can change over time in the simulation
+  if new Agent behaviors are created in the simulation.
+- **Get Steps : `env.get_steps(behavior_name: str)`** Returns a tuple
+  `DecisionSteps, TerminalSteps` corresponding to the behavior_name given as
+  input. The `DecisionSteps` contains information about the state of the agents
+  **that need an action this step** and have the behavior behavior_name. The
+  `TerminalSteps` contains information about the state of the agents **whose
+  episode ended** and have the behavior behavior_name. Both `DecisionSteps` and
+  `TerminalSteps` contain information such as the observations, the rewards and
+  the agent identifiers. `DecisionSteps` also contains action masks for the next
+  action while `TerminalSteps` contains the reason for termination (did the
+  Agent reach its maximum step and was interrupted). The data is in `np.array`
+  of which the first dimension is always the number of agents note that the
+  number of agents is not guaranteed to remain constant during the simulation
+  and it is not unusual to have either `DecisionSteps` or `TerminalSteps`
+  contain no Agents at all.
+- **Set Actions :`env.set_actions(behavior_name: str, action: ActionTuple)`** Sets
+  the actions for a whole agent group. `action` is an `ActionTuple`, which
+  is made up of a 2D `np.array` of `dtype=np.int32` for discrete actions, and
+  `dtype=np.float32` for continuous actions. The first dimension of `np.array`
+  in the tuple is the number of agents that requested a decision since the
+  last call to `env.step()`. The second dimension is the number of discrete or
+  continuous actions for the corresponding array.
+- **Set Action for Agent :
+  `env.set_action_for_agent(agent_group: str, agent_id: int, action: ActionTuple)`**
+  Sets the action for a specific Agent in an agent group. `agent_group` is the
+  name of the group the Agent belongs to and `agent_id` is the integer
+  identifier of the Agent. `action` is an `ActionTuple` as described above.
+**Note:** If no action is provided for an agent group between two calls to
+`env.step()` then the default action will be all zeros.
+
+#### DecisionSteps and DecisionStep
+
+`DecisionSteps` (with `s`) contains information about a whole batch of Agents
+while `DecisionStep` (no `s`) only contains information about a single Agent.
+
+A `DecisionSteps` has the following fields :
+
+- `obs` is a list of numpy arrays observations collected by the group of agent.
+  The first dimension of the array corresponds to the batch size of the group
+  (number of agents requesting a decision since the last call to `env.step()`).
+- `reward` is a float vector of length batch size. Corresponds to the rewards
+  collected by each agent since the last simulation step.
+- `agent_id` is an int vector of length batch size containing unique identifier
+  for the corresponding Agent. This is used to track Agents across simulation
+  steps.
+- `action_mask` is an optional list of two dimensional arrays of booleans which is only
+  available when using multi-discrete actions. Each array corresponds to an
+  action branch. The first dimension of each array is the batch size and the
+  second contains a mask for each action of the branch. If true, the action is
+  not available for the agent during this simulation step.
+
+It also has the two following methods:
+
+- `len(DecisionSteps)` Returns the number of agents requesting a decision since
+  the last call to `env.step()`.
+- `DecisionSteps[agent_id]` Returns a `DecisionStep` for the Agent with the
+  `agent_id` unique identifier.
+
+A `DecisionStep` has the following fields:
+
+- `obs` is a list of numpy arrays observations collected by the agent. (Each
+  array has one less dimension than the arrays in `DecisionSteps`)
+- `reward` is a float. Corresponds to the rewards collected by the agent since
+  the last simulation step.
+- `agent_id` is an int and an unique identifier for the corresponding Agent.
+- `action_mask` is an optional list of one dimensional arrays of booleans which is only
+  available when using multi-discrete actions. Each array corresponds to an
+  action branch. Each array contains a mask for each action of the branch. If
+  true, the action is not available for the agent during this simulation step.
+
+#### TerminalSteps and TerminalStep
+
+Similarly to `DecisionSteps` and `DecisionStep`, `TerminalSteps` (with `s`)
+contains information about a whole batch of Agents while `TerminalStep` (no `s`)
+only contains information about a single Agent.
+
+A `TerminalSteps` has the following fields :
+
+- `obs` is a list of numpy arrays observations collected by the group of agent.
+  The first dimension of the array corresponds to the batch size of the group
+  (number of agents requesting a decision since the last call to `env.step()`).
+- `reward` is a float vector of length batch size. Corresponds to the rewards
+  collected by each agent since the last simulation step.
+- `agent_id` is an int vector of length batch size containing unique identifier
+  for the corresponding Agent. This is used to track Agents across simulation
+  steps.
+ - `interrupted` is an array of booleans of length batch size. Is true if the
+ associated Agent was interrupted since the last decision step. For example,
+ if the Agent reached the maximum number of steps for the episode.
+
+It also has the two following methods:
+
+- `len(TerminalSteps)` Returns the number of agents requesting a decision since
+  the last call to `env.step()`.
+- `TerminalSteps[agent_id]` Returns a `TerminalStep` for the Agent with the
+  `agent_id` unique identifier.
+
+A `TerminalStep` has the following fields:
+
+- `obs` is a list of numpy arrays observations collected by the agent. (Each
+  array has one less dimension than the arrays in `TerminalSteps`)
+- `reward` is a float. Corresponds to the rewards collected by the agent since
+  the last simulation step.
+- `agent_id` is an int and an unique identifier for the corresponding Agent.
+ - `interrupted` is a bool. Is true if the Agent was interrupted since the last
+ decision step. For example, if the Agent reached the maximum number of steps for
+ the episode.
+
+#### BehaviorSpec
+
+A `BehaviorSpec` has the following fields :
+
+- `observation_specs` is a List of `ObservationSpec` objects : Each `ObservationSpec`
+  corresponds to an observation's properties: `shape` is a tuple of ints that
+  corresponds to the shape of the observation (without the number of agents dimension).
+  `dimension_property` is a tuple of flags containing extra information about how the
+  data should be processed in the corresponding dimension. `observation_type` is an enum
+  corresponding to what type of observation is generating the data (i.e., default, goal,
+  etc). Note that the `ObservationSpec` have the same ordering as the ordering of observations
+  in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep.
+- `action_spec` is an `ActionSpec` namedtuple that defines the number and types
+  of actions for the Agent.
+
+An `ActionSpec` has the following fields and properties:
+- `continuous_size` is the number of floats that constitute the continuous actions.
+- `discrete_size` is the number of branches (the number of independent actions) that
+  constitute the multi-discrete actions.
+- `discrete_branches` is a Tuple of ints. Each int corresponds to the number of
+  different options for each branch of the action. For example:
+  In a game direction input (no movement, left, right) and
+  jump input (no jump, jump) there will be two branches (direction and jump),
+  the first one with 3 options and the second with 2 options. (`discrete_size = 2`
+  and `discrete_action_branches = (3,2,)`)
+
+
+### Communicating additional information with the Environment
+
+In addition to the means of communicating between Unity and python described
+above, we also provide methods for sharing agent-agnostic information. These
+additional methods are referred to as side channels. ML-Agents includes two
+ready-made side channels, described below. It is also possible to create custom
+side channels to communicate any additional data between a Unity environment and
+Python. Instructions for creating custom side channels can be found
+[here](Custom-SideChannels.md).
+
+Side channels exist as separate classes which are instantiated, and then passed
+as list to the `side_channels` argument of the constructor of the
+`UnityEnvironment` class.
+
+```python
+channel = MyChannel()
+
+env = UnityEnvironment(side_channels = [channel])
+```
+
+**Note** : A side channel will only send/receive messages when `env.step` or
+`env.reset()` is called.
+
+#### EngineConfigurationChannel
+
+The `EngineConfiguration` side channel allows you to modify the time-scale,
+resolution, and graphics quality of the environment. This can be useful for
+adjusting the environment to perform better during training, or be more
+interpretable during inference.
+
+`EngineConfigurationChannel` has two methods :
+
+- `set_configuration_parameters` which takes the following arguments:
+  - `width`: Defines the width of the display. (Must be set alongside height)
+  - `height`: Defines the height of the display. (Must be set alongside width)
+  - `quality_level`: Defines the quality level of the simulation.
+  - `time_scale`: Defines the multiplier for the deltatime in the simulation. If
+    set to a higher value, time will pass faster in the simulation but the
+    physics may perform unpredictably.
+  - `target_frame_rate`: Instructs simulation to try to render at a specified
+    frame rate.
+  - `capture_frame_rate` Instructs the simulation to consider time between
+    updates to always be constant, regardless of the actual frame rate.
+- `set_configuration` with argument config which is an `EngineConfig` NamedTuple
+  object.
+
+For example, the following code would adjust the time-scale of the simulation to
+be 2x realtime.
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
+
+channel = EngineConfigurationChannel()
+
+env = UnityEnvironment(side_channels=[channel])
+
+channel.set_configuration_parameters(time_scale = 2.0)
+
+i = env.reset()
+...
+```
+
+#### EnvironmentParameters
+
+The `EnvironmentParameters` will allow you to get and set pre-defined numerical
+values in the environment. This can be useful for adjusting environment-specific
+settings, or for reading non-agent related information from the environment. You
+can call `get_property` and `set_property` on the side channel to read and write
+properties.
+
+`EnvironmentParametersChannel` has one methods:
+
+- `set_float_parameter` Sets a float parameter in the Unity Environment.
+  - key: The string identifier of the property.
+  - value: The float value of the property.
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel
+
+channel = EnvironmentParametersChannel()
+
+env = UnityEnvironment(side_channels=[channel])
+
+channel.set_float_parameter("parameter_1", 2.0)
+
+i = env.reset()
+...
+```
+
+Once a property has been modified in Python, you can access it in C# after the
+next call to `step` as follows:
+
+```csharp
+var envParameters = Academy.Instance.EnvironmentParameters;
+float property1 = envParameters.GetWithDefault("parameter_1", 0.0f);
+```
+
+#### Custom side channels
+
+For information on how to make custom side channels for sending additional data
+types, see the documentation [here](Custom-SideChannels.md).
diff --git a/docs/Python-On-Off-Policy-Trainer-Documentation.md b/docs/Python-On-Off-Policy-Trainer-Documentation.md
new file mode 100644
index 0000000000000000000000000000000000000000..4f13bdf72ebc14dd92707b843875006f0183dfec
--- /dev/null
+++ b/docs/Python-On-Off-Policy-Trainer-Documentation.md
@@ -0,0 +1,787 @@
+# Table of Contents
+
+* [mlagents.trainers.trainer.on\_policy\_trainer](#mlagents.trainers.trainer.on_policy_trainer)
+  * [OnPolicyTrainer](#mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer)
+    * [\_\_init\_\_](#mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer.__init__)
+    * [add\_policy](#mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer.add_policy)
+* [mlagents.trainers.trainer.off\_policy\_trainer](#mlagents.trainers.trainer.off_policy_trainer)
+  * [OffPolicyTrainer](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer)
+    * [\_\_init\_\_](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.__init__)
+    * [save\_model](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.save_model)
+    * [save\_replay\_buffer](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.save_replay_buffer)
+    * [load\_replay\_buffer](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.load_replay_buffer)
+    * [add\_policy](#mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.add_policy)
+* [mlagents.trainers.trainer.rl\_trainer](#mlagents.trainers.trainer.rl_trainer)
+  * [RLTrainer](#mlagents.trainers.trainer.rl_trainer.RLTrainer)
+    * [end\_episode](#mlagents.trainers.trainer.rl_trainer.RLTrainer.end_episode)
+    * [create\_optimizer](#mlagents.trainers.trainer.rl_trainer.RLTrainer.create_optimizer)
+    * [save\_model](#mlagents.trainers.trainer.rl_trainer.RLTrainer.save_model)
+    * [advance](#mlagents.trainers.trainer.rl_trainer.RLTrainer.advance)
+* [mlagents.trainers.trainer.trainer](#mlagents.trainers.trainer.trainer)
+  * [Trainer](#mlagents.trainers.trainer.trainer.Trainer)
+    * [\_\_init\_\_](#mlagents.trainers.trainer.trainer.Trainer.__init__)
+    * [stats\_reporter](#mlagents.trainers.trainer.trainer.Trainer.stats_reporter)
+    * [parameters](#mlagents.trainers.trainer.trainer.Trainer.parameters)
+    * [get\_max\_steps](#mlagents.trainers.trainer.trainer.Trainer.get_max_steps)
+    * [get\_step](#mlagents.trainers.trainer.trainer.Trainer.get_step)
+    * [threaded](#mlagents.trainers.trainer.trainer.Trainer.threaded)
+    * [should\_still\_train](#mlagents.trainers.trainer.trainer.Trainer.should_still_train)
+    * [reward\_buffer](#mlagents.trainers.trainer.trainer.Trainer.reward_buffer)
+    * [save\_model](#mlagents.trainers.trainer.trainer.Trainer.save_model)
+    * [end\_episode](#mlagents.trainers.trainer.trainer.Trainer.end_episode)
+    * [create\_policy](#mlagents.trainers.trainer.trainer.Trainer.create_policy)
+    * [add\_policy](#mlagents.trainers.trainer.trainer.Trainer.add_policy)
+    * [get\_policy](#mlagents.trainers.trainer.trainer.Trainer.get_policy)
+    * [advance](#mlagents.trainers.trainer.trainer.Trainer.advance)
+    * [publish\_policy\_queue](#mlagents.trainers.trainer.trainer.Trainer.publish_policy_queue)
+    * [subscribe\_trajectory\_queue](#mlagents.trainers.trainer.trainer.Trainer.subscribe_trajectory_queue)
+* [mlagents.trainers.settings](#mlagents.trainers.settings)
+  * [deep\_update\_dict](#mlagents.trainers.settings.deep_update_dict)
+  * [RewardSignalSettings](#mlagents.trainers.settings.RewardSignalSettings)
+    * [structure](#mlagents.trainers.settings.RewardSignalSettings.structure)
+  * [ParameterRandomizationSettings](#mlagents.trainers.settings.ParameterRandomizationSettings)
+    * [\_\_str\_\_](#mlagents.trainers.settings.ParameterRandomizationSettings.__str__)
+    * [structure](#mlagents.trainers.settings.ParameterRandomizationSettings.structure)
+    * [unstructure](#mlagents.trainers.settings.ParameterRandomizationSettings.unstructure)
+    * [apply](#mlagents.trainers.settings.ParameterRandomizationSettings.apply)
+  * [ConstantSettings](#mlagents.trainers.settings.ConstantSettings)
+    * [\_\_str\_\_](#mlagents.trainers.settings.ConstantSettings.__str__)
+    * [apply](#mlagents.trainers.settings.ConstantSettings.apply)
+  * [UniformSettings](#mlagents.trainers.settings.UniformSettings)
+    * [\_\_str\_\_](#mlagents.trainers.settings.UniformSettings.__str__)
+    * [apply](#mlagents.trainers.settings.UniformSettings.apply)
+  * [GaussianSettings](#mlagents.trainers.settings.GaussianSettings)
+    * [\_\_str\_\_](#mlagents.trainers.settings.GaussianSettings.__str__)
+    * [apply](#mlagents.trainers.settings.GaussianSettings.apply)
+  * [MultiRangeUniformSettings](#mlagents.trainers.settings.MultiRangeUniformSettings)
+    * [\_\_str\_\_](#mlagents.trainers.settings.MultiRangeUniformSettings.__str__)
+    * [apply](#mlagents.trainers.settings.MultiRangeUniformSettings.apply)
+  * [CompletionCriteriaSettings](#mlagents.trainers.settings.CompletionCriteriaSettings)
+    * [need\_increment](#mlagents.trainers.settings.CompletionCriteriaSettings.need_increment)
+  * [Lesson](#mlagents.trainers.settings.Lesson)
+  * [EnvironmentParameterSettings](#mlagents.trainers.settings.EnvironmentParameterSettings)
+    * [structure](#mlagents.trainers.settings.EnvironmentParameterSettings.structure)
+  * [TrainerSettings](#mlagents.trainers.settings.TrainerSettings)
+    * [structure](#mlagents.trainers.settings.TrainerSettings.structure)
+  * [CheckpointSettings](#mlagents.trainers.settings.CheckpointSettings)
+    * [prioritize\_resume\_init](#mlagents.trainers.settings.CheckpointSettings.prioritize_resume_init)
+  * [RunOptions](#mlagents.trainers.settings.RunOptions)
+    * [from\_argparse](#mlagents.trainers.settings.RunOptions.from_argparse)
+
+<a name="mlagents.trainers.trainer.on_policy_trainer"></a>
+# mlagents.trainers.trainer.on\_policy\_trainer
+
+<a name="mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer"></a>
+## OnPolicyTrainer Objects
+
+```python
+class OnPolicyTrainer(RLTrainer)
+```
+
+The PPOTrainer is an implementation of the PPO algorithm.
+
+<a name="mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str)
+```
+
+Responsible for collecting experiences and training an on-policy model.
+
+**Arguments**:
+
+- `behavior_name`: The name of the behavior associated with trainer config
+- `reward_buff_cap`: Max reward history to track in the reward buffer
+- `trainer_settings`: The parameters for the trainer.
+- `training`: Whether the trainer is set for training.
+- `load`: Whether the model should be loaded.
+- `seed`: The seed the model will be initialized with
+- `artifact_path`: The directory within which to store artifacts from this trainer.
+
+<a name="mlagents.trainers.trainer.on_policy_trainer.OnPolicyTrainer.add_policy"></a>
+#### add\_policy
+
+```python
+ | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None
+```
+
+Adds policy to trainer.
+
+**Arguments**:
+
+- `parsed_behavior_id`: Behavior identifiers that the policy should belong to.
+- `policy`: Policy to associate with name_behavior_id.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer"></a>
+# mlagents.trainers.trainer.off\_policy\_trainer
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer"></a>
+## OffPolicyTrainer Objects
+
+```python
+class OffPolicyTrainer(RLTrainer)
+```
+
+The SACTrainer is an implementation of the SAC algorithm, with support
+for discrete actions and recurrent networks.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str)
+```
+
+Responsible for collecting experiences and training an off-policy model.
+
+**Arguments**:
+
+- `behavior_name`: The name of the behavior associated with trainer config
+- `reward_buff_cap`: Max reward history to track in the reward buffer
+- `trainer_settings`: The parameters for the trainer.
+- `training`: Whether the trainer is set for training.
+- `load`: Whether the model should be loaded.
+- `seed`: The seed the model will be initialized with
+- `artifact_path`: The directory within which to store artifacts from this trainer.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.save_model"></a>
+#### save\_model
+
+```python
+ | save_model() -> None
+```
+
+Saves the final training model to memory
+Overrides the default to save the replay buffer.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.save_replay_buffer"></a>
+#### save\_replay\_buffer
+
+```python
+ | save_replay_buffer() -> None
+```
+
+Save the training buffer's update buffer to a pickle file.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.load_replay_buffer"></a>
+#### load\_replay\_buffer
+
+```python
+ | load_replay_buffer() -> None
+```
+
+Loads the last saved replay buffer from a file.
+
+<a name="mlagents.trainers.trainer.off_policy_trainer.OffPolicyTrainer.add_policy"></a>
+#### add\_policy
+
+```python
+ | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None
+```
+
+Adds policy to trainer.
+
+<a name="mlagents.trainers.trainer.rl_trainer"></a>
+# mlagents.trainers.trainer.rl\_trainer
+
+<a name="mlagents.trainers.trainer.rl_trainer.RLTrainer"></a>
+## RLTrainer Objects
+
+```python
+class RLTrainer(Trainer)
+```
+
+This class is the base class for trainers that use Reward Signals.
+
+<a name="mlagents.trainers.trainer.rl_trainer.RLTrainer.end_episode"></a>
+#### end\_episode
+
+```python
+ | end_episode() -> None
+```
+
+A signal that the Episode has ended. The buffer must be reset.
+Get only called when the academy resets.
+
+<a name="mlagents.trainers.trainer.rl_trainer.RLTrainer.create_optimizer"></a>
+#### create\_optimizer
+
+```python
+ | @abc.abstractmethod
+ | create_optimizer() -> TorchOptimizer
+```
+
+Creates an Optimizer object
+
+<a name="mlagents.trainers.trainer.rl_trainer.RLTrainer.save_model"></a>
+#### save\_model
+
+```python
+ | save_model() -> None
+```
+
+Saves the policy associated with this trainer.
+
+<a name="mlagents.trainers.trainer.rl_trainer.RLTrainer.advance"></a>
+#### advance
+
+```python
+ | advance() -> None
+```
+
+Steps the trainer, taking in trajectories and updates if ready.
+Will block and wait briefly if there are no trajectories.
+
+<a name="mlagents.trainers.trainer.trainer"></a>
+# mlagents.trainers.trainer.trainer
+
+<a name="mlagents.trainers.trainer.trainer.Trainer"></a>
+## Trainer Objects
+
+```python
+class Trainer(abc.ABC)
+```
+
+This class is the base class for the mlagents_envs.trainers
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(brain_name: str, trainer_settings: TrainerSettings, training: bool, load: bool, artifact_path: str, reward_buff_cap: int = 1)
+```
+
+Responsible for collecting experiences and training a neural network model.
+
+**Arguments**:
+
+- `brain_name`: Brain name of brain to be trained.
+- `trainer_settings`: The parameters for the trainer (dictionary).
+- `training`: Whether the trainer is set for training.
+- `artifact_path`: The directory within which to store artifacts from this trainer
+- `reward_buff_cap`:
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.stats_reporter"></a>
+#### stats\_reporter
+
+```python
+ | @property
+ | stats_reporter()
+```
+
+Returns the stats reporter associated with this Trainer.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.parameters"></a>
+#### parameters
+
+```python
+ | @property
+ | parameters() -> TrainerSettings
+```
+
+Returns the trainer parameters of the trainer.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.get_max_steps"></a>
+#### get\_max\_steps
+
+```python
+ | @property
+ | get_max_steps() -> int
+```
+
+Returns the maximum number of steps. Is used to know when the trainer should be stopped.
+
+**Returns**:
+
+The maximum number of steps of the trainer
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.get_step"></a>
+#### get\_step
+
+```python
+ | @property
+ | get_step() -> int
+```
+
+Returns the number of steps the trainer has performed
+
+**Returns**:
+
+the step count of the trainer
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.threaded"></a>
+#### threaded
+
+```python
+ | @property
+ | threaded() -> bool
+```
+
+Whether or not to run the trainer in a thread. True allows the trainer to
+update the policy while the environment is taking steps. Set to False to
+enforce strict on-policy updates (i.e. don't update the policy when taking steps.)
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.should_still_train"></a>
+#### should\_still\_train
+
+```python
+ | @property
+ | should_still_train() -> bool
+```
+
+Returns whether or not the trainer should train. A Trainer could
+stop training if it wasn't training to begin with, or if max_steps
+is reached.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.reward_buffer"></a>
+#### reward\_buffer
+
+```python
+ | @property
+ | reward_buffer() -> Deque[float]
+```
+
+Returns the reward buffer. The reward buffer contains the cumulative
+rewards of the most recent episodes completed by agents using this
+trainer.
+
+**Returns**:
+
+the reward buffer.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.save_model"></a>
+#### save\_model
+
+```python
+ | @abc.abstractmethod
+ | save_model() -> None
+```
+
+Saves model file(s) for the policy or policies associated with this trainer.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.end_episode"></a>
+#### end\_episode
+
+```python
+ | @abc.abstractmethod
+ | end_episode()
+```
+
+A signal that the Episode has ended. The buffer must be reset.
+Get only called when the academy resets.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.create_policy"></a>
+#### create\_policy
+
+```python
+ | @abc.abstractmethod
+ | create_policy(parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec) -> Policy
+```
+
+Creates a Policy object
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.add_policy"></a>
+#### add\_policy
+
+```python
+ | @abc.abstractmethod
+ | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None
+```
+
+Adds policy to trainer.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.get_policy"></a>
+#### get\_policy
+
+```python
+ | get_policy(name_behavior_id: str) -> Policy
+```
+
+Gets policy associated with name_behavior_id
+
+**Arguments**:
+
+- `name_behavior_id`: Fully qualified behavior name
+
+**Returns**:
+
+Policy associated with name_behavior_id
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.advance"></a>
+#### advance
+
+```python
+ | @abc.abstractmethod
+ | advance() -> None
+```
+
+Advances the trainer. Typically, this means grabbing trajectories
+from all subscribed trajectory queues (self.trajectory_queues), and updating
+a policy using the steps in them, and if needed pushing a new policy onto the right
+policy queues (self.policy_queues).
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.publish_policy_queue"></a>
+#### publish\_policy\_queue
+
+```python
+ | publish_policy_queue(policy_queue: AgentManagerQueue[Policy]) -> None
+```
+
+Adds a policy queue to the list of queues to publish to when this Trainer
+makes a policy update
+
+**Arguments**:
+
+- `policy_queue`: Policy queue to publish to.
+
+<a name="mlagents.trainers.trainer.trainer.Trainer.subscribe_trajectory_queue"></a>
+#### subscribe\_trajectory\_queue
+
+```python
+ | subscribe_trajectory_queue(trajectory_queue: AgentManagerQueue[Trajectory]) -> None
+```
+
+Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from.
+
+**Arguments**:
+
+- `trajectory_queue`: Trajectory queue to read from.
+
+<a name="mlagents.trainers.settings"></a>
+# mlagents.trainers.settings
+
+<a name="mlagents.trainers.settings.deep_update_dict"></a>
+#### deep\_update\_dict
+
+```python
+deep_update_dict(d: Dict, update_d: Mapping) -> None
+```
+
+Similar to dict.update(), but works for nested dicts of dicts as well.
+
+<a name="mlagents.trainers.settings.RewardSignalSettings"></a>
+## RewardSignalSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class RewardSignalSettings()
+```
+
+<a name="mlagents.trainers.settings.RewardSignalSettings.structure"></a>
+#### structure
+
+```python
+ | @staticmethod
+ | structure(d: Mapping, t: type) -> Any
+```
+
+Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with
+cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
+the special Enum selection of RewardSignalSettings classes.
+
+<a name="mlagents.trainers.settings.ParameterRandomizationSettings"></a>
+## ParameterRandomizationSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class ParameterRandomizationSettings(abc.ABC)
+```
+
+<a name="mlagents.trainers.settings.ParameterRandomizationSettings.__str__"></a>
+#### \_\_str\_\_
+
+```python
+ | __str__() -> str
+```
+
+Helper method to output sampler stats to console.
+
+<a name="mlagents.trainers.settings.ParameterRandomizationSettings.structure"></a>
+#### structure
+
+```python
+ | @staticmethod
+ | structure(d: Union[Mapping, float], t: type) -> "ParameterRandomizationSettings"
+```
+
+Helper method to a ParameterRandomizationSettings class. Meant to be registered with
+cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
+the special Enum selection of ParameterRandomizationSettings classes.
+
+<a name="mlagents.trainers.settings.ParameterRandomizationSettings.unstructure"></a>
+#### unstructure
+
+```python
+ | @staticmethod
+ | unstructure(d: "ParameterRandomizationSettings") -> Mapping
+```
+
+Helper method to a ParameterRandomizationSettings class. Meant to be registered with
+cattr.register_unstructure_hook() and called with cattr.unstructure().
+
+<a name="mlagents.trainers.settings.ParameterRandomizationSettings.apply"></a>
+#### apply
+
+```python
+ | @abc.abstractmethod
+ | apply(key: str, env_channel: EnvironmentParametersChannel) -> None
+```
+
+Helper method to send sampler settings over EnvironmentParametersChannel
+Calls the appropriate sampler type set method.
+
+**Arguments**:
+
+- `key`: environment parameter to be sampled
+- `env_channel`: The EnvironmentParametersChannel to communicate sampler settings to environment
+
+<a name="mlagents.trainers.settings.ConstantSettings"></a>
+## ConstantSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class ConstantSettings(ParameterRandomizationSettings)
+```
+
+<a name="mlagents.trainers.settings.ConstantSettings.__str__"></a>
+#### \_\_str\_\_
+
+```python
+ | __str__() -> str
+```
+
+Helper method to output sampler stats to console.
+
+<a name="mlagents.trainers.settings.ConstantSettings.apply"></a>
+#### apply
+
+```python
+ | apply(key: str, env_channel: EnvironmentParametersChannel) -> None
+```
+
+Helper method to send sampler settings over EnvironmentParametersChannel
+Calls the constant sampler type set method.
+
+**Arguments**:
+
+- `key`: environment parameter to be sampled
+- `env_channel`: The EnvironmentParametersChannel to communicate sampler settings to environment
+
+<a name="mlagents.trainers.settings.UniformSettings"></a>
+## UniformSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class UniformSettings(ParameterRandomizationSettings)
+```
+
+<a name="mlagents.trainers.settings.UniformSettings.__str__"></a>
+#### \_\_str\_\_
+
+```python
+ | __str__() -> str
+```
+
+Helper method to output sampler stats to console.
+
+<a name="mlagents.trainers.settings.UniformSettings.apply"></a>
+#### apply
+
+```python
+ | apply(key: str, env_channel: EnvironmentParametersChannel) -> None
+```
+
+Helper method to send sampler settings over EnvironmentParametersChannel
+Calls the uniform sampler type set method.
+
+**Arguments**:
+
+- `key`: environment parameter to be sampled
+- `env_channel`: The EnvironmentParametersChannel to communicate sampler settings to environment
+
+<a name="mlagents.trainers.settings.GaussianSettings"></a>
+## GaussianSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class GaussianSettings(ParameterRandomizationSettings)
+```
+
+<a name="mlagents.trainers.settings.GaussianSettings.__str__"></a>
+#### \_\_str\_\_
+
+```python
+ | __str__() -> str
+```
+
+Helper method to output sampler stats to console.
+
+<a name="mlagents.trainers.settings.GaussianSettings.apply"></a>
+#### apply
+
+```python
+ | apply(key: str, env_channel: EnvironmentParametersChannel) -> None
+```
+
+Helper method to send sampler settings over EnvironmentParametersChannel
+Calls the gaussian sampler type set method.
+
+**Arguments**:
+
+- `key`: environment parameter to be sampled
+- `env_channel`: The EnvironmentParametersChannel to communicate sampler settings to environment
+
+<a name="mlagents.trainers.settings.MultiRangeUniformSettings"></a>
+## MultiRangeUniformSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class MultiRangeUniformSettings(ParameterRandomizationSettings)
+```
+
+<a name="mlagents.trainers.settings.MultiRangeUniformSettings.__str__"></a>
+#### \_\_str\_\_
+
+```python
+ | __str__() -> str
+```
+
+Helper method to output sampler stats to console.
+
+<a name="mlagents.trainers.settings.MultiRangeUniformSettings.apply"></a>
+#### apply
+
+```python
+ | apply(key: str, env_channel: EnvironmentParametersChannel) -> None
+```
+
+Helper method to send sampler settings over EnvironmentParametersChannel
+Calls the multirangeuniform sampler type set method.
+
+**Arguments**:
+
+- `key`: environment parameter to be sampled
+- `env_channel`: The EnvironmentParametersChannel to communicate sampler settings to environment
+
+<a name="mlagents.trainers.settings.CompletionCriteriaSettings"></a>
+## CompletionCriteriaSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class CompletionCriteriaSettings()
+```
+
+CompletionCriteriaSettings contains the information needed to figure out if the next
+lesson must start.
+
+<a name="mlagents.trainers.settings.CompletionCriteriaSettings.need_increment"></a>
+#### need\_increment
+
+```python
+ | need_increment(progress: float, reward_buffer: List[float], smoothing: float) -> Tuple[bool, float]
+```
+
+Given measures, this method returns a boolean indicating if the lesson
+needs to change now, and a float corresponding to the new smoothed value.
+
+<a name="mlagents.trainers.settings.Lesson"></a>
+## Lesson Objects
+
+```python
+@attr.s(auto_attribs=True)
+class Lesson()
+```
+
+Gathers the data of one lesson for one environment parameter including its name,
+the condition that must be fullfiled for the lesson to be completed and a sampler
+for the environment parameter. If the completion_criteria is None, then this is
+the last lesson in the curriculum.
+
+<a name="mlagents.trainers.settings.EnvironmentParameterSettings"></a>
+## EnvironmentParameterSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class EnvironmentParameterSettings()
+```
+
+EnvironmentParameterSettings is an ordered list of lessons for one environment
+parameter.
+
+<a name="mlagents.trainers.settings.EnvironmentParameterSettings.structure"></a>
+#### structure
+
+```python
+ | @staticmethod
+ | structure(d: Mapping, t: type) -> Dict[str, "EnvironmentParameterSettings"]
+```
+
+Helper method to structure a Dict of EnvironmentParameterSettings class. Meant
+to be registered with cattr.register_structure_hook() and called with
+cattr.structure().
+
+<a name="mlagents.trainers.settings.TrainerSettings"></a>
+## TrainerSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class TrainerSettings(ExportableSettings)
+```
+
+<a name="mlagents.trainers.settings.TrainerSettings.structure"></a>
+#### structure
+
+```python
+ | @staticmethod
+ | structure(d: Mapping, t: type) -> Any
+```
+
+Helper method to structure a TrainerSettings class. Meant to be registered with
+cattr.register_structure_hook() and called with cattr.structure().
+
+<a name="mlagents.trainers.settings.CheckpointSettings"></a>
+## CheckpointSettings Objects
+
+```python
+@attr.s(auto_attribs=True)
+class CheckpointSettings()
+```
+
+<a name="mlagents.trainers.settings.CheckpointSettings.prioritize_resume_init"></a>
+#### prioritize\_resume\_init
+
+```python
+ | prioritize_resume_init() -> None
+```
+
+Prioritize explicit command line resume/init over conflicting yaml options.
+if both resume/init are set at one place use resume
+
+<a name="mlagents.trainers.settings.RunOptions"></a>
+## RunOptions Objects
+
+```python
+@attr.s(auto_attribs=True)
+class RunOptions(ExportableSettings)
+```
+
+<a name="mlagents.trainers.settings.RunOptions.from_argparse"></a>
+#### from\_argparse
+
+```python
+ | @staticmethod
+ | from_argparse(args: argparse.Namespace) -> "RunOptions"
+```
+
+Takes an argparse.Namespace as specified in `parse_command_line`, loads input configuration files
+from file paths, and converts to a RunOptions instance.
+
+**Arguments**:
+
+- `args`: collection of command-line parameters passed to mlagents-learn
+
+**Returns**:
+
+RunOptions representing the passed in arguments, with trainer config, curriculum and sampler
+configs loaded from files.
diff --git a/docs/Python-Optimizer-Documentation.md b/docs/Python-Optimizer-Documentation.md
new file mode 100644
index 0000000000000000000000000000000000000000..9b7e1b993c6d8d55f3f19e45c4b6d96bccc487d7
--- /dev/null
+++ b/docs/Python-Optimizer-Documentation.md
@@ -0,0 +1,87 @@
+# Table of Contents
+
+* [mlagents.trainers.optimizer.torch\_optimizer](#mlagents.trainers.optimizer.torch_optimizer)
+  * [TorchOptimizer](#mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer)
+    * [create\_reward\_signals](#mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer.create_reward_signals)
+    * [get\_trajectory\_value\_estimates](#mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer.get_trajectory_value_estimates)
+* [mlagents.trainers.optimizer.optimizer](#mlagents.trainers.optimizer.optimizer)
+  * [Optimizer](#mlagents.trainers.optimizer.optimizer.Optimizer)
+    * [update](#mlagents.trainers.optimizer.optimizer.Optimizer.update)
+
+<a name="mlagents.trainers.optimizer.torch_optimizer"></a>
+# mlagents.trainers.optimizer.torch\_optimizer
+
+<a name="mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer"></a>
+## TorchOptimizer Objects
+
+```python
+class TorchOptimizer(Optimizer)
+```
+
+<a name="mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer.create_reward_signals"></a>
+#### create\_reward\_signals
+
+```python
+ | create_reward_signals(reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]) -> None
+```
+
+Create reward signals
+
+**Arguments**:
+
+- `reward_signal_configs`: Reward signal config.
+
+<a name="mlagents.trainers.optimizer.torch_optimizer.TorchOptimizer.get_trajectory_value_estimates"></a>
+#### get\_trajectory\_value\_estimates
+
+```python
+ | get_trajectory_value_estimates(batch: AgentBuffer, next_obs: List[np.ndarray], done: bool, agent_id: str = "") -> Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]]
+```
+
+Get value estimates and memories for a trajectory, in batch form.
+
+**Arguments**:
+
+- `batch`: An AgentBuffer that consists of a trajectory.
+- `next_obs`: the next observation (after the trajectory). Used for boostrapping
+    if this is not a termiinal trajectory.
+- `done`: Set true if this is a terminal trajectory.
+- `agent_id`: Agent ID of the agent that this trajectory belongs to.
+
+**Returns**:
+
+A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)],
+    the final value estimate as a Dict of [name, float], and optionally (if using memories)
+    an AgentBufferField of initial critic memories to be used during update.
+
+<a name="mlagents.trainers.optimizer.optimizer"></a>
+# mlagents.trainers.optimizer.optimizer
+
+<a name="mlagents.trainers.optimizer.optimizer.Optimizer"></a>
+## Optimizer Objects
+
+```python
+class Optimizer(abc.ABC)
+```
+
+Creates loss functions and auxillary networks (e.g. Q or Value) needed for training.
+Provides methods to update the Policy.
+
+<a name="mlagents.trainers.optimizer.optimizer.Optimizer.update"></a>
+#### update
+
+```python
+ | @abc.abstractmethod
+ | update(batch: AgentBuffer, num_sequences: int) -> Dict[str, float]
+```
+
+Update the Policy based on the batch that was passed in.
+
+**Arguments**:
+
+- `batch`: AgentBuffer that contains the minibatch of data used for this update.
+- `num_sequences`: Number of recurrent sequences found in the minibatch.
+
+**Returns**:
+
+A Dict containing statistics (name, value) from the update (e.g. loss)
diff --git a/docs/Python-PettingZoo-API-Documentation.md b/docs/Python-PettingZoo-API-Documentation.md
new file mode 100644
index 0000000000000000000000000000000000000000..233e45e805c675234dacefa44307db05eeff724a
--- /dev/null
+++ b/docs/Python-PettingZoo-API-Documentation.md
@@ -0,0 +1,246 @@
+# Table of Contents
+
+* [mlagents\_envs.envs.pettingzoo\_env\_factory](#mlagents_envs.envs.pettingzoo_env_factory)
+  * [PettingZooEnvFactory](#mlagents_envs.envs.pettingzoo_env_factory.PettingZooEnvFactory)
+    * [env](#mlagents_envs.envs.pettingzoo_env_factory.PettingZooEnvFactory.env)
+* [mlagents\_envs.envs.unity\_aec\_env](#mlagents_envs.envs.unity_aec_env)
+  * [UnityAECEnv](#mlagents_envs.envs.unity_aec_env.UnityAECEnv)
+    * [\_\_init\_\_](#mlagents_envs.envs.unity_aec_env.UnityAECEnv.__init__)
+    * [step](#mlagents_envs.envs.unity_aec_env.UnityAECEnv.step)
+    * [observe](#mlagents_envs.envs.unity_aec_env.UnityAECEnv.observe)
+    * [last](#mlagents_envs.envs.unity_aec_env.UnityAECEnv.last)
+* [mlagents\_envs.envs.unity\_parallel\_env](#mlagents_envs.envs.unity_parallel_env)
+  * [UnityParallelEnv](#mlagents_envs.envs.unity_parallel_env.UnityParallelEnv)
+    * [\_\_init\_\_](#mlagents_envs.envs.unity_parallel_env.UnityParallelEnv.__init__)
+    * [reset](#mlagents_envs.envs.unity_parallel_env.UnityParallelEnv.reset)
+* [mlagents\_envs.envs.unity\_pettingzoo\_base\_env](#mlagents_envs.envs.unity_pettingzoo_base_env)
+  * [UnityPettingzooBaseEnv](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv)
+    * [observation\_spaces](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.observation_spaces)
+    * [observation\_space](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.observation_space)
+    * [action\_spaces](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.action_spaces)
+    * [action\_space](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.action_space)
+    * [side\_channel](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.side_channel)
+    * [reset](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.reset)
+    * [seed](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.seed)
+    * [render](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.render)
+    * [close](#mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.close)
+
+<a name="mlagents_envs.envs.pettingzoo_env_factory"></a>
+# mlagents\_envs.envs.pettingzoo\_env\_factory
+
+<a name="mlagents_envs.envs.pettingzoo_env_factory.PettingZooEnvFactory"></a>
+## PettingZooEnvFactory Objects
+
+```python
+class PettingZooEnvFactory()
+```
+
+<a name="mlagents_envs.envs.pettingzoo_env_factory.PettingZooEnvFactory.env"></a>
+#### env
+
+```python
+ | env(seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]) -> UnityAECEnv
+```
+
+Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper
+
+**Arguments**:
+
+- `seed`: The seed for the action spaces of the agents.
+- `kwargs`: Any argument accepted by `UnityEnvironment`class except file_name
+
+<a name="mlagents_envs.envs.unity_aec_env"></a>
+# mlagents\_envs.envs.unity\_aec\_env
+
+<a name="mlagents_envs.envs.unity_aec_env.UnityAECEnv"></a>
+## UnityAECEnv Objects
+
+```python
+class UnityAECEnv(UnityPettingzooBaseEnv,  AECEnv)
+```
+
+Unity AEC (PettingZoo) environment wrapper.
+
+<a name="mlagents_envs.envs.unity_aec_env.UnityAECEnv.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(env: BaseEnv, seed: Optional[int] = None)
+```
+
+Initializes a Unity AEC environment wrapper.
+
+**Arguments**:
+
+- `env`: The UnityEnvironment that is being wrapped.
+- `seed`: The seed for the action spaces of the agents.
+
+<a name="mlagents_envs.envs.unity_aec_env.UnityAECEnv.step"></a>
+#### step
+
+```python
+ | step(action: Any) -> None
+```
+
+Sets the action of the active agent and get the observation, reward, done
+and info of the next agent.
+
+**Arguments**:
+
+- `action`: The action for the active agent
+
+<a name="mlagents_envs.envs.unity_aec_env.UnityAECEnv.observe"></a>
+#### observe
+
+```python
+ | observe(agent_id)
+```
+
+Returns the observation an agent currently can make. `last()` calls this function.
+
+<a name="mlagents_envs.envs.unity_aec_env.UnityAECEnv.last"></a>
+#### last
+
+```python
+ | last(observe=True)
+```
+
+returns observation, cumulative reward, done, info for the current agent (specified by self.agent_selection)
+
+<a name="mlagents_envs.envs.unity_parallel_env"></a>
+# mlagents\_envs.envs.unity\_parallel\_env
+
+<a name="mlagents_envs.envs.unity_parallel_env.UnityParallelEnv"></a>
+## UnityParallelEnv Objects
+
+```python
+class UnityParallelEnv(UnityPettingzooBaseEnv,  ParallelEnv)
+```
+
+Unity Parallel (PettingZoo) environment wrapper.
+
+<a name="mlagents_envs.envs.unity_parallel_env.UnityParallelEnv.__init__"></a>
+#### \_\_init\_\_
+
+```python
+ | __init__(env: BaseEnv, seed: Optional[int] = None)
+```
+
+Initializes a Unity Parallel environment wrapper.
+
+**Arguments**:
+
+- `env`: The UnityEnvironment that is being wrapped.
+- `seed`: The seed for the action spaces of the agents.
+
+<a name="mlagents_envs.envs.unity_parallel_env.UnityParallelEnv.reset"></a>
+#### reset
+
+```python
+ | reset() -> Dict[str, Any]
+```
+
+Resets the environment.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env"></a>
+# mlagents\_envs.envs.unity\_pettingzoo\_base\_env
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv"></a>
+## UnityPettingzooBaseEnv Objects
+
+```python
+class UnityPettingzooBaseEnv()
+```
+
+Unity Petting Zoo base environment.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.observation_spaces"></a>
+#### observation\_spaces
+
+```python
+ | @property
+ | observation_spaces() -> Dict[str, spaces.Space]
+```
+
+Return the observation spaces of all the agents.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.observation_space"></a>
+#### observation\_space
+
+```python
+ | observation_space(agent: str) -> Optional[spaces.Space]
+```
+
+The observation space of the current agent.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.action_spaces"></a>
+#### action\_spaces
+
+```python
+ | @property
+ | action_spaces() -> Dict[str, spaces.Space]
+```
+
+Return the action spaces of all the agents.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.action_space"></a>
+#### action\_space
+
+```python
+ | action_space(agent: str) -> Optional[spaces.Space]
+```
+
+The action space of the current agent.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.side_channel"></a>
+#### side\_channel
+
+```python
+ | @property
+ | side_channel() -> Dict[str, Any]
+```
+
+The side channels of the environment. You can access the side channels
+of an environment with `env.side_channel[<name-of-channel>]`.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.reset"></a>
+#### reset
+
+```python
+ | reset()
+```
+
+Resets the environment.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.seed"></a>
+#### seed
+
+```python
+ | seed(seed=None)
+```
+
+Reseeds the environment (making the resulting environment deterministic).
+`reset()` must be called after `seed()`, and before `step()`.
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.render"></a>
+#### render
+
+```python
+ | render(mode="human")
+```
+
+NOT SUPPORTED.
+
+Displays a rendered frame from the environment, if supported.
+Alternate render modes in the default environments are `'rgb_array'`
+which returns a numpy array and is supported by all environments outside of classic,
+and `'ansi'` which returns the strings printed (specific to classic environments).
+
+<a name="mlagents_envs.envs.unity_pettingzoo_base_env.UnityPettingzooBaseEnv.close"></a>
+#### close
+
+```python
+ | close() -> None
+```
+
+Close the environment.
diff --git a/docs/Python-PettingZoo-API.md b/docs/Python-PettingZoo-API.md
new file mode 100644
index 0000000000000000000000000000000000000000..9af94d2dbb8d7579d57058cf0884f3252b677e76
--- /dev/null
+++ b/docs/Python-PettingZoo-API.md
@@ -0,0 +1,57 @@
+# Unity ML-Agents PettingZoo Wrapper
+
+With the increasing interest in multi-agent training with a gym-like API, we provide a
+PettingZoo Wrapper around the [Petting Zoo API](https://www.pettingzoo.ml/). Our wrapper
+provides interfaces on top of our `UnityEnvironment` class, which is the default way of
+interfacing with a Unity environment via Python.
+
+## Installation and Examples
+
+The PettingZoo wrapper is part of the `mlgents_envs` package. Please refer to the
+[mlagents_envs installation instructions](ML-Agents-Envs-README.md).
+
+[[Colab] PettingZoo Wrapper Example](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop-python-api-ga/ml-agents-envs/colabs/Colab_PettingZoo.ipynb)
+
+This colab notebook demonstrates the example usage of the wrapper, including installation,
+basic usages, and an example with our
+[Striker vs Goalie environment](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md#strikers-vs-goalie)
+which is a multi-agents environment with multiple different behavior names.
+
+## API interface
+
+This wrapper is compatible with PettingZoo API. Please check out
+[PettingZoo API page](https://www.pettingzoo.ml/api) for more details.
+Here's an example of interacting with wrapped environment:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.envs import UnityToPettingZooWrapper
+
+unity_env = UnityEnvironment("StrikersVsGoalie")
+env = UnityToPettingZooWrapper(unity_env)
+env.reset()
+for agent in env.agent_iter():
+    observation, reward, done, info = env.last()
+    action = policy(observation, agent)
+    env.step(action)
+```
+
+## Notes
+- There is support for both [AEC](https://www.pettingzoo.ml/api#interacting-with-environments)
+  and [Parallel](https://www.pettingzoo.ml/api#parallel-api) PettingZoo APIs.
+- The AEC wrapper is compatible with PettingZoo (PZ) API interface but works in a slightly
+  different way under the hood. For the AEC API, Instead of stepping the environment in every `env.step(action)`,
+  the PZ wrapper will store the action, and will only perform environment stepping when all the
+  agents requesting for actions in the current step have been assigned an action. This is for
+  performance, considering that the communication between Unity and python is more efficient
+  when data are sent in batches.
+- Since the actions for the AEC wrapper are stored without applying them to the environment until
+  all the actions are queued, some components of the API might behave in unexpected way. For example, a call
+  to `env.reward` should return the instantaneous reward for that particular step, but the true
+  reward would only be available when an actual environment step is performed. It's recommended that
+  you follow the API definition for training (access rewards from `env.last()` instead of
+  `env.reward`) and the underlying mechanism shouldn't affect training results.
+- The environments will automatically reset when it's done, so `env.agent_iter(max_step)` will
+  keep going on until the specified max step is reached (default: `2**63`). There is no need to
+  call `env.reset()` except for the very beginning of instantiating an environment.
+
diff --git a/docs/Readme.md b/docs/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..a7d6fb0dca23e4d46a379bebfb57518ce7a20903
--- /dev/null
+++ b/docs/Readme.md
@@ -0,0 +1,202 @@
+# Unity ML-Agents Toolkit
+
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/)
+
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](../LICENSE.md)
+
+([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
+([all releases](https://github.com/Unity-Technologies/ml-agents/releases))
+
+**The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source
+project that enables games and simulations to serve as environments for
+training intelligent agents. We provide implementations (based on PyTorch)
+of state-of-the-art algorithms to enable game developers and hobbyists to easily
+train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the
+provided simple-to-use Python API to train Agents using reinforcement learning,
+imitation learning, neuroevolution, or any other methods. These trained agents can be
+used for multiple purposes, including controlling NPC behavior (in a variety of
+settings such as multi-agent and adversarial), automated testing of game builds
+and evaluating different game design decisions pre-release. The ML-Agents
+Toolkit is mutually beneficial for both game developers and AI researchers as it
+provides a central platform where advances in AI can be evaluated on Unity’s
+rich environments and then made accessible to the wider research and game
+developer communities.
+
+## Features
+- 17+ [example Unity environments](Learning-Environment-Examples.md)
+- Support for multiple environment configurations and training scenarios
+- Flexible Unity SDK that can be integrated into your game or custom Unity scene
+- Support for training single-agent, multi-agent cooperative, and multi-agent
+  competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).
+- Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).
+- Quickly and easily add your own [custom training algorithm](Python-Custom-Trainer-Plugin.md) and/or components.
+- Easily definable Curriculum Learning scenarios for complex tasks
+- Train robust agents using environment randomization
+- Flexible agent control with On Demand Decision Making
+- Train using multiple concurrent Unity environment instances
+- Utilizes the [Unity Inference Engine](Unity-Inference-Engine.md) to
+  provide native cross-platform support
+- Unity environment [control from Python](Python-LLAPI.md)
+- Wrap Unity learning environments as a [gym](Python-Gym-API.md) environment
+- Wrap Unity learning environments as a [PettingZoo](Python-PettingZoo-API.md) environment
+
+See our [ML-Agents Overview](ML-Agents-Overview.md) page for detailed
+descriptions of all these features. Or go straight to our [web docs](https://unity-technologies.github.io/ml-agents/).
+## Releases & Documentation
+
+**Our latest, stable release is `Release 20`. Click
+[here](Getting-Started.md)
+to get started with the latest release of ML-Agents.**
+
+**You can also check out our new [web docs](https://unity-technologies.github.io/ml-agents/)!**
+
+The table below lists all our releases, including our `main` branch which is
+under active development and may be unstable. A few helpful guidelines:
+- The [Versioning page](Versioning.md) overviews how we manage our GitHub
+  releases and the versioning process for each of the ML-Agents components.
+- The [Releases page](https://github.com/Unity-Technologies/ml-agents/releases)
+  contains details of the changes between releases.
+- The [Migration page](Migrating.md) contains details on how to upgrade
+  from earlier releases of the ML-Agents Toolkit.
+- The **Documentation** links in the table below include installation and usage
+  instructions specific to each release. Remember to always use the
+  documentation that corresponds to the release version you're using.
+- The `com.unity.ml-agents` package is [verified](https://docs.unity3d.com/2020.1/Documentation/Manual/pack-safe.html)
+  for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x.
+
+| **Version** | **Release Date** | **Source** | **Documentation** | **Download** | **Python Package** | **Unity Package** |
+|:-------:|:------:|:-------------:|:-------:|:------------:|:------------:|:------------:|
+| **Release 20** | **November 21, 2022** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_20)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_20.zip)** | **[0.30.0](https://pypi.org/project/mlagents/0.30.0/)** | **[2.3.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@2.3/manual/index.html)** |
+| **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) | -- | -- |
+| **Verified Package 1.0.8** | **May 26, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.8)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_20_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.8.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.8](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** |
+
+If you are a researcher interested in a discussion of Unity as an AI platform,
+see a pre-print of our
+[reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627).
+
+If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you
+cite the following paper as a reference:
+
+```
+@article{juliani2020,
+  title={Unity: A general platform for intelligent agents},
+  author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny},
+  journal={arXiv preprint arXiv:1809.02627},
+  url={https://arxiv.org/pdf/1809.02627.pdf},
+  year={2020}
+}
+```
+
+Additionally, if you use the MA-POCA trainer in your research, we ask that you
+cite the following paper as a reference:
+
+```
+@article{cohen2022,
+  title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning},
+  author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy},
+  journal={RL in Games Workshop AAAI 2022},
+  url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf},
+  year={2022}
+}
+```
+
+
+
+## Additional Resources
+
+We have a Unity Learn course,
+[ML-Agents: Hummingbirds](https://learn.unity.com/course/ml-agents-hummingbirds),
+that provides a gentle introduction to Unity and the ML-Agents Toolkit.
+
+We've also partnered with
+[CodeMonkeyUnity](https://www.youtube.com/c/CodeMonkeyUnity) to create a
+[series of tutorial videos](https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110)
+on how to implement and use the ML-Agents Toolkit.
+
+We have also published a series of blog posts that are relevant for ML-Agents:
+
+- (July 12, 2021)
+  [ML-Agents plays Dodgeball](https://blog.unity.com/technology/ml-agents-plays-dodgeball)
+- (May 5, 2021)
+  [ML-Agents v2.0 release: Now supports training complex cooperative behaviors](https://blogs.unity3d.com/2021/05/05/ml-agents-v2-0-release-now-supports-training-complex-cooperative-behaviors/)
+- (December 28, 2020)
+  [Happy holidays from the Unity ML-Agents team!](https://blogs.unity3d.com/2020/12/28/happy-holidays-from-the-unity-ml-agents-team/)
+- (November 20, 2020)
+  [How Eidos-Montréal created Grid Sensors to improve observations for training agents](https://blogs.unity3d.com/2020/11/20/how-eidos-montreal-created-grid-sensors-to-improve-observations-for-training-agents/)
+- (November 11, 2020)
+  [2020 AI@Unity interns shoutout](https://blogs.unity3d.com/2020/11/11/2020-aiunity-interns-shoutout/)
+- (May 12, 2020)
+  [Announcing ML-Agents Unity Package v1.0!](https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/)
+- (February 28, 2020)
+  [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
+- (November 11, 2019)
+  [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
+- (October 21, 2019)
+  [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
+- (April 15, 2019)
+  [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
+- (March 1, 2019)
+  [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
+- (December 17, 2018)
+  [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
+- (October 2, 2018)
+  [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
+- (September 11, 2018)
+  [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
+- (June 26, 2018)
+  [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
+- (June 19, 2018)
+  [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
+- (May 24, 2018)
+  [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
+- (March 15, 2018)
+  [ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/)
+- (December 11, 2017)
+  [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+- (December 8, 2017)
+  [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
+- (September 19, 2017)
+  [Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)
+- Overviewing reinforcement learning concepts
+  ([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
+  and
+  [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
+
+### More from Unity
+
+- [Unity Robotics](https://github.com/Unity-Technologies/Unity-Robotics-Hub)
+- [Unity Computer Vision](https://github.com/Unity-Technologies/com.unity.perception)
+
+## Community and Feedback
+
+The ML-Agents Toolkit is an open-source project and we encourage and welcome
+contributions. If you wish to contribute, be sure to review our
+[contribution guidelines](CONTRIBUTING.md) and
+[code of conduct](../CODE_OF_CONDUCT.md).
+
+For problems with the installation and setup of the ML-Agents Toolkit, or
+discussions about how to best setup or train your agents, please create a new
+thread on the
+[Unity ML-Agents forum](https://forum.unity.com/forums/ml-agents.453/) and make
+sure to include as much detail as possible. If you run into any other problems
+using the ML-Agents Toolkit or have a specific feature request, please
+[submit a GitHub issue](https://github.com/Unity-Technologies/ml-agents/issues).
+
+Please tell us which samples you would like to see shipped with the ML-Agents Unity
+package by replying to
+[this forum thread](https://forum.unity.com/threads/feedback-wanted-shipping-sample-s-with-the-ml-agents-package.1073468/).
+
+
+Your opinion matters a great deal to us. Only by hearing your thoughts on the
+Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few
+minutes to
+[let us know about it](https://unitysoftware.co1.qualtrics.com/jfe/form/SV_55pQKCZ578t0kbc).
+
+For any other questions or feedback, connect directly with the ML-Agents team at
+ml-agents@unity3d.com.
+
+## Privacy
+
+In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics.
+Please refer to "Information that is passively collected by Unity" in the
+[Unity Privacy Policy](https://unity3d.com/legal/privacy-policy).
diff --git a/docs/Training-Configuration-File.md b/docs/Training-Configuration-File.md
new file mode 100644
index 0000000000000000000000000000000000000000..d47b6ab103cfe1d25aead7ae9e712a6e175310cc
--- /dev/null
+++ b/docs/Training-Configuration-File.md
@@ -0,0 +1,231 @@
+# Training Configuration File
+
+**Table of Contents**
+
+- [Common Trainer Configurations](#common-trainer-configurations)
+- [Trainer-specific Configurations](#trainer-specific-configurations)
+  - [PPO-specific Configurations](#ppo-specific-configurations)
+  - [SAC-specific Configurations](#sac-specific-configurations)
+- [Reward Signals](#reward-signals)
+  - [Extrinsic Rewards](#extrinsic-rewards)
+  - [Curiosity Intrinsic Reward](#curiosity-intrinsic-reward)
+  - [GAIL Intrinsic Reward](#gail-intrinsic-reward)
+  - [RND Intrinsic Reward](#rnd-intrinsic-reward)
+  - [Reward Signal Settings for SAC](#reward-signal-settings-for-sac)
+- [Behavioral Cloning](#behavioral-cloning)
+- [Memory-enhanced Agents using Recurrent Neural Networks](#memory-enhanced-agents-using-recurrent-neural-networks)
+- [Self-Play](#self-play)
+  - [Note on Reward Signals](#note-on-reward-signals)
+  - [Note on Swap Steps](#note-on-swap-steps)
+
+## Common Trainer Configurations
+
+One of the first decisions you need to make regarding your training run is which
+trainer to use: PPO, SAC, or POCA. There are some training configurations that are
+common to both trainers (which we review now) and others that depend on the
+choice of the trainer (which we review on subsequent sections).
+
+| **Setting**              | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| :----------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `trainer_type`                | (default = `ppo`) The type of trainer to use: `ppo`,  `sac`, or `poca`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `summary_freq`           | (default = `50000`) Number of experiences that needs to be collected before generating and displaying training statistics. This determines the granularity of the graphs in Tensorboard.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| `time_horizon`           | (default = `64`) How many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions. <br><br> Typical range: `32` - `2048` |
+| `max_steps`              | (default = `500000`) Total number of steps (i.e., observation collected and action taken) that must be taken in the environment (or across all environments if using multiple in parallel) before ending the training process. If you have multiple agents with the same behavior name within your environment, all steps taken by those agents will contribute to the same `max_steps` count. <br><br>Typical range: `5e5` - `1e7`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| `keep_checkpoints`         | (default = `5`) The maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the checkpoint_interval option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. |
+| `even_checkpoints`         | (default = `false`) If set to true, ignores `checkpoint_interval` and evenly distributes checkpoints throughout training based on `keep_checkpoints`and `max_steps`, i.e. `checkpoint_interval = max_steps / keep_checkpoints`. Useful for cataloging agent behavior throughout training. |
+| `checkpoint_interval`         | (default = `500000`) The number of experiences collected between each checkpoint by the trainer. A maximum of `keep_checkpoints` checkpoints are saved before old ones are deleted. Each checkpoint saves the `.onnx` files in `results/` folder.|
+| `init_path`              | (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. <br><br>You can provide either the file name or the full path to the checkpoint, e.g. `{checkpoint_name.pt}` or `./models/{run-id}/{behavior_name}/{checkpoint_name.pt}`. This option is provided in case you want to initialize different behaviors from different runs or initialize from an older checkpoint; in most cases, it is sufficient to use the `--initialize-from` CLI parameter to initialize all models from the same run.                                                                                                                                  |
+| `threaded`               | (default = `false`) Allow environments to step while updating the model. This might result in a training speedup, especially when using SAC. For best performance, leave setting to `false` when using self-play.                                                                                                                                                                                                                      |
+| `hyperparameters -> learning_rate`          | (default = `3e-4`) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. <br><br>Typical range: `1e-5` - `1e-3`                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| `hyperparameters -> batch_size`             | Number of experiences in each iteration of gradient descent. **This should always be multiple times smaller than `buffer_size`**. If you are using continuous actions, this value should be large (on the order of 1000s). If you are using only discrete actions, this value should be smaller (on the order of 10s). <br><br> Typical range: (Continuous - PPO): `512` - `5120`; (Continuous - SAC): `128` - `1024`; (Discrete, PPO & SAC): `32` - `512`.                                                                                                                                                                                                                                                               |
+| `hyperparameters -> buffer_size`            | (default = `10240` for PPO and `50000` for SAC)<br> **PPO:** Number of experiences to collect before updating the policy model. Corresponds to how many experiences should be collected before we do any learning or updating of the model. **This should be multiple times larger than `batch_size`**. Typically a larger `buffer_size` corresponds to more stable training updates. <br> **SAC:** The max size of the experience buffer - on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. <br><br>Typical range: PPO: `2048` - `409600`; SAC: `50000` - `1000000`                                                                                                                                                      |
+| `hyperparameters -> learning_rate_schedule` | (default = `linear` for PPO and `constant` for SAC) Determines how learning rate changes over time. For PPO, we recommend decaying learning rate until max_steps so learning converges more stably. However, for some cases (e.g. training for an unknown amount of time) this feature can be disabled. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally. <br><br>`linear` decays the learning_rate linearly, reaching 0 at max_steps, while `constant` keeps the learning rate constant for the entire training run.                                                                                                           |
+| `network_settings -> hidden_units`           | (default = `128`) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. <br><br> Typical range: `32` - `512`                                                                                                                                                                                                                                                                                    |
+| `network_settings -> num_layers`             | (default = `2`) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. <br><br> Typical range: `1` - `3`                                                                                                                                                                                                                                                                                                                                                    |
+| `network_settings -> normalize`              | (default = `false`) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems.                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| `network_settings -> vis_encode_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that can capture more granular spatial relationships and is optimized for board games. `fully_connected` uses a single fully connected dense layer as encoder without any convolutional layers. <br><br> Due to the size of convolution kernel, there is a minimum observation size limitation that each encoder type can handle - `simple`: 20x20, `nature_cnn`: 36x36, `resnet`: 15 x 15, `match3`: 5x5.  `fully_connected` doesn't have convolutional layers and thus no size limits, but since it has less representation power it should be reserved for very small inputs. Note that using the `match3` CNN with very large visual input might result in a huge observation encoding and thus potentially slow down training or cause memory issues.                                                                                                                                                                                              |
+| `network_settings -> conditioning_type`       | (default = `hyper`) Conditioning type for the policy using goal observations. <br><br> `none` treats the goal observations as regular observations, `hyper` (default) uses a HyperNetwork with goal observations as input to generate some of the weights of the policy. Note that when using `hyper` the number of parameters of the network increases greatly. Therefore, it is recommended to reduce the number of `hidden_units` when using this `conditioning_type`
+
+
+## Trainer-specific Configurations
+
+Depending on your choice of a trainer, there are additional trainer-specific
+configurations. We present them below in two separate tables, but keep in mind
+that you only need to include the configurations for the trainer selected (i.e.
+the `trainer` setting above).
+
+### PPO-specific Configurations
+
+| **Setting** | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| :---------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `hyperparameters -> beta`      | (default = `5.0e-3`) Strength of the entropy regularization, which makes the policy "more random." This ensures that agents properly explore the action space during training. Increasing this will ensure more random actions are taken. This should be adjusted such that the entropy (measurable from TensorBoard) slowly decreases alongside increases in reward. If entropy drops too quickly, increase beta. If entropy drops too slowly, decrease `beta`. <br><br>Typical range: `1e-4` - `1e-2`                                                                                                                                                                     |
+| `hyperparameters -> epsilon`   | (default = `0.2`) Influences how rapidly the policy can evolve during training. Corresponds to the acceptable threshold of divergence between the old and new policies during gradient descent updating. Setting this value small will result in more stable updates, but will also slow the training process. <br><br>Typical range: `0.1` - `0.3`                                                                                                                                                                                                                                                                                                                      |
+| `hyperparameters -> beta_schedule` | (default = `learning_rate_schedule`) Determines how beta changes over time. <br><br>`linear` decays beta linearly, reaching 0 at max_steps, while `constant` keeps beta constant for the entire training run. If not explicitly set, the default beta schedule will be set to `hyperparameters -> learning_rate_schedule`.                                                                                                           |
+| `hyperparameters -> epsilon_schedule` | (default = `learning_rate_schedule `) Determines how epsilon changes over time (PPO only). <br><br>`linear` decays epsilon linearly, reaching 0 at max_steps, while `constant` keeps the epsilon constant for the entire training run. If not explicitly set, the default epsilon schedule will be set to `hyperparameters -> learning_rate_schedule`.
+| `hyperparameters -> lambd`     | (default = `0.95`) Regularization parameter (lambda) used when calculating the Generalized Advantage Estimate ([GAE](https://arxiv.org/abs/1506.02438)). This can be thought of as how much the agent relies on its current value estimate when calculating an updated value estimate. Low values correspond to relying more on the current value estimate (which can be high bias), and high values correspond to relying more on the actual rewards received in the environment (which can be high variance). The parameter provides a trade-off between the two, and the right value can lead to a more stable training process. <br><br>Typical range: `0.9` - `0.95` |
+| `hyperparameters -> num_epoch` | (default = `3`) Number of passes to make through the experience buffer when performing gradient descent optimization.The larger the batch_size, the larger it is acceptable to make this. Decreasing this will ensure more stable updates, at the cost of slower learning. <br><br>Typical range: `3` - `10`                                                                                                                                                                                                                                                                                                                                                           |
+| `hyperparameters -> shared_critic` | (default = `False`) Whether or not the policy and value function networks share a backbone. It may be useful to use a shared backbone when learning from image observations.
+
+### SAC-specific Configurations
+
+| **Setting**          | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| :------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `hyperparameters -> buffer_init_steps`  | (default = `0`) Number of experiences to collect into the buffer before updating the policy model. As the untrained policy is fairly random, pre-filling the buffer with random actions is useful for exploration. Typically, at least several episodes of experiences should be pre-filled. <br><br>Typical range: `1000` - `10000`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| `hyperparameters -> init_entcoef` | (default = `1.0`) How much the agent should explore in the beginning of training. Corresponds to the initial entropy coefficient set at the beginning of training. In SAC, the agent is incentivized to make its actions entropic to facilitate better exploration. The entropy coefficient weighs the true reward with a bonus entropy reward. The entropy coefficient is [automatically adjusted](https://arxiv.org/abs/1812.05905) to a preset target entropy, so the `init_entcoef` only corresponds to the starting value of the entropy bonus. Increase init_entcoef to explore more in the beginning, decrease to converge to a solution faster. <br><br>Typical range: (Continuous): `0.5` - `1.0`; (Discrete): `0.05` - `0.5`                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| `hyperparameters -> save_replay_buffer` | (default = `false`) Whether to save and load the experience replay buffer as well as the model when quitting and re-starting training. This may help resumes go more smoothly, as the experiences collected won't be wiped. Note that replay buffers can be very large, and will take up a considerable amount of disk space. For that reason, we disable this feature by default.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `hyperparameters -> tau` | (default = `0.005`) How aggressively to update the target network used for bootstrapping value estimation in SAC. Corresponds to the magnitude of the target Q update during the SAC model update. In SAC, there are two neural networks: the target and the policy. The target network is used to bootstrap the policy's estimate of the future rewards at a given state, and is fixed while the policy is being updated. This target is then slowly updated according to tau. Typically, this value should be left at 0.005. For simple problems, increasing tau to 0.01 might reduce the time it takes to learn, at the cost of stability. <br><br>Typical range: `0.005` - `0.01`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `hyperparameters -> steps_per_update` | (default = `1`) Average ratio of agent steps (actions) taken to updates made of the agent's policy. In SAC, a single "update" corresponds to grabbing a batch of size `batch_size` from the experience replay buffer, and using this mini batch to update the models. Note that it is not guaranteed that after exactly `steps_per_update` steps an update will be made, only that the ratio will hold true over many steps. Typically, `steps_per_update` should be greater than or equal to 1. Note that setting `steps_per_update` lower will improve sample efficiency (reduce the number of steps required to train) but increase the CPU time spent performing updates. For most environments where steps are fairly fast (e.g. our example environments) `steps_per_update` equal to the number of agents in the scene is a good balance. For slow environments (steps take 0.1 seconds or more) reducing `steps_per_update` may improve training speed. We can also change `steps_per_update` to lower than 1 to update more often than once per step, though this will usually result in a slowdown unless the environment is very slow. <br><br>Typical range: `1` - `20` |
+| `hyperparameters -> reward_signal_num_update` | (default = `steps_per_update`) Number of steps per mini batch sampled and used for updating the reward signals. By default, we update the reward signals once every time the main policy is updated. However, to imitate the training procedure in certain imitation learning papers (e.g. [Kostrikov et. al](http://arxiv.org/abs/1809.02925), [Blondé et. al](http://arxiv.org/abs/1809.02064)), we may want to update the reward signal (GAIL) M times for every update of the policy. We can change `steps_per_update` of SAC to N, as well as `reward_signal_steps_per_update` under `reward_signals` to N / M to accomplish this. By default, `reward_signal_steps_per_update` is set to `steps_per_update`. |
+
+### MA-POCA-specific Configurations
+MA-POCA uses the same configurations as PPO, and there are no additional POCA-specific parameters.
+
+**NOTE**: Reward signals other than Extrinsic Rewards have not been extensively tested with MA-POCA,
+though they can still be added and used for training on a your-mileage-may-vary basis.
+
+## Reward Signals
+
+The `reward_signals` section enables the specification of settings for both
+extrinsic (i.e. environment-based) and intrinsic reward signals (e.g. curiosity
+and GAIL). Each reward signal should define at least two parameters, `strength`
+and `gamma`, in addition to any class-specific hyperparameters. Note that to
+remove a reward signal, you should delete its entry entirely from
+`reward_signals`. At least one reward signal should be left defined at all
+times. Provide the following configurations to design the reward signal for your
+training run.
+
+### Extrinsic Rewards
+
+Enable these settings to ensure that your training run incorporates your
+environment-based reward signal:
+
+| **Setting**             | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+| :---------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `extrinsic -> strength` | (default = `1.0`) Factor by which to multiply the reward given by the environment. Typical ranges will vary depending on the reward signal. <br><br>Typical range: `1.00`                                                                                                                                                                                                                                                                                              |
+| `extrinsic -> gamma`    | (default = `0.99`) Discount factor for future rewards coming from the environment. This can be thought of as how far into the future the agent should care about possible rewards. In situations when the agent should be acting in the present in order to prepare for rewards in the distant future, this value should be large. In cases when rewards are more immediate, it can be smaller. Must be strictly smaller than 1. <br><br>Typical range: `0.8` - `0.995` |
+
+### Curiosity Intrinsic Reward
+
+To enable curiosity, provide these settings:
+
+| **Setting**                  | **Description**                                                                                                                                                                                                                                                                                                                       |
+| :--------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `curiosity -> strength`      | (default = `1.0`) Magnitude of the curiosity reward generated by the intrinsic curiosity module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. <br><br>Typical range: `0.001` - `0.1` |
+| `curiosity -> gamma`         | (default = `0.99`) Discount factor for future rewards. <br><br>Typical range: `0.8` - `0.995`                                                                                                                                                                                                                                                            |
+| `curiosity -> network_settings` | Please see the documentation for `network_settings` under [Common Trainer Configurations](#common-trainer-configurations). The network specs used by the intrinsic curiosity model. The value should of `hidden_units` should be small enough to encourage the ICM to compress the original observation, but also not too small to prevent it from learning to differentiate between expected and actual observations. <br><br>Typical range: `64` - `256` |
+| `curiosity -> learning_rate` | (default = `3e-4`) Learning rate used to update the intrinsic curiosity module. This should typically be decreased if training is unstable, and the curiosity loss is unstable. <br><br>Typical range: `1e-5` - `1e-3`                                                                                                      |
+
+### GAIL Intrinsic Reward
+
+To enable GAIL (assuming you have recorded demonstrations), provide these
+settings:
+
+| **Setting**             | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| :---------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `gail -> strength`      | (default = `1.0`) Factor by which to multiply the raw reward. Note that when using GAIL with an Extrinsic Signal, this value should be set lower if your demonstrations are suboptimal (e.g. from a human), so that a trained agent will focus on receiving extrinsic rewards instead of exactly copying the demonstrations. Keep the strength below about 0.1 in those cases. <br><br>Typical range: `0.01` - `1.0`                                                                              |
+| `gail -> gamma`         | (default = `0.99`) Discount factor for future rewards. <br><br>Typical range: `0.8` - `0.9`                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `gail -> demo_path`     | (Required, no default) The path to your .demo file or directory of .demo files.                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `gail -> network_settings` | Please see the documentation for `network_settings` under [Common Trainer Configurations](#common-trainer-configurations). The network specs for the GAIL discriminator. The value of `hidden_units` should be small enough to encourage the discriminator to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior. Dramatically increasing this size will also negatively affect training times. <br><br>Typical range: `64` - `256`                                                           |
+| `gail -> learning_rate` | (Optional, default = `3e-4`) Learning rate used to update the discriminator. This should typically be decreased if training is unstable, and the GAIL loss is unstable. <br><br>Typical range: `1e-5` - `1e-3`                                                                                                                                                                                                                                                                  |
+| `gail -> use_actions`   | (default = `false`) Determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower. |
+| `gail -> use_vail`      | (default = `false`) Enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be "too good" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand.                                                                       |
+
+### RND Intrinsic Reward
+
+Random Network Distillation (RND) is only available for the PyTorch trainers.
+To enable RND, provide these settings:
+
+| **Setting**                  | **Description**                                                                                                                                                                                                                                                                                                                       |
+| :--------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `rnd -> strength`      | (default = `1.0`) Magnitude of the curiosity reward generated by the intrinsic rnd module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. <br><br>Typical range: `0.001` - `0.01` |
+| `rnd -> gamma`         | (default = `0.99`) Discount factor for future rewards. <br><br>Typical range: `0.8` - `0.995`                                                                                                                                                                                                                                                            |
+| `rnd -> network_settings` | Please see the documentation for `network_settings` under [Common Trainer Configurations](#common-trainer-configurations). The network specs for the RND model. |
+| `curiosity -> learning_rate` | (default = `3e-4`) Learning rate used to update the RND module. This should be large enough for the RND module to quickly learn the state representation, but small enough to allow for stable learning. <br><br>Typical range: `1e-5` - `1e-3`
+
+
+## Behavioral Cloning
+
+To enable Behavioral Cloning as a pre-training option (assuming you have
+recorded demonstrations), provide the following configurations under the
+`behavioral_cloning` section:
+
+| **Setting**          | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| :------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `demo_path`          | (Required, no default) The path to your .demo file or directory of .demo files.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `strength`           | (default = `1.0`) Learning rate of the imitation relative to the learning rate of PPO, and roughly corresponds to how strongly we allow BC to influence the policy. <br><br>Typical range: `0.1` - `0.5`                                                                                                                                                                                                                                                                                                                                                                     |
+| `steps`              | (default = `0`) During BC, it is often desirable to stop using demonstrations after the agent has "seen" rewards, and allow it to optimize past the available demonstrations and/or generalize outside of the provided demonstrations. steps corresponds to the training steps over which BC is active. The learning rate of BC will anneal over the steps. Set the steps to 0 for constant imitation over the entire training run.                                                                                                                                        |
+| `batch_size`         | (default = `batch_size` of trainer) Number of demonstration experiences used for one iteration of a gradient descent update. If not specified, it will default to the `batch_size` of the trainer. <br><br>Typical range: (Continuous): `512` - `5120`; (Discrete): `32` - `512`                                                                                                                                                                                                                                                                                                                              |
+| `num_epoch`          | (default = `num_epoch` of trainer) Number of passes through the experience buffer during gradient descent. If not specified, it will default to the number of epochs set for PPO. <br><br>Typical range: `3` - `10`                                                                                                                                                                                                                                                                                                                                                                           |
+| `samples_per_update` | (default = `0`) Maximum number of samples to use during each imitation update. You may want to lower this if your demonstration dataset is very large to avoid overfitting the policy on demonstrations. Set to 0 to train over all of the demonstrations at each update step. <br><br>Typical range: `buffer_size`
+
+## Memory-enhanced Agents using Recurrent Neural Networks
+
+You can enable your agents to use memory by adding a `memory` section under `network_settings`,
+and setting `memory_size` and `sequence_length`:
+
+| **Setting**       | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| :---------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `network_settings -> memory -> memory_size` | (default = `128`) Size of the memory an agent must keep. In order to use a LSTM, training requires a sequence of experiences instead of single experiences. Corresponds to the size of the array of floating point numbers used to store the hidden state of the recurrent neural network of the policy. This value must be a multiple of 2, and should scale with the amount of information you expect the agent will need to remember in order to successfully complete the task. <br><br>Typical range: `32` - `256` |
+| `network_settings -> memory -> sequence_length` | (default = `64`) Defines how long the sequences of experiences must be while training. Note that if this number is too small, the agent will not be able to remember things over longer periods of time. If this number is too large, the neural network will take longer to train. <br><br>Typical range: `4` - `128`                                                                                                                                                                                                 |
+
+A few considerations when deciding to use memory:
+
+- LSTM does not work well with continuous actions. Please use
+  discrete actions for better results.
+- Adding a recurrent layer increases the complexity of the neural network, it is
+  recommended to decrease `num_layers` when using recurrent.
+- It is required that `memory_size` be divisible by 2.
+
+## Self-Play
+
+Training with self-play adds additional confounding factors to the usual issues
+faced by reinforcement learning. In general, the tradeoff is between the skill
+level and generality of the final policy and the stability of learning. Training
+against a set of slowly or unchanging adversaries with low diversity results in
+a more stable learning process than training against a set of quickly changing
+adversaries with high diversity. With this context, this guide discusses the
+exposed self-play hyperparameters and intuitions for tuning them.
+
+If your environment contains multiple agents that are divided into teams, you
+can leverage our self-play training option by providing these configurations for
+each Behavior:
+
+| **Setting**                       | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| :-------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `save_steps`                      | (default = `20000`) Number of _trainer steps_ between snapshots. For example, if `save_steps=10000` then a snapshot of the current policy will be saved every `10000` trainer steps. Note, trainer steps are counted per agent. For more information, please see the [migration doc](Migrating.md) after v0.13. <br><br>A larger value of `save_steps` will yield a set of opponents that cover a wider range of skill levels and possibly play styles since the policy receives more training. As a result, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. This value is also dependent on how intrinsically difficult the environment is for the agent. <br><br> Typical range: `10000` - `100000`                                                                                                                                                                                                                                                                                            |
+| `team_change`                     | (default = `5 * save_steps`) Number of _trainer_steps_ between switching the learning team. This is the number of trainer steps the teams associated with a specific ghost trainer will train before a different team becomes the new learning team. It is possible that, in asymmetric games, opposing teams require fewer trainer steps to make similar performance gains. This enables users to train a more complicated team of agents for more trainer steps than a simpler team of agents per team switch. <br><br>A larger value of `team-change` will allow the agent to train longer against it's opponents. The longer an agent trains against the same set of opponents the more able it will be to defeat them. However, training against them for too long may result in overfitting to the particular opponent strategies and so the agent may fail against the next batch of opponents. <br><br> The value of `team-change` will determine how many snapshots of the agent's policy are saved to be used as opponents for the other team. So, we recommend setting this value as a function of the `save_steps` parameter discussed previously. <br><br> Typical range: 4x-10x where x=`save_steps` |
+| `swap_steps`                      | (default = `10000`) Number of _ghost steps_ (not trainer steps) between swapping the opponents policy with a different snapshot. A 'ghost step' refers to a step taken by an agent _that is following a fixed policy and not learning_. The reason for this distinction is that in asymmetric games, we may have teams with an unequal number of agents e.g. a 2v1 scenario like our Strikers Vs Goalie example environment. The team with two agents collects twice as many agent steps per environment step as the team with one agent. Thus, these two values will need to be distinct to ensure that the same number of trainer steps corresponds to the same number of opponent swaps for each team. The formula for `swap_steps` if a user desires `x` swaps of a team with `num_agents` agents against an opponent team with `num_opponent_agents` agents during `team-change` total steps is: `(num_agents / num_opponent_agents) * (team_change / x)` <br><br> Typical range: `10000` - `100000`                                                                                                                                                                                                 |
+| `play_against_latest_model_ratio` | (default = `0.5`) Probability an agent will play against the latest opponent policy. With probability 1 - `play_against_latest_model_ratio`, the agent will play against a snapshot of its opponent from a past iteration. <br><br> A larger value of `play_against_latest_model_ratio` indicates that an agent will be playing against the current opponent more often. Since the agent is updating it's policy, the opponent will be different from iteration to iteration. This can lead to an unstable learning environment, but poses the agent with an [auto-curricula](https://openai.com/blog/emergent-tool-use/) of more increasingly challenging situations which may lead to a stronger final policy. <br><br> Typical range: `0.0` - `1.0`                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| `window`                          | (default = `10`) Size of the sliding window of past snapshots from which the agent's opponents are sampled. For example, a `window` size of 5 will save the last 5 snapshots taken. Each time a new snapshot is taken, the oldest is discarded. A larger value of `window` means that an agent's pool of opponents will contain a larger diversity of behaviors since it will contain policies from earlier in the training run. Like in the `save_steps` hyperparameter, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. <br><br> Typical range: `5` - `30`                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+
+### Note on Reward Signals
+
+We make the assumption that the final reward in a trajectory corresponds to the
+outcome of an episode. A final reward of +1 indicates winning, -1 indicates
+losing and 0 indicates a draw. The ELO calculation (discussed below) depends on
+this final reward being either +1, 0, -1.
+
+The reward signal should still be used as described in the documentation for the
+other trainers. However, we encourage users to be a bit more conservative when
+shaping reward functions due to the instability and non-stationarity of learning
+in adversarial games. Specifically, we encourage users to begin with the
+simplest possible reward function (+1 winning, -1 losing) and to allow for more
+iterations of training to compensate for the sparsity of reward.
+
+### Note on Swap Steps
+
+As an example, in a 2v1 scenario, if we want the swap to occur x=4 times during
+team-change=200000 steps, the swap_steps for the team of one agent is:
+
+swap_steps = (1 / 2) \* (200000 / 4) = 25000 The swap_steps for the team of two
+agents is:
+
+swap_steps = (2 / 1) \* (200000 / 4) = 100000 Note, with equal team sizes, the
+first term is equal to 1 and swap_steps can be calculated by just dividing the
+total steps by the desired number of swaps.
+
+A larger value of swap_steps means that an agent will play against the same
+fixed opponent for a longer number of training iterations. This results in a
+more stable training scenario, but leaves the agent open to the risk of
+overfitting it's behavior for this particular opponent. Thus, when a new
+opponent is swapped, the agent may lose more often than expected.
diff --git a/docs/Training-ML-Agents.md b/docs/Training-ML-Agents.md
new file mode 100644
index 0000000000000000000000000000000000000000..2457963a96810c47feded2a15b3c619c9cf8158b
--- /dev/null
+++ b/docs/Training-ML-Agents.md
@@ -0,0 +1,623 @@
+# Training ML-Agents
+
+**Table of Contents**
+
+- [Training ML-Agents](#training-ml-agents)
+  - [Training with mlagents-learn](#training-with-mlagents-learn)
+    - [Starting Training](#starting-training)
+      - [Observing Training](#observing-training)
+      - [Stopping and Resuming Training](#stopping-and-resuming-training)
+      - [Loading an Existing Model](#loading-an-existing-model)
+  - [Training Configurations](#training-configurations)
+    - [Adding CLI Arguments to the Training Configuration file](#adding-cli-arguments-to-the-training-configuration-file)
+      - [Environment settings](#environment-settings)
+      - [Engine settings](#engine-settings)
+      - [Checkpoint settings](#checkpoint-settings)
+      - [Torch settings:](#torch-settings)
+    - [Behavior Configurations](#behavior-configurations)
+    - [Default Behavior Settings](#default-behavior-settings)
+    - [Environment Parameters](#environment-parameters)
+      - [Environment Parameter Randomization](#environment-parameter-randomization)
+        - [Supported Sampler Types](#supported-sampler-types)
+        - [Training with Environment Parameter Randomization](#training-with-environment-parameter-randomization)
+      - [Curriculum](#curriculum)
+        - [Training with a Curriculum](#training-with-a-curriculum)
+    - [Training Using Concurrent Unity Instances](#training-using-concurrent-unity-instances)
+
+For a broad overview of reinforcement learning, imitation learning and all the
+training scenarios, methods and options within the ML-Agents Toolkit, see
+[ML-Agents Toolkit Overview](ML-Agents-Overview.md).
+
+Once your learning environment has been created and is ready for training, the
+next step is to initiate a training run. Training in the ML-Agents Toolkit is
+powered by a dedicated Python package, `mlagents`. This package exposes a
+command `mlagents-learn` that is the single entry point for all training
+workflows (e.g. reinforcement leaning, imitation learning, curriculum learning).
+Its implementation can be found at
+[ml-agents/mlagents/trainers/learn.py](../ml-agents/mlagents/trainers/learn.py).
+
+## Training with mlagents-learn
+
+### Starting Training
+
+`mlagents-learn` is the main training utility provided by the ML-Agents Toolkit.
+It accepts a number of CLI options in addition to a YAML configuration file that
+contains all the configurations and hyperparameters to be used during training.
+The set of configurations and hyperparameters to include in this file depend on
+the agents in your environment and the specific training method you wish to
+utilize. Keep in mind that the hyperparameter values can have a big impact on
+the training performance (i.e. your agent's ability to learn a policy that
+solves the task). In this page, we will review all the hyperparameters for all
+training methods and provide guidelines and advice on their values.
+
+To view a description of all the CLI options accepted by `mlagents-learn`, use
+the `--help`:
+
+```sh
+mlagents-learn --help
+```
+
+The basic command for training is:
+
+```sh
+mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier>
+```
+
+where
+
+- `<trainer-config-file>` is the file path of the trainer configuration YAML.
+  This contains all the hyperparameter values. We offer a detailed guide on the
+  structure of this file and the meaning of the hyperparameters (and advice on
+  how to set them) in the dedicated
+  [Training Configurations](#training-configurations) section below.
+- `<env_name>`**(Optional)** is the name (including path) of your
+  [Unity executable](Learning-Environment-Executable.md) containing the agents
+  to be trained. If `<env_name>` is not passed, the training will happen in the
+  Editor. Press the **Play** button in Unity when the message _"Start training
+  by pressing the Play button in the Unity Editor"_ is displayed on the screen.
+- `<run-identifier>` is a unique name you can use to identify the results of
+  your training runs.
+
+See the
+[Getting Started Guide](Getting-Started.md#training-a-new-model-with-reinforcement-learning)
+for a sample execution of the `mlagents-learn` command.
+
+#### Observing Training
+
+Regardless of which training methods, configurations or hyperparameters you
+provide, the training process will always generate three artifacts, all found
+in the `results/<run-identifier>` folder:
+
+1. Summaries: these are training metrics that
+   are updated throughout the training process. They are helpful to monitor your
+   training performance and may help inform how to update your hyperparameter
+   values. See [Using TensorBoard](Using-Tensorboard.md) for more details on how
+   to visualize the training metrics.
+1. Models: these contain the model checkpoints that
+   are updated throughout training and the final model file (`.onnx`). This final
+   model file is generated once either when training completes or is
+   interrupted.
+1. Timers file (under `results/<run-identifier>/run_logs`): this contains aggregated
+   metrics on your training process, including time spent on specific code
+   blocks. See [Profiling in Python](Profiling-Python.md) for more information
+   on the timers generated.
+
+These artifacts are updated throughout the training
+process and finalized when training is completed or is interrupted.
+
+#### Stopping and Resuming Training
+
+To interrupt training and save the current progress, hit `Ctrl+C` once and wait
+for the model(s) to be saved out.
+
+To resume a previously interrupted or completed training run, use the `--resume`
+flag and make sure to specify the previously used run ID.
+
+If you would like to re-run a previously interrupted or completed training run
+and re-use the same run ID (in this case, overwriting the previously generated
+artifacts), then use the `--force` flag.
+
+#### Loading an Existing Model
+
+You can also use this mode to run inference of an already-trained model in
+Python by using both the `--resume` and `--inference` flags. Note that if you
+want to run inference in Unity, you should use the
+[Unity Inference Engine](Getting-Started.md#running-a-pre-trained-model).
+
+Additionally, if the network architecture changes, you may still load an existing model,
+but ML-Agents will only load the parts of the model it can load and ignore all others. For instance,
+if you add a new reward signal, the existing model will load but the new reward signal
+will be initialized from scratch. If you have a model with a visual encoder (CNN) but
+change the `hidden_units`, the CNN will be loaded but the body of the network will be
+initialized from scratch.
+
+Alternatively, you might want to start a new training run but _initialize_ it
+using an already-trained model. You may want to do this, for instance, if your
+environment changed and you want a new model, but the old behavior is still
+better than random. You can do this by specifying
+`--initialize-from=<run-identifier>`, where `<run-identifier>` is the old run
+ID.
+
+## Training Configurations
+
+The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods
+and options. As such, specific training runs may require different training
+configurations and may generate different artifacts and TensorBoard statistics.
+This section offers a detailed guide into how to manage the different training
+set-ups withing the toolkit.
+
+More specifically, this section offers a detailed guide on the command-line
+flags for `mlagents-learn` that control the training configurations:
+
+- `<trainer-config-file>`: defines the training hyperparameters for each
+  Behavior in the scene, and the set-ups for the environment parameters
+  (Curriculum Learning and Environment Parameter Randomization)
+
+It is important to highlight that successfully training a Behavior in the
+ML-Agents Toolkit involves tuning the training hyperparameters and
+configuration. This guide contains some best practices for tuning the training
+process when the default parameters don't seem to be giving the level of
+performance you would like. We provide sample configuration files for our
+example environments in the [config/](../config/) directory. The
+`config/ppo/3DBall.yaml` was used to train the 3D Balance Ball in the
+[Getting Started](Getting-Started.md) guide. That configuration file uses the
+PPO trainer, but we also have configuration files for SAC and GAIL.
+
+Additionally, the set of configurations you provide depend on the training
+functionalities you use (see [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
+for a description of all the training functionalities). Each functionality you
+add typically has its own training configurations. For instance:
+
+- Use PPO or SAC?
+- Use Recurrent Neural Networks for adding memory to your agents?
+- Use the intrinsic curiosity module?
+- Ignore the environment reward signal?
+- Pre-train using behavioral cloning? (Assuming you have recorded
+  demonstrations.)
+- Include the GAIL intrinsic reward signals? (Assuming you have recorded
+  demonstrations.)
+- Use self-play? (Assuming your environment includes multiple agents.)
+
+The trainer config file, `<trainer-config-file>`, determines the features you will
+use during training, and the answers to the above questions will dictate its contents.
+The rest of this guide breaks down the different sub-sections of the trainer config file
+and explains the possible settings for each. If you need a list of all the trainer
+configurations, please see [Training Configuration File](Training-Configuration-File.md).
+
+**NOTE:** The configuration file format has been changed between 0.17.0 and 0.18.0 and
+between 0.18.0 and onwards. To convert
+an old set of configuration files (trainer config, curriculum, and sampler files) to the new
+format, a script has been provided. Run `python -m mlagents.trainers.upgrade_config -h` in your
+console to see the script's usage.
+
+### Adding CLI Arguments to the Training Configuration file
+
+Additionally, within the training configuration YAML file, you can also add the
+CLI arguments (such as `--num-envs`).
+
+Reminder that a detailed description of all the CLI arguments can be found by
+using the help utility:
+
+```sh
+mlagents-learn --help
+```
+
+These additional CLI arguments are grouped into environment, engine, checkpoint and torch.
+The available settings and example values are shown below.
+
+#### Environment settings
+
+```yaml
+env_settings:
+  env_path: FoodCollector
+  env_args: null
+  base_port: 5005
+  num_envs: 1
+  seed: -1
+  max_lifetime_restarts: 10
+  restarts_rate_limit_n: 1
+  restarts_rate_limit_period_s: 60
+```
+
+#### Engine settings
+
+```yaml
+engine_settings:
+  width: 84
+  height: 84
+  quality_level: 5
+  time_scale: 20
+  target_frame_rate: -1
+  capture_frame_rate: 60
+  no_graphics: false
+```
+
+#### Checkpoint settings
+
+```yaml
+checkpoint_settings:
+  run_id: foodtorch
+  initialize_from: null
+  load_model: false
+  resume: false
+  force: true
+  train_model: false
+  inference: false
+```
+
+#### Torch settings:
+
+```yaml
+torch_settings:
+  device: cpu
+```
+
+### Behavior Configurations
+
+The primary section of the trainer config file is a
+set of configurations for each Behavior in your scene. These are defined under
+the sub-section `behaviors` in your trainer config file. Some of the
+configurations are required while others are optional. To help us get started,
+below is a sample file that includes all the possible settings if we're using a
+PPO trainer with all the possible training functionalities enabled (memory,
+behavioral cloning, curiosity, GAIL and self-play). You will notice that
+curriculum and environment parameter randomization settings are not part of the `behaviors`
+configuration, but in their own section called `environment_parameters`.
+
+```yaml
+behaviors:
+  BehaviorPPO:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      beta: 5.0e-3
+      beta_schedule: constant
+      epsilon: 0.2
+      epsilon_schedule: linear
+      lambd: 0.95
+      num_epoch: 3
+      shared_critic: False
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encode_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      # memory
+      memory:
+        sequence_length: 64
+        memory_size: 256
+
+    # Trainer configurations common to all trainers
+    max_steps: 5.0e5
+    time_horizon: 64
+    summary_freq: 10000
+    keep_checkpoints: 5
+    checkpoint_interval: 50000
+    threaded: false
+    init_path: null
+
+    # behavior cloning
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+      strength: 0.5
+      steps: 150000
+      batch_size: 512
+      num_epoch: 3
+      samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # curiosity module
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        encoding_size: 256
+        learning_rate: 3.0e-4
+
+      # GAIL
+      gail:
+        strength: 0.01
+        gamma: 0.99
+        encoding_size: 128
+        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+        learning_rate: 3.0e-4
+        use_actions: false
+        use_vail: false
+
+    # self-play
+    self_play:
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      save_steps: 50000
+      swap_steps: 2000
+      team_change: 100000
+```
+
+Here is an equivalent file if we use an SAC trainer instead. Notice that the
+configurations for the additional functionalities (memory, behavioral cloning,
+curiosity and self-play) remain unchanged.
+
+```yaml
+behaviors:
+  BehaviorSAC:
+    trainer_type: sac
+
+    # Trainer configs common to PPO/SAC (excluding reward signals)
+    # same as PPO config
+
+    # SAC-specific configs (replaces the hyperparameters section above)
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      # Same as PPO config
+
+      # SAC-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.5
+      reward_signal_steps_per_update: 10.0
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      # Same as PPO config
+
+    # Trainer configurations common to all trainers
+      # <Same as PPO config>
+
+    # pre-training using behavior cloning
+    behavioral_cloning:
+      # same as PPO config
+
+    reward_signals:
+      # environment reward
+      extrinsic:
+        # same as PPO config
+
+      # curiosity module
+      curiosity:
+        # same as PPO config
+
+      # GAIL
+      gail:
+        # same as PPO config
+
+    # self-play
+    self_play:
+      # same as PPO config
+```
+
+We now break apart the components of the configuration file and describe what
+each of these parameters mean and provide guidelines on how to set them. See
+[Training Configuration File](Training-Configuration-File.md) for a detailed
+description of all the configurations listed above, along with their defaults.
+Unless otherwise specified, omitting a configuration will revert it to its default.
+
+### Default Behavior Settings
+
+In some cases, you may want to specify a set of default configurations for your Behaviors.
+This may be useful, for instance, if your Behavior names are generated procedurally by
+the environment and not known before runtime, or if you have many Behaviors with very similar
+settings. To specify a default configuraton, insert a `default_settings` section in your YAML.
+This section should be formatted exactly like a configuration for a Behavior.
+
+```yaml
+default_settings:
+  # < Same as Behavior configuration >
+behaviors:
+  # < Same as above >
+```
+
+Behaviors found in the environment that aren't specified in the YAML will now use the `default_settings`,
+and unspecified settings in behavior configurations will default to the values in `default_settings` if
+specified there.
+
+### Environment Parameters
+
+In order to control the `EnvironmentParameters` in the Unity simulation during training,
+you need to add a section called `environment_parameters`. For example you can set the
+value of an `EnvironmentParameter` called `my_environment_parameter` to `3.0` with
+the following code :
+
+```yml
+behaviors:
+  BehaviorY:
+    # < Same as above >
+
+# Add this section
+environment_parameters:
+  my_environment_parameter: 3.0
+```
+
+Inside the Unity simulation, you can access your Environment Parameters by doing :
+
+```csharp
+Academy.Instance.EnvironmentParameters.GetWithDefault("my_environment_parameter", 0.0f);
+```
+
+#### Environment Parameter Randomization
+
+To enable environment parameter randomization, you need to edit the `environment_parameters`
+section of your training configuration yaml file. Instead of providing a single float value
+for your environment parameter, you can specify a sampler instead. Here is an example with
+three environment parameters called `mass`, `length` and `scale`:
+
+```yml
+behaviors:
+  BehaviorY:
+    # < Same as above >
+
+# Add this section
+environment_parameters:
+  mass:
+    sampler_type: uniform
+    sampler_parameters:
+        min_value: 0.5
+        max_value: 10
+
+  length:
+    sampler_type: multirangeuniform
+    sampler_parameters:
+        intervals: [[7, 10], [15, 20]]
+
+  scale:
+    sampler_type: gaussian
+    sampler_parameters:
+        mean: 2
+        st_dev: .3
+```
+
+
+| **Setting**                  | **Description**                                                                                                                                                                                                                                                                                                                         |
+| :--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `sampler_type`               | A string identifier for the type of sampler to use for this `Environment Parameter`.                                                                                                                                                                                                    |
+| `sampler_parameters` | The parameters for a given `sampler_type`. Samplers of different types can have different `sampler_parameters` |
+
+##### Supported Sampler Types
+
+Below is a list of the `sampler_type` values supported by the toolkit.
+
+- `uniform` - Uniform sampler
+  - Uniformly samples a single float value from a range with a given minimum
+    and maximum value (inclusive).
+  - **parameters** - `min_value`, `max_value`
+- `gaussian` - Gaussian sampler
+  - Samples a single float value from a normal distribution with a given mean
+    and standard deviation.
+  - **parameters** - `mean`, `st_dev`
+- `multirange_uniform` - Multirange uniform sampler
+  - First, samples an interval from a set of intervals in proportion to relative
+    length of the intervals. Then, uniformly samples a single float value from the
+    sampled interval (inclusive). This sampler can take an arbitrary number of
+    intervals in a list in the following format:
+    [[`interval_1_min`, `interval_1_max`], [`interval_2_min`,
+    `interval_2_max`], ...]
+  - **parameters** - `intervals`
+
+The implementation of the samplers can be found in the
+[Samplers.cs file](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents/Runtime/Sampler.cs).
+
+##### Training with Environment Parameter Randomization
+
+After the sampler configuration is defined, we proceed by launching `mlagents-learn`
+and specify trainer configuration with  parameter randomization enabled. For example,
+if we wanted to train the 3D ball agent with parameter randomization, we would run
+
+```sh
+mlagents-learn config/ppo/3DBall_randomize.yaml --run-id=3D-Ball-randomize
+```
+
+We can observe progress and metrics via TensorBoard.
+
+#### Curriculum
+
+To enable curriculum learning, you need to add a `curriculum` sub-section to your environment
+parameter. Here is one example with the environment parameter `my_environment_parameter` :
+
+```yml
+behaviors:
+  BehaviorY:
+    # < Same as above >
+
+# Add this section
+environment_parameters:
+  my_environment_parameter:
+    curriculum:
+      - name: MyFirstLesson # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: my_behavior
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.2
+        value: 0.0
+      - name: MySecondLesson # This is the start of the second lesson
+        completion_criteria:
+          measure: progress
+          behavior: my_behavior
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.6
+          require_reset: true
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 4.0
+            max_value: 7.0
+      - name: MyLastLesson
+        value: 8.0
+```
+
+Note that this curriculum __only__ applies to `my_environment_parameter`. The `curriculum` section
+contains a list of `Lessons`. In the example, the lessons are named `MyFirstLesson`, `MySecondLesson`
+and `MyLastLesson`.
+Each `Lesson` has 3 fields :
+
+ - `name` which is a user defined name for the lesson (The name of the lesson will be displayed in
+ the console when the lesson changes)
+ - `completion_criteria` which determines what needs to happen in the simulation before the lesson
+ can be considered complete. When that condition is met, the curriculum moves on to the next
+ `Lesson`. Note that you do not need to specify a `completion_criteria` for the last `Lesson`
+ - `value` which is the value the environment parameter will take during the lesson. Note that this
+ can be a float or a sampler.
+
+ There are the different settings of the `completion_criteria` :
+
+
+| **Setting**         | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| :------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `measure`           | What to measure learning progress, and advancement in lessons by.<br><br> `reward` uses a measure of received reward, `progress` uses the ratio of steps/max_steps, while `Elo` is available only for self-play situations and uses Elo score as a curriculum completion measure.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| `behavior`        | Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them.                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| `threshold`        | Determines at what point in value of `measure` the lesson should be increased.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| `min_lesson_length` | The minimum number of episodes that should be completed before the lesson can change. If `measure` is set to `reward`, the average cumulative reward of the last `min_lesson_length` episodes will be used to determine if the lesson should change. Must be nonnegative. <br><br> **Important**: the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if `min_lesson_length` is `100`, the lesson will increment after the average cumulative reward of the last `100` episodes exceeds the current threshold. The mean reward logged to the console is dictated by the `summary_freq` parameter defined above. |
+| `signal_smoothing`  | Whether to weight the current progress measure by previous values.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| `require_reset`  | Whether changing lesson requires the environment to reset (default: false)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+##### Training with a Curriculum
+
+Once we have specified our metacurriculum and curricula, we can launch
+`mlagents-learn` to point to the config file containing
+our curricula and PPO will train using Curriculum Learning. For example, to
+train agents in the Wall Jump environment with curriculum learning, we can run:
+
+```sh
+mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum
+```
+
+We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated
+the run, you can resume it using `--resume` and lesson progress will start off where it
+ended.
+
+
+### Training Using Concurrent Unity Instances
+
+In order to run concurrent Unity instances during training, set the number of
+environment instances using the command line option `--num-envs=<n>` when you
+invoke `mlagents-learn`. Optionally, you can also set the `--base-port`, which
+is the starting port used for the concurrent Unity instances.
+
+Some considerations:
+
+- **Buffer Size** - If you are having trouble getting an agent to train, even
+  with multiple concurrent Unity instances, you could increase `buffer_size` in
+  the trainer config file. A common practice is to multiply
+  `buffer_size` by `num-envs`.
+- **Resource Constraints** - Invoking concurrent Unity instances is constrained
+  by the resources on the machine. Please use discretion when setting
+  `--num-envs=<n>`.
+- **Result Variation Using Concurrent Unity Instances** - If you keep all the
+  hyperparameters the same, but change `--num-envs=<n>`, the results and model
+  would likely change.
diff --git a/docs/Training-Plugins.md b/docs/Training-Plugins.md
new file mode 100644
index 0000000000000000000000000000000000000000..24ba45d6a8d42b5d548444e4488329edebe3485a
--- /dev/null
+++ b/docs/Training-Plugins.md
@@ -0,0 +1,59 @@
+# Customizing Training via Plugins
+
+ML-Agents provides support for running your own python implementations of specific interfaces during the training
+process. These interfaces are currently fairly limited, but will be expanded in the future.
+
+**Note:** Plugin interfaces should currently be considered "in beta", and they may change in future releases.
+
+## How to Write Your Own Plugin
+[This video](https://www.youtube.com/watch?v=fY3Y_xPKWNA) explains the basics of how to create a plugin system using
+setuptools, and is the same approach that ML-Agents' plugin system is based on.
+
+The `ml-agents-plugin-examples` directory contains a reference implementation of each plugin interface, so it's a good
+starting point.
+
+### setup.py
+If you don't already have a `setup.py` file for your python code, you'll need to add one. `ml-agents-plugin-examples`
+has a [minimal example](../ml-agents-plugin-examples/setup.py) of this.
+
+In the call to `setup()`, you'll need to add to the `entry_points` dictionary for each plugin interface that you
+implement. The form of this is `{entry point name}={plugin module}:{plugin function}`. For example, in
+ `ml-agents-plugin-examples`:
+```python
+entry_points={
+    ML_AGENTS_STATS_WRITER: [
+        "example=mlagents_plugin_examples.example_stats_writer:get_example_stats_writer"
+    ]
+}
+```
+* `ML_AGENTS_STATS_WRITER` (which is a string constant, `mlagents.stats_writer`) is the name of the plugin interface.
+This must be one of the provided interfaces ([see below](#plugin-interfaces)).
+* `example` is the plugin implementation name. This can be anything.
+* `mlagents_plugin_examples.example_stats_writer` is the plugin module. This points to the module where the
+plugin registration function is defined.
+* `get_example_stats_writer` is the plugin registration function. This is called when running `mlagents-learn`. The
+arguments and expected return type for this are different for each plugin interface.
+
+### Local Installation
+Once you've defined `entry_points` in your `setup.py`, you will need to run
+```
+pip install -e [path to your plugin code]
+```
+in the same python virtual environment that you have `mlagents` installed.
+
+## Plugin Interfaces
+
+### StatsWriter
+The StatsWriter class receives various information from the training process, such as the average Agent reward in
+each summary period. By default, we log this information to the console and write it to
+[TensorBoard](Using-Tensorboard.md).
+
+#### Interface
+The `StatsWriter.write_stats()` method must be implemented in any derived classes. It takes a "category" parameter,
+which typically is the behavior name of the Agents being trained, and a dictionary of `StatSummary` values with
+string keys. Additionally, `StatsWriter.on_add_stat()` may be extended to register a callback handler for each stat
+emission.
+
+#### Registration
+The `StatsWriter` registration function takes a `RunOptions` argument and returns a list of `StatsWriter`s. An
+example implementation is provided in [`mlagents_plugin_examples`](../ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py)
diff --git a/docs/Training-on-Amazon-Web-Service.md b/docs/Training-on-Amazon-Web-Service.md
new file mode 100644
index 0000000000000000000000000000000000000000..f220a5eec64be93520a8363a1fbac8eb71daa1a5
--- /dev/null
+++ b/docs/Training-on-Amazon-Web-Service.md
@@ -0,0 +1,328 @@
+# Training on Amazon Web Service
+
+:warning: **Note:** We no longer use this guide ourselves and so it may not work
+correctly. We've decided to keep it up just in case it is helpful to you.
+
+This page contains instructions for setting up an EC2 instance on Amazon Web
+Service for training ML-Agents environments.
+
+## Pre-configured AMI
+
+We've prepared a pre-configured AMI for you with the ID: `ami-016ff5559334f8619`
+in the `us-east-1` region. It was created as a modification of
+[Deep Learning AMI (Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C).
+The AMI has been tested with p2.xlarge instance. Furthermore, if you want to
+train without headless mode, you need to enable X Server.
+
+After launching your EC2 instance using the ami and ssh into it, run the
+following commands to enable it:
+
+```sh
+# Start the X Server, press Enter to come to the command line
+$ sudo /usr/bin/X :0 &
+
+# Check if Xorg process is running
+# You will have a list of processes running on the GPU, Xorg should be in the
+# list, as shown below
+$ nvidia-smi
+
+# Thu Jun 14 20:27:26 2018
+# +-----------------------------------------------------------------------------+
+# | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
+# |-------------------------------+----------------------+----------------------+
+# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+# |===============================+======================+======================|
+# |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |
+# | N/A   35C    P8    31W / 149W |      9MiB / 11441MiB |      0%      Default |
+# +-------------------------------+----------------------+----------------------+
+#
+# +-----------------------------------------------------------------------------+
+# | Processes:                                                       GPU Memory |
+# |  GPU       PID   Type   Process name                             Usage      |
+# |=============================================================================|
+# |    0      2331      G   /usr/lib/xorg/Xorg                             8MiB |
+# +-----------------------------------------------------------------------------+
+
+# Make the ubuntu use X Server for display
+$ export DISPLAY=:0
+```
+
+## Configuring your own instance
+
+You could also choose to configure your own instance. To begin with, you will
+need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN.
+In this tutorial we used the
+[Deep Learning AMI (Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C)
+listed under AWS Marketplace with a p2.xlarge instance.
+
+### Installing the ML-Agents Toolkit on the instance
+
+After launching your EC2 instance using the ami and ssh into it:
+
+1. Activate the python3 environment
+
+   ```sh
+   source activate python3
+   ```
+
+2. Clone the ML-Agents repo and install the required Python packages
+
+   ```sh
+   git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git
+   cd ml-agents/ml-agents/
+   pip3 install -e .
+   ```
+
+### Setting up X Server (optional)
+
+X Server setup is only necessary if you want to do training that requires visual
+observation input. _Instructions here are adapted from this
+[Medium post](https://medium.com/towards-data-science/how-to-run-unity-on-amazon-cloud-or-without-monitor-3c10ce022639)
+on running general Unity applications in the cloud._
+
+Current limitations of the Unity Engine require that a screen be available to
+render to when using visual observations. In order to make this possible when
+training on a remote server, a virtual screen is required. We can do this by
+installing Xorg and creating a virtual screen. Once installed and created, we
+can display the Unity environment in the virtual environment, and train as we
+would on a local machine. Ensure that `headless` mode is disabled when building
+linux executables which use visual observations.
+
+#### Install and setup Xorg:
+
+    ```sh
+    # Install Xorg
+    $ sudo apt-get update
+    $ sudo apt-get install -y xserver-xorg mesa-utils
+    $ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024
+
+    # Get the BusID information
+    $ nvidia-xconfig --query-gpu-info
+
+    # Add the BusID information to your /etc/X11/xorg.conf file
+    $ sudo sed -i 's/    BoardName      "Tesla K80"/    BoardName      "Tesla K80"\n    BusID          "0:30:0"/g' /etc/X11/xorg.conf
+
+    # Remove the Section "Files" from the /etc/X11/xorg.conf file
+    # And remove two lines that contain Section "Files" and EndSection
+    $ sudo vim /etc/X11/xorg.conf
+    ```
+
+#### Update and setup Nvidia driver:
+
+    ```sh
+    # Download and install the latest Nvidia driver for ubuntu
+    # Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt
+    $ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run
+    $ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.87.run --accept-license --no-questions --ui=none
+
+    # Disable Nouveau as it will clash with the Nvidia driver
+    $ sudo echo 'blacklist nouveau'  | sudo tee -a /etc/modprobe.d/blacklist.conf
+    $ sudo echo 'options nouveau modeset=0'  | sudo tee -a /etc/modprobe.d/blacklist.conf
+    $ sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
+    $ sudo update-initramfs -u
+    ```
+
+#### Restart the EC2 instance:
+
+    ```sh
+    sudo reboot now
+    ```
+
+#### Make sure there are no Xorg processes running:
+
+```sh
+# Kill any possible running Xorg processes
+# Note that you might have to run this command multiple times depending on
+# how Xorg is configured.
+$ sudo killall Xorg
+
+# Check if there is any Xorg process left
+# You will have a list of processes running on the GPU, Xorg should not be in
+# the list, as shown below.
+$ nvidia-smi
+
+# Thu Jun 14 20:21:11 2018
+# +-----------------------------------------------------------------------------+
+# | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
+# |-------------------------------+----------------------+----------------------+
+# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+# |===============================+======================+======================|
+# |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |
+# | N/A   37C    P8    31W / 149W |      0MiB / 11441MiB |      0%      Default |
+# +-------------------------------+----------------------+----------------------+
+#
+# +-----------------------------------------------------------------------------+
+# | Processes:                                                       GPU Memory |
+# |  GPU       PID   Type   Process name                             Usage      |
+# |=============================================================================|
+# |  No running processes found                                                 |
+# +-----------------------------------------------------------------------------+
+
+```
+
+#### Start X Server and make the ubuntu use X Server for display:
+
+    ```console
+    # Start the X Server, press Enter to come back to the command line
+    $ sudo /usr/bin/X :0 &
+
+    # Check if Xorg process is running
+    # You will have a list of processes running on the GPU, Xorg should be in the list.
+    $ nvidia-smi
+
+    # Make the ubuntu use X Server for display
+    $ export DISPLAY=:0
+    ```
+
+#### Ensure the Xorg is correctly configured:
+
+    ```sh
+    # For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html.
+    $ glxgears
+    # If Xorg is configured correctly, you should see the following message
+
+    # Running synchronized to the vertical refresh.  The framerate should be
+    # approximately the same as the monitor refresh rate.
+    # 137296 frames in 5.0 seconds = 27459.053 FPS
+    # 141674 frames in 5.0 seconds = 28334.779 FPS
+    # 141490 frames in 5.0 seconds = 28297.875 FPS
+
+    ```
+
+## Training on EC2 instance
+
+1. In the Unity Editor, load a project containing an ML-Agents environment (you
+   can use one of the example environments if you have not created your own).
+2. Open the Build Settings window (menu: File > Build Settings).
+3. Select Linux as the Target Platform, and x86_64 as the target architecture
+   (the default x86 currently does not work).
+4. Check Headless Mode if you have not setup the X Server. (If you do not use
+   Headless Mode, you have to setup the X Server to enable training.)
+5. Click Build to build the Unity environment executable.
+6. Upload the executable to your EC2 instance within `ml-agents` folder.
+7. Change the permissions of the executable.
+
+   ```sh
+   chmod +x <your_env>.x86_64
+   ```
+
+8. (Without Headless Mode) Start X Server and use it for display:
+
+   ```sh
+   # Start the X Server, press Enter to come back to the command line
+   $ sudo /usr/bin/X :0 &
+
+   # Check if Xorg process is running
+   # You will have a list of processes running on the GPU, Xorg should be in the list.
+   $ nvidia-smi
+
+   # Make the ubuntu use X Server for display
+   $ export DISPLAY=:0
+   ```
+
+9. Test the instance setup from Python using:
+
+   ```python
+   from mlagents_envs.environment import UnityEnvironment
+
+   env = UnityEnvironment(<your_env>)
+   ```
+
+   Where `<your_env>` corresponds to the path to your environment executable.
+
+   You should receive a message confirming that the environment was loaded
+   successfully.
+
+10. Train your models
+
+    ```console
+    mlagents-learn <trainer-config-file> --env=<your_env> --train
+    ```
+
+## FAQ
+
+### The <Executable_Name>\_Data folder hasn't been copied cover
+
+If you've built your Linux executable, but forget to copy over the corresponding
+<Executable_Name>\_Data folder, you will see error message like the following:
+
+```sh
+Set current directory to /home/ubuntu/ml-agents/ml-agents
+Found path: /home/ubuntu/ml-agents/ml-agents/3dball_linux.x86_64
+no boot config - using default values
+
+(Filename:  Line: 403)
+
+There is no data folder
+```
+
+### Unity Environment not responding
+
+If you didn't setup X Server or hasn't launched it properly, or your environment
+somehow crashes, or you haven't `chmod +x` your Unity Environment, all of these
+will cause connection between Unity and Python to fail. Then you will see
+something like this:
+
+```console
+Logging to /home/ubuntu/.config/unity3d/<Some_Path>/Player.log
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py", line 63, in __init__
+    aca_params = self.send_academy_parameters(rl_init_parameters_in)
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py", line 489, in send_academy_parameters
+    return self.communicator.initialize(inputs).rl_initialization_output
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/rpc_communicator.py", line 60, in initialize
+mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
+         The environment does not need user interaction to launch
+         The environment and the Python interface have compatible versions.
+```
+
+It would be also really helpful to check your
+/home/ubuntu/.config/unity3d/<Some_Path>/Player.log to see what happens with
+your Unity environment.
+
+### Could not launch X Server
+
+When you execute:
+
+```sh
+sudo /usr/bin/X :0 &
+```
+
+You might see something like:
+
+```sh
+X.Org X Server 1.18.4
+...
+(==) Log file: "/var/log/Xorg.0.log", Time: Thu Oct 11 21:10:38 2018
+(==) Using config file: "/etc/X11/xorg.conf"
+(==) Using system config directory "/usr/share/X11/xorg.conf.d"
+(EE)
+Fatal server error:
+(EE) no screens found(EE)
+(EE)
+Please consult the X.Org Foundation support
+         at http://wiki.x.org
+ for help.
+(EE) Please also check the log file at "/var/log/Xorg.0.log" for additional information.
+(EE)
+(EE) Server terminated with error (1). Closing log file.
+```
+
+And when you execute:
+
+```sh
+nvidia-smi
+```
+
+You might see something like:
+
+```sh
+NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
+```
+
+This means the NVIDIA's driver needs to be updated. Refer to
+[this section](Training-on-Amazon-Web-Service.md#update-and-setup-nvidia-driver)
+for more information.
diff --git a/docs/Training-on-Microsoft-Azure.md b/docs/Training-on-Microsoft-Azure.md
new file mode 100644
index 0000000000000000000000000000000000000000..6e5fff8d77bca456fd1f35cbc2a9abdcbc7f95fc
--- /dev/null
+++ b/docs/Training-on-Microsoft-Azure.md
@@ -0,0 +1,210 @@
+# Training on Microsoft Azure (works with ML-Agents Toolkit v0.3)
+
+:warning: **Note:** We no longer use this guide ourselves and so it may not work
+correctly. We've decided to keep it up just in case it is helpful to you.
+
+This page contains instructions for setting up training on Microsoft Azure
+through either
+[Azure Container Instances](https://azure.microsoft.com/services/container-instances/)
+or Virtual Machines. Non "headless" training has not yet been tested to verify
+support.
+
+## Pre-Configured Azure Virtual Machine
+
+A pre-configured virtual machine image is available in the Azure Marketplace and
+is nearly completely ready for training. You can start by deploying the
+[Data Science Virtual Machine for Linux (Ubuntu)](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/microsoft-dsvm.ubuntu-1804)
+into your Azure subscription.
+
+Note that, if you choose to deploy the image to an
+[N-Series GPU optimized VM](https://docs.microsoft.com/azure/virtual-machines/linux/sizes-gpu),
+training will, by default, run on the GPU. If you choose any other type of VM,
+training will run on the CPU.
+
+## Configuring your own Instance
+
+Setting up your own instance requires a number of package installations. Please
+view the documentation for doing so [here](#custom-instances).
+
+## Installing ML-Agents
+
+1. [Move](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/copy-files-to-linux-vm-using-scp)
+   the `ml-agents` sub-folder of this ml-agents repo to the remote Azure
+   instance, and set it as the working directory.
+2. Install the required packages:
+   Torch: `pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html` and
+   MLAgents: `python -m pip install mlagents==0.30.0`
+
+## Testing
+
+To verify that all steps worked correctly:
+
+1. In the Unity Editor, load a project containing an ML-Agents environment (you
+   can use one of the example environments if you have not created your own).
+2. Open the Build Settings window (menu: File > Build Settings).
+3. Select Linux as the Target Platform, and x86_64 as the target architecture.
+4. Check Headless Mode.
+5. Click Build to build the Unity environment executable.
+6. Upload the resulting files to your Azure instance.
+7. Test the instance setup from Python using:
+
+```python
+from mlagents_envs.environment import UnityEnvironment
+
+env = UnityEnvironment(file_name="<your_env>", seed=1, side_channels=[])
+```
+
+Where `<your_env>` corresponds to the path to your environment executable (i.e. `/home/UserName/Build/yourFile`).
+
+You should receive a message confirming that the environment was loaded
+successfully.
+
+**Note:** When running your environment in headless mode, you must append `--no-graphics` to your mlagents-learn command, as it won't train otherwise.
+You can test this simply by aborting a training and check if it says "Model Saved" or "Aborted", or see if it generated the .onnx in the result folder.
+
+## Running Training on your Virtual Machine
+
+To run your training on the VM:
+
+1. [Move](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/copy-files-to-linux-vm-using-scp)
+   your built Unity application to your Virtual Machine.
+2. Set the directory where the ML-Agents Toolkit was installed to your working
+   directory.
+3. Run the following command:
+
+```sh
+mlagents-learn <trainer_config> --env=<your_app> --run-id=<run_id> --train
+```
+
+Where `<your_app>` is the path to your app (i.e.
+`~/unity-volume/3DBallHeadless`) and `<run_id>` is an identifier you would like
+to identify your training run with.
+
+If you've selected to run on a N-Series VM with GPU support, you can verify that
+the GPU is being used by running `nvidia-smi` from the command line.
+
+## Monitoring your Training Run with TensorBoard
+
+Once you have started training, you can
+[use TensorBoard to observe the training](Using-Tensorboard.md).
+
+1. Start by
+   [opening the appropriate port for web traffic to connect to your VM](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/nsg-quickstart-portal).
+
+   - Note that you don't need to generate a new `Network Security Group` but
+     instead, go to the **Networking** tab under **Settings** for your VM.
+   - As an example, you could use the following settings to open the Port with
+     the following Inbound Rule settings:
+     - Source: Any
+     - Source Port Ranges: \*
+     - Destination: Any
+     - Destination Port Ranges: 6006
+     - Protocol: Any
+     - Action: Allow
+     - Priority: (Leave as default)
+
+2. Unless you started the training as a background process, connect to your VM
+   from another terminal instance.
+3. Run the following command from your terminal
+   `tensorboard --logdir results --host 0.0.0.0`
+4. You should now be able to open a browser and navigate to
+   `<Your_VM_IP_Address>:6060` to view the TensorBoard report.
+
+## Running on Azure Container Instances
+
+[Azure Container Instances](https://azure.microsoft.com/services/container-instances/)
+allow you to spin up a container, on demand, that will run your training and
+then be shut down. This ensures you aren't leaving a billable VM running when it
+isn't needed. Using ACI enables you to offload training of your models without
+needing to install Python and TensorFlow on your own computer.
+
+## Custom Instances
+
+This page contains instructions for setting up a custom Virtual Machine on
+Microsoft Azure so you can running ML-Agents training in the cloud.
+
+1. Start by
+   [deploying an Azure VM](https://docs.microsoft.com/azure/virtual-machines/linux/quick-create-portal)
+   with Ubuntu Linux (tests were done with 16.04 LTS). To use GPU support, use a
+   N-Series VM.
+2. SSH into your VM.
+3. Start with the following commands to install the Nvidia driver:
+
+   ```sh
+   wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
+
+   sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb
+
+   sudo apt-get update
+
+   sudo apt-get install cuda-drivers
+
+   sudo reboot
+   ```
+
+4. After a minute you should be able to reconnect to your VM and install the
+   CUDA toolkit:
+
+   ```sh
+   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
+
+   sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
+
+   sudo apt-get update
+
+   sudo apt-get install cuda-8-0
+   ```
+
+5. You'll next need to download cuDNN from the Nvidia developer site. This
+   requires a registered account.
+
+6. Navigate to [http://developer.nvidia.com](http://developer.nvidia.com) and
+   create an account and verify it.
+
+7. Download (to your own computer) cuDNN from
+   [this url](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v6/prod/8.0_20170307/Ubuntu16_04_x64/libcudnn6_6.0.20-1+cuda8.0_amd64-deb).
+
+8. Copy the deb package to your VM:
+
+   ```sh
+   scp libcudnn6_6.0.21-1+cuda8.0_amd64.deb <VMUserName>@<VMIPAddress>:libcudnn6_6.0.21-1+cuda8.0_amd64.deb
+   ```
+
+9. SSH back to your VM and execute the following:
+
+   ```console
+   sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb
+
+   export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH
+   . ~/.profile
+
+   sudo reboot
+   ```
+
+10. After a minute, you should be able to SSH back into your VM. After doing so,
+    run the following:
+
+    ```sh
+    sudo apt install python-pip
+    sudo apt install python3-pip
+    ```
+
+11. At this point, you need to install TensorFlow. The version you install
+    should be tied to if you are using GPU to train:
+
+    ```sh
+    pip3 install tensorflow-gpu==1.4.0 keras==2.0.6
+    ```
+
+    Or CPU to train:
+
+    ```sh
+    pip3 install tensorflow==1.4.0 keras==2.0.6
+    ```
+
+12. You'll then need to install additional dependencies:
+
+    ```sh
+    pip3 install pillow
+    pip3 install numpy
+    ```
diff --git a/docs/Tutorial-Custom-Trainer-Plugin.md b/docs/Tutorial-Custom-Trainer-Plugin.md
new file mode 100644
index 0000000000000000000000000000000000000000..986030611f10b9d110e3a80aee4addf855666c28
--- /dev/null
+++ b/docs/Tutorial-Custom-Trainer-Plugin.md
@@ -0,0 +1,300 @@
+### Step 1: Write your custom trainer class
+Before you start writing your code, make sure to use your favorite environment management tool(e.g. `venv` or `conda`) to create and activate a Python virtual environment. The following command uses `conda`, but other tools work similarly:
+```shell
+conda create -n trainer-env python=3.8.13
+conda activate trainer-env
+```
+
+Users of the plug-in system are responsible for implementing the trainer class subject to the API standard. Let us follow an example by implementing a custom trainer named "YourCustomTrainer". You can either extend `OnPolicyTrainer` or `OffPolicyTrainer` classes depending on the training strategies you choose.
+
+Please refer to the internal [PPO implementation](../ml-agents/mlagents/trainers/ppo/trainer.py) for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow.
+
+Your custom trainers are responsible for collecting experiences and training the models. Your custom trainer class acts like a co-ordinator to the policy and optimizer. To start implementing methods in the class, create a policy class objects from method `create_policy`:
+
+
+```python
+def create_policy(
+    self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+) -> TorchPolicy:
+
+    actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor
+    actor_kwargs: Dict[str, Any] = {
+        "conditional_sigma": False,
+        "tanh_squash": False,
+    }
+    if self.shared_critic:
+        reward_signal_configs = self.trainer_settings.reward_signals
+        reward_signal_names = [
+            key.value for key, _ in reward_signal_configs.items()
+        ]
+        actor_cls = SharedActorCritic
+        actor_kwargs.update({"stream_names": reward_signal_names})
+
+    policy = TorchPolicy(
+        self.seed,
+        behavior_spec,
+        self.trainer_settings.network_settings,
+        actor_cls,
+        actor_kwargs,
+    )
+    return policy
+
+```
+
+Depending on whether you use shared or separate network architecture for your policy, we provide `SimpleActor` and `SharedActorCritic` from `mlagents.trainers.torch_entities.networks` that you can choose from. In our example above, we use a `SimpleActor`.
+
+Next, create an optimizer class object from `create_optimizer` method and connect it to the policy object you created above:
+
+
+```python
+def create_optimizer(self) -> TorchOptimizer:
+    return TorchPPOOptimizer(  # type: ignore
+        cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore
+    )  # type: ignore
+
+```
+
+There are a couple of abstract methods(`_process_trajectory` and `_update_policy`) inherited from `RLTrainer` that you need to implement in your custom trainer class. `_process_trajectory` takes a trajectory and processes it, putting it into the update buffer. Processing involves calculating value and advantage targets for the model updating step. Given input `trajectory: Trajectory`, users are responsible for processing the data in the trajectory and append `agent_buffer_trajectory` to the back of the update buffer by calling `self._append_to_update_buffer(agent_buffer_trajectory)`, whose output will be used in updating the model in `optimizer` class.
+
+A typical `_process_trajectory` function(incomplete) will convert a trajectory object to an agent buffer then get all value estimates from the trajectory by calling `self.optimizer.get_trajectory_value_estimates`. From the returned dictionary of value estimates we extract reward signals keyed by their names:
+
+```python
+def _process_trajectory(self, trajectory: Trajectory) -> None:
+    super()._process_trajectory(trajectory)
+    agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+    agent_buffer_trajectory = trajectory.to_agentbuffer()
+
+    # Get all value estimates
+    (
+        value_estimates,
+        value_next,
+        value_memories,
+    ) =  self.optimizer.get_trajectory_value_estimates(
+        agent_buffer_trajectory,
+        trajectory.next_obs,
+        trajectory.done_reached and not trajectory.interrupted,
+    )
+
+    for name, v in value_estimates.items():
+        agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(
+            v
+        )
+        self._stats_reporter.add_stat(
+            f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
+            np.mean(v),
+        )
+
+    # Evaluate all reward functions
+    self.collected_rewards["environment"][agent_id] += np.sum(
+        agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+    )
+    for name, reward_signal in self.optimizer.reward_signals.items():
+        evaluate_result = (
+            reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+        )
+        agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(
+            evaluate_result
+        )
+        # Report the reward signals
+        self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+    self._append_to_update_buffer(agent_buffer_trajectory)
+
+```
+
+A trajectory will be a list of dictionaries of strings mapped to `Anything`. When calling `forward` on a policy, the argument will include an “experience� dictionary from the last step. The `forward` method will generate an action and the next “experience� dictionary. Examples of fields in the “experience� dictionary include observation, action, reward, done status, group_reward, LSTM memory state, etc.
+
+
+
+### Step 2: implement your custom optimizer for the trainer.
+We will show you an example we implemented - `class TorchPPOOptimizer(TorchOptimizer)`, which takes a Policy and a Dict of trainer parameters and creates an Optimizer that connects to the policy. Your optimizer should include a value estimator and a loss function in the `update` method.
+
+Before writing your optimizer class, first define setting class `class PPOSettings(OnPolicyHyperparamSettings)` for your custom optimizer:
+
+
+
+```python
+class PPOSettings(OnPolicyHyperparamSettings):
+    beta: float = 5.0e-3
+    epsilon: float = 0.2
+    lambd: float = 0.95
+    num_epoch: int = 3
+    shared_critic: bool = False
+    learning_rate_schedule: ScheduleType = ScheduleType.LINEAR
+    beta_schedule: ScheduleType = ScheduleType.LINEAR
+    epsilon_schedule: ScheduleType = ScheduleType.LINEAR
+
+```
+
+You should implement `update` function following interface:
+
+
+```python
+def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+
+```
+
+In which losses and other metrics are calculated from an `AgentBuffer` that is generated from your trainer class, depending on which model you choose to implement the loss functions will be different. In our case we calculate value loss from critic and trust region policy loss. A typical pattern(incomplete) of the calculations will look like the following:
+
+
+```python
+run_out = self.policy.actor.get_stats(
+    current_obs,
+    actions,
+    masks=act_masks,
+    memories=memories,
+    sequence_length=self.policy.sequence_length,
+)
+
+log_probs = run_out["log_probs"]
+entropy = run_out["entropy"]
+
+values, _ = self.critic.critic_pass(
+    current_obs,
+    memories=value_memories,
+    sequence_length=self.policy.sequence_length,
+)
+policy_loss = ModelUtils.trust_region_policy_loss(
+    ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]),
+    log_probs,
+    old_log_probs,
+    loss_masks,
+    decay_eps,
+)
+loss = (
+    policy_loss
+    + 0.5 * value_loss
+    - decay_bet * ModelUtils.masked_mean(entropy, loss_masks)
+)
+
+```
+
+Finally update the model and return the a dictionary including calculated losses and updated decay learning rate:
+
+
+```python
+ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+self.optimizer.zero_grad()
+loss.backward()
+
+self.optimizer.step()
+update_stats = {
+    "Losses/Policy Loss": torch.abs(policy_loss).item(),
+    "Losses/Value Loss": value_loss.item(),
+    "Policy/Learning Rate": decay_lr,
+    "Policy/Epsilon": decay_eps,
+    "Policy/Beta": decay_bet,
+}
+
+```
+
+### Step 3: Integrate your custom trainer into the plugin system
+
+By integrating a custom trainer into the plugin system, a user can use their published packages which have their implementations. To do that, you need to add a setup.py file. In the call to setup(), you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function}. For example:
+
+
+
+```python
+entry_points={
+        ML_AGENTS_TRAINER_TYPE: [
+            "your_trainer_type=your_package.your_custom_trainer:get_type_and_setting"
+        ]
+    },
+```
+
+Some key elements in the code:
+
+```
+ML_AGENTS_TRAINER_TYPE: a string constant for trainer type
+your_trainer_type: name your trainer type, used in configuration file
+your_package: your pip installable package containing custom trainer implementation
+```
+
+Also define `get_type_and_setting` method in `YourCustomTrainer` class:
+
+
+```python
+def get_type_and_setting():
+    return {YourCustomTrainer.get_trainer_name(): YourCustomTrainer}, {
+        YourCustomTrainer.get_trainer_name(): YourCustomSetting
+    }
+
+```
+
+Finally, specify trainer type in the config file:
+
+
+```python
+behaviors:
+  3DBall:
+    trainer_type: your_trainer_type
+...
+```
+
+### Step 4: Install your custom trainer and run training:
+Before installing your custom trainer package, make sure you have `ml-agents-env` and `ml-agents` installed
+
+```shell
+pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents
+```
+
+Install your cutom trainer package(if your package is pip installable):
+```shell
+pip3 install your_custom_package
+```
+Or follow our internal implementations:
+```shell
+pip3 install -e ./ml-agents-trainer-plugin
+```
+
+Following the previous installations your package is added as an entrypoint and you can use a config file with new
+trainers:
+```shell
+mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name>
+--env <env-executable>
+```
+
+### Validate your implementations:
+Create a clean Python environment with Python 3.8+ and activate it before you start, if you haven't done so already:
+```shell
+conda create -n trainer-env python=3.8.13
+conda activate trainer-env
+```
+
+Make sure you follow previous steps and install all required packages. We are testing internal implementations in this tutorial, but ML-Agents users can run similar validations once they have their own implementations installed:
+```shell
+pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents
+pip3 install -e ./ml-agents-trainer-plugin
+```
+Once your package is added as an `entrypoint`, you can add to the config file the new trainer type. Check if trainer type is specified in the config file `a2c_3DBall.yaml`:
+```
+trainer_type: a2c
+```
+
+Test if custom trainer package is installed by running:
+```shell
+mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id test-trainer
+```
+
+You can also list all trainers installed in the registry. Type `python` in your shell to open a REPL session. Run the python code below, you should be able to see all trainer types currently installed:
+```python
+>>> import pkg_resources
+>>> for entry in pkg_resources.iter_entry_points('mlagents.trainer_type'):
+...     print(entry)
+...
+default = mlagents.plugins.trainer_type:get_default_trainer_types
+a2c = mlagents_trainer_plugin.a2c.a2c_trainer:get_type_and_setting
+dqn = mlagents_trainer_plugin.dqn.dqn_trainer:get_type_and_setting
+```
+
+If it is properly installed, you will see Unity logo and message indicating training will start:
+```
+[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
+```
+
+If you see the following error message, it could be due to trainer type is wrong or the trainer type specified is not installed:
+```shell
+mlagents.trainers.exception.TrainerConfigError: Invalid trainer type a2c was found
+```
+
diff --git a/docs/Unity-Environment-Registry.md b/docs/Unity-Environment-Registry.md
new file mode 100644
index 0000000000000000000000000000000000000000..c5caa68cbdea64d0c5b45c3f776bacbb4f23f7ae
--- /dev/null
+++ b/docs/Unity-Environment-Registry.md
@@ -0,0 +1,60 @@
+# Unity Environment Registry [Experimental]
+
+The Unity Environment Registry is a database of pre-built Unity environments that can be easily used without having to install the Unity Editor. It is a great way to get started with our [UnityEnvironment API](Python-LLAPI.md).
+
+## Loading an Environment from the Registry
+
+To get started, you can access the default registry we provide with our [Example Environments](Learning-Environment-Examples.md). The Unity Environment Registry implements a _Mapping_, therefore, you can access an entry with its identifier with the square brackets `[ ]`. Use the following code to list all of the environment identifiers present in the default registry:
+
+```python
+from mlagents_envs.registry import default_registry
+
+environment_names = list(default_registry.keys())
+for name in environment_names:
+   print(name)
+```
+
+The `make()` method on a registry value will return a `UnityEnvironment` ready to be used. All arguments passed to the make method will be passed to the constructor of the `UnityEnvironment` as well. Refer to the documentation on the [Python-API](Python-LLAPI.md) for more information about the arguments of the `UnityEnvironment` constructor. For example, the following code will create the environment under the identifier `"my-env"`, reset it, perform a few steps and finally close it:
+
+```python
+from mlagents_envs.registry import default_registry
+
+env = default_registry["my-env"].make()
+env.reset()
+for _ in range(10):
+  env.step()
+env.close()
+```
+
+## Create and share your own registry
+
+In order to share the `UnityEnvironemnt` you created, you must :
+ - [Create a Unity executable](Learning-Environment-Executable.md) of your environment for each platform (Linux, OSX and/or Windows)
+ - Place each executable in a `zip` compressed folder
+ - Upload each zip file online to your preferred hosting platform
+ - Create a `yaml` file that will contain the description and path to your environment
+ - Upload the `yaml` file online
+The `yaml` file must have the following format :
+
+```yaml
+environments:
+  - <environment-identifier>:
+     expected_reward: <expected-reward-float>
+     description: <description-of-the-environment>
+     linux_url: <url-to-the-linux-zip-folder>
+     darwin_url: <url-to-the-osx-zip-folder>
+     win_url: <url-to-the-windows-zip-folder>
+     additional_args:
+      - <an-optional-list-of-command-line-arguments-for-the-executable>
+      - ...
+```
+
+Your users can now use your environment with the following code :
+```python
+from mlagents_envs.registry import UnityEnvRegistry
+
+registry = UnityEnvRegistry()
+registry.register_from_yaml("url-or-path-to-your-yaml-file")
+```
+ __Note__: The `"url-or-path-to-your-yaml-file"` can be either a url or a local path.
+
diff --git a/docs/Unity-Inference-Engine.md b/docs/Unity-Inference-Engine.md
new file mode 100644
index 0000000000000000000000000000000000000000..31a7c137c8837fe99d4fe73316da3ee6ab0c2004
--- /dev/null
+++ b/docs/Unity-Inference-Engine.md
@@ -0,0 +1,47 @@
+# Unity Inference Engine
+
+The ML-Agents Toolkit allows you to use pre-trained neural network models inside
+your Unity games. This support is possible thanks to the
+[Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html)
+(codenamed Barracuda). The Unity Inference Engine uses
+[compute shaders](https://docs.unity3d.com/Manual/class-ComputeShader.html) to
+run the neural network within Unity.
+
+## Supported devices
+
+See the Unity Inference Engine documentation for a list of the
+[supported platforms](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html#supported-platforms).
+
+Scripting Backends : The Unity Inference Engine is generally faster with
+**IL2CPP** than with **Mono** for Standalone builds. In the Editor, It is not
+possible to use the Unity Inference Engine with GPU device selected when Editor
+Graphics Emulation is set to **OpenGL(ES) 3.0 or 2.0 emulation**. Also there
+might be non-fatal build time errors when target platform includes Graphics API
+that does not support **Unity Compute Shaders**.
+
+## Using the Unity Inference Engine
+
+When using a model, drag the model file into the **Model** field in the
+Inspector of the Agent. Select the **Inference Device** : CPU or GPU you want to
+use for Inference.
+
+**Note:** For most of the models generated with the ML-Agents Toolkit, CPU will
+be faster than GPU. You should use the GPU only if you use the ResNet visual
+encoder or have a large number of agents with visual observations.
+
+# Unsupported use cases
+## Externally trained models
+The ML-Agents Toolkit only supports the models created with our trainers. Model
+loading expects certain conventions for constants and tensor names. While it is
+possible to construct a model that follows these conventions, we don't provide
+any additional help for this. More details can be found in
+[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
+and
+[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
+
+If you wish to run inference on an externally trained model, you should use
+Barracuda directly, instead of trying to run it through ML-Agents.
+
+## Model inference outside of Unity
+We do not provide support for inference anywhere outside of Unity. The `.onnx` files produced by training use the open format ONNX; if you wish to convert a `.onnx` file to another
+format or run inference with them, refer to their documentation.
diff --git a/docs/Using-Docker.md b/docs/Using-Docker.md
new file mode 100644
index 0000000000000000000000000000000000000000..000478f3456ffe3cb4fbaf372efdefaa50702ffa
--- /dev/null
+++ b/docs/Using-Docker.md
@@ -0,0 +1,177 @@
+# Using Docker For ML-Agents (Deprecated)
+
+:warning: **Note:** We no longer use this guide ourselves and so it may not work
+correctly. We've decided to keep it up just in case it is helpful to you.
+
+We currently offer a solution for Windows and Mac users who would like to do
+training or inference using Docker. This option may be appealing to those who
+would like to avoid installing Python and TensorFlow themselves. The current
+setup forces both TensorFlow and Unity to _only_ rely on the CPU for
+computations. Consequently, our Docker simulation does not use a GPU and uses
+[`Xvfb`](https://en.wikipedia.org/wiki/Xvfb) to do visual rendering. `Xvfb` is a
+utility that enables `ML-Agents` (or any other application) to do rendering
+virtually i.e. it does not assume that the machine running `ML-Agents` has a GPU
+or a display attached to it. This means that rich environments which involve
+agents using camera-based visual observations might be slower.
+
+## Requirements
+
+- [Docker](https://www.docker.com)
+- Unity _Linux Build Support_ Component. Make sure to select the _Linux Build
+  Support_ component when installing Unity.
+
+<p align="center">
+  <img src="images/unity_linux_build_support.png"
+       alt="Linux Build Support"
+       width="500" border="10" />
+</p>
+
+## Setup
+
+- [Download](https://unity3d.com/get-unity/download) the Unity Installer and add
+  the _Linux Build Support_ Component
+
+- [Download](https://www.docker.com/community-edition#/download) and install
+  Docker if you don't have it setup on your machine.
+
+- Since Docker runs a container in an environment that is isolated from the host
+  machine, a mounted directory in your host machine is used to share data, e.g.
+  the trainer configuration file, Unity executable and
+  TensorFlow graph. For convenience, we created an empty `unity-volume`
+  directory at the root of the repository for this purpose, but feel free to use
+  any other directory. The remainder of this guide assumes that the
+  `unity-volume` directory is the one used.
+
+## Usage
+
+Using Docker for ML-Agents involves three steps: building the Unity environment
+with specific flags, building a Docker container and, finally, running the
+container. If you are not familiar with building a Unity environment for
+ML-Agents, please read through our
+[Getting Started with the 3D Balance Ball Example](Getting-Started.md) guide
+first.
+
+### Build the Environment (Optional)
+
+_If you want to used the Editor to perform training, you can skip this step._
+
+Since Docker typically runs a container sharing a (linux) kernel with the host
+machine, the Unity environment **has** to be built for the **linux platform**.
+When building a Unity environment, please select the following options from the
+the Build Settings window:
+
+- Set the _Target Platform_ to `Linux`
+- Set the _Architecture_ to `x86_64`
+
+Then click `Build`, pick an environment name (e.g. `3DBall`) and set the output
+directory to `unity-volume`. After building, ensure that the file
+`<environment-name>.x86_64` and subdirectory `<environment-name>_Data/` are
+created under `unity-volume`.
+
+![Build Settings For Docker](images/docker_build_settings.png)
+
+### Build the Docker Container
+
+First, make sure the Docker engine is running on your machine. Then build the
+Docker container by calling the following command at the top-level of the
+repository:
+
+```sh
+docker build -t <image-name> .
+```
+
+Replace `<image-name>` with a name for the Docker image, e.g.
+`balance.ball.v0.1`.
+
+### Run the Docker Container
+
+Run the Docker container by calling the following command at the top-level of
+the repository:
+
+```sh
+docker run -it --name <container-name> \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           -p 5005:5005 \
+           -p 6006:6006 \
+           <image-name>:latest \
+           <trainer-config-file> \
+           --env=<environment-name> \
+           --train \
+           --run-id=<run-id>
+```
+
+Notes on argument values:
+
+- `<container-name>` is used to identify the container (in case you want to
+  interrupt and terminate it). This is optional and Docker will generate a
+  random name if this is not set. _Note that this must be unique for every run
+  of a Docker image._
+- `<image-name>` references the image name used when building the container.
+- `<environment-name>` **(Optional)**: If you are training with a linux
+  executable, this is the name of the executable. If you are training in the
+  Editor, do not pass a `<environment-name>` argument and press the **Play**
+  button in Unity when the message _"Start training by pressing the Play button
+  in the Unity Editor"_ is displayed on the screen.
+- `source`: Reference to the path in your host OS where you will store the Unity
+  executable.
+- `target`: Tells Docker to mount the `source` path as a disk with this name.
+- `trainer-config-file`, `train`, `run-id`: ML-Agents arguments passed to
+  `mlagents-learn`. `trainer-config-file` is the filename of the trainer config
+  file, `train` trains the algorithm, and `run-id` is used to tag each
+  experiment with a unique identifier. We recommend placing the trainer-config
+  file inside `unity-volume` so that the container has access to the file.
+
+To train with a `3DBall` environment executable, the command would be:
+
+```sh
+docker run -it --name 3DBallContainer.first.trial \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           -p 5005:5005 \
+           -p 6006:6006 \
+           balance.ball.v0.1:latest 3DBall \
+           /unity-volume/trainer_config.yaml \
+           --env=/unity-volume/3DBall \
+           --train \
+           --run-id=3dball_first_trial
+```
+
+For more detail on Docker mounts, check out
+[these](https://docs.docker.com/storage/bind-mounts/) docs from Docker.
+
+**NOTE** If you are training using docker for environments that use visual
+observations, you may need to increase the default memory that Docker allocates
+for the container. For example, see
+[here](https://docs.docker.com/docker-for-mac/#advanced) for instructions for
+Docker for Mac.
+
+### Running Tensorboard
+
+You can run Tensorboard to monitor your training instance on
+http://localhost:6006:
+
+```sh
+docker exec -it <container-name> tensorboard --logdir /unity-volume/results --host 0.0.0.0
+```
+
+With our previous 3DBall example, this command would look like this:
+
+```sh
+docker exec -it 3DBallContainer.first.trial tensorboard --logdir /unity-volume/results --host 0.0.0.0
+```
+
+For more details on Tensorboard, check out the documentation about
+[Using Tensorboard](Using-Tensorboard.md).
+
+### Stopping Container and Saving State
+
+If you are satisfied with the training progress, you can stop the Docker
+container while saving state by either using `Ctrl+C` or `⌘+C` (Mac) or by using
+the following command:
+
+```sh
+docker kill --signal=SIGINT <container-name>
+```
+
+`<container-name>` is the name of the container specified in the earlier
+`docker run` command. If you didn't specify one, you can find the randomly
+generated identifier by running `docker container ls`.
diff --git a/docs/Using-Tensorboard.md b/docs/Using-Tensorboard.md
new file mode 100644
index 0000000000000000000000000000000000000000..feaf800fe8458c27950b45a7f9d37fa7a70fd3b5
--- /dev/null
+++ b/docs/Using-Tensorboard.md
@@ -0,0 +1,136 @@
+# Using TensorBoard to Observe Training
+
+The ML-Agents Toolkit saves statistics during learning session that you can view
+with a TensorFlow utility named,
+[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
+
+The `mlagents-learn` command saves training statistics to a folder named
+`results`, organized by the `run-id` value you assign to a training session.
+
+In order to observe the training process, either during training or afterward,
+start TensorBoard:
+
+1. Open a terminal or console window:
+1. Navigate to the directory where the ML-Agents Toolkit is installed.
+1. From the command line run: `tensorboard --logdir results --port 6006`
+1. Open a browser window and navigate to
+   [localhost:6006](http://localhost:6006).
+
+**Note:** The default port TensorBoard uses is 6006. If there is an existing
+session running on port 6006 a new session can be launched on an open port using
+the --port option.
+
+**Note:** If you don't assign a `run-id` identifier, `mlagents-learn` uses the
+default string, "ppo". You can delete the folders under the `results` directory
+to clear out old statistics.
+
+On the left side of the TensorBoard window, you can select which of the training
+runs you want to display. You can select multiple run-ids to compare statistics.
+The TensorBoard window also provides options for how to display and smooth
+graphs.
+
+## The ML-Agents Toolkit training statistics
+
+The ML-Agents training program saves the following statistics:
+
+![Example TensorBoard Run](images/mlagents-TensorBoard.png)
+
+### Environment Statistics
+
+- `Environment/Lesson` - Plots the progress from lesson to lesson. Only
+  interesting when performing curriculum training.
+
+- `Environment/Cumulative Reward` - The mean cumulative episode reward over all
+  agents. Should increase during a successful training session.
+
+- `Environment/Episode Length` - The mean length of each episode in the
+  environment for all agents.
+
+### Is Training
+
+- `Is Training` - A boolean indicating if the agent is updating its model.
+
+### Policy Statistics
+
+- `Policy/Entropy` (PPO; SAC) - How random the decisions of the model are.
+  Should slowly decrease during a successful training process. If it decreases
+  too quickly, the `beta` hyperparameter should be increased.
+
+- `Policy/Learning Rate` (PPO; SAC) - How large a step the training algorithm
+  takes as it searches for the optimal policy. Should decrease over time.
+
+- `Policy/Entropy Coefficient` (SAC) - Determines the relative importance of the
+  entropy term. This value is adjusted automatically so that the agent retains
+  some amount of randomness during training.
+
+- `Policy/Extrinsic Reward` (PPO; SAC) - This corresponds to the mean cumulative
+  reward received from the environment per-episode.
+
+- `Policy/Value Estimate` (PPO; SAC) - The mean value estimate for all states
+  visited by the agent. Should increase during a successful training session.
+
+- `Policy/Curiosity Reward` (PPO/SAC+Curiosity) - This corresponds to the mean
+  cumulative intrinsic reward generated per-episode.
+
+- `Policy/Curiosity Value Estimate` (PPO/SAC+Curiosity) - The agent's value
+  estimate for the curiosity reward.
+
+- `Policy/GAIL Reward` (PPO/SAC+GAIL) - This corresponds to the mean cumulative
+  discriminator-based reward generated per-episode.
+
+- `Policy/GAIL Value Estimate` (PPO/SAC+GAIL) - The agent's value estimate for
+  the GAIL reward.
+
+- `Policy/GAIL Policy Estimate` (PPO/SAC+GAIL) - The discriminator's estimate
+  for states and actions generated by the policy.
+
+- `Policy/GAIL Expert Estimate` (PPO/SAC+GAIL) - The discriminator's estimate
+  for states and actions drawn from expert demonstrations.
+
+### Learning Loss Functions
+
+- `Losses/Policy Loss` (PPO; SAC) - The mean magnitude of policy loss function.
+  Correlates to how much the policy (process for deciding actions) is changing.
+  The magnitude of this should decrease during a successful training session.
+
+- `Losses/Value Loss` (PPO; SAC) - The mean loss of the value function update.
+  Correlates to how well the model is able to predict the value of each state.
+  This should increase while the agent is learning, and then decrease once the
+  reward stabilizes.
+
+- `Losses/Forward Loss` (PPO/SAC+Curiosity) - The mean magnitude of the forward
+  model loss function. Corresponds to how well the model is able to predict the
+  new observation encoding.
+
+- `Losses/Inverse Loss` (PPO/SAC+Curiosity) - The mean magnitude of the inverse
+  model loss function. Corresponds to how well the model is able to predict the
+  action taken between two observations.
+
+- `Losses/Pretraining Loss` (BC) - The mean magnitude of the behavioral cloning
+  loss. Corresponds to how well the model imitates the demonstration data.
+
+- `Losses/GAIL Loss` (GAIL) - The mean magnitude of the GAIL discriminator loss.
+  Corresponds to how well the model imitates the demonstration data.
+
+### Self-Play
+
+- `Self-Play/ELO` (Self-Play) -
+  [ELO](https://en.wikipedia.org/wiki/Elo_rating_system) measures the relative
+  skill level between two players. In a proper training run, the ELO of the
+  agent should steadily increase.
+
+## Exporting Data from TensorBoard
+To export timeseries data in CSV or JSON format, check the "Show data download
+links" in the upper left. This will enable download links below each chart.
+
+![Example TensorBoard Run](images/TensorBoard-download.png)
+
+## Custom Metrics from Unity
+
+To get custom metrics from a C# environment into TensorBoard, you can use the
+`StatsRecorder`:
+
+```csharp
+var statsRecorder = Academy.Instance.StatsRecorder;
+statsRecorder.Add("MyMetric", 1.0);
+```
diff --git a/docs/Using-Virtual-Environment.md b/docs/Using-Virtual-Environment.md
new file mode 100644
index 0000000000000000000000000000000000000000..5ec0592a54bb85f808917a7461197c4cc6644efe
--- /dev/null
+++ b/docs/Using-Virtual-Environment.md
@@ -0,0 +1,69 @@
+# Using Virtual Environment
+
+## What is a Virtual Environment?
+
+A Virtual Environment is a self contained directory tree that contains a Python
+installation for a particular version of Python, plus a number of additional
+packages. To learn more about Virtual Environments see
+[here](https://docs.python.org/3/library/venv.html).
+
+## Why should I use a Virtual Environment?
+
+A Virtual Environment keeps all dependencies for the Python project separate
+from dependencies of other projects. This has a few advantages:
+
+1. It makes dependency management for the project easy.
+1. It enables using and testing of different library versions by quickly
+   spinning up a new environment and verifying the compatibility of the code
+   with the different version.
+
+## Python Version Requirement (Required)
+This guide has been tested with Python 3.8.13 through Python 3.10.x. Newer versions might not
+have support for the dependent libraries, so are not recommended.
+
+## Installing Pip (Required)
+
+1. Download the `get-pip.py` file using the command
+   `curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py`
+1. Run the following `python3 get-pip.py`
+1. Check pip version using `pip3 -V`
+
+Note (for Ubuntu users): If the
+`ModuleNotFoundError: No module named 'distutils.util'` error is encountered,
+then python3-distutils needs to be installed. Install python3-distutils using
+`sudo apt-get install python3-distutils`
+
+## Mac OS X Setup
+
+1. Create a folder where the virtual environments will reside
+   `$ mkdir ~/python-envs`
+1. To create a new environment named `sample-env` execute
+   `$ python3 -m venv ~/python-envs/sample-env`
+1. To activate the environment execute
+   `$ source ~/python-envs/sample-env/bin/activate`
+1. Upgrade to the latest pip version using `$ pip3 install --upgrade pip`
+1. Upgrade to the latest setuptools version using
+   `$ pip3 install --upgrade setuptools`
+1. To deactivate the environment execute `$ deactivate` (you can reactivate the
+   environment using the same `activate` command listed above)
+
+## Ubuntu Setup
+
+1. Install the python3-venv package using `$ sudo apt-get install python3-venv`
+1. Follow the steps in the Mac OS X installation.
+
+## Windows Setup
+
+1. Create a folder where the virtual environments will reside `md python-envs`
+1. To create a new environment named `sample-env` execute
+   `python -m venv python-envs\sample-env`
+1. To activate the environment execute `python-envs\sample-env\Scripts\activate`
+1. Upgrade to the latest pip version using `pip install --upgrade pip`
+1. To deactivate the environment execute `deactivate` (you can reactivate the
+   environment using the same `activate` command listed above)
+
+Note:
+- Verify that you are using a Python version between 3.8.13 and 3.10.x. Launch a
+  command prompt using `cmd` and execute `python --version` to verify the version.
+- Python3 installation may require admin privileges on Windows.
+- This guide is for Windows 10 using a 64-bit architecture only.
diff --git a/docs/Versioning.md b/docs/Versioning.md
new file mode 100644
index 0000000000000000000000000000000000000000..0f1db0a06243b595122e0b3cb9936ede0cf0c42d
--- /dev/null
+++ b/docs/Versioning.md
@@ -0,0 +1,95 @@
+# ML-Agents Versioning
+
+## Context
+As the ML-Agents project evolves into a more mature product, we want to communicate the process
+we use to version our packages and the data that flows into, through, and out of them clearly.
+Our project now has four packages (1 Unity, 3 Python) along with artifacts that are produced as
+well as consumed.  This document covers the versioning for these packages and artifacts.
+
+## GitHub Releases
+Up until now, all packages were in lockstep in-terms of versioning. As a result, the GitHub releases
+were tagged with the version of all those packages (e.g. v0.15.0, v0.15.1) and labeled accordingly.
+With the decoupling of package versions, we now need to revisit our GitHub release tagging.
+The proposal is that we move towards an integer release numbering for our repo and each such
+release will call out specific version upgrades of each package. For instance, with
+[the April 30th release](https://github.com/Unity-Technologies/ml-agents/releases/tag/release_1),
+we will have:
+- GitHub Release 1 (branch name: *release_1_branch*)
+  - com.unity.ml-agents release 1.0.0
+  - ml-agents release 0.16.0
+  - ml-agents-envs release 0.16.0
+  - gym-unity release 0.16.0
+
+Our release cadence will not be affected by these versioning changes.  We will keep having
+monthly releases to fix bugs and release new features.
+
+## Packages
+All of the software packages, and their generated artifacts will be versioned.  Any automation
+tools will not be versioned.
+
+### Unity package
+Package name: com.unity.ml-agents
+- Versioned following [Semantic Versioning Guidelines](https://www.semver.org)
+- This package consumes an artifact of the training process: the `.nn` file.  These files
+    are integer versioned and currently at version 2. The com.unity.ml-agents package
+    will need to support the version of `.nn` files which existed at its 1.0.0 release.
+    For example, consider that com.unity.ml-agents is at version 1.0.0 and the NN files
+    are at version 2.  If the NN files change to version 3, the next release of
+    com.unity.ml-agents at version 1.1.0 guarantees it will be able to read both of these
+    formats.  If the NN files were to change to version 4 and com.unity.ml-agents to
+    version 2.0.0, support for NN versions 2 and 3 could be dropped for com.unity.ml-agents
+    version 2.0.0.
+- This package produces one artifact, the `.demo` files.  These files will have integer
+    versioning. This means their version will increment by 1 at each change.  The
+    com.unity.ml-agents package must be backward compatible with version changes
+    that occur between minor versions.
+- To summarize, the artifacts produced and consumed by com.unity.ml-agents are guaranteed
+    to be supported for 1.x.x versions of com.unity.ml-agents.  We intend to provide stability
+    for our users by moving to a 1.0.0 release of com.unity.ml-agents.
+
+
+### Python Packages
+Package names: ml-agents / ml-agents-envs / gym-unity
+- The python packages remain in "Beta."  This means that breaking changes to the public
+    API of the python packages can change without having to have a major version bump.
+    Historically, the python and C# packages were in version lockstep.  This is no longer
+    the case.  The python packages will remain in lockstep with each other for now, while the
+    C# package will follow its own versioning as is appropriate.  However, the python package
+    versions may diverge in the future.
+- While the python packages will remain in Beta for now, we acknowledge that the most
+    heavily used portion of our python interface is the `mlagents-learn` CLI and strive
+    to make this part of our API backward compatible. We are actively working on this and
+    expect to have a stable CLI in the next few weeks.
+
+## Communicator
+
+Packages which communicate: com.unity.ml-agents / ml-agents-envs
+
+Another entity of the ML-Agents Toolkit that requires versioning is the communication layer
+between C# and Python, which will follow also semantic versioning.  This guarantees a level of
+backward compatibility between different versions of C# and Python packages which communicate.
+Any Communicator version 1.x.x of the Unity package should be compatible with any 1.x.x
+Communicator Version in Python.
+
+An RLCapabilities struct keeps track of which features exist. This struct is passed from C# to
+Python, and another from Python to C#.  With this feature level granularity, we can notify users
+more specifically about feature limitations based on what's available in both C# and Python.
+These notifications will be logged to the python terminal, or to the Unity Editor Console.
+
+
+## Side Channels
+
+The communicator is what manages data transfer between Unity and Python for the core
+training loop. Side Channels are another means of data transfer between Unity and Python.
+Side Channels are not versioned, but have been designed to support backward compatibility
+for what they are. As of today, we provide 4 side channels:
+- FloatProperties: shared float data between Unity - Python (bidirectional)
+- RawBytes: raw data that can be sent Unity - Python (bidirectional)
+- EngineConfig: a set of numeric fields in a pre-defined order sent from Python to Unity
+- Stats: (name, value, agg) messages sent from Unity to Python
+
+Aside from the specific implementations of side channels we provide (and use ourselves),
+the Side Channel interface is made available for users to create their own custom side
+channels. As such, we guarantee that the built in SideChannel interface between Unity and
+Python is backward compatible in packages that share the same major version.
+
diff --git a/docs/com.unity.ml-agents.extensions.md b/docs/com.unity.ml-agents.extensions.md
new file mode 100644
index 0000000000000000000000000000000000000000..48a1449efa645858fa8885a839dd7b676152f1d2
--- /dev/null
+++ b/docs/com.unity.ml-agents.extensions.md
@@ -0,0 +1 @@
+{!../com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md!}
diff --git a/docs/com.unity.ml-agents.md b/docs/com.unity.ml-agents.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d3d8066876d35b51e7f90900cad1079411f3849
--- /dev/null
+++ b/docs/com.unity.ml-agents.md
@@ -0,0 +1 @@
+{!../com.unity.ml-agents/Documentation~/com.unity.ml-agents.md!}
diff --git a/docs/dox-ml-agents.conf b/docs/dox-ml-agents.conf
new file mode 100644
index 0000000000000000000000000000000000000000..bfdb11ead7966ea63bbe4f5d3382774ab04fafbe
--- /dev/null
+++ b/docs/dox-ml-agents.conf
@@ -0,0 +1,2444 @@
+# Doxyfile 1.8.13
+
+# To generate the C# API documentation, run:
+#
+# doxygen dox-ml-agents.conf
+#
+# from the ml-agents-docs directory
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the config file
+# that follow. The default is UTF-8 which is also the encoding used for all text
+# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
+# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
+# for the list of possible encodings.
+# The default value is: UTF-8.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
+
+PROJECT_NAME           = "Unity ML-Agents Toolkit"
+
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
+
+PROJECT_NUMBER         =
+
+# Using the PROJECT_BRIEF tag one can provide an optional one line description
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
+
+PROJECT_BRIEF          =
+
+# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
+# in the documentation. The maximum height of the logo should not exceed 55
+# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
+# the logo to the output directory.
+
+PROJECT_LOGO           = doxygen/logo.png
+
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+# into which the generated documentation will be written. If a relative path is
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+
+OUTPUT_DIRECTORY       =
+
+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
+
+CREATE_SUBDIRS         = NO
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
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+# The default value is: NO.
+
+ALLOW_UNICODE_NAMES    = NO
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+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
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+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
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+
+OUTPUT_LANGUAGE        = English
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+BRIEF_MEMBER_DESC      = YES
+
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+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
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+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
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+
+ABBREVIATE_BRIEF       = "The $name class" \
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+ALWAYS_DETAILED_SEC    = NO
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+# inherited members of a class in the documentation of that class as if those
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+
+INLINE_INHERITED_MEMB  = NO
+
+# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
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+
+FULL_PATH_NAMES        = NO
+
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
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+
+STRIP_FROM_INC_PATH    =
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+SHORT_NAMES            = NO
+
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
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+
+JAVADOC_AUTOBRIEF      = YES
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+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
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+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines.
+
+ALIASES                =
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+# A mapping has the form "name=value". For example adding "class=itcl::class"
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+TCL_SUBST              =
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+OPTIMIZE_OUTPUT_JAVA   = YES
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+
+OPTIMIZE_FOR_FORTRAN   = NO
+
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+# sources. Doxygen will then generate output that is tailored for VHDL.
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+
+OPTIMIZE_OUTPUT_VHDL   = NO
+
+# Doxygen selects the parser to use depending on the extension of the files it
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+# Fortran. In the later case the parser tries to guess whether the code is fixed
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+# the files are not read by doxygen.
+
+EXTENSION_MAPPING      =
+
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See http://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
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+# The default value is: YES.
+
+MARKDOWN_SUPPORT       = YES
+
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+TOC_INCLUDE_HEADINGS   = 0
+
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+# be prevented in individual cases by putting a % sign in front of the word or
+# globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# If one adds a struct or class to a group and this option is enabled, then also
+# any nested class or struct is added to the same group. By default this option
+# is disabled and one has to add nested compounds explicitly via \ingroup.
+# The default value is: NO.
+
+GROUP_NESTED_COMPOUNDS = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = YES
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = YES
+
+# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = NO
+
+# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = YES
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO,
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. If set to YES, local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO, only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = NO
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO, these classes will be included in the various overviews. This option
+# has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# (class|struct|union) declarations. If set to NO, these declarations will be
+# included in the documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO, these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES, upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# and Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES, the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = YES
+
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            = doxygen/doxlayout.xml
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = NO
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered.
+# The default value is: NO.
+
+WARN_AS_ERROR          = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  = ../com.unity.ml-agents/Runtime/
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: http://www.gnu.org/software/libiconv) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
+# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf.
+
+FILE_PATTERNS          = *.cs \
+                         *.md \
+                         *.py
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       =
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       =
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             = images
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE = Unity-Agents-Overview.md
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# function all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see http://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse-libclang=ON option for CMake.
+# The default value is: NO.
+
+#CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+#CLANG_OPTIONS          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 2
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = ./html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            = doxygen/header.html
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            = doxygen/footer.html
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        = doxygen/doxygenbase.css
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefore more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  = doxygen/unity.css
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the style sheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = NO
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: http://developer.apple.com/tools/xcode/), introduced with
+# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the master .chm file (NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = YES
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# http://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = NO
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from http://www.mathjax.org before deployment.
+# The default value is: http://cdn.mathjax.org/mathjax/latest.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using Javascript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = NO
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when enabling USE_PDFLATEX this option is only used for generating
+# bitmaps for formulas in the HTML output, but not in the Makefile that is
+# written to the output directory.
+# The default file is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         = latex
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES, to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's config
+# file, i.e. a series of assignments. You only have to provide replacements,
+# missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's config file. A template extensions file can be generated
+# using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sf.net) file that captures the
+# structure of the code including all documentation. Note that this feature is
+# still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO, the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# in the source code. If set to NO, only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES, the include files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             =
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
+
+TAGFILES               =
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
+
+GENERATE_TAGFILE       =
+
+# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
+# the class index. If set to NO, only the inherited external classes will be
+# listed.
+# The default value is: NO.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
+# in the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
+
+EXTERNAL_GROUPS        = NO
+
+# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+EXTERNAL_PAGES         = NO
+
+# The PERL_PATH should be the absolute path and name of the perl script
+# interpreter (i.e. the result of 'which perl').
+# The default file (with absolute path) is: /usr/bin/perl.
+
+#PERL_PATH              = /usr/bin/perl
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
+
+CLASS_DIAGRAMS         = NO
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. Doxygen will then run the mscgen tool (see:
+# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
+# documentation. The MSCGEN_PATH tag allows you to specify the directory where
+# the mscgen tool resides. If left empty the tool is assumed to be found in the
+# default search path.
+
+#MSCGEN_PATH            =
+
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+DIA_PATH               =
+
+# If set to YES the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: NO.
+
+HAVE_DOT               = NO
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NUM_THREADS        = 0
+
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CLASS_GRAPH            = YES
+
+# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# graph for each documented class showing the direct and indirect implementation
+# dependencies (inheritance, containment, and class references variables) of the
+# class with other documented classes.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+COLLABORATION_GRAPH    = YES
+
+# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
+# groups, showing the direct groups dependencies.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GROUP_GRAPHS           = YES
+
+# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
+# collaboration diagrams in a style similar to the OMG's Unified Modeling
+# Language.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LOOK               = NO
+
+# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
+# class node. If there are many fields or methods and many nodes the graph may
+# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
+# number of items for each type to make the size more manageable. Set this to 0
+# for no limit. Note that the threshold may be exceeded by 50% before the limit
+# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
+# but if the number exceeds 15, the total amount of fields shown is limited to
+# 10.
+# Minimum value: 0, maximum value: 100, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LIMIT_NUM_FIELDS   = 10
+
+# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
+# collaboration graphs will show the relations between templates and their
+# instances.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDE_GRAPH          = YES
+
+# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDED_BY_GRAPH      = YES
+
+# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command. Disabling a call graph can be
+# accomplished by means of the command \hidecallgraph.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command. Disabling a caller graph can be
+# accomplished by means of the command \hidecallergraph.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GRAPHICAL_HIERARCHY    = YES
+
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# dependencies a directory has on other directories in a graphical way. The
+# dependency relations are determined by the #include relations between the
+# files in the directories.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DIRECTORY_GRAPH        = YES
+
+# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
+# generated by dot. For an explanation of the image formats see the section
+# output formats in the documentation of the dot tool (Graphviz (see:
+# http://www.graphviz.org/)).
+# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
+# to make the SVG files visible in IE 9+ (other browsers do not have this
+# requirement).
+# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
+# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and
+# png:gdiplus:gdiplus.
+# The default value is: png.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_IMAGE_FORMAT       = png
+
+# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
+# enable generation of interactive SVG images that allow zooming and panning.
+#
+# Note that this requires a modern browser other than Internet Explorer. Tested
+# and working are Firefox, Chrome, Safari, and Opera.
+# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
+# the SVG files visible. Older versions of IE do not have SVG support.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INTERACTIVE_SVG        = NO
+
+# The DOT_PATH tag can be used to specify the path where the dot tool can be
+# found. If left blank, it is assumed the dot tool can be found in the path.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_PATH               =
+
+# The DOTFILE_DIRS tag can be used to specify one or more directories that
+# contain dot files that are included in the documentation (see the \dotfile
+# command).
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOTFILE_DIRS           =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
+MSCFILE_DIRS           =
+
+# The DIAFILE_DIRS tag can be used to specify one or more directories that
+# contain dia files that are included in the documentation (see the \diafile
+# command).
+
+DIAFILE_DIRS           =
+
+# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file. If left blank, it is assumed
+# PlantUML is not used or called during a preprocessing step. Doxygen will
+# generate a warning when it encounters a \startuml command in this case and
+# will not generate output for the diagram.
+
+PLANTUML_JAR_PATH      =
+
+# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a
+# configuration file for plantuml.
+
+PLANTUML_CFG_FILE      =
+
+# When using plantuml, the specified paths are searched for files specified by
+# the !include statement in a plantuml block.
+
+PLANTUML_INCLUDE_PATH  =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
+# that will be shown in the graph. If the number of nodes in a graph becomes
+# larger than this value, doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that doxygen if the number of direct
+# children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
+# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# Minimum value: 0, maximum value: 10000, default value: 50.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_GRAPH_MAX_NODES    = 50
+
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
+# generated by dot. A depth value of 3 means that only nodes reachable from the
+# root by following a path via at most 3 edges will be shown. Nodes that lay
+# further from the root node will be omitted. Note that setting this option to 1
+# or 2 may greatly reduce the computation time needed for large code bases. Also
+# note that the size of a graph can be further restricted by
+# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+# Minimum value: 0, maximum value: 1000, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+MAX_DOT_GRAPH_DEPTH    = 0
+
+# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
+# background. This is disabled by default, because dot on Windows does not seem
+# to support this out of the box.
+#
+# Warning: Depending on the platform used, enabling this option may lead to
+# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
+# read).
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_TRANSPARENT        = NO
+
+# Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
+# files in one run (i.e. multiple -o and -T options on the command line). This
+# makes dot run faster, but since only newer versions of dot (>1.8.10) support
+# this, this feature is disabled by default.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_MULTI_TARGETS      = NO
+
+# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# explaining the meaning of the various boxes and arrows in the dot generated
+# graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GENERATE_LEGEND        = YES
+
+# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot
+# files that are used to generate the various graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_CLEANUP            = YES
diff --git a/docs/doxygen/Readme.md b/docs/doxygen/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..b92b864e14d11c52175740c4e9e6cfebb8a21e66
--- /dev/null
+++ b/docs/doxygen/Readme.md
@@ -0,0 +1,5 @@
+# Doxygen files
+
+To generate the API reference as HTML files, run:
+
+    doxygen dox-ml-agents.conf
diff --git a/docs/doxygen/doxygenbase.css b/docs/doxygen/doxygenbase.css
new file mode 100644
index 0000000000000000000000000000000000000000..5cabd87318c05763587f926df6cda463e5e95513
--- /dev/null
+++ b/docs/doxygen/doxygenbase.css
@@ -0,0 +1,1600 @@
+/* The standard CSS for doxygen 1.8.14 */
+
+body, table, div, p, dl {
+	font: 400 14px/22px Roboto,sans-serif;
+}
+
+p.reference, p.definition {
+	font: 400 14px/22px Roboto,sans-serif;
+}
+
+/* @group Heading Levels */
+
+h1.groupheader {
+	font-size: 150%;
+}
+
+.title {
+	font: 400 14px/28px Roboto,sans-serif;
+	font-size: 150%;
+	font-weight: bold;
+	margin: 10px 2px;
+}
+
+h2.groupheader {
+	border-bottom: 1px solid #879ECB;
+	color: #354C7B;
+	font-size: 150%;
+	font-weight: normal;
+	margin-top: 1.75em;
+	padding-top: 8px;
+	padding-bottom: 4px;
+	width: 100%;
+}
+
+h3.groupheader {
+	font-size: 100%;
+}
+
+h1, h2, h3, h4, h5, h6 {
+	-webkit-transition: text-shadow 0.5s linear;
+	-moz-transition: text-shadow 0.5s linear;
+	-ms-transition: text-shadow 0.5s linear;
+	-o-transition: text-shadow 0.5s linear;
+	transition: text-shadow 0.5s linear;
+	margin-right: 15px;
+}
+
+h1.glow, h2.glow, h3.glow, h4.glow, h5.glow, h6.glow {
+	text-shadow: 0 0 15px cyan;
+}
+
+dt {
+	font-weight: bold;
+}
+
+div.multicol {
+	-moz-column-gap: 1em;
+	-webkit-column-gap: 1em;
+	-moz-column-count: 3;
+	-webkit-column-count: 3;
+}
+
+p.startli, p.startdd {
+	margin-top: 2px;
+}
+
+p.starttd {
+	margin-top: 0px;
+}
+
+p.endli {
+	margin-bottom: 0px;
+}
+
+p.enddd {
+	margin-bottom: 4px;
+}
+
+p.endtd {
+	margin-bottom: 2px;
+}
+
+/* @end */
+
+caption {
+	font-weight: bold;
+}
+
+span.legend {
+        font-size: 70%;
+        text-align: center;
+}
+
+h3.version {
+        font-size: 90%;
+        text-align: center;
+}
+
+div.qindex, div.navtab{
+	background-color: #EBEFF6;
+	border: 1px solid #A3B4D7;
+	text-align: center;
+}
+
+div.qindex, div.navpath {
+	width: 100%;
+	line-height: 140%;
+}
+
+div.navtab {
+	margin-right: 15px;
+}
+
+/* @group Link Styling */
+
+a {
+	color: #3D578C;
+	font-weight: normal;
+	text-decoration: none;
+}
+
+.contents a:visited {
+	color: #4665A2;
+}
+
+a:hover {
+	text-decoration: underline;
+}
+
+a.qindex {
+	font-weight: bold;
+}
+
+a.qindexHL {
+	font-weight: bold;
+	background-color: #9CAFD4;
+	color: #ffffff;
+	border: 1px double #869DCA;
+}
+
+.contents a.qindexHL:visited {
+        color: #ffffff;
+}
+
+a.el {
+	font-weight: bold;
+}
+
+a.elRef {
+}
+
+a.code, a.code:visited, a.line, a.line:visited {
+	color: #4665A2; 
+}
+
+a.codeRef, a.codeRef:visited, a.lineRef, a.lineRef:visited {
+	color: #4665A2; 
+}
+
+/* @end */
+
+dl.el {
+	margin-left: -1cm;
+}
+
+pre.fragment {
+        border: 1px solid #C4CFE5;
+        background-color: #FBFCFD;
+        padding: 4px 6px;
+        margin: 4px 8px 4px 2px;
+        overflow: auto;
+        word-wrap: break-word;
+        font-size:  9pt;
+        line-height: 125%;
+        font-family: monospace, fixed;
+        font-size: 105%;
+}
+
+div.fragment {
+        padding: 0px;
+        margin: 4px 8px 4px 2px;
+	background-color: #FBFCFD;
+	border: 1px solid #C4CFE5;
+}
+
+div.line {
+	font-family: monospace, fixed;
+        font-size: 13px;
+	min-height: 13px;
+	line-height: 1.0;
+	text-wrap: unrestricted;
+	white-space: -moz-pre-wrap; /* Moz */
+	white-space: -pre-wrap;     /* Opera 4-6 */
+	white-space: -o-pre-wrap;   /* Opera 7 */
+	white-space: pre-wrap;      /* CSS3  */
+	word-wrap: break-word;      /* IE 5.5+ */
+	text-indent: -53px;
+	padding-left: 53px;
+	padding-bottom: 0px;
+	margin: 0px;
+	-webkit-transition-property: background-color, box-shadow;
+	-webkit-transition-duration: 0.5s;
+	-moz-transition-property: background-color, box-shadow;
+	-moz-transition-duration: 0.5s;
+	-ms-transition-property: background-color, box-shadow;
+	-ms-transition-duration: 0.5s;
+	-o-transition-property: background-color, box-shadow;
+	-o-transition-duration: 0.5s;
+	transition-property: background-color, box-shadow;
+	transition-duration: 0.5s;
+}
+
+div.line:after {
+    content:"\000A";
+    white-space: pre;
+}
+
+div.line.glow {
+	background-color: cyan;
+	box-shadow: 0 0 10px cyan;
+}
+
+
+span.lineno {
+	padding-right: 4px;
+	text-align: right;
+	border-right: 2px solid #0F0;
+	background-color: #E8E8E8;
+        white-space: pre;
+}
+span.lineno a {
+	background-color: #D8D8D8;
+}
+
+span.lineno a:hover {
+	background-color: #C8C8C8;
+}
+
+.lineno {
+	-webkit-touch-callout: none;
+	-webkit-user-select: none;
+	-khtml-user-select: none;
+	-moz-user-select: none;
+	-ms-user-select: none;
+	user-select: none;
+}
+
+div.ah, span.ah {
+	background-color: black;
+	font-weight: bold;
+	color: #ffffff;
+	margin-bottom: 3px;
+	margin-top: 3px;
+	padding: 0.2em;
+	border: solid thin #333;
+	border-radius: 0.5em;
+	-webkit-border-radius: .5em;
+	-moz-border-radius: .5em;
+	box-shadow: 2px 2px 3px #999;
+	-webkit-box-shadow: 2px 2px 3px #999;
+	-moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px;
+	background-image: -webkit-gradient(linear, left top, left bottom, from(#eee), to(#000),color-stop(0.3, #444));
+	background-image: -moz-linear-gradient(center top, #eee 0%, #444 40%, #000 110%);
+}
+
+div.classindex ul {
+        list-style: none;
+        padding-left: 0;
+}
+
+div.classindex span.ai {
+        display: inline-block;
+}
+
+div.groupHeader {
+	margin-left: 16px;
+	margin-top: 12px;
+	font-weight: bold;
+}
+
+div.groupText {
+	margin-left: 16px;
+	font-style: italic;
+}
+
+body {
+	background-color: white;
+	color: black;
+        margin: 0;
+}
+
+div.contents {
+	margin-top: 10px;
+	margin-left: 12px;
+	margin-right: 8px;
+}
+
+td.indexkey {
+	background-color: #EBEFF6;
+	font-weight: bold;
+	border: 1px solid #e6e6e6;
+	margin: 2px 0px 2px 0;
+	padding: 2px 10px;
+        white-space: nowrap;
+        vertical-align: top;
+}
+
+td.indexvalue {
+	background-color: #EBEFF6;
+	border: 1px solid #e6e6e6;
+	padding: 2px 10px;
+	margin: 2px 0px;
+}
+
+tr.memlist {
+	background-color: #EEF1F7;
+}
+
+p.formulaDsp {
+	text-align: center;
+}
+
+img.formulaDsp {
+	
+}
+
+img.formulaInl {
+	vertical-align: middle;
+}
+
+div.center {
+	text-align: center;
+        margin-top: 0px;
+        margin-bottom: 0px;
+        padding: 0px;
+}
+
+div.center img {
+	border: 0px;
+}
+
+address.footer {
+	text-align: right;
+	padding-right: 12px;
+}
+
+img.footer {
+	border: 0px;
+	vertical-align: middle;
+}
+
+/* @group Code Colorization */
+
+span.keyword {
+	color: #008000
+}
+
+span.keywordtype {
+	color: #604020
+}
+
+span.keywordflow {
+	color: #e08000
+}
+
+span.comment {
+	color: #800000
+}
+
+span.preprocessor {
+	color: #806020
+}
+
+span.stringliteral {
+	color: #002080
+}
+
+span.charliteral {
+	color: #008080
+}
+
+span.vhdldigit { 
+	color: #ff00ff 
+}
+
+span.vhdlchar { 
+	color: #000000 
+}
+
+span.vhdlkeyword { 
+	color: #700070 
+}
+
+span.vhdllogic { 
+	color: #ff0000 
+}
+
+blockquote {
+        background-color: #F7F8FB;
+        border-left: 2px solid #e6e6e6;
+        margin: 0 24px 0 4px;
+        padding: 0 12px 0 16px;
+}
+
+/* @end */
+
+/*
+.search {
+	color: #003399;
+	font-weight: bold;
+}
+
+form.search {
+	margin-bottom: 0px;
+	margin-top: 0px;
+}
+
+input.search {
+	font-size: 75%;
+	color: #000080;
+	font-weight: normal;
+	background-color: #e8eef2;
+}
+*/
+
+td.tiny {
+	font-size: 75%;
+}
+
+.dirtab {
+	padding: 4px;
+	border-collapse: collapse;
+	border: 1px solid #e6e6e6;
+}
+
+th.dirtab {
+	background: #EBEFF6;
+	font-weight: bold;
+}
+
+hr {
+	height: 0px;
+	border: none;
+	border-top: 1px solid #e6e6e6;
+}
+
+hr.footer {
+	height: 1px;
+}
+
+/* @group Member Descriptions */
+
+table.memberdecls {
+	border-spacing: 0px;
+	padding: 0px;
+}
+
+.memberdecls td, .fieldtable tr {
+	-webkit-transition-property: background-color, box-shadow;
+	-webkit-transition-duration: 0.5s;
+	-moz-transition-property: background-color, box-shadow;
+	-moz-transition-duration: 0.5s;
+	-ms-transition-property: background-color, box-shadow;
+	-ms-transition-duration: 0.5s;
+	-o-transition-property: background-color, box-shadow;
+	-o-transition-duration: 0.5s;
+	transition-property: background-color, box-shadow;
+	transition-duration: 0.5s;
+}
+
+.memberdecls td.glow, .fieldtable tr.glow {
+	background-color: cyan;
+	box-shadow: 0 0 15px cyan;
+}
+
+.mdescLeft, .mdescRight,
+.memItemLeft, .memItemRight,
+.memTemplItemLeft, .memTemplItemRight, .memTemplParams {
+	background-color: #F9FAFC;
+	border: none;
+	margin: 4px;
+	padding: 1px 0 0 8px;
+}
+
+.mdescLeft, .mdescRight {
+	padding: 0px 8px 4px 8px;
+	color: #555;
+}
+
+.memSeparator {
+        border-bottom: 1px solid #e6e6e6;
+        line-height: 1px;
+        margin: 0px;
+        padding: 0px;
+}
+
+.memItemLeft, .memTemplItemLeft {
+        white-space: nowrap;
+}
+
+.memItemRight {
+	width: 100%;
+}
+
+.memTemplParams {
+	color: #4665A2;
+        white-space: nowrap;
+	font-size: 80%;
+}
+
+/* @end */
+
+/* @group Member Details */
+
+/* Styles for detailed member documentation */
+
+.memtitle {
+	display: none;
+	padding: 8px 0px 8px 8px;
+	border-top: 1px solid #e6e6e6;
+	border-left: 1px solid #e6e6e6;
+	border-right: 1px solid #e6e6e6;
+	/* border-top-right-radius: 4px; 
+	border-top-left-radius: 4px; */
+	margin-bottom: -1px;
+	/* background-image: url('nav_f.png'); */
+	background-repeat: repeat-x;
+	background-color: #E2E8F2;
+	line-height: 1.25;
+	font-weight: 300;
+	/* float:left; */
+	/* added */
+	width: 100%;
+}
+
+.permalink
+{
+        font-size: 65%;
+        display: inline-block;
+        vertical-align: middle;
+}
+
+.memtemplate {
+	font-size: 80%;
+	color: #4665A2;
+	font-weight: normal;
+	margin-left: 9px;
+}
+
+.memnav {
+	background-color: #EBEFF6;
+	border: 1px solid #e6e6e6;
+	text-align: center;
+	margin: 2px;
+	margin-right: 15px;
+	padding: 2px;
+}
+
+.mempage {
+	width: 100%;
+}
+
+.memitem {
+	padding: 0;
+	margin-bottom: 10px;
+	margin-right: 5px;
+        -webkit-transition: box-shadow 0.5s linear;
+        -moz-transition: box-shadow 0.5s linear;
+        -ms-transition: box-shadow 0.5s linear;
+        -o-transition: box-shadow 0.5s linear;
+        transition: box-shadow 0.5s linear;
+        display: table !important;
+        width: 100%;
+}
+
+.memitem.glow {
+         box-shadow: 0 0 15px cyan;
+}
+
+.memname {
+        font-weight: 400;
+        margin-left: 6px;
+}
+
+.memname td {
+    font-weight: 900;
+	vertical-align: bottom;
+}
+
+.memproto, dl.reflist dt {
+        border-top: 1px solid #e6e6e6;
+        border-left: 1px solid #e6e6e6;
+        border-right: 1px solid #e6e6e6;
+		border-bottom: 1px dotted #e6e6e6;
+        padding: 6px 0px 6px 0px;
+        color: #253555;
+        font-weight: bold;
+        //text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9);
+        background-color: #f8f8f8;
+        /* opera specific markup */
+        //box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15);
+        //border-top-right-radius: 4px;
+        /* firefox specific markup */
+        //-moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px;
+        //-moz-border-radius-topright: 4px;
+        /* webkit specific markup */
+        //-webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15);
+        //-webkit-border-top-right-radius: 4px;
+
+}
+
+.overload {
+        font-family: "courier new",courier,monospace;
+	font-size: 65%;
+}
+
+.memdoc, dl.reflist dd {
+        border-bottom: 1px solid #e6e6e6;      
+        border-left: 1px solid #e6e6e6;      
+        border-right: 1px solid #e6e6e6; 
+        padding: 6px 10px 2px 10px;
+        border-top-width: 0;
+        //background-image:url('nav_g.png');
+        //background-repeat:repeat-x;
+        background-color: #FFFFFF;
+        /* opera specific markup */
+        //border-bottom-left-radius: 4px;
+        //border-bottom-right-radius: 4px;
+        //box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15);
+        /* firefox specific markup */
+        //-moz-border-radius-bottomleft: 4px;
+        //-moz-border-radius-bottomright: 4px;
+       // -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px;
+        /* webkit specific markup */
+        //-webkit-border-bottom-left-radius: 4px;
+        //-webkit-border-bottom-right-radius: 4px;
+       // -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15);
+}
+
+dl.reflist dt {
+        padding: 5px;
+}
+
+dl.reflist dd {
+        margin: 0px 0px 10px 0px;
+        padding: 5px;
+}
+
+.paramkey {
+	text-align: right;
+}
+
+.paramtype {
+	white-space: nowrap;
+}
+
+.paramname {
+	color: #602020;
+	white-space: nowrap;
+}
+.paramname em {
+	font-style: normal;
+}
+.paramname code {
+        line-height: 14px;
+}
+
+.params, .retval, .exception, .tparams {
+        margin-left: 0px;
+        padding-left: 0px;
+}       
+
+.params .paramname, .retval .paramname {
+        font-weight: bold;
+        vertical-align: top;
+}
+        
+.params .paramtype {
+        font-style: italic;
+        vertical-align: top;
+}       
+        
+.params .paramdir {
+        font-family: "courier new",courier,monospace;
+        vertical-align: top;
+}
+
+table.mlabels {
+	border-spacing: 0px;
+}
+
+td.mlabels-left {
+	width: 100%;
+	padding: 0px;
+}
+
+td.mlabels-right {
+	vertical-align: bottom;
+	padding: 0px;
+	white-space: nowrap;
+}
+
+span.mlabels {
+        margin-left: 8px;
+}
+
+span.mlabel {
+        background-color: #728DC1;
+        border-top:1px solid #e6e6e6;
+        border-left:1px solid #e6e6e6;
+        border-right:1px solid #e6e6e6;
+        border-bottom:1px solid #e6e6e6;
+	text-shadow: none;
+	color: white;
+	margin-right: 4px;
+	padding: 2px 3px;
+	border-radius: 3px;
+	font-size: 7pt;
+	white-space: nowrap;
+	vertical-align: middle;
+}
+
+
+
+/* @end */
+
+/* these are for tree view inside a (index) page */
+
+div.directory {
+        margin: 10px 0px;
+        border-top: 1px solid #e6e6e6;
+        border-bottom: 1px solid #e6e6e6;
+        width: 100%;
+}
+
+.directory table {
+        border-collapse:collapse;
+}
+
+.directory td {
+        margin: 0px;
+        padding: 0px;
+	vertical-align: top;
+}
+
+.directory td.entry {
+        white-space: nowrap;
+        padding-right: 6px;
+	padding-top: 3px;
+}
+
+.directory td.entry a {
+        outline:none;
+}
+
+.directory td.entry a img {
+        border: none;
+}
+
+.directory td.desc {
+        width: 100%;
+        padding-left: 6px;
+	padding-right: 6px;
+	padding-top: 3px;
+	border-left: 1px solid rgba(0,0,0,0.05);
+}
+
+.directory tr.even {
+	padding-left: 6px;
+	background-color: #F7F8FB;
+}
+
+.directory img {
+	vertical-align: -30%;
+}
+
+.directory .levels {
+        white-space: nowrap;
+        width: 100%;
+        text-align: right;
+        font-size: 9pt;
+}
+
+.directory .levels span {
+        cursor: pointer;
+        padding-left: 2px;
+        padding-right: 2px;
+	color: #3D578C;
+}
+
+.arrow {
+    color: #9CAFD4;
+    -webkit-user-select: none;
+    -khtml-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+    cursor: pointer;
+    font-size: 80%;
+    display: inline-block;
+    width: 16px;
+    height: 22px;
+}
+
+.icon {
+    font-family: Arial, Helvetica;
+    font-weight: bold;
+    font-size: 12px;
+    height: 14px;
+    width: 16px;
+    display: inline-block;
+    background-color: #728DC1;
+    color: white;
+    text-align: center;
+    border-radius: 4px;
+    margin-left: 2px;
+    margin-right: 2px;
+}
+
+.icona {
+    width: 24px;
+    height: 22px;
+    display: inline-block;
+}
+
+.iconfopen {
+    width: 24px;
+    height: 18px;
+    margin-bottom: 4px;
+    background-image:url('folderopen.png');
+    background-position: 0px -4px;
+    background-repeat: repeat-y;
+    vertical-align:top;
+    display: inline-block;
+}
+
+.iconfclosed {
+    width: 24px;
+    height: 18px;
+    margin-bottom: 4px;
+    background-image:url('folderclosed.png');
+    background-position: 0px -4px;
+    background-repeat: repeat-y;
+    vertical-align:top;
+    display: inline-block;
+}
+
+.icondoc {
+    width: 24px;
+    height: 18px;
+    margin-bottom: 4px;
+    background-image:url('doc.png');
+    background-position: 0px -4px;
+    background-repeat: repeat-y;
+    vertical-align:top;
+    display: inline-block;
+}
+
+table.directory {
+    font: 400 14px Roboto,sans-serif;
+}
+
+/* @end */
+
+div.dynheader {
+        margin-top: 8px;
+	-webkit-touch-callout: none;
+	-webkit-user-select: none;
+	-khtml-user-select: none;
+	-moz-user-select: none;
+	-ms-user-select: none;
+	user-select: none;
+}
+
+address {
+	font-style: normal;
+	color: #2A3D61;
+}
+
+table.doxtable caption {
+	caption-side: top;
+}
+
+table.doxtable {
+	border-collapse:collapse;
+        margin-top: 4px;
+        margin-bottom: 4px;
+}
+
+table.doxtable td, table.doxtable th {
+	border: 1px solid #2D4068;
+	padding: 3px 7px 2px;
+}
+
+table.doxtable th {
+	background-color: #374F7F;
+	color: #FFFFFF;
+	font-size: 110%;
+	padding-bottom: 4px;
+	padding-top: 5px;
+}
+
+table.fieldtable {
+        /*width: 100%;*/
+        margin-bottom: 10px;
+        border: 1px solid #e6e6e6;
+        border-spacing: 0px;
+        -moz-border-radius: 4px;
+        -webkit-border-radius: 4px;
+        border-radius: 4px;
+        -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px;
+        -webkit-box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15);
+        box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15);
+}
+
+.fieldtable td, .fieldtable th {
+        padding: 3px 7px 2px;
+}
+
+.fieldtable td.fieldtype, .fieldtable td.fieldname {
+        white-space: nowrap;
+        border-right: 1px solid #e6e6e6;
+        border-bottom: 1px solid #e6e6e6;
+        vertical-align: top;
+}
+
+.fieldtable td.fieldname {
+        padding-top: 3px;
+}
+
+.fieldtable td.fielddoc {
+        border-bottom: 1px solid #e6e6e6;
+        /*width: 100%;*/
+}
+
+.fieldtable td.fielddoc p:first-child {
+        margin-top: 0px;
+}       
+        
+.fieldtable td.fielddoc p:last-child {
+        margin-bottom: 2px;
+}
+
+.fieldtable tr:last-child td {
+        border-bottom: none;
+}
+
+.fieldtable th {
+        background-image:url('nav_f.png');
+        background-repeat:repeat-x;
+        background-color: #E2E8F2;
+        font-size: 90%;
+        color: #253555;
+        padding-bottom: 4px;
+        padding-top: 5px;
+        text-align:left;
+        font-weight: 400;
+        -moz-border-radius-topleft: 4px;
+        -moz-border-radius-topright: 4px;
+        -webkit-border-top-left-radius: 4px;
+        -webkit-border-top-right-radius: 4px;
+        border-top-left-radius: 4px;
+        border-top-right-radius: 4px;
+        border-bottom: 1px solid #e6e6e6;
+}
+
+
+.tabsearch {
+	top: 0px;
+	left: 10px;
+	height: 36px;
+	background-image: url('tab_b.png');
+	z-index: 101;
+	overflow: hidden;
+	font-size: 13px;
+}
+
+.navpath ul
+{
+	font-size: 11px;
+	background-image:url('tab_b.png');
+	background-repeat:repeat-x;
+	background-position: 0 -5px;
+	height:30px;
+	line-height:30px;
+	color:#8AA0CC;
+	border:solid 1px #e6e6e6;
+	overflow:hidden;
+	margin:0px;
+	padding:0px;
+}
+
+.navpath li
+{
+	list-style-type:none;
+	float:left;
+	padding-left:10px;
+	padding-right:15px;
+	background-image:url('bc_s.png');
+	background-repeat:no-repeat;
+	background-position:right;
+	color:#364D7C;
+}
+
+.navpath li.navelem a
+{
+	height:32px;
+	display:block;
+	text-decoration: none;
+	outline: none;
+	color: #283A5D;
+	font-family: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif;
+	text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9);
+	text-decoration: none;        
+}
+
+.navpath li.navelem a:hover
+{
+	color:#6884BD;
+}
+
+.navpath li.footer
+{
+        list-style-type:none;
+        float:right;
+        padding-left:10px;
+        padding-right:15px;
+        background-image:none;
+        background-repeat:no-repeat;
+        background-position:right;
+        color:#364D7C;
+        font-size: 8pt;
+}
+
+
+div.summary
+{
+	float: right;
+	font-size: 8pt;
+	padding-right: 5px;
+	width: 50%;
+	text-align: right;
+}       
+
+div.summary a
+{
+	white-space: nowrap;
+}
+
+table.classindex
+{
+        margin: 10px;
+        white-space: nowrap;
+        margin-left: 3%;
+        margin-right: 3%;
+        width: 94%;
+        border: 0;
+        border-spacing: 0; 
+        padding: 0;
+}
+
+div.ingroups
+{
+	font-size: 8pt;
+	width: 50%;
+	text-align: left;
+}
+
+div.ingroups a
+{
+	white-space: nowrap;
+}
+
+div.header
+{
+        background-image:url('nav_h.png');
+        background-repeat:repeat-x;
+	background-color: #F9FAFC;
+	margin:  0px;
+	border-bottom: 1px solid #e6e6e6;
+}
+
+div.headertitle
+{
+	padding: 5px 5px 5px 10px;
+}
+
+dl
+{
+        padding: 0 0 0 10px;
+}
+
+/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug */
+dl.section
+{
+	margin-left: 0px;
+	padding-left: 0px;
+}
+
+dl.note
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #e6e6e6;
+}
+
+dl.warning, dl.attention
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #FF0000;
+}
+
+dl.pre, dl.post, dl.invariant
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #00D000;
+}
+
+dl.deprecated
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #505050;
+}
+
+dl.todo
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #00C0E0;
+}
+
+dl.test
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #3030E0;
+}
+
+dl.bug
+{
+        margin-left:-7px;
+        padding-left: 3px;
+        border-left:4px solid;
+        border-color: #C08050;
+}
+
+dl.section dd {
+	margin-bottom: 6px;
+}
+
+
+#projectlogo
+{
+	text-align: center;
+	vertical-align: bottom;
+	border-collapse: separate;
+}
+ 
+#projectlogo img
+{ 
+	border: 0px none;
+}
+ 
+#projectalign
+{
+        vertical-align: middle;
+}
+
+#projectname
+{
+	font: 300% Tahoma, Arial,sans-serif;
+	margin: 0px;
+	padding: 2px 0px;
+}
+    
+#projectbrief
+{
+	font: 120% Tahoma, Arial,sans-serif;
+	margin: 0px;
+	padding: 0px;
+}
+
+#projectnumber
+{
+	font: 50% Tahoma, Arial,sans-serif;
+	margin: 0px;
+	padding: 0px;
+}
+
+#titlearea
+{
+	padding: 0px;
+	margin: 0px;
+	width: 100%;
+	border-bottom: 1px solid #5373B4;
+}
+
+.image
+{
+        text-align: center;
+}
+
+.dotgraph
+{
+        text-align: center;
+}
+
+.mscgraph
+{
+        text-align: center;
+}
+
+.plantumlgraph
+{
+        text-align: center;
+}
+
+.diagraph
+{
+        text-align: center;
+}
+
+.caption
+{
+	font-weight: bold;
+}
+
+div.zoom
+{
+	border: 1px solid #90A5CE;
+}
+
+dl.citelist {
+        margin-bottom:50px;
+}
+
+dl.citelist dt {
+        color:#334975;
+        float:left;
+        font-weight:bold;
+        margin-right:10px;
+        padding:5px;
+}
+
+dl.citelist dd {
+        margin:2px 0;
+        padding:5px 0;
+}
+
+div.toc {
+        padding: 14px 25px;
+        background-color: #F4F6FA;
+        border: 1px solid #D8DFEE;
+        border-radius: 7px 7px 7px 7px;
+        float: right;
+        height: auto;
+        margin: 0 8px 10px 10px;
+        width: 200px;
+}
+
+div.toc li {
+        background: url("bdwn.png") no-repeat scroll 0 5px transparent;
+        font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif;
+        margin-top: 5px;
+        padding-left: 10px;
+        padding-top: 2px;
+}
+
+div.toc h3 {
+        font: bold 12px/1.2 Arial,FreeSans,sans-serif;
+	color: #4665A2;
+        border-bottom: 0 none;
+        margin: 0;
+}
+
+div.toc ul {
+        list-style: none outside none;
+        border: medium none;
+        padding: 0px;
+}       
+
+div.toc li.level1 {
+        margin-left: 0px;
+}
+
+div.toc li.level2 {
+        margin-left: 15px;
+}
+
+div.toc li.level3 {
+        margin-left: 30px;
+}
+
+div.toc li.level4 {
+        margin-left: 45px;
+}
+
+.inherit_header {
+        font-weight: bold;
+        color: gray;
+        cursor: pointer;
+	-webkit-touch-callout: none;
+	-webkit-user-select: none;
+	-khtml-user-select: none;
+	-moz-user-select: none;
+	-ms-user-select: none;
+	user-select: none;
+}
+
+.inherit_header td {
+        padding: 6px 0px 2px 5px;
+}
+
+.inherit {
+        display: none;
+}
+
+tr.heading h2 {
+        margin-top: 12px;
+        margin-bottom: 4px;
+}
+
+/* tooltip related style info */
+
+.ttc {
+        position: absolute;
+        display: none;
+}
+
+#powerTip {
+	cursor: default;
+	white-space: nowrap;
+	background-color: white;
+	border: 1px solid gray;
+	border-radius: 4px 4px 4px 4px;
+	box-shadow: 1px 1px 7px gray;
+	display: none;
+	font-size: smaller;
+	max-width: 80%;
+	opacity: 0.9;
+	padding: 1ex 1em 1em;
+	position: absolute;
+	z-index: 2147483647;
+}
+
+#powerTip div.ttdoc {
+        color: grey;
+	font-style: italic;
+}
+
+#powerTip div.ttname a {
+        font-weight: bold;
+}
+
+#powerTip div.ttname {
+        font-weight: bold;
+}
+
+#powerTip div.ttdeci {
+        color: #006318;
+}
+
+#powerTip div {
+        margin: 0px;
+        padding: 0px;
+        font: 12px/16px Roboto,sans-serif;
+}
+
+#powerTip:before, #powerTip:after {
+	content: "";
+	position: absolute;
+	margin: 0px;
+}
+
+#powerTip.n:after,  #powerTip.n:before,
+#powerTip.s:after,  #powerTip.s:before,
+#powerTip.w:after,  #powerTip.w:before,
+#powerTip.e:after,  #powerTip.e:before,
+#powerTip.ne:after, #powerTip.ne:before,
+#powerTip.se:after, #powerTip.se:before,
+#powerTip.nw:after, #powerTip.nw:before,
+#powerTip.sw:after, #powerTip.sw:before {
+	border: solid transparent;
+	content: " ";
+	height: 0;
+	width: 0;
+	position: absolute;
+}
+
+#powerTip.n:after,  #powerTip.s:after,
+#powerTip.w:after,  #powerTip.e:after,
+#powerTip.nw:after, #powerTip.ne:after,
+#powerTip.sw:after, #powerTip.se:after {
+	border-color: rgba(255, 255, 255, 0);
+}
+
+#powerTip.n:before,  #powerTip.s:before,
+#powerTip.w:before,  #powerTip.e:before,
+#powerTip.nw:before, #powerTip.ne:before,
+#powerTip.sw:before, #powerTip.se:before {
+	border-color: rgba(128, 128, 128, 0);
+}
+
+#powerTip.n:after,  #powerTip.n:before,
+#powerTip.ne:after, #powerTip.ne:before,
+#powerTip.nw:after, #powerTip.nw:before {
+	top: 100%;
+}
+
+#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after {
+	border-top-color: #ffffff;
+	border-width: 10px;
+	margin: 0px -10px;
+}
+#powerTip.n:before {
+	border-top-color: #808080;
+	border-width: 11px;
+	margin: 0px -11px;
+}
+#powerTip.n:after, #powerTip.n:before {
+	left: 50%;
+}
+
+#powerTip.nw:after, #powerTip.nw:before {
+	right: 14px;
+}
+
+#powerTip.ne:after, #powerTip.ne:before {
+	left: 14px;
+}
+
+#powerTip.s:after,  #powerTip.s:before,
+#powerTip.se:after, #powerTip.se:before,
+#powerTip.sw:after, #powerTip.sw:before {
+	bottom: 100%;
+}
+
+#powerTip.s:after, #powerTip.se:after, #powerTip.sw:after {
+	border-bottom-color: #ffffff;
+	border-width: 10px;
+	margin: 0px -10px;
+}
+
+#powerTip.s:before, #powerTip.se:before, #powerTip.sw:before {
+	border-bottom-color: #808080;
+	border-width: 11px;
+	margin: 0px -11px;
+}
+
+#powerTip.s:after, #powerTip.s:before {
+	left: 50%;
+}
+
+#powerTip.sw:after, #powerTip.sw:before {
+	right: 14px;
+}
+
+#powerTip.se:after, #powerTip.se:before {
+	left: 14px;
+}
+
+#powerTip.e:after, #powerTip.e:before {
+	left: 100%;
+}
+#powerTip.e:after {
+	border-left-color: #ffffff;
+	border-width: 10px;
+	top: 50%;
+	margin-top: -10px;
+}
+#powerTip.e:before {
+	border-left-color: #808080;
+	border-width: 11px;
+	top: 50%;
+	margin-top: -11px;
+}
+
+#powerTip.w:after, #powerTip.w:before {
+	right: 100%;
+}
+#powerTip.w:after {
+	border-right-color: #ffffff;
+	border-width: 10px;
+	top: 50%;
+	margin-top: -10px;
+}
+#powerTip.w:before {
+	border-right-color: #808080;
+	border-width: 11px;
+	top: 50%;
+	margin-top: -11px;
+}
+
+@media print
+{
+  #top { display: none; }
+  #side-nav { display: none; }
+  #nav-path { display: none; }
+  body { overflow:visible; }
+  h1, h2, h3, h4, h5, h6 { page-break-after: avoid; }
+  .summary { display: none; }
+  .memitem { page-break-inside: avoid; }
+  #doc-content
+  {
+    margin-left:0 !important;
+    height:auto !important;
+    width:auto !important;
+    overflow:inherit;
+    display:inline;
+  }
+}
+
+/* @group Markdown */
+
+/*
+table.markdownTable {
+	border-collapse:collapse;
+        margin-top: 4px;
+        margin-bottom: 4px;
+}
+
+table.markdownTable td, table.markdownTable th {
+	border: 1px solid #2D4068;
+	padding: 3px 7px 2px;
+}
+
+table.markdownTableHead tr {
+}
+
+table.markdownTableBodyLeft td, table.markdownTable th {
+	border: 1px solid #2D4068;
+	padding: 3px 7px 2px;
+}
+
+th.markdownTableHeadLeft th.markdownTableHeadRight th.markdownTableHeadCenter th.markdownTableHeadNone {
+	background-color: #374F7F;
+	color: #FFFFFF;
+	font-size: 110%;
+	padding-bottom: 4px;
+	padding-top: 5px;
+}
+
+th.markdownTableHeadLeft {
+	text-align: left
+}
+
+th.markdownTableHeadRight {
+	text-align: right
+}
+
+th.markdownTableHeadCenter {
+	text-align: center
+}
+*/
+
+table.markdownTable {
+	border-collapse:collapse;
+        margin-top: 4px;
+        margin-bottom: 4px;
+}
+
+table.markdownTable td, table.markdownTable th {
+	border: 1px solid #2D4068;
+	padding: 3px 7px 2px;
+}
+
+table.markdownTable tr {
+}
+
+th.markdownTableHeadLeft, th.markdownTableHeadRight, th.markdownTableHeadCenter, th.markdownTableHeadNone {
+	background-color: #374F7F;
+	color: #FFFFFF;
+	font-size: 110%;
+	padding-bottom: 4px;
+	padding-top: 5px;
+}
+
+th.markdownTableHeadLeft, td.markdownTableBodyLeft {
+	text-align: left
+}
+
+th.markdownTableHeadRight, td.markdownTableBodyRight {
+	text-align: right
+}
+
+th.markdownTableHeadCenter, td.markdownTableBodyCenter {
+	text-align: center
+}
+
+
+/* @end */
diff --git a/docs/doxygen/footer.html b/docs/doxygen/footer.html
new file mode 100644
index 0000000000000000000000000000000000000000..38a9ba04c128f260ba0aaafa761b95e02de4e5e4
--- /dev/null
+++ b/docs/doxygen/footer.html
@@ -0,0 +1,14 @@
+<!-- HTML footer for doxygen 1.8.14-->
+<!-- start footer part -->
+<!--BEGIN GENERATE_TREEVIEW-->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    $navpath
+  </ul>
+</div>
+<!--END GENERATE_TREEVIEW-->
+<!--BEGIN !GENERATE_TREEVIEW-->
+<hr class="footer"/>
+<!--END !GENERATE_TREEVIEW-->
+</body>
+</html>
diff --git a/docs/doxygen/header.html b/docs/doxygen/header.html
new file mode 100644
index 0000000000000000000000000000000000000000..ff6f2f138fee29eb6f0e29c48a9ee9eb5e84be4a
--- /dev/null
+++ b/docs/doxygen/header.html
@@ -0,0 +1,56 @@
+<!-- HTML header for doxygen 1.8.14-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen $doxygenversion"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
+<!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
+<link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="$relpath^jquery.js"></script>
+<script type="text/javascript" src="$relpath^dynsections.js"></script>
+$treeview
+$search
+$mathjax
+<link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" />
+$extrastylesheet
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+
+<!--BEGIN TITLEAREA-->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <!--BEGIN PROJECT_LOGO-->
+  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
+  <!--END PROJECT_LOGO-->
+  <!--BEGIN PROJECT_NAME-->
+  <td id="projectalign" style="padding-left: 0.5em;">
+   <div id="projectname">$projectname
+   <!--BEGIN PROJECT_NUMBER-->&#160;<span id="projectnumber">$projectnumber</span><!--END PROJECT_NUMBER-->
+   </div>
+   <!--BEGIN PROJECT_BRIEF--><div id="projectbrief">$projectbrief</div><!--END PROJECT_BRIEF-->
+  </td>
+  <!--END PROJECT_NAME-->
+  <!--BEGIN !PROJECT_NAME-->
+   <!--BEGIN PROJECT_BRIEF-->
+    <td style="padding-left: 0.5em;">
+    <div id="projectbrief">$projectbrief</div>
+    </td>
+   <!--END PROJECT_BRIEF-->
+  <!--END !PROJECT_NAME-->
+  <!--BEGIN DISABLE_INDEX-->
+   <!--BEGIN SEARCHENGINE-->
+   <td>$searchbox</td>
+   <!--END SEARCHENGINE-->
+  <!--END DISABLE_INDEX-->
+ </tr>
+ </tbody>
+</table>
+</div>
+<!--END TITLEAREA-->
+<!-- end header part -->
diff --git a/docs/doxygen/logo.png b/docs/doxygen/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..a73c309d3aeba395003acc7278900d00f84600ac
Binary files /dev/null and b/docs/doxygen/logo.png differ
diff --git a/docs/doxygen/navtree.css b/docs/doxygen/navtree.css
new file mode 100644
index 0000000000000000000000000000000000000000..b06183d56cfa3fb015ffa22cf700a6a2be727106
--- /dev/null
+++ b/docs/doxygen/navtree.css
@@ -0,0 +1,146 @@
+#nav-tree .children_ul {
+  margin:0;
+  padding:4px;
+}
+
+#nav-tree ul {
+  list-style:none outside none;
+  margin:0px;
+  padding:0px;
+}
+
+#nav-tree li {
+  white-space:nowrap;
+  margin:0px;
+  padding:0px;
+}
+
+#nav-tree .plus {
+  margin:0px;
+}
+
+#nav-tree .selected {
+  background-image: url('tab_a.png');
+  background-repeat:repeat-x;
+  color: #fff;
+  text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0);
+}
+
+#nav-tree img {
+  margin:0px;
+  padding:0px;
+  border:0px;
+  vertical-align: middle;
+}
+
+#nav-tree a {
+  text-decoration:none;
+  padding:0px;
+  margin:0px;
+  outline:none;
+}
+
+#nav-tree .label {
+  margin:0px;
+  padding:0px;
+  font: 12px 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif;
+}
+
+#nav-tree .label a {
+  padding:2px;
+}
+
+#nav-tree .selected a {
+  text-decoration:none;
+  color:#fff;
+}
+
+#nav-tree .children_ul {
+  margin:0px;
+  padding:0px;
+}
+
+#nav-tree .item {
+  margin:0px;
+  padding:0px;
+}
+
+#nav-tree {
+  padding: 0px 0px;
+  background-color: #FAFAFF; 
+  font-size:14px;
+  overflow:auto;
+}
+
+#doc-content {
+  overflow:auto;
+  display:block;
+  padding:0px;
+  margin:0px;
+  -webkit-overflow-scrolling : touch; /* iOS 5+ */
+}
+
+#side-nav {
+  padding:0 6px 0 0;
+  margin: 0px;
+  display:block;
+  position: absolute;
+  left: 0px;
+  width: 250px;
+}
+
+.ui-resizable .ui-resizable-handle {
+  display:block;
+}
+
+.ui-resizable-e {
+  /* background-image:url("splitbar.png"); */
+  background-size:100%;
+  background-repeat:no-repeat;
+  background-attachment: scroll;
+  cursor:ew-resize;
+  height:100%;
+  right:0;
+  top:0;
+  width:6px;
+}
+
+.ui-resizable-handle {
+  display:none;
+  font-size:0.1px;
+  position:absolute;
+  z-index:1;
+}
+
+#nav-tree-contents {
+  margin: 6px 0px 0px 0px;
+}
+
+#nav-tree {
+  background-image:url('nav_h.png');
+  background-repeat:repeat-x;
+  background-color: #F9FAFC;
+  -webkit-overflow-scrolling : touch; /* iOS 5+ */
+}
+
+#nav-sync {
+  position:absolute;
+  top:5px;
+  right:24px;
+  z-index:0;
+}
+
+#nav-sync img {
+  opacity:0.3;
+}
+
+#nav-sync img:hover {
+  opacity:0.9;
+}
+
+@media print
+{
+  #nav-tree { display: none; }
+  div.ui-resizable-handle { display: none; position: relative; }
+}
+
diff --git a/docs/doxygen/splitbar.png b/docs/doxygen/splitbar.png
new file mode 100644
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diff --git a/docs/doxygen/unity.css b/docs/doxygen/unity.css
new file mode 100644
index 0000000000000000000000000000000000000000..f24a0d3abf3e61cb335d7780d3bf8d966a3a0559
--- /dev/null
+++ b/docs/doxygen/unity.css
@@ -0,0 +1,410 @@
+/* ====================================================
+ * Company: Unity Technologies
+ * Author:  Rickard Andersson, rickard@unity3d.com
+======================================================= */
+
+/***************************************
+ ==== Doxygen Style overides
+****************************************/
+
+#titlearea
+{
+	background-color: #222c37;
+}
+
+#projectlogo
+{
+	vertical-align: middle;
+	padding: 0px 0px 5px 5px;
+}
+
+#projectname
+{
+	vertical-align: middle;
+	color: white;
+}
+
+.sm-dox {
+    background-image: none;
+	background-color :#e6e6e6;
+	color: #999; 
+	border-style: solid; 
+	border-width: thin;
+}
+
+.navpath ul{
+    background-image: none;
+	background-color :#e6e6e6;
+	color: #999; 
+	border-style: dotted; 
+	border-width: thin;	
+}
+
+.ui-resizable-e {
+  background-image:none;
+  background-size:100%;
+  background-repeat:no-repeat;
+  background-attachment: scroll;
+  cursor:ew-resize;
+  height:100%;
+  right:0;
+  top:0;
+  width:6px;
+}
+
+/****************************************
+  ==== RESETS & EXTRAS
+****************************************/
+
+html,body,div { margin: 0; padding: 0; }
+dl,dt,dd,h1,h2,h3,h4,h5,h6,pre,form,p,blockquote,input,figure { margin: 0 0 0 10px; padding: 0; }
+h1,h2,h3,h4,h5,h6,pre,code,cite,code,th { font-size: 1em; font-weight: normal; font-style: normal; }
+article, aside, details, figcaption, figure, footer, header, hgroup, menu, nav, section, video { display: block; }
+iframe { border: none; } caption,th { text-align: left; } table { border-collapse: collapse; border-spacing: 0; }
+img, img a, img a:hover { border: 0; display: block; max-width: 100%; }
+hr { color: black; border-style: solid; border-width: 1px; }
+hr.section { color: silver; border-style: solid; border-width: 1px; }
+::-moz-selection { background: #999; color: #fff; text-shadow: none; }
+::selection { background: #999; color: #fff; text-shadow: none; }
+.clear:after { visibility: hidden; display: block; font-size: 0; content: " "; clear: both; height: 0; }.clear { display: inline-table; clear: both; }
+/* Hides from IE-mac \*/ * html .clear { height: 1%; } .clear { display: block; } /* End hide from IE-mac */
+.left { float: left !important; } .right { float: right !important; }
+.hide {display: none !important;} .hidden { visibility: hidden; opacity: 0; }
+.mb0 { margin-bottom: 0 !important; } .mb5 { margin-bottom: 5px !important; } .mb10 { margin-bottom: 10px !important; } .mb20 { margin-bottom: 20px !important; }
+.mt10 { margin-top: 10px; } .mr0 { margin-right: 0 !important; } .mr10 { margin-right: 10px !important; }
+.cl { color: #99a0a7; } .cw { color: #fff !important; } .lh42 { line-height: 42px;} .rel { position: relative; }
+
+.otherversionswrapper{ position:relative;display:inline;margin-left:2%; }
+.otherversionscontent{ position:absolute;top:100%;left:0px;background-color:white;border:1px solid grey;border-radius:12px;width: 150%;max-height:300%;overflow-y:scroll;text-align:center;padding:0.5% 0%; }
+.otherversionsitem{ display:block; }
+
+/****************************************
+  ==== FORM
+****************************************/
+
+input, select, textarea { font-family: 'Open Sans','Nanum Gothic',sans-serif; outline: none; margin: 0 0 20px 0; -webkit-appearance: none; }
+button::-moz-focus-inner, input[type="reset"]::-moz-focus-inner, input[type="button"]::-moz-focus-inner, input[type="submit"]::-moz-focus-inner, input[type="file"] > input[type="button"]::-moz-focus-inner { padding: 0px; }
+input[type="text"], input[type="tel"], input[type="email"], input[type="url"], input[type="password"], select, textarea { width: 100%; font-size: 0.9375em; display: block; outline-width: 0; border: #00cccc 1px solid; line-height: 18px; padding: 10px 13px; color: #455463 !important; resize: none; -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; -webkit-border-radius: 3px; -moz-border-radius: 3px; border-radius: 3px; }
+input[type="text"]:focus, input[type="tel"]:focus, input[type="email"]:focus, input[type="url"]:focus, input[type="password"]:focus, select:focus, textarea:focus { border-color: #455463 !important; }
+textarea { height: 100px; }
+label { display: inline-block; font-size: 0.9375em; margin: 0 0 7px 0; font-weight: bold; cursor: pointer; text-transform: uppercase; }
+label span.r { color: #00cccc; }
+input[type="text"].error, input[type="tel"].error, input[type="email"].error, input[type="password"].error, textarea.error { padding: 10px 45px 10px 13px; background: #fff url(../images/error-red.png) right 15px no-repeat; }
+
+/****************************************
+  ==== MESSAGES
+****************************************/
+
+div.message { padding: 10px 15px; margin: 0 0 15px 0; font-size: 0.875em; }
+div.message-error { background: #ffe2d7; }
+div.message-warning { background: #fff9d7; }
+div.message-ok { background: #ebffce; }
+
+/****************************************
+  ==== LOADING
+****************************************/
+
+div.loading { width: 60px; height: 18px; position: absolute; left: 50%; top: 50%; z-index: 15; margin: -9px 0 0 -30px; }
+div.loading div { width: 18px; height: 18px; display: block; float: left; margin: 0 1px; background-color: #29e5b7; -webkit-border-radius: 100%; -moz-border-radius: 100%; border-radius: 100%; -webkit-box-shadow: 0px 20px 20px rgba(255,255,255,0.2); -moz-box-shadow: 0px 20px 20px rgba(255,255,255,0.2); box-shadow: 0px 20px 20px rgba(255,255,255,0.2); -webkit-animation: bouncedelay 1.4s infinite ease-in-out; -moz-animation: bouncedelay 1.4s infinite ease-in-out; -o-animation: bouncedelay 1.4s infinite ease-in-out; animation: bouncedelay 1.4s infinite ease-in-out; -webkit-animation-fill-mode: both; -moz-animation-fill-mode: both; -o-animation-fill-mode: both; animation-fill-mode: both; }
+div.loading div:nth-child(1){ -webkit-animation-delay: -0.32s; -moz-animation-delay: -0.32s; -o-animation-delay: -0.32s; animation-delay: -0.32s; }
+div.loading div:nth-child(2){ -webkit-animation-delay: -0.16s; -moz-animation-delay: -0.16s; -o-animation-delay: -0.16s; animation-delay: -0.16s; }
+/* Loading animation */
+@-webkit-keyframes bouncedelay {
+  0%, 80%, 100% { -webkit-transform: scale(0.0) }
+  40% { -webkit-transform: scale(1.0) }
+}
+@-moz-keyframes bouncedelay {
+  0%, 80%, 100% { -moz-transform: scale(0.0) }
+  40% { -moz-transform: scale(1.0) }
+}
+@-o-keyframes bouncedelay {
+  0%, 80%, 100% { -o-transform: scale(0.0) }
+  40% { -o-transform: scale(1.0) }
+}
+@keyframes bouncedelay {
+  0%, 80%, 100% { transform: scale(0.0) }
+  40% { transform: scale(1.0) }
+}
+
+/****************************************
+  ==== TOOLTIP
+****************************************/
+
+.tt { position: relative; }
+.tt div.tip { height: 20px; line-height: 20px; position: absolute; left: -100000px; z-index: 30; background: #444; color: #fff; border: 0; font-size: 0.6875em; font-weight: normal; padding: 0 5px; white-space: nowrap; border-radius: 2px; -moz-border-radius: 2px; -webkit-border-radius: 2px; text-align: center; }
+.tt div.b:after { content: ""; display: block; position: absolute; top: -8px; left: 50%; font-size: 0px; line-height: 0%; width: 0px; margin: 0 0 0 -3px; border-top: 4px solid transparent; border-left: 4px solid transparent; border-bottom: 4px solid #444; border-right: 4px solid transparent; }
+.tt div.t:after { content: ""; display: block; position: absolute; bottom: -8px; left: 50%; font-size: 0px; line-height: 0%; width: 0px; margin: 0 0 0 -4px; border-top: 4px solid #444; border-left: 4px solid transparent; border-bottom: 4px solid transparent; border-right: 4px solid transparent; }
+
+/****************************************
+  ==== TYPO
+****************************************/
+
+body { font: 16px/135% 'Open Sans', sans-serif; color: #455463; font-style: normal; font-weight: normal; overflow: auto; overflow-y: scroll; -webkit-text-size-adjust: none; -ms-text-size-adjust: none; -webkit-tap-highlight-color: rgba(0,0,0,0); -webkit-font-smoothing: antialiased; }
+h1 { font-size: 2em; line-height: 1em; color: #1b2229; font-weight: 700; margin: 0 0 20px 0; word-wrap: break-word; }
+h1.inherit { margin: 0 15px 5px 0; }
+h1 a { text-decoration: underline; color: #1b2229; }
+h1 a:hover { text-decoration: none; color: #1b2229; }
+h2 { font-size: 1.5em; line-height: 1em; color: #1b2229; font-weight: 700; margin: 20px 0 5px 10px; }
+div.subsection div.subsection h2 { font-size: 1.125em; color: #455463; }
+h3 { font-size: 1.25em; line-height: 1.2em; font-weight: bold; margin: 10px 0 10px 10px; color: #455463; }
+h4 { font-size: 1em; line-height: 1em; font-weight: bold; margin: 10px 0 10px 10px; color: #455463; }
+p { max-width: 1100px; font-size: 0.875em; margin: 0 0 15px 10px; }
+a { color: #b83c82; text-decoration: underline; outline: none; cursor: pointer; }
+a:hover, a:focus, a:active { color: #b83c82; text-decoration: none; outline: none; }
+.cn { color: #455463; } .cn:hover { color: #455463; } .b, strong { font-weight: 700; }
+ul.l { list-style-type: none; }
+ul.l li { padding: 0 0 4px 30px; background: none; position: relative; font-size: 0.875em; }
+ul.l li:before { content: "\2022"; font-size: 1.8em; position: absolute; top: 0; left: 0; color: #455463; }
+ul.l li a { color: #333; }
+
+/****************************************
+  ==== BUTTONS
+****************************************/
+
+.blue-btn, .gray-btn { height: 42px; line-height: 42px; display: block; float: left; padding: 0 20px; color: #fff; font-size: 0.8125em; text-align: center; cursor: pointer; text-decoration: none; border: 0; outline: none; font-family: 'avalonbold','Open Sans',sans-serif; text-transform: uppercase; -webkit-border-radius: 3px; -moz-border-radius: 3px; border-radius: 3px; }
+input.blue-btn, input.gray-btn { padding: 0 20px 4px 20px; }
+.blue-btn { background-color: #00cccc; }
+.blue-btn:hover { color: #fff; background-color: #222c37; }
+.gray-btn { background-color: #222c37; }
+.gray-btn:hover { color: #fff; background-color: #222c37; }
+.bbtn { height: 50px; line-height: 50px; padding: 0 40px !important; font-size: 1.0em; }
+.sbtn { height: 24px; line-height: 24px; padding: 0 10px !important; font-size: 0.75em; }
+.dbtn, .dbtn:hover, .dbtn:active { cursor: default; background-color: #ccc; color: #f0f0f0; }
+.centerbtn { float: none; display: inline-block; margin: 0; }
+
+/****************************************
+  ==== HEADER
+****************************************/
+
+div.header-wrapper { width: 100%; height: 100px; position: fixed; z-index: 30; }
+div.header { width: 100%; height: 60px; background: white; }
+div.header .content { min-width: 860px; margin: 0 auto; padding: 0 20px; position: relative; }
+div.header .content .menu { width: 100%; float: left; margin: 0 -170px 0 0; position: relative; }
+div.header .content .spacer { margin: 0 170px 0 0; }
+div.header .menu .logo { width: 271px; height: 34px; float: left; padding: 13px 0 0 0; }
+div.header .menu .logo a { width: 271px; height: 34px; display: block; background: url(../images/sprites.png) 0 0 no-repeat; }
+div.header .menu ul { float: right; list-style-type: none; margin: 10px 0; padding: 0 30px 0 0; }
+div.header .menu ul li { float: left; margin: 0 1px 0 0; font-size: 1em; font-family: 'avalon', sans-serif; }
+div.header .menu ul li a { height: 40px; line-height: 40px; display: block; float: left; margin: 0 15px; text-decoration: none; cursor: pointer; color: #fff; -webkit-transition: color .15s; -moz-transition: color .15s; -ms-transition: color .15s; -o-transition: color .15s; transition: color .15s; }
+div.header .menu ul li a:hover { color: #00cccc; }
+div.header .menu ul li a.selected { color: #00cccc; position: relative; font-family: 'avalonbold', sans-serif; line-height: 44px; }
+div.header .menu ul li a.selected:after { content: ''; display: block; width: 100%; height: 1px; position: absolute; bottom: 11px; left: 0; background: #00cccc; }
+div.header div.search-form { float: right; position: relative; }
+div.header div.search-form:before { content: ''; width: 18px; height: 60px; display: block; position: absolute; top: 0; left: -17px; background: url(../images/shard-left.png) 0 0 no-repeat; }
+div.header div.search-form input.field { width: 350px; line-height: 18px; padding: 20px 50px 20px 20px; border: 0; margin: 0; -webkit-border-radius: 0; -moz-border-radius: 0; border-radius: 0; }
+div.header div.search-form input.field:focus { background: #fff !important; }
+div.header div.search-form input.submit { width: 30px; height: 30px; border: 0; margin: 0; cursor: pointer; outline-width: 0; text-indent: -9999em; position: absolute; top: 15px; right: 10px; background: url(../images/sprites.png) 5px -54px no-repeat; }
+div.header .more { width: 170px; height: 60px; float: right; position: relative; }
+div.header .more .filler { width: 800%; height: 60px; background: #00cccc; position: absolute; z-index: 14; left: 0px; top: 0; }
+div.header .more .filler:before { content: ''; width: 18px; height: 60px; display: block; position: absolute; top: 0; left: -1px; background: url(../images/shard-right.png) 0 0 no-repeat; }
+div.header .more ul { float: right; position: relative; z-index: 15; list-style: none; margin: 15px 15px 15px 0; }
+div.header .more ul li { float: left; margin: 0 0 0 20px; font-size: 1em; font-family: 'avalon', sans-serif; }
+div.header .more ul li a { height: 30px; line-height: 30px; display: block; float: left; padding: 0 31px 0 0; text-decoration: none; cursor: pointer; color: #fff; background: url(../images/sprites.png) right -112px no-repeat; }
+
+/****************************************
+  ==== TOOLBAR
+****************************************/
+
+div.toolbar { width: 100%; height: 40px; background: #e6e6e6; border-bottom: #222c37 0px solid; }
+div.toolbar div.content { margin: 0 auto; padding: 0 15px; position: relative; }
+div.toolbar div.script-lang { padding: 7px 0; margin: 0 15px 0 0; float: right; position: relative; }
+div.toolbar div.script-lang ul { list-style-type: none; float: left; }
+div.toolbar div.script-lang ul li { width: 46px; height: 26px; line-height: 26px; display: block; float: left; margin: 0 0 0 1px; text-align: center; font-size: 0.75em; cursor: pointer; background: #fff; }
+div.toolbar div.script-lang ul li:hover, div.toolbar div.script-lang ul li.selected { background-color: #222c37; color: #fff; }
+div.toolbar div.script-lang div.dialog { width: 300px; background: #fff; position: absolute; top: 45px; right: 0; z-index: 10; -webkit-box-shadow: 0 1px 20px rgba(34,44,55,0.3); -moz-box-shadow: 0 1px 20px rgba(34,44,55,0.3); box-shadow: 0 1px 20px rgba(34,44,55,0.3); }
+div.toolbar div.script-lang div.dialog:before { content: ''; display: block; position: absolute; top: -16px; right: 60px; border: transparent 8px solid; border-bottom-color: #19e3b1; }
+div.toolbar div.script-lang div.dialog-content { border-top: #19e3b1 5px solid; padding: 15px; }
+div.toolbar div.script-lang div.dialog-content h2 { float: left; margin: 0 0 10px 0; font-size: 1.125em; font-family: 'avalonbold', sans-serif; text-transform: uppercase; }
+div.toolbar div.script-lang div.dialog-content p { padding: 10px 0 0 0; margin: 0; border-top: #e6e6e6 1px solid; font-size: 0.8125em; line-height: 1.3em; }
+div.toolbar div.script-lang div.dialog div.close { width: 18px; height: 18px; float: right; cursor: pointer; background: url(../images/sprites.png) 0 -106px no-repeat; }
+div.lang-switcher { padding: 7px 0; float: right; position: relative; }
+div.lang-switcher div.current { float: left; }
+div.lang-switcher div.lbl { float: left; line-height: 26px; padding: 0 3px 0 0; font-size: 0.8125em; cursor: pointer; }
+div.lang-switcher div.current div.arrow { width: 7px; height: 26px; float: left; background: url(../images/sprites.png) -484px -24px no-repeat; cursor: pointer; }
+div.lang-switcher div.lang-list { width: 150px; position: absolute; top: 36px; right: 0; z-index: 9999; background: #fff; border-top: #19e3b1 4px solid; -webkit-box-shadow: 0 1px 5px rgba(0,0,0,0.3); -moz-box-shadow: 0 1px 5px rgba(0,0,0,0.3); box-shadow: 0 1px 5px rgba(0,0,0,0.3); display: none; }
+div.lang-switcher div.lang-list:before { content: ""; display: block; position: absolute; top: -20px; left: 50%; font-size: 0px; line-height: 0%; width: 0px; margin: 0 0 0 -5px; border: transparent 8px solid; border-bottom-color: #19e3b1; }
+div.lang-switcher div.lang-list ul { list-style-type: none; }
+div.lang-switcher div.lang-list li { font-size: 0.8125em; border-bottom: #e6e6e6 1px solid; }
+div.lang-switcher div.lang-list li:last-child { border: 0; } 
+div.lang-switcher div.lang-list a { display: block; padding: 7px 10px; color: #455463; text-decoration: none; }
+div.lang-switcher div.lang-list a:hover { background: #f0f0f0; }
+div.version-number { padding: 9px 7px; float: left; position: relative; }
+
+/****************************************
+  ==== LAYOUT
+****************************************/
+
+div.master-wrapper { min-width: 860px; margin: 0 auto 40px auto; padding: 100px 0 20px 0; }
+div.content-wrap { width: 100%; float: right; margin: 0 0 0 -380px; position: relative; }
+div.content-block { margin: 0 0 0 380px; }
+div.content-wrap div.content { min-width: 460px; padding: 40px 40px 0 0; }
+div.content-wrap div.content div.section { padding: 0 10px; margin: 0 0 50px 0; min-height: 200px; }
+
+/****************************************
+  ==== FOOTER
+****************************************/
+
+div.footer-wrapper { width: 100%; height: 40px; margin: 0 auto; }
+div.footer { height: 40px; line-height: 40px; margin: 0 10px; font-size: 0.8125em; border-top: #e6e6e6 1px solid; }
+div.footer div.copy { float: left; }
+div.footer div.copy a { color: #455463; font-size: 130%; font-weight: bold; }
+div.footer div.menu { float: right; }
+div.footer div.menu a { color: #455463; margin: 0 0 0 15px; }
+
+/****************************************
+  ==== SIDEBAR
+****************************************/
+
+div.sidebar { width: 340px; margin: 0 40px 0 0; float: left; position: relative; z-index: 2; }
+div.sidebar-wrap { width: 339px; position: fixed; border-right: #e6e6e6 1px solid; }
+div.sidebar p { padding: 10px 20px; background: #222c37; margin: 0; }
+div.sidebar-menu h2 { margin: 20px 0; padding: 0 0 10px 0; border-bottom: #e6e6e6 1px solid; }
+div.sidebar-menu ul { list-style-type: none; margin: 0 0 20px 0; position: relative; }
+div.sidebar-menu ul li { font-size: 0.875em; word-wrap: break-word; margin: 0 0 8px 0; line-height: 1.3em; padding: 0 0 0 22px; position: relative; }
+div.sidebar-menu ul li.nl span { cursor: pointer; }
+div.sidebar-menu ul li div.arrow { width: 12px; height: 12px; cursor: pointer; border: #19e3b1 1px solid; position: absolute; top: 2px; left: 0; background: #19e3b1 url(../images/sprites.png) 0 0 no-repeat; }
+div.sidebar-menu ul li div.collapsed { background-position: -51px -61px; }
+div.sidebar-menu ul li div.expanded { background-position: -51px -95px; }
+div.sidebar-menu ul li a { display: block; color: #455463; word-wrap: break-word; text-decoration: none; }
+div.sidebar-menu ul li a:hover { text-decoration: underline; }
+div.sidebar-menu ul li a.current { background: #222c37; color: #fff; padding: 5px 8px; text-decoration: none; }
+div.sidebar-menu ul li ul { margin: 8px 0 8px 0; }
+div.sidebar-menu ul li ul li ul:before { left: -15px; }
+div.sidebar-menu ul li ul li div.arrow { left: 0; }
+div.sidebar-menu ul li ul li { font-size: 1em; padding: 0 0 0 22px; }
+div.sidebar-menu ul li ul li ul li { font-size: 1em;  }
+
+/****************************************
+  ==== SIDEBAR CUSTOM SCROLLER
+****************************************/
+
+.mCSB_container { width: auto; margin: 0 30px 0 20px; overflow: hidden; }
+.mCSB_container.mCS_no_scrollbar { margin: 0 20px; }
+.mCS_disabled>.mCustomScrollBox>.mCSB_container.mCS_no_scrollbar, .mCS_destroyed>.mCustomScrollBox>.mCSB_container.mCS_no_scrollbar { margin-right: 30px; }
+.mCustomScrollBox>.mCSB_scrollTools { width: 16px; height: 100%; top: 0; right: 0; z-index: 14; opacity: 0.5; filter: "alpha(opacity=50)"; -ms-filter:"alpha(opacity=50)"; }
+.mCustomScrollBox:hover>.mCSB_scrollTools { opacity: 1; filter: "alpha(opacity=100)"; -ms-filter: "alpha(opacity=100)"; }
+.mCSB_scrollTools .mCSB_draggerContainer { width: 8px; position: absolute; top: 0; bottom: 0; right: 0; z-index: 14; height: auto; }
+.mCSB_scrollTools a+.mCSB_draggerContainer { margin: 20px 0; }
+.mCSB_scrollTools .mCSB_draggerRail { width: 8px; height: 100%; margin: 0 auto; background: #ccf5f5; }
+.mCSB_scrollTools .mCSB_dragger { width: 100%; height: 30px; cursor: pointer; }
+.mCSB_scrollTools .mCSB_dragger .mCSB_dragger_bar { width: 8px; height: 100%; margin: 0 auto; text-align: center; background: #00cccc; }
+.mCSB_scrollTools .mCSB_dragger:hover .mCSB_dragger_bar { background: #00cccc; }
+.mCSB_scrollTools .mCSB_dragger:active .mCSB_dragger_bar, .mCSB_scrollTools .mCSB_dragger.mCSB_dragger_onDrag .mCSB_dragger_bar { background: #00cccc; }
+.mCustomScrollBox { -ms-touch-action: none; }
+
+/****************************************
+  ==== HISTORY TABLE
+****************************************/
+
+.history-table { width: 100%; }
+.history-table .link { width: 70%; }
+.history-table .type { width: 15%; }
+.history-table .namespace { width: 15%; }
+.history-table th { padding: 5px 10px; font-size: 0.875em; font-weight: bold; background: #f0f0f0; }
+.history-table td { padding: 7px 10px; font-size: 0.875em; border-bottom: #e6e6e6 1px solid; }
+
+/****************************************
+  ==== CONTENT
+****************************************/
+
+div.signature { margin: 0 0 40px 0; font-size: 0.875em; }
+div.signature div.sig-block { margin: 0 0 3px 0; }
+div.signature .sig-kw { font-weight: bold; }
+.switch-link { margin: 10px 0 0 0; }
+.suggest { float: left; margin: 10px 5px 0 0; position: relative; }
+
+table.list { width: 100%; margin: 0px 0 30px 0; font-size: 0.875em; }
+table.list tbody { border-top: #e6e6e6 0px solid; }
+table.list tr { -webkit-transition: background .15s; -moz-transition: background .15s; -ms-transition: background .15s; -o-transition: background .15s; transition: background .15s; }
+table.list tr:nth-child(odd) { background: #f8f8f8; }
+table.list tr:hover { outline: #00cccc 1px solid; }
+table.list td { vertical-align: top; padding: 7px 10px; }
+table.list td.lbl { width: 18%; }
+table.list td.desc { width: 82%; }
+table.list td.name { font-weight: 700; }
+.content .section ul {  list-style-type: none; margin: 0 0 20px 0; }
+.content .section ul li, .content .section ol li ul li { padding: 0 0 3px 20px; background: none; position: relative; font-size: 0.875em; }
+.content .section ul li:before { content: "\2022"; font-size: 1.8em; position: absolute; top: 0; left: 0; color: #455463; }
+.content .section ul li ul, .content .section ul li ul li ul { margin: 0 0 5px 0; }
+.content .section ul li ul li, .content .section ul li ul li ul li, .content .section ol li ul li { font-size: 1em; }
+.content .section ol { margin: 0 0 20px 0; padding: 0 0 0 20px; }
+.content .section ol li { padding: 0 0 5px 0; font-size: 0.875em; }
+.content .section ul li ol { margin: 0 0 5px 0; }
+.content .section ul li ol li:before { display: none; }
+.content figure { margin: 0 0 30px 0; }
+.content figure img { margin: 0 0 10px 0; }
+.content figure figcaption { margin: 0 0 10px 0; font-size: 0.875em; color: #99a0a7; }
+
+/****************************************
+  ==== SUGGESTION FORM
+****************************************/
+
+.suggest .suggest-wrap { width: 400px; position: absolute; top: 36px; left: 50%; z-index: 15; margin: 0 0 0 -200px; background: #fff; -webkit-box-shadow: 0 1px 20px rgba(34,44,55,0.3); -moz-box-shadow: 0 1px 20px rgba(34,44,55,0.3); box-shadow: 0 1px 20px rgba(34,44,55,0.3); }
+.suggest .suggest-wrap:before { content: ''; display: block; position: absolute; top: -16px; left: 50%; margin: 0 0 0 -8px; border: transparent 8px solid; border-bottom-color: #222c37; }
+.suggest .suggest-wrap .suggest-form { padding: 20px; border-top: #222c37 5px solid; }
+.suggest .suggest-wrap label { font-size: 0.875em; }
+.suggest-failed, .suggest-success { width: 100%; position: absolute; top: 0; left: 0; padding: 20px; border-top: #222c37 5px solid; -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; }
+
+/****************************************
+  ==== SEARCH RESULTS
+****************************************/
+
+div.search-results h2 { padding: 0 0 15px 0 !important; border-bottom: #e6e6e6 1px solid; font-weight: 400; }
+div.search-results h2 span.q { font-weight: bold; }
+div.search-results b { word-wrap: break-word; }
+div.search-results div.result { width: 90%; margin: 0 0 20px 0; }
+div.search-results div.result a.title { color: #ff0066; font-size: 1em; }
+div.search-results div.result p strong { font-weight: 400 !important; }
+
+/****************************************
+  ==== CODE SNIPPETS
+****************************************/
+
+pre { font-family: Consolas,Monaco,'Andale Mono',monospace; padding: 20px; margin: 0 0 30px 0; border: #ddd 1px solid; background: #fff; font-size: 0.9375em; color: #455463; overflow: auto; }
+.code, .codelisting { white-space: pre; }
+pre .comment { color: #19e3b1; }
+pre .hl-comment { color: #19e3b1; }
+
+.doc-prop, .doc-menu, .doc-inspector, .doc-keyword {
+  font-family: Consolas, Monaco, 'Andale Mono', monospace;
+}
+
+/****************************************
+  ==== MEDIA QUERIES
+****************************************/
+
+@media only screen and (max-width: 1260px) {
+
+div.header .content { padding: 0 0 0 20px; }
+div.header .content .menu { width: 100%; margin: 0; }
+div.header .content .spacer { margin: 0; }
+div.header .more { display: none; }
+div.header div.search-form input.field { width: 300px; }
+div.content-wrap { margin: 0 0 0 -360px; }
+div.content-block { margin: 0 0 0 360px; }
+div.content-wrap div.content { padding: 20px 20px 0 0; }
+div.sidebar { margin: 0 20px 0 0; }
+div.footer-wrapper { height: auto; }
+div.footer { height: auto; line-height: 21px; padding: 10px 0 0 0; }
+div.footer div.copy, div.footer div.menu { float: none; }
+div.footer div.menu a { margin: 0 15px 0 0; }
+
+}
+
+@media only screen and (max-width: 900px) {
+
+div.master-wrapper { padding: 0 0 20px 0;  }
+div.header-wrapper { width: auto; position: relative; float: left; }
+div.sidebar-wrap { position: static; }
+div.content-wrap { width: 480px; float: left; margin: 0; }
+div.content-block { margin: 0; }
+
+}
+@media only screen and (-moz-min-device-pixel-ratio: 2), only screen and (-o-min-device-pixel-ratio: 2/1), only screen and (-webkit-min-device-pixel-ratio: 2) {
+
+div.header .menu .logo a, div.header .more ul li a, div.toolbar div.script-lang div.dialog div.close, div.lang-switcher div.current div.arrow, div.sidebar-menu ul li div.arrow { background-image: url(../images/sprites@2x.png); -webkit-background-size: 500px 250px; -moz-background-size: 500px 250px; -o-background-size: 500px 250px; background-size: 500px 250px; } 
+input[type="text"].error, input[type="tel"].error, input[type="email"].error, input[type="password"].error, textarea.error { background-image: url(../images/error-red.png); -webkit-background-size: 24px 12px; -moz-background-size: 24px 12px; background-size: 24px 12px; }
+div.header div.search-form:before { background-image: url(../images/shard-left@2x.png); -webkit-background-size: 18px 60px; -moz-background-size: 18px 60px; background-size: 18px 60px; }
+div.header .more .filler:before { background-image: url(../images/shard-right@2x.png); -webkit-background-size: 18px 60px; -moz-background-size: 18px 60px; background-size: 18px 60px; }
+  
+}
diff --git a/docs/extra.css b/docs/extra.css
new file mode 100644
index 0000000000000000000000000000000000000000..39f517eca67cceb5e3cd059ffd2a6efb782ae123
--- /dev/null
+++ b/docs/extra.css
@@ -0,0 +1,9 @@
+.wy-nav-top, .wy-side-nav-search {
+    background: #439b47;
+}
+
+.md-typeset__scrollwrap {
+    margin: 1em -.8rem;
+    overflow-x: visible;
+    touch-action: auto;
+}
diff --git a/docs/images/3dball_big.png b/docs/images/3dball_big.png
new file mode 100644
index 0000000000000000000000000000000000000000..c5a786825ba7fcfa7de3d4dfc15a253ea10b6938
Binary files /dev/null and b/docs/images/3dball_big.png differ
diff --git a/docs/images/3dball_learning_brain.png b/docs/images/3dball_learning_brain.png
new file mode 100644
index 0000000000000000000000000000000000000000..68757fa6ce7af95c422eb6cea230bdf3f15cfd6b
Binary files /dev/null and b/docs/images/3dball_learning_brain.png differ
diff --git a/docs/images/3dball_small.png b/docs/images/3dball_small.png
new file mode 100644
index 0000000000000000000000000000000000000000..2591e0c3f6e006cb5e8ae90bc6f4dc0ff50153ae
Binary files /dev/null and b/docs/images/3dball_small.png differ
diff --git a/docs/images/TensorBoard-download.png b/docs/images/TensorBoard-download.png
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+<img src="images/image-banner.png" align="middle" width="3000"/>
+{!README.md!}
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+{!../ml-agents/README.md!}
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+<img src="docs/images/image-banner.png" align="middle" width="3000"/>
+
+# 유니티 ML-Agents 툴킷
+
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/)
+
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
+
+([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
+([all releases](https://github.com/Unity-Technologies/ml-agents/releases))
+
+**유니티 기계학습 ��전트 툴킷** (ML-Agents) � 게임 컨�츠 � 게임� �함한 다양한 시뮬레�션�서 활용하기 위한 지능형 ��전트를 훈련시키는 환경� 제공하는 오픈 소스 프로�트입니다.
+ML-Agents는 게임 개발� 들� 2D, 3D � 가�현실/�강현실 게임�서 지능형 ��전트를 쉽게 �육할 수 있�� 최신 알고리즘� 구현(PyTorch 기반)� 제공합니다.
+간단한 파�� API를 사용하여 강화 학습, 모방 학습, 신경 진화 등 다른 방법� 활용하여 ��전트를 �육할 수 있습니다.
+학습� ��전트는 NPC 행� 제어(다중 ��전트 � �대� ��전트와 같� 다양한 설정), 게임 빌드 테스트 ��화, 그리고 출시 전 게임 설계(밸런스) 검� 등� �함한 다양한 용�로 활용할 수 있습니다.
+ML-Agents 툴킷� 유니티� �유로운 환경�서 �공지능 ��전트를 개발하기 위한 기반� 제공하며, �틀 통해 연구� � 게임 개발� 등 광범위한 커뮤니티� 접근할 수 있기 때문� 게임 개발�와 �공지능 연구� 모��게 �호 ��� �니다.
+
+## 특징
+
+- 15+ [유니티 환경 예제](docs/Learning-Environment-Examples.md)
+- 다양한 환경 구성 � �육 시나리오 지�
+- 게임�나 커스텀 유니티 씬� 통합� 수 있는 유연한 유니티 SDK
+- Proximal Policy Optimization (PPO) 와 Soft Actor-Critic (SAC) � � 가지 심층 강화 학습 알고리즘� �용한 훈련
+- Behavioral Cloning �나 Generative Adversarial Imitation Learning � 통한 모방 학습� 대한 내장 지�
+- �대�(Adversarial) 시나리오�서 ��전트를 �육하기 위한 Self-play 메커니즘
+- 복잡한 작업� 대해 쉽게 정�할 수 있는 커리�럼 학습 시나리오
+- 환경 랜�화를 사용하여 강력한 ��전트 학습
+- 온 디맨드 �사 결정� 통한 유연한 ��전트 제어
+- 여러 개� 유니티 환경 �스턴스를 �시� 사용하는 학습
+- 네�티브 �로스 플랫�� 지�하기 위해 [유니티 추론(Inference) 엔진](docs/Unity-Inference-Engine.md) �용
+- 유니티 환경 [파���서 제어](docs/Python-API.md)
+- [gym](gym-unity/README.md) 과 같� 유니티 학습 환경 제공
+
+� 모든 기능� 대한 �세한 설명� [ML-Agents 개요](docs/ML-Agents-Overview.md) 페�지를 참조하십시오.
+
+## 릴리즈 & 설명서
+
+**최신� 안정� 릴리즈는 `Release 12` 입니다. �릭해서 ML-Agents� 최신 릴리스를 시작하세요.** [여기](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md)
+
+아래 표�는 현재 개발� 진행 중�며 불안정할 수 있는 `master` 브랜치를 �함한 모든 릴리스가 나와 있습니다. 몇 가지 유용한 지침:
+- [버전 관리 페�지](docs/Versioning.md) 는 GitHub 릴리즈를 관리하는 방법과 � ML-Agents 구성 요소� 대한 버전 관리 프로세스를 간략히 설명합니다.
+- [릴리즈 페�지](https://github.com/Unity-Technologies/ml-agents/releases) 는 릴리스 간� 변경 사항� 대한 세부 정보가 �함�어 있습니다.
+- [마�그레�션(Migration) 페�지](docs/Migrating.md) 는 �전 릴리스� ML-Agents 툴킷�서 업그레�드하는 방법� 대한 세부 정보가 �함�어 있습니다.
+- 아래 표� **설명서** ���는 � 릴리스� 대한 설치 � 사용 지침� �함�어 있습니다. 사용 중� 릴리스 버전� 해당하는 설명서를 항� 사용해야 합니다.
+
+| **버전** | **릴리즈 날짜** | **소스** | **설명서** | **다운로드** |
+|:-------:|:------:|:-------------:|:-------:|:------------:|
+| **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) |
+| **Release 12** | **December 22, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_12)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_12.zip)** |
+| **Release 11** | December 21, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_11) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_11_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_11.zip) |
+| **Release 10** | November 18, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_10) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_10.zip) |
+| **Release 9** | November 4, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_9) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_9.zip) |
+| **Release 8** | October 14, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_8) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_8.zip) |
+| **Release 7** | September 16, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_7) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_7.zip) |
+| **Release 6** | August 12, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_6) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_6.zip) |
+| **Release 5** | July 31, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_5) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_5_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_5.zip) |
+
+## �용
+
+�공지능 플랫�으로서� 유니티� 대한 논�� 관심� 있는 연구��면, 프리프린트를 참조하시오.
+[ � ML-Agents 툴킷� 대한 참조 문서](https://arxiv.org/abs/1809.02627).
+
+유니티 �는 ML-Agents 툴킷� 사용하여 연구를 수행하는 경우, 다� 논문� 참조 �료로 �용할 것� 요청합니다.
+Juliani, A., Berges, V., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., Lange, D. (2020). Unity: A General Platform for Intelligent Agents. _arXiv preprint [arXiv:1809.02627](https://arxiv.org/abs/1809.02627)._ https://github.com/Unity-Technologies/ml-agents.
+
+## 추가 리소스
+
+유니티 � ML-Agents 툴킷� 대해 �세히 소개하는 유니티 학습 과정� 있습니다. [ML-Agents: 벌새](https://learn.unity.com/course/ml-agents-hummingbirds)
+
+�한 [CodeMonkeyUnity](https://www.youtube.com/c/CodeMonkeyUnity)와 제휴하여 ML-Agents 툴킷� 구현 � 사용 방법� 대한 [튜토리얼 비디오](https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110)� 제작했습니다.
+
+�한 ML-Agents 관련 블로그 게시물� 게시했습니다.
+
+- (December 28, 2020)
+  [Happy holidays from the Unity ML-Agents team!](https://blogs.unity3d.com/2020/12/28/happy-holidays-from-the-unity-ml-agents-team/)
+- (November 20, 2020)
+  [How Eidos-Montréal created Grid Sensors to improve observations for training agents](https://blogs.unity3d.com/2020/11/20/how-eidos-montreal-created-grid-sensors-to-improve-observations-for-training-agents/)
+- (November 11, 2020)
+  [2020 AI@Unity interns shoutout](https://blogs.unity3d.com/2020/11/11/2020-aiunity-interns-shoutout/)
+- (May 12, 2020)
+  [Announcing ML-Agents Unity Package v1.0!](https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/)
+- (February 28, 2020)
+  [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
+- (November 11, 2019)
+  [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
+- (October 21, 2019)
+  [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
+- (April 15, 2019)
+  [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
+- (March 1, 2019)
+  [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
+- (December 17, 2018)
+  [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
+- (October 2, 2018)
+  [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
+- (September 11, 2018)
+  [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
+- (June 26, 2018)
+  [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
+- (June 19, 2018)
+  [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
+- (May 24, 2018)
+  [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
+- (March 15, 2018)
+  [ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/)
+- (December 11, 2017)
+  [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+- (December 8, 2017)
+  [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
+- (September 19, 2017)
+  [Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)
+- Overviewing reinforcement learning concepts
+  ([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
+  and
+  [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
+
+
+## 커뮤니티 그리고 피드백
+
+ML-Agents 툴킷� 오픈소스 프로�트�며 컨트리뷰션� 환�합니다. 만약 컨트리뷰션� �하시는 경우
+[컨트리뷰션 가�드��](com.unity.ml-agents/CONTRIBUTING.md) 과 [행� 규칙](CODE_OF_CONDUCT.md) � 검토해주십시오.
+
+ML-Agents 툴킷 설치 � 설정과 관련� 문제 �는 ��전트를 가장 잘 설정하거나 �육하는 방법� 대한 논�는 [유니티 ML-Agents �럼](https://forum.unity.com/forums/ml-agents.453/) � 새 스레드를 작성하십시오. 가능한 많� 세부 정보를 �함해야 합니다.
+ML-Agents 툴킷� 사용하여 다른 문제가 발�하거나 특정 기능 요청� 있는 경우 [�슈 제출](https://github.com/Unity-Technologies/ml-agents/issues) 부�합니다.
+
+여러분� �견� 저��게 매우 중요합니다. 유니티 ML-Agents 툴킷� 관련� 여러분� �견� 통해서 저�는 계�해서 발전하고 성장할 수 있습니다. 단 몇 분만 사용하여 [저��게 알려주세요](https://github.com/Unity-Technologies/ml-agents/issues/1454).
+
+다른 �견과 피드백� ML-Agents 팀과 �접 연�부�드립니다. (ml-agents@unity3d.com)
+
+
+## 개�정보
+
+Unity ML-Agents 툴킷� 대한 개발� 경험� 개선하기 위해, 우리는 �디터 내부 분�� 추가했습니다.
+[유니티 개� 정보 보호 정책](https://unity3d.com/legal/privacy-policy) � "Unity가 기본�으로 수집하는 정보"를 참조하시기 바�니다.
+
+
+## ��센스
+
+[Apache License 2.0](LICENSE)
+
+
+## 한글 번역
+
+유니티 ML-Agents 관련 문서� 한글 번역� [장현준(Hyeonjun Jang)][https://github.com/JangHyeonJun],  [민규� (Kyushik Min)]([https://github.com/Kyushik](https://github.com/Kyushik))� �해 진행�었습니다. 내용� 오류나 오탈�가 있는 경우 � 문서� 번역� 진행한 사람� �메�� 통해 연�주시면 �사드리겠습니다.
+
+장현준: totok682@naver.com
+
+민규�: kyushikmin@gmail.com
+
+최태�: chlxogur_@naver.com
diff --git a/localized_docs/KR/docs/Installation-Anaconda-Windows.md b/localized_docs/KR/docs/Installation-Anaconda-Windows.md
new file mode 100644
index 0000000000000000000000000000000000000000..71dc8e4870d77fe1662f435e9e9ce7c3b083d602
--- /dev/null
+++ b/localized_docs/KR/docs/Installation-Anaconda-Windows.md
@@ -0,0 +1,304 @@
+# Windows ����ڸ� ���� ML-Agents Toolkit ��ġ ���
+
+ML-Agents toolkit�� Windows 10�� �����մϴ�. �ٸ� ������ Windows ���ε� ML-Agents toolkit��
+����� �� ������ �������� �ʾҽ��ϴ�. ����, ML-Agents toolkit�� Windows VM(Bootcamp �Ǵ� ���� ó��
+ȯ�� ����)�� ��� ���� �������� �ʾҽ��ϴ� .
+
+ML-Agents toolkit�� ����ϱ� ����, �Ʒ��� ����Ȱ� ó�� Python�� �䱸�Ǵ� Python ��Ű���� ��ġ�ؾ� �մϴ�.
+�� ���̵�� ���� GPU ��� �н�(�����ڸ� ����)�� ���� ���� ����� �ٷ�ϴ�.
+����, ML-Agents toolkit�� ���� GPU ��� �н��� �ʿ����� ������ ���� ���� �Ǵ� Ư�� ���׿� �ʿ��� �� �ֽ��ϴ�.
+
+## �ܰ� 1: Anaconda�� ���� Python ��ġ
+
+Windows ������ Anaconda�� [�ٿ�ε�](https://www.anaconda.com/download/#windows)�ϰ� ��ġ�Ͻʽÿ�.
+Anaconda�� ��������ν�, �ٸ� ���� ������ Python�� �и��� ȯ�濡�� ������ �� �ֽ��ϴ�.
+Python 2�� ���̻� �������� �ʱ� ������ Python 3.8�� �ʿ��մϴ�. �� ���̵忡�� �츮��
+Python 3.8 ������ Anaconda 5.1 ������ ����� ���Դϴ�.
+([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe)
+�Ǵ� [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe)
+��ũ).
+
+<p align="center">
+  <img src="images/anaconda_install.PNG"
+       alt="Anaconda Install"
+       width="500" border="10" />
+</p>
+
+����Ʈ _advanced installation options_�� �����ϴ� ���� ��õ������ ��Ȳ�� ���� ������ �ɼ��� �����Ͻʽÿ�.
+
+<p align="center">
+  <img src="images/anaconda_default.PNG" alt="Anaconda Install" width="500" border="10" />
+</p>
+
+��ġ �Ŀ� �ݵ�� __Anaconda Navigator__ �� ���� ������ �Ϸ��ؾ� �մϴ�.
+Windows Ž�� â����, _anaconda navigator_ �� Ÿ�����Ͽ� Anaconda Navigator �� �� �� �ֽ��ϴ�.
+
+ȯ�� ������ �����Ǿ����� �ʴٸ� `conda` ��ɾ Ÿ�������� ��
+"conda is not recognized as internal or external command" ��� ������ ���� ���Դϴ�.
+�̸� �ذ��ϱ� ���� ��Ȯ�� ȯ�� ���� ������ �ʿ��մϴ�.
+
+Ž�� â���� `ȯ�� ����`�� Ÿ���� �Ͽ� (������ Ű�� �����ų� ���� �Ʒ� ������ ��ư�� ���� �� �� �ֽ��ϴ�).
+ __�ý��� ȯ�� ���� ����__ �ɼ��� �ҷ��ɴϴ�.
+
+<p align="center">
+  <img src="images/edit_env_var_kr.png"
+       alt="edit env variables"
+       width="250" border="10" />
+</p>
+
+�� �ɼǿ��� __ȯ�� ����__ ��ư�� Ŭ���ϰ�. �Ʒ� __�ý��� ����__���� "Path" ������ ���� Ŭ���ϰ� __���� �����__�� Ŭ���Ͽ� ���� �� path�� �߰��Ͻʽÿ�.
+
+```console
+%UserProfile%\Anaconda3\Scripts
+%UserProfile%\Anaconda3\Scripts\conda.exe
+%UserProfile%\Anaconda3
+%UserProfile%\Anaconda3\python.exe
+```
+
+## �ܰ� 2: ���ο� Conda ȯ�� ���� �� Ȱ��ȭ
+
+ML-Agents toolkit�� �Բ� ����� ���ο� [Conda ȯ��](https://conda.io/docs/)�� ���� ���Դϴ�.
+�� �۾��� ��ġ�� ��� ��Ű���� �� ȯ�濡�� ���ѵȴٴ� ���� �ǹ��մϴ�. �̴� �ٸ� ȯ���̳� �ٸ� ���̽� ��ġ��
+������ ��ġ�� �ʽ��ϴ�. ML-Agents�� ������ ������ �׻� Conda ȯ���� Ȱ��ȭ ���Ѿ� �մϴ�.
+
+���ο� Conda ȯ���� ����� ����, ���ο� Anaconda ������Ʈ(Ž�� â���� _Anaconda Prompt_�� Ŭ��)�� ���� ����
+��ɾ Ÿ���� �Ͻʽÿ�:
+
+```sh
+conda create -n ml-agents python=3.8
+```
+
+�� ��Ű���� ��ġ�ϱ� ���� �޼����� ���� ��� `y`�� Ÿ�����ϰ� ���͸� �����ʽÿ� _(���ͳ��� ����Ǿ��ִ��� Ȯ���Ͻʽÿ�)_.
+�� �䱸�Ǵ� ��Ű������ �ݵ�� ��ġ�ؾ� �մϴ�. ���ο� Conda ȯ�濡�� Python 3.8 ������ ���Ǹ� ml-agents�� ȣ��˴ϴ�.
+
+<p align="center">
+  <img src="images/conda_new.PNG" alt="Anaconda Install" width="500" border="10" />
+</p>
+
+�ռ� ���� ȯ���� �̿��ϱ� ���� �ݵ�� Ȱ��ȭ�� �ؾ��մϴ�. _(���Ŀ� ���� ��ɾ� ���� ȯ���� ������ �� �ֽ��ϴ�)_.
+���� Anaconda ������Ʈ���� ���� ��ɾ Ÿ���� �Ͻʽÿ�:
+
+```sh
+activate ml-agents
+```
+
+Ȱ��ȭ �Ŀ� `(ml-agents)`��� ���ڰ� ������ �� �տ� ��Ÿ���� ���� �� �� �ֽ��ϴ�.
+
+��������, `tensorflow`�� ��ġ�մϴ�. ���̽� ��Ű���� ��ġ�ϱ� ���� ����ϴ� `pip`��� ��Ű�� ���� �ý��۸� ����Ͽ� ��ġ�� �� �ֽ��ϴ�.
+�ֽ� ������ TensorFlow�� �۵����� ���� �� �����Ƿ�, ��ġ ������ 1.7.1���� Ȯ���ؾ� �մϴ�. ���� Anaconda ������Ʈ â����
+���� ��ɾ Ÿ���� �Ͻʽÿ�._(���ͳ��� ����Ǿ� �ִ��� Ȯ���Ͽ� �ֽʽÿ�)_:
+
+```sh
+pip install tensorflow==1.7.1
+```
+
+## �ܰ� 3: �ʼ� ���̽� ��Ű�� ��ġ
+
+ML-Agents toolkit�� ���� ���̽� ��Ű���� �������Դϴ�. `pip`�� ����Ͽ� �� ���̽� ���Ӽ����� ��ġ�Ͻʽÿ�.
+
+ML-Agents Toolkit ����� ����Ұ� ���� ��ǻ�Ϳ� �����Ǿ����� �ʾҴٸ� �����Ͻʽÿ�. Git�� ([�ٿ�ε�](https://git-scm.com/download/win))�ϰ�
+�����Ų �� ���� ��ɾ Anaconda ������Ʈâ�� �Է��Ͽ� ������ �� �ֽ��ϴ�. _(���� �� ������Ʈ â�� �����ִٸ� `activate ml-agents`�� Ÿ�����Ͽ�
+ml-agents Conda ȯ���� Ȱ��ȭ �Ǿ��ִ��� Ȯ���Ͻʽÿ�)_:
+
+```sh
+git clone https://github.com/Unity-Technologies/ml-agents.git
+```
+
+���� Git�� ����ϰ� ���� �ʴٸ� ������ [��ũ](https://github.com/Unity-Technologies/ml-agents/archive/main.zip)���� ��� ������ �ٿ�ε� �� �� �ֽ��ϴ�.
+
+`UnitySDK` ���� ���丮���� ������Ʈ�� �߰��� ����Ƽ �ּ��� ���ԵǾ� �ֽ��ϴ�. ���� �����ϴµ� ������ �Ǵ� ���� [���� ȯ��](Learning-Environment-Examples.md)���� �ֽ��ϴ�.
+
+`ml-agents` ���� ���丮���� ����Ƽ ȯ��� �԰� ����ϴ� ���� ��ȭ�н� Ʈ���̳� ���̽� ��Ű���� ���ԵǾ� �ֽ��ϴ�.
+
+`ml-agents-envs` ���� ���丮���� `ml-agents` ��Ű���� ���ӵǴ� ����Ƽ�� �������̽��� ���� ���̽� API�� ���ԵǾ� �ֽ��ϴ�.
+
+`gym-unity` ���� ���丮���� OpenAI Gym�� �������̽��� ���� ��Ű���� ���ԵǾ� �ֽ��ϴ�.
+
+`mlagents-learn`�� ������ �� Ʈ���̳��� ȯ�� ���� ������ �� ���丮 �ȿ� �ʿ��ϹǷ�, ������ �ٿ�ε� �� ���丮�� ��ġ�� ����Ͻʽÿ�.
+���ͳ��� ����Ǿ����� Ȯ���ϰ� Anaconda ������Ʈ���� ���� ��ɾ Ÿ���� �Ͻʽÿ�t:
+
+```console
+pip install mlagents
+```
+
+ML-Agents toolkit�� ������ �� �ʿ��� ��� ���̽� ��Ű���� ��ġ�� �Ϸ��� ���Դϴ�.
+
+Windows���� ���� pip�� ����Ͽ� Ư�� ���̽� ��Ű���� ��ġ�� �� ��Ű���� ij���� �д� ���� ���� ���� �ֽ��ϴ�.
+������ ���� ������ �ذ��� �� �� �ֽ��ϴ�:
+
+```console
+pip install mlagents --no-cache-dir
+```
+
+`--no-cache-dir`�� pip���� ij���� ��Ȱ��ȭ �Ѵٴ� ���Դϴ�.
+
+
+### ������ ���� ��ġ
+
+���� `ml-agents` �Ǵ� `ml-agents-envs`�� �����ϰ� �ʹٸ�, PyPi�� �ƴ� ������ ����ҷ� ���� ��Ű���� ��ġ�ؾ� �մϴ�.
+�̸� ����, `ml-agents` �� `ml-agents-envs` �� ���� ��ġ�ؾ� �մϴ�.
+
+�������� ������ `C:\Downloads`�� ��ġ�� �ֽ��ϴ�. ������ �����ϰų� �ٿ�ε��� ��
+Anaconda ������Ʈ���� ml-agents ���丮 ���� ml-agents ���� ���丮�� �����Ͻʽÿ�:
+
+```console
+cd C:\Downloads\ml-agents
+```
+
+������� ���� ���丮���� ������ �����Ͻʽÿ�:
+
+```console
+cd ml-agents-envs
+pip install -e .
+cd ..
+cd ml-agents
+pip install -e .
+```
+
+`-e` �÷��׸� ����Ͽ� pip�� ���� �ϸ� ���̽� ������ ���� ������ �� �ְ� `mlagents-learn`�� ������ �� �ݿ��˴ϴ�.
+`mlagents` ��Ű���� `mlagents_envs`�� �������̰�, �ٸ� ������ ��ġ�ϸ� PyPi�� ���� `mlagents_envs` �� ��ġ�� �� �ֱ� ������
+�� ������� ��Ű���� ��ġ�ϴ� ���� �߿��մϴ�.
+
+## (�ɼ�) Step 4: ML-Agents Toolkit�� ����� GPU �н�
+
+ML-Agents toolkit�� ���� GPU�� �ʿ����� ������ �н� �߿� PPO �˰��� �ӵ��� ũ�� ������ ���մϴ�(������ ���Ŀ� GPU�� ������ �� �� �ֽ��ϴ�).
+�� ���̵�� GPU�� ����� �н��� �ϰ� ���� ��� ����ڸ� ���� ���̵� �Դϴ�. ���� GPU�� CUDA�� ȣȯ�Ǵ��� Ȯ���ؾ� �մϴ�.
+[����](https://developer.nvidia.com/cuda-gpus) Nvidia ���������� Ȯ���� �ֽʽÿ�.
+
+���� ML-Agents toolkit �� CUDA 9.0 ������ cuDNN 7.0.5 ������ �����˴ϴ�.
+
+### Nvidia CUDA toolkit ��ġ
+
+Nvidia ��ī�̺꿡�� CUDA ��Ŷ(toolkit) 9.0�� [�ٿ�ε�](https://developer.nvidia.com/cuda-toolkit-archive)�ϰ� ��ġ�Ͻʽÿ�.
+ML-Agents toolkit�� �����Ű�� ���� CUDA ��Ŷ�� GPU ���� ���̺귯��,
+�����-����ȭ ����, C/C++(���־� ��Ʃ��� 2017) �����Ϸ�, ��Ÿ�� ���̺귯���� �����մϴ�.
+�� ���̵忡���� [9.0.176](https://developer.nvidia.com/compute/cuda/9.0/Prod/network_installers/cuda_9.0.176_win10_network-exe))������ ����մϴ�.
+
+��ġ�ϱ� ��, __���� ���� ����Ƽ �Ǵ� ���־� ��Ʃ����� �����ߴ���__ Ȯ���Ͽ� �ֽʽÿ�.
+
+�ν��緯�� �����ϰ� Express �ɼ��� �����Ͻʽÿ�. CUDA ��Ŷ�� ��ġ�� ���丮�� ������ �ֽʽÿ�. �� ���̵忡����,
+`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0` ��ο� ��ġ�մϴ�.
+
+### Nvidia cuDNN ���̺귯�� ��ġ
+
+Nvidia���� cuDNN ���̺귯���� [�ٿ�ε�](https://developer.nvidia.com/cudnn)�ϰ� ��ġ�Ͻʽÿ�.
+cuDNN�� ���� �Ű���� ���� �⺻�� �Ǵ� GPU ���� ���̺귯��. �ٿ�ε� ���� Nvidia Developer Program�� �����ؾ��� ���Դϴ�(����).
+
+<p align="center">
+  <img src="images/cuDNN_membership_required.png"
+       alt="cuDNN membership required"
+       width="500" border="10" />
+</p>
+
+�����ϰ� cuDNN [�ٿ�ε� ������](https://developer.nvidia.com/cudnn)�� ���ư��ʽÿ�.
+ª�� �������翡 �����ؾ� �� ���� �ֽ��ϴ�. When you get to the list
+cuDNN ������ ����Ʈ���� __�ܰ� 1���� ��ġ�� CUDA ��Ŷ�� �´� ������ �ٿ�ε��ϰ� �ִ��� Ȯ���Ͻʽÿ�.__ �� ���̵忡����,
+CUDA ��Ŷ 9.0 ������ ���� 7.0.5 ������ ����մϴ�
+([�ٿ�ε� ��ũ](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/9.0_20171129/cudnn-9.0-windows10-x64-v7)).
+
+cuDNN ������ �ٿ�ε� �� �Ŀ�, CUDA ��Ŷ ���丮�ȿ� ������ ����(���� ����)�ؾ� �մϴ�.
+cuDNN zip ���� �ȿ��� ������ ���� `bin`, `include`, �׸��� `lib`�� �ֽ��ϴ�.
+
+<p align="center">
+  <img src="images/cudnn_zip_files.PNG"
+       alt="cuDNN zip files"
+       width="500" border="10" />
+</p>
+
+�� ������ ������ CUDA ��Ŷ ���丮�ȿ� �����Ͻʽÿ�.
+CUDA ��Ŷ ���丮�� `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`�� ��ġ�� �ֽ��ϴ�.
+
+<p align="center">
+  <img src="images/cuda_toolkit_directory.PNG"
+       alt="cuda toolkit directory"
+       width="500" border="10" />
+</p>
+
+### ȯ�� ���� ����
+
+1���� ȯ�� ������ 2���� ��� ������ �߰��ؾ� �մϴ�.
+
+ȯ�� ������ �����ϱ� ����, Ž�� â���� `ȯ�� ����`�� Ÿ���� �Ͽ� (������ Ű�� �����ų� ���� �Ʒ� ������ ��ư�� ���� �� �� �ֽ��ϴ�).
+ __�ý��� ȯ�� ���� ����__ �ɼ��� �ҷ��ɴϴ�.
+
+<p align="center">
+  <img src="images/edit_env_var_kr.png"
+       alt="edit env variables"
+       width="250" border="10" />
+</p>
+
+�� �ɼǿ��� __ȯ�� ����__ ��ư�� Ŭ���ϰ� �ý��� ������ __���� �����__�� Ŭ���Ͻʽÿ� _(����� ������ �ƴ� �Ʒ��� __�ý��� ����__���� Ȯ���Ͻʽÿ�).
+
+<p align="center">
+  <img src="images/new_system_variable_kr.PNG"
+       alt="new system variable"
+       width="500" border="10" />
+</p>
+
+__���� �̸�__�� `CUDA_HOME`���� �����ϰ� ���� ������ CUDA ��Ŷ ���丮 ��θ� �Է��� �ֽʽÿ�.
+�� ���̵忡�� ���丮 ��δ� `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0` �Դϴ�. Ȯ�� ��ư�� �����ֽʽÿ�.
+
+<p align="center">
+  <img src="images/system_variable_name_value_kr.PNG"
+       alt="system variable names and values"
+       width="500" border="10" />
+</p>
+
+2���� ��� ���� ������ ���� __ȯ�� ����__ â �׸��� �Ʒ� �ι�° �ڽ��� __�ý��� ����__�ȿ���,
+`Path` ������ ã�� Ŭ���ϰ� __����__��ư�� �����ʽÿ�. ����Ʈ�� 2���� ���丮�� �߰��� ���Դϴ�. �� ���丮�� ������ �����ϴ�:
+
+```console
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64
+```
+
+���� ���丮 ��ġ�� ��ġ�� ���丮 ������� Ȯ���Ͻʽÿ�. _��ҹ��ڿ� �����Ͻʽÿ�_.
+
+<p align="center">
+    <img src="images/path_variables_kr.PNG"
+        alt="Path variables"
+        width="500" border="10" />
+</p>
+
+### TensorFlow GPU ��ġ
+
+����, `pip`�� ����Ͽ� 1.7.1. ���� `tensorflow-gpu`�� ��ġ�Ͻʽÿ� . ml-agents Conda ȯ���� Ȱ��ȭ ��Ų Anaconda ������Ʈ����
+CPU�� TensorFlow �����ϰ� GPU�� TensorFlow�� ��ġ�ϱ� ���� ���� ��ɾ Ÿ���� �Ͻʽÿ� _(���ͳ��� ����Ǿ� �ִ��� Ȯ���Ͻʽÿ�)_:
+
+```sh
+pip uninstall tensorflow
+pip install tensorflow-gpu==1.7.1
+```
+
+����������, ��� ���� ����� ��ġ�Ǿ� �ְ�, Tensorflow���� GPU�� �ν��ϰ� �ִ��� �׽�Ʈ�ؾ��մϴ�.
+���� Anaconda ������Ʈ���� Python�� ȣ���Ͽ� ���ϴ�:
+
+```sh
+python
+```
+
+�׸��� ���� ��ɾ Ÿ���� �Ͻʽÿ�:
+
+```python
+import tensorflow as tf
+
+sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
+```
+
+������ ����� ������ �� �� ���� ���Դϴ�:
+
+```console
+Found device 0 with properties ...
+```
+
+## Acknowledgments
+
+�� ���̵带 ����� ���� ���� �ʾ��� �ۼ���
+[Jason Weimann](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/)
+��
+[Nitish S. Mutha](http://blog.nitishmutha.com/tensorflow/2017/01/22/TensorFlow-with-gpu-for-windows.html)
+���� ����帳�ϴ�.
+
+## �ѱ� ����
+
+�ش� ������ �ѱ� ������ [������ (Hyeonjun Jang)]([https://github.com/janghyeonjun](https://github.com/janghyeonjun))�� ���� ����Ǿ����ϴ�. ����� ������ ��Ż�ڰ� �ִ� ��� totok682@naver.com ���� �����ֽø� ����帮�ڽ��ϴ�.
diff --git a/localized_docs/KR/docs/Installation.md b/localized_docs/KR/docs/Installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..633c2ea67139e4b26534db4262f80bef27a58d3c
--- /dev/null
+++ b/localized_docs/KR/docs/Installation.md
@@ -0,0 +1,104 @@
+# 설치
+
+ML-Agents를 설치하고 사용하기 위해 유니티를 설치해야 하고 � Repository(저장소)를
+Clone(복제)하고 추가종�성� 가지는 Python(파��)� 설치해야합니다. 아래 Subsection(하위섹션)�서는 Docker(�커) 설정 외��
+� 단계를 개괄�으로 설명합니다.
+
+## **Unity 2018.4** �는 �후� 버전� 설치하십시오.
+
+[다운로드](https://store.unity.com/kr/download)하고 설치하십시오. 만약 저�� �커 설정(차후� 소개할)� 사용하고 싶다면,
+유니티를 설치할 때, Linux Build Support를 설정하십시오.
+
+<p align="center">
+  <img src="images/unity_linux_build_support.png"
+       alt="Linux Build Support"
+       width="500" border="10" />
+</p>
+
+## Windows 사용�
+Windows�서 환경� 설정하기 위해, [세부 사항](Installation-Anaconda-Windows.md)� 설정 방법� 대해 작성하였습니다.
+Mac과 Linux는 다� 가�드를 확�해주십시오.
+
+## Mac �는 Unix 사용�
+
+### ML-Agents Toolkit 저장소 복제
+
+유니티 설치 후� ML-Agents Toolkit 깃허브 저장소를 설치하고 싶� 것입니다.
+
+```sh
+git clone https://github.com/Unity-Technologies/ml-agents.git
+```
+
+`UnitySDK` 하위 디렉토리�는 프로�트� 추가할 유니티 애셋� �함�어 있습니다.
+�한 시작하는� �움� �는 많� [예제 환경](Learning-Environment-Examples.md)들� 있습니다.
+
+`ml-agents` 하위 디렉토리�는 유니티 환경과 함게 사용하는 심층 강화학습 트레�너 파�� 패키지가 �함�어 있습니다.
+
+`ml-agents-envs` 하위 디렉토리�는 `ml-agents` 패키지� 종��는 유니티� �터페�스를 위한 파�� API가 �함�어 있습니다.
+
+`gym-unity` 하위 디렉토리�는 OpenAI Gym� �터페�스를 위한 패키지가 �함�어 있습니다.
+
+### 파��과 mlagents 패키지 설치
+
+ML-Agents toolkit� 사용하기 위해 [setup.py file](../ml-agents/setup.py)� 나열� 종�성과 함께 파�� 3.8� 필요합니다.
+주요 종�성� �부는 다�� �함합니다:
+
+- [TensorFlow](Background-TensorFlow.md) (Requires a CPU w/ AVX support)
+- [Jupyter](Background-Jupyter.md)
+
+Python 3.8� 만약 설치�어 있지 않다면, [다운로드](https://www.python.org/downloads/)하고 설치하십시오.
+
+만약 당신� 파�� 환경� `pip3`� �함하지 않는다면, 다�
+[지시사항](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
+� 따�서 설치하십시오.
+
+종�성과 `mlagents` 파�� 패키지를 설치하기 위해 다� 명령어를 실행하십시오:
+
+```sh
+pip3 install mlagents
+```
+
+� 명령어를 통해 PyPi로 부터(복제� 저장소가 아닌) `ml-agents`가 설치� 것입니다.
+만약 성공�으로 설치를 완료 했다면, `mlagents-learn --help` 명령어를 실행할 수 있� 것입니다.
+명령어를 실행하면 유니티 로고와 `mlagents-learn`�서 사용할 수 있는 명령어 �� 매개변수들� 볼 수 있습니다.
+
+**주�:**
+
+- 현재 Python 3.8 �는 Python 3.5� 지�하지 않습니다.
+- 만약 Anaconda를 사용하고 TensorFlow� 문제가 있다면, 다�
+  [��](https://www.tensorflow.org/install/pip)�서 Anaconda 환경�서 어떻게 TensorFlow를 설치하는지 확�하십시오.
+### 개발� 위한 설치방법
+
+만약 `ml-agents` �는 `ml-agents-envs`를 수정하고 싶다면, PyPi가 아닌 복제� 저장소로 부터 패키지를 설치해야 합니다.
+�를 위해, `ml-agents`와 `ml-agents-envs`를 �� 설치해야 합니다. 저장소� 루트 디렉토리�서 다� 명령어를 실행하십시오:
+
+```sh
+cd ml-agents-envs
+pip3 install -e ./
+cd ..
+cd ml-agents
+pip3 install -e ./
+```
+
+`-e` 플래그를 사용하여 pip를 실행 하면 파�� 파�� �접 변경할 수 있고 `mlagents-learn`를 실행할 때 반��니다.
+`mlagents` 패키지가 `mlagents_envs`� �존��고, 다른 순서로 설치하면 PyPi로 부터 `mlagents_envs`를
+설치할 수 있기 때문� � 순서대로 패키지를 설치하는 것� 중요합니다.
+
+## �커 기반 설치
+
+만약 ML-Agents를 위해 �커를 사용하고 싶다면, [� 가�드](Using-Docker.md)를 따�하십시오.
+
+## 다� 단계
+
+[기초 가�드](Basic-Guide.md) 페�지�는 유니티 내�서 ML-Agents toolkit� 설정 � 학습� 모� 실행,
+환경 구축, 학습 방법� 대한 여러 짧� 튜토리얼� �함하고 있습니다.
+
+## �움�
+
+ML-Agents와 관련� 문제가 발�하면 저�� [FAQ](FAQ.md)와 [제약 사항](Limitations.md) 페�지를 참고해 주십시오.
+만약 문제� 대한 아무것� 찾� 수 없다면 OS, Pythons 버전 � 정확한 오류 메세지와 함께 [�슈 제출](https://github.com/Unity-Technologies/ml-agents/issues)� 해주십시오.
+
+
+## 한글 번역
+
+해당 문서� 한글 번역� [장현준 (Hyeonjun Jang)]([https://github.com/janghyeonjun](https://github.com/janghyeonjun))� �해 진행�었습니다. 내용� 오류나 오탈�가 있는 경우 totok682@naver.com 으로 연�주시면 �사드리겠습니다.
diff --git a/localized_docs/KR/docs/Training-Imitation-Learning.md b/localized_docs/KR/docs/Training-Imitation-Learning.md
new file mode 100644
index 0000000000000000000000000000000000000000..a293a4f01450641c5f3572ac645b38297ed2c0b6
--- /dev/null
+++ b/localized_docs/KR/docs/Training-Imitation-Learning.md
@@ -0,0 +1,85 @@
+# 모방학습� 통한 ��전트 학습
+
+��전트가 시행착오를 통해 스스로 학습하는 것보다 단순히 ��전트가 수행하기를 �하는 행�� 우리가 알려주는 것� � �관�� 수 있습니다. 위�병 NPC를 학습하기 위한 [실행 예시](ML-Agents-Overview.md#running-example-training-npc-behaviors) 문서 내용� 대해 ��해보겠습니다. 보� 함수를 �용하여 위�병� 행�� 간접�으로 학습하는 것� 아니� 게임�서 얻어진 관측 (observation)과 게임 컨트롤러를 통해 얻어진 행�들 (actions)� 실제 ��터를 통해 위�병� 행�� 결정하�� 학습합니다. 모방학습 (Imitation Learning)� 실제 플레�를 통해 얻어진 관측과 행� ��터 �� �용하여 ��전트� 정책� 학습합니다. [비디오 ��](https://youtu.be/kpb8ZkMBFYs).
+
+## 시범 (Demonstration) ��터 기�
+
+유니티 �디터를 �용하여 ��전트� 플레�를 기�하고 �셋으로 저장하는 것� 가능합니다. �런 플레� ��터�는 기�� 진행하는 �안� 관측, 행� 그리고 보� 정보가 �함�니다. �것들� ��터를 통해 관리가 가능하며 Behavioral Cloning과 같� 오프�� 학습� 사용� 수 있습니다. (아래 내용 참고)
+
+��전트� 플레� ��터를 기�하기 위해서는 씬(Scene)�서 `Agent` 컴�넌트를 �함하고 있는 GameObject� `Demonstration Recorder` 컴�넌트를 추가해주어야 합니다. �단 추가�고나면 ��전트로부터 플레� ��터를 기�할 수 있게 �니다.
+
+<p align="center">
+  <img src="images/demo_component.png"
+       alt="BC Teacher Helper"
+       width="375" border="10" />
+</p>
+
+`Record`가 체��는 경우 씬� 실행�면 ��터가 �성�니다. 환경� 난��� 따� 모방학습� 사용하기 위해 몇분�서 몇시간 정� 플레� ��터를 수집해야합니다. 충분한 ��터가 기��었으면 유니티 ��서 게임� 실행� 정지합니다. 그렇게 하면 `.demo` 파�� `Assets/Demonstations` �� 내부� �성�니다. � 파��는 ��전트� 플레� ��터가 저장�어 있습니다. � 파�� �릭하면 �스펙터 �� �모 파�� 대한 정보를 아래와 같� 알려�니다.
+
+<p align="center">
+  <img src="images/demo_inspector.png"
+       alt="BC Teacher Helper"
+       width="375" border="10" />
+</p>
+
+
+## Behavioral Cloning� 통한 학습
+
+모방학습� 위한 다양한 알고리즘� 존재하며 모방학습 알고리즘 중 가장 간단한 알고리즘� Behavioral Cloning 입니다. � 알고리즘� 마치 �미지 분류를 위한 지�학습 (Supervised Learning)�나 기타 고전�� 머신러� 기법들처럼 전문가� 플레�로부터 수집� ��터를 �접�으로 모방하�� 정책 (Policy)� 학습합니다.
+
+
+### 오프�� 학습
+
+오프�� Behavioral Cloning�서 우리는 ��전트� 행�� 학습하기 위해 `Demonstration Recorder`를 통해 �성� `demo` 파�� ��터 셋으로 �용합니다.
+
+1. 전문가� 플레� ��터를 모방하�� 학습하는 ��전트 선�
+2. `Demonstration Recorder`를 �용하여 전문가� 플레�를 기�합니다. (위� 내용 참고)
+   앞으로 설명� 위해 � 기�� 파�� �름� `AgentRecording.demo`�고 하겠습니다.
+3. 씬� 빌드하고 ��전트�게 러� 브레� (Learning Brain)� 할당합니다. 그리고 아카�미� Broadcast Hub�서 � 브레�� Control� 체�해�니다. 브레�� 대한 정보가 필요하시면 다�� [문서](Learning-Environment-Design-Brains.md)를 참고해주세요.
+4.  `config/offline_bc_config.yaml` 파�� 열어�니다.
+5. `demo_path` 파�미터를 스� 2�서 기�한 �모 파�� 경로로 수정해�니다. �번 예시� 경우 설정� 경로는 다�과 같습니다:  `./UnitySDK/Assets/Demonstrations/AgentRecording.demo`
+6. `./config/offline_bc_config.yaml` � 설정 파�미터로 하는 mlagent-learn� 실행하며 `--run-id` 와 `--train` � 입력합니다. 빌드� 환경� standalone으로 컴파��었거나 �디터�서 train� �략� 경우 `--env` 파�미터� 빌드� 환경� 경로를 기입해주세요.
+   ​
+7. (선��) �서 보드를 활용하여 학습 성능� 확�해보세요!.
+
+위 방법� �모 파�� �용하여 ��전트가 �접�으로 전문가� 행�� 따�하�� �공신경�� 학습하는 기법입니다. 환경� 학습� 진행�는 �안 ��전트� 성능� �가하기 위해 실행�며 사용� 것입니다.
+
+### 온�� 학습
+
+미리 �성� �모 파� 없� 학습� 진행�는 �안 실시간으로 전문가� 플레� ��터를 제공하며 ��전트를 학습하는 것� 간으합니다. � 방법� 다�� 단계를 따� 진행�니다:
+without pre-recording a demonstration file. The steps to do this are as follows:
+
+1. 먼저 �개� 브레�들� �성합니다. 하나는 "선�님"� � 것�고 하나는 "학�"� � 것입니다. �번 예시�서는 �개� 브레� �셋� �름� �� "Teacher"와 "Student"로 설정할 것입니다.
+2. "Teacher" 브레�� 반드시 **플레�어 브레� (Player Brain)**�어야 합니다.
+3. "Student" 브레�� 반드시 **러� 브레� (Learning Brain)**�어야 합니다.
+4. "Teacher" 브레�과 "Student" 브레�� 파�미터는 ��전트�서 설정한대로 ��하게 설정�어야 합니다.
+5. "Teacher" 브레�과 "Student" 브레�� 아카�미� `Broadcast Hub`� 추가하고 "Student" 브레�� `Control` 체�박스� 체�를 해�니다.
+6. 브레�들� �하는 ��전트들�게 연결해�니다. (하나� ��전트는 선�님으로 설정�어야 하며 �어� 하나� ��전트는 학�으로 설정�어야 합니다).
+7. `config/online_bc_config.yaml` 파��서, "Student" 브레�� 대한 항목� 추가해야합니다.  `trainer` 파�미터를 `online_bc`로 설정하고 `brain_to_imitate` 파�미터를 선�님 ��전트� 브레� �름� "Teacher"로 설정합니다. 추가�으로 � 순간마다 얼마나 많� 학습� 진행할지 결정하는 `batches_per_epoch`를 설정합니다. ��전트를 � 오랜 기간�안 학습하고 싶� 경우  `max_steps` 값� �가시켜주세요.
+8. `mlagents-learn config/online_bc_config.yaml
+   ​--train —slow`를 통해 학습과정� 실행하고 화면� _"Start training by pressing the Play button in the Unity Editor"_ �는 메세지가 출력�면 유니티� :arrow_forward: 버튼� 눌러주세요
+9. 유니티 윈�우 ��서 선�님 브레�� 가진 ��전트를 제어하면서 �하는대로 플레� ��터를 �성합니다.
+10. 학� 브레�� 가진 ��전트(들)� 살펴보면 선�님 브레�� 가진 ��전트� 플레�와 유사하게 행�하기 시작합니다.
+11. 학� ��전트들� �하는대로 행�하게 �면 커멘드 ���서 `CTL+C`를 눌러서 학습� 중단하십시오.
+12. �성� `*.nn` 파�� Assets ��� 하위 ��� `TFModels`  ��로 ��시키고 � 파��  `러�` 브레�� 사용하세요.
+
+**BC Teacher Helper**
+
+� 편리한 사용� 위해서, `BC Teacher Helper` 컴�넌트를 선�님 ��전트� 사용할 수 있습니다.
+
+<p align="center">
+  <img src="images/bc_teacher_helper.png"
+       alt="BC Teacher Helper"
+       width="375" border="10" />
+</p>
+
+�것� 사용하면 다�과 같� 키보드 단축키를 사용할 수 있습니다:
+
+1. 기�� 시작하거나 중단할 수 있습니다. �것� ��전트를 통해 게임� 플레�하� ��전트가 학습� �지 않�� 사용할 때 유용합니다. �것� 대한 기본�� 실행� 키보드�  `R`  버튼� 누르면 �니다.
+2. 트레�� 버�를 리셋합니다. � 명령� 통해 ��전트가 최근� 경험� 대한 버�를 비우�� 설정합니다. �것� ��전트가 빠르게 새로운 행�� 배우게 하고싶�때 사용하면 유용합니다. 버�를 리셋하기 위한 기본 명령� 키보드�  `C` 버튼� 누르면 �니다.
+
+
+
+## 한글 번역
+
+해당 문서� 한글 번역� [민규� (Kyushik Min)]([https://github.com/Kyushik](https://github.com/Kyushik))� �해 진행�었습니다. 내용� 오류나 오탈�가 있는 경우 kyushikmin@gmail.com 으로 연�주시면 �사드리겠습니다.
diff --git a/localized_docs/KR/docs/Training-PPO.md b/localized_docs/KR/docs/Training-PPO.md
new file mode 100644
index 0000000000000000000000000000000000000000..18f32986f50e1e2ff3ac459d551158e1393b5e63
--- /dev/null
+++ b/localized_docs/KR/docs/Training-PPO.md
@@ -0,0 +1,151 @@
+# Proximal Policy Optimization를 �용한 학습
+
+ML-Agents는 [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/) �는 강화학습 기법� 사용합니다.
+PPO는 ��전트� 관측 (Observation)� 통해 ��전트가 주어진 �태�서 최선� 행�� 선�할 수 있�� 하는 ���� 함수를 �공신경�� �용하여 근사하는 기법입니다.  ML-agents� PPO 알고리즘� �서플로우로 구현�었으며 별�� 파�� 프로세스 (소켓 통신� 통해 실행중� 유니티 프로그램과 통신)�서 실행�니다.
+
+��전트를 학습하기 위해서 사용�는 ��전트가 최대화하�� 시�하는 보� 시그�� 하나 혹� 그 �� 설정해야합니다.  사용 가능한 보� 시그�들과 관련� 하��파�미터� 대해서는 [보� 시그�](Reward-Signals.md) 문서를 참고해주십시오.
+
+`learn.py`를 �용하여 학습 프로그램� 실행하는 방법� [ML-Agents 학습](Training-ML-Agents.md) 문서를 참고해주십시오.
+
+만약 ��전트�게 기억력� 부여하기 위해 순환 신경� (Recurrent Neural Network, RNN)� 사용하는 경우, 순환 신경�� 대한 구체�� 학습 방법� 설명하는 [순환 신경� 사용하기](Feature-Memory.md) 문서를 참고해주십시오.
+
+
+만약 ��전트�게 제시� 문제� 난��를 �차�으로 �가시키며 학습하는 커리�럼 학습 (Curriculum Learning)� 사용하는 경우 [커리�럼 학습� 통한 ��전트 학습](Training-Curriculum-Learning.md) 문서를 참고해주십니오.
+
+모방 학습 (Imitation Learning)� 대한 정보를 얻고 싶으시다면  [모방 학습� 통한 ��전트 학습](Training-Imitation-Learning.md) 문서를 참고해주십시오.
+
+
+
+## PPO 학습� 위한 하��파�미터
+
+강화학습 모�� 성공�으로 학습하기 위해서는 학습과 관련� 하��파�미터 튜�� 필요합니다. � 가�드는 기본�� 파�미터들� �용하여 학습했� 때 사용�가 �하는 성능� 만족하지 못한 경우 파�미터 튜�� 수행하는 방법� 대해 설명합니다.
+
+## 하��파�미터
+
+### Reward Signals
+
+강화학습�서 목표는 보�� 최대로 하는 정책 (Policy)� 학습하는 것입니다. 기본�으로 보�� 환경으로부터 주어집니다. 그러나 우리는 다양한 다른 행�� 통해 ��전트�게 보�� 주는 것� ��해볼 수 있습니다. 예를 들어 ��전트가 새로운 �태를 �험했� 때 ��전트�게 보�� 줄 수 있습니다. �런 보� 시그�� 추가하여 학습 과정� �움� 줄 수� 있습니다.
+
+`reward_signals`는 [보� 시그�](Reward-Signals.md)� 정�합니다. ML-Agents는 기본�으로 �개� 보� 시그�� 제공합니다. 하나는 외부 (환경) 보��며 다른 하나는 호기심 (Curiosity) 보�입니다. � 호기심 보�� 외부 보�� �소성� 가지는 환경 (Sparse Extrinsic Reward Environment)�서 � 다양한 �험� 수행할 수 있�� �와�니다.
+
+### Lambda
+
+`lambd` 는 `람다(lambda)` 파�미터를 �미하며 �반화� �� 추정 (Generalized Advantage Estimate, [GAE]((https://arxiv.org/abs/1506.02438))) 계산� 사용�니다. �는 업��트� 가치를 예측할 때 현재 예측� 가치� 얼마나 �존할지 결정하는 값입니다. � 값� 낮으면 현재 예측� 가치� � �존하는 것� �미하며 (높� 편향 (bias) 발� 가능), 값� 높으면 환경� 통해 얻� 실제 보�� � �존하는 것� �미합니다 (높� 분산 (variance) 발� 가능). 즉 � 파�미터를 어떻게 선�하�� 따� � 특성간� 트레�드오프 (trade-off)가 존재합니다. �한 � 파�미터를 �절하게 선�하면 � 안정�� 학습� 가능합니다.
+
+�반�� 범위: `0.9` - `0.95`
+
+### Buffer Size
+
+`buffer_size` 는 모� 학습� 시작하기 전 얼마나 많� 경험들(관측, 행�, 보� 등)� 저장할지 결정합니다.  **� 값� `batch_size`� 배수로 설정�어야 합니다.** �반�으로 � `buffer_size`는 � 안정�� 학습� 가능하게 합니다.
+
+�반�� 범위: `2048` - `409600`
+
+### Batch Size
+
+`batch_size` 는 한번� 경사하강(Gradient Descent) 업��트를 수행할 때 사용할 경험들� 수를 �미합니다. **� 값� 항� `buffer_size`� 약수로 설정�어야 합니다.** 만약 연��� 행� 공간 (Continuous Action Space) 환경� 사용하는 경우 � 값� �게 설정�어야 합니다 (1000� 단위). 만약 �산�� 행� 공간 (Discrete Action Space) 환경� 사용하는 경우 � 값� � 작게 설정�어야 합니다. (10� 단위).
+
+�반�� 범위 (연��� 행�): `512` - `5120`
+
+�반�� 범위 (�산�� 행�): `32` - `512`
+
+### Number of Epochs
+
+`num_epoch` 는 경사 하강 (Gradient Descent) 학습 �안 경험 버� (Experience Buffer) ��터� 대해 학습� 몇번 수행할 지 결정합니다. `batch_size`가 �수� � 값� 커져야합니다. � 값� 줄�면 � 안정�� 업��트가 보장�지만 학습 ��가 �려집니다.
+
+�반�� 범위: `3` - `10`
+
+### Learning Rate
+
+`learning_rate` 는 경사 하강 (Gradient Descent) 학습� 정�를 결정합니다. 학습� 불안정하고 ��전트가 얻는 보�� �가하지 않는 경우 �반�으로 학습률� �소시킵니다.
+
+�반�� 범위: `1e-5` - `1e-3`
+
+### Time Horizon
+
+`time_horizon` � 경험 버� (Experience Buffer)� 저장하기 전 ��전트당 수집할 경험� 스� 수를 �미합니다. �피소드가 �나기 전� � 한�� �달하면 가치 �가를 통해 ��전트� 현재 �태로부터 기대�는 전체 보�� 예측합니다. 따�서 � 값� 설정� 따� � 편향�지만 분산� 커질수� 있고 (긴 time horizon), � 편향 (bias)�지만 분산 (variance)� 작아질 수� 있습니다 (짧� time horizon). 한 �피소드 �안 보�� 빈번하게 발�하는 경우나 �피소드가 엄청나게 긴 경우�는 time horizon 값� 작게 설정하는 것� ���입니다. � 값� ��전트가 취하는 �련� 행� 내�서 중요한 행�� 모� �착할 수 있� 만� � 값� 가져야 합니다.
+
+�반�� 범위: `32` - `2048`
+
+### Max Steps
+
+`max_steps` � 학습 과정 �안 얼마나 많� 시뮬레�션 스� (프레임 스킵� 곱한만�) � 실행할지 결정합니다. � 값� 복잡한 문제�수� �게 설정해야합니다.
+
+�반�� 범위: `5e5` - `1e7`
+
+### Beta
+
+`beta` 는 엔트로피 정규화 (Entropy Regularization)� 정�를 결정하며 �를 통해 정책� � 랜�하게 만들 수 있습니다. � 값� 통해 ��전트는 학습 �안 액션 공간� �절하게 �험할 수 있습니다. � 값� �가시키면 ��전트가 � 많� 랜� 행�� 취하게 �니다. 엔트로피 (�서보드를 통해 측정 가능)는 보�� �가함� 따� 서서히 �기를 �소시켜야합니다. 만약 엔트로피가 너무 빠르게 떨어지면 `beta`를 �가시켜야합니다. 만약 엔트로피가 너무 �리게 떨어지면 `beta`를 �소시켜야 합니다.
+
+�반�� 범위: 1e-4 - 1e-2
+
+### Epsilon
+
+`epsilon` � 경사 하강 업��트 �안 사용하는 �전 정책과 새로운 정책 사�� 비율� �정 범위� �기로 제한하는 값입니다. � 값� 작게 설정�면 � 안정�� 학습� 가능하지만 학습� �리게 진행� 것입니다.
+
+�반�� 범위: `0.1` - `0.3`
+
+### Normalize
+
+`normalize`는 벡터 관측 (Vector Observation) 입력� 정규화 (Normalization)할지 결정합니다. � 정규화는 벡터 관측� �� �균 � 분산� 기반으로 수행합니다. 정규화는 복잡하고 연��� 제어 문제�서 �움� � 수 있지만 단순하고 �산�� 제어 문제�서는 정규화를 사용하는 것� 좋지 않� 수 있습니다.
+
+### Number of Layers
+
+`num_layers` 는 관측 입력 후 혹� 시�� 관측 (Visual Observation)� CNN �코딩 �후 몇개� �닉층 (Hidden Layer)� 사용할지 결정합니다. 간단한 문제�서는 �� 수� 층� 사용하여 빠르고 효율�으로 학습해야합니다. 복잡한 제어 문제�서는 많� 층� 사용할 필요가 있습니다.
+
+�반�� 범위: `1` - `3`
+
+### Hidden Units
+
+`hidden_units` � �공신경�� � 완전연결층 (Fully Connected Layer)� 몇개� 유닛� 사용할지 결정합니다. 최�� 행�� 관측 입력� 간단한 조합으로 결정�는 단순한 문제� 대해서는 � 값� 작게 설정합니다. 최�� 행�� 관측 입력� 복잡한 관계� �해 결정�는 어려운 문제� 대해서는 � 값� �게 설정합니다.
+
+�반�� 범위: `32` - `512`
+
+## (선��) 순환신경�� 하��파�미터
+
+아래� 하��파�미터들�  `use_recurrent` � 참(True)으로 결정� 경우�만 사용합니다.
+
+### Sequence Length
+
+`sequence_length` 는 학습 �안 네트워�를 통과하는 연��� 경험들� 길�를 �미합니다. ��전트가 긴 시간� 대해 기억해야하는 정보가 있다면 � 값� 충분히 길게 설정해야합니다. 예를 들어 ��전트가 물체� ��를 기억해야하는 경우 � 값� 작게 설정해� 괜찮습니다. 만약 ��전트가 �피소드 초반� 한번 주어진 정보를 계� 기억해야한다면 � 값� �게 설정해야 합니다.
+
+�반�� 범위: `4` - `128`
+
+### Memory Size
+
+`memory_size` 는 순환신경�� �닉 �태(hidden state)를 저장하는� 사용�는 배열� �기를 �미합니다. � 값� 반드시 4� 배수로 설정�어야 하며 ��전트가 임무를 성공�으로 완수하기 위해서 기억해야하는 정보� 양� 따� �기를 조절해야합니다.
+
+�반�� 범위: `64` - `512`
+
+## Training Statistics
+
+학습� �태를 확�하려면 �서보드 (TensorBoard)를 사용해야합니다. �서보드를 실행하고 사용하는 것� 대한 정보를 알고싶으신 경우 � [문서](./Getting-Started-with-Balance-Ball.md#observing-training-progress)를 참고해주십시오.
+
+### Cumulative Reward
+
+보�� �반�으로 지��으로 �가하는 경향� 가져야합니다. 작� 기복� 발�할수는 있습니다. 문제� 복잡�� 따� 수백만 스�� 학습� 진행�어� 보�� �가하지 않�수� 있습니다.
+
+### Entropy
+
+� 값� 브레�� 결정� 얼마나 무작위�지 나타냅니다. � 값� 학습� 진행�는 �안 지��으로 �소해야합니다. 만약 � 값� 너무 빠르게 �소하거나 아예 �소하지 않는 경우 `beta`� �기를 조절해야합니다. (�산�� 행� 공간� 사용하는 경우)
+
+### Learning Rate
+
+� 값� 시간� 지남� 따� 선형�으로 �소합니다.
+
+### Policy Loss
+
+� 값들� 학습� 진행�는 �안 진�합니다. �반�으로 � 값들� 1보다 작아야합니다.
+
+### Value Estimate
+
+� 값들� 누� 보�� �가함� 따� 커져야합니다. � 값들� 주어진 시��서 ��전트가 스스로 받� 것�� 예측하는 미래� 보�� 얼마나 �것�지를 나타냅니다.
+
+### Value Loss
+
+� 값들� 보�� �가하면 �가하고 보�� 안정�면 �소합니다.
+
+
+
+## 한글 번역
+
+해당 문서� 한글 번역� [민규� (Kyushik Min)]([https://github.com/Kyushik](https://github.com/Kyushik))� �해 진행�었습니다. 내용� 오류나 오탈�가 있는 경우 kyushikmin@gmail.com 으로 연�주시면 �사드리겠습니다.
diff --git a/localized_docs/KR/docs/Using-Docker.md b/localized_docs/KR/docs/Using-Docker.md
new file mode 100644
index 0000000000000000000000000000000000000000..352ecbb0def88c14c50b4fe02eeb207cfc12b363
--- /dev/null
+++ b/localized_docs/KR/docs/Using-Docker.md
@@ -0,0 +1,123 @@
+# ML-Agents 용 �커 사용법
+
+�커를 사용해 추론과 학습� 하고�하는 Windows와 Mac 사용�를 위한 솔루션� 제공합니다.
+�것� Python과 TensorFlow 설치를 피하고� 하는 분�게 매력�� 옵션� � 것입니다. 현재 설정� TensorFlow와 Unity가 _CPU를 통해서만_
+계산하�� 합니다. 따�서 �커 시뮬레�션� GPU를 사용하지 않고 시�� 렌��� 위해 [`Xvfb`](https://en.wikipedia.org/wiki/Xvfb)를 사용합니다.
+`Xvfb`는 `ML-Agents`(�는 다른 �용 프로그램)가 가�으로 렌��� 할 수 있게하는 유틸리티 입니다. 즉, `ML-Agents`를 실행하는 기계가 GPU를 가지고 있거나
+디스플레�를 가지고 있다고 가정하지 않습니다. �것� 카메� 기반� 시�� 관찰 요소가 �함� 환경� �욱 �려질 수� 있�� �미합니다.
+
+## 요구사항
+
+- 유니티 _Linux Build Support_ 컴�넌트
+- [�커](https://www.docker.com)
+
+## 설치
+
+- 유니티 �스톨러를 [다운로드](https://unity3d.com/kr/get-unity/download)하고 _Linux Build Support_ 컴�넌트를 추가하십시오.
+
+- �커가 설치�어 있지 않다면 [다운로드](https://www.docker.com/community-edition#/download)하고 설치 하십시오.
+
+- 호스트 머신과 분리� 환경�서 �커를 실행하기 때문�, 호스트 머신안� 마운트� 디렉토리는 트레�너 환경 설정 파�,
+  유니티 실행 파�, 커리�럼 파�과 TensorFlow 그래프와 같� ��터를 공유하기위해 사용�니다.
+  �를 위해, 편�� 비어있는 `unity-volume` 디렉토리를 저장소� 루트� 만들었으나, 다른 디렉토리� 사용� �유롭게 할 수 있습니다.
+  � 가�드� 나머지 부분�서는 `unity-volume` 디렉토리가 사용�다고 가정하고 진행�니다.
+
+## 사용법
+
+ML-Agents 용 �커 사용�는 세 단계가 �함�니다.: 특정 플래그를 사용하여 유니티 환경 빌드, �커 컨테�너 빌드
+마지막으로, 컨테�너 실행. 만약 ML-Agents 용 유니티 환경 빌드� �숙하지 않다면, [3D 밸런스 볼 예제와 함께 시작하기](Getting-Started-with-Balance-Ball.md) 가�드를 먼저 �으십시오.
+
+### 환경 빌드 (옵션)
+
+_학습� 위해 �디터 사용� �한다면 � 단계를 건너뛸 수 있습니다._
+
+�커는 �반�으로 호스트 머신과 (리눅스) 커�� 공유하는 컨테�너를 실행하기 때문�,
+유니티 환경� 리눅스 플랫�� 구축�어야 합니다. 유니티 환경� 빌드할 때, 빌드 세팅 창(Build Settings window)�서
+다� 옵션� 선�해 주십시오:
+
+- 타겟 플랫�� `리눅스`로 설정 (Set the _Target Platform_ to `Linux`)
+- _아키�처_를 `x86_64'로 설정 (Set the _Architecture_ to `x86_64`)
+- 환경�서 시��� 관찰� 필요로 하지않는다면, `headless` 옵션� 선�할 수 있습니다 (아래 사진 참조).
+
+`빌드` (Build)를 �릭하고, 환경 �름� 선�하고 (예시: `3DBall`) 출력 디레토리를 `unity-volume`으로 설정하십시오.
+빌드 후�, 파� `<환경 �름>.x86_64` 와 하위디렉토리 `<환경 �름>_Data/` 가 `unity-volume` � �성 �어있는지 확�하십시오.
+
+![�커를 위한 빌드 설정](images/docker_build_settings.png)
+
+### �커 컨테�너 빌드
+
+첫 번째, �커 머신� 시스템�서 작�하는지 확�하십시오. 저장소� 최�단�서 다� 명령어를 호출하여
+�커 컨테�너를 빌드하십시오:
+
+```sh
+docker build -t <image-name> .
+```
+
+`<image-name>`� �커 �미지 �름으로 바꾸십시오, 예시: `balance.ball.v0.1`.
+
+### �커 컨테�너 실행
+
+저장소� 최�단�서 다� 명령어를 호출하여 �커 컨테�너를 실행하십시오:
+
+```sh
+docker run --name <container-name> \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           -p 5005:5005 \
+           <image-name>:latest \
+           --docker-target-name=unity-volume \
+           <trainer-config-file> \
+           --env=<environment-name> \
+           --train \
+           --run-id=<run-id>
+```
+
+�수(argument) 값 정보:
+
+- `<container-name>` � 컨테�너를 구분하기위해 사용�니다 (컨테�너를 �터럽트하거나 종료시킬 때).
+�것� 선�사항�며 설정하지 않았� 경우 �커는 랜�한 �름� �성합니다. _�커 �미지를 실행할 때마다
+고유한 �름� 가져야함� 유�하십시오._
+- `<image-name>` 컨테�너를 빌드할 때 사용할 image name� 참조합니다.
+- `<environment-name>` __(옵션)__: 리눅스 실행파�과 함께 학습� 할 경우, �수 값� 실행파�� �름� �다.
+�디터�서 학습� 할 경우, `<environment-name>` �수를 전달하지 �고 유니티�서 _"Start training by pressing
+  the Play button in the Unity Editor"_ 메세지가 화면� 표시� 때 :arrow_forward: 버튼� 누르십시오.
+- `source`: 유니티 실행파�� 저장할 호스트 운�체제� 경로를 참조합니다.
+- `target`: �커가`source` 경로� � �름� 가진 디스�로 마운트하�� 합니다.
+- `docker-target-name`: ML-Agents 파�� 패키지�게 유니티 실행파�� �고 그래프를 저장할 수 있는 디스�� �름� 알려준다.
+**그러므로 `target`과 ��한 값� 가져야 합니다.**
+- `trainer-config-file`, `train`, `run-id`: ML-Agents ��들� `mlagents-learn`로 전달�니다. 트레�너 설정 파�� �름 `trainer-config-file`,
+알고리즘� 학습하는 `train`, 그리고 � 실험� 고유한 �별�를 태깅하는� 사용�는 `run-id`.
+컨테�너가 파�� 접근할 수 있�� trainer-config 파�� `unity-volume` 안� 둘 것� 권장합니다.
+
+`3DBall` 환경 실행파�� 학습하기 위해 다� 명령어가 사용�니다:
+
+```sh
+docker run --name 3DBallContainer.first.trial \
+           --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
+           -p 5005:5005 \
+           balance.ball.v0.1:latest 3DBall \
+           --docker-target-name=unity-volume \
+           trainer_config.yaml \
+           --env=3DBall
+           --train \
+           --run-id=3dball_first_trial
+```
+
+�커 마운트� 대한 세부 사항� �커� [� 문서](https://docs.docker.com/storage/bind-mounts/)를 참고해 주십시오.
+
+**참고** �커를 사용해 시��� 관찰� �함한 환경� 학습할 경우, 콘테�너를 위해 할당한 �커� 디�트 메모리를 늘려야할 것입니다.
+예를 들어, [여기](https://docs.docker.com/docker-for-mac/#advanced) Mac 사용�를 위한 �커 지시사항� �주십시오.
+
+### 컨테�너 중지 � �태 저장
+
+학습 진행 �황� 만족했� 경우, �태를 저장하는 �안 `Ctrl+C` or `⌘+C` (Mac) 키를 사용하거나 다� 명령어를 통해 �커 컨테�너를 중지할 수 있습니다:
+
+```sh
+docker kill --signal=SIGINT <container-name>
+```
+
+`<container-name>` � `docker run` 명령어� 지정� 컨테�너 �름입니다. 지정하지 않으면 무작위로 �성�며`docker container ls`를 통해 확�할 수 있습니다.
+
+
+## 한글 번역
+
+해당 문서� 한글 번역� [장현준 (Hyeonjun Jang)]([https://github.com/janghyeonjun](https://github.com/janghyeonjun))� �해 진행�었습니다. 내용� 오류나 오탈�가 있는 경우 totok682@naver.com 으로 연�주시면 �사드리겠습니다.
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+<img src="https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/image-banner.png" align="middle" width="3000"/>
+
+# Unity ML-Agents Toolkit Version Release 7
+
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/)
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
+
+([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
+([all releases](https://github.com/Unity-Technologies/ml-agents/releases))
+
+**The Unity Machine Learning Agents Toolkit (ML-Agents)** - open-source проект,
+предназначенный дл� обучени� и�ку��твенного интеллекта (агента) через взаимодей�твие �о �редой, -
+игрой или �имул�цией, - и�пользу� различные методы машинного обучени�:
+обучение � подкреплением (reinforcement learning), имитационное обучение (imitation learning),
+нейро�волюци� (neuroevolution) и др. �ред�твами Python API. В проекте реализованы также и �овременные
+алгоритмы (на о�нове TensorFlow), чтобы дать возможно�ть как разработчикам игр так и любым другим,
+кто увлечен темой AI, обучать и�ку��твенный интеллект дл� 2D, 3D и VR/AR игр. Применение таких агентов
+бе�чи�ленно: например, вы можете и�пользовать их дл� управлени� NPC (опций также много - будь то
+обучение дей�тви�м в кооперативе или друг против друга), дл� те�тировани� различных вер�ий �борок
+игры, а также дл� оценки гейм дизайнер�ких решений. ML-Agents объедин�ет разработчиков игр и
+и��ледователей AI, так как предо�тавл�ет единую платформу, в рамках которой новые разработки
+в �фере и�ку��твенного интеллекта могут быть проте�тированы через движок Unity и, как �лед�твие,
+�тать до�тупнее большему количе�тву и тех, и других.
+
+## О�обенно�ти:
+
+- Более [15 примеров на Unity](docs/Learning-Environment-Examples.md).
+- Большие возможно�ти по конфигурации �реды и тренировочных �ценариев.
+- Unity SDK, который легко в�троить в код вашей игры или в ка�томную �цену в Unity
+- Два алгоритма глубинного обучени� � подкреплением (deep reinforcement learning):
+Proximal Policy Optimization (PPO) и Soft Actor-Critic (SAC). Первый алгоритм �тарает�� узнать,
+какой будет наилучший шаг в конкретной �итуации, тогда как второй - узнать в целом правила
+игры/�и�темы/�имул�ции, их закон и дей�твовать �огла�но �тому у�военному закону изменени� �реды.
+- В�троенна� поддержка дл� имитационного обучени� (Imitation Learning), которое можно �делать
+либо через клонирование поведени� (Behavioral Cloning), либо через генеративно-�о�т�зательное
+имитационное обучение (Generative Adversarial Imitation Learning - GAIL), когда одна ча�ть алгоритма
+генерирует поведение, а друга� определ�ет, похоже данное поведение на то, которое было дано как и�ходное,
+например, �амим пользователем в виде запи�и его дей�твий. Генераци� прои�ходит до тех пор, пока
+�генерированное поведение не будет определено как неотличимое или очень близкое к и�ходному.
+- Возможно�ть дл� агента игры � �амим �обой, е�ли агент обучает�� в контек�те �ценари� “�о�т�зание�:
+например, игра в футбол, где е�ть две команды.
+- ML-Agents позвол�ет на�троить череду �цен, где кажда� нова� �цена - �то у�ложнение �цены предыдущей,
+например, добавление новой преграды. �е в�егда по�тавленную задачу агент �может научить��
+выполн�ть, е�ли �реда �лишком �ложна� изначально. Дайте ему �начала �ценку попроще, когда
+он научить�� ее проходить, его перене�ет на уровень по�ложнее.
+- Обучение агента, у�тойчивого к изменени�м, � помощью возможно�ти �лучайного генерации �лементов �цены
+- Гибкий контроль агента: демон�траци� выученного поведени� только при определенных у�лови�х.
+�апример, NPC входит в контек�т “атака� - атакует так, как научил�� ранее в рамках обучающего �ценари�.
+- Обучение агента �разу на множе�тве �цен одновременно. Пред�тавьте, как он играет в футбол �разу
+на де��ти �тадионах, набира��ь опыта одновременно на них в�ех. Выгл�дит �то в Unity также,
+как и пред�тавл�ет��.
+- И�пользование [Unity Inference Engine](docs/Unity-Inference-Engine.md) дл� поддержки кро��платформенно�ти.
+- Контроль через [Python API](docs/Python-API.md) �цен.
+- Возможно�ть обернуть Unity �реду дл� обучени� как [gym](gym-unity/README.md).
+
+Дл� более детального ознакомлени� � данными о�обенно�т�ми �м. [Обзор ML-Agents] (docs/ML-Agents-Overview.md).
+
+## Релизы и Документаци�
+
+**�аш по�ледний �табильный релиз - �то `7-ой Релиз` (Release 7).
+См. [зде�ь](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md),
+чтобы начать работать � �амой по�ледней вер�ий ML-Agents.**
+
+Таблица внизу - �пи�ок в�ех наших релизов, включа� main ветку, над которой мы ведем активную работу
+и котора� может быть не�табильной. Полезна� информаци�:
+
+[Управление вер�и�ми](docs/Versioning.md) - опи�ание того, как мы работам � GitHub.
+[Релизы](https://github.com/Unity-Technologies/ml-agents/releases) - об изменени�х между вер�и�ми
+[Миграци�](docs/Migrating.md) - как перейти � более ранней вер�ии ML-Agents на новую.
+С�ылки на **документацию** - как у�тановить и начать пользовать�� ML-Agents в зави�имо�ти от вер�ии.
+В�егда и�пользуйте только ту документацию, котора� отно�ит�� к той вер�ии, которую вы у�тановили:
+
+| **Version** | **Дата релиза** | **Source** | **Документаци�** | **Загрузка** |
+|:-------:|:------:|:-------------:|:-------:|:------------:|
+| **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) |
+| **Release 7** | **16 Сент�бр�, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_7)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_7.zip)** |
+| **Release 6** | 12 �вгу�та, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_6) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_6.zip) |
+| **Release 5** | 31 Июл�, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_5) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_5_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_5.zip) |
+| **Release 4** | 15 Июл�, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_4) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_4_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_4.zip) |
+| **Release 3** | 10 Июн�, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_3) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_3_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_3.zip) |
+| **Release 2** | 20 Ма�, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_2) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_2_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_2.zip) |
+| **Release 1** | 30 �прел�, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_1) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_1_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_1.zip) |
+
+## Цитирование
+
+Е�ли ва� интере�ует Unity как платформа дл� изучени� AI, �м. [нашу работу Unity и ML-Agents](https://arxiv.org/abs/1809.02627).
+Е�ли вы и�пользуете Unity или ML-Agents дл� и��ледователь�кой работы, пожалуй�та, указывайте
+в �пи�ке и�пользуемой литературы �ледующую работу:
+Juliani, A., Berges, V., Teng, E., Cohen, A., Harper, J., Elion, C., Goy,
+C., Gao, Y., Henry, H., Mattar, M., Lange, D. (2020). Unity: A General Platform for
+Intelligent Agents. _arXiv preprint
+[arXiv:1809.02627].(https://arxiv.org/abs/1809.02627)._
+https://github.com/Unity-Technologies/ml-agents.
+
+## Дополнительные и�точники:
+
+Мы опубликовали �ерию �татей на нашем блоге про ML-Agents (**пока без перевода на ру��кий**):
+
+- (12 Ма�, 2020)
+[Announcing ML-Agents Unity Package v1.0!](https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/)
+- (28 Феврал�, 2020)
+[Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
+- (11 �о�бр�, 2019)
+[Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
+- (21 Окт�бр�, 2019)
+[The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
+- (15 �прел�, 2019)
+[Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
+- (1 Марта, 2019)
+[Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
+- (17 Декабр�, 2018)
+[ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
+- (2 Окт�бр�, 2018)
+[Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
+- (11 Сент�бр�, 2018)
+[ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
+- (26 Июн�, 2018)
+[Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
+- (19 Июн�, 2018)
+[Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
+- (24 Ма�, 2018)
+[Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
+- (15 Марта, 2018)
+[ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/)
+- (11 Декабр�, 2017)
+[Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+- (8 Декабр�, 2017)
+[Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
+- (19 Сент�бр�, 2017)
+[Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)
+- Обзор обучени� � подкреплением (
+[multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
+и
+[Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
+
+Дополнительные материалы от других авторов:
+- [A Game Developer Learns Machine Learning] (https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
+- [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
+- [ML-Agents Penguins tutorial](https://learn.unity.com/project/ml-agents-penguins)
+
+## Community and Feedback
+
+ML-Agents Toolkit - open-source проект, по�тому мы рады любой помощи. Е�ли вы хотите нам помочь,
+ознакомьте�ь, дл� начала, пожалуй�та, дл� � [гайдом, как �делать �то правильно](com.unity.ml-agents/CONTRIBUTING.md),
+и [кодек�ом поведени�](CODE_OF_CONDUCT.md).
+
+Е�ли возникли проблемы � у�тановкой и на�тройкой ML-Agents, е�ли вы хотите об�удить как лучше в�его
+обучать агентов и пр., пожалуй�та, по�мотрите возможные решени� на [форуме Unity ML-Agents](https://forum.unity.com/forums/ml-agents.453/).
+Е�ли вы не найдете нужной вам информации, начните новую тему, дав подробное опи�ани� вашей проблемы. Е�ли вы обнаружили
+какие-то баги или ошибки во врем� работы � ML-Agents, пожалуй�та, �ообщите об �том [зде�ь](https://github.com/Unity-Technologies/ml-agents/issues).
+
+�ам важно знать ваше мнение. Только на его о�нове проект Unity ML-Agents и продолжает развивать��.
+Пожалуй�та, уделите не�колько минут и [поделите�ь](https://github.com/Unity-Technologies/ml-agents/issues/1454)
+� нами тем, что могло бы улучшить наш проект.
+
+По в�ем о�тальным вопро�ам или отзыву, пишите �разу на адре� команды разработчиков ML-Agents - ml-agents@unity3d.com.
+
+## Лицензи�
+
+Apache License 2.0
diff --git "a/localized_docs/RU/docs/\320\235\320\260\321\207\320\260\320\273\320\276 \321\200\320\260\320\261\320\276\321\202\321\213.md" "b/localized_docs/RU/docs/\320\235\320\260\321\207\320\260\320\273\320\276 \321\200\320\260\320\261\320\276\321\202\321\213.md"
new file mode 100644
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--- /dev/null
+++ "b/localized_docs/RU/docs/\320\235\320\260\321\207\320\260\320\273\320\276 \321\200\320\260\320\261\320\276\321\202\321\213.md"	
@@ -0,0 +1,258 @@
+# �ачало работы
+
+В данной �татье мы разберем шаг за шагом один из [наших примеров](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md),
+обучим в нем и�ку��твенный интеллект (агента - Agent) и применим
+полученную модель в Unity �цене (scene). По�ле прочтени� �татьи, вы �можете
+обучить агента в любой другой �цене из наших примеров. Е�ли вы не работали
+� движком [Unity](https://unity.com/products), пожалуй�та, ознакомьте�ь � нашей �татьей
+[Background: Unity](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-Unity.md).
+Также, е�ли �лово�очетание “машинное обучение� вам ничего не говорит,
+прочтите дл� бы�трого ознакомлени� о нем зде�ь
+[Background: Machine Learning](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-Machine-Learning.md).
+
+![Головы � м�чами](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/balance.png)
+
+�ашей �ценой будет **3D Balance Ball**. �генты в ней - �иние кубы-платформы, у каждого
+на голове по м�чу. В�е они - копии друг друга. Каждый куб-агент пытает��
+предотвратить падение м�чика, у каждого из них е�ть возможно�ть двигать
+головой вверх-вниз (кивать), вправо-лево (качать). В данной �цене, - или иначе, �реде,
+куб-платформа - �то **�гент**, который получает награду за каждый момент времени, когда ему
+удает�� удержать м�ч на голове. �гент также наказывает�� (отрицательна� награда),
+е�ли м�ч падает � его головы. Цель обучени� - научить агента удерживать м�чик на голове.
+
+В�е, начинаем!
+
+## У�тановка
+Е�ли вы еще не у�тановили ML-Agents, �ледуйте �той
+ин�трукции по у�тановке(https://github.com/Unity-Technologies/ml-agents/tree/main/docs/localized/RU/docs/У�тановка.md).
+
+1. Далее, откройте в Unity Project, в котором наход�т�� примеры:
+1. Запу�тите Unity Hub
+1. В Projects нажмите на **Add** вверху окна.
+Перейдите в папку � ML-Agents Toolkit, найдите папку `Projects` и нажмите **Open**.
+1. В окне **Project** перейдите на `Assets/ML-Agents/Examples/3DBall/Scenes` и откройте файл-�цену `3DBall`.
+
+## О �реде/cцене Unity
+
+�гент - �то автономный actor (официальное название в документации), который взаимодей�твует
+�о _�редой_, �обира� о ней данные. В Unity, �реда �то �цена (scene), в которой е�ть один
+или более объектов - �гентов, а также, конечно, и другие объекты, � которыми взаимодей�твует агент.
+
+![Unity-Editor](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/mlagents-3DBallHierarchy.png)
+
+Обратите внимание: В Unity каждый объект �цены - �то объект кла��а GameObject. GameObject �то
+буквально контейнер дл� в�его, что ка�ает�� объекта: его физики, графики, поведени� и пр.,
+называемое компонентами. Чтобы по�мотреть в�е компоненты, �о�тавл�ющие GameObject,
+выберите GameObject в окне Scene и откройте окно Inspector. Inspector показывает
+в�е компоненты GameObject. Первое, что мы можете заметить, открыв �цену 3D Balance Ball,
+что она �о�тоит не из одного, а не�кольких кубов - агентов. Каждый такой агент в �цене
+�то незави�имый агент, но у них на в�ех одно Поведение (Behavior). Это �делано дл� у�корени�
+проце��а обучени�: в�е 12 агентов вно��т �вой вклад одновременно.
+
+## �гент
+
+�гент - �то actor (дей�твующие лицо), который делает наблюдени�
+(“видит/�лушает/пр.� - принимает заданные �игналы) и �овершает дей�тви� в �реде.
+В 3D Balance Ball компоненты �гента помещены в 12  "�гентов" - GameObjects.
+�гент как объект обладает парой �вой�тв, которые вли�ют на его поведение:
+
+- **Behavior Parameters (Параметры поведени�)** — У каждого �гента должно быть Поведение.
+Поведение определ�ет решени� �гента.
+- **Max Step (Мак�имальное количе�тво шагов)** — Определ�ет, �колько шагов допу�кает��
+�делать в рамках одной �имул�ции до завершени� �пизода, до�тижение мак�имального
+значени� также означает завершение. В 3D Balance Ball �гент перезапу�кает�� по�ле
+5000 шагов. Иными �ловами: каждый �пизод - �то �имул�ци�, в рамках которой куб
+пытает�� удержать шарик. Симул�ци� заканчивает�� либо по проше�твию 5000 шагов,
+либо по�ле определенного времени.
+
+#### Параметры поведени�: Векторное про�тран�тво наблюдений (Vector Observation Space)
+
+Перед тем как прин�ть тот или иной шаг/дей�твие, агент �обирает наблюдени� о �реде,
+ее �о�то�нии. Векторное про�тран�тво наблюдений �то вектор �о значени�ми типа
+float (� плавающей точкой, например, 3.14), которые пред�тавл�ют �обой информацию о мире,
+позвол�ющими агенту прин�ть решение о том или ином дей�твии.
+Параметры поведени� в примере 3D Balance Ball и�пользую `вектор размерно�тью 8`:
+�то значит, что в нем хранит�� 8 �лементов наблюдени�: `x` и `z` компоненты ротации агента, `x`,  `y`, `z`
+компоненты отно�ительного ме�тонахождени� шарика, а также его `�коро�ть` (�о�то�щую из трех компонентов).
+
+#### Параметры поведени�: Векторное про�тран�тво дей�твий (Vector Action Space)
+Дей�тви� �гента даны в форме ма��ива � типом float. ML-Agents Toolkit кла��ифицирует
+дей�тви� на два типа: ди�кретные и непрерывные. Пример 3D Balance Ball и�пользует
+непрерывное про�тран�тво дей�твий, пред�тавл�ющий �обой вектор чи�ел,
+которые могут мен�ть�� �коль угодно мало. По��ним: в примере размер
+`вектора дей�твий -  2`: контроль `x` and `z` компонент ротации, чтобы
+удержать шарик на голове. Так вот, изменение (без учета выделенной пам�ти)
+может быть равным 0,1. Может быть равным и 0.11, а может и на 0.1000001…
+и так далее - �то и е�ть �вой�тво непрерывно�ти.
+Еще пример дл� понимани�: �кажем, ваш ро�т �о�тавл�ет 180 �м.
+Е�ли мы его начнем измер�ть более точной линейкой, окажет��, что он
+на �амом деле 180,01 cм. Е�ли еще более точной линейкой, то 180,012345 �м.
+И так далее, при точно�ти линейки, �трем�щей�� к бе�конечно�ти,
+уточнение вашего ро�та также �тремить�� к бе�конечно�ти.
+
+## Запу�к заранее обученной (предтренированной) модели
+
+Мы включили в �вои примеры заранее обученные модели (файлы � ра�ширением `.nn`)
+и и�пользовали [Unity Inference Engine](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Unity-Inference-Engine.md),
+чтобы запу�тить их в Unity. В �том разделе мы во�пользуем�� одной
+из таких моделей дл� 3D Ball.
+
+1. В окне **Project** пройдите в папку `Assets/ML-Agents/Examples/3DBall/Prefabs`.
+   Ра�кройте `3DBall` и кликните на префаб (prefab) `Agent`. Вы должны будете
+   увидеть префаб `Agent` в окне **Inspector**.
+
+   **Обратите внимание**: Кубы-платформы в �цене `3DBall` были �деланы и�пользу� префаб `3DBall`.
+   По�тому при необходимо�ти вне�ти изменени� в каждую из платформ,
+   вы можете про�то изменить �ам префаб вме�то �того.
+
+   ![Platform Prefab](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/platform_prefab.png)
+
+1. В окне **Project**, перене�ите модель (Model) поведени� **3DBall**, наход�щую�� в `Assets/ML-Agents/Examples/3DBall/TFModels`
+   в �вой�тво `Model` в компоненте `Behavior Parameters (Script)` в окне **Inspector** GameObject’a Agent.
+
+   ![3dball learning brain](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/3dball_learning_brain.png)
+
+1. Теперь каждый `�гент` на каждой платформе `3DBall` в окне **Hierarchy**
+   должен �одержать модель поведени� **3DBall** в `Behavior Parameters`.
+   **Обратите внимание** : Вы можете измен�ть разные игровые объекты на �цене,
+   выбрав их разом, и�пользу� �троку пои�ка в Hierarchy �цены (Scene Hierarchy).
+
+1. В **Inference Device** дл� данной модели выберите CPU.
+
+1. �ажмите на кнопку **Play** в Unity - наши платформы будут удерживать м�чик, и�пользу� ранее обученную модель.
+
+## Обучение новой модели � и�пользованием обучени� � подкреплением (Reinforcement Learning)
+
+В любой �реде, которую вы �делаете �ами, вам нужно будет обучить агентов � нул�, чтобы
+у ва� �гененировал�� файл модели (поведени�). В данном разделе мы покажаем как и�пользовать алгоритмы
+обучени� � подкреплением (reinforcement learning), �вл�ющие�� ча�тью пакета ML-Agents
+Python. �иже пред�тавлена команда `mlagents-learn`, чтобы управл�ть как обучением, так и генерацией модели.
+
+### Обучение �реды
+
+1. Откройте командную �троку (терминал).
+1. Выберите папку � клонированным репозиторием.
+  Обратите внимание: е�ли вы и�пользовали дефолтную у�тановку,
+  вам должно быть до�тупно выполнение `mlagents-learn` из любой директории/папки.
+1. Выполните `mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun`.
+   - `config/ppo/3DBall.yaml` - путь к файлу � дефолтной конфигурации обучени�, который мы предо�тавили.
+   Папка config/ppo �одержит файлы конфигов обучени� дл� в�ех наших примеров, включа� 3DBall.
+   - `run-id` - уникальное им� дл� данной обучающий �е��ии.
+1. Когда отобразит�� �ообщение _"Start training by pressing the Play button in the Unity Editor"_
+(_“�ачните обучение нажатием кнопки “Play� в редакторе Unity�_) -
+нажмите **Play** в Unity, чтобы начать обучение в Editor.
+
+Е�ли `mlagents-learn` запущено корректно и начал�� проце�� обучени�,
+вы должны будете увидеть что-то вроде �того:
+
+```console
+INFO:mlagents_envs:
+'Ball3DAcademy' started successfully!
+Unity Academy name: Ball3DAcademy
+
+INFO:mlagents_envs:Connected new brain:
+Unity brain name: 3DBallLearning
+        Number of Visual Observations (per agent): 0
+        Vector Observation space size (per agent): 8
+        Number of stacked Vector Observation: 1
+        Vector Action space type: continuous
+        Vector Action space size (per agent): [2]
+        Vector Action descriptions: ,
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
+        batch_size:          64
+        beta:                0.001
+        buffer_size:         12000
+        epsilon:             0.2
+        gamma:               0.995
+        hidden_units:        128
+        lambd:               0.99
+        learning_rate:       0.0003
+        max_steps:           5.0e4
+        normalize:           True
+        num_epoch:           3
+        num_layers:          2
+        time_horizon:        1000
+        sequence_length:     64
+        summary_freq:        1000
+        use_recurrent:       False
+        memory_size:         256
+        use_curiosity:       False
+        curiosity_strength:  0.01
+        curiosity_enc_size:  128
+        output_path: ./results/first3DBallRun/3DBallLearning
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
+```
+
+Обратите внимание, что �редн�� награда (Mean Reward), увеличивает�� от одной
+фазы обучени� к другой. Это �игнал, что проце�� обучени� проходит у�пешно.
+
+**Примечание:** Вы можете обучать агента, и�пользу� вме�то Editor’a и�полн�емые файлы.
+См. ин�трукцию [Using an Executable](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Executable.md).
+
+### �аблюдение за тренировочным проце��ом
+
+�ачав обучать модель � помощью `mlagents-learn` в директории `ml-agents` по�вит�� папка `results`.
+Чтобы более детально по�мотреть проце�� обучени�, вы можете и�пользовать [TensorBoard](https://www.tensorflow.org/tensorboard).
+Выполните в командной �троке �ледующую команду:
+
+```sh
+tensorboard --logdir results
+```
+
+В вашем браузере наберите `localhost:6006`, чтобы по�мотреть �тати�тики TensorBoard.
+В рамках �того примера �амой важной из них �вл�ет�� `Environment/Cumulative Reward`
+(�реда/�уммарна� награда за �пизод), котора� должно увеличивать�� в проце��е обучени�,
+приближа��ь к 100 - мак�имально возможное значение, которого может до�тигнуть агент.
+
+![Example TensorBoard Run](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/mlagents-TensorBoard.png)
+
+## Внедрение модели в �реду Unity
+
+Когда проце�� обучени� завершен и модель �охранена
+(об �том будет �пециальное уведомление - `Saved Model`),
+вы можете добавить ее в проект Unity и и�пользовать ее в рамках
+тех Agents, на базе которых она обучала�ь (иначе говор�, применить �нова к тем же кубам-платформам).
+**Обратите внимание:**  �е закрывайте окно Unity, когда по�вит�� уведомление Saved Model`.
+Подождите пока �то не будет �делать автоматиче�ки или нажмите
+`Ctrl+C` в командной �троке. Е�ли вы закроете окно вручную, `.nn` file � обученной моделью
+не будет �к�портирован в папку `ml-agents`.
+
+Е�ли вы прервали обучение через `Ctrl+C` и хотите его продолжить,
+выполните ту же команду, припи�ав к ней `--resume`:
+
+```sh
+mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume
+```
+
+Ваша обученна� модель будет находит�� в каталоге вида `results/<run-identifier>/<behavior_name>.nn`,
+где `<behavior_name>` �то название поведени� (`Behavior Name`) агента, на базе которого и
+была обучена модель. Этот файл �одержит в �ебе по�леднюю �генерированную вер�ию.
+Теперь вы можете применить модель к вашим �гентам, �леду� ин�трукции,
+котора� идентична той, что была [выше](#запу�к-заранее-обученной-предтренированной-модели):
+
+1. Перене�ите файл модели в `Project/Assets/ML-Agents/Examples/3DBall/TFModels/`.
+1. Откройте Unity Editor (редактор Unity) и выберите **3DBall** �
+  цену как опи�ано [выше](#запу�к-заранее-обученной-предтренированной-модели).
+1. Выберете **3DBall** префаб (prefab) объекта �гента.
+1. Переме�тите `<behavior_name>.nn` файл из окна Project,
+  которых находит�� в Editor’e, в **Model** в **Ball3DAgent** в окне Inspector.
+1. �ажмите кнопку **Play** вверху Editor’a.
+
+### Следующие шаги
+
+- Дл� дополнительной информации о ML-Agents Toolkit,
+  �м. [Обзор ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/ML-Agents-Overview.md).
+- [Создание �воих �цен](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Create-New.md)
+  дл� обучени� агентов.
+- Обзор более �ложных �ред обучени�, которые е�ть в каче�тве
+  примера в [ML-Agents - Example Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md)
+- Информаци� про различные опции обучени� - [Training ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md)
diff --git "a/localized_docs/RU/docs/\320\243\321\201\321\202\320\260\320\275\320\276\320\262\320\272\320\260.md" "b/localized_docs/RU/docs/\320\243\321\201\321\202\320\260\320\275\320\276\320\262\320\272\320\260.md"
new file mode 100644
index 0000000000000000000000000000000000000000..bbcf313781204562ffbe30a40f6f490b0d11e63f
--- /dev/null
+++ "b/localized_docs/RU/docs/\320\243\321\201\321\202\320\260\320\275\320\276\320\262\320\272\320\260.md"
@@ -0,0 +1,154 @@
+# У�тановка
+
+ML-Agents Toolkit �о�тоит из не�кольких компонентов:
+
+- Специальный пакет Unity ([`com.unity.ml-agents`](https://github.com/Unity-Technologies/ml-agents/tree/main/com.unity.ml-agents))
+� Unity C# SDK, который будет интегрирован в Unity �цену.
+
+- Три Python пакета:
+  - ([`mlagents`](https://github.com/Unity-Technologies/ml-agents/tree/main/ml-agents)) - пакет � алгоритмами машинного обучени�,
+  которые позвол�ют обучить и�ку��твенный интеллект. Большин�тву пользователей ML-Agents до�таточно напр�мую у�тановить mlagents.
+  - ([`mlagents_envs`](https://github.com/Unity-Technologies/ml-agents/tree/main/ml-agents-envs) �одержит Python
+  API дл� взаимодей�тви� � Unity �ценой. Этот пакет управл�ет передачей данных между Unity �ценой и алгоритмами
+  машинного обучени�, реализованных на Python. Пакет mlagents зави�ит от mlagents_envs.
+  - ([`gym_unity`](https://github.com/Unity-Technologies/ml-agents/tree/main/gym-unity)) - позвол�ет обернуть вашу �цену
+  в Unity в �реду OpenAI Gym.
+- Unity [Project](https://github.com/Unity-Technologies/ml-agents/tree/main/Project),
+�одержащий [примеры �цены](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md),
+где реализованы различные возможно�ти ML-Agents дл� нагл�дно�ти.
+
+Итого, чтобы у�тановить и и�пользовать ML-Agents, вам нужно:
+- У�тановить Unity (2022.3 или более позднюю вер�ию)
+- У�тановить Python (3.8.13 или более позднюю вер�ию)
+- Клонировать �тот репозиторий (Опционально)
+  - __Примечание:__ е�ли вы не �клонируете репозиторий, тогда у ва� не будет до�тупа к примерам и конфигураци�м обучени�.
+  Также, раздел "�ачало работы" подразумевает, что вы �делали клон репозитори�.
+- У�тановить пакет `com.unity.ml-agents` Unity
+- У�тановить пакет `mlagents` Python
+
+### У�тановка **Unity 2022.3** или более поздней вер�ии
+
+[Загрузите](https://unity3d.com/get-unity/download) и у�тановите движок Unity.
+Мы очень рекомендуем у�тановить Unity через Unity Hub, так как по�ледний позвол�ет управл�ть разными вер�и�ми движка.
+
+### У�тановка **Python 3.8.13** или более поздней вер�ии
+
+Мы рекомендуем [у�тановить](https://www.python.org/downloads/) Python 3.8.
+Е�ли вы и�пользуете Windows, пожалуй�та, у�тановите x86-64 вер�ию, а не x86.
+Е�ли у ва� нет �и�темы управлени� пакетами `pip3` дл� Python, пожалуй�та,
+во�пользуйте�ь [�тими ин�трукци�ми](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
+дл� ее у�тановки. И хот� мы более не поддерживаем у�тановку пакетов дл� Anaconda на Windows,
+ин�трукци� в�е еще до�тупна - [Windows Anaconda Installation (Deprecated)](Installation-Anaconda-Windows.md)
+
+### Клонирование репозитори� ML-Agents Toolkit (Опционально)
+
+Теперь, когда вы у�тановили Unity и Python, вы можете начать у�тановку пакетов.
+Дл� �того не об�зательно клонировать репозиторий, но е�ли вы хотите проте�тировать
+наши примеры, �делать �то будет необходимо (некоторые наши уроки и гайды �того требуют)
+
+```sh
+git clone --branch release_7 https://github.com/Unity-Technologies/ml-agents.git
+```
+Флаг `--branch release_7` у�тановит по�ледную �табильную рабочую вер�ию.
+Е�ли ее не набрать, у�тановит�� ма�тер вер�и�, котора�, возможно, будет работать не�табильно.
+
+#### Продвинута� локальна� у�тановка дл� разработки
+
+Вам нужно будет �клонировать репозиторий, е�ли вы планируете вно�ить изменени�
+или что-то добавл�ть в ML-Agents дл� �воих целей. Или вы планируете �делать
+�ти изменени� общедо�тупными, пожалуй�та, �клонируйте main branch
+(без флага `--branch release_7` в команде выше).
+Далее �м. [гайд про публикацию правок](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents/CONTRIBUTING.md).
+
+### У�тановка пакета `com.unity.ml-agents` дл� Unity
+Unity ML-Agents C# SDK �то пакет Unity. Вы можете у�тановить его пр�мо из пакетного менеджера,
+Unity Package Manager. Пожалуй�та, убедите�ь, что вы нажали 'Preview Packages' в 'Advanced',
+чтобы найти �тот пакет.
+
+**ОБР�ТИТЕ В�ИМ��ИЕ:** Е�ли вы не видите пакет ML-Agents в Package Manager,
+пожалуй�та, �ледуйте шагам, напи�анных [ниже](#продвинута�-локальна�-у�тановка-дл�-разработки-1)
+
+#### Продвинута� локальна� у�тановка дл� разработки
+
+Вы можете добавить �амо�то�тельно com.unity.ml-agents (из репозитори�, который вы �клонировали)
+к вашему проекту:
+1. `Меню Window` -> `Package Manager`.
+1. В `Package Manaer` кликнете на кнопку “+�.
+1. Выберите `Add package from disk`...
+1. Зайдите в папку `com.unity.ml-agents`
+1. Выберите файл `package.json`.
+
+**ПРИМЕЧ��ИЕ:** В Unity 2018.4 кпопка `+` находит�� внизу �права около �пи�ка пакетов.
+� в Unity 2019.3 она - �верху �лева.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/unity_package_manager_window.png"
+       alt="Unity Package Manager Window"
+       height="300"
+       border="10" />
+  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/unity_package_json.png"
+     alt="package.json"
+     height="300"
+     border="10" />
+</p>
+
+Е�ли вы �обираете�ь проте�тировать примеры из нашей документации, про�то откройте папку `Projects` в Unity и вперед!
+
+### У�тановка пакета `mlagents` Python
+
+У�тановка `mlagents` Python требует у�тановки и других Python пакетов, от которых зави�ит `mlagents`.
+По�тому е�ли у ва� у�тановлены их более ранние вер�ии, могут быть ошибки у�тановки.
+Чтобы �того избежать, мы рекомендуем и�пользовать Python Virtual Environments.
+Virtual Environments позвол�ет управл�ть ранее у�тановленными вер�и�ми так,
+что дл� одного проекта будут одни зави�имо�ти, а дл� другого - другие на каждой
+из операционных �и�тем - Mac / Windows / Linux.
+Гайд по [Virtual Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Using-Virtual-Environment.md).
+
+Чтобы у�тановить `mlagents` Python, активируйте вашу виртуальную �реду
+и выполните �ледующее в командной �троке:
+
+```sh
+pip3 install mlagents
+```
+
+Обратите внимание, что �та команда у�тановит `mlagents`,
+и�пользу� PyPi, а не из клонированного репозитори�.
+Е�ли у�тановка прошла у�пешно, вы �можете выполнить
+команду `mlagents-learn --help`, по�ле
+и�полнени� которой вы увидите набор до�тупных команд `mlagents-learn`.
+У�танавлива� mlagents, вы также у�танавливаете и в�е то, на базе чего он �делан -
+�м. [setup.py file](https://github.com/Unity-Technologies/ml-agents/blob/main/ml-agents/setup.py).
+В том чи�ле [TensorFlow](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-TensorFlow.md)
+(требует поддержки CPU w/ AVX).
+
+#### Продвинута� локальна� у�тановка дл� разработки
+Е�ли вы хотите �не�ти изменени� в пакеты `mlagents` или `mlagents_envs`,
+вам �ледует у�тановить их из клонированного репозитори�, а не через PyPi.
+У�тановить и тот и другой пакет нужно будет по отдельно�ти.
+Перейдите к корень репозитори�, выполните �ледующие команды:
+
+```sh
+pip3 install -e ./ml-agents-envs
+pip3 install -e ./ml-agents
+```
+
+Выполн�� команды � �уффик�ом `� -e`, вы вно�ите изменени� в файлы Python напр�мую.
+Важно: у�тановить данные пакеты надо ровно в таком пор�дке, как �то указано выше,
+так как `mlagents` зави�ит от `mlagents_envs`. В ином �лучае  `mlagents_envs`
+будут загружены и у�тановлены через PyPi.
+
+### Следующие шаги
+
+Гайд ["�ачало работы"](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/localized/RU/docs/�ачало-работы.md)
+�одержит в �ебе �ерию коротких обучающих уроков по на�тройке ML-Agents внутри Unity
+и запу�к предобученной модели, а также уроки по �озданию и дополнению �цен,
+где будет обучать�� ваш агент.
+
+### Помощь
+
+Е�ли у ва� возникли трудно�ти � ML-Agents, пожалуй�та, обратите�ь к [FAQ](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/FAQ.md)
+и к �транице [Limitations](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Limitations.md).
+Е�ли �то не решило вашу проблему, �оздайте запро� [зде�ь](https://github.com/Unity-Technologies/ml-agents/issues),
+предо�тавив информацию о операционной �и�теме, вер�ии Python и �ообщение об ошибки (е�ли оно е�ть).
+
+
diff --git a/localized_docs/TR/README.md b/localized_docs/TR/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..51109e5f57d25deed0a39bf68c2ed46e9a511374
--- /dev/null
+++ b/localized_docs/TR/README.md
@@ -0,0 +1,65 @@
+<img src="docs/images/image-banner.png" align="middle" width="3000"/>
+
+# Unity ML-Agents Toolkit(Release 8) Türkçe Dokümantasyon Sayfası
+
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/)
+
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
+
+([son sürüm](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
+([tüm sürümler](https://github.com/Unity-Technologies/ml-agents/releases))
+
+**Unity Machine Learning Agents Araç Seti** (ML-Agents) oyun ve simülasyon projeleri için akıllı ajanlar(botlar, etkiye uğrayan temsilciler) eğitecek ortamları sağlayan açık kaynaklı bir projedir. Ajanlar(Agents) pekiştirmeli öğrenme, taklit ederek öğrenme, nöroevrim veya diğer makine öğrenimi metodlarıyla kullanımı kolay Python API ile eğitilebilir. Ayrıca; oyun geliştiricilere ve hobi olarak yapanlara 2B, 3B ve VR / AR oyunlarına yönelik akıllı ajanları kolayca eğitmesini sağlamak için son teknoloji algoritmaları (TensorFlow tabanlı) kolayca uygulamalarını da sağlıyoruz. Bu eğitimli ajanlar(karakterler), NPC davranışı kontrol etme(çeşitli
+çoklu bot ve rakip gibi ayarlanabilen), oyun yapılarının otomatik testleri, farklı oyun tasarımlarının yayın öncesi değerlendirilmesi gibi birden çok amaç için kullanılabilir. ML-Agents Araç Seti hem oyun geliştiricileri hem de yapay zeka araştırmacıları için karşılıklı olarak faydalıdır. Yapay zekadaki gelişmelerin Unity'nin zengin ortamlarında değerlendirilebileceği, daha geniş araştırma ve oyun geliştirici toplulukları için erişilebilir hale getirilebileceği merkezi bir platform sağlar.
+
+**Kısacası;** Unity oyun motorunda kendi başına eğitildikleri yönde kararlar alabilen karakter, araç gibi objeleri eğitmemizi sağlayan bir araç setidir.
+
+
+## Özellikleri
+
+- 15+ [örnek Unity ortamları](docs/Learning-Environment-Examples.md)
+- Birden çok ortam yapılandırması ve eğitim senaryosu desteği
+- Oyununuza veya özel Unity sahnenize entegre edilebilen esnek Unity SDK
+- İki derin güçlendirme öğrenme algoritması, Proximal Policy Optimization (PPO) ve
+  Soft Actor-Critic (SAC) kullanarak eÄŸitme
+- Davranışsal Klonlama veya Üretken Tartışmalı Taklit Öğrenme yoluyla
+  Taklit Öğrenme için yerleşik destek
+- Düşman senaryolarında ajanları(agents) eğitmek için kendi kendine oyun mekanizması
+- Karmaşık görevler için kolayca tanımlanabilen Müfredat Öğrenme(Curriculum Learning) senaryoları
+- Rastgele ortamlar kullanarak kararlı ajanlar eğitin
+- İsteğe Bağlı Karar Verme ile esnek ajan kontrolü
+- Birden çok eş zamanlı Unity ortamı örneği kullanarak eğitim
+- [Unity Inference Engine](docs/Unity-Inference-Engine.md) desteÄŸi
+- [Python API ile](docs/Python-API.md) Unity ortamını kontrol etme
+- Unity - [gym](gym-unity/README.md) desteÄŸi
+
+[ML-Agents Genel Bakış](docs/ML-Agents-Overview.md) sayfasında tüm özellikleri açıklanmıştır.
+
+## Sürümler & Dokümantasyon
+
+**Unity ML-Agents Araç Seti son kararlı sürümü `Release 8` 'dir.
+[Buradan](docs/Readme.md) en son ML-Agents Türkçe dokümantasyonuna giderek öğrenmeye ve kullanmaya başlayabilirsiniz.**
+
+- [Sürümler sayfasında](https://github.com/Unity-Technologies/ml-agents/releases) tüm versiyonları inceleyebilirsiniz.
+- [Yapılan değişiklikler sayfasına](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Migrating.md) giderek güncellemelerle hangi özelliklerin değiştiğini inceleyebilirsiniz.
+- [İngilizce](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Readme.md) orjinal dokümantasyonuna linkten erişebilirsiniz.
+
+## Makale
+
+Bir YapayZeka platformu olarak Unity tartışmasıyla ilgilenen bir araştırmacıysanız, [Unity ve ML-Agents Toolkit](https://arxiv.org/abs/1809.02627) hakkındaki referans makalemizin bir ön baskısına bakın.
+
+Ek kaynaklara [buradan](https://github.com/Unity-Technologies/ml-agents#additional-resources) ulaÅŸabilirsiniz.
+
+## Topluluk ve Geri Bildirim
+
+Topluluğuza katılmak için [Unity ML-Agents forum](https://forum.unity.com/forums/ml-agents.453/) linkine veya [GitHub issue başvurusu](https://github.com/Unity-Technologies/ml-agents/issues) ile belirli özellik taleplerinde bulunabilirsiniz.
+
+Diğer sorularınız veya geri bildirimleriniz için doğrudan ulaşabileceğiniz mail adresi : ml-agents@unity3d.com
+
+Dokümantasyonu hazırlayan : Eydi Gözeneli
+Hatalı çeviriler ve geri bildirim için iletişim : w3.eydi@gmail.com
+
+
+## License
+
+[Apache License 2.0](LICENSE)
diff --git a/localized_docs/TR/docs/Getting-Started.md b/localized_docs/TR/docs/Getting-Started.md
new file mode 100644
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--- /dev/null
+++ b/localized_docs/TR/docs/Getting-Started.md
@@ -0,0 +1,178 @@
+# Başlangıç Rehberi
+
+Bu rehber, Unity'de [örnek ortamlarımızdan](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Examples.md) birini açma, içinde bir Agent 'i eğitme ve eğitilmiş modeli Unity ortamına yerleştirme sürecini baştan sona anlatır. Bu rehberi bitirdikten sonra örnek ortamlardan herhangi birini kullanabilecek hale geleceksiniz. [Unity Engine](https://unity.com/products) 'e aşina değilseniz, [Background: Unity](https://unity.com/products) sayfasına bakarak yararlı noktaları öğrenebilirsiniz. Ek olarak, makine öğrenimine aşina değilseniz, kısa bir genel bakış ve ipuçları için [Background: Machine Learning](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Background-Machine-Learning.md) sayfamızı görüntüleyin.
+
+![3D Balance Ball](images/balance.png)
+
+Bu rehber için, biz bir dizi `agent(temsilci)` küpleri ve topları içeren **3D Balance Ball** ortamını kullanacağız (hepsi birbirinin kopyası). Her ajan(agent - temsilci) küpü yatay ve dikey olarak dönerek topun düşmesini önlemeye çalışır. Bu Unity ortamında, ajan **Agent** küp her adımında topu dengelediğinde ödül kazanacaktır. Bir temsilci`(agent)` ayrıca, topu düşürdüğü için olumsuz bir ödülle cezalandırılır. Eğitim sürecinin amacı, temsilcilerin topu kafalarında dengelemeyi öğrenmelerini sağlamaktır. Ödül - ceza mekanizmasıyla yapay zekayı eğitmeyi amaçlıyoruz.
+
+Hadi başlayalım!
+
+## Kurulum
+
+Henüz yapmadıysanız, [kurulum talimatlarını](Installation.md) izleyin. Daha sonra Unity ortamlarını içeren `Project` klasörünü açın:
+
+1. Unity Hub'ı başlatın.
+1. Projects iletişim kutusunda, pencerenin üst kısmındaki `Add` seçeneğini seçin.
+1. Açılan dosya diyalog penceresini kullanarak indirdiğiniz ML-Agents Toolkit içindeki `Project` klasörünü bulun ve `Open`'a tıklayın. Açılması için güncelleme isterse, projeyi güncelleyin.
+1. Unity içerisinde **Project** penceresinde
+   `Assets/ML-Agents/Examples/3DBall/Scenes` dosyasını bulun ve  `3DBall` sahne dosyasını açın.
+
+## Unity Ortamlarını Anlamak
+
+Bir ajan`(agent)`, çevreyi gözlemleyen ve onunla etkileşime giren özerk bir aktördür. Unity bağlamında, ortam, bir veya daha fazla Ajan nesnesini ve tabii ki bir ajanın etkileşimde bulunduğu diğer varlıkları içeren bir sahnedir.
+
+![Unity Editor](images/mlagents-3DBallHierarchy.png)
+
+**Note:** Unity'de, bir sahnedeki her şeyin temel nesnesi _GameObject_'tir. GameObject, temelde davranışlar, grafikler, fizik vb. dahil diğer her şey için bir kapsayıcıdır. Bir GameObject'i oluşturan bileşenleri görmek için, Sahne penceresinde GameObject'i seçin ve Inspector penceresini açın. Inspector penceresi, GameObject üzerindeki her bileşeni gösterir.
+
+3D Balance Ball sahnesini açtıktan sonra fark edebileceğiniz ilk şey, bir değil, birkaç ajan küpü içermesidir. Sahnedeki her ajan küpü bağımsız bir ajandır, ancak hepsi aynı davranışı paylaşır. On iki temsilcinin tümü eğitime paralel olarak katkıda bulunduğundan, 3D Balance Ball eğitimini bu şekilde hızlandırabilirsiniz.
+
+### Agent
+
+Temsilci`(agent - ajan)`, ortamı gözlemleyen ve çevreyi harekete geçiren aktördür. 3B Balance Ball sahne ortamında, Agent bileşenleri on iki `Agent` GameObject'e yerleştirilir. Temel Agent nesnesinin davranışını etkileyen birkaç özelliği vardır:
+
+- **Behavior Parameters** — Her Agent'in bir Behavior(Davranış Modeli) olmalıdır. Davranış`(Behavior)` ajanın nasıl karar vereceğini belirler.
+- **Max Step** — Agent'ın bölümü bitirmeden önce kaç simülasyon adımı gerçekleştireceğini belirler. 3D Balance Ball'da, bir Ajan 5000 adımdan sonra yeniden başlatılır.
+
+#### Davranış Parametreleri(Behavior Parameters): Vektör Gözlem Uzayı(Vector Observation Space)
+
+Bir karar vermeden önce, bir temsilci dünyadaki durumu hakkındaki gözlemini toplar. Vektör gözlemi, ajanın karar vermesi için ilgili bilgileri içeren kayan nokta sayılarının bir vektörüdür.
+
+3D Balance Ball örneğinin davranış(behavior) parametreleri, 8 Uzay Boyutu`(Space Size)` kullanır.
+Bu, Ajanın gözlemlerini içeren özellik vektörünün sekiz öğe içerdiği anlamına gelir: agent küpün dönüşünün `x` ve `z` bileşenleri ve topun göreceli konumu ve hızının `x`, `y` ve `z` bileşenleri.
+
+#### Davranış parametreleri : Vektör Eylem Alanı - (Behavior Parameters: Vector Action Space)
+
+Bir Ajana çeşitli değerler dizisi şeklinde talimatlar verilir.
+ML-Agent Toolkit, eylemleri iki türe sınıflandırır: sürekli ve ayrık(continuous and discrete). 3D Balance Ball örneği, sürekli olarak değişebilen bir sayı vektörü olan sürekli eylem alanını kullanmak üzere programlanmıştır. Daha spesifik olarak, topu kafasında dengede tutmak için kendisine uygulanacak `x` ve `z` dönüşlerinin miktarını kontrol etmek için 2 Uzay Boyutu`(Space Size)` kullanılabilir.
+
+## Önceden eğitilmiş modeli çalıştırma
+
+Ajanlarımız (`.nn` dosyaları) için önceden eğitilmiş modeller ortamımıza ekliyoruz  ve bu modelleri Unity içinde çalıştırmak için [Unity Inference Engine]([Unity Inference Engine](Unity-Inference-Engine.md)'i kullanıyoruz. Bu bölümde, 3D Ball örneği için önceden eğitilmiş modeli bir sinir ağı kullanacağız.
+
+1. **Project** penceresinde `Assets/ML-Agents/Examples/3DBall/Prefabs` klasörüne gidin. `3DBall`'ın içerisini genişletin ve `Agent` hazır yapısına tıklayın. `Agent` hazır yapısını **Inspector** penceresinde görmelisiniz.
+
+   **Not**: `3DBall` sahnesindeki platformlar, `3DBall` prefab kullanılarak oluşturuldu. 12 platformu tek tek güncellemek yerine, `3DBall` prefabını değiştirerek diğer platformların da yapısını güncelleyebilirsiniz.
+
+   ![Platform Prefab](images/platform_prefab.png)
+
+1. **Project** penceresinde, `Assets/ML-Agents/Examples/3DBall/TFModels`'te bulunan `3DBall` sinir ağı modelini(`.nn` uzantılı dosya), **Agent** GameObject **Inspector** penceresindeki `Behavior Parameters (Script)` bileşeni altındaki `Model` özelliğine sürükleyin.
+
+   ![3dball learning brain](images/3dball_learning_brain.png)
+
+1. **Hierarchy** pencerelerindeki her bir `3DBall` altındaki her Agent'ın artık `Behavior Parameters`'inde `Model` olarak `3DBall`'ı içerdiğini fark etmelisiniz. Prefab'daki modeli değiştirdiğimizde bütün nesneleri etkiledik.
+   **Not** : Sahne **Hierarchy**'deki arama çubuğunu kullanarak bir sahnedeki birden fazla oyun nesnesini aynı anda seçerek değiştirebilirsiniz.
+1. **Inference Device**'yi bu model için `CPU` olarak kullanılacak şekilde ayarlayın.
+1. Unity editöründeki **Oynat(Play)** düğmesine tıklayın ve platformların önceden eğitilmiş modeli kullanarak topları dengelediğini göreceksiniz.
+
+## Pekiştirmeli(Reinforcement) Öğrenme ile yeni bir model eğitmek
+
+Bu ortamdaki aracılar için önceden eğitilmiş `.nn` dosyaları sağlasak da, kendi oluşturduğunuz herhangi bir ortamda yeni bir model dosyası oluşturmak için sıfırdan eğitim aracıları gerekir. Bu bölümde, bunu başarmak için ML-Agents Python paketinin parçası olan pekiştirmeli öğrenme algoritmalarının nasıl kullanılacağını göstereceğiz. Hem eğitim hem de çıkarım aşamalarını yapılandırmak için kullanılan argümanları kabul eden uygun bir `mlagents-learn` komutu sağladık.
+
+### Ortamı eğitmek
+
+1. Bir komut veya terminal penceresi açın.
+1. Terminalde `ml-agent` deposunu klonladığınız klasöre gidin. **Not**: [Varsayılan kurulumu](Installation.md) izlediyseniz, `mlagents-learn`'ü herhangi bir dizinden çalıştırabilmelisiniz.
+1. `mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun` komutunu çalıştırın.
+   - `config/ppo/3DBall.yaml` sağladığımız varsayılan eğitimin konfigürasyon dosyasıdır. `config/ppo` klasörü, 3DBall dahil tüm örnek ortamlarımız için eğitim yapılandırma dosyalarını içerir.
+   - `run-id` bu eğitime özel benzersiz bir addır.
+1. Ekranda _"Start training by pressing the Play button in the Unity Editor"_ mesajı görüntülendiğinde, editör'de eğitime başlamak için Unity'deki **Play(Oynat)** düğmesine basabilirsiniz.
+
+`mlagents-learn` doğru şekilde çalışırsa ve eğitime başlarsa, şuna benzer bir şey görmelisiniz:
+
+```console
+INFO:mlagents_envs:
+'Ball3DAcademy' started successfully!
+Unity Academy name: Ball3DAcademy
+
+INFO:mlagents_envs:Connected new brain:
+Unity brain name: 3DBallLearning
+        Number of Visual Observations (per agent): 0
+        Vector Observation space size (per agent): 8
+        Number of stacked Vector Observation: 1
+        Vector Action space type: continuous
+        Vector Action space size (per agent): [2]
+        Vector Action descriptions: ,
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
+        batch_size:          64
+        beta:                0.001
+        buffer_size:         12000
+        epsilon:             0.2
+        gamma:               0.995
+        hidden_units:        128
+        lambd:               0.99
+        learning_rate:       0.0003
+        max_steps:           5.0e4
+        normalize:           True
+        num_epoch:           3
+        num_layers:          2
+        time_horizon:        1000
+        sequence_length:     64
+        summary_freq:        1000
+        use_recurrent:       False
+        memory_size:         256
+        use_curiosity:       False
+        curiosity_strength:  0.01
+        curiosity_enc_size:  128
+        output_path: ./results/first3DBallRun/3DBallLearning
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
+INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
+```
+
+Eğitim ilerledikçe ekrana yazdırılan `Mean Reward` değerinin nasıl arttığına dikkat edin. Bu, eğitimin başarılı olduğunun olumlu bir işaretidir.
+
+**Not**: Editör yerine yürütülebilir bir dosya kullanarak eğitim yapabilirsiniz. Bunu yapmak için, [Using an Executable](Learning-Environment-Executable.md) bölümündeki talimatları izleyin.
+
+### Eğitim İlerlemesini Gözlemlemek
+
+`mlagents-learn` kullanarak eğitime başladığınızda, önceki bölümde anlatıldığı şekilde `ml-agent` dizini bir `results` dizini içerecektir. Eğitim sürecini daha detaylı gözlemlemek için TensorBoard'u kullanabilirsiniz. Komut satırından şunu çalıştırın:
+
+```sh
+tensorboard --logdir results
+```
+
+Ardından, aşağıda gösterildiği gibi TensorBoard özet istatistiklerini görüntülemek için tarayıcınızda `localhost:6006` adresine gidin. Bu bölümün amaçları doğrultusunda, en önemli istatistik, eğitim boyunca artması gereken ve sonunda temsilcinin toplayabileceği maksimum ödül olan `100`'e yaklaşan `Environment/Cumulative` ödülüdür.
+
+![Example TensorBoard Run](images/mlagents-TensorBoard.png)
+
+## Modeli Unity Ortamına yerleştirme
+
+Eğitim süreci tamamlandığında ve eğitim süreci modeli (`Saved Model` mesajı ile gösterilir) kaydedildikten sonra, onu Unity projesine ekleyebilir ve uyumlu Agents'lar ile (modeli oluşturan ajanlar) kullanabilirsiniz. **Not:**
+`Saved Model` mesajı görünmeden Unity penceresini kapatmayın. Eğitim sürecinin pencereyi kapatmasını bekleyin veya komut satırı isteminde **<kbd>Ctrl</kbd> + <kbd>C</kbd>** tuşlarına basın. Pencereyi manuel olarak kapatırsanız, eğitimli modeli içeren `.nn` dosyası `ml-agents` klasörüne aktarılmaz.
+
+`Ctrl + C` kullanarak eğitimden erken çıkarsanız ve eğitime tekrar kaldığı yerden devam etmek isterseniz, aynı komutu yeniden çalıştırın ve aşağıdaki gibi `--resume` işaretini ekleyin:
+
+```sh
+mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume
+```
+
+Eğitimli modeliniz `results/<run-identifier>/<behavior_name>.nn` konumunda olacaktır; burada `<behavior_name>`, modele karşılık gelen aracıların Davranış Adının`(Behavior Name)` adıdır. Bu dosya, modelinizin en son kontrol noktasına karşılık gelir. Artık bu eğitimli modeli, yukarıda açıklanan adımlara benzer şekilde aşağıdaki adımları izleyerek Ajanlarınıza`(Agents)` yerleştirebilirsiniz.
+
+1. Model dosyanızı `Project/Assets/ML-Agents/Examples/3DBall/TFModels/` içine taşıyın.
+1. Unity Editor'ü açın ve yukarıda açıklandığı gibi **3DBall** sahnesini seçin.
+1. **3DBall** prefab Agents nesnesini seçin.
+1. `<behavior_name(davranış_adı)>.nn` dosyasını editörün Proje penceresinden **Ball3DAgent** inspector penceresindeki **Model** yer tutucusuna sürükleyin.
+1. Editörün üst kısmındaki **Oynat** düğmesine basın.
+
+## Sonraki adımlar
+<!--
+- For more information on the ML-Agents Toolkit, in addition to helpful
+  background, check out the [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
+  page. -->
+- Kendi Öğrenme Ortamınızı oluşturmaya yönelik "Merhaba Dünya" tanıtımı için [Making a New Learning Environment](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Create-New.md) sayfasına göz atın.
+<!--
+- For an overview on the more complex example environments that are provided in
+  this toolkit, check out the
+  [Example Environments](Learning-Environment-Examples.md) page.
+- For more information on the various training options available, check out the
+  [Training ML-Agents](Training-ML-Agents.md) page.
+-->
diff --git a/localized_docs/TR/docs/Installation.md b/localized_docs/TR/docs/Installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..6399512d6b4ba5f20659df7fc9d02e547602a195
--- /dev/null
+++ b/localized_docs/TR/docs/Installation.md
@@ -0,0 +1,113 @@
+# Kurulum
+
+ML-Agents Araç Seti birkaç bileşen içermektedir:
+
+- Unity paketi ([`com.unity.ml-agents`](../com.unity.ml-agents/)) Unity sahnenize entegre edilecek Unity C# SDK içerir.
+- Python paketleri:
+  - [`mlagents`](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/ml-agents) Unity sahnenizdeki davranışları eğitmenizi sağlayan makine öğrenimi algoritmalarını içerir. Bu nedenle `mlagents` paketini kurmanız gerekecek.
+  - [`mlagents_envs`](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/ml-agents-envs) Unity sahnesiyle etkileşime girmek için Python API içermektedir. Unity sahnesi ile Python makine öğrenimi algoritmaları arasında veri mesajlaşmasını kolaylaştıran temel bir katmandır.
+    Sonuç olarak, `mlagents,` `mlagents_envs` apisine bağımlıdır.
+  - [`gym_unity`](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/gym-unity) OpenAI Gym arayüzünü destekleyen Unity sahneniz için bir Python kapsayıcı sağlar.
+  <!-- düzenle learning-envir... -->
+- Unity [Project](../Project/) klasörü
+  [örnek ortamlar](Learning-Environment-Examples.md) ile başlamanıza yardımcı olacak araç setinin çeşitli özelliklerini vurgulayan sahneler içermektedir.
+
+ML-Agents Toolkit 'i kurmanız için gerekenler:
+
+- Unity yükleyin (2022.3 veya daha sonraki bir sürüm)
+- Python yükleyin (3.8.13 veya daha yüksek bir sürüm)
+- Bu depoyu klonlayın (İsteğe bağlı)
+  - __Not:__ Depoyu klonlamazsanız, örnek ortamlara ve eğitim yapılandırmalarına erişemezsiniz. Ek olarak, [Başlangıç Rehberi](Getting-Started.md) depoyu klonladığınızı varsayar.
+- `com.unity.ml-agents` ML-Agents Unity paketini yükleyin.
+- `mlagents` Python paketini yüklemek.
+
+### **Unity 2022.3** veya Sonraki Bir Sürüm Yükleyin
+
+[İndir](https://unity3d.com/get-unity/download) ve Unity'i yükle. Şiddetli bir şekilde Unity Hub üzerinden kurmanızı ve bu şekilde birden fazla Unity sürümünü yönetmenizi öneriyoruz.
+
+### **Python 3.8.13** veya Daha Yüksek Bir Sürüm Yükleyin
+
+Python 3.8 veya daha yüksek bir sürümü [yüklemenizi](https://www.python.org/downloads/) öneriyoruz. Eğer, Windows kullanıyorsanız, lütfen x86-64 versiyonunu kurun ve asla sadece x86 isimli versiyonu kurmayın. Python ortamınız `pip3` içermiyorsa, [talimatları](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers) takip ederek yükleyebilirsiniz.
+
+Windows'ta Anaconda kurulumu için destek sağlamıyor olsak da,
+önceki [Windows için Anaconda Yüklemesi (Kullanımdan Kaldırılan) Rehberine](Installation-Anaconda-Windows.md) bakabilirsiniz.
+
+### ML-Agent Toolkit Deposunu Klonlayın (İsteğe Bağlı)
+
+Artık Unity ve Python'u kurduğunuza göre, Unity ve Python paketlerini kurabilirsiniz. Bu paketleri yüklemek için depoyu klonlamanıza gerek yoktur, ancak örnek ortamlarımızı ve eğitim yapılandırmalarımızı bunlarla denemek için indirmek isterseniz depoyu klonlamayı seçebilirsiniz (bizim bazı eğitim serilerimiz / rehberlerimiz örnek ortamlarımıza erişiminizin olduğunu varsayar).
+
+```sh
+git clone --branch release_8 https://github.com/Unity-Technologies/ml-agents.git
+```
+
+`--branch release_8` seçeneği en son kararlı sürümün etiketine geçecektir. Bunu ihmal ederek yüklerseniz, potansiyel olarak kararsız olan `master` dalı seçilecektir.
+
+#### Gelişmiş: Geliştirme için Yerel Kurulum
+
+Eğer, ML-Agent Toolkit'i amaçlarınız için değiştirmeyi veya genişletmeyi planlıyorsanız, depoyu klonlamanız gerekecektir. Bu değişikliklere tekrar katkıda bulunmayı planlıyorsanız, `master` dalını klonladığınızdan emin olun (yukarıdaki komuttan `--branch release_8` 'i çıkarın). ML-Agent Toolkit'e katkıda bulunma hakkında daha fazla bilgi için [Katkı Yönergelerimize](../com.unity.ml-agents/CONTRIBUTING.md) bakın.
+
+### `com.unity.ml-agent` Unity paketini kurun
+
+Unity ML-Agents C# SDK, bir Unity paketidir. Unity `com.unity.ml-agent` paketini [doğrudan Paket Yöneticisi](https://docs.unity3d.com/Manual/upm-ui-install.html) kayıt defterinden kurabilirsiniz.
+Lütfen bulmak için 'Advanced' açılır menüsünde 'Preview Packages' seçeneğini etkinleştirdiğinizden emin olun.
+
+**NOT:** Paket Yöneticisi'nde listelenen ML-Agent paketini görmüyorsanız, lütfen [aşağıdaki gelişmiş kurulum talimatlarını]() izleyin.
+
+#### Gelişmiş: Geliştirme için Yerel Kurulum
+
+[Yerel](https://docs.unity3d.com/Manual/upm-ui-local.html) `com.unity.ml-agent` paketini (yeni klonladığınız depodan) projenize şu şekilde ekleyebilirsiniz:
+
+1. Menüye gidin ve `Window` -> `Package Manager` seçeneğini seçin.
+1. Paket yöneticisi penceresinde `+` düğmesine tıklayın.
+1. `Add package from disk...` 'i seçin.
+1. `com.unity.ml-agents` klasörüne gidin.
+1. `package.json` dosyasını seçin.
+
+**NOT:** Unity 2018.4'te `+` düğmesi paket listesinin sağ altındadır ve Unity 2019.3'te paket listesinin sol üst tarafındadır.
+
+<p align="center">
+  <img src="images/unity_package_manager_window.png"
+       alt="Unity Package Manager Window"
+       height="300"
+       border="10" />
+  <img src="images/unity_package_json.png"
+     alt="package.json"
+     height="300"
+     border="10" />
+</p>
+
+Dokümantasyonumuzdaki örnekleri takip edecekseniz, Unity'de `Project` klasörünü açabilir ve hemen kurcalamaya başlayabilirsiniz.
+
+### `mlagents` Python paketi kurulumu
+
+`mlagents` Python paketini kurmak, mlagentlerin bağlı olduğu diğer Python paketlerinin kurulmasını içerir. Dolayısıyla, makinenizde bu bağımlılıklardan herhangi birinin daha eski sürümleri zaten kurulu ise kurulum sorunlarıyla karşılaşabilirsiniz. Sonuç olarak, `mlagents` yüklemek için desteklediğimiz yol, Python Sanal Ortamlarından yararlanmaktır. Sanal Ortamlar, her proje için bağımlılıkları izole etmek için bir mekanizma sağlar ve Mac / Windows / Linux'ta desteklenir. [Sanal Ortamlar hakkında özel bir rehber](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Using-Virtual-Environment.md) sunuyoruz.
+
+`mlagents` Python paketini kurmak için sanal ortamınızı etkinleştirin ve komut satırından çalıştırın:
+
+```sh
+pip3 install mlagents
+```
+
+`mlagents` için klonlanmış depo yerine PyPi'den yüklenmesini şiddetle tavsiye ediyoruz. Eğer doğru bir şekilde kurduysanız, `mlagents-learn --help` komutunu çalıştırabilmeniz gerekir, ardından `mlagents-learn` ile kullanabileceğiniz komut satırı parametrelerini görürsünüz.
+
+`mlagents` paketini yükleyerek, [setup.py dosyasında](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/ml-agents/setup.py) listelenen bağımlılıklar da yüklenir. Bunlara
+[TensorFlow](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Background-TensorFlow.md) dahildir. (AVX destekli bir CPU gerektirir).
+
+#### Gelişmiş: Geliştirme için Yerel Kurulum
+
+`mlagents` veya `mlagents_envs` üzerinde değişiklikler yapmayı planlıyorsanız, paketleri PyPi yerine klonlanmış depodan yüklemelisiniz. Bunu yapmak için `mlagents` ve `mlagents_env` dosyalarını ayrı ayrı yüklemeniz gerekir. Deponun kök dizininden şunu çalıştırmalısınız:
+
+```sh
+pip3 install -e ./ml-agents-envs
+pip3 install -e ./ml-agents
+```
+
+Pip'i `-e` bayrağıyla çalıştırmak, Python dosyalarında doğrudan değişiklik yapmanıza ve `mlagents-learn` çalıştırdığınızda bunların yansıtılmasına izin verir. `mlagents` paketi `mlagents_envs` paketine bağlı olduğundan, bu paketleri bu sırayla kurmak önemlidir ve diğer sırayla yüklemek `mlagents_envs` paketini PyPi'den indirecektir.
+
+## Sonraki Adımlar
+
+[Başlangıç Rehberi](Getting-Started.md), Unity içinde ML-Agents Toolkit'i kurma, önceden eğitilmiş bir model çalıştırma, ortamları oluşturma ve eğitimle ilgili birkaç kısa öğretici içermektedir.
+
+## Yardım
+
+ML-Agent'larla ilgili herhangi bir sorunla karşılaşırsanız, [SSS](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/FAQ.md) sayfamıza ve [Sınırlamalar](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Limitations.md) sayfalarımıza bakın. Hiçbir şey bulamazsanız, lütfen bir [sorun gönderin(issue)](https://github.com/Unity-Technologies/ml-agents/issues) ve işletim sisteminiz, Python sürümünüz, mümkünse tam hata mesajı ile ilgili bilgileri verdiğinizden emin olun.
diff --git a/localized_docs/TR/docs/Readme.md b/localized_docs/TR/docs/Readme.md
new file mode 100644
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@@ -0,0 +1,81 @@
+# Unity ML-Agents Araç Seti Türkçe Dokümantasyon
+
+## Kurulum & Düzenleme
+
+- [Kurulum](Installation.md)
+<!--  - [Using Virtual Environment](Using-Virtual-Environment.md)-->
+
+## Başlangıç
+
+- [Başlangıç Rehberi](Getting-Started.md)
+<!-- Buradan devam et.
+- [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
+  - [Background: Unity](Background-Unity.md)
+  - [Background: Machine Learning](Background-Machine-Learning.md)
+  - [Background: TensorFlow](Background-TensorFlow.md)
+- [Example Environments](Learning-Environment-Examples.md)
+
+## Creating Learning Environments
+
+- [Making a New Learning Environment](Learning-Environment-Create-New.md)
+- [Designing a Learning Environment](Learning-Environment-Design.md)
+  - [Designing Agents](Learning-Environment-Design-Agents.md)
+- [Using an Executable Environment](Learning-Environment-Executable.md)
+
+## Training & Inference
+
+- [Training ML-Agents](Training-ML-Agents.md)
+  - [Training Configuration File](Training-Configuration-File.md)
+  - [Using TensorBoard to Observe Training](Using-Tensorboard.md)
+  - [Profiling Trainers](Profiling-Python.md)
+- [Unity Inference Engine](Unity-Inference-Engine.md)
+
+## Extending ML-Agents
+
+- [Creating Custom Side Channels](Custom-SideChannels.md)
+- [Creating Custom Samplers for Environment Parameter Randomization](Training-ML-Agents.md#defining-a-new-sampler-type)
+
+## Python Tutorial with Google Colab
+
+- [Using a UnityEnvironment](https://colab.research.google.com/drive/1Qg6E5kmf9n4G8rc5lXHIM_cQzMUFGH-g#forceEdit=true&sandboxMode=true)
+- [Q-Learning with a UnityEnvironment](https://colab.research.google.com/drive/1nkOztXzU91MHEbuQ1T9GnynYdL_LRsHG#forceEdit=true&sandboxMode=true)
+- [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/drive/1-g7CwEpk9nJ7SgWXfoCUf8pSLUW1b48i#scrollTo=pbVXrmEsLXDt&forceEdit=true&sandboxMode=true)
+
+## Help
+
+- [Migrating from earlier versions of ML-Agents](Migrating.md)
+- [Frequently Asked Questions](FAQ.md)
+- [ML-Agents Glossary](Glossary.md)
+- [Limitations](Limitations.md)
+
+## API Docs
+
+- [API Reference](API-Reference.md)
+- [How to use the Python API](Python-LLAPI.md)
+- [How to use the Unity Environment Registry](Unity-Environment-Registry.md)
+- [Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)](Python-Gym-API.md)
+
+## Translations
+
+To make the Unity ML-Agents Toolkit accessible to the global research and Unity
+developer communities, we're attempting to create and maintain translations of
+our documentation. We've started with translating a subset of the documentation
+to one language (Chinese), but we hope to continue translating more pages and to
+other languages. Consequently, we welcome any enhancements and improvements from
+the community.
+
+- [Chinese](localized/zh-CN/)
+- [Korean](localized/KR/)
+
+## Deprecated Docs
+
+We no longer use them ourselves and so they may not be up-to-date. We've decided
+to keep them up just in case they are helpful to you.
+
+- [Windows Anaconda Installation](Installation-Anaconda-Windows.md)
+- [Using Docker](Using-Docker.md)
+- [Training on the Cloud with Amazon Web Services](Training-on-Amazon-Web-Service.md)
+- [Training on the Cloud with Microsoft Azure](Training-on-Microsoft-Azure.md)
+- [Using the Video Recorder](https://github.com/Unity-Technologies/video-recorder)
+
+-->
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+<img src="docs/images/unity-wide.png" align="middle" width="3000"/>
+
+# Unity ML-Agents 工具包(Beta) v0.3.1
+
+**注�:** 本文档为v0.3版本文档的部分翻译版，目�并�会��英文版文档更新而更新。若�查看更新更全的英文版文档，请查看[这里](https://github.com/Unity-Technologies/ml-agents)。
+
+**Unity Machine Learning Agents** (ML-Agents) 是一款开�的 Unity �件，
+使得我们得以在游�环境和模拟环境中训练智能 agent。您�以使用 reinforcement learning（强化学习）�imitation learning（模仿学习）�neuroevolution（神�进化）或其他机器学习方法， 通过简�易用的 Python API进行控制，对 Agent 进行训练。我们还�供最先进算法的实现方�（基于
+TensorFlow），让游�开�者和业余爱好者能够轻�地
+训练用于 2D�3D 和 VR/AR 游�的智能 agent。
+这些�过训练的 agent �用于多�目的，
+包括控制 NPC 行为（采用��设置，
+例如多个 agent 和对抗）�对游�内部版本进行自动化测试�以�评估��游�设计决策的预�布版本。ML-Agents 对于游�开�者和 AI 研究人员�方
+都有利，因为它�供了一个集中的平�，
+使得我们得以在 Unity 的丰富环境中测试 AI 的最新进展，
+并使结果为更多的研究者和游�开�者所用。
+
+## 功能
+* 用于控制 Unity 环境的 Python API
+* 10 多个示例 Unity 环境
+* 支�多�环境�置方案和训练方案
+* 使用 deep reinforcement learning（深度强化学习）技术训练带记忆的Agent
+* �轻�定义的 Curriculum Learning（课程学习）方案
+* 通过广播 Agent 行为实现监�学习
+* 内置 Imitation Learning（模仿学习）支�
+* 通过按需决策功能实现�活的 Agent 控制
+* 在环境中�查看神�网络的输出
+* 通过 Docker 实现简化设置（测试功能）
+
+## 文档和�考
+
+**除了安装和使用说明外，如需更多信�，
+请�阅我们的[文档主页](docs/Readme.md)。**如果您使用的
+是 v0.3 之�的 ML-Agents 版本，强烈建议您�考
+我们的[关于�移到 v0.3 的指�](/docs/Migrating.md)。
+
+我们还�布了一系列与 ML-Agents 相关的�客文章：
+- reinforcement learning（强化学习）概念概述
+([多臂强盗](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
+和 [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
+- [在实际游�中使用机器学习 Agent：�学者指�](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+- [文章](https://blogs.unity3d.com/2018/02/28/introducing-the-winners-of-the-first-ml-agents-challenge/)公布我们
+[第一个 ML-Agents 挑战](https://connect.unity.com/challenges/ml-agents-1)的获胜者
+- [文章](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
+概述如何利用 Unity 作为模拟器�设计更安全的城市。
+
+除了我们自己的文档外，这里还有一些�外的相关文章：
+- [Unity AI - Unity 3D 人工智能](https://www.youtube.com/watch?v=bqsfkGbBU6k)
+- [游�开�者学习机器学习](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
+- [在 Intel 体系结构上�独研究 Unity Technologies ML-Agents](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
+
+## 社区和�馈
+
+ML-Agents 是一个开�项目，我们鼓励并欢迎大家贡献自己的力�。
+如果您想�出贡献，请务必查看我们的
+[贡献准则](/com.unity.ml-agents/CONTRIBUTING.md)和
+[行为准则](/CODE_OF_CONDUCT.md)。
+
+您�以通过 Unity Connect 和 GitHub 与我们以�更广泛的社区进行交�：
+* 加入我们的
+[Unity 机器学习频�](https://connect.unity.com/messages/c/035fba4f88400000)
+与使用 ML-Agents 的其他人以�对机器学习充满热情的 Unity 开�者
+交�。我们使用该频��展示关于 ML-Agents
+（在更广泛的范围内，还包括游�中的机器学习）的最新动�。
+* 如果您在使用 ML-Agents 时�到任何问题，请
+[�交问题](https://github.com/Unity-Technologies/ml-agents/issues)并
+确��供尽�能多的详细信�。
+
+对于任何其他问题或�馈，请直接与 ML-Agents 团队�系，
+电�邮件地�为 ml-agents@unity3d.com。
+
+## 许��
+
+[Apache 许�� 2.0](LICENSE)
diff --git a/localized_docs/zh-CN/docs/Getting-Started-with-Balance-Ball.md b/localized_docs/zh-CN/docs/Getting-Started-with-Balance-Ball.md
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+# 3D Balance Ball 环境入门
+
+本教程介�在 Unity 中打开 ML-Agents 示例环境�
+构建 Unity �执行文件�在其中训练 agent 以�
+最终将�过训练的模型嵌入到 Unity 环境中的端到端过程。
+
+ML-Agents 包�大�[示例环境](Learning-Environment-Examples.md)，
+您�以通过这些环境�了解 ML-Agents 的
+��使用方�。这些环境也�以用作新环境的模�
+或用作测试新 ML 算法的方法。阅读本教程�，
+您应该能够了解并构建示例环境。
+
+![3D Balance Ball](images/balance.png)
+
+本演练过程将使用 **3D Balance Ball** 环境。3D Balance Ball 包�
+大�平�和�（它们都是彼此的副本）。
+�个平�都会试图通过水平或垂直旋转的方�
+防止�掉�。在这�环境下，平�是一个 **agent**，
+�以在对�进行平衡的�一步中获得奖励。agent 也会
+因为掉�而得到负奖励（惩罚）。训练过程的
+目标是让平�学会��掉�。
+
+让我们开始��
+
+## 安装
+
+为了安装和设置 ML-Agents�Python 所�赖的库和 Unity 软件，
+请�阅[安装说明](Installation.md)。
+
+## 了解 Unity 环境 (3D Balance Ball)
+
+agent 是一�观测并与_环境_交互的
+自主�与者 (actor)。在 ML-Agent的语境下，环境是一个包�一个 Academy， 一个或多个 Brain， 一个或多个Agent， Agent 与其他实体交互的场景。
+
+![Unity Editor](images/mlagents-3DBallHierarchy.png)
+
+**注�：**在 Unity 中，场景内所有元素的基础对象�为
+_游�对象_(GameObject)。游�对象本质上是其他任何元素
+（包括行为�图形�物�等）的容器。�查看组�游�对象的组件，
+请在 Scene 窗�中选择 GameObject，然�打开
+Inspector 窗�。Inspector 会显示游�对象上的�个组件。
+
+在打开 3D Balance Ball 场景�，您�能会首先注�到它包�的
+�是一个平�，而是多个平�。场景中的�个平�都是
+独立的 agent，但它们全部共享�一个 Brain。3D Balance Ball 通过
+这�方��以加快训练速度，因为所有 12 个 agent �以并行�与训练任务。
+
+### Academy
+
+在这个示例场景中的 Academy 对象是 Ball3DAcademy 游�对象。
+当您在 Inspector 中查看该 Academy 组件时，�以看到若干
+用于控制环境工作方�的属性。例如，Inspector中�以看到
+**Training** 和 **Inference Configuration** 属性， 在其中我们�以设置之�生�的 Unity �执行文件的
+图形和 Time Scale 属性。Academy 在训练期间使用
+**Training Configuration**，而在�训练时使用
+**Inference Configuration**。（*Inference* 等�于**�**进行训练的任何时候，此时 agent �以使用�过训练的模型控制，或用写定的代�控制，或让玩家直接控制。）
+通常情况下，您需�为 **Training configuration** 设置低图形质�
+和高Time Scale，而为 **Inference Configuration** 设置高图形质�和
+`1.0` 的Time Scale。
+
+**注�：**如果您想在训练期间观测环境，则�以调整
+**Inference Configuration** 设置�使用更大的窗�和更接近
+1:1 的时间刻度。当你�正�训练时一定��新设置这些�数；
+�则，训练�能需�很长时间。
+
+对于环境，�一个需�关注的方�是 Academy 的实现。
+由于 Academy 基类是抽象的，您必须始终定义一个�类。
+您�以实现以下三个函数，但这些函数都是�选的：
+
+* Academy.InitializeAcademy() — �动环境时调用一次。
+* Academy.AcademyStep() — 在
+Agent.AgentAction() 之�（以� agent 收集其观测结果之�）的�个模拟步骤调用。
+* Academy.AcademyReset() — 在 Academy 开始或�新开始模拟
+（包括第一次）时调用。
+
+3D Balance Ball 环境�使用这些函数（�个 agent 在需�时
+会自行�置），但许多环境都会使用这些函数�
+控制 agent 周围的环境。
+
+### Brain
+
+场景中的 Ball3DBrain 游�对象包� Brain 组件，
+是 Academy 对象的�级。（场景中的所有 Brain 对象都必须是
+Academy 的�级。）3D Balance Ball 环境中的所有 agent 使用
+�一个 Brain 实例。
+Brain �存储关于 agent 的任何信�，
+�是将 agent 收集的观测结果��到决策过程，
+然�将所选的动作返回给 agent。因此，所有 agent �共享
+�一个 Brain，但会独立行动。Brain 设置�以�供很多
+关于 agent 工作方�的信�。
+
+**Brain Type** 决定了 agent 如何决策。
+**External** 和 **Internal** 类型需���使用：训练 agent 时使用 **External**，
+而在采用�过训练的模型时使用 **Internal**。
+**Heuristic** Brain �许您通过扩展 Decision 类�对 agent 的逻辑进行
+手动编�。最�，**Player** Brain �让您将键盘命令
+映射到动作，这样在测试 agent 和环境时
+会�常有用。如果这些类型的 Brain 都�能满足您的需求，您�以
+实现自己的 CoreBrain �创建自有的类型。
+
+在本教程中，进行训练时，需�将 **Brain Type** 设置为 **External**；
+当您将�过训练的模型嵌入到 Unity 应用程�中时，需�将
+**Brain Type** 更改为 **Internal**。
+
+**��观测空间**
+
+在决策之�，agent 会收集有关自己在环境中所处的状�的
+观测结果。ML-Agents 将观测分为两类：
+**Continuous** 和 **Discrete**。**Continuous** ��观测空间
+会收集浮点数��中的观测结果。**Discrete**
+��观测空间是一个状�表的索引。大多数示例环境
+都使用连续的��观测空间。
+
+3D Balance Ball 示例中所用的 Brain 实例使用 **State Size** 为 8 的
+**Continuous** ��观测空间。这�味�
+包� agent 观测结果的特���包�八个元素：
+平�旋转的 `x` 和 `z` 分�以��相对�置和
+速度的 `x`�`y` 和 `z` 分�。（观测结果值
+在 agent 的 `CollectObservations()` 函数中进行定义。）
+
+**���动空间**
+
+Brain 以*动作*的形�� agent �供指令。与状�
+一样，ML-Agents 将动作分为两�类型：**Continuous**
+���动空间是一个�以连续�化的数字��。��
+�个元素的�义都是由 agent 逻辑定义的（PPO 训练过程是一个了解agent的哪�状�更好的过程，这个过程是通过学习��agent的��状�会对应多少奖励�实现的）。
+例如，一个元素�能表示施加到 agent �个
+`Rigidbody` 上的力或扭矩。**Discrete** ���动空间将其动作
+定义为一个表。�供给 agent 的具体动作是这个表的
+索引。
+
+根�设计，3D Balance Ball 示例会使用这两�类型的���动
+空间。
+您�以�试使用两�设置进行训练，观测是�有
+差异。（使用离散�动空间时将 `Vector Action Space Size` 设置为 4，
+而使用连续�动空间时将其设置为 2。）
+
+### Agent
+
+Agent 是在环境中进行观测并采�动作的�与者。
+在 3D Balance Ball 环境中，Agent 组件被放置在 12 个
+平�游�对象上。基础 Agent 对象有一些影�其行为的
+属性：
+
+* **Brain** — �个 Agent 必须有一个 Brain。Brain 决定了 agent 如何
+决策。3D Balance Ball 场景中的所有 agent 共享�一个
+Brain。
+* **Visual Observations** — 定义 agent 用�观测其环境的
+任何 Camera 对象。3D Balance Ball �使用摄�机观测。
+* **Max Step** — 定义在 agent 决定自己完�之��以�生多少个
+模拟步骤。在 3D Balance Ball 中，agent 在 5000 步之��新开始。
+* **Reset On Done** — 定义 agent 是�在完�时�新开始。
+3D Balance Ball 将此项设置为 true，因此 agent 在达到
+**Max Step** 计数�或在掉���新开始。
+
+也许 agent 更有趣的方�在于 Agent �类的
+实现。在创建 agent 时，您必须扩展 Agent 基类。
+Ball3DAgent �类定义了以下方法：
+
+* Agent.AgentReset() — Agent �置时（包括会�开始时）
+调用。Ball3DAgent 类使用�置函数��置
+平�和�。该函数会将�置值�机化，从而使
+训练�局�于特定的开始�置和平�
+姿�。
+* Agent.CollectObservations() — 在�个模拟步骤调用。负责
+收集 agent 对环境的观测结果。由于分�给
+agent 的 Brain 实例设置为状�大�为 8 的连续��观测空间，
+因此 `CollectObservations()` 必须调用 8 次
+`AddVectorObs`。
+* Agent.AgentAction() — 在�个模拟步骤调用。接收 Brain 选择的
+动作。Ball3DAgent 示例�以处�连续和离散
+�动空间类型。在此环境中，两�状�类型之间实际上
+没有太大的差别：这两����动空间在�一步都会
+导致平�旋转�生��化。`AgentAction()` 函数
+为 agent 分�奖励；在此示例中，agent 在�一步
+将���在平�上时收到较�的正奖励，
+而在掉�时收到更大的负奖励。agent 在掉�时还会被标记为
+完�状�，因此会�置一个用于下一模拟步骤的
+新�。
+
+## 构建环境
+
+第一步是打开包� 3D Balance Ball 环境的
+Unity 场景：
+
+1. �动 Unity。
+2. 在 Projects 对�框上，选择窗�顶部的 **Open** 选项。
+3. 使用��打开的文件对�框，找到 ML-Agents 项目内的
+`unity-environment` 文件夹，然��击 **Open**。
+4. 在 `Project` 窗�中，找到文件夹
+`Assets/ML-Agents/Examples/3DBall/`。
+5. �击 `Scene` 文件以加载包� Balance Ball 环境的
+场景。
+
+![3DBall 场景](images/mlagents-Open3DBall.png)
+
+由于我们�建立此环境�进行训练，因此我们需�
+将 agent 使用的 Brain 设置为 **External**。这样 agent 在
+进行决策时能够与外部训练过程进行通信。
+
+1. 在 **Scene** 窗�中，�击 Ball3DAcademy 对象�边的三角形
+图标。
+2. 选择其�对象 `Ball3DBrain`。
+3. 在 Inspector 窗�中，将 **Brain Type** 设置为 `External`。
+
+![将 Brain 设置为 External](images/mlagents-SetExternalBrain.png)
+
+接下�，我们希望设置场景以便在训练过程�动我们的环境�执行文件时
+正确播放场景。这�味�：
+* 环境应用程�在���行
+* 没有对�需�互动
+* 正确的场景会自动加载
+
+1. 打开 Player Settings（��：**Edit** > **Project Settings** > **Player**）。
+2. 在 **Resolution and Presentation** 下方：
+    - 确�选中 **Run in Background**。
+    - 确� **Display Resolution Dialog** 设置为 Disabled。
+3. 打开 Build Settings 窗�（��：**File** > **Build Settings**）。
+4. 选择目标平�。
+    -（�选）选择“Development Build�以便
+    [记录调试消�](https://docs.unity3d.com/Manual/LogFiles.html)。
+5. 如果 **Scenes in Build** 列表中显示了任何场景，请确�
+唯一选中的是 3DBall Scene。（如果该列表为空，则表示
+仅当�场景包�在编译中）。
+6. �击 *Build*：
+    a. 在 File 对�框中，导航至 ML-Agents 目录中的 `python`
+    文件夹。
+    b. 指定文件�，然��击 **Save**。
+
+![编译窗�](images/mlagents-BuildWindow.png)
+
+## 使用 Reinforcement Learning（强化学习）�训练 Brain
+
+有了一个包�模拟环境的 Unity �执行文件�，现在我们
+�以执行训练。为了首先确�您的环境和 Python
+API 能正常工作，您�以使用 `python/Basics`
+[Jupyter 笔记本](/docs/Background-Jupyter.md)。
+此笔记本包�了 API 功能的简�演练。
+在 `Basics` 中，务必将 `env_name` 设置为您先�构建的
+环境文件的�称。
+
+### 使用 PPO 进行训练
+
+为了训练 agent 对�进行正确平衡，我们将使用一�称为 Proximal Policy Optimization (PPO) 的
+Reinforcement Learning（强化学习）算法。
+与其他许多 RL 算法相比，这�算法��明是一�安全�
+有效且更通用的方法，因此我们选择它作为与 ML-Agents
+一起使用的示例算法。有关 PPO 的更多信�，
+请�阅 OpenAI 近期�布的[�客文章](https://blog.openai.com/openai-baselines-ppo/)，
+其中对 PPO 进行了说明。
+
+
+为了训练 Balance Ball 环境中的 agent，我们将使用 Python
+包。我们�供了一个�为 `learn.py` 的方便的 Python 包装脚本，此脚本会接�用于�置训练和预测阶段的�数。
+
+
+我们将�这个脚本传递我们刚�构建的环境�执行文件的路径。（�选）我们�以
+使用 `run_id` �识别实验并创建用于存储模型和摘�统计信�的文件夹。当使用
+TensorBoard �观测训练统计信�时，将�次训练的此项设置为顺�值
+将会很有用。也就是说，第一次训练时为“BalanceBall1�，
+第二次训练时为“BalanceBall2�，�此类推。如果�这样�，�次训练的
+摘�信�都会�存在�一个目录中，并且全部将包�在
+�一个图中。
+
+总之，转到命令行，进入 `ml-agents` 目录并输入：
+
+```
+python3 python/learn.py <env_name> --run-id=<run-identifier> --train
+```
+
+`--train` 标志告诉 ML-Agents 以训练模��行。`env_name` 应该是刚�创建的 Unity �执行文件的�字。
+
+
+### 观测训练进度
+
+开始使用 `learn.py` 按照��部分所述的方�进行训练�，`ml-agents` 文件夹将
+包�一个 `results` 目录。为了更详细地观测训练过程，
+您�以使用 TensorBoard。从命令行中�行：
+
+`tensorboard --logdir results`
+
+然�导航至 `localhost:6006`。
+
+从 TensorBoard 中，您将看到摘�统计信�：
+
+* Lesson - �有在进行
+[课程训练](/docs/Training-Curriculum-Learning.md)时�有�义。
+3D Balance Ball 环境中�使用此项。
+* Cumulative Reward - 所有 agent 的平�累积场景奖励。
+在�功训练期间应该增大。
+* Entropy - 模型决策的�机程度。在�功训练过程中
+应该缓慢��。如果��得太快，应增大 `beta`
+超�数。
+* Episode Length - 所有 agent 在环境中�个场景的
+平�长度。
+* Learning Rate - 训练算法�索最优 policy 时需�多大的
+步骤。��时间推移应该��。
+* Policy Loss - policy 功能更新的平��失。与 policy
+（决定动作的过程）的�化程度相关。此项的幅度
+在�功训练期间应该��。
+* Value Estimate - agent 访问的所有状�的平�价值估算。
+在�功训练期间应该增大。
+* Value Loss - 价值功能更新的平��失。与模型
+对�个状�的价值进行预测的能力相关。此项
+在�功训练期间应该��。
+
+![TensorBoard �行示例](images/mlagents-TensorBoard.png)
+
+## 将�过训练的 Brain 嵌入到 Unity 环境中（测试功能）
+
+一旦训练过程完�，并且训练过程�存了模型
+（通过 `Saved Model` 消��看出），您便�以将该模型添加到 Unity 项目中，
+然�将其用于 Brain 类型为 **Internal** 的 agent。
+
+### 设置 TensorFlowSharp 支�
+
+由于 TensorFlowSharp 支��处于实验阶段，因此默认情况下会
+将其�用。为了�用这项支�，必须�循以下步骤。请注�，
+�有完�这些步骤��能使用 `Internal` Brain 模�。
+
+1. 确� TensorFlowSharp �件�于 `Assets` 文件夹中。
+�在
+[此处](https://s3.amazonaws.com/unity-ml-agents/0.5/TFSharpPlugin.unitypackage)下载一个包� TF# 的 Plugins 文件夹。
+下载�，�击并将其导入。您�以在 Project 选项�中
+（�于 `Assets` > `ML-Agents` > `Plugins` > `Computer` 下）
+检查 TensorFlow 的相关文件�查看是�安装�功
+2. 转到 `Edit` > `Project Settings` > `Player`
+3. 对于�个目标平�
+（**`PC, Mac and Linux Standalone`**�**`iOS`** 或 **`Android`**）：
+    1.转到 `Other Settings`。
+    2.选择 `Scripting Runtime Version` 为
+    `Experimental (.NET 4.6 Equivalent)`
+    3.在 `Scripting Defined Symbols` 中，添加标志 `ENABLE_TENSORFLOW`。
+    输入�，按 Enter。
+4. 转到 `File` > `Save Project`
+5. �新�动 Unity Editor。
+
+### 将�过训练的模型嵌入到 Unity 中
+
+1. �过训练的模型存储在 `ml-agents` 文件夹中的 `models/<run-identifier>` 内。训练
+完��，该�置会有一个 `<env_name>.bytes` 文件，其中的 `<env_name>` 是训练期间使用的�执行文件的
+�称。
+2. 将 `<env_name>.bytes` 从 `python/models/ppo/` 移入
+`unity-environment/Assets/ML-Agents/Examples/3DBall/TFModels/`。
+3. 打开 Unity Editor，然�选择 `3DBall` 场景（如上所述）。
+4. 从 Scene 层级视图中选择 `Ball3DBrain` 对象。
+5. 将 `Type of Brain` 更改为 `Internal`。
+6. 将 `<env_name>.bytes` 文件从 Editor 的 Project 窗�拖入
+`3DBallBrain` Inspector 窗�中的 `Graph Model` ��区域。
+7. 按 Editor 顶部的 Play 按钮。
+
+如果您正确执行了这些步骤，您现在应该能够
+看到 Unity 编辑器中有这个用于控制平衡�行为的
+训练模型。从这里开始，您便�以�新构建 Unity 的�执行文件，
+并�独�行该�执行文件，在其中内置 agent 新学到的行为。
diff --git a/localized_docs/zh-CN/docs/Installation.md b/localized_docs/zh-CN/docs/Installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..52f6d0e0a477812547145484f19076017c21bea8
--- /dev/null
+++ b/localized_docs/zh-CN/docs/Installation.md
@@ -0,0 +1,66 @@
+# 安装和设置
+
+为了安装和使用 ML-Agents，您需�安装 Unity，克隆这个代�仓库，
+并安装 Python 以�其他所�赖的库。下�的�个�节
+都会概述�个步骤，此外还会介�尚在测试阶段的 Docker 的�置方法。
+
+## 安装 **Unity 2017.1** 或更高版本
+
+[下载](https://store.unity.com/download) 并安装 Unity。如果您想
+使用我们的 Docker 设置（��介�），请确�在安装 Unity 时选择
+_Linux Build Support_ 组件。
+
+<p align="center">
+    <img src="images/unity_linux_build_support.png"
+        alt="Linux Build Support"
+        width="500" border="10" />
+</p>
+
+## 克隆 ml-agents 代�仓库
+
+安装完��，您需�克隆 ML-Agents GitHub 代�仓库。
+
+    git clone git@github.com:Unity-Technologies/ml-agents.git
+
+此代�仓库中的 `unity-environment` 目录包�了�添加到项目中的
+Unity Assets。`python` 目录包�训练代�。
+这两个目录都�于代�仓库的根目录。
+
+## 安装 Python（以��赖项）
+
+为了使用 ML-Agents，您需�安装 Python 3 以�
+[�求文件](../python/requirements.txt)中列出的�赖项。
+一些主�的�赖项包括：
+- [TensorFlow](/docs/Background-TensorFlow.md)
+- [Jupyter](/docs/Background-Jupyter.md)
+
+### Windows 用户
+
+如果您是刚接触 Python 和 TensorFlow 的 Windows 用户，请�循[此指�](/docs/Installation-Anaconda-Windows.md)�设置 Python 环境。
+
+### Mac 和 Unix 用户
+
+如果您的 Python 环境�包括 `pip3`，请�阅这些
+[说明](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
+以了解其安装方法。
+
+�安装�赖项，请进入代�仓库的 `python` �目录，
+然�从命令行中�行：
+
+    pip3 install .
+
+## 基于 Docker 的安装（测试阶段）
+
+如果您想使用 Docker for ML-Agents，请按照
+[该指�](/docs/Using-Docker.md)进行�作。
+
+## Unity 包
+
+您�以通过 Unity 包的形�下载TensorFlowSharp �件 ([AWS S3链接](https://s3.amazonaws.com/unity-ml-agents/0.5/TFSharpPlugin.unitypackage)，[百度盘链接](https://pan.baidu.com/s/1s0mJN8lvuxTcYbs2kL2FqA))
+
+## 帮助
+
+如果您在安装 ML-Agents 时�到任何问题，请
+[�交问题](https://github.com/Unity-Technologies/ml-agents/issues)，并在�交时
+�供有关�作系统�Python 版本和相关错误消�（如�能）的
+信�。
diff --git a/localized_docs/zh-CN/docs/Learning-Environment-Create-New.md b/localized_docs/zh-CN/docs/Learning-Environment-Create-New.md
new file mode 100644
index 0000000000000000000000000000000000000000..ea73ada781079e8eeaf17eaff68673e6ee6f5a91
--- /dev/null
+++ b/localized_docs/zh-CN/docs/Learning-Environment-Create-New.md
@@ -0,0 +1,434 @@
+# 创建新的学习环境
+
+本教程将介�创建 Unity 环境的过程。Unity 环境是用 Unity 引擎构建的应用程�，�用于训练 Reinforcement Learning（强化学习）agent。
+
+![简�的 ML-Agents 环境](images/mlagents-NewTutSplash.png)
+
+在此示例中，我们将训练一个�滚动到一个�机放置的立方体。这个�还�学习��从平�上掉�。
+
+## 概述
+
+在 Unity 项目中使用 ML-Agents 涉�以下基本步骤：
+
+1. 创建一个容纳 agent 的环境。从包�少�对象的简�物�模拟环境到整个游�或生�系统，环境的形��以多�多样。
+2. 实现一个 Academy �类，并将这个�类添加到包�该环境的 Unity 场景中的游�对象 (GameObject)。此游�对象将用作场景中任何 Brain 对象的父级。您的 Academy 类�以实现一些�选的方法，从而独立于任何 agent �对场景进行更新。例如，您�以在环境中添加�移动或删除 agent 和其他实体。
+3. 将一个或多个 Brain 对象作为 Academy 的�级添加到场景中。
+4. 实现 Agent �类。Agent �类定义了必�的代�供 agent 用于观测自身环境�执行指定动作以�计算用于强化训练的奖励。您还�以实现�选方法，从而在 agent 完�任务或任务失败时�置 agent。
+5. 将 Agent �类添加到相应的游�对象，通常是在场景内表示模拟中的 agent 的对象。必须为�个 Agent 对象分�一个 Brain 对象。
+6. 如果�进行训练，应将 Brain 类型设置为 External 并[执行训练过程](/docs/Training-ML-Agents.md)。
+
+**注�：**如果您�熟悉 Unity，而本教程中未充分讲解 Editor 任务的情况下，请�阅 Unity 手册中的[了解界�](https://docs.unity3d.com/Manual/LearningtheInterface.html)。
+
+如果尚未安装，请�循[安装说明](Installation.md)。
+
+## 设置 Unity 项目
+
+�完�的第一项任务是创建一个新的 Unity 项目并将 ML-Agents Assets导入其中：
+
+1. �动 Unity Editor 并创建一个�为“RollerBall�的新项目。
+
+2. 在你的文件系统中，找到包�您所克隆的 ML-Agents 代�仓库的文件夹。
+
+3. 将 `ML-Agents` 文件夹从 `unity-environments/Assets` 拖到 Unity Editor Project 窗�中。
+
+您的 Unity **Project** 窗�应包�以下Assets：
+
+![Project 窗�](images/mlagents-NewProject.png)
+
+## 创建环境：
+
+接下�，我们将创建一个�常简�的场景�充当我们的 ML-Agents 环境。该环境的“physical�组件包括一个 Plane（充当 agent 移动的基础地�）�一个 Cube（充当 agent 寻找的目标）以�一个 Sphere（表示 agent 本身）。
+
+**创建地�平�：**
+
+1. 在 Hierarchy 窗�中�键�击，选择 3D Object > Plane。
+2. 将游�对象命�为“Floor�。
+3. 选择 Plane 以便在 Inspector 窗�中查看其属性。
+4. 将 Transform 设置为 Position = (0,0,0)�Rotation = (0,0,0)�Scale = (1,1,1)。
+5. 在 Plane 的 Mesh Renderer 上，展开 Materials 属性并将默认�质更改为 *floor*。
+
+（�设置新�质，请�击当��质�称�边的�圆圈图标。此时将打开 **Object Picker** 对�框，然�便�从项目中当�所有�质的列表中选择��的�质。）
+
+![Inspector 窗�中的 Floor（地�）](images/mlagents-NewTutFloor.png)
+
+**添加目标立方体**
+
+1. 在 Hierarchy 窗�中�键�击，选择 3D Object > Cube。
+2. 将游�对象命�为“Target�
+3. 选择 Target 以便在 Inspector 窗�中查看其属性。
+4. 将 Transform 设置为 Position = (3,0.5,3)�Rotation = (0,0,0)�Scale = (1,1,1)。
+5. 在 Cube 的 Mesh Renderer 上，展开 Materials 属性并将默认�质更改为 *block*。
+
+![Inspector 窗�中的目标立方体](images/mlagents-NewTutBlock.png)
+
+**添加 Agent �体**
+
+1. 在 Hierarchy 窗�中�键�击，选择 3D Object > Sphere。
+2. 将游�对象命�为“RollerAgent�
+3. 选择 Target 以便在 Inspector 窗�中查看其属性。
+4. 将 Transform 设置为 Position = (0,0.5,0)�Rotation = (0,0,0)�Scale = (1,1,1)。
+5. 在 Sphere 的 Mesh Renderer 上，展开 Materials 属性并将默认�质更改为 *checker 1*。
+6. �击 **Add Component**。
+7. � Sphere 添加 Physics/Rigidbody 组件。（添加 Rigidbody）
+
+![Inspector 窗�中的 Agent 游�对象](images/mlagents-NewTutSphere.png)
+
+请注�，我们将在本教程的��部分创建一个 Agent �类以作为组件添加到此游�对象。
+
+**添加空游�对象�容纳 Academy 和 Brain**
+
+1. 在 Hierarchy 窗�中�键�击，选择 Create Empty。
+2. 将游�对象命�为“Academy�
+3. �键�击 Academy 游�对象，然�选择 Create Empty。
+4. 将 Academy 的此�级命�为“Brain�。
+
+![场景层级视图](images/mlagents-NewTutHierarchy.png)
+
+您�以调整摄�机角度以便在�行时更好地查看场景。接下�的步骤是创建并添加 ML-Agent 组件。
+
+## 实现 Academy
+
+Academy 对象�在场景中�调 ML-Agents 并驱动模拟中的决策部分。�个 ML-Agent 场景需�一个 Academy 实例。由于 Academy 基类是抽象的，�使�需�对特定环境使用任何方法，您也必须创建自己的�类。
+
+首先，将一个 New Script 组件添加到先�创建的 Academy 游�对象：
+
+1. 选择 Academy 游�对象以便在 Inspector 窗�中查看该对象。
+2. �击 **Add Component**。
+3. 在组件列表中�击 **New Script**（�于底部）。
+4. 将该脚本命�为“RollerAcademy�。
+5. �击 **Create and Add**。
+
+接下�，编辑新的 `RollerAcademy` 脚本：
+
+1. 在 Unity Project 窗�中，�击 `RollerAcademy` 脚本，在代�编辑器中将其打开。（默认情况下，新脚本直接放置于 **Assets** 文件夹中。）
+2. 在编辑器中，将基类从 `MonoBehaviour` 更改为 `Academy`。
+3. 删除默认添加的 `Start()` 和 `Update()` 方法。
+
+在这样的基本场景中，我们�需� Academy �始化��置或以其他方�控制环境中的任何对象，因此我们采用最简�的 Academy 实现：
+
+```csharp
+public class RollerAcademy : Academy { }
+```
+
+Academy 属性的默认设置也适�该环境，因此我们无需在 Inspector 窗�中更改 RollerAcademy 组件的任何设置。
+
+![Academy 属性](images/mlagents-NewTutAcademy.png)
+
+## 添加 Brain
+
+Brain 对象内部�装了决策过程。Agent 将观测结果��给自己的 Brain，并期望收到决策。Brain Type 设置决定了 Brain 如何决策。与 Academy 和 Agent 类��，您�需�创建自己的 Brain �类。
+
+�创建 Brain：
+
+1. 选择先�创建的 Brain 游�对象以便在 Inspector 窗�中显示该对象的属性。
+2. �击 **Add Component**。
+3. 选择 **Scripts/Brain** 组件以将其添加到该游�对象。
+
+我们���讲解 Brain 属性，但现在将 Brain Type �留为 **Player**。
+
+![Brain 默认属性](images/mlagents-NewTutBrain.png)
+
+## 实现 Agent
+
+�创建 Agent：
+
+1. 选择 RollerAgent 游�对象以便在 Inspector 窗�中查看该对象。
+2. �击 **Add Component**。
+3. 在组件列表中�击 **New Script**（�于底部）。
+4. 将该脚本命�为“RollerAgent�。
+5. �击 **Create and Add**。
+
+然�，编辑新的 `RollerAgent` 脚本：
+
+1. 在 Unity Project 窗�中，�击 `RollerAgent` 脚本，在代�编辑器中将其打开。
+2. 在编辑器中，将基类从 `MonoBehaviour` 更改为 `Agent`。
+3. 删除 `Update()` 方法。我们将使用 `Start()` 函数，所以暂时将其�留。
+
+到目�为止介�的这些，都是为了将 ML-Agents 添加到任何 Unity 项目而需�执行的基本步骤。接下�，我们将添加逻辑，让我们的 agent 能够使用 reinforcement learning（强化学习）技术学习滚动到立方体。
+
+在这�简�的情况下，我们�使用 Academy 对象�控制环境。以�，如果我们想�改�环境，例如在模拟之�或模拟过程中更改地�的大�，或者添加或删除 agent以�其他物体，我们�以在 Academy 中实现相应的方法。但现在，我们会让 Agent 在�功或失败时完�对自己和目标进行�置的所有工作。
+
+**�始化和�置 Agent**
+
+agent 到达目标时会将自己标记为完�状�，而 agent �置函数会将目标移动到�机�置。�外，如果 agent 从平�上滚下，�置函数会将其放回到地�上。
+
+为了移动目标游�对象，我们需�它的 Transform 对象的引用（这个对象中存储了游�对象在 3D 世界中的�置�方�和比例）。��得此引用，请将一个类型为 `Transform` 的公共字段添加到 RollerAgent 类中。Unity 中的组件的公共字段会显示在 Inspector 窗�中，�许您选择在 Unity Editor 中将哪个游�对象用作目标。��置 agent 的速度（并在��用力移动 agent），我们需�引用 Rigidbody 组件。[Rigidbody](https://docs.unity3d.com/ScriptReference/Rigidbody.html) 是 Unity 用于物�模拟的主�元素。（请�阅[物�](https://docs.unity3d.com/Manual/PhysicsSection.html)以查看 Unity 物�的完整文档。）由于 Rigidbody 组件与我们的 Agent 脚本�于�一个游�对象上，因此�得此引用的最佳方�是使用 `GameObject.GetComponent<T>()`，我们�以在脚本的 `Start()` 方法中调用它。
+
+到目�为止，我们的 RollerAgent 脚本如下所示：
+
+```csharp
+using System.Collections.Generic;
+using UnityEngine;
+
+public class RollerAgent : Agent
+{
+    Rigidbody rBody;
+    void Start () {
+        rBody = GetComponent<Rigidbody>();
+    }
+
+    public Transform Target;
+    public override void AgentReset()
+    {
+        if (this.transform.position.y < -1.0)
+        {
+            // agent 掉�
+            this.transform.position = Vector3.zero;
+            this.rBody.angularVelocity = Vector3.zero;
+            this.rBody.velocity = Vector3.zero;
+        }
+        else
+        {
+            // 将目标移动到新的�置
+            Target.position = new Vector3(Random.value * 8 - 4,
+                                          0.5f,
+                                          Random.value * 8 - 4);
+        }
+    }
+}
+```
+
+接下�，让我们实现 Agent.CollectObservations() 函数。
+
+**观测环境**
+
+Agent 将我们收集的信���给 Brain，由 Brain 使用这些信���决策。当您训练 agent（或使用�过训练的模型）时，数�将作为特���输入到神�网络中。为了让 agent �功学习�个任务，我们需��供正确的信�。为了决定�收集哪些信�，一个好的�验法则是考虑您在计算问题的分�解决方案时需�用到什么。
+
+在我们的示例中，我们的 agent 收集的信�包括：
+
+* 目标的�置。一般�说，为了进行更一般化的训练，较好的�法是使用其他对象的相对�置而�是�对�置。请注�，agent 仅收集 x 和 z �标，因为地�与 x-z 平�平行，并且目标�置的 y 分��会改�。
+
+```csharp
+// 计算相对�置
+Vector3 relativePosition = Target.position - this.transform.position;
+
+// 相对�置
+AddVectorObs(relativePosition.x / 5);
+AddVectorObs(relativePosition.z / 5);
+```
+
+* agent 本身在地�范围内的�置。收集此数�时收集的是 agent 与地��个边的�离。
+
+```csharp
+// 与平�边缘的�离
+AddVectorObs((this.transform.position.x + 5) / 5);
+AddVectorObs((this.transform.position.x - 5) / 5);
+AddVectorObs((this.transform.position.z + 5) / 5);
+AddVectorObs((this.transform.position.z - 5) / 5);
+```
+
+* agent 的速度。这有助于 agent 学习去控制自己的速度，使其�会越过目标和从平�上滚下。
+
+```csharp
+// Agent 速度
+AddVectorObs(rBody.velocity.x / 5);
+AddVectorObs(rBody.velocity.z / 5);
+```
+
+所有值都除以 5 �将神�网络的输入归一化到范围 [-1,1]。（因为平�的跨度为 10 个��，所以使用数字 5。）
+
+总的�说，状�观测结果包� 8 个值，当我们开始设置 Brain 属性时，我们需�使用连续状�空间：
+
+```csharp
+List<float> observation = new List<float>();
+public override void CollectObservations()
+{
+    // 计算相对�置
+    Vector3 relativePosition = Target.position - this.transform.position;
+
+    // 相对�置
+    AddVectorObs(relativePosition.x/5);
+    AddVectorObs(relativePosition.z/5);
+
+    // 与平�边缘的�离
+    AddVectorObs((this.transform.position.x + 5)/5);
+    AddVectorObs((this.transform.position.x - 5)/5);
+    AddVectorObs((this.transform.position.z + 5)/5);
+    AddVectorObs((this.transform.position.z - 5)/5);
+
+    // Agent 速度
+    AddVectorObs(rBody.velocity.x/5);
+    AddVectorObs(rBody.velocity.z/5);
+}
+```
+
+Agent 代�的最�一部分是 Agent.AgentAction() 函数，此函数接收 Brain 的决策。
+
+**动作**
+
+Brain 的决策以动作数组的形�传递给 `AgentAction()` 函数。此数组中的元素数�由 agent 的 Brain 的 `Vector Action Space Type` 和 `Vector Action Space Size` 设置确定。RollerAgent 使用连续���动空间，并需� Brain �供的两个连续控制信�。因此，我们�将 Brain `Vector Action Size` 设置为 2。第一个元素 `action[0]` 确定沿 x 轴施加的力；`action[1]` 确定沿 z 轴施加的力。（如果我们�许 agent 以三维方�移动，那么我们需�将 `Vector Action Size` 设置为 3。）注�，Brain 并�知�动作数组中的值是什么��。训练过程�是根�观测输入�调整动作值，然�看看会得到什么样的奖励。
+
+RollerAgent 使用 `Rigidbody.AddForce` 函数将 action[] 数组中的值应用到其 Rigidbody 组件 `rBody`：
+
+```csharp
+Vector3 controlSignal = Vector3.zero;
+controlSignal.x = Mathf.Clamp(action[0], -1, 1);
+controlSignal.z = Mathf.Clamp(action[1], -1, 1);
+rBody.AddForce(controlSignal * speed);
+```
+
+agent 将动作值�制在 [-1,1] 范围内，原因有两个。首先，学习算法对于�试�常大的值的动机较�（因为�会对 agent 行为产生任何影�），这样�以��神�网络计算中出现数值�稳定的问题。其次，我们没有在别的地方采�措施阻止神�网络返回过大的值，所以我们希望在任何情况下都将它们�制在��的范围内。
+
+**奖励**
+
+Reinforcement learning（强化学习）需�奖励。应在 `AgentAction()` 函数中分�奖励。学习算法使用在模拟和学习过程的�个步骤中分�给 agent 的奖励�确定是�为 agent �供了最佳动作。您会希望 agent 完�分�的任务（在此情况下是指到达目标立方体）时奖励 agent，而在 agent 彻底失败（掉下平�）时惩罚 agent。有时，您�以通过一些�奖励�鼓励帮助 agent 完�任务的行为，从而加快训练速度。例如，如果 agent 在�个步骤中�近目标，则 RollerAgent 奖励系统会�供�奖励，并会在�个步骤�供很�的负奖励�促使 agent 快速完�其任务。
+
+RollerAgent 会计算到达目标所需的�离。当到达目标时，代�将 Agent.reward ��增加 1.0，并通过将 agent 设置为 done �将 agent 标记为完�。
+
+```csharp
+float distanceToTarget = Vector3.Distance(this.transform.position,
+                                          Target.position);
+// 已到达目标
+if (distanceToTarget < 1.42f)
+{
+    Done();
+    AddReward(1.0f);
+}
+```
+
+**注�：**当您将 agent 标记为完�状�时，它将�止活动，直到�置为止。您�以通过设置 Inspector 中的 Agent.ResetOnDone 属性， 以便当它被标记为完�状�时，立��置 agent。你也�以等待 Academy 自己�置环境。本教程中的 RollerBall 环境的�置�赖于 `ResetOnDone` 机制，并且我们目�没有为 Academy 设置 `Max Steps` �制，在这样的�件下 Academy ��会自己�置环境。
+
+为了鼓励 agent 继续往�，我们还�在它进一步接近目标时�供奖励（�存先�的步�测�值）：
+
+```csharp
+// 进一步接近
+if (distanceToTarget < previousDistance)
+{
+    AddReward(0.1f);
+}
+```
+
+此外还�以为了鼓励 agent 更快完�任务而在�个步骤中分�负奖励：
+
+```csharp
+// 时间惩罚
+AddReward(-0.05f);
+```
+
+最�，为了惩罚 agent 掉下平�，会为它分�一个大的负奖励，�仅如此，当它掉下平��，我们还�将 agent 设置为完�状�，以便它在下一步�置自己：
+
+```csharp
+// 掉下平�
+if (this.transform.position.y < -1.0)
+{
+    Done();
+    AddReward(-1.0f);
+}
+```
+
+**AgentAction()**
+
+利用上�列出的动作和奖励逻辑，`AgentAction()` 函数的最终版本如下所示：
+
+```csharp
+public float speed = 10;
+private float previousDistance = float.MaxValue;
+
+public override void AgentAction(float[] vectorAction, string textAction)
+{
+    // 奖励
+    float distanceToTarget = Vector3.Distance(this.transform.position,
+                                              Target.position);
+
+    // 已到达目标
+    if (distanceToTarget < 1.42f)
+    {
+        Done();
+        AddReward(1.0f);
+    }
+
+    // 进一步接近
+    if (distanceToTarget < previousDistance)
+    {
+        AddReward(0.1f);
+    }
+
+    // 时间惩罚
+    AddReward(-0.05f);
+
+    // 掉下平�
+    if (this.transform.position.y < -1.0)
+    {
+        Done();
+        AddReward(-1.0f);
+    }
+    previousDistance = distanceToTarget;
+
+    // 动作，大� = 2
+    Vector3 controlSignal = Vector3.zero;
+    controlSignal.x = Mathf.Clamp(vectorAction[0], -1, 1);
+    controlSignal.z = Mathf.Clamp(vectorAction[1], -1, 1);
+    rBody.AddForce(controlSignal * speed);
+ }
+```
+
+请注�函数之�定义的 `speed` 和 `previousDistance` 类��。由于 `speed` 是公共��，您�以从 Inspector 窗�中设置该值。
+
+## 最终 Editor 设置
+
+现在，所有游�对象和 ML-Agent 组件都已准备就绪，因此�以在 Unity Editor 中将一切串起�了。这包括将 Brain 对象分�给 Agent，并设置 Brain 属性，使这些属性与我们的 agent 代�兼容。
+
+1. 在 Hierarchy 窗�中展开 Academy 游�对象，使 Brain 对象��。
+2. 选中 RollerAgent 游�对象以便，在 Inspector 窗�中显示该对象的属性。
+3. 将 Brain 对象从 Hierarchy 窗�拖到 RollerAgent 的 Brain 字段。
+
+![将 Brain 分�给 RollerAgent](images/mlagents-NewTutAssignBrain.png)
+
+�外，将 Target 游�对象从 Hierarchy 窗�拖到 RollerAgent 的 Target 字段。
+
+最�，选择 Brain 游�对象以便能够在 Inspector 窗�中查看该对象的属性。设置以下属性：
+
+* `Vector Observation Space Type` = **Continuous**
+* `Vector Observation Space Size` = 8
+* `Vector Action Space Type` = **Continuous**
+* `Vector Action Space Size` = 2
+* `Brain Type` = **Player**
+
+现在您已准备好在训练之�测试环境。
+
+## 测试环境
+
+在开始长时间进行训练之�，手动测试您的环境始终是一�明智的�法。我们将 Brain 设置为 **Player** 类型的原因是，我们�以使用直接的键盘控制方��控制 agent。但首先，您需�将键盘定义为动作映射。尽管 RollerAgent 的 `Action Size` 仅为 2，但对于�个动作，我们将用一个键指定正值，�用一个键指定负值，因此总共为四个键。
+
+1. 选择 Brain 游�对象以便在 Inspector 中查看该对象的属性。
+2. 将 **Brain Type** 设置为 **Player**。
+3. 展开 **Continuous Player Actions**（仅在使用 **Player* Brain 时��）。
+4. 将 **Size** 设置为 4。
+5. 设置以下映射：
+
+| 元素   | 键 | 索引 | 值 |
+| :------------ | :---: | :------: | :------: |
+| 元素 0 | D   | 0        | 1        |
+| 元素 1 | A    | 0        | -1       |
+| 元素 2 | W  | 1        | 1        |
+| 元素 3 | S   | 1        | -1       |
+
+**Index** 值对应于传递给 `AgentAction()` 函数的动作数组的索引。**Value** 在 **Key** 被按下时分�给 action[Index]。
+
+按 **Play** �行场景，并用 WASD 键在平�上移动 agent。确�在 Unity Editor Console 窗�中没有显示任何错误，并且 agent 在到达目标或掉下平�时会�置。请注�，对于较��的调试，ML-Agents SDK �供了一个方便的 Monitor 类，您�以使用该类轻�地在 Game 窗�中显示 agent 状�信�。
+
+您�以执行一个�外的测试是，首先使用 `python/Basics`
+[Jupyter Notebook](/docs/Background-Jupyter.md)
+确�您的环境和 Python API 能正常工作。在 `Basics` 中，务必将
+`env_name` 设置为您生�的此环境对应的�执行文件的
+�称。
+
+现在�以开始训练 Agent 了。为了准备进行训练，必须首先将 **Brain Type** 从 **Player** 更改为 **External**。此�的过程与[训练 ML-Agents](/docs/Training-ML-Agents.md) 中所述的过程相�。
+
+## 回顾：场景布局
+
+本节简�回顾了在 Unity 环境中使用 Agent 时
+如何组织场景。
+
+您需�在场景中包�三�游�对象�能使用 Unity ML-Agents 工具包：
+ * Academy
+ * Brain
+ * Agent
+
+切记：
+ * 场景中�能有一个 Academy 游�对象。
+ * 您�以拥有多个 Brain 游�对象，但它们必须是 Academy 游�对象的�级。
+
+您的场景层级视图应该如以下示例所示：
+
+![场景层级视图](images/scene-hierarchy.png)
diff --git a/localized_docs/zh-CN/docs/Learning-Environment-Design.md b/localized_docs/zh-CN/docs/Learning-Environment-Design.md
new file mode 100644
index 0000000000000000000000000000000000000000..a18b58a39ef59185565fe3b601381afc74098d03
--- /dev/null
+++ b/localized_docs/zh-CN/docs/Learning-Environment-Design.md
@@ -0,0 +1,89 @@
+# Unity 中的 Reinforcement Learning（强化学习）
+
+Reinforcement learning（强化学习）是一�人工智能技术，通过奖励期望的行为�训练 _agent_ 执行任务。在 reinforcement learning（强化学习）过程中，agent 会探索自己所处的环境，观测事物的状�，并根�这些观测结果采�相应动作。如果该动作带�了更好的状�，agent 会得到正奖励。如果该动作带�的状��太�想，则 agent �会得到奖励或会得到负奖励（惩罚）。�� agent 在训练期间�断学习，它会优化自己的决策能力，以便��时间的推移获得最高奖励。
+
+ML-Agents 使用一�称为 [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/) 的 reinforcement learning（强化学习）技术。PPO 使用神�网络�逼近�想函数；这��想函数将 agent 的观测结果映射为 agent 在给定状�下�以采�的最佳动作。ML-Agents PPO 算法在 TensorFlow 中实现，并在�独的 Python 过程中�行（通过一个socket与正在�行的 Unity 应用程�进行通信）。
+
+**注�：**如果您并��专门研究机器学习和 reinforcement learning（强化学习）主题，�想训练 agent 完�任务，则�以将 PPO 训练视为一个_黑盒_。在 Unity 内部以�在 Python 训练方�有一些与训练相关的�数�进行调整，但您�需�深入了解算法本身就�以�功创建和训练 agent。[训练 ML-Agents](/docs/Training-ML-Agents.md)�供了执行训练过程的�步�作程�。
+
+## 模拟和训练过程
+
+训练和模拟过程以 ML-Agents Academy 类编排的步骤进行。Academy 与场景中的 Agent 和 Brain 对象一起�作�步完�模拟。当 Academy 已达到其最大步数或场景中的所有 agent �_完�_时，一个训练场景�完�。
+
+在训练期间，处于外部的 Python 进程会在训练过程中与 Academy �断进行通信以便�行一系列场景，�时会收集数�并优化其神�网络模型。分�给 agent 的 Brain 类型决定了我们是�进行训练。**External** Brain 会与外部过程进行通信以训练 TensorFlow 模型。�功完�训练�，您�以将�过训练的模型文件添加到您的 Unity 项目中，以便�供给 **Internal** Brain �控制agent的行为。
+
+ML-Agents Academy 类按如下方�编排 agent 模拟循环：
+
+1. 调用您的 Academy �类的 `AcademyReset()` 函数。
+2. 对场景中的�个 agent 调用 `AgentReset()` 函数。
+3. 对场景中的�个 agent 调用 `CollectObservations()` 函数。
+4. 使用�个 agent 的 Brain 类�决定 agent 的下一动作。
+5. 调用您的�类的 `AcademyAct()` 函数。
+6. 对场景中的�个 agent 调用 `AgentAction()` 函数，传入由 agent 的 Brain 选择的动作。（如果 agent 已完�，则�调用此函数。）
+7. 如果 agent 已达到其 `Max Step` 计数或者已将其自身标记为 `done`，则调用 agent 的 `AgentOnDone()` 函数。或者，如果�个 agent 在场景结�之�已完�，您�以将其设置为�新开始。在这�情况下，Academy 会调用 `AgentReset()` 函数。
+8. 当 Academy 达到其自身的 `Max Step` 计数时，它会通过调用您的 Academy �类的 `AcademyReset()` 函数��次开始下一场景。
+
+�创建训练环境，请扩展 Academy 和 Agent 类以实现上述方法。`Agent.CollectObservations()` 和 `Agent.AgentAction()` 函数必须实现；而其他方法是�选的，�是�需�实现它们�决于您的具体情况。
+
+**注�：**在这里用到的 Python API 也�用于其他目的。例如，借助于该 API，您�以将 Unity 用作您自己的机器学习算法的模拟引擎。请�阅 [Python API](/docs/Python-LLAPI.md) 以了解更多信�。
+
+## 组织 Unity 场景
+
+为了在 Unity 场景中训练和使用 ML-Agents，该场景必须包�一个 Academy �类实例，若干个 Brain 游�对象和 Agent �类实例。场景中的任何 Brain 游�对象都必须附加到 Hierarchy 视图中的 Academy 游�对象的�级下。Agent �类实例应该附到代表该 agent 的游�对象下。
+
+[Screenshot of scene hierarchy]
+
+您必须为�个 agent 分�一个 Brain，但�以在多个 agent 之间共享 Brain。�个 agent 都将进行自己的观测并独立行动，但会使用相�的决策逻辑，而对于 **Internal** Brain，则会使用相�的�过训练的 TensorFlow 模型。
+
+### Academy
+
+Academy 对象会指挥多个 agent 的决策过程。一个场景中有且仅能有一个Academy 对象。
+
+您必须创建 Academy 类的�类（因为Academy类是抽象类）。创建 Academy 的�类时，你�以实现以下方法（全部都是�选方法）：
+
+* `InitializeAcademy()` — 第一次�动时准备环境。
+* `AcademyReset()` — 为下一轮的模拟准备环境和 agent。你�以使用这个函数在场景中放入并�始化实体。
+* `AcademyStep()` — 为下一模拟步骤准备环境。Academy 基类首先调用此函数，然��调用当�步骤的任何 `AgentAction()` 方法。您�以使用此函数在 agent 采�动作之�更新场景中的其他对象。请注�，在 Academy 调用此方法之�，agent 已收集了自己的观测结果并选择了动作。
+
+Academy 基类还定义了若干�以在 Unity Editor Inspector 中设置的��属性。对于训练而言，这些属性中最��的是 `Max Steps`，它决定了�个训练场景的�续时间。Academy 的步骤计数器达到此值�，它将调用 `AcademyReset()` 函数�开始下一轮模拟。
+
+  请�阅 [Academy](/docs/Learning-Environment-Design-Academy.md) 以查看 Academy 属性�其用途的完整列表。
+
+### Brain
+
+Brain 内部�装了决策过程。Brain 对象必须放在 Hierarchy 视图中的 Academy 的�级。我们必须为�个 Agent 分�一个 Brain，但�以在多个 Agent 之间共享�一个 Brain。
+
+当我们使用 Brain 类的时候�需�使用其�类，而应该直接使用 Brain 这个类。Brain 的行为�决于 Brain 的类型。在训练期间，应将 agent 上连接的 Brain 的 Brain Type 设置为 **External**。�使用�过训练的模型，请将模型文件导入 Unity 项目，并将对应 Brain 的 Brain  Type 更改为 **Internal**。请�阅 [Brain](/docs/Learning-Environment-Design-Brains.md) 以了解有关使用��类型的 Brain 的详细信�。如果四�内置的类型�能满足您的需求，您�以扩展 CoreBrain 类以创建其它的 Brain 类型。
+
+Brain 类有若干�以使用 Inspector 窗�进行设置的��属性。对于使用 Brain 的 agent，这些属性必须�当。例如，`Vector Observation Space Size` 属性必须与 agent 创建的特���的长度完全匹�。请�阅 [Agent](/docs/Learning-Environment-Design-Agents.md) 以获�有关创建 agent 和正确设置 Brain 实例的信�。
+
+请�阅 [Brain](/docs/Learning-Environment-Design-Brains.md) 以查看 Brain 属性的完整列表。
+
+### Agent
+
+Agent 类代表场景中负责收集观测结果并采�动作的一个�与者 (actor)。我们在�置的时候通常会把Agent 类的脚本附在这个�与者对应的游�对象上。例如，附加到足�比赛中的�员对象，或车辆模拟中的汽车对象上。此外，必须为�个 Agent 类的脚本分�一个 Brain。
+
+�创建 agent，请扩展 Agent 类并实现基本的 `CollectObservations()` 和 `AgentAction()` 方法：
+
+* `CollectObservations()` — 收集 agent 对其环境的观测结果。
+* `AgentAction()` — 执行由 agent 的 Brain 选择的动作，并为当�状�分�奖励。
+
+这些函数的实现决定了分�给此 agent 的 Brain 的属性�如何设置。
+
+您还必须确定 Agent 如何完�任务，以�当它超时�如何处�。agent 完�其任务（或彻底失败）�，您�以在 `AgentAction()` 函数中手动将 agent 设置为完�。您还�以将 agent 的 `Max Steps` 属性设置为正值，这样 agent 在执行了此数�的步骤�会认为自己已完�。Academy 达到自己的 `Max Steps` 计数�，会开始下一场景。如果将 agent 的 `ResetOnDone` 属性设置为 true，则 agent �以在一个场景中多次�试自己的任务。（在 `Agent.AgentReset()` 函数中�以设置 agent 的�始化逻辑，为下一次的任务�好准备。）
+
+请�阅 [Agent](/docs/Learning-Environment-Design-Agents.md) 以详细了解如何编写一个你自己的 agent。
+
+## 环境
+
+ML-Agents 中的_环境_�以是 Unity 中构建的任何场景。Unity 场景为 agent �供了观察�行动和学习的环境。如何设置 Unity 场景实际上�决于您的目标。您�能想�试图解决�个特定的reinforcement learning（强化学习）问题，这�情况下您�以在�一个场景内�进行训练�进行测试。或者，您�能想�训练 agent 在��的游�或模拟�件下的�出�些行为，这�情况下创建更有针对性的训练场景�能会更加高效和实用。
+
+训练和测试（或正常游�）场景都必须包�一个 Academy 对象�控制 agent 的决策过程。Academy 定义了若干�以针对训练场景与测试场景进行��设置的属性。Academy 的 **Configuration** 属性用于控制渲染和时间刻度。您�以设置 **Training Configuration** �最大�度缩短 Unity 用于渲染图形的时间，从而加快训练速度。您�能还需�调整其他 Academy 功能设置。例如，`Max Steps` 的大�应尽�能�，从而尽�缩短训练时间，但也�能太�，必须大到足以让 agent 完�任务并在学习过程中有一些�外的“徘徊�考�(wandering) 时间。在测试场景中，您通常根本�希望 Academy �置场景；如果是这样，应将 `Max Steps` 设置为零。
+
+在 Unity 中创建训练环境时，必须设置场景以便�以通过外部训练过程�控制场景。注�以下几点：
+
+* 在训练程��动�，Unity �执行文件会被自动打开，然�训练场景会自动开始训练。
+* 场景中至少须包括一个 **External** Brain。
+* Academy 必须在�一轮训练�将场景�置为有效的�始状�。
+* 训练场景必须有明确的结�状�，为此需�使用 `Max Steps`，或让�个 agent 将自身设置为 `done`。
+
diff --git a/localized_docs/zh-CN/docs/Learning-Environment-Examples.md b/localized_docs/zh-CN/docs/Learning-Environment-Examples.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa096cb7fa75e83a3530acab1b655254c9ecccfc
--- /dev/null
+++ b/localized_docs/zh-CN/docs/Learning-Environment-Examples.md
@@ -0,0 +1,224 @@
+# 学习环境示例
+
+Unity ML-Agents 工具包中内置了一些�建好的学习环境的示例，并且我们还在�断增加新的示例，这些示例演示了该平�的��功能。示例环境�于
+`unity-environment/Assets/ML-Agents/Examples` 中，并且我们在下文中进行了简�的介�。
+此外，我们的
+[首届 ML-Agents 挑战赛](https://connect.unity.com/challenges/ml-agents-1)
+中包�了由社区�员创建的一些环境示例。
+
+此页仅概述了我们�供的示例环境。如果想�更多地学习如何设计和构建您自己的环境，请�阅
+[创建新的学习环境](Learning-Environment-Create-New.md)
+页�。
+
+如果您想�交自己的环境，请�阅我们的
+[贡献指�](/com.unity.ml-agents/CONTRIBUTING.md)页�。
+
+## Basic
+
+![Basic](images/basic.png)
+
+* 训练环境：一�线性移动任务，在此任务中 agent 必须�左或��移动到奖励状�。
+* 目标：移动到最高奖励状�。
+* Agent设置：环境包�一个 agent，上�附带了�个 Brain。
+* Agent 奖励函数设置：
+    * 达到次优状�时 +0.1。
+    * 达到最优状�时 +1.0。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（离散��）一个��，对应于当�状�。
+    * ���动空间：（离散��）两个�能的动作（�左移动���移动）。
+    * 视觉观测：0
+* �置�数：无
+
+## 3DBall: 3D Balance Ball
+
+![3D Balance Ball](images/balance.png)
+
+* 训练环境：一�平衡�任务，在此任务中 agent 需�控制平�。
+* 目标：agent 必须平衡平�，以尽�能长时间在平�上����掉�。
+* Agent设置：环境包� 12 个全部链接到�个 Brain 的�类 agent。
+* Agent 奖励函数设置：
+    * �在平�上���掉下的�一步都 +0.1。
+    * �掉下平�时 -1.0。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）8 个，对应于平�的旋转以��的�置�旋转和速度。
+    * ��观测空间（困难版本，因为观测到的信��少了）：（连续��）5 个��，对应于平�的旋转以��的�置和旋转。
+    * ���动空间：（连续��）2 个，其中一个值对应于 X 旋转，而�一个值对应于 Z 旋转。
+    * 视觉观测：0
+* �置�数：无
+
+## GridWorld
+
+![GridWorld](images/gridworld.png)
+
+* 训练环境：�一个典型版本的的grid-world任务。场景包� agent�目标和障�。
+* 目标：agent 必须在网格中�开障�的�时移动到目标。
+* Agent设置：环境包�一个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置：
+    * �一步 -0.01。
+    * agent 导航到目标网格�置时 +1.0（场景结�）。
+    * agent 移动到障�物时 -1.0（场景结�）。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：无
+    * ���动空间：（离散��）4 个，对应于基本方�的移动。
+    * 视觉观测：一个对应于 GridWorld 自上而下的视图。
+* �置�数：三个�数，分别对应于网格大��障�物数�和目标数�。
+
+
+## Tennis
+
+![Tennis](images/tennis.png)
+
+* 训练环境：agent 控制��将�弹过�网的�人游�。
+* 目标：agent 必须在彼此之间弹起网�，�时�能丢�或击�出界。
+* Agent设置：环境包�两个链接到�个 Brain TennisBrain）的 agent。在训练之�，您�以将�一个�为 MyBrain 的 Brain 附加到其中一个 agent，从而与�过训练的模型进行游�比赛。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * agent 击�过网时 +0.1。
+    * agent 让��入自己的范围或者击�出界时 -0.1。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）8 个，分别对应于�和��的�置和速度。
+    * ���动空间：（连续��）2 个，分别对应于���网或远离�网的�动，以�上下的�动。
+    * 视觉观测：无
+* �置�数：一个�数，对应于�的大�。
+
+## Push Block
+
+![Push](images/push.png)
+
+* 训练环境：一个平�，agent �以在该平�上推动方�。
+* 目标：agent 必须将方�推�目标。
+* Agent设置：环境包�一个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置：
+    * �一步 -0.0025。
+    * 方�接触到目标时 +1.0。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）15 个，分别对应于 agent�方�和目标的�置和速度。
+    * ���动空间：（连续��）2 个，分别对应于 X 和 Z 方�的移动。
+    * 视觉观测：无。
+* �置�数：无。
+
+## Wall Jump
+
+![Wall](images/wall.png)
+
+* 训练环境：一个平�环境，agent �以在该环境中跳过墙。
+* 目标：agent 必须利用一个方�越过墙并到达目标。
+* Agent设置：环境包�一个链接到两个�� Brain 的 agent。agent 链接到的 Brain 根�墙的高度而�化。
+* Agent 奖励函数设置：
+    * �一步 -0.0005。
+    * agent 接触到目标时 +1.0。
+    * agent 掉下平�时 -1.0。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）16 个，分别对应于 agent�方�和目标的�置和速度以�墙的高度。
+    * ���动空间：（离散��）74 个，分别对应于 14 个射线投射，�个射线投射�检测 4 个�能的物体，加上 agent 的全局�置以� agent 是��地。
+    * 视觉观测：无。
+* �置�数：4，对应于�能的墙�的高度。
+
+## Reacher
+
+![Tennis](images/reacher.png)
+
+* 训练环境：�以移动到目标�置的�关节臂。
+* 目标：agent 必须将手移动到目标�置，并��在此处。
+* Agent设置：环境包� 32 个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * 当 agent 的手处于目标�置时，�过一步 +0.1。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）26 个，对应于两个机械臂 Rigidbody 的�置�旋转�速度和角速度。
+    * ���动空间：（连续��）4 个，对应于两个关节的两个方�上的转动。
+    * 视觉观测：无
+* �置�数：两个，对应于目标大�和目标移动速度。
+
+## Crawler
+
+![Crawler](images/crawler.png)
+
+* 训练环境：一�有 4 个手臂的生物，�个手臂分两节
+* 目标：agent 必须沿 x 轴移动其身体，并且���跌倒。
+* Agent设置：环境包� 3 个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * +1 乘以 x 方�的速度
+    * 跌倒时 -1。
+    * -0.01 乘以动作平方
+    * -0.05 乘以 y �置�化
+    * -0.05 乘以 z 方�的速度
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）117 个，对应于�个肢体的�置�旋转�速度和角速度以�身体的加速度和角速度。
+    * ���动空间：（连续��）12 个，对应于适用于 12 个关节的扭矩。
+    * 视觉观测：无
+* �置�数：无
+
+## Banana Collector
+
+![Banana](images/banana.png)
+
+* 训练环境：一个包�多个 agent 的环境，这些 agent 争相收集香蕉。
+* 目标：agent 必须学习尽�能接近更多的黄色香蕉，�时�开红色香蕉。
+* Agent设置：环境包� 10 个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * 接触黄色香蕉时 +1
+    * 接触红色香蕉时 -1。
+* Brain 设置：一个有以下观测/�动空间的 Brain。
+    * ��观测空间：（连续��）51 个，对应于 agent 的速度， agent �进方�，以� agent 对周围物体进行基于射线的感知。
+    * ���动空间：（连续��）3 个，对应于��移动，绕 y 轴旋转，以�是�使用激光使其他 agent 瘫痪。
+    * 视觉观测（�选）：�个 agent 的第一人称视图。
+* �置�数：无
+
+## Hallway
+
+![Hallway](images/hallway.png)
+
+* 训练环境：在一个环境中，agent 需�在房间内查找信��记�信�并使用信�移动到正确目标。
+* 目标：移动到与房间内的方�的颜色相对应的目标。
+* Agent设置：环境包�一个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * 移动到正确目标时 +1。
+    * 移动到错误目标时 -0.1。
+    * 时间��惩罚 -0.0003。
+* Brain 设置：一个有以下观测/�动空间的 Brain：
+    * ��观测空间：（连续��）30 个，对应于针对物体�目标和墙�的射线投射检测信�。
+    * ���动空间：（离散��）4 个，对应于 agent 旋转和�进/�退�动。
+    * 视觉观测（�选）：agent 的第一人称视图。
+* �置�数：无
+
+## Bouncer
+
+![Bouncer](images/bouncer.png)
+
+* 训练环境：在一个环境中，agent 需�按需决策。agent 必须决定在接触地�时如何进行下一次弹跳。
+* 目标：抓�漂浮的香蕉。跳跃次数有�。
+* Agent设置：环境包�一个链接到�个 Brain 的 agent。
+* Agent 奖励函数设置（agent互相之间独立）：
+    * 抓�香蕉时 +1。
+    * 弹跳出界时 -1。
+    * -0.05 乘以跳跃力的在�个方�上的平方和。能�消耗惩罚。
+* Brain 设置：一个有以下观测/�动空间的 Brain 的设置：
+    * ��观测空间：（连续��）6 个，对应于 agent 和香蕉的局部�置。
+    * ���动空间：（连续��）3 个，对应于 agent 为跳跃所用的力。
+    * 视觉观测：无
+* �置�数：无
+
+## Soccer Twos
+
+![SoccerTwos](images/soccer.png)
+
+* 训练环境：在一个环境中，四个 agent 在 2 对 2 玩具足�比赛中比赛。
+* 目标：
+    * �锋：让�进入对手的�门。
+    * 守门员：防止�进入自己的�门。
+* Agent设置：环境包�四个 agent，其中两个链接到一个 Brain（�锋），两个链接到�一个 Brain（守门员）。
+* Agent 奖励函数设置（agent互相之间�独立）：
+    * �锋：
+        * �进入对手�门时 +1。
+        * �进入自己队的�门时 -0.1。
+        * 存在性惩罚 -0.001。
+    * 守门员：
+        * �进入自己队的�门时 -1。
+        * �进入对手�门时 +0.1。
+        * 存在性奖励 +0.001。
+* Brain 设置：两个有以下观测/�动空间的 Brain：
+    * ��观测空间：（连续��）112，对应于局部的 14 个射线投射，�个射线投射�检测 7 个�能的物体类型，以�物体的�离。感知范围是 agent ��的 180 度视角。
+    * ���动空间：（离散��）
+        * �锋：6 个，对应于�进��退�侧身移动以�旋转。
+        * 守门员：4 个，对应于�进��退�侧身移动。
+    * 视觉观测：无
+* �置�数：无
diff --git a/localized_docs/zh-CN/docs/ML-Agents-Overview.md b/localized_docs/zh-CN/docs/ML-Agents-Overview.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c77cc4b98b4c47049c030a210229f348ac0f0f3
--- /dev/null
+++ b/localized_docs/zh-CN/docs/ML-Agents-Overview.md
@@ -0,0 +1,445 @@
+# ML-Agents 概述
+
+**Unity Machine Learning Agents** (ML-Agents) 是一款开�的 Unity �件，使我们得以在游�和其它模拟环境中训练智能的 agent。您�以使用 reinforcement learning（强化学习）�
+imitation learning（模仿学习）�neuroevolution（神�进化）或其他机器学习方法
+通过简�易用的 Python API 对 Agent 进行训练。我们还�供最先进算法的实现方�（基于
+TensorFlow），让游�开�者和业余爱好者能够轻�地
+训练用于 2D�3D 和 VR/AR 游�的智能 agent。
+这些�过训练的 agent �用于多�目的，
+包括控制 NPC 行为（采用��设置，
+例如多个 agent 和对抗）�对游�内部版本进行自动化测试
+以�评估��游�设计决策的预�布版本。ML-Agents 对于游�开�者和 AI 研究人员�方
+都有利，因为它�供了一个集中的平�，
+�以在 Unity 的丰富环境中评估 AI 的进步情况，
+然��供给更广泛的研究和游�开�者社区。
+
+根�您的背景（如研究人员�游�开�人员�业余爱好者），
+您现在�能在脑海中会有�常��的问题。
+为了让您更轻�地过渡到 ML-Agents，
+我们�供了多个��页�，其中包括有关
+[Unity 引擎](/docs/Background-Unity.md)�
+[机器学习](/docs/Background-Machine-Learning.md)和
+[TensorFlow](/docs/Background-TensorFlow.md) 的概述和有用资�。如果您�熟悉 Unity 场景，�了解基本的机器学习概念，或者以�没有�说过 TensorFlow，**强烈**建议您�览相关的背景知识页�。
+
+此页�的其余部分深入介�了 ML-Agents�包括其��组件�
+支�的��的训练模�以�这些训练模�对应的应用场景。读完�
+您应该对 ML-Agents 的_功能_有一个很好的了解。��的文档页�
+�供了 ML-Agents _用法_的示例。
+
+## �行示例：训练 NPC 行为
+
+为了帮助解释此页�中的�料和术语，我们将通篇使用一个
+虚构的�行示例。我们将探讨
+在游�中训练�玩家角色 (NPC) 行为的问题。
+（NPC 是一个永远��人类玩家控制的游�角色，
+其行为由游�开�者预先定义。）更具体地说，
+我们�设我们正在开�一个由玩家控制士兵的多玩家战争主题
+游�。在此游�中，我们有一�担任军医的 NPC，他会负责寻找
+和���伤的玩家。最�，我们�设有两支军队，
+�支军队有五�队员和一� NPC 军医。
+
+军医的行为相当��。此角色首先需����伤，
+因此需�检测何时处于�险之中并转移至安全
+�置。其次，此角色需�了解其队员中哪些人�伤
+而需�帮助。如果多人�伤，则需�评估�伤程度
+并决定首先帮助哪个伤者。最�，
+一�优秀的军医总能把自己置于一个�以快速帮助队员的
+�置。符�所有这些特��味�军医需�在任何情况下
+测�环境的若干属性（例如，队员�置�
+敌人�置�哪一�队员�伤以��伤程度），
+然�决定采�的行动（例如，躲�敌人的�力�
+�往帮助其队员之一）。鉴于环境中存在有大�的���数
+以�军医�以采�的大��能的行动，
+手工定义和实现此类��的行为�具挑战性，
+并且很容易出错。
+
+凭借 ML-Agents，您�以使用��方法�_训练_这类 NPC
+（称为 **agent**）的行为。基本�念很简�。
+我们需�在游�（称为**环境**）的�个时刻定义
+三个实体：
+- **观测** - 军医对环境的感知。
+观测�以是数字和/或视觉形�。数字观测会从 agent 的角度
+测�环境的属性。对于
+我们的军医�说，这将是军医�以看到的战场属性。
+根�游�和 agent 的��，数字观测的数��以是_离散_的
+或_连续_的形�。对于大多数有趣的环境，agent 将需�若干连续的
+数字观测，而对于具有少�独特�置的简�环境，
+离散观测就足够了。
+�一方�，视觉观测是附加到 agent 的摄�头
+所生�的图�，代表� agent 在该时间点看到的
+内容。我们通常会将 agent 的观测与环境（或游�）的
+**状�**混淆。环境状�表示包�所有游�角色的
+整个场景的相关信�。但是，agent 观测
+仅包� agent 了解的信�，通常是
+环境状�的一个�集。例如，军医观测�能包括
+军医�知�的�身敌人的信�。
+- **动作** - 军医�采�的动作。与观测
+类似，根�环境和 agent 的��性，
+动作�以是连续的或离散的。就军医而言，
+如果环境是一个�是基于�置的简�网格世界，
+那么采用四个值（东�西���北）之一的离散动作
+就足够了。但是，如果环境更加��并且军医�以
+自由移动，那么使用两个连续动作（一个对应于方�，
+�一个对应于速度）更�适。
+- **奖励信�** - 一个表示军医行为的标�值。
+请注�，�需�在�个时刻都�供奖励信�，
+�有在军医执行好的或�的动作时
+��供。例如，军医在死亡时会得到很大的负奖励，
+�当���伤的队员时会得到适度的正奖励，
+而在�伤队员因缺�救助而死亡时
+会得到适度的负奖励。请注�，奖励信�表示如何将任务的目标
+传达给 agent，所以采用的设置方�需�确�当获得的奖励达到最大值时，agent能够产生我们最期望的行为。
+
+在定义这三个实体（**reinforcement learning（强化学习）任务**
+的模�）之�，我们现在�以
+_训练_军医的行为了。为此需�通过许多试验�
+模拟环境，��时间的推移，通过最大化未�奖励，
+使军医能够针对�次的观测学会采�
+最佳的动作。关键在于，军医学习能够使奖励得到最大值的动作的过程，
+便是在学习使自己�为一�优秀军医（�拯救最多生命的
+军医）的过程。在 **reinforcement learning（强化学习）**
+技术中，所学习的行为称为 **policy**，
+我们想学习的 policy 本质上是一个从�个�能的观测到该观测下最优动作的映射。请注�，
+通过�行模拟�学习 policy 的过程被称为
+**训练阶段**，而让 NPC 使用其学习到的 policy
+玩游�的过程被称为**预测阶段**。
+
+ML-Agents �供了所有必�工具，从而使我们得以利用 Unity 作为
+模拟引擎�学习 Unity 环境中��对象的 policy。
+在接下�的几节中，我们将讨论 ML-Agents 如何实现这一目的以�
+它所�供的功能有什么。
+
+## 主�组件
+
+ML-Agents 是一个 Unity �件，它包�三个高级组件：
+* **学习环境** - 其中包� Unity 场景和所有游�
+角色。
+* **Python API** - 其中包�用于训练（学习�个行为或 policy）的
+所有机器学习算法。请注�，
+与学习环境��，Python API �是 Unity 的一部分，而是�于外部
+并通过 External Communicator 与 Unity 进行通信。
+* **External Communicator** - 它将 Unity 环境与 Python API
+连接起�。它�于 Unity 环境中。
+
+<p align="center">
+    <img src="images/learning_environment_basic.png"
+        alt="Simplified ML-Agents Scene Block Diagram"
+        width="700" border="10" />
+</p>
+
+_ML-Agents 的简化框图。_
+
+学习环境包�三个�帮助组织 Unity 场景的
+附加组件：
+* **Agent** - 它�以被附加到一个 Unity 游�对象上（场景中的
+任何角色），负责生�它的观测结果�执行它接收的动作
+并适时分�奖励（正/负）。
+�个 Agent �与一个 Brain 相关�。
+* **Brain** - 它�装了 Agent 的决策逻辑。
+实质上，Brain 中�存��个 Agent 的 policy，
+决定了 Agent 在��情况下应采�的动作。更具体地说，
+它是从 Agent 接收观测结果和奖励并返回动作的
+组件。
+* **Academy** - 它指挥 agent 的观测和决策过程。
+在 Academy 内，�以指定若干环境�数，
+例如渲染质�和环境�行速度�数。
+External Communicator �于 Academy 内。
+
+�个学习环境都会有一个全局的 Academy ，与�一个游�角色一一对应的多个 Agent。虽然�个 Agent 必须与一个 Brain 相连，
+但具有相似观测和动作的多个 Agent �关�到
+�一个 Brain。在我们的示例游�中，我们有两个�自拥有
+自己军医的军队。因此，在我们的学习环境中将有两个 Agent，
+��军医对应一个 Agent，但这两个军医都�以关�到�一个 Brain。
+请注�，这两个军医与�一个 Brain 相连的原因是，他们的观测和动作_空间_
+是相似的。这并��味�在��情况下
+他们都会有相�的观测和动作_值_。���说，
+Brain 定义了所有�能的观测和动作的空间，
+而与之相连的 Agent（在本示例中是指军医）�以�自拥有
+自己独特的观测和动作值。如果我们将游�
+扩展到包��克驾驶员 NPC，那么附加到这些角色的
+Agent �能与连接到军医的 Agent 共享一个 Brain（军医和驾驶员
+有��的动作）。
+
+<p align="center">
+    <img src="images/learning_environment_example.png"
+        alt="Example ML-Agents Scene Block Diagram"
+        border="10" />
+</p>
+
+_示例游�的 ML-Agents 的示例框图。_
+
+我们尚未讨论 ML-Agents 如何训练行为以� Python API 和
+External Communicator 的作用。在我们深入了解这些细节之�，
+让我们总结一下先�的组件。�个游�角色上附有一个 Agent，
+而�个 Agent 都连接到一个 Brain。Brain 从 Agent 处接收观测结果和奖励并返回动作。Academy 除了能够控制环境�数之外，还�确�所有 Agent 和 Brain 都处于�步状�。那么，Brain 如何控制 Agent 的动作呢？
+
+实际上，我们有四���类型的 Brain，它们�以实现广泛的训练和预测情形：
+* **External** - 使用 Python API 进行决策。这�情况下，
+Brain 收集的观测结果和奖励通过 External Communicator
+转�给 Python API。Python API ��返回 Agent 需�采�的相应动作。
+* **Internal** - 使用嵌入�
+[TensorFlow](/docs/Background-TensorFlow.md) 模型进行决策。
+嵌入� TensorFlow 模型包�了学到的 policy，Brain 直接使用
+此模型�确定�个 Agent 的动作。
+* **Player** - 使用键盘或控制器的实际输入进行
+决策。这�情况下，人类玩家负责控制 Agent，由 Brain 收集的
+观测结果和奖励�用于控制 Agent。
+* **Heuristic** - 使用写死的逻辑行为进行决策，目�市�上大多数游�角色行为都是这么定义的。这�类型有助于调试
+具有写死逻辑行为的 Agent。也有助于把这�由写死逻辑指挥的 Agent 与
+训练好的 Agent 进行比较。在我们的示例中，一旦我们
+为军医训练了 Brain，我们便�以为一个军队的军医分�
+�过训练的 Brain，而为�一个军队的军医分�具有写死逻辑行为的
+Heuristic Brain。然�，我们�以评估哪个军医的效率更高。
+
+根�目�所述，External Communicator 和 Python API 似乎
+�能由 External Brain 所用。实际并�如此。
+我们�以�置 Internal�Player 和 Heuristic 类型的 Brain，
+使它们也能通过 External Communicator（一�称为 _broadcasting_ 的功能）
+将观测结果�奖励和动作��给 Python API。我们很快就会
+看到，这样�以实现其他的训练模�。
+
+<p align="center">
+    <img src="images/learning_environment.png"
+        alt="ML-Agents Scene Block Diagram"
+        border="10" />
+</p>
+
+_一个包�多个 Agent 和 Brain 的场景的
+�能�置示例。_
+
+## 训练模�
+
+鉴于 ML-Agents 的�活性，我们�以通过多�方�进行训练
+和预测。
+
+### 内置的训练和预测
+
+如�所述，ML-Agents 附带了多�用于训练智能 agent 的
+最先进算法的实现。在此模�下，
+Brain 类型在训练期间设置为 External，在预测期间设置为 Internal。
+更具体地说，在训练期间，场景中的所有军医
+通过 External Communicator 将他们的观测结果��到 Python API
+（这是采用 External Brain 时的行为）。Python API 会处�这些观测结果并�回
+�个军医�采�的动作。在训练期间，这些动作大多是
+探索性的，旨在帮助 Python API 学习��军医的
+最佳 policy。训练结��，便�导出�个军医
+所学的 policy。由于我们所有的实现都基于 TensorFlow，
+因此所学的 policy �是一个 TensorFlow 模型文件。然�在预测阶段，
+我们将 Brain 类型切�为 Internal，并加入从训练阶段
+生�的 TensorFlow 模型。现在，在预测阶段，军医
+�然继续生�他们的观测结果，但��将结果��到
+Python API，而是�入他们的嵌入了的 TensorFlow 模型，
+以便生��个军医在�个时间点上�采�的_最佳_动作。
+
+总结一下：我们的实现是基于 TensorFlow 的，因此，
+在训练期间，Python API 使用收到的观测结果�学习
+TensorFlow 模型。然�在预测过程中该模型将嵌入到
+Internal Brain 中，以便为连接到该 Brain 的所有 Agent 生�
+最佳动作。**请注�，我们的 Internal Brain 目�是实验性的，
+因为它仅�于 TensorFlow 模型并会利用第三方
+[TensorFlowSharp](https://github.com/migueldeicaza/TensorFlowSharp)
+ 库。**
+
+[3D Balance Ball 示例入门](Getting-Started-with-Balance-Ball.md)
+教程使用 **3D Balance Ball** 示例环境介�了此训练模�。
+
+### 自定义训练和预测
+
+先�的模�中使用 External Brain 类型进行训练，
+从而生� Internal Brain 类型�以�解和使用的 TensorFlow
+模型。然而，ML-Agents 的任何用户都�以利用自己的算法
+进行训练和预测。在这�情况下，训练阶段和预测阶段
+的 Brain 类型都会设置为 External，并且场景中所有 Agent 的行为
+都将在 Python 中接�控制。
+
+我们目�没有教程介�这�模�，但您�以在[这里](/docs/Python-LLAPI.md)
+了解有关 Python API 的更多信�。
+
+### Curriculum Learning（课程学习）
+
+此模�是_内置训练和预测_的扩展，
+在训练��环境中的��行为时特别
+有用。Curriculum learning（课程学习）是一�训练机器学习模型
+的方�，这�训练方�会��引入问题较难�解的方�，
+使该模型总是�到最佳挑战。这��路已�存在了
+很长一段时间，这也是我们人类通常学习的方�。比如
+任何童年时期的�等教育，课程和主题都会
+进行排�。例如，先教算术，�教代数。
+�样，先教代数，�教微积分。�期课程中学到的技能和知识为
+�期的课程�供了基础。机器学习
+也是�样的��，对较简�任务的训练�以为将�
+较难的任务�供基础。
+
+<p align="center">
+    <img src="images/math.png"
+        alt="Example Math Curriculum"
+        width="700"
+        border="10" />
+</p>
+
+_数学课程的示例。从简�主题到��主题的课程进度安排，
+�一个课程基于�一个课程。_
+
+当我们考虑 reinforcement learning（强化学习）的实际原�时，
+学习信�是在整个训练过程中�尔收到的奖励。
+训练 agent 完�此任务时的起点将是一个
+�机 policy。该起始 policy 将使 agent ��转圈，
+这样的行为在��环境中�能永远�会获得奖励或�少
+获得奖励。因此，通过在训练开始时简化环境，
+我们�让 agent 将�机 policy 快速更新为更有�义的 policy，
+�，��环境����化，policy 也会�断
+改进。在我们的示例中，我们�以考虑当�个队�包�一个
+玩家时，首先训练军医，然����增加玩家人数
+（�环境��度）。ML-Agents 支�在
+Academy 内设置自定义环境�数。因此，
+�以根�训练进度动�调整与难度或��性相关的
+环境�素（比如游�对象）。
+
+[使用 Curriculum Learning（课程学习）进行训练](/docs/Training-Curriculum-Learning.md)
+教程使用 **Wall Area** 示例环境介�了此训练模�。
+
+### Imitation Learning（模仿学习）
+
+简�地展示我们希望 agent 执行的行为，
+而�是试图通过试错法�让它学习，这�方�
+通常会更直观。例如，这�模��是通过
+设置奖励功能�训练军医，而是通过游�控制器�供军医
+应该如何表现的示例。更具体地说，
+在这�模�下，训练期间的 Brain 类型设置为 Player，
+并且所有使用控制器执行的动作（�仅包括 agent 观测）
+都将被记录并��到 Python API。imitation learning（模仿学习）
+算法��将会使用这些�自人类玩家的观测结果以�他们对应的动作�
+学习 policy。
+
+[使用 Imitation Learning（模仿学习）进行训练](/docs/Training-Imitation-Learning.md)教程
+使用 **Banana Collector** 示例环境介�了此训练模�。
+
+## �活的训练方案
+
+虽然迄今为止的讨论都主�集中在使用 ML-Agents 对�个 agent 进行训练
+方�，但�时实现多�训练方案也是�能的。
+我们很高兴看到社区创建了许多新奇而有趣的
+环境。刚开始训练智能 agent 的新用户�以�考以下一些示例
+�获得�感：
+* � Agent。与�个 Brain 相连的�个 Agent，拥有自己的奖励
+信�。这是传统的 agent 训练方�。一个示例是
+任何�人游�，比如 Chicken。
+[视频链接](https://www.youtube.com/watch?v=fiQsmdwEGT8&feature=youtu.be)。
+* �步� Agent。与�个 Brain 相连的多个独立 Agent，
+具有独立奖励信�。传统训练方案的
+并行版本，这�以加速和稳定训练
+过程。当您在一个环境中拥有�一角色的多个版本
+而这些角色版本应该学习类似行为时，这很有用。一个�能的示例是训练
+�二个机械臂�时打开一扇门。
+[视频链接](https://www.youtube.com/watch?v=fq0JBaiCYNA)。
+* 对抗性自我模拟。与�个 Brain 相连的两个相互作用的 Agent，
+具有��奖励信�。在�人游�中，对抗性自我模拟
+�以让 agent �得越�越熟练，�时始终拥有完美匹�的
+对手：自身。这是训练 AlphaGo 时采用的策略，
+最近被 OpenAI 用于训练一�人类对战的 1 比 1 Dota 2 agent。
+* �作性多 Agent。与�个或多个�� Brain 相连的多个相互作用的 Agent，具有共享的奖励信�。在此方案中，所有 agent 必须共�完�一项�能�独完�的任务。示例包括这样的环境：�个 agent �能访问部分信�，并且需�共享这些信��能完�任务或�作解决难题。
+* 竞争性多 Agent。与�个或多个�� Brain 相连的多个
+相互作用的 Agent，具有��奖励信�。在此
+方案中，agent 必须相互竞争�能赢得比赛，
+或获得一些有�的资�。所有的团队�动都属于
+这�情况。
+* 生�系统。与�个或多个�� Brain 相连的多个
+相互作用的 Agent，具有独立奖励信�。这�方案�以
+看�是创造一个�世界，在这个�世界中，拥有��目标的动物
+都�以相互影�，比如�能有斑马�大象和长颈鹿的稀树�原，
+或者城市环境中的自动驾驶模拟。
+
+## 其他功能
+
+除了�供�活的训练方案外，ML-Agents 还
+包�其他功能，�用于�高训练过程的�活性和
+解释能力。
+
+* **按需决策** - 使用 ML-Agents �以让 agent 仅在需�时
+�请求决策，而�是在环境的�一步都
+请求决策。这�方案�用于训练基于回�的游��
+agent 必须对事件作出�应的游�，
+或 agent �以采��续时间��的动作
+的游�。在�一步决策与按需决策之间切�
+�需点击一下按钮��实现。您�以在
+[此处](/docs/Learning-Environment-Design-Agents.md#on-demand-decision-making)了解更多关于按需决策功能的
+信�。
+
+* **记忆增强 Agent** - 在�些情况下，
+agent 必须学会记�过去�能�出
+最好的决策。当 agent �能部分观测环境时，
+跟踪过去的观测结果�以帮助 agent 学习。我们在
+教练中�供了一�_长短期记忆_
+([LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory))
+ 的实现，使 agent 能够存储�在未�步骤中
+使用的记忆。您�以在
+[此处](/docs/Feature-Memory.md)了解有关在训练中�用 LSTM 的更多信�。
+
+* **监控 Agent 的决策过程** - 由于 ML-Agents 中的通信
+是��通�通信，因此我们在 Unity 中�供了一个 agent Monitor 类，
+这个类�以显示�过训练的 agent 的�个方�，
+例如 agent 对自己在 Unity 环境中的表现（称为**价值估算**）
+的感知。通过利用作为�视化工具的 Unity 并实时�供
+这些输出，研究人员和开�人员�以更轻�地
+调试 agent 的行为。您�以在
+[此处](/docs/Feature-Monitor.md)了解更多关于使用 Monitor 类的信�。
+
+* **��的视觉观测** - 在其他平�中，agent 的观测�能
+仅�于�个��或图�，与之��的是，ML-Agents �许
+�个 agent 使用多个摄�机进行观测。因此，agent �以
+学习整��自多个视觉�的信�。这在多�情况下
+都会很有用，例如训练需�多个摄�头且摄�头具有
+��视角的自动驾驶车辆，或�能需�整�空中视觉和
+第一人称视觉的导航 agent。您�以在
+[此处](/docs/Learning-Environment-Design-Agents.md#multiple-visual-observations)了解更多关于� agent 添加视觉观测的
+信�。
+
+* **Broadcasting** - 如�所述，默认情况下，External Brain 会将
+其所有 Agent 的观测结果��到 Python API。这对
+训练或预测很有帮助。Broadcasting 是一��以为
+其他三�模�（Player�Internal�Heuristic）�用的功能，
+这�情况下的 Agent 观测和动作也会��到 Python API
+（尽管 Agent **�**� Python API 控制）。Imitation Learning（模仿学习）
+会利用这一功能，此情况下会使用 Player Brain 的观测和动作
+�通过示范的方�学习 agent 的 policy。
+�过，这对 Heuristic 和 Internal Brain 也有帮助，
+特别是在调试 agent 行为时。您�以在
+[此处](/docs/Learning-Environment-Design-Brains.md#using-the-broadcast-feature)了解更多关于使用 broadcasting 功能的
+信�。
+
+* **Docker 设置（测试功能）** - 为了便于在�直接安装
+Python 或 TensorFlow 的情况下设置 ML-Agents，
+我们�供了关于如何创建和�行 Docker 容器的
+[指�](/docs/Using-Docker.md)。由于渲染视觉观测的�制，
+该功能被标记为测试功能。
+
+* **AWS 上的云训练** - 为了便于在 Amazon Web Services (AWS)
+机器上使用 ML-Agents，我们�供了一份
+[指�](/docs/Training-on-Amazon-Web-Service.md)
+让您了解如何设置 EC2 实例以�公共的预�置 Amazon
+Machine Image (AMI)。
+
+* **Microsoft Azure 上的云训练** - 为了便于在 Microsoft Azure
+机器上使用 ML-Agents，我们�供了一份
+[指�](/docs/Training-on-Microsoft-Azure.md)
+让您了解如何设置 virtual machine instance 实例以�公共的预�置 Data Science VM。
+
+* **Cloud Training on Microsoft Azure** - To facilitate using ML-Agents on
+Azure machines, we provide a
+[guide](Training-on-Microsoft-Azure.md)
+on how to set-up virtual machine instances in addition to a pre-configured data science image.
+
+## 总结和�续步骤
+
+简�总结一下：ML-Agents 使 Unity 中构建的游�和模拟环境
+�以�为训练智能 agent 的平�。它旨在
+使研究人员和开�人员�以在 Unity 中采用多�训练模�和方案，应用
+机器学习技术，推动机器学习技术的�展。
+
+为了帮助您使用 ML-Agents，我们制作了几个
+关于[安装 ML-Agents](Installation.md)�
+3D Balance Ball 环境
+[入门](Getting-Started-with-Balance-Ball.md)（这是我们众多
+[示例环境](Learning-Environment-Examples.md)之一）以�
+[创建您自己的环境](Learning-Environment-Create-New.md)的深入教程。
+
diff --git a/localized_docs/zh-CN/docs/Readme.md b/localized_docs/zh-CN/docs/Readme.md
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+++ b/localized_docs/zh-CN/docs/Readme.md
@@ -0,0 +1,44 @@
+# Unity ML-Agents 工具包文档
+
+## 入门
+ * [ML-Agents 概述*](ML-Agents-Overview.md)
+    * [背景：Unity](/docs/Background-Unity.md)
+    * [背景：机器学习](/docs/Background-Machine-Learning.md)
+    * [背景：TensorFlow](/docs/Background-TensorFlow.md)
+ * [安装和设置*](Installation.md)
+    * [背景：Jupyter Notebooks](/docs/Background-Jupyter.md)
+    * [Docker 设置（测试功能）](/docs/Using-Docker.md)
+ * [教你快速入门--3D Balance Ball 训练环境示例*](Getting-Started-with-Balance-Ball.md)
+ * [官方训练环境示例*](Learning-Environment-Examples.md)
+
+## 创建训练环境
+ * [从头创建一个训练环境*](Learning-Environment-Create-New.md)
+ * [设计训练环境*](Learning-Environment-Design.md)
+     * [Agent](/docs/Learning-Environment-Design-Agents.md)
+     * [Academy](/docs/Learning-Environment-Design-Academy.md)
+     * [Brain](/docs/Learning-Environment-Design-Brains.md)：[Player](/docs/Learning-Environment-Design-Player-Brains.md)�[Heuristic](/docs/Learning-Environment-Design-Heuristic-Brains.md)�[Internal 和 External](/docs/Learning-Environment-Design-External-Internal-Brains.md)
+ * [训练环境设计�点](/docs/Learning-Environment-Best-Practices.md)
+ * [如何使用 Monitor 功能](/docs/Feature-Monitor.md)
+ * [如何使用 TensorFlowSharp �件（测试功能）](/docs/Using-TensorFlow-Sharp-in-Unity.md)
+
+## 进行训练
+ * [如何用 ML-Agents 进行训练](/docs/Training-ML-Agents.md)
+ * [Proximal Policy Optimization 训练�点](/docs/Training-PPO.md)
+ * [Curriculum Learning（课程学习）训练�点](/docs/Training-Curriculum-Learning.md)
+ * [Imitation Learning（模仿学习）训练�点](/docs/Training-Imitation-Learning.md)
+ * [LSTM 训练�点](/docs/Feature-Memory.md)
+ * [如何使用 Amazon Web Services 进行云端训练](/docs/Training-on-Amazon-Web-Service.md)
+ * [如何使用 Microsoft Azure 进行云端训练](/docs/Training-on-Microsoft-Azure.md)
+ * [如何使用 TensorBoard 观察训练过程](/docs/Using-Tensorboard.md)
+
+## 帮助
+ * [如何从�版本�级到 ML-Agents v0.3](/docs/Migrating.md)
+ * [常�问题](/docs/FAQ.md)
+ * [ML-Agents 术语表](/docs/Glossary.md)
+ * [ML-Agents 尚未实现功能](/docs/Limitations.md)
+
+## API 文档
+ * [API �考](/docs/API-Reference.md)
+ * [如何使用 Python API](/docs/Python-LLAPI.md)
+
+**注:** 有翻译版的文档会在�上角标注*�。
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diff --git a/localized_docs/zh-CN/docs/images/unity-logo-rgb.png b/localized_docs/zh-CN/docs/images/unity-logo-rgb.png
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diff --git a/localized_docs/zh-CN/docs/images/visual-observation.png b/localized_docs/zh-CN/docs/images/visual-observation.png
new file mode 100644
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diff --git a/localized_docs/zh-CN/docs/images/wall.png b/localized_docs/zh-CN/docs/images/wall.png
new file mode 100644
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Binary files /dev/null and b/localized_docs/zh-CN/docs/images/wall.png differ
diff --git a/markdown-link-check.fast.json b/markdown-link-check.fast.json
new file mode 100644
index 0000000000000000000000000000000000000000..1120d2696ea96edce98c6b4745abdc0e66c1afcd
--- /dev/null
+++ b/markdown-link-check.fast.json
@@ -0,0 +1,20 @@
+{
+    "ignorePatterns": [
+        {
+            "pattern": "^http://localhost",
+            "comment": "Ignore local tensorboard links"
+        },
+        {
+            "pattern": "^https://developer.nvidia.com/compute/machine-learning/cudnn/secure",
+            "comment": "Requires login"
+        },
+        {
+            "pattern": "^https?://",
+            "comment": "Skip external links for fast runs."
+        },
+        {
+            "pattern": "mailto:",
+            "comment": "mailto checking broken"
+        }
+    ]
+}
diff --git a/markdown-link-check.full.json b/markdown-link-check.full.json
new file mode 100644
index 0000000000000000000000000000000000000000..57f2875228f7a48bbdf62258cf9440c54c23aac3
--- /dev/null
+++ b/markdown-link-check.full.json
@@ -0,0 +1,28 @@
+{
+    "ignorePatterns": [
+        {
+            "pattern": "^http://localhost",
+            "comment": "Ignore local tensorboard links"
+        },
+        {
+            "pattern": "^https://developer.nvidia.com/compute/machine-learning/cudnn/secure",
+            "comment": "Requires login"
+        },
+        {
+            "pattern": "https://github.com/Unity-Technologies/ml-agents/(tree|archive)/release_[0-9]+.*",
+            "comment": "Allow future release tags"
+        },
+        {
+            "pattern": "https://forum.unity.com/",
+            "comment": "Returns 307 (temporary redirect) which causes link check to fail."
+        },
+        {
+            "pattern": "mailto:",
+            "comment": "mailto checking broken"
+        },
+        {
+            "pattern": "https://www.researchgate.net/",
+            "comment": "Issue with GHE certs / firewall"
+        }
+    ]
+}
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000000000000000000000000000000000000..91cc61759fb7a0f5ee0bed43f6dad2644523168d
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,43 @@
+site_name: Unity ML-Agents Toolkit
+site_url: https://unity-technologies.github.io/ml-agents/
+repo_url: https://github.com/Unity-Technologies/ml-agents
+edit_uri: edit/main/docs/
+site_description: The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents.
+site_author: Unity Technologies
+copyright: com.unity.ml-agents copyright © 2017 - 2022 Unity Technologies
+nav:
+- Home: index.md
+- ML-Agents Overview: ML-Agents-Overview.md
+- Toolkit Documentation: ML-Agents-Toolkit-Documentation.md
+- Background:
+    - Machine Learning: Background-Machine-Learning.md
+    - PyTorch: Background-PyTorch.md
+    - Unity: Background-Unity.md
+- Interfacing with Unity Builds:
+    - Getting started with the Gym API: Python-Gym-API.md
+    - Getting started with the PettingZoo API: Python-PettingZoo-API.md
+    - Getting started with the LLAPI: Python-LLAPI.md
+- Python API Docs:
+    - Gym API Documentation: Python-Gym-API-Documentation.md
+    - Petting Zoo Documentation: Python-PettingZoo-API-Documentation.md
+    - LLAPI Documentation: Python-LLAPI-Documentation.md
+    - On/Off Policy Trainer: Python-On-Off-Policy-Trainer-Documentation.md
+- Tutorials:
+    - Custom Trainer Plugin: Tutorial-Custom-Trainer-Plugin.md
+- About:
+    - FAQs: FAQ.md
+    - Limitations: Limitations.md
+    - Migrating: Migrating.md
+    - Versioning: Versioning.md
+theme:
+  name: material
+  logo: images/unity-logo.png
+  favicon: images/unity-logo-black.png
+  palette:
+    primary: blue
+    accent: light blue
+extra_css:
+  - extra.css
+markdown_extensions:
+    - markdown_include.include:
+        base_path: docs
diff --git a/ml-agents-envs/README.md b/ml-agents-envs/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d2194aebc072eebe1131ecf6994ff4eb98d25af
--- /dev/null
+++ b/ml-agents-envs/README.md
@@ -0,0 +1,44 @@
+# Unity ML-Agents Python Interface
+
+The `mlagents_envs` Python package is part of the
+[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
+`mlagents_envs` provides three Python APIs that allows direct interaction with the
+Unity game engine:
+- A single agent API (Gym API)
+- A gym-like multi-agent API (PettingZoo API)
+- A low-level API (LLAPI)
+
+The LLAPI is used by the trainer implementation in `mlagents`.
+`mlagents_envs` can be used independently of `mlagents` for Python
+communication.
+
+## Installation
+
+Install the `mlagents_envs` package with:
+
+```sh
+python -m pip install mlagents_envs==0.30.0
+```
+
+## Usage & More Information
+
+See
+- [Gym API Guide](../docs/Python-Gym-API.md)
+- [PettingZoo API Guide](../docs/Python-PettingZoo-API.md)
+- [Python API Guide](../docs/Python-LLAPI.md)
+
+for more information on how to use the API to interact with a Unity environment.
+
+For more information on the ML-Agents Toolkit and how to instrument a Unity
+scene with the ML-Agents SDK, check out the main
+[ML-Agents Toolkit documentation](../docs/Readme.md).
+
+## Limitations
+
+- `mlagents_envs` uses localhost ports to exchange data between Unity and
+  Python. As such, multiple instances can have their ports collide, leading to
+  errors. Make sure to use a different port if you are using multiple instances
+  of `UnityEnvironment`.
+- Communication between Unity and the Python `UnityEnvironment` is not secure.
+- On Linux, ports are not released immediately after the communication closes.
+  As such, you cannot reuse ports right after closing a `UnityEnvironment`.
diff --git a/ml-agents-envs/colabs/Colab_PettingZoo.ipynb b/ml-agents-envs/colabs/Colab_PettingZoo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..63b37619327a1f20571c7811b4dca04ff0885cfb
--- /dev/null
+++ b/ml-agents-envs/colabs/Colab_PettingZoo.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ML-Agents PettingZoo Wrapper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title Install Rendering Dependencies { display-mode: \"form\" }\n",
+    "#@markdown (You only need to run this code when using Colab's hosted runtime)\n",
+    "\n",
+    "import os\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def progress(value, max=100):\n",
+    "    return HTML(\"\"\"\n",
+    "        <progress\n",
+    "            value='{value}'\n",
+    "            max='{max}',\n",
+    "            style='width: 100%'\n",
+    "        >\n",
+    "            {value}\n",
+    "        </progress>\n",
+    "    \"\"\".format(value=value, max=max))\n",
+    "\n",
+    "pro_bar = display(progress(0, 100), display_id=True)\n",
+    "\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  INSTALL_XVFB = True\n",
+    "except ImportError:\n",
+    "  INSTALL_XVFB = 'COLAB_ALWAYS_INSTALL_XVFB' in os.environ\n",
+    "\n",
+    "if INSTALL_XVFB:\n",
+    "  with open('frame-buffer', 'w') as writefile:\n",
+    "    writefile.write(\"\"\"#taken from https://gist.github.com/jterrace/2911875\n",
+    "XVFB=/usr/bin/Xvfb\n",
+    "XVFBARGS=\":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\"\n",
+    "PIDFILE=./frame-buffer.pid\n",
+    "case \"$1\" in\n",
+    "  start)\n",
+    "    echo -n \"Starting virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  stop)\n",
+    "    echo -n \"Stopping virtual X frame buffer: Xvfb\"\n",
+    "    /sbin/start-stop-daemon --stop --quiet --pidfile $PIDFILE\n",
+    "    rm $PIDFILE\n",
+    "    echo \".\"\n",
+    "    ;;\n",
+    "  restart)\n",
+    "    $0 stop\n",
+    "    $0 start\n",
+    "    ;;\n",
+    "  *)\n",
+    "        echo \"Usage: /etc/init.d/xvfb {start|stop|restart}\"\n",
+    "        exit 1\n",
+    "esac\n",
+    "exit 0\n",
+    "    \"\"\")\n",
+    "  pro_bar.update(progress(5, 100))\n",
+    "  !apt-get install daemon >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(10, 100))\n",
+    "  !apt-get install wget >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(20, 100))\n",
+    "  !wget http://security.ubuntu.com/ubuntu/pool/main/libx/libxfont/libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(30, 100))\n",
+    "  !wget --output-document xvfb.deb http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.12_amd64.deb >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(40, 100))\n",
+    "  !dpkg -i libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(50, 100))\n",
+    "  !dpkg -i xvfb.deb >/dev/null 2>&1\n",
+    "  pro_bar.update(progress(70, 100))\n",
+    "  !rm libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb\n",
+    "  pro_bar.update(progress(80, 100))\n",
+    "  !rm xvfb.deb\n",
+    "  pro_bar.update(progress(90, 100))\n",
+    "  !bash frame-buffer start\n",
+    "  os.environ[\"DISPLAY\"] = \":1\"\n",
+    "pro_bar.update(progress(100, 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installing ml-agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "  import mlagents\n",
+    "  print(\"ml-agents already installed\")\n",
+    "except ImportError:\n",
+    "  !git clone -b main --single-branch https://github.com/Unity-Technologies/ml-agents.git\n",
+    "  !python -m pip install -q ./ml-agents/ml-agents-envs\n",
+    "  !python -m pip install -q ./ml-agents/ml-agents\n",
+    "  print(\"Installed ml-agents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run the Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "List of available environments:\n",
+    "* Basic\n",
+    "* ThreeDBall\n",
+    "* ThreeDBallHard\n",
+    "* GridWorld\n",
+    "* Hallway\n",
+    "* VisualHallway\n",
+    "* CrawlerDynamicTarget\n",
+    "* CrawlerStaticTarget\n",
+    "* Bouncer\n",
+    "* SoccerTwos\n",
+    "* PushBlock\n",
+    "* VisualPushBlock\n",
+    "* WallJump\n",
+    "* Tennis\n",
+    "* Reacher\n",
+    "* Pyramids\n",
+    "* VisualPyramids\n",
+    "* Walker\n",
+    "* FoodCollector\n",
+    "* VisualFoodCollector\n",
+    "* StrikersVsGoalie\n",
+    "* WormStaticTarget\n",
+    "* WormDynamicTarget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start Environment with PettingZoo Wrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YSf-WhxbqtLw"
+   },
+   "outputs": [],
+   "source": [
+    "# -----------------\n",
+    "# This code is used to close an env that might not have been closed before\n",
+    "try:\n",
+    "  env.close()\n",
+    "except:\n",
+    "  pass\n",
+    "# -----------------\n",
+    "\n",
+    "import numpy as np\n",
+    "from mlagents_envs.envs import StrikersVsGoalie # import unity environment\n",
+    "env = StrikersVsGoalie.env()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stepping the environment\n",
+    "\n",
+    "Example of interacting with the environment in basic RL loop. It follows the same interface as described in [PettingZoo API page](https://www.pettingzoo.ml/api)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dhtl0mpeqxYi"
+   },
+   "outputs": [],
+   "source": [
+    "num_cycles = 10\n",
+    "\n",
+    "env.reset()\n",
+    "for agent in env.agent_iter(env.num_agents * num_cycles):\n",
+    "    prev_observe, reward, done, info = env.last()\n",
+    "    if isinstance(prev_observe, dict) and 'action_mask' in prev_observe:\n",
+    "        action_mask = prev_observe['action_mask']\n",
+    "    if done:\n",
+    "        action = None\n",
+    "    else:\n",
+    "        action = env.action_spaces[agent].sample() # randomly choose an action for example\n",
+    "    env.step(action)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional Environment API\n",
+    "\n",
+    "All the API described in the `Additional Environment API` section in the [PettingZoo API page](https://www.pettingzoo.ml/api) are all supported. A few examples are shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# `agents`: a list of the names of all current agents\n",
+    "print(\"Agent names:\", env.agents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# `agent_selection`: the currently agent that an action can be taken for.\n",
+    "print(\"Current agent:\", env.agent_selection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# `observation_spaces`: a dict of the observation spaces of every agent, keyed by name.\n",
+    "print(\"Observation space of current agent:\", env.observation_spaces[env.agent_selection])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# `action_spaces`: a dict of the observation spaces of every agent, keyed by name.\n",
+    "print(\"Action space of current agent:\", env.action_spaces[env.agent_selection])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Close the Environment to free the port it is using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "a7KatdThq7OV"
+   },
+   "outputs": [],
+   "source": [
+    "env.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Colab-UnityEnvironment-1-Run.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ml-agents-envs/mlagents_envs.egg-info/PKG-INFO b/ml-agents-envs/mlagents_envs.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..f88c8b57123f0daabd96a1da0b3da65b531172c2
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/PKG-INFO
@@ -0,0 +1,14 @@
+Metadata-Version: 2.1
+Name: mlagents-envs
+Version: 0.31.0.dev0
+Summary: Unity Machine Learning Agents Interface
+Home-page: https://github.com/Unity-Technologies/ml-agents
+Author: Unity Technologies
+Author-email: ML-Agents@unity3d.com
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.8.13,<=3.10.12
diff --git a/ml-agents-envs/mlagents_envs.egg-info/SOURCES.txt b/ml-agents-envs/mlagents_envs.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3e8c0a8b2dcdd73bc8d604af54385fa8873b6d6e
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/SOURCES.txt
@@ -0,0 +1,76 @@
+README.md
+setup.py
+mlagents_envs/__init__.py
+mlagents_envs/base_env.py
+mlagents_envs/communicator.py
+mlagents_envs/env_utils.py
+mlagents_envs/environment.py
+mlagents_envs/exception.py
+mlagents_envs/logging_util.py
+mlagents_envs/mock_communicator.py
+mlagents_envs/rpc_communicator.py
+mlagents_envs/rpc_utils.py
+mlagents_envs/timers.py
+mlagents_envs.egg-info/PKG-INFO
+mlagents_envs.egg-info/SOURCES.txt
+mlagents_envs.egg-info/dependency_links.txt
+mlagents_envs.egg-info/not-zip-safe
+mlagents_envs.egg-info/requires.txt
+mlagents_envs.egg-info/top_level.txt
+mlagents_envs/communicator_objects/__init__.py
+mlagents_envs/communicator_objects/agent_action_pb2.py
+mlagents_envs/communicator_objects/agent_info_action_pair_pb2.py
+mlagents_envs/communicator_objects/agent_info_pb2.py
+mlagents_envs/communicator_objects/brain_parameters_pb2.py
+mlagents_envs/communicator_objects/capabilities_pb2.py
+mlagents_envs/communicator_objects/command_pb2.py
+mlagents_envs/communicator_objects/custom_reset_parameters_pb2.py
+mlagents_envs/communicator_objects/demonstration_meta_pb2.py
+mlagents_envs/communicator_objects/engine_configuration_pb2.py
+mlagents_envs/communicator_objects/header_pb2.py
+mlagents_envs/communicator_objects/observation_pb2.py
+mlagents_envs/communicator_objects/space_type_pb2.py
+mlagents_envs/communicator_objects/training_analytics_pb2.py
+mlagents_envs/communicator_objects/unity_input_pb2.py
+mlagents_envs/communicator_objects/unity_message_pb2.py
+mlagents_envs/communicator_objects/unity_output_pb2.py
+mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py
+mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py
+mlagents_envs/communicator_objects/unity_rl_input_pb2.py
+mlagents_envs/communicator_objects/unity_rl_output_pb2.py
+mlagents_envs/communicator_objects/unity_to_external_pb2.py
+mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py
+mlagents_envs/envs/__init__.py
+mlagents_envs/envs/env_helpers.py
+mlagents_envs/envs/pettingzoo_env_factory.py
+mlagents_envs/envs/unity_aec_env.py
+mlagents_envs/envs/unity_gym_env.py
+mlagents_envs/envs/unity_parallel_env.py
+mlagents_envs/envs/unity_pettingzoo_base_env.py
+mlagents_envs/registry/__init__.py
+mlagents_envs/registry/base_registry_entry.py
+mlagents_envs/registry/binary_utils.py
+mlagents_envs/registry/remote_registry_entry.py
+mlagents_envs/registry/unity_env_registry.py
+mlagents_envs/side_channel/__init__.py
+mlagents_envs/side_channel/default_training_analytics_side_channel.py
+mlagents_envs/side_channel/engine_configuration_channel.py
+mlagents_envs/side_channel/environment_parameters_channel.py
+mlagents_envs/side_channel/float_properties_channel.py
+mlagents_envs/side_channel/incoming_message.py
+mlagents_envs/side_channel/outgoing_message.py
+mlagents_envs/side_channel/raw_bytes_channel.py
+mlagents_envs/side_channel/side_channel.py
+mlagents_envs/side_channel/side_channel_manager.py
+mlagents_envs/side_channel/stats_side_channel.py
+tests/test_env_utils.py
+tests/test_envs.py
+tests/test_gym.py
+tests/test_pettingzoo_wrapper.py
+tests/test_registry.py
+tests/test_rpc_communicator.py
+tests/test_rpc_utils.py
+tests/test_set_action.py
+tests/test_side_channel.py
+tests/test_steps.py
+tests/test_timers.py
\ No newline at end of file
diff --git a/ml-agents-envs/mlagents_envs.egg-info/dependency_links.txt b/ml-agents-envs/mlagents_envs.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/ml-agents-envs/mlagents_envs.egg-info/not-zip-safe b/ml-agents-envs/mlagents_envs.egg-info/not-zip-safe
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/not-zip-safe
@@ -0,0 +1 @@
+
diff --git a/ml-agents-envs/mlagents_envs.egg-info/requires.txt b/ml-agents-envs/mlagents_envs.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a800958e0e82c594378ca352265086dd1491b8b
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/requires.txt
@@ -0,0 +1,9 @@
+cloudpickle
+grpcio>=1.11.0
+Pillow>=4.2.1
+protobuf<3.20,>=3.6
+pyyaml>=3.1.0
+gym>=0.21.0
+pettingzoo==1.15.0
+numpy==1.21.2
+filelock>=3.4.0
diff --git a/ml-agents-envs/mlagents_envs.egg-info/top_level.txt b/ml-agents-envs/mlagents_envs.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..833e77edf5f93decd1aa3f9f114174f12faad07d
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs.egg-info/top_level.txt
@@ -0,0 +1 @@
+mlagents_envs
diff --git a/ml-agents-envs/mlagents_envs/__init__.py b/ml-agents-envs/mlagents_envs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b8c9d285f3d51ce2d20c3ded236dfcd4a428caa
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/__init__.py
@@ -0,0 +1,5 @@
+# Version of the library that will be used to upload to pypi
+__version__ = "0.31.0.dev0"
+
+# Git tag that will be checked to determine whether to trigger upload to pypi
+__release_tag__ = None
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diff --git a/ml-agents-envs/mlagents_envs/base_env.py b/ml-agents-envs/mlagents_envs/base_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..a993d8a7c509c34d1a2621195c840e4c9e14a099
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/base_env.py
@@ -0,0 +1,616 @@
+"""
+Python Environment API for the ML-Agents Toolkit
+The aim of this API is to expose Agents evolving in a simulation
+to perform reinforcement learning on.
+This API supports multi-agent scenarios and groups similar Agents (same
+observations, actions spaces and behavior) together. These groups of Agents are
+identified by their BehaviorName.
+For performance reasons, the data of each group of agents is processed in a
+batched manner. Agents are identified by a unique AgentId identifier that
+allows tracking of Agents across simulation steps. Note that there is no
+guarantee that the number or order of the Agents in the state will be
+consistent across simulation steps.
+A simulation steps corresponds to moving the simulation forward until at least
+one agent in the simulation sends its observations to Python again. Since
+Agents can request decisions at different frequencies, a simulation step does
+not necessarily correspond to a fixed simulation time increment.
+"""
+
+from abc import ABC, abstractmethod
+from collections.abc import Mapping
+from typing import (
+    List,
+    NamedTuple,
+    Tuple,
+    Optional,
+    Dict,
+    Iterator,
+    Any,
+    Mapping as MappingType,
+)
+from enum import IntFlag, Enum
+import numpy as np
+
+from mlagents_envs.exception import UnityActionException
+
+AgentId = int
+GroupId = int
+BehaviorName = str
+
+
+class DecisionStep(NamedTuple):
+    """
+    Contains the data a single Agent collected since the last
+    simulation step.
+     - obs is a list of numpy arrays observations collected by the agent.
+     - reward is a float. Corresponds to the rewards collected by the agent
+     since the last simulation step.
+     - agent_id is an int and an unique identifier for the corresponding Agent.
+     - action_mask is an optional list of one dimensional array of booleans.
+     Only available when using multi-discrete actions.
+     Each array corresponds to an action branch. Each array contains a mask
+     for each action of the branch. If true, the action is not available for
+     the agent during this simulation step.
+    """
+
+    obs: List[np.ndarray]
+    reward: float
+    agent_id: AgentId
+    action_mask: Optional[List[np.ndarray]]
+    group_id: int
+    group_reward: float
+
+
+class DecisionSteps(Mapping):
+    """
+    Contains the data a batch of similar Agents collected since the last
+    simulation step. Note that all Agents do not necessarily have new
+    information to send at each simulation step. Therefore, the ordering of
+    agents and the batch size of the DecisionSteps are not fixed across
+    simulation steps.
+     - obs is a list of numpy arrays observations collected by the batch of
+     agent. Each obs has one extra dimension compared to DecisionStep: the
+     first dimension of the array corresponds to the batch size of the batch.
+     - reward is a float vector of length batch size. Corresponds to the
+     rewards collected by each agent since the last simulation step.
+     - agent_id is an int vector of length batch size containing unique
+     identifier for the corresponding Agent. This is used to track Agents
+     across simulation steps.
+     - action_mask is an optional list of two dimensional array of booleans.
+     Only available when using multi-discrete actions.
+     Each array corresponds to an action branch. The first dimension of each
+     array is the batch size and the second contains a mask for each action of
+     the branch. If true, the action is not available for the agent during
+     this simulation step.
+    """
+
+    def __init__(self, obs, reward, agent_id, action_mask, group_id, group_reward):
+        self.obs: List[np.ndarray] = obs
+        self.reward: np.ndarray = reward
+        self.agent_id: np.ndarray = agent_id
+        self.action_mask: Optional[List[np.ndarray]] = action_mask
+        self.group_id: np.ndarray = group_id
+        self.group_reward: np.ndarray = group_reward
+        self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
+
+    @property
+    def agent_id_to_index(self) -> Dict[AgentId, int]:
+        """
+        :returns: A Dict that maps agent_id to the index of those agents in
+        this DecisionSteps.
+        """
+        if self._agent_id_to_index is None:
+            self._agent_id_to_index = {}
+            for a_idx, a_id in enumerate(self.agent_id):
+                self._agent_id_to_index[a_id] = a_idx
+        return self._agent_id_to_index
+
+    def __len__(self) -> int:
+        return len(self.agent_id)
+
+    def __getitem__(self, agent_id: AgentId) -> DecisionStep:
+        """
+        returns the DecisionStep for a specific agent.
+        :param agent_id: The id of the agent
+        :returns: The DecisionStep
+        """
+        if agent_id not in self.agent_id_to_index:
+            raise KeyError(f"agent_id {agent_id} is not present in the DecisionSteps")
+        agent_index = self._agent_id_to_index[agent_id]  # type: ignore
+        agent_obs = []
+        for batched_obs in self.obs:
+            agent_obs.append(batched_obs[agent_index])
+        agent_mask = None
+        if self.action_mask is not None:
+            agent_mask = []
+            for mask in self.action_mask:
+                agent_mask.append(mask[agent_index])
+        group_id = self.group_id[agent_index]
+        return DecisionStep(
+            obs=agent_obs,
+            reward=self.reward[agent_index],
+            agent_id=agent_id,
+            action_mask=agent_mask,
+            group_id=group_id,
+            group_reward=self.group_reward[agent_index],
+        )
+
+    def __iter__(self) -> Iterator[Any]:
+        yield from self.agent_id
+
+    @staticmethod
+    def empty(spec: "BehaviorSpec") -> "DecisionSteps":
+        """
+        Returns an empty DecisionSteps.
+        :param spec: The BehaviorSpec for the DecisionSteps
+        """
+        obs: List[np.ndarray] = []
+        for sen_spec in spec.observation_specs:
+            obs += [np.zeros((0,) + sen_spec.shape, dtype=np.float32)]
+        return DecisionSteps(
+            obs=obs,
+            reward=np.zeros(0, dtype=np.float32),
+            agent_id=np.zeros(0, dtype=np.int32),
+            action_mask=None,
+            group_id=np.zeros(0, dtype=np.int32),
+            group_reward=np.zeros(0, dtype=np.float32),
+        )
+
+
+class TerminalStep(NamedTuple):
+    """
+    Contains the data a single Agent collected when its episode ended.
+     - obs is a list of numpy arrays observations collected by the agent.
+     - reward is a float. Corresponds to the rewards collected by the agent
+     since the last simulation step.
+     - interrupted is a bool. Is true if the Agent was interrupted since the last
+     decision step. For example, if the Agent reached the maximum number of steps for
+     the episode.
+     - agent_id is an int and an unique identifier for the corresponding Agent.
+    """
+
+    obs: List[np.ndarray]
+    reward: float
+    interrupted: bool
+    agent_id: AgentId
+    group_id: GroupId
+    group_reward: float
+
+
+class TerminalSteps(Mapping):
+    """
+    Contains the data a batch of Agents collected when their episode
+    terminated. All Agents present in the TerminalSteps have ended their
+    episode.
+     - obs is a list of numpy arrays observations collected by the batch of
+     agent. Each obs has one extra dimension compared to DecisionStep: the
+     first dimension of the array corresponds to the batch size of the batch.
+     - reward is a float vector of length batch size. Corresponds to the
+     rewards collected by each agent since the last simulation step.
+     - interrupted is an array of booleans of length batch size. Is true if the
+     associated Agent was interrupted since the last decision step. For example, if the
+     Agent reached the maximum number of steps for the episode.
+     - agent_id is an int vector of length batch size containing unique
+     identifier for the corresponding Agent. This is used to track Agents
+     across simulation steps.
+    """
+
+    def __init__(self, obs, reward, interrupted, agent_id, group_id, group_reward):
+        self.obs: List[np.ndarray] = obs
+        self.reward: np.ndarray = reward
+        self.interrupted: np.ndarray = interrupted
+        self.agent_id: np.ndarray = agent_id
+        self.group_id: np.ndarray = group_id
+        self.group_reward: np.ndarray = group_reward
+        self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
+
+    @property
+    def agent_id_to_index(self) -> Dict[AgentId, int]:
+        """
+        :returns: A Dict that maps agent_id to the index of those agents in
+        this TerminalSteps.
+        """
+        if self._agent_id_to_index is None:
+            self._agent_id_to_index = {}
+            for a_idx, a_id in enumerate(self.agent_id):
+                self._agent_id_to_index[a_id] = a_idx
+        return self._agent_id_to_index
+
+    def __len__(self) -> int:
+        return len(self.agent_id)
+
+    def __getitem__(self, agent_id: AgentId) -> TerminalStep:
+        """
+        returns the TerminalStep for a specific agent.
+        :param agent_id: The id of the agent
+        :returns: obs, reward, done, agent_id and optional action mask for a
+        specific agent
+        """
+        if agent_id not in self.agent_id_to_index:
+            raise KeyError(f"agent_id {agent_id} is not present in the TerminalSteps")
+        agent_index = self._agent_id_to_index[agent_id]  # type: ignore
+        agent_obs = []
+        for batched_obs in self.obs:
+            agent_obs.append(batched_obs[agent_index])
+        group_id = self.group_id[agent_index]
+        return TerminalStep(
+            obs=agent_obs,
+            reward=self.reward[agent_index],
+            interrupted=self.interrupted[agent_index],
+            agent_id=agent_id,
+            group_id=group_id,
+            group_reward=self.group_reward[agent_index],
+        )
+
+    def __iter__(self) -> Iterator[Any]:
+        yield from self.agent_id
+
+    @staticmethod
+    def empty(spec: "BehaviorSpec") -> "TerminalSteps":
+        """
+        Returns an empty TerminalSteps.
+        :param spec: The BehaviorSpec for the TerminalSteps
+        """
+        obs: List[np.ndarray] = []
+        for sen_spec in spec.observation_specs:
+            obs += [np.zeros((0,) + sen_spec.shape, dtype=np.float32)]
+        return TerminalSteps(
+            obs=obs,
+            reward=np.zeros(0, dtype=np.float32),
+            interrupted=np.zeros(0, dtype=bool),
+            agent_id=np.zeros(0, dtype=np.int32),
+            group_id=np.zeros(0, dtype=np.int32),
+            group_reward=np.zeros(0, dtype=np.float32),
+        )
+
+
+class _ActionTupleBase(ABC):
+    """
+    An object whose fields correspond to action data of continuous and discrete
+    spaces. Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size),
+    respectively. Note, this also holds when continuous or discrete size is
+    zero.
+    """
+
+    def __init__(
+        self,
+        continuous: Optional[np.ndarray] = None,
+        discrete: Optional[np.ndarray] = None,
+    ):
+        self._continuous: Optional[np.ndarray] = None
+        self._discrete: Optional[np.ndarray] = None
+        if continuous is not None:
+            self.add_continuous(continuous)
+        if discrete is not None:
+            self.add_discrete(discrete)
+
+    @property
+    def continuous(self) -> np.ndarray:
+        return self._continuous
+
+    @property
+    def discrete(self) -> np.ndarray:
+        return self._discrete
+
+    def add_continuous(self, continuous: np.ndarray) -> None:
+        if continuous.dtype != np.float32:
+            continuous = continuous.astype(np.float32, copy=False)
+        if self._discrete is None:
+            self._discrete = np.zeros(
+                (continuous.shape[0], 0), dtype=self.discrete_dtype
+            )
+        self._continuous = continuous
+
+    def add_discrete(self, discrete: np.ndarray) -> None:
+        if discrete.dtype != self.discrete_dtype:
+            discrete = discrete.astype(self.discrete_dtype, copy=False)
+        if self._continuous is None:
+            self._continuous = np.zeros((discrete.shape[0], 0), dtype=np.float32)
+        self._discrete = discrete
+
+    @property
+    @abstractmethod
+    def discrete_dtype(self) -> np.dtype:
+        pass
+
+
+class ActionTuple(_ActionTupleBase):
+    """
+    An object whose fields correspond to actions of different types.
+    Continuous and discrete actions are numpy arrays of type float32 and
+    int32, respectively and are type checked on construction.
+    Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size),
+    respectively. Note, this also holds when continuous or discrete size is
+    zero.
+    """
+
+    @property
+    def discrete_dtype(self) -> np.dtype:
+        """
+        The dtype of a discrete action.
+        """
+        return np.int32
+
+
+class ActionSpec(NamedTuple):
+    """
+    A NamedTuple containing utility functions and information about the action spaces
+    for a group of Agents under the same behavior.
+    - num_continuous_actions is an int corresponding to the number of floats which
+    constitute the action.
+    - discrete_branch_sizes is a Tuple of int where each int corresponds to
+    the number of discrete actions available to the agent on an independent action branch.
+    """
+
+    continuous_size: int
+    discrete_branches: Tuple[int, ...]
+
+    def __eq__(self, other):
+        return (
+            self.continuous_size == other.continuous_size
+            and self.discrete_branches == other.discrete_branches
+        )
+
+    def __str__(self):
+        return f"Continuous: {self.continuous_size}, Discrete: {self.discrete_branches}"
+
+    # For backwards compatibility
+    def is_discrete(self) -> bool:
+        """
+        Returns true if this Behavior uses discrete actions
+        """
+        return self.discrete_size > 0 and self.continuous_size == 0
+
+    # For backwards compatibility
+    def is_continuous(self) -> bool:
+        """
+        Returns true if this Behavior uses continuous actions
+        """
+        return self.discrete_size == 0 and self.continuous_size > 0
+
+    @property
+    def discrete_size(self) -> int:
+        """
+        Returns a an int corresponding to the number of discrete branches.
+        """
+        return len(self.discrete_branches)
+
+    def empty_action(self, n_agents: int) -> ActionTuple:
+        """
+        Generates ActionTuple corresponding to an empty action (all zeros)
+        for a number of agents.
+        :param n_agents: The number of agents that will have actions generated
+        """
+        _continuous = np.zeros((n_agents, self.continuous_size), dtype=np.float32)
+        _discrete = np.zeros((n_agents, self.discrete_size), dtype=np.int32)
+        return ActionTuple(continuous=_continuous, discrete=_discrete)
+
+    def random_action(self, n_agents: int) -> ActionTuple:
+        """
+        Generates ActionTuple corresponding to a random action (either discrete
+        or continuous) for a number of agents.
+        :param n_agents: The number of agents that will have actions generated
+        """
+        _continuous = np.random.uniform(
+            low=-1.0, high=1.0, size=(n_agents, self.continuous_size)
+        )
+        _discrete = np.zeros((n_agents, self.discrete_size), dtype=np.int32)
+        if self.discrete_size > 0:
+            _discrete = np.column_stack(
+                [
+                    np.random.randint(
+                        0,
+                        self.discrete_branches[i],  # type: ignore
+                        size=(n_agents),
+                        dtype=np.int32,
+                    )
+                    for i in range(self.discrete_size)
+                ]
+            )
+        return ActionTuple(continuous=_continuous, discrete=_discrete)
+
+    def _validate_action(
+        self, actions: ActionTuple, n_agents: int, name: str
+    ) -> ActionTuple:
+        """
+        Validates that action has the correct action dim
+        for the correct number of agents and ensures the type.
+        """
+        _expected_shape = (n_agents, self.continuous_size)
+        if actions.continuous.shape != _expected_shape:
+            raise UnityActionException(
+                f"The behavior {name} needs a continuous input of dimension "
+                f"{_expected_shape} for (<number of agents>, <action size>) but "
+                f"received input of dimension {actions.continuous.shape}"
+            )
+        _expected_shape = (n_agents, self.discrete_size)
+        if actions.discrete.shape != _expected_shape:
+            raise UnityActionException(
+                f"The behavior {name} needs a discrete input of dimension "
+                f"{_expected_shape} for (<number of agents>, <action size>) but "
+                f"received input of dimension {actions.discrete.shape}"
+            )
+        return actions
+
+    @staticmethod
+    def create_continuous(continuous_size: int) -> "ActionSpec":
+        """
+        Creates an ActionSpec that is homogenously continuous
+        """
+        return ActionSpec(continuous_size, ())
+
+    @staticmethod
+    def create_discrete(discrete_branches: Tuple[int]) -> "ActionSpec":
+        """
+        Creates an ActionSpec that is homogenously discrete
+        """
+        return ActionSpec(0, discrete_branches)
+
+
+class DimensionProperty(IntFlag):
+    """
+    The dimension property of a dimension of an observation.
+    """
+
+    UNSPECIFIED = 0
+    """
+    No properties specified.
+    """
+
+    NONE = 1
+    """
+    No Property of the observation in that dimension. Observation can be processed with
+    Fully connected networks.
+    """
+
+    TRANSLATIONAL_EQUIVARIANCE = 2
+    """
+    Means it is suitable to do a convolution in this dimension.
+    """
+
+    VARIABLE_SIZE = 4
+    """
+    Means that there can be a variable number of observations in this dimension.
+    The observations are unordered.
+    """
+
+
+class ObservationType(Enum):
+    """
+    An Enum which defines the type of information carried in the observation
+    of the agent.
+    """
+
+    DEFAULT = 0
+    """
+    Observation information is generic.
+    """
+
+    GOAL_SIGNAL = 1
+    """
+    Observation contains goal information for current task.
+    """
+
+
+class ObservationSpec(NamedTuple):
+    """
+    A NamedTuple containing information about the observation of Agents.
+    - shape is a Tuple of int : It corresponds to the shape of
+    an observation's dimensions.
+    - dimension_property is a Tuple of DimensionProperties flag, one flag for each
+    dimension.
+    - observation_type is an enum of ObservationType.
+    """
+
+    shape: Tuple[int, ...]
+    dimension_property: Tuple[DimensionProperty, ...]
+    observation_type: ObservationType
+
+    # Optional name. For observations coming from com.unity.ml-agents, this
+    # will be the ISensor name.
+    name: str
+
+
+class BehaviorSpec(NamedTuple):
+    """
+    A NamedTuple containing information about the observation and action
+    spaces for a group of Agents under the same behavior.
+    - observation_specs is a List of ObservationSpec NamedTuple containing
+    information about the information of the Agent's observations such as their shapes.
+    The order of the ObservationSpec is the same as the order of the observations of an
+    agent.
+    - action_spec is an ActionSpec NamedTuple.
+    """
+
+    observation_specs: List[ObservationSpec]
+    action_spec: ActionSpec
+
+
+class BehaviorMapping(Mapping):
+    def __init__(self, specs: Dict[BehaviorName, BehaviorSpec]):
+        self._dict = specs
+
+    def __len__(self) -> int:
+        return len(self._dict)
+
+    def __getitem__(self, behavior: BehaviorName) -> BehaviorSpec:
+        return self._dict[behavior]
+
+    def __iter__(self) -> Iterator[Any]:
+        yield from self._dict
+
+
+class BaseEnv(ABC):
+    @abstractmethod
+    def step(self) -> None:
+        """
+        Signals the environment that it must move the simulation forward
+        by one step.
+        """
+
+    @abstractmethod
+    def reset(self) -> None:
+        """
+        Signals the environment that it must reset the simulation.
+        """
+
+    @abstractmethod
+    def close(self) -> None:
+        """
+        Signals the environment that it must close.
+        """
+
+    @property
+    @abstractmethod
+    def behavior_specs(self) -> MappingType[str, BehaviorSpec]:
+        """
+        Returns a Mapping from behavior names to behavior specs.
+        Agents grouped under the same behavior name have the same action and
+        observation specs, and are expected to behave similarly in the
+        environment.
+        Note that new keys can be added to this mapping as new policies are instantiated.
+        """
+
+    @abstractmethod
+    def set_actions(self, behavior_name: BehaviorName, action: ActionTuple) -> None:
+        """
+        Sets the action for all of the agents in the simulation for the next
+        step. The Actions must be in the same order as the order received in
+        the DecisionSteps.
+        :param behavior_name: The name of the behavior the agents are part of
+        :param action: ActionTuple tuple of continuous and/or discrete action.
+        Actions are np.arrays with dimensions  (n_agents, continuous_size) and
+        (n_agents, discrete_size), respectively.
+        """
+
+    @abstractmethod
+    def set_action_for_agent(
+        self, behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple
+    ) -> None:
+        """
+        Sets the action for one of the agents in the simulation for the next
+        step.
+        :param behavior_name: The name of the behavior the agent is part of
+        :param agent_id: The id of the agent the action is set for
+        :param action: ActionTuple tuple of continuous and/or discrete action
+        Actions are np.arrays with dimensions  (1, continuous_size) and
+        (1, discrete_size), respectively. Note, this initial dimensions of 1 is because
+        this action is meant for a single agent.
+        """
+
+    @abstractmethod
+    def get_steps(
+        self, behavior_name: BehaviorName
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        """
+        Retrieves the steps of the agents that requested a step in the
+        simulation.
+        :param behavior_name: The name of the behavior the agents are part of
+        :return: A tuple containing :
+         - A DecisionSteps NamedTuple containing the observations,
+         the rewards, the agent ids and the action masks for the Agents
+         of the specified behavior. These Agents need an action this step.
+         - A TerminalSteps NamedTuple containing the observations,
+         rewards, agent ids and interrupted flags of the agents that had their
+         episode terminated last step.
+        """
diff --git a/ml-agents-envs/mlagents_envs/communicator.py b/ml-agents-envs/mlagents_envs/communicator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2223f34d3a4b4177b69867125b3e449be76f920d
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator.py
@@ -0,0 +1,43 @@
+from typing import Callable, Optional
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+
+
+# Function to call while waiting for a connection timeout.
+# This should raise an exception if it needs to break from waiting for the timeout.
+PollCallback = Callable[[], None]
+
+
+class Communicator:
+    def __init__(self, worker_id=0, base_port=5005):
+        """
+        Python side of the communication. Must be used in pair with the right Unity Communicator equivalent.
+
+        :int worker_id: Offset from base_port. Used for training multiple environments simultaneously.
+        :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
+        """
+
+    def initialize(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> UnityOutputProto:
+        """
+        Used to exchange initialization parameters between Python and the Environment
+        :param inputs: The initialization input that will be sent to the environment.
+        :param poll_callback: Optional callback to be used while polling the connection.
+        :return: UnityOutput: The initialization output sent by Unity
+        """
+
+    def exchange(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> Optional[UnityOutputProto]:
+        """
+        Used to send an input and receive an output from the Environment
+        :param inputs: The UnityInput that needs to be sent the Environment
+        :param poll_callback: Optional callback to be used while polling the connection.
+        :return: The UnityOutputs generated by the Environment
+        """
+
+    def close(self):
+        """
+        Sends a shutdown signal to the unity environment, and closes the connection.
+        """
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/__init__.py b/ml-agents-envs/mlagents_envs/communicator_objects/__init__.py
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diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c57d8b4cb179db52449f32c638f841f4a2146a2
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py
@@ -0,0 +1,92 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/agent_action.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/agent_action.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n5mlagents_envs/communicator_objects/agent_action.proto\x12\x14\x63ommunicator_objects\"\x8c\x01\n\x10\x41gentActionProto\x12!\n\x19vector_actions_deprecated\x18\x01 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02\x12\x1a\n\x12\x63ontinuous_actions\x18\x06 \x03(\x02\x12\x18\n\x10\x64iscrete_actions\x18\x07 \x03(\x05J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x05\x10\x06\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_AGENTACTIONPROTO = _descriptor.Descriptor(
+  name='AgentActionProto',
+  full_name='communicator_objects.AgentActionProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='vector_actions_deprecated', full_name='communicator_objects.AgentActionProto.vector_actions_deprecated', index=0,
+      number=1, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='communicator_objects.AgentActionProto.value', index=1,
+      number=4, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='continuous_actions', full_name='communicator_objects.AgentActionProto.continuous_actions', index=2,
+      number=6, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='discrete_actions', full_name='communicator_objects.AgentActionProto.discrete_actions', index=3,
+      number=7, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=80,
+  serialized_end=220,
+)
+
+DESCRIPTOR.message_types_by_name['AgentActionProto'] = _AGENTACTIONPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+AgentActionProto = _reflection.GeneratedProtocolMessageType('AgentActionProto', (_message.Message,), dict(
+  DESCRIPTOR = _AGENTACTIONPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.agent_action_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.AgentActionProto)
+  ))
+_sym_db.RegisterMessage(AgentActionProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b3ddc28f0338de4d38227a83722b35c236d33acd
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi
@@ -0,0 +1,52 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class AgentActionProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    vector_actions_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
+    value = ... # type: builtin___float
+    continuous_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
+    discrete_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+
+    def __init__(self,
+        *,
+        vector_actions_deprecated : typing___Optional[typing___Iterable[builtin___float]] = None,
+        value : typing___Optional[builtin___float] = None,
+        continuous_actions : typing___Optional[typing___Iterable[builtin___float]] = None,
+        discrete_actions : typing___Optional[typing___Iterable[builtin___int]] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> AgentActionProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",u"discrete_actions",u"value",u"vector_actions_deprecated"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",b"continuous_actions",u"discrete_actions",b"discrete_actions",u"value",b"value",u"vector_actions_deprecated",b"vector_actions_deprecated"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..667b91a13dcc95d76f754920fa79c2e69a65a791
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.py
@@ -0,0 +1,83 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/agent_info_action_pair.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import agent_info_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__info__pb2
+from mlagents_envs.communicator_objects import agent_action_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__action__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/agent_info_action_pair.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n?mlagents_envs/communicator_objects/agent_info_action_pair.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/agent_info.proto\x1a\x35mlagents_envs/communicator_objects/agent_action.proto\"\x91\x01\n\x18\x41gentInfoActionPairProto\x12\x38\n\nagent_info\x18\x01 \x01(\x0b\x32$.communicator_objects.AgentInfoProto\x12;\n\x0b\x61\x63tion_info\x18\x02 \x01(\x0b\x32&.communicator_objects.AgentActionProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__info__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_agent__action__pb2.DESCRIPTOR,])
+
+
+
+
+_AGENTINFOACTIONPAIRPROTO = _descriptor.Descriptor(
+  name='AgentInfoActionPairProto',
+  full_name='communicator_objects.AgentInfoActionPairProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='agent_info', full_name='communicator_objects.AgentInfoActionPairProto.agent_info', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='action_info', full_name='communicator_objects.AgentInfoActionPairProto.action_info', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=198,
+  serialized_end=343,
+)
+
+_AGENTINFOACTIONPAIRPROTO.fields_by_name['agent_info'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__info__pb2._AGENTINFOPROTO
+_AGENTINFOACTIONPAIRPROTO.fields_by_name['action_info'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__action__pb2._AGENTACTIONPROTO
+DESCRIPTOR.message_types_by_name['AgentInfoActionPairProto'] = _AGENTINFOACTIONPAIRPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+AgentInfoActionPairProto = _reflection.GeneratedProtocolMessageType('AgentInfoActionPairProto', (_message.Message,), dict(
+  DESCRIPTOR = _AGENTINFOACTIONPAIRPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.agent_info_action_pair_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoActionPairProto)
+  ))
+_sym_db.RegisterMessage(AgentInfoActionPairProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e3711aa562362f07b1595d98f9fa1e35de629f52
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_action_pair_pb2.pyi
@@ -0,0 +1,57 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.agent_action_pb2 import (
+    AgentActionProto as mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto,
+)
+
+from mlagents_envs.communicator_objects.agent_info_pb2 import (
+    AgentInfoProto as mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class AgentInfoActionPairProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+    @property
+    def agent_info(self) -> mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto: ...
+
+    @property
+    def action_info(self) -> mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto: ...
+
+    def __init__(self,
+        *,
+        agent_info : typing___Optional[mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto] = None,
+        action_info : typing___Optional[mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> AgentInfoActionPairProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"action_info",u"agent_info"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_info",u"agent_info"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"action_info",b"action_info",u"agent_info",b"agent_info"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_info",b"action_info",u"agent_info",b"agent_info"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..57bb77aa575e0bfc5187b14cc3aec946046678e3
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py
@@ -0,0 +1,123 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/agent_info.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import observation_pb2 as mlagents__envs_dot_communicator__objects_dot_observation__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/agent_info.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n3mlagents_envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a\x34mlagents_envs/communicator_objects/observation.proto\"\xf9\x01\n\x0e\x41gentInfoProto\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12<\n\x0cobservations\x18\r \x03(\x0b\x32&.communicator_objects.ObservationProto\x12\x10\n\x08group_id\x18\x0e \x01(\x05\x12\x14\n\x0cgroup_reward\x18\x0f \x01(\x02J\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_observation__pb2.DESCRIPTOR,])
+
+
+
+
+_AGENTINFOPROTO = _descriptor.Descriptor(
+  name='AgentInfoProto',
+  full_name='communicator_objects.AgentInfoProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=0,
+      number=7, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='done', full_name='communicator_objects.AgentInfoProto.done', index=1,
+      number=8, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=2,
+      number=9, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='id', full_name='communicator_objects.AgentInfoProto.id', index=3,
+      number=10, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='action_mask', full_name='communicator_objects.AgentInfoProto.action_mask', index=4,
+      number=11, type=8, cpp_type=7, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='observations', full_name='communicator_objects.AgentInfoProto.observations', index=5,
+      number=13, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='group_id', full_name='communicator_objects.AgentInfoProto.group_id', index=6,
+      number=14, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='group_reward', full_name='communicator_objects.AgentInfoProto.group_reward', index=7,
+      number=15, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=132,
+  serialized_end=381,
+)
+
+_AGENTINFOPROTO.fields_by_name['observations'].message_type = mlagents__envs_dot_communicator__objects_dot_observation__pb2._OBSERVATIONPROTO
+DESCRIPTOR.message_types_by_name['AgentInfoProto'] = _AGENTINFOPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+AgentInfoProto = _reflection.GeneratedProtocolMessageType('AgentInfoProto', (_message.Message,), dict(
+  DESCRIPTOR = _AGENTINFOPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.agent_info_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoProto)
+  ))
+_sym_db.RegisterMessage(AgentInfoProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..821d242d6d245600a5e80ec7366d64b603c6c222
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.pyi
@@ -0,0 +1,67 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.observation_pb2 import (
+    ObservationProto as mlagents_envs___communicator_objects___observation_pb2___ObservationProto,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class AgentInfoProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    reward = ... # type: builtin___float
+    done = ... # type: builtin___bool
+    max_step_reached = ... # type: builtin___bool
+    id = ... # type: builtin___int
+    action_mask = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___bool]
+    group_id = ... # type: builtin___int
+    group_reward = ... # type: builtin___float
+
+    @property
+    def observations(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]: ...
+
+    def __init__(self,
+        *,
+        reward : typing___Optional[builtin___float] = None,
+        done : typing___Optional[builtin___bool] = None,
+        max_step_reached : typing___Optional[builtin___bool] = None,
+        id : typing___Optional[builtin___int] = None,
+        action_mask : typing___Optional[typing___Iterable[builtin___bool]] = None,
+        observations : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]] = None,
+        group_id : typing___Optional[builtin___int] = None,
+        group_reward : typing___Optional[builtin___float] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> AgentInfoProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"done",u"group_id",u"group_reward",u"id",u"max_step_reached",u"observations",u"reward"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"done",b"done",u"group_id",b"group_id",u"group_reward",b"group_reward",u"id",b"id",u"max_step_reached",b"max_step_reached",u"observations",b"observations",u"reward",b"reward"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccd9140d3cc7b7f8ef9a4092480572fbd04d911e
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py
@@ -0,0 +1,170 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/brain_parameters.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import space_type_pb2 as mlagents__envs_dot_communicator__objects_dot_space__type__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/brain_parameters.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n9mlagents_envs/communicator_objects/brain_parameters.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/space_type.proto\"\x8b\x01\n\x0f\x41\x63tionSpecProto\x12\x1e\n\x16num_continuous_actions\x18\x01 \x01(\x05\x12\x1c\n\x14num_discrete_actions\x18\x02 \x01(\x05\x12\x1d\n\x15\x64iscrete_branch_sizes\x18\x03 \x03(\x05\x12\x1b\n\x13\x61\x63tion_descriptions\x18\x04 \x03(\t\"\xb6\x02\n\x14\x42rainParametersProto\x12%\n\x1dvector_action_size_deprecated\x18\x03 \x03(\x05\x12-\n%vector_action_descriptions_deprecated\x18\x05 \x03(\t\x12Q\n#vector_action_space_type_deprecated\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x13\n\x0bis_training\x18\x08 \x01(\x08\x12:\n\x0b\x61\x63tion_spec\x18\t \x01(\x0b\x32%.communicator_objects.ActionSpecProtoJ\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x04\x10\x05\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_space__type__pb2.DESCRIPTOR,])
+
+
+
+
+_ACTIONSPECPROTO = _descriptor.Descriptor(
+  name='ActionSpecProto',
+  full_name='communicator_objects.ActionSpecProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='num_continuous_actions', full_name='communicator_objects.ActionSpecProto.num_continuous_actions', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_discrete_actions', full_name='communicator_objects.ActionSpecProto.num_discrete_actions', index=1,
+      number=2, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='discrete_branch_sizes', full_name='communicator_objects.ActionSpecProto.discrete_branch_sizes', index=2,
+      number=3, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='action_descriptions', full_name='communicator_objects.ActionSpecProto.action_descriptions', index=3,
+      number=4, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=137,
+  serialized_end=276,
+)
+
+
+_BRAINPARAMETERSPROTO = _descriptor.Descriptor(
+  name='BrainParametersProto',
+  full_name='communicator_objects.BrainParametersProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='vector_action_size_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_size_deprecated', index=0,
+      number=3, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='vector_action_descriptions_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_descriptions_deprecated', index=1,
+      number=5, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='vector_action_space_type_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_space_type_deprecated', index=2,
+      number=6, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='brain_name', full_name='communicator_objects.BrainParametersProto.brain_name', index=3,
+      number=7, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='is_training', full_name='communicator_objects.BrainParametersProto.is_training', index=4,
+      number=8, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='action_spec', full_name='communicator_objects.BrainParametersProto.action_spec', index=5,
+      number=9, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=279,
+  serialized_end=589,
+)
+
+_BRAINPARAMETERSPROTO.fields_by_name['vector_action_space_type_deprecated'].enum_type = mlagents__envs_dot_communicator__objects_dot_space__type__pb2._SPACETYPEPROTO
+_BRAINPARAMETERSPROTO.fields_by_name['action_spec'].message_type = _ACTIONSPECPROTO
+DESCRIPTOR.message_types_by_name['ActionSpecProto'] = _ACTIONSPECPROTO
+DESCRIPTOR.message_types_by_name['BrainParametersProto'] = _BRAINPARAMETERSPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+ActionSpecProto = _reflection.GeneratedProtocolMessageType('ActionSpecProto', (_message.Message,), dict(
+  DESCRIPTOR = _ACTIONSPECPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.brain_parameters_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.ActionSpecProto)
+  ))
+_sym_db.RegisterMessage(ActionSpecProto)
+
+BrainParametersProto = _reflection.GeneratedProtocolMessageType('BrainParametersProto', (_message.Message,), dict(
+  DESCRIPTOR = _BRAINPARAMETERSPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.brain_parameters_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.BrainParametersProto)
+  ))
+_sym_db.RegisterMessage(BrainParametersProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..19611e1c42c04497575112fe2b8fa0fac3a55f47
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.pyi
@@ -0,0 +1,88 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.space_type_pb2 import (
+    SpaceTypeProto as mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class ActionSpecProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    num_continuous_actions = ... # type: builtin___int
+    num_discrete_actions = ... # type: builtin___int
+    discrete_branch_sizes = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+    action_descriptions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
+
+    def __init__(self,
+        *,
+        num_continuous_actions : typing___Optional[builtin___int] = None,
+        num_discrete_actions : typing___Optional[builtin___int] = None,
+        discrete_branch_sizes : typing___Optional[typing___Iterable[builtin___int]] = None,
+        action_descriptions : typing___Optional[typing___Iterable[typing___Text]] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> ActionSpecProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_descriptions",u"discrete_branch_sizes",u"num_continuous_actions",u"num_discrete_actions"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_descriptions",b"action_descriptions",u"discrete_branch_sizes",b"discrete_branch_sizes",u"num_continuous_actions",b"num_continuous_actions",u"num_discrete_actions",b"num_discrete_actions"]) -> None: ...
+
+class BrainParametersProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    vector_action_size_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+    vector_action_descriptions_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
+    vector_action_space_type_deprecated = ... # type: mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto
+    brain_name = ... # type: typing___Text
+    is_training = ... # type: builtin___bool
+
+    @property
+    def action_spec(self) -> ActionSpecProto: ...
+
+    def __init__(self,
+        *,
+        vector_action_size_deprecated : typing___Optional[typing___Iterable[builtin___int]] = None,
+        vector_action_descriptions_deprecated : typing___Optional[typing___Iterable[typing___Text]] = None,
+        vector_action_space_type_deprecated : typing___Optional[mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto] = None,
+        brain_name : typing___Optional[typing___Text] = None,
+        is_training : typing___Optional[builtin___bool] = None,
+        action_spec : typing___Optional[ActionSpecProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> BrainParametersProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"action_spec"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_spec",u"brain_name",u"is_training",u"vector_action_descriptions_deprecated",u"vector_action_size_deprecated",u"vector_action_space_type_deprecated"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"action_spec",b"action_spec"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_spec",b"action_spec",u"brain_name",b"brain_name",u"is_training",b"is_training",u"vector_action_descriptions_deprecated",b"vector_action_descriptions_deprecated",u"vector_action_size_deprecated",b"vector_action_size_deprecated",u"vector_action_space_type_deprecated",b"vector_action_space_type_deprecated"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..35b8fbdef15752e0c75bdcd329afbc391d68e6f4
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py
@@ -0,0 +1,113 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/capabilities.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/capabilities.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n5mlagents_envs/communicator_objects/capabilities.proto\x12\x14\x63ommunicator_objects\"\xec\x01\n\x18UnityRLCapabilitiesProto\x12\x1a\n\x12\x62\x61seRLCapabilities\x18\x01 \x01(\x08\x12#\n\x1b\x63oncatenatedPngObservations\x18\x02 \x01(\x08\x12 \n\x18\x63ompressedChannelMapping\x18\x03 \x01(\x08\x12\x15\n\rhybridActions\x18\x04 \x01(\x08\x12\x19\n\x11trainingAnalytics\x18\x05 \x01(\x08\x12!\n\x19variableLengthObservation\x18\x06 \x01(\x08\x12\x18\n\x10multiAgentGroups\x18\x07 \x01(\x08\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_UNITYRLCAPABILITIESPROTO = _descriptor.Descriptor(
+  name='UnityRLCapabilitiesProto',
+  full_name='communicator_objects.UnityRLCapabilitiesProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='baseRLCapabilities', full_name='communicator_objects.UnityRLCapabilitiesProto.baseRLCapabilities', index=0,
+      number=1, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='concatenatedPngObservations', full_name='communicator_objects.UnityRLCapabilitiesProto.concatenatedPngObservations', index=1,
+      number=2, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='compressedChannelMapping', full_name='communicator_objects.UnityRLCapabilitiesProto.compressedChannelMapping', index=2,
+      number=3, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='hybridActions', full_name='communicator_objects.UnityRLCapabilitiesProto.hybridActions', index=3,
+      number=4, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='trainingAnalytics', full_name='communicator_objects.UnityRLCapabilitiesProto.trainingAnalytics', index=4,
+      number=5, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='variableLengthObservation', full_name='communicator_objects.UnityRLCapabilitiesProto.variableLengthObservation', index=5,
+      number=6, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='multiAgentGroups', full_name='communicator_objects.UnityRLCapabilitiesProto.multiAgentGroups', index=6,
+      number=7, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=80,
+  serialized_end=316,
+)
+
+DESCRIPTOR.message_types_by_name['UnityRLCapabilitiesProto'] = _UNITYRLCAPABILITIESPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityRLCapabilitiesProto = _reflection.GeneratedProtocolMessageType('UnityRLCapabilitiesProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYRLCAPABILITIESPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.capabilities_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLCapabilitiesProto)
+  ))
+_sym_db.RegisterMessage(UnityRLCapabilitiesProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1c6a1f7030b8ba7402a51ae676be27b5e6f325f3
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.pyi
@@ -0,0 +1,53 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityRLCapabilitiesProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    baseRLCapabilities = ... # type: builtin___bool
+    concatenatedPngObservations = ... # type: builtin___bool
+    compressedChannelMapping = ... # type: builtin___bool
+    hybridActions = ... # type: builtin___bool
+    trainingAnalytics = ... # type: builtin___bool
+    variableLengthObservation = ... # type: builtin___bool
+    multiAgentGroups = ... # type: builtin___bool
+
+    def __init__(self,
+        *,
+        baseRLCapabilities : typing___Optional[builtin___bool] = None,
+        concatenatedPngObservations : typing___Optional[builtin___bool] = None,
+        compressedChannelMapping : typing___Optional[builtin___bool] = None,
+        hybridActions : typing___Optional[builtin___bool] = None,
+        trainingAnalytics : typing___Optional[builtin___bool] = None,
+        variableLengthObservation : typing___Optional[builtin___bool] = None,
+        multiAgentGroups : typing___Optional[builtin___bool] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityRLCapabilitiesProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",u"compressedChannelMapping",u"concatenatedPngObservations",u"hybridActions",u"multiAgentGroups",u"trainingAnalytics",u"variableLengthObservation"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",b"baseRLCapabilities",u"compressedChannelMapping",b"compressedChannelMapping",u"concatenatedPngObservations",b"concatenatedPngObservations",u"hybridActions",b"hybridActions",u"multiAgentGroups",b"multiAgentGroups",u"trainingAnalytics",b"trainingAnalytics",u"variableLengthObservation",b"variableLengthObservation"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..397f31098025a193a2365d150057ad1c3fc90c36
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.py
@@ -0,0 +1,64 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/command.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf.internal import enum_type_wrapper
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/command.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n0mlagents_envs/communicator_objects/command.proto\x12\x14\x63ommunicator_objects*-\n\x0c\x43ommandProto\x12\x08\n\x04STEP\x10\x00\x12\t\n\x05RESET\x10\x01\x12\x08\n\x04QUIT\x10\x02\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+_COMMANDPROTO = _descriptor.EnumDescriptor(
+  name='CommandProto',
+  full_name='communicator_objects.CommandProto',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='STEP', index=0, number=0,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='RESET', index=1, number=1,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='QUIT', index=2, number=2,
+      options=None,
+      type=None),
+  ],
+  containing_type=None,
+  options=None,
+  serialized_start=74,
+  serialized_end=119,
+)
+_sym_db.RegisterEnumDescriptor(_COMMANDPROTO)
+
+CommandProto = enum_type_wrapper.EnumTypeWrapper(_COMMANDPROTO)
+STEP = 0
+RESET = 1
+QUIT = 2
+
+
+DESCRIPTOR.enum_types_by_name['CommandProto'] = _COMMANDPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4378a8a6985cb42b198f1eb4206097170316ffa4
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/command_pb2.pyi
@@ -0,0 +1,39 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    List as typing___List,
+    Tuple as typing___Tuple,
+    cast as typing___cast,
+)
+
+
+builtin___int = int
+builtin___str = str
+
+
+class CommandProto(builtin___int):
+    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+    @classmethod
+    def Name(cls, number: builtin___int) -> builtin___str: ...
+    @classmethod
+    def Value(cls, name: builtin___str) -> 'CommandProto': ...
+    @classmethod
+    def keys(cls) -> typing___List[builtin___str]: ...
+    @classmethod
+    def values(cls) -> typing___List['CommandProto']: ...
+    @classmethod
+    def items(cls) -> typing___List[typing___Tuple[builtin___str, 'CommandProto']]: ...
+    STEP = typing___cast('CommandProto', 0)
+    RESET = typing___cast('CommandProto', 1)
+    QUIT = typing___cast('CommandProto', 2)
+STEP = typing___cast('CommandProto', 0)
+RESET = typing___cast('CommandProto', 1)
+QUIT = typing___cast('CommandProto', 2)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4300d1016aa7e7dc6dcdea0f27fb21d641abb39
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.py
@@ -0,0 +1,64 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/custom_reset_parameters.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/custom_reset_parameters.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n@mlagents_envs/communicator_objects/custom_reset_parameters.proto\x12\x14\x63ommunicator_objects\"\x1c\n\x1a\x43ustomResetParametersProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_CUSTOMRESETPARAMETERSPROTO = _descriptor.Descriptor(
+  name='CustomResetParametersProto',
+  full_name='communicator_objects.CustomResetParametersProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=90,
+  serialized_end=118,
+)
+
+DESCRIPTOR.message_types_by_name['CustomResetParametersProto'] = _CUSTOMRESETPARAMETERSPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+CustomResetParametersProto = _reflection.GeneratedProtocolMessageType('CustomResetParametersProto', (_message.Message,), dict(
+  DESCRIPTOR = _CUSTOMRESETPARAMETERSPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.custom_reset_parameters_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.CustomResetParametersProto)
+  ))
+_sym_db.RegisterMessage(CustomResetParametersProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8127e971df920b40022396f4ca7523c8323a0c52
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/custom_reset_parameters_pb2.pyi
@@ -0,0 +1,23 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+
+builtin___bytes = bytes
+
+
+class CustomResetParametersProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+    def __init__(self,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> CustomResetParametersProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b86fee455033d420e151518070a1e5174c1cd0a
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.py
@@ -0,0 +1,99 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/demonstration_meta.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/demonstration_meta.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n;mlagents_envs/communicator_objects/demonstration_meta.proto\x12\x14\x63ommunicator_objects\"\x8d\x01\n\x16\x44\x65monstrationMetaProto\x12\x13\n\x0b\x61pi_version\x18\x01 \x01(\x05\x12\x1a\n\x12\x64\x65monstration_name\x18\x02 \x01(\t\x12\x14\n\x0cnumber_steps\x18\x03 \x01(\x05\x12\x17\n\x0fnumber_episodes\x18\x04 \x01(\x05\x12\x13\n\x0bmean_reward\x18\x05 \x01(\x02\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_DEMONSTRATIONMETAPROTO = _descriptor.Descriptor(
+  name='DemonstrationMetaProto',
+  full_name='communicator_objects.DemonstrationMetaProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='api_version', full_name='communicator_objects.DemonstrationMetaProto.api_version', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='demonstration_name', full_name='communicator_objects.DemonstrationMetaProto.demonstration_name', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='number_steps', full_name='communicator_objects.DemonstrationMetaProto.number_steps', index=2,
+      number=3, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='number_episodes', full_name='communicator_objects.DemonstrationMetaProto.number_episodes', index=3,
+      number=4, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='mean_reward', full_name='communicator_objects.DemonstrationMetaProto.mean_reward', index=4,
+      number=5, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=86,
+  serialized_end=227,
+)
+
+DESCRIPTOR.message_types_by_name['DemonstrationMetaProto'] = _DEMONSTRATIONMETAPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+DemonstrationMetaProto = _reflection.GeneratedProtocolMessageType('DemonstrationMetaProto', (_message.Message,), dict(
+  DESCRIPTOR = _DEMONSTRATIONMETAPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.demonstration_meta_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.DemonstrationMetaProto)
+  ))
+_sym_db.RegisterMessage(DemonstrationMetaProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ba3c306d51d165a2de007780bd28c54929e80a76
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/demonstration_meta_pb2.pyi
@@ -0,0 +1,50 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class DemonstrationMetaProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    api_version = ... # type: builtin___int
+    demonstration_name = ... # type: typing___Text
+    number_steps = ... # type: builtin___int
+    number_episodes = ... # type: builtin___int
+    mean_reward = ... # type: builtin___float
+
+    def __init__(self,
+        *,
+        api_version : typing___Optional[builtin___int] = None,
+        demonstration_name : typing___Optional[typing___Text] = None,
+        number_steps : typing___Optional[builtin___int] = None,
+        number_episodes : typing___Optional[builtin___int] = None,
+        mean_reward : typing___Optional[builtin___float] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> DemonstrationMetaProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"api_version",u"demonstration_name",u"mean_reward",u"number_episodes",u"number_steps"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"api_version",b"api_version",u"demonstration_name",b"demonstration_name",u"mean_reward",b"mean_reward",u"number_episodes",b"number_episodes",u"number_steps",b"number_steps"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f4656ca8cb6048231b50892373ec94968b12622
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.py
@@ -0,0 +1,106 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/engine_configuration.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/engine_configuration.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n=mlagents_envs/communicator_objects/engine_configuration.proto\x12\x14\x63ommunicator_objects\"\x95\x01\n\x18\x45ngineConfigurationProto\x12\r\n\x05width\x18\x01 \x01(\x05\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\x15\n\rquality_level\x18\x03 \x01(\x05\x12\x12\n\ntime_scale\x18\x04 \x01(\x02\x12\x19\n\x11target_frame_rate\x18\x05 \x01(\x05\x12\x14\n\x0cshow_monitor\x18\x06 \x01(\x08\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_ENGINECONFIGURATIONPROTO = _descriptor.Descriptor(
+  name='EngineConfigurationProto',
+  full_name='communicator_objects.EngineConfigurationProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='width', full_name='communicator_objects.EngineConfigurationProto.width', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='height', full_name='communicator_objects.EngineConfigurationProto.height', index=1,
+      number=2, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='quality_level', full_name='communicator_objects.EngineConfigurationProto.quality_level', index=2,
+      number=3, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='time_scale', full_name='communicator_objects.EngineConfigurationProto.time_scale', index=3,
+      number=4, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='target_frame_rate', full_name='communicator_objects.EngineConfigurationProto.target_frame_rate', index=4,
+      number=5, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='show_monitor', full_name='communicator_objects.EngineConfigurationProto.show_monitor', index=5,
+      number=6, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=88,
+  serialized_end=237,
+)
+
+DESCRIPTOR.message_types_by_name['EngineConfigurationProto'] = _ENGINECONFIGURATIONPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+EngineConfigurationProto = _reflection.GeneratedProtocolMessageType('EngineConfigurationProto', (_message.Message,), dict(
+  DESCRIPTOR = _ENGINECONFIGURATIONPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.engine_configuration_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.EngineConfigurationProto)
+  ))
+_sym_db.RegisterMessage(EngineConfigurationProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d4eef07a94c81302586dc8d021d4c64b137c7595
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.pyi
@@ -0,0 +1,51 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class EngineConfigurationProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    width = ... # type: builtin___int
+    height = ... # type: builtin___int
+    quality_level = ... # type: builtin___int
+    time_scale = ... # type: builtin___float
+    target_frame_rate = ... # type: builtin___int
+    show_monitor = ... # type: builtin___bool
+
+    def __init__(self,
+        *,
+        width : typing___Optional[builtin___int] = None,
+        height : typing___Optional[builtin___int] = None,
+        quality_level : typing___Optional[builtin___int] = None,
+        time_scale : typing___Optional[builtin___float] = None,
+        target_frame_rate : typing___Optional[builtin___int] = None,
+        show_monitor : typing___Optional[builtin___bool] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> EngineConfigurationProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"height",u"quality_level",u"show_monitor",u"target_frame_rate",u"time_scale",u"width"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"height",b"height",u"quality_level",b"quality_level",u"show_monitor",b"show_monitor",u"target_frame_rate",b"target_frame_rate",u"time_scale",b"time_scale",u"width",b"width"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5aa8eb89545b4c97e4eea1f3fd4c92e5cad2b463
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.py
@@ -0,0 +1,78 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/header.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/header.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n/mlagents_envs/communicator_objects/header.proto\x12\x14\x63ommunicator_objects\".\n\x0bHeaderProto\x12\x0e\n\x06status\x18\x01 \x01(\x05\x12\x0f\n\x07message\x18\x02 \x01(\tB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_HEADERPROTO = _descriptor.Descriptor(
+  name='HeaderProto',
+  full_name='communicator_objects.HeaderProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='status', full_name='communicator_objects.HeaderProto.status', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='message', full_name='communicator_objects.HeaderProto.message', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=73,
+  serialized_end=119,
+)
+
+DESCRIPTOR.message_types_by_name['HeaderProto'] = _HEADERPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+HeaderProto = _reflection.GeneratedProtocolMessageType('HeaderProto', (_message.Message,), dict(
+  DESCRIPTOR = _HEADERPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.header_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.HeaderProto)
+  ))
+_sym_db.RegisterMessage(HeaderProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ab7fdd632365edcb77328c47e57acb7ea0f7ed3c
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.pyi
@@ -0,0 +1,44 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class HeaderProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    status = ... # type: builtin___int
+    message = ... # type: typing___Text
+
+    def __init__(self,
+        *,
+        status : typing___Optional[builtin___int] = None,
+        message : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> HeaderProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"message",u"status"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"message",b"message",u"status",b"status"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..838ca1d87dabe02d7355fc627fe5b131196ab8bf
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py
@@ -0,0 +1,224 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/observation.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf.internal import enum_type_wrapper
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/observation.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\x8f\x03\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x12\"\n\x1a\x63ompressed_channel_mapping\x18\x05 \x03(\x05\x12\x1c\n\x14\x64imension_properties\x18\x06 \x03(\x05\x12\x44\n\x10observation_type\x18\x07 \x01(\x0e\x32*.communicator_objects.ObservationTypeProto\x12\x0c\n\x04name\x18\x08 \x01(\t\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01*@\n\x14ObservationTypeProto\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x0f\n\x0bGOAL_SIGNAL\x10\x01\"\x04\x08\x02\x10\x02\"\x04\x08\x03\x10\x03\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+_COMPRESSIONTYPEPROTO = _descriptor.EnumDescriptor(
+  name='CompressionTypeProto',
+  full_name='communicator_objects.CompressionTypeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='NONE', index=0, number=0,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='PNG', index=1, number=1,
+      options=None,
+      type=None),
+  ],
+  containing_type=None,
+  options=None,
+  serialized_start=480,
+  serialized_end=521,
+)
+_sym_db.RegisterEnumDescriptor(_COMPRESSIONTYPEPROTO)
+
+CompressionTypeProto = enum_type_wrapper.EnumTypeWrapper(_COMPRESSIONTYPEPROTO)
+_OBSERVATIONTYPEPROTO = _descriptor.EnumDescriptor(
+  name='ObservationTypeProto',
+  full_name='communicator_objects.ObservationTypeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='DEFAULT', index=0, number=0,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='GOAL_SIGNAL', index=1, number=1,
+      options=None,
+      type=None),
+  ],
+  containing_type=None,
+  options=None,
+  serialized_start=523,
+  serialized_end=587,
+)
+_sym_db.RegisterEnumDescriptor(_OBSERVATIONTYPEPROTO)
+
+ObservationTypeProto = enum_type_wrapper.EnumTypeWrapper(_OBSERVATIONTYPEPROTO)
+NONE = 0
+PNG = 1
+DEFAULT = 0
+GOAL_SIGNAL = 1
+
+
+
+_OBSERVATIONPROTO_FLOATDATA = _descriptor.Descriptor(
+  name='FloatData',
+  full_name='communicator_objects.ObservationProto.FloatData',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='data', full_name='communicator_objects.ObservationProto.FloatData.data', index=0,
+      number=1, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=433,
+  serialized_end=458,
+)
+
+_OBSERVATIONPROTO = _descriptor.Descriptor(
+  name='ObservationProto',
+  full_name='communicator_objects.ObservationProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='shape', full_name='communicator_objects.ObservationProto.shape', index=0,
+      number=1, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='compression_type', full_name='communicator_objects.ObservationProto.compression_type', index=1,
+      number=2, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='compressed_data', full_name='communicator_objects.ObservationProto.compressed_data', index=2,
+      number=3, type=12, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b(""),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='float_data', full_name='communicator_objects.ObservationProto.float_data', index=3,
+      number=4, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='compressed_channel_mapping', full_name='communicator_objects.ObservationProto.compressed_channel_mapping', index=4,
+      number=5, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='dimension_properties', full_name='communicator_objects.ObservationProto.dimension_properties', index=5,
+      number=6, type=5, cpp_type=1, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='observation_type', full_name='communicator_objects.ObservationProto.observation_type', index=6,
+      number=7, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='name', full_name='communicator_objects.ObservationProto.name', index=7,
+      number=8, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_OBSERVATIONPROTO_FLOATDATA, ],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+    _descriptor.OneofDescriptor(
+      name='observation_data', full_name='communicator_objects.ObservationProto.observation_data',
+      index=0, containing_type=None, fields=[]),
+  ],
+  serialized_start=79,
+  serialized_end=478,
+)
+
+_OBSERVATIONPROTO_FLOATDATA.containing_type = _OBSERVATIONPROTO
+_OBSERVATIONPROTO.fields_by_name['compression_type'].enum_type = _COMPRESSIONTYPEPROTO
+_OBSERVATIONPROTO.fields_by_name['float_data'].message_type = _OBSERVATIONPROTO_FLOATDATA
+_OBSERVATIONPROTO.fields_by_name['observation_type'].enum_type = _OBSERVATIONTYPEPROTO
+_OBSERVATIONPROTO.oneofs_by_name['observation_data'].fields.append(
+  _OBSERVATIONPROTO.fields_by_name['compressed_data'])
+_OBSERVATIONPROTO.fields_by_name['compressed_data'].containing_oneof = _OBSERVATIONPROTO.oneofs_by_name['observation_data']
+_OBSERVATIONPROTO.oneofs_by_name['observation_data'].fields.append(
+  _OBSERVATIONPROTO.fields_by_name['float_data'])
+_OBSERVATIONPROTO.fields_by_name['float_data'].containing_oneof = _OBSERVATIONPROTO.oneofs_by_name['observation_data']
+DESCRIPTOR.message_types_by_name['ObservationProto'] = _OBSERVATIONPROTO
+DESCRIPTOR.enum_types_by_name['CompressionTypeProto'] = _COMPRESSIONTYPEPROTO
+DESCRIPTOR.enum_types_by_name['ObservationTypeProto'] = _OBSERVATIONTYPEPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+ObservationProto = _reflection.GeneratedProtocolMessageType('ObservationProto', (_message.Message,), dict(
+
+  FloatData = _reflection.GeneratedProtocolMessageType('FloatData', (_message.Message,), dict(
+    DESCRIPTOR = _OBSERVATIONPROTO_FLOATDATA,
+    __module__ = 'mlagents_envs.communicator_objects.observation_pb2'
+    # @@protoc_insertion_point(class_scope:communicator_objects.ObservationProto.FloatData)
+    ))
+  ,
+  DESCRIPTOR = _OBSERVATIONPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.observation_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.ObservationProto)
+  ))
+_sym_db.RegisterMessage(ObservationProto)
+_sym_db.RegisterMessage(ObservationProto.FloatData)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6427c5085102aee34d2022c804f3fb3a928a0c8b
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.pyi
@@ -0,0 +1,122 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    List as typing___List,
+    Optional as typing___Optional,
+    Text as typing___Text,
+    Tuple as typing___Tuple,
+    cast as typing___cast,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+builtin___str = str
+
+
+class CompressionTypeProto(builtin___int):
+    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+    @classmethod
+    def Name(cls, number: builtin___int) -> builtin___str: ...
+    @classmethod
+    def Value(cls, name: builtin___str) -> 'CompressionTypeProto': ...
+    @classmethod
+    def keys(cls) -> typing___List[builtin___str]: ...
+    @classmethod
+    def values(cls) -> typing___List['CompressionTypeProto']: ...
+    @classmethod
+    def items(cls) -> typing___List[typing___Tuple[builtin___str, 'CompressionTypeProto']]: ...
+    NONE = typing___cast('CompressionTypeProto', 0)
+    PNG = typing___cast('CompressionTypeProto', 1)
+NONE = typing___cast('CompressionTypeProto', 0)
+PNG = typing___cast('CompressionTypeProto', 1)
+
+class ObservationTypeProto(builtin___int):
+    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+    @classmethod
+    def Name(cls, number: builtin___int) -> builtin___str: ...
+    @classmethod
+    def Value(cls, name: builtin___str) -> 'ObservationTypeProto': ...
+    @classmethod
+    def keys(cls) -> typing___List[builtin___str]: ...
+    @classmethod
+    def values(cls) -> typing___List['ObservationTypeProto']: ...
+    @classmethod
+    def items(cls) -> typing___List[typing___Tuple[builtin___str, 'ObservationTypeProto']]: ...
+    DEFAULT = typing___cast('ObservationTypeProto', 0)
+    GOAL_SIGNAL = typing___cast('ObservationTypeProto', 1)
+DEFAULT = typing___cast('ObservationTypeProto', 0)
+GOAL_SIGNAL = typing___cast('ObservationTypeProto', 1)
+
+class ObservationProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    class FloatData(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+        data = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
+
+        def __init__(self,
+            *,
+            data : typing___Optional[typing___Iterable[builtin___float]] = None,
+            ) -> None: ...
+        @classmethod
+        def FromString(cls, s: builtin___bytes) -> ObservationProto.FloatData: ...
+        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        if sys.version_info >= (3,):
+            def ClearField(self, field_name: typing_extensions___Literal[u"data"]) -> None: ...
+        else:
+            def ClearField(self, field_name: typing_extensions___Literal[u"data",b"data"]) -> None: ...
+
+    shape = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+    compression_type = ... # type: CompressionTypeProto
+    compressed_data = ... # type: builtin___bytes
+    compressed_channel_mapping = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+    dimension_properties = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
+    observation_type = ... # type: ObservationTypeProto
+    name = ... # type: typing___Text
+
+    @property
+    def float_data(self) -> ObservationProto.FloatData: ...
+
+    def __init__(self,
+        *,
+        shape : typing___Optional[typing___Iterable[builtin___int]] = None,
+        compression_type : typing___Optional[CompressionTypeProto] = None,
+        compressed_data : typing___Optional[builtin___bytes] = None,
+        float_data : typing___Optional[ObservationProto.FloatData] = None,
+        compressed_channel_mapping : typing___Optional[typing___Iterable[builtin___int]] = None,
+        dimension_properties : typing___Optional[typing___Iterable[builtin___int]] = None,
+        observation_type : typing___Optional[ObservationTypeProto] = None,
+        name : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> ObservationProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"compressed_data",u"float_data",u"observation_data"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",u"compressed_data",u"compression_type",u"dimension_properties",u"float_data",u"name",u"observation_data",u"observation_type",u"shape"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"compressed_data",b"compressed_data",u"float_data",b"float_data",u"observation_data",b"observation_data"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",b"compressed_channel_mapping",u"compressed_data",b"compressed_data",u"compression_type",b"compression_type",u"dimension_properties",b"dimension_properties",u"float_data",b"float_data",u"name",b"name",u"observation_data",b"observation_data",u"observation_type",b"observation_type",u"shape",b"shape"]) -> None: ...
+    def WhichOneof(self, oneof_group: typing_extensions___Literal[u"observation_data",b"observation_data"]) -> typing_extensions___Literal["compressed_data","float_data"]: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..df62c23319c2be5890aa5d021aad9f8590820dc3
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.py
@@ -0,0 +1,59 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/space_type.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf.internal import enum_type_wrapper
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/space_type.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n3mlagents_envs/communicator_objects/space_type.proto\x12\x14\x63ommunicator_objects*.\n\x0eSpaceTypeProto\x12\x0c\n\x08\x64iscrete\x10\x00\x12\x0e\n\ncontinuous\x10\x01\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+_SPACETYPEPROTO = _descriptor.EnumDescriptor(
+  name='SpaceTypeProto',
+  full_name='communicator_objects.SpaceTypeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='discrete', index=0, number=0,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='continuous', index=1, number=1,
+      options=None,
+      type=None),
+  ],
+  containing_type=None,
+  options=None,
+  serialized_start=77,
+  serialized_end=123,
+)
+_sym_db.RegisterEnumDescriptor(_SPACETYPEPROTO)
+
+SpaceTypeProto = enum_type_wrapper.EnumTypeWrapper(_SPACETYPEPROTO)
+discrete = 0
+continuous = 1
+
+
+DESCRIPTOR.enum_types_by_name['SpaceTypeProto'] = _SPACETYPEPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4527ffcfed18dfe8b4953cae2bce1cc269dee887
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.pyi
@@ -0,0 +1,37 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    List as typing___List,
+    Tuple as typing___Tuple,
+    cast as typing___cast,
+)
+
+
+builtin___int = int
+builtin___str = str
+
+
+class SpaceTypeProto(builtin___int):
+    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+    @classmethod
+    def Name(cls, number: builtin___int) -> builtin___str: ...
+    @classmethod
+    def Value(cls, name: builtin___str) -> 'SpaceTypeProto': ...
+    @classmethod
+    def keys(cls) -> typing___List[builtin___str]: ...
+    @classmethod
+    def values(cls) -> typing___List['SpaceTypeProto']: ...
+    @classmethod
+    def items(cls) -> typing___List[typing___Tuple[builtin___str, 'SpaceTypeProto']]: ...
+    discrete = typing___cast('SpaceTypeProto', 0)
+    continuous = typing___cast('SpaceTypeProto', 1)
+discrete = typing___cast('SpaceTypeProto', 0)
+continuous = typing___cast('SpaceTypeProto', 1)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2701dac8586bf004fd61430e2c0aa55a9f412691
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.py
@@ -0,0 +1,257 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/training_analytics.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/training_analytics.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n;mlagents_envs/communicator_objects/training_analytics.proto\x12\x14\x63ommunicator_objects\"\xee\x01\n\x1eTrainingEnvironmentInitialized\x12\x18\n\x10mlagents_version\x18\x01 \x01(\t\x12\x1d\n\x15mlagents_envs_version\x18\x02 \x01(\t\x12\x16\n\x0epython_version\x18\x03 \x01(\t\x12\x15\n\rtorch_version\x18\x04 \x01(\t\x12\x19\n\x11torch_device_type\x18\x05 \x01(\t\x12\x10\n\x08num_envs\x18\x06 \x01(\x05\x12\"\n\x1anum_environment_parameters\x18\x07 \x01(\x05\x12\x13\n\x0brun_options\x18\x08 \x01(\t\"\xbd\x03\n\x1bTrainingBehaviorInitialized\x12\x15\n\rbehavior_name\x18\x01 \x01(\t\x12\x14\n\x0ctrainer_type\x18\x02 \x01(\t\x12 \n\x18\x65xtrinsic_reward_enabled\x18\x03 \x01(\x08\x12\x1b\n\x13gail_reward_enabled\x18\x04 \x01(\x08\x12 \n\x18\x63uriosity_reward_enabled\x18\x05 \x01(\x08\x12\x1a\n\x12rnd_reward_enabled\x18\x06 \x01(\x08\x12\"\n\x1a\x62\x65havioral_cloning_enabled\x18\x07 \x01(\x08\x12\x19\n\x11recurrent_enabled\x18\x08 \x01(\x08\x12\x16\n\x0evisual_encoder\x18\t \x01(\t\x12\x1a\n\x12num_network_layers\x18\n \x01(\x05\x12 \n\x18num_network_hidden_units\x18\x0b \x01(\x05\x12\x18\n\x10trainer_threaded\x18\x0c \x01(\x08\x12\x19\n\x11self_play_enabled\x18\r \x01(\x08\x12\x1a\n\x12\x63urriculum_enabled\x18\x0e \x01(\x08\x12\x0e\n\x06\x63onfig\x18\x0f \x01(\tB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+)
+
+
+
+
+_TRAININGENVIRONMENTINITIALIZED = _descriptor.Descriptor(
+  name='TrainingEnvironmentInitialized',
+  full_name='communicator_objects.TrainingEnvironmentInitialized',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='mlagents_version', full_name='communicator_objects.TrainingEnvironmentInitialized.mlagents_version', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='mlagents_envs_version', full_name='communicator_objects.TrainingEnvironmentInitialized.mlagents_envs_version', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='python_version', full_name='communicator_objects.TrainingEnvironmentInitialized.python_version', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='torch_version', full_name='communicator_objects.TrainingEnvironmentInitialized.torch_version', index=3,
+      number=4, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='torch_device_type', full_name='communicator_objects.TrainingEnvironmentInitialized.torch_device_type', index=4,
+      number=5, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_envs', full_name='communicator_objects.TrainingEnvironmentInitialized.num_envs', index=5,
+      number=6, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_environment_parameters', full_name='communicator_objects.TrainingEnvironmentInitialized.num_environment_parameters', index=6,
+      number=7, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='run_options', full_name='communicator_objects.TrainingEnvironmentInitialized.run_options', index=7,
+      number=8, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=86,
+  serialized_end=324,
+)
+
+
+_TRAININGBEHAVIORINITIALIZED = _descriptor.Descriptor(
+  name='TrainingBehaviorInitialized',
+  full_name='communicator_objects.TrainingBehaviorInitialized',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='behavior_name', full_name='communicator_objects.TrainingBehaviorInitialized.behavior_name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='trainer_type', full_name='communicator_objects.TrainingBehaviorInitialized.trainer_type', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='extrinsic_reward_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.extrinsic_reward_enabled', index=2,
+      number=3, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='gail_reward_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.gail_reward_enabled', index=3,
+      number=4, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='curiosity_reward_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.curiosity_reward_enabled', index=4,
+      number=5, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='rnd_reward_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.rnd_reward_enabled', index=5,
+      number=6, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='behavioral_cloning_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.behavioral_cloning_enabled', index=6,
+      number=7, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='recurrent_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.recurrent_enabled', index=7,
+      number=8, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='visual_encoder', full_name='communicator_objects.TrainingBehaviorInitialized.visual_encoder', index=8,
+      number=9, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_network_layers', full_name='communicator_objects.TrainingBehaviorInitialized.num_network_layers', index=9,
+      number=10, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_network_hidden_units', full_name='communicator_objects.TrainingBehaviorInitialized.num_network_hidden_units', index=10,
+      number=11, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='trainer_threaded', full_name='communicator_objects.TrainingBehaviorInitialized.trainer_threaded', index=11,
+      number=12, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='self_play_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.self_play_enabled', index=12,
+      number=13, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='curriculum_enabled', full_name='communicator_objects.TrainingBehaviorInitialized.curriculum_enabled', index=13,
+      number=14, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='config', full_name='communicator_objects.TrainingBehaviorInitialized.config', index=14,
+      number=15, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=327,
+  serialized_end=772,
+)
+
+DESCRIPTOR.message_types_by_name['TrainingEnvironmentInitialized'] = _TRAININGENVIRONMENTINITIALIZED
+DESCRIPTOR.message_types_by_name['TrainingBehaviorInitialized'] = _TRAININGBEHAVIORINITIALIZED
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+TrainingEnvironmentInitialized = _reflection.GeneratedProtocolMessageType('TrainingEnvironmentInitialized', (_message.Message,), dict(
+  DESCRIPTOR = _TRAININGENVIRONMENTINITIALIZED,
+  __module__ = 'mlagents_envs.communicator_objects.training_analytics_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.TrainingEnvironmentInitialized)
+  ))
+_sym_db.RegisterMessage(TrainingEnvironmentInitialized)
+
+TrainingBehaviorInitialized = _reflection.GeneratedProtocolMessageType('TrainingBehaviorInitialized', (_message.Message,), dict(
+  DESCRIPTOR = _TRAININGBEHAVIORINITIALIZED,
+  __module__ = 'mlagents_envs.communicator_objects.training_analytics_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.TrainingBehaviorInitialized)
+  ))
+_sym_db.RegisterMessage(TrainingBehaviorInitialized)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..53709ca8e8f444f32d016eda22c762c700a45829
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/training_analytics_pb2.pyi
@@ -0,0 +1,101 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class TrainingEnvironmentInitialized(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    mlagents_version = ... # type: typing___Text
+    mlagents_envs_version = ... # type: typing___Text
+    python_version = ... # type: typing___Text
+    torch_version = ... # type: typing___Text
+    torch_device_type = ... # type: typing___Text
+    num_envs = ... # type: builtin___int
+    num_environment_parameters = ... # type: builtin___int
+    run_options = ... # type: typing___Text
+
+    def __init__(self,
+        *,
+        mlagents_version : typing___Optional[typing___Text] = None,
+        mlagents_envs_version : typing___Optional[typing___Text] = None,
+        python_version : typing___Optional[typing___Text] = None,
+        torch_version : typing___Optional[typing___Text] = None,
+        torch_device_type : typing___Optional[typing___Text] = None,
+        num_envs : typing___Optional[builtin___int] = None,
+        num_environment_parameters : typing___Optional[builtin___int] = None,
+        run_options : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> TrainingEnvironmentInitialized: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"mlagents_envs_version",u"mlagents_version",u"num_environment_parameters",u"num_envs",u"python_version",u"run_options",u"torch_device_type",u"torch_version"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"mlagents_envs_version",b"mlagents_envs_version",u"mlagents_version",b"mlagents_version",u"num_environment_parameters",b"num_environment_parameters",u"num_envs",b"num_envs",u"python_version",b"python_version",u"run_options",b"run_options",u"torch_device_type",b"torch_device_type",u"torch_version",b"torch_version"]) -> None: ...
+
+class TrainingBehaviorInitialized(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    behavior_name = ... # type: typing___Text
+    trainer_type = ... # type: typing___Text
+    extrinsic_reward_enabled = ... # type: builtin___bool
+    gail_reward_enabled = ... # type: builtin___bool
+    curiosity_reward_enabled = ... # type: builtin___bool
+    rnd_reward_enabled = ... # type: builtin___bool
+    behavioral_cloning_enabled = ... # type: builtin___bool
+    recurrent_enabled = ... # type: builtin___bool
+    visual_encoder = ... # type: typing___Text
+    num_network_layers = ... # type: builtin___int
+    num_network_hidden_units = ... # type: builtin___int
+    trainer_threaded = ... # type: builtin___bool
+    self_play_enabled = ... # type: builtin___bool
+    curriculum_enabled = ... # type: builtin___bool
+    config = ... # type: typing___Text
+
+    def __init__(self,
+        *,
+        behavior_name : typing___Optional[typing___Text] = None,
+        trainer_type : typing___Optional[typing___Text] = None,
+        extrinsic_reward_enabled : typing___Optional[builtin___bool] = None,
+        gail_reward_enabled : typing___Optional[builtin___bool] = None,
+        curiosity_reward_enabled : typing___Optional[builtin___bool] = None,
+        rnd_reward_enabled : typing___Optional[builtin___bool] = None,
+        behavioral_cloning_enabled : typing___Optional[builtin___bool] = None,
+        recurrent_enabled : typing___Optional[builtin___bool] = None,
+        visual_encoder : typing___Optional[typing___Text] = None,
+        num_network_layers : typing___Optional[builtin___int] = None,
+        num_network_hidden_units : typing___Optional[builtin___int] = None,
+        trainer_threaded : typing___Optional[builtin___bool] = None,
+        self_play_enabled : typing___Optional[builtin___bool] = None,
+        curriculum_enabled : typing___Optional[builtin___bool] = None,
+        config : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> TrainingBehaviorInitialized: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"behavior_name",u"behavioral_cloning_enabled",u"config",u"curiosity_reward_enabled",u"curriculum_enabled",u"extrinsic_reward_enabled",u"gail_reward_enabled",u"num_network_hidden_units",u"num_network_layers",u"recurrent_enabled",u"rnd_reward_enabled",u"self_play_enabled",u"trainer_threaded",u"trainer_type",u"visual_encoder"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"behavior_name",b"behavior_name",u"behavioral_cloning_enabled",b"behavioral_cloning_enabled",u"config",b"config",u"curiosity_reward_enabled",b"curiosity_reward_enabled",u"curriculum_enabled",b"curriculum_enabled",u"extrinsic_reward_enabled",b"extrinsic_reward_enabled",u"gail_reward_enabled",b"gail_reward_enabled",u"num_network_hidden_units",b"num_network_hidden_units",u"num_network_layers",b"num_network_layers",u"recurrent_enabled",b"recurrent_enabled",u"rnd_reward_enabled",b"rnd_reward_enabled",u"self_play_enabled",b"self_play_enabled",u"trainer_threaded",b"trainer_threaded",u"trainer_type",b"trainer_type",u"visual_encoder",b"visual_encoder"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c4a24f3922f209ed928119b97c0f67ea38e7b15
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.py
@@ -0,0 +1,83 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_input.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import unity_rl_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2
+from mlagents_envs.communicator_objects import unity_rl_initialization_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_input.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a\x37mlagents_envs/communicator_objects/unity_rl_input.proto\x1a\x46mlagents_envs/communicator_objects/unity_rl_initialization_input.proto\"\xa4\x01\n\x0fUnityInputProto\x12\x39\n\x08rl_input\x18\x01 \x01(\x0b\x32\'.communicator_objects.UnityRLInputProto\x12V\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x35.communicator_objects.UnityRLInitializationInputProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYINPUTPROTO = _descriptor.Descriptor(
+  name='UnityInputProto',
+  full_name='communicator_objects.UnityInputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='rl_input', full_name='communicator_objects.UnityInputProto.rl_input', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='rl_initialization_input', full_name='communicator_objects.UnityInputProto.rl_initialization_input', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=208,
+  serialized_end=372,
+)
+
+_UNITYINPUTPROTO.fields_by_name['rl_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2._UNITYRLINPUTPROTO
+_UNITYINPUTPROTO.fields_by_name['rl_initialization_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2._UNITYRLINITIALIZATIONINPUTPROTO
+DESCRIPTOR.message_types_by_name['UnityInputProto'] = _UNITYINPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityInputProto = _reflection.GeneratedProtocolMessageType('UnityInputProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYINPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_input_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityInputProto)
+  ))
+_sym_db.RegisterMessage(UnityInputProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..937acf1ad685f5a14dd427fced217453c96f6bf0
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.pyi
@@ -0,0 +1,57 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2 import (
+    UnityRLInitializationInputProto as mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto,
+)
+
+from mlagents_envs.communicator_objects.unity_rl_input_pb2 import (
+    UnityRLInputProto as mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityInputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+    @property
+    def rl_input(self) -> mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto: ...
+
+    @property
+    def rl_initialization_input(self) -> mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto: ...
+
+    def __init__(self,
+        *,
+        rl_input : typing___Optional[mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto] = None,
+        rl_initialization_input : typing___Optional[mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityInputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"rl_initialization_input",u"rl_input"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"rl_initialization_input",u"rl_input"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"rl_initialization_input",b"rl_initialization_input",u"rl_input",b"rl_input"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"rl_initialization_input",b"rl_initialization_input",u"rl_input",b"rl_input"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e01b52298f8b05f477802c53ea5ab5a9082f9b0e
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.py
@@ -0,0 +1,92 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_message.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import unity_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__output__pb2
+from mlagents_envs.communicator_objects import unity_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__input__pb2
+from mlagents_envs.communicator_objects import header_pb2 as mlagents__envs_dot_communicator__objects_dot_header__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_message.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n6mlagents_envs/communicator_objects/unity_message.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/unity_output.proto\x1a\x34mlagents_envs/communicator_objects/unity_input.proto\x1a/mlagents_envs/communicator_objects/header.proto\"\xc0\x01\n\x11UnityMessageProto\x12\x31\n\x06header\x18\x01 \x01(\x0b\x32!.communicator_objects.HeaderProto\x12<\n\x0cunity_output\x18\x02 \x01(\x0b\x32&.communicator_objects.UnityOutputProto\x12:\n\x0bunity_input\x18\x03 \x01(\x0b\x32%.communicator_objects.UnityInputProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__output__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__input__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_header__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYMESSAGEPROTO = _descriptor.Descriptor(
+  name='UnityMessageProto',
+  full_name='communicator_objects.UnityMessageProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='header', full_name='communicator_objects.UnityMessageProto.header', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='unity_output', full_name='communicator_objects.UnityMessageProto.unity_output', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='unity_input', full_name='communicator_objects.UnityMessageProto.unity_input', index=2,
+      number=3, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=239,
+  serialized_end=431,
+)
+
+_UNITYMESSAGEPROTO.fields_by_name['header'].message_type = mlagents__envs_dot_communicator__objects_dot_header__pb2._HEADERPROTO
+_UNITYMESSAGEPROTO.fields_by_name['unity_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__output__pb2._UNITYOUTPUTPROTO
+_UNITYMESSAGEPROTO.fields_by_name['unity_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__input__pb2._UNITYINPUTPROTO
+DESCRIPTOR.message_types_by_name['UnityMessageProto'] = _UNITYMESSAGEPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityMessageProto = _reflection.GeneratedProtocolMessageType('UnityMessageProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYMESSAGEPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_message_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityMessageProto)
+  ))
+_sym_db.RegisterMessage(UnityMessageProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d8f85dbe8f3402d1eacd09c814cebb2a992a9668
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.pyi
@@ -0,0 +1,65 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.header_pb2 import (
+    HeaderProto as mlagents_envs___communicator_objects___header_pb2___HeaderProto,
+)
+
+from mlagents_envs.communicator_objects.unity_input_pb2 import (
+    UnityInputProto as mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto,
+)
+
+from mlagents_envs.communicator_objects.unity_output_pb2 import (
+    UnityOutputProto as mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityMessageProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+    @property
+    def header(self) -> mlagents_envs___communicator_objects___header_pb2___HeaderProto: ...
+
+    @property
+    def unity_output(self) -> mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto: ...
+
+    @property
+    def unity_input(self) -> mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto: ...
+
+    def __init__(self,
+        *,
+        header : typing___Optional[mlagents_envs___communicator_objects___header_pb2___HeaderProto] = None,
+        unity_output : typing___Optional[mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto] = None,
+        unity_input : typing___Optional[mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityMessageProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"header",u"unity_input",u"unity_output"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"header",u"unity_input",u"unity_output"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"header",b"header",u"unity_input",b"unity_input",u"unity_output",b"unity_output"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"header",b"header",u"unity_input",b"unity_input",u"unity_output",b"unity_output"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..400fdb00f736a6b6a496b270b20bb65bdcc304f6
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.py
@@ -0,0 +1,83 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_output.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import unity_rl_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2
+from mlagents_envs.communicator_objects import unity_rl_initialization_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_output.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n5mlagents_envs/communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a\x38mlagents_envs/communicator_objects/unity_rl_output.proto\x1aGmlagents_envs/communicator_objects/unity_rl_initialization_output.proto\"\xa9\x01\n\x10UnityOutputProto\x12;\n\trl_output\x18\x01 \x01(\x0b\x32(.communicator_objects.UnityRLOutputProto\x12X\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x36.communicator_objects.UnityRLInitializationOutputProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYOUTPUTPROTO = _descriptor.Descriptor(
+  name='UnityOutputProto',
+  full_name='communicator_objects.UnityOutputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='rl_output', full_name='communicator_objects.UnityOutputProto.rl_output', index=0,
+      number=1, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='rl_initialization_output', full_name='communicator_objects.UnityOutputProto.rl_initialization_output', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=211,
+  serialized_end=380,
+)
+
+_UNITYOUTPUTPROTO.fields_by_name['rl_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2._UNITYRLOUTPUTPROTO
+_UNITYOUTPUTPROTO.fields_by_name['rl_initialization_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2._UNITYRLINITIALIZATIONOUTPUTPROTO
+DESCRIPTOR.message_types_by_name['UnityOutputProto'] = _UNITYOUTPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityOutputProto = _reflection.GeneratedProtocolMessageType('UnityOutputProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYOUTPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_output_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityOutputProto)
+  ))
+_sym_db.RegisterMessage(UnityOutputProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..55950f767da27f2d9805dff9418d8869ad5873b0
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.pyi
@@ -0,0 +1,57 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2 import (
+    UnityRLInitializationOutputProto as mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto,
+)
+
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import (
+    UnityRLOutputProto as mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto,
+)
+
+from typing import (
+    Optional as typing___Optional,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityOutputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+    @property
+    def rl_output(self) -> mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto: ...
+
+    @property
+    def rl_initialization_output(self) -> mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto: ...
+
+    def __init__(self,
+        *,
+        rl_output : typing___Optional[mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto] = None,
+        rl_initialization_output : typing___Optional[mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityOutputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"rl_initialization_output",u"rl_output"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"rl_initialization_output",u"rl_output"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"rl_initialization_output",b"rl_initialization_output",u"rl_output",b"rl_output"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"rl_initialization_output",b"rl_initialization_output",u"rl_output",b"rl_output"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d111397adaa25c1d73a00291092fb98358ef0ff7
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py
@@ -0,0 +1,102 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import capabilities_pb2 as mlagents__envs_dot_communicator__objects_dot_capabilities__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_rl_initialization_input.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\nFmlagents_envs/communicator_objects/unity_rl_initialization_input.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/capabilities.proto\"\xc0\x01\n\x1fUnityRLInitializationInputProto\x12\x0c\n\x04seed\x18\x01 \x01(\x05\x12\x1d\n\x15\x63ommunication_version\x18\x02 \x01(\t\x12\x17\n\x0fpackage_version\x18\x03 \x01(\t\x12\x44\n\x0c\x63\x61pabilities\x18\x04 \x01(\x0b\x32..communicator_objects.UnityRLCapabilitiesProto\x12\x11\n\tnum_areas\x18\x05 \x01(\x05\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_capabilities__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYRLINITIALIZATIONINPUTPROTO = _descriptor.Descriptor(
+  name='UnityRLInitializationInputProto',
+  full_name='communicator_objects.UnityRLInitializationInputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='seed', full_name='communicator_objects.UnityRLInitializationInputProto.seed', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='communication_version', full_name='communicator_objects.UnityRLInitializationInputProto.communication_version', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='package_version', full_name='communicator_objects.UnityRLInitializationInputProto.package_version', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='capabilities', full_name='communicator_objects.UnityRLInitializationInputProto.capabilities', index=3,
+      number=4, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='num_areas', full_name='communicator_objects.UnityRLInitializationInputProto.num_areas', index=4,
+      number=5, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=152,
+  serialized_end=344,
+)
+
+_UNITYRLINITIALIZATIONINPUTPROTO.fields_by_name['capabilities'].message_type = mlagents__envs_dot_communicator__objects_dot_capabilities__pb2._UNITYRLCAPABILITIESPROTO
+DESCRIPTOR.message_types_by_name['UnityRLInitializationInputProto'] = _UNITYRLINITIALIZATIONINPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityRLInitializationInputProto = _reflection.GeneratedProtocolMessageType('UnityRLInitializationInputProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYRLINITIALIZATIONINPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInitializationInputProto)
+  ))
+_sym_db.RegisterMessage(UnityRLInitializationInputProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f5029976106c0f993f9a4e3213b27fa9608c6678
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.pyi
@@ -0,0 +1,58 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.capabilities_pb2 import (
+    UnityRLCapabilitiesProto as mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto,
+)
+
+from typing import (
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityRLInitializationInputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    seed = ... # type: builtin___int
+    communication_version = ... # type: typing___Text
+    package_version = ... # type: typing___Text
+    num_areas = ... # type: builtin___int
+
+    @property
+    def capabilities(self) -> mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto: ...
+
+    def __init__(self,
+        *,
+        seed : typing___Optional[builtin___int] = None,
+        communication_version : typing___Optional[typing___Text] = None,
+        package_version : typing___Optional[typing___Text] = None,
+        capabilities : typing___Optional[mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto] = None,
+        num_areas : typing___Optional[builtin___int] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityRLInitializationInputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"capabilities"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"capabilities",u"communication_version",u"num_areas",u"package_version",u"seed"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"capabilities",b"capabilities"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"capabilities",b"capabilities",u"communication_version",b"communication_version",u"num_areas",b"num_areas",u"package_version",b"package_version",u"seed",b"seed"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..155af96d825b5ee5ae1f1df3adaa0abf35c65db2
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py
@@ -0,0 +1,111 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import capabilities_pb2 as mlagents__envs_dot_communicator__objects_dot_capabilities__pb2
+from mlagents_envs.communicator_objects import brain_parameters_pb2 as mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_rl_initialization_output.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\nGmlagents_envs/communicator_objects/unity_rl_initialization_output.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/capabilities.proto\x1a\x39mlagents_envs/communicator_objects/brain_parameters.proto\"\x8c\x02\n UnityRLInitializationOutputProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1d\n\x15\x63ommunication_version\x18\x02 \x01(\t\x12\x10\n\x08log_path\x18\x03 \x01(\t\x12\x44\n\x10\x62rain_parameters\x18\x05 \x03(\x0b\x32*.communicator_objects.BrainParametersProto\x12\x17\n\x0fpackage_version\x18\x07 \x01(\t\x12\x44\n\x0c\x63\x61pabilities\x18\x08 \x01(\x0b\x32..communicator_objects.UnityRLCapabilitiesProtoJ\x04\x08\x06\x10\x07\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_capabilities__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYRLINITIALIZATIONOUTPUTPROTO = _descriptor.Descriptor(
+  name='UnityRLInitializationOutputProto',
+  full_name='communicator_objects.UnityRLInitializationOutputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='communicator_objects.UnityRLInitializationOutputProto.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='communication_version', full_name='communicator_objects.UnityRLInitializationOutputProto.communication_version', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='log_path', full_name='communicator_objects.UnityRLInitializationOutputProto.log_path', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='brain_parameters', full_name='communicator_objects.UnityRLInitializationOutputProto.brain_parameters', index=3,
+      number=5, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='package_version', full_name='communicator_objects.UnityRLInitializationOutputProto.package_version', index=4,
+      number=7, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='capabilities', full_name='communicator_objects.UnityRLInitializationOutputProto.capabilities', index=5,
+      number=8, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=212,
+  serialized_end=480,
+)
+
+_UNITYRLINITIALIZATIONOUTPUTPROTO.fields_by_name['brain_parameters'].message_type = mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2._BRAINPARAMETERSPROTO
+_UNITYRLINITIALIZATIONOUTPUTPROTO.fields_by_name['capabilities'].message_type = mlagents__envs_dot_communicator__objects_dot_capabilities__pb2._UNITYRLCAPABILITIESPROTO
+DESCRIPTOR.message_types_by_name['UnityRLInitializationOutputProto'] = _UNITYRLINITIALIZATIONOUTPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityRLInitializationOutputProto = _reflection.GeneratedProtocolMessageType('UnityRLInitializationOutputProto', (_message.Message,), dict(
+  DESCRIPTOR = _UNITYRLINITIALIZATIONOUTPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInitializationOutputProto)
+  ))
+_sym_db.RegisterMessage(UnityRLInitializationOutputProto)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cce72df8b56e9e8773151c43fc7fa770fbe6804b
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.pyi
@@ -0,0 +1,71 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
+    BrainParametersProto as mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto,
+)
+
+from mlagents_envs.communicator_objects.capabilities_pb2 import (
+    UnityRLCapabilitiesProto as mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityRLInitializationOutputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    name = ... # type: typing___Text
+    communication_version = ... # type: typing___Text
+    log_path = ... # type: typing___Text
+    package_version = ... # type: typing___Text
+
+    @property
+    def brain_parameters(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]: ...
+
+    @property
+    def capabilities(self) -> mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto: ...
+
+    def __init__(self,
+        *,
+        name : typing___Optional[typing___Text] = None,
+        communication_version : typing___Optional[typing___Text] = None,
+        log_path : typing___Optional[typing___Text] = None,
+        brain_parameters : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]] = None,
+        package_version : typing___Optional[typing___Text] = None,
+        capabilities : typing___Optional[mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityRLInitializationOutputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def HasField(self, field_name: typing_extensions___Literal[u"capabilities"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"brain_parameters",u"capabilities",u"communication_version",u"log_path",u"name",u"package_version"]) -> None: ...
+    else:
+        def HasField(self, field_name: typing_extensions___Literal[u"capabilities",b"capabilities"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"brain_parameters",b"brain_parameters",u"capabilities",b"capabilities",u"communication_version",b"communication_version",u"log_path",b"log_path",u"name",b"name",u"package_version",b"package_version"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e14f3fd291f3cedd03446926fadfd2f4e589b8
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.py
@@ -0,0 +1,179 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_rl_input.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import agent_action_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__action__pb2
+from mlagents_envs.communicator_objects import command_pb2 as mlagents__envs_dot_communicator__objects_dot_command__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_rl_input.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n7mlagents_envs/communicator_objects/unity_rl_input.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/agent_action.proto\x1a\x30mlagents_envs/communicator_objects/command.proto\"\xfe\x02\n\x11UnityRLInputProto\x12P\n\ragent_actions\x18\x01 \x03(\x0b\x32\x39.communicator_objects.UnityRLInputProto.AgentActionsEntry\x12\x33\n\x07\x63ommand\x18\x04 \x01(\x0e\x32\".communicator_objects.CommandProto\x12\x14\n\x0cside_channel\x18\x05 \x01(\x0c\x1aM\n\x14ListAgentActionProto\x12\x35\n\x05value\x18\x01 \x03(\x0b\x32&.communicator_objects.AgentActionProto\x1aq\n\x11\x41gentActionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12K\n\x05value\x18\x02 \x01(\x0b\x32<.communicator_objects.UnityRLInputProto.ListAgentActionProto:\x02\x38\x01J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__action__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_command__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO = _descriptor.Descriptor(
+  name='ListAgentActionProto',
+  full_name='communicator_objects.UnityRLInputProto.ListAgentActionProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='value', full_name='communicator_objects.UnityRLInputProto.ListAgentActionProto.value', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=365,
+  serialized_end=442,
+)
+
+_UNITYRLINPUTPROTO_AGENTACTIONSENTRY = _descriptor.Descriptor(
+  name='AgentActionsEntry',
+  full_name='communicator_objects.UnityRLInputProto.AgentActionsEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='communicator_objects.UnityRLInputProto.AgentActionsEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='communicator_objects.UnityRLInputProto.AgentActionsEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=444,
+  serialized_end=557,
+)
+
+_UNITYRLINPUTPROTO = _descriptor.Descriptor(
+  name='UnityRLInputProto',
+  full_name='communicator_objects.UnityRLInputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='agent_actions', full_name='communicator_objects.UnityRLInputProto.agent_actions', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='command', full_name='communicator_objects.UnityRLInputProto.command', index=1,
+      number=4, type=14, cpp_type=8, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='side_channel', full_name='communicator_objects.UnityRLInputProto.side_channel', index=2,
+      number=5, type=12, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b(""),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO, _UNITYRLINPUTPROTO_AGENTACTIONSENTRY, ],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=187,
+  serialized_end=569,
+)
+
+_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO.fields_by_name['value'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__action__pb2._AGENTACTIONPROTO
+_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO.containing_type = _UNITYRLINPUTPROTO
+_UNITYRLINPUTPROTO_AGENTACTIONSENTRY.fields_by_name['value'].message_type = _UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO
+_UNITYRLINPUTPROTO_AGENTACTIONSENTRY.containing_type = _UNITYRLINPUTPROTO
+_UNITYRLINPUTPROTO.fields_by_name['agent_actions'].message_type = _UNITYRLINPUTPROTO_AGENTACTIONSENTRY
+_UNITYRLINPUTPROTO.fields_by_name['command'].enum_type = mlagents__envs_dot_communicator__objects_dot_command__pb2._COMMANDPROTO
+DESCRIPTOR.message_types_by_name['UnityRLInputProto'] = _UNITYRLINPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityRLInputProto = _reflection.GeneratedProtocolMessageType('UnityRLInputProto', (_message.Message,), dict(
+
+  ListAgentActionProto = _reflection.GeneratedProtocolMessageType('ListAgentActionProto', (_message.Message,), dict(
+    DESCRIPTOR = _UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO,
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
+    # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInputProto.ListAgentActionProto)
+    ))
+  ,
+
+  AgentActionsEntry = _reflection.GeneratedProtocolMessageType('AgentActionsEntry', (_message.Message,), dict(
+    DESCRIPTOR = _UNITYRLINPUTPROTO_AGENTACTIONSENTRY,
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
+    # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInputProto.AgentActionsEntry)
+    ))
+  ,
+  DESCRIPTOR = _UNITYRLINPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInputProto)
+  ))
+_sym_db.RegisterMessage(UnityRLInputProto)
+_sym_db.RegisterMessage(UnityRLInputProto.ListAgentActionProto)
+_sym_db.RegisterMessage(UnityRLInputProto.AgentActionsEntry)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+_UNITYRLINPUTPROTO_AGENTACTIONSENTRY.has_options = True
+_UNITYRLINPUTPROTO_AGENTACTIONSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..563f8fa981c3ac9767edbe762716dc77021274e2
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.pyi
@@ -0,0 +1,105 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.agent_action_pb2 import (
+    AgentActionProto as mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto,
+)
+
+from mlagents_envs.communicator_objects.command_pb2 import (
+    CommandProto as mlagents_envs___communicator_objects___command_pb2___CommandProto,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Mapping as typing___Mapping,
+    MutableMapping as typing___MutableMapping,
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityRLInputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    class ListAgentActionProto(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+        @property
+        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto]: ...
+
+        def __init__(self,
+            *,
+            value : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto]] = None,
+            ) -> None: ...
+        @classmethod
+        def FromString(cls, s: builtin___bytes) -> UnityRLInputProto.ListAgentActionProto: ...
+        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        if sys.version_info >= (3,):
+            def ClearField(self, field_name: typing_extensions___Literal[u"value"]) -> None: ...
+        else:
+            def ClearField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> None: ...
+
+    class AgentActionsEntry(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+        key = ... # type: typing___Text
+
+        @property
+        def value(self) -> UnityRLInputProto.ListAgentActionProto: ...
+
+        def __init__(self,
+            *,
+            key : typing___Optional[typing___Text] = None,
+            value : typing___Optional[UnityRLInputProto.ListAgentActionProto] = None,
+            ) -> None: ...
+        @classmethod
+        def FromString(cls, s: builtin___bytes) -> UnityRLInputProto.AgentActionsEntry: ...
+        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        if sys.version_info >= (3,):
+            def HasField(self, field_name: typing_extensions___Literal[u"value"]) -> builtin___bool: ...
+            def ClearField(self, field_name: typing_extensions___Literal[u"key",u"value"]) -> None: ...
+        else:
+            def HasField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> builtin___bool: ...
+            def ClearField(self, field_name: typing_extensions___Literal[u"key",b"key",u"value",b"value"]) -> None: ...
+
+    command = ... # type: mlagents_envs___communicator_objects___command_pb2___CommandProto
+    side_channel = ... # type: builtin___bytes
+
+    @property
+    def agent_actions(self) -> typing___MutableMapping[typing___Text, UnityRLInputProto.ListAgentActionProto]: ...
+
+    def __init__(self,
+        *,
+        agent_actions : typing___Optional[typing___Mapping[typing___Text, UnityRLInputProto.ListAgentActionProto]] = None,
+        command : typing___Optional[mlagents_envs___communicator_objects___command_pb2___CommandProto] = None,
+        side_channel : typing___Optional[builtin___bytes] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityRLInputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"agent_actions",u"command",u"side_channel"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"agent_actions",b"agent_actions",u"command",b"command",u"side_channel",b"side_channel"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..099d0b0040e98418950442adc34410e908a52a08
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.py
@@ -0,0 +1,170 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_rl_output.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import agent_info_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__info__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_rl_output.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n8mlagents_envs/communicator_objects/unity_rl_output.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/agent_info.proto\"\xb9\x02\n\x12UnityRLOutputProto\x12L\n\nagentInfos\x18\x02 \x03(\x0b\x32\x38.communicator_objects.UnityRLOutputProto.AgentInfosEntry\x12\x14\n\x0cside_channel\x18\x03 \x01(\x0c\x1aI\n\x12ListAgentInfoProto\x12\x33\n\x05value\x18\x01 \x03(\x0b\x32$.communicator_objects.AgentInfoProto\x1an\n\x0f\x41gentInfosEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12J\n\x05value\x18\x02 \x01(\x0b\x32;.communicator_objects.UnityRLOutputProto.ListAgentInfoProto:\x02\x38\x01J\x04\x08\x01\x10\x02\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__info__pb2.DESCRIPTOR,])
+
+
+
+
+_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO = _descriptor.Descriptor(
+  name='ListAgentInfoProto',
+  full_name='communicator_objects.UnityRLOutputProto.ListAgentInfoProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='value', full_name='communicator_objects.UnityRLOutputProto.ListAgentInfoProto.value', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=258,
+  serialized_end=331,
+)
+
+_UNITYRLOUTPUTPROTO_AGENTINFOSENTRY = _descriptor.Descriptor(
+  name='AgentInfosEntry',
+  full_name='communicator_objects.UnityRLOutputProto.AgentInfosEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='communicator_objects.UnityRLOutputProto.AgentInfosEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='communicator_objects.UnityRLOutputProto.AgentInfosEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=333,
+  serialized_end=443,
+)
+
+_UNITYRLOUTPUTPROTO = _descriptor.Descriptor(
+  name='UnityRLOutputProto',
+  full_name='communicator_objects.UnityRLOutputProto',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='agentInfos', full_name='communicator_objects.UnityRLOutputProto.agentInfos', index=0,
+      number=2, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='side_channel', full_name='communicator_objects.UnityRLOutputProto.side_channel', index=1,
+      number=3, type=12, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b(""),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO, _UNITYRLOUTPUTPROTO_AGENTINFOSENTRY, ],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=136,
+  serialized_end=449,
+)
+
+_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO.fields_by_name['value'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__info__pb2._AGENTINFOPROTO
+_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO.containing_type = _UNITYRLOUTPUTPROTO
+_UNITYRLOUTPUTPROTO_AGENTINFOSENTRY.fields_by_name['value'].message_type = _UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO
+_UNITYRLOUTPUTPROTO_AGENTINFOSENTRY.containing_type = _UNITYRLOUTPUTPROTO
+_UNITYRLOUTPUTPROTO.fields_by_name['agentInfos'].message_type = _UNITYRLOUTPUTPROTO_AGENTINFOSENTRY
+DESCRIPTOR.message_types_by_name['UnityRLOutputProto'] = _UNITYRLOUTPUTPROTO
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+UnityRLOutputProto = _reflection.GeneratedProtocolMessageType('UnityRLOutputProto', (_message.Message,), dict(
+
+  ListAgentInfoProto = _reflection.GeneratedProtocolMessageType('ListAgentInfoProto', (_message.Message,), dict(
+    DESCRIPTOR = _UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO,
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
+    # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLOutputProto.ListAgentInfoProto)
+    ))
+  ,
+
+  AgentInfosEntry = _reflection.GeneratedProtocolMessageType('AgentInfosEntry', (_message.Message,), dict(
+    DESCRIPTOR = _UNITYRLOUTPUTPROTO_AGENTINFOSENTRY,
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
+    # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLOutputProto.AgentInfosEntry)
+    ))
+  ,
+  DESCRIPTOR = _UNITYRLOUTPUTPROTO,
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
+  # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLOutputProto)
+  ))
+_sym_db.RegisterMessage(UnityRLOutputProto)
+_sym_db.RegisterMessage(UnityRLOutputProto.ListAgentInfoProto)
+_sym_db.RegisterMessage(UnityRLOutputProto.AgentInfosEntry)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+_UNITYRLOUTPUTPROTO_AGENTINFOSENTRY.has_options = True
+_UNITYRLOUTPUTPROTO_AGENTINFOSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0ace7bdd120e95d4c14883e779f89724e8faebe3
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.pyi
@@ -0,0 +1,99 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from mlagents_envs.communicator_objects.agent_info_pb2 import (
+    AgentInfoProto as mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    Mapping as typing___Mapping,
+    MutableMapping as typing___MutableMapping,
+    Optional as typing___Optional,
+    Text as typing___Text,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+class UnityRLOutputProto(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    class ListAgentInfoProto(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+
+        @property
+        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto]: ...
+
+        def __init__(self,
+            *,
+            value : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto]] = None,
+            ) -> None: ...
+        @classmethod
+        def FromString(cls, s: builtin___bytes) -> UnityRLOutputProto.ListAgentInfoProto: ...
+        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        if sys.version_info >= (3,):
+            def ClearField(self, field_name: typing_extensions___Literal[u"value"]) -> None: ...
+        else:
+            def ClearField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> None: ...
+
+    class AgentInfosEntry(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+        key = ... # type: typing___Text
+
+        @property
+        def value(self) -> UnityRLOutputProto.ListAgentInfoProto: ...
+
+        def __init__(self,
+            *,
+            key : typing___Optional[typing___Text] = None,
+            value : typing___Optional[UnityRLOutputProto.ListAgentInfoProto] = None,
+            ) -> None: ...
+        @classmethod
+        def FromString(cls, s: builtin___bytes) -> UnityRLOutputProto.AgentInfosEntry: ...
+        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+        if sys.version_info >= (3,):
+            def HasField(self, field_name: typing_extensions___Literal[u"value"]) -> builtin___bool: ...
+            def ClearField(self, field_name: typing_extensions___Literal[u"key",u"value"]) -> None: ...
+        else:
+            def HasField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> builtin___bool: ...
+            def ClearField(self, field_name: typing_extensions___Literal[u"key",b"key",u"value",b"value"]) -> None: ...
+
+    side_channel = ... # type: builtin___bytes
+
+    @property
+    def agentInfos(self) -> typing___MutableMapping[typing___Text, UnityRLOutputProto.ListAgentInfoProto]: ...
+
+    def __init__(self,
+        *,
+        agentInfos : typing___Optional[typing___Mapping[typing___Text, UnityRLOutputProto.ListAgentInfoProto]] = None,
+        side_channel : typing___Optional[builtin___bytes] = None,
+        ) -> None: ...
+    @classmethod
+    def FromString(cls, s: builtin___bytes) -> UnityRLOutputProto: ...
+    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
+    if sys.version_info >= (3,):
+        def ClearField(self, field_name: typing_extensions___Literal[u"agentInfos",u"side_channel"]) -> None: ...
+    else:
+        def ClearField(self, field_name: typing_extensions___Literal[u"agentInfos",b"agentInfos",u"side_channel",b"side_channel"]) -> None: ...
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6053ccc46a4b550649b8f20238b15117f8748e88
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.py
@@ -0,0 +1,58 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: mlagents_envs/communicator_objects/unity_to_external.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from mlagents_envs.communicator_objects import unity_message_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__message__pb2
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='mlagents_envs/communicator_objects/unity_to_external.proto',
+  package='communicator_objects',
+  syntax='proto3',
+  serialized_pb=_b('\n:mlagents_envs/communicator_objects/unity_to_external.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents_envs/communicator_objects/unity_message.proto2v\n\x14UnityToExternalProto\x12^\n\x08\x45xchange\x12\'.communicator_objects.UnityMessageProto\x1a\'.communicator_objects.UnityMessageProto\"\x00\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  ,
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.DESCRIPTOR,])
+
+
+
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+
+DESCRIPTOR.has_options = True
+DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
+
+_UNITYTOEXTERNALPROTO = _descriptor.ServiceDescriptor(
+  name='UnityToExternalProto',
+  full_name='communicator_objects.UnityToExternalProto',
+  file=DESCRIPTOR,
+  index=0,
+  options=None,
+  serialized_start=140,
+  serialized_end=258,
+  methods=[
+  _descriptor.MethodDescriptor(
+    name='Exchange',
+    full_name='communicator_objects.UnityToExternalProto.Exchange',
+    index=0,
+    containing_service=None,
+    input_type=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
+    output_type=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
+    options=None,
+  ),
+])
+_sym_db.RegisterServiceDescriptor(_UNITYTOEXTERNALPROTO)
+
+DESCRIPTOR.services_by_name['UnityToExternalProto'] = _UNITYTOEXTERNALPROTO
+
+# @@protoc_insertion_point(module_scope)
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.pyi b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8bba4ff746e52bceb29a235b9fb5b0846ff1caaa
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.pyi
@@ -0,0 +1,6 @@
+# @generated by generate_proto_mypy_stubs.py.  Do not edit!
+import sys
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
diff --git a/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..bedeb100594c7ba9728c2118b526a18de5f2421d
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py
@@ -0,0 +1,46 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+import grpc
+
+from mlagents_envs.communicator_objects import unity_message_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__message__pb2
+
+
+class UnityToExternalProtoStub(object):
+  # missing associated documentation comment in .proto file
+  pass
+
+  def __init__(self, channel):
+    """Constructor.
+
+    Args:
+      channel: A grpc.Channel.
+    """
+    self.Exchange = channel.unary_unary(
+        '/communicator_objects.UnityToExternalProto/Exchange',
+        request_serializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
+        response_deserializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
+        )
+
+
+class UnityToExternalProtoServicer(object):
+  # missing associated documentation comment in .proto file
+  pass
+
+  def Exchange(self, request, context):
+    """Sends the academy parameters
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+
+def add_UnityToExternalProtoServicer_to_server(servicer, server):
+  rpc_method_handlers = {
+      'Exchange': grpc.unary_unary_rpc_method_handler(
+          servicer.Exchange,
+          request_deserializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
+          response_serializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
+      ),
+  }
+  generic_handler = grpc.method_handlers_generic_handler(
+      'communicator_objects.UnityToExternalProto', rpc_method_handlers)
+  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/ml-agents-envs/mlagents_envs/env_utils.py b/ml-agents-envs/mlagents_envs/env_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..73393d945617a2e3ac0c3489939ae9575d5d8d80
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/env_utils.py
@@ -0,0 +1,130 @@
+import glob
+import os
+import subprocess
+from sys import platform
+from typing import Optional, List
+from mlagents_envs.logging_util import get_logger, DEBUG
+from mlagents_envs.exception import UnityEnvironmentException
+
+
+logger = get_logger(__name__)
+
+
+def get_platform():
+    """
+    returns the platform of the operating system : linux, darwin or win32
+    """
+    return platform
+
+
+def validate_environment_path(env_path: str) -> Optional[str]:
+    """
+    Strip out executable extensions of the env_path
+    :param env_path: The path to the executable
+    """
+    env_path = (
+        env_path.strip()
+        .replace(".app", "")
+        .replace(".exe", "")
+        .replace(".x86_64", "")
+        .replace(".x86", "")
+    )
+    true_filename = os.path.basename(os.path.normpath(env_path))
+    logger.debug(f"The true file name is {true_filename}")
+
+    if not (glob.glob(env_path) or glob.glob(env_path + ".*")):
+        return None
+
+    cwd = os.getcwd()
+    launch_string = None
+    true_filename = os.path.basename(os.path.normpath(env_path))
+    if get_platform() == "linux" or get_platform() == "linux2":
+        candidates = glob.glob(os.path.join(cwd, env_path) + ".x86_64")
+        if len(candidates) == 0:
+            candidates = glob.glob(os.path.join(cwd, env_path) + ".x86")
+        if len(candidates) == 0:
+            candidates = glob.glob(env_path + ".x86_64")
+        if len(candidates) == 0:
+            candidates = glob.glob(env_path + ".x86")
+        if len(candidates) == 0:
+            if os.path.isfile(env_path):
+                candidates = [env_path]
+        if len(candidates) > 0:
+            launch_string = candidates[0]
+
+    elif get_platform() == "darwin":
+        candidates = glob.glob(
+            os.path.join(cwd, env_path + ".app", "Contents", "MacOS", true_filename)
+        )
+        if len(candidates) == 0:
+            candidates = glob.glob(
+                os.path.join(env_path + ".app", "Contents", "MacOS", true_filename)
+            )
+        if len(candidates) == 0:
+            candidates = glob.glob(
+                os.path.join(cwd, env_path + ".app", "Contents", "MacOS", "*")
+            )
+        if len(candidates) == 0:
+            candidates = glob.glob(
+                os.path.join(env_path + ".app", "Contents", "MacOS", "*")
+            )
+        if len(candidates) > 0:
+            launch_string = candidates[0]
+    elif get_platform() == "win32":
+        candidates = glob.glob(os.path.join(cwd, env_path + ".exe"))
+        if len(candidates) == 0:
+            candidates = glob.glob(env_path + ".exe")
+        if len(candidates) == 0:
+            # Look for e.g. 3DBall\UnityEnvironment.exe
+            crash_handlers = set(
+                glob.glob(os.path.join(cwd, env_path, "UnityCrashHandler*.exe"))
+            )
+            candidates = [
+                c
+                for c in glob.glob(os.path.join(cwd, env_path, "*.exe"))
+                if c not in crash_handlers
+            ]
+        if len(candidates) > 0:
+            launch_string = candidates[0]
+    return launch_string
+
+
+def launch_executable(file_name: str, args: List[str]) -> subprocess.Popen:
+    """
+    Launches a Unity executable and returns the process handle for it.
+    :param file_name: the name of the executable
+    :param args: List of string that will be passed as command line arguments
+    when launching the executable.
+    """
+    launch_string = validate_environment_path(file_name)
+    if launch_string is None:
+        raise UnityEnvironmentException(
+            f"Couldn't launch the {file_name} environment. Provided filename does not match any environments."
+        )
+    else:
+        logger.debug(f"The launch string is {launch_string}")
+        logger.debug(f"Running with args {args}")
+        # Launch Unity environment
+        subprocess_args = [launch_string] + args
+        # std_out_option = DEVNULL means the outputs will not be displayed on terminal.
+        # std_out_option = None is default behavior: the outputs are displayed on terminal.
+        std_out_option = subprocess.DEVNULL if logger.level > DEBUG else None
+        try:
+            return subprocess.Popen(
+                subprocess_args,
+                # start_new_session=True means that signals to the parent python process
+                # (e.g. SIGINT from keyboard interrupt) will not be sent to the new process on POSIX platforms.
+                # This is generally good since we want the environment to have a chance to shutdown,
+                # but may be undesirable in come cases; if so, we'll add a command-line toggle.
+                # Note that on Windows, the CTRL_C signal will still be sent.
+                start_new_session=True,
+                stdout=std_out_option,
+                stderr=std_out_option,
+            )
+        except PermissionError as perm:
+            # This is likely due to missing read or execute permissions on file.
+            raise UnityEnvironmentException(
+                f"Error when trying to launch environment - make sure "
+                f"permissions are set correctly. For example "
+                f'"chmod -R 755 {launch_string}"'
+            ) from perm
diff --git a/ml-agents-envs/mlagents_envs/environment.py b/ml-agents-envs/mlagents_envs/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..18731a20bb198c4be456d5fe8772fb2dae3e4574
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/environment.py
@@ -0,0 +1,518 @@
+import atexit
+from distutils.version import StrictVersion
+
+import numpy as np
+import os
+import subprocess
+from typing import Dict, List, Optional, Tuple, Mapping as MappingType
+
+import mlagents_envs
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.side_channel.side_channel import SideChannel
+from mlagents_envs.side_channel import DefaultTrainingAnalyticsSideChannel
+from mlagents_envs.side_channel.side_channel_manager import SideChannelManager
+from mlagents_envs import env_utils
+
+from mlagents_envs.base_env import (
+    BaseEnv,
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorSpec,
+    ActionTuple,
+    BehaviorName,
+    AgentId,
+    BehaviorMapping,
+)
+from mlagents_envs.timers import timed, hierarchical_timer
+from mlagents_envs.exception import (
+    UnityEnvironmentException,
+    UnityActionException,
+    UnityTimeOutException,
+    UnityCommunicatorStoppedException,
+)
+
+from mlagents_envs.communicator_objects.command_pb2 import STEP, RESET
+from mlagents_envs.rpc_utils import behavior_spec_from_proto, steps_from_proto
+
+from mlagents_envs.communicator_objects.unity_rl_input_pb2 import UnityRLInputProto
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
+from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.capabilities_pb2 import UnityRLCapabilitiesProto
+from mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2 import (
+    UnityRLInitializationInputProto,
+)
+
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+
+from .rpc_communicator import RpcCommunicator
+import signal
+
+logger = get_logger(__name__)
+
+
+class UnityEnvironment(BaseEnv):
+    # Communication protocol version.
+    # When connecting to C#, this must be compatible with Academy.k_ApiVersion.
+    # We follow semantic versioning on the communication version, so existing
+    # functionality will work as long the major versions match.
+    # This should be changed whenever a change is made to the communication protocol.
+    # Revision history:
+    #  * 1.0.0 - initial version
+    #  * 1.1.0 - support concatenated PNGs for compressed observations.
+    #  * 1.2.0 - support compression mapping for stacked compressed observations.
+    #  * 1.3.0 - support action spaces with both continuous and discrete actions.
+    #  * 1.4.0 - support training analytics sent from python trainer to the editor.
+    #  * 1.5.0 - support variable length observation training and multi-agent groups.
+    API_VERSION = "1.5.0"
+
+    # Default port that the editor listens on. If an environment executable
+    # isn't specified, this port will be used.
+    DEFAULT_EDITOR_PORT = 5004
+
+    # Default base port for environments. Each environment will be offset from this
+    # by it's worker_id.
+    BASE_ENVIRONMENT_PORT = 5005
+
+    # Command line argument used to pass the port to the executable environment.
+    _PORT_COMMAND_LINE_ARG = "--mlagents-port"
+
+    @staticmethod
+    def _raise_version_exception(unity_com_ver: str) -> None:
+        raise UnityEnvironmentException(
+            f"The communication API version is not compatible between Unity and python. "
+            f"Python API: {UnityEnvironment.API_VERSION}, Unity API: {unity_com_ver}.\n "
+            f"Please find the versions that work best together from our release page.\n"
+            "https://github.com/Unity-Technologies/ml-agents/releases"
+        )
+
+    @staticmethod
+    def _check_communication_compatibility(
+        unity_com_ver: str, python_api_version: str, unity_package_version: str
+    ) -> bool:
+        unity_communicator_version = StrictVersion(unity_com_ver)
+        api_version = StrictVersion(python_api_version)
+        if unity_communicator_version.version[0] == 0:
+            if (
+                unity_communicator_version.version[0] != api_version.version[0]
+                or unity_communicator_version.version[1] != api_version.version[1]
+            ):
+                # Minor beta versions differ.
+                return False
+        elif unity_communicator_version.version[0] != api_version.version[0]:
+            # Major versions mismatch.
+            return False
+        else:
+            # Major versions match, so either:
+            # 1) The versions are identical, in which case there's no compatibility issues
+            # 2) The Unity version is newer, in which case we'll warn or fail on the Unity side if trying to use
+            #    unsupported features
+            # 3) The trainer version is newer, in which case new trainer features might be available but unused by C#
+            # In any of the cases, there's no reason to warn about mismatch here.
+            logger.info(
+                f"Connected to Unity environment with package version {unity_package_version} "
+                f"and communication version {unity_com_ver}"
+            )
+        return True
+
+    @staticmethod
+    def _get_capabilities_proto() -> UnityRLCapabilitiesProto:
+        capabilities = UnityRLCapabilitiesProto()
+        capabilities.baseRLCapabilities = True
+        capabilities.concatenatedPngObservations = True
+        capabilities.compressedChannelMapping = True
+        capabilities.hybridActions = True
+        capabilities.trainingAnalytics = True
+        capabilities.variableLengthObservation = True
+        capabilities.multiAgentGroups = True
+        return capabilities
+
+    @staticmethod
+    def _warn_csharp_base_capabilities(
+        caps: UnityRLCapabilitiesProto, unity_package_ver: str, python_package_ver: str
+    ) -> None:
+        if not caps.baseRLCapabilities:
+            logger.warning(
+                "WARNING: The Unity process is not running with the expected base Reinforcement Learning"
+                " capabilities. Please be sure upgrade the Unity Package to a version that is compatible with this "
+                "python package.\n"
+                f"Python package version: {python_package_ver}, C# package version: {unity_package_ver}"
+                f"Please find the versions that work best together from our release page.\n"
+                "https://github.com/Unity-Technologies/ml-agents/releases"
+            )
+
+    def __init__(
+        self,
+        file_name: Optional[str] = None,
+        worker_id: int = 0,
+        base_port: Optional[int] = None,
+        seed: int = 0,
+        no_graphics: bool = False,
+        timeout_wait: int = 60,
+        additional_args: Optional[List[str]] = None,
+        side_channels: Optional[List[SideChannel]] = None,
+        log_folder: Optional[str] = None,
+        num_areas: int = 1,
+    ):
+        """
+        Starts a new unity environment and establishes a connection with the environment.
+        Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
+        Ensure that the network where training takes place is secure.
+
+        :string file_name: Name of Unity environment binary.
+        :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
+        If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used.
+        :int worker_id: Offset from base_port. Used for training multiple environments simultaneously.
+        :bool no_graphics: Whether to run the Unity simulator in no-graphics mode
+        :int timeout_wait: Time (in seconds) to wait for connection from environment.
+        :list args: Addition Unity command line arguments
+        :list side_channels: Additional side channel for no-rl communication with Unity
+        :str log_folder: Optional folder to write the Unity Player log file into.  Requires absolute path.
+        """
+        atexit.register(self._close)
+        self._additional_args = additional_args or []
+        self._no_graphics = no_graphics
+        # If base port is not specified, use BASE_ENVIRONMENT_PORT if we have
+        # an environment, otherwise DEFAULT_EDITOR_PORT
+        if base_port is None:
+            base_port = (
+                self.BASE_ENVIRONMENT_PORT if file_name else self.DEFAULT_EDITOR_PORT
+            )
+        self._port = base_port + worker_id
+        self._buffer_size = 12000
+        # If true, this means the environment was successfully loaded
+        self._loaded = False
+        # The process that is started. If None, no process was started
+        self._process: Optional[subprocess.Popen] = None
+        self._timeout_wait: int = timeout_wait
+        self._communicator = self._get_communicator(worker_id, base_port, timeout_wait)
+        self._worker_id = worker_id
+        if side_channels is None:
+            side_channels = []
+        default_training_side_channel: Optional[
+            DefaultTrainingAnalyticsSideChannel
+        ] = None
+        if DefaultTrainingAnalyticsSideChannel.CHANNEL_ID not in [
+            _.channel_id for _ in side_channels
+        ]:
+            default_training_side_channel = DefaultTrainingAnalyticsSideChannel()
+            side_channels.append(default_training_side_channel)
+        self._side_channel_manager = SideChannelManager(side_channels)
+        self._log_folder = log_folder
+        self.academy_capabilities: UnityRLCapabilitiesProto = None  # type: ignore
+
+        # If the environment name is None, a new environment will not be launched
+        # and the communicator will directly try to connect to an existing unity environment.
+        # If the worker-id is not 0 and the environment name is None, an error is thrown
+        if file_name is None and worker_id != 0:
+            raise UnityEnvironmentException(
+                "If the environment name is None, "
+                "the worker-id must be 0 in order to connect with the Editor."
+            )
+        if file_name is not None:
+            try:
+                self._process = env_utils.launch_executable(
+                    file_name, self._executable_args()
+                )
+            except UnityEnvironmentException:
+                self._close(0)
+                raise
+        else:
+            logger.info(
+                f"Listening on port {self._port}. "
+                f"Start training by pressing the Play button in the Unity Editor."
+            )
+        self._loaded = True
+
+        rl_init_parameters_in = UnityRLInitializationInputProto(
+            seed=seed,
+            communication_version=self.API_VERSION,
+            package_version=mlagents_envs.__version__,
+            capabilities=UnityEnvironment._get_capabilities_proto(),
+            num_areas=num_areas,
+        )
+        try:
+            aca_output = self._send_academy_parameters(rl_init_parameters_in)
+            aca_params = aca_output.rl_initialization_output
+        except UnityTimeOutException:
+            self._close(0)
+            raise
+
+        if not UnityEnvironment._check_communication_compatibility(
+            aca_params.communication_version,
+            UnityEnvironment.API_VERSION,
+            aca_params.package_version,
+        ):
+            self._close(0)
+            UnityEnvironment._raise_version_exception(aca_params.communication_version)
+
+        UnityEnvironment._warn_csharp_base_capabilities(
+            aca_params.capabilities,
+            aca_params.package_version,
+            UnityEnvironment.API_VERSION,
+        )
+
+        self._env_state: Dict[str, Tuple[DecisionSteps, TerminalSteps]] = {}
+        self._env_specs: Dict[str, BehaviorSpec] = {}
+        self._env_actions: Dict[str, ActionTuple] = {}
+        self._is_first_message = True
+        self._update_behavior_specs(aca_output)
+        self.academy_capabilities = aca_params.capabilities
+        if default_training_side_channel is not None:
+            default_training_side_channel.environment_initialized()
+
+    @staticmethod
+    def _get_communicator(worker_id, base_port, timeout_wait):
+        return RpcCommunicator(worker_id, base_port, timeout_wait)
+
+    def _executable_args(self) -> List[str]:
+        args: List[str] = []
+        if self._no_graphics:
+            args += ["-nographics", "-batchmode"]
+        args += [UnityEnvironment._PORT_COMMAND_LINE_ARG, str(self._port)]
+
+        # If the logfile arg isn't already set in the env args,
+        # try to set it to an output directory
+        logfile_set = "-logfile" in (arg.lower() for arg in self._additional_args)
+        if self._log_folder and not logfile_set:
+            log_file_path = os.path.join(
+                self._log_folder, f"Player-{self._worker_id}.log"
+            )
+            args += ["-logFile", log_file_path]
+        # Add in arguments passed explicitly by the user.
+        args += self._additional_args
+        return args
+
+    def _update_behavior_specs(self, output: UnityOutputProto) -> None:
+        init_output = output.rl_initialization_output
+        for brain_param in init_output.brain_parameters:
+            # Each BrainParameter in the rl_initialization_output should have at least one AgentInfo
+            # Get that agent, because we need some of its observations.
+            agent_infos = output.rl_output.agentInfos[brain_param.brain_name]
+            if agent_infos.value:
+                agent = agent_infos.value[0]
+                new_spec = behavior_spec_from_proto(brain_param, agent)
+                self._env_specs[brain_param.brain_name] = new_spec
+                logger.info(f"Connected new brain: {brain_param.brain_name}")
+
+    def _update_state(self, output: UnityRLOutputProto) -> None:
+        """
+        Collects experience information from all external brains in environment at current step.
+        """
+        for brain_name in self._env_specs.keys():
+            if brain_name in output.agentInfos:
+                agent_info_list = output.agentInfos[brain_name].value
+                self._env_state[brain_name] = steps_from_proto(
+                    agent_info_list, self._env_specs[brain_name]
+                )
+            else:
+                self._env_state[brain_name] = (
+                    DecisionSteps.empty(self._env_specs[brain_name]),
+                    TerminalSteps.empty(self._env_specs[brain_name]),
+                )
+        self._side_channel_manager.process_side_channel_message(output.side_channel)
+
+    def reset(self) -> None:
+        if self._loaded:
+            outputs = self._communicator.exchange(
+                self._generate_reset_input(), self._poll_process
+            )
+            if outputs is None:
+                raise UnityCommunicatorStoppedException("Communicator has exited.")
+            self._update_behavior_specs(outputs)
+            rl_output = outputs.rl_output
+            self._update_state(rl_output)
+            self._is_first_message = False
+            self._env_actions.clear()
+        else:
+            raise UnityEnvironmentException("No Unity environment is loaded.")
+
+    @timed
+    def step(self) -> None:
+        if self._is_first_message:
+            return self.reset()
+        if not self._loaded:
+            raise UnityEnvironmentException("No Unity environment is loaded.")
+        # fill the blanks for missing actions
+        for group_name in self._env_specs:
+            if group_name not in self._env_actions:
+                n_agents = 0
+                if group_name in self._env_state:
+                    n_agents = len(self._env_state[group_name][0])
+                self._env_actions[group_name] = self._env_specs[
+                    group_name
+                ].action_spec.empty_action(n_agents)
+        step_input = self._generate_step_input(self._env_actions)
+        with hierarchical_timer("communicator.exchange"):
+            outputs = self._communicator.exchange(step_input, self._poll_process)
+        if outputs is None:
+            raise UnityCommunicatorStoppedException("Communicator has exited.")
+        self._update_behavior_specs(outputs)
+        rl_output = outputs.rl_output
+        self._update_state(rl_output)
+        self._env_actions.clear()
+
+    @property
+    def behavior_specs(self) -> MappingType[str, BehaviorSpec]:
+        return BehaviorMapping(self._env_specs)
+
+    def _assert_behavior_exists(self, behavior_name: str) -> None:
+        if behavior_name not in self._env_specs:
+            raise UnityActionException(
+                f"The group {behavior_name} does not correspond to an existing "
+                f"agent group in the environment"
+            )
+
+    def set_actions(self, behavior_name: BehaviorName, action: ActionTuple) -> None:
+        self._assert_behavior_exists(behavior_name)
+        if behavior_name not in self._env_state:
+            return
+        action_spec = self._env_specs[behavior_name].action_spec
+        num_agents = len(self._env_state[behavior_name][0])
+        action = action_spec._validate_action(action, num_agents, behavior_name)
+        self._env_actions[behavior_name] = action
+
+    def set_action_for_agent(
+        self, behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple
+    ) -> None:
+        self._assert_behavior_exists(behavior_name)
+        if behavior_name not in self._env_state:
+            return
+        action_spec = self._env_specs[behavior_name].action_spec
+        action = action_spec._validate_action(action, 1, behavior_name)
+        if behavior_name not in self._env_actions:
+            num_agents = len(self._env_state[behavior_name][0])
+            self._env_actions[behavior_name] = action_spec.empty_action(num_agents)
+        try:
+            index = np.where(self._env_state[behavior_name][0].agent_id == agent_id)[0][
+                0
+            ]
+        except IndexError as ie:
+            raise IndexError(
+                "agent_id {} is did not request a decision at the previous step".format(
+                    agent_id
+                )
+            ) from ie
+        if action_spec.continuous_size > 0:
+            self._env_actions[behavior_name].continuous[index] = action.continuous[0, :]
+        if action_spec.discrete_size > 0:
+            self._env_actions[behavior_name].discrete[index] = action.discrete[0, :]
+
+    def get_steps(
+        self, behavior_name: BehaviorName
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        self._assert_behavior_exists(behavior_name)
+        return self._env_state[behavior_name]
+
+    def _poll_process(self) -> None:
+        """
+        Check the status of the subprocess. If it has exited, raise a UnityEnvironmentException
+        :return: None
+        """
+        if not self._process:
+            return
+        poll_res = self._process.poll()
+        if poll_res is not None:
+            exc_msg = self._returncode_to_env_message(self._process.returncode)
+            raise UnityEnvironmentException(exc_msg)
+
+    def close(self):
+        """
+        Sends a shutdown signal to the unity environment, and closes the socket connection.
+        """
+        if self._loaded:
+            self._close()
+        else:
+            raise UnityEnvironmentException("No Unity environment is loaded.")
+
+    def _close(self, timeout: Optional[int] = None) -> None:
+        """
+        Close the communicator and environment subprocess (if necessary).
+
+        :int timeout: [Optional] Number of seconds to wait for the environment to shut down before
+            force-killing it.  Defaults to `self.timeout_wait`.
+        """
+        if timeout is None:
+            timeout = self._timeout_wait
+        self._loaded = False
+        self._communicator.close()
+        if self._process is not None:
+            # Wait a bit for the process to shutdown, but kill it if it takes too long
+            try:
+                self._process.wait(timeout=timeout)
+                logger.debug(self._returncode_to_env_message(self._process.returncode))
+            except subprocess.TimeoutExpired:
+                logger.warning("Environment timed out shutting down. Killing...")
+                self._process.kill()
+            # Set to None so we don't try to close multiple times.
+            self._process = None
+
+    @timed
+    def _generate_step_input(
+        self, vector_action: Dict[str, ActionTuple]
+    ) -> UnityInputProto:
+        rl_in = UnityRLInputProto()
+        for b in vector_action:
+            n_agents = len(self._env_state[b][0])
+            if n_agents == 0:
+                continue
+            for i in range(n_agents):
+                action = AgentActionProto()
+                if vector_action[b].continuous is not None:
+                    action.vector_actions_deprecated.extend(
+                        vector_action[b].continuous[i]
+                    )
+                    action.continuous_actions.extend(vector_action[b].continuous[i])
+                if vector_action[b].discrete is not None:
+                    action.vector_actions_deprecated.extend(
+                        vector_action[b].discrete[i]
+                    )
+                    action.discrete_actions.extend(vector_action[b].discrete[i])
+                rl_in.agent_actions[b].value.extend([action])
+                rl_in.command = STEP
+        rl_in.side_channel = bytes(
+            self._side_channel_manager.generate_side_channel_messages()
+        )
+        return self._wrap_unity_input(rl_in)
+
+    def _generate_reset_input(self) -> UnityInputProto:
+        rl_in = UnityRLInputProto()
+        rl_in.command = RESET
+        rl_in.side_channel = bytes(
+            self._side_channel_manager.generate_side_channel_messages()
+        )
+        return self._wrap_unity_input(rl_in)
+
+    def _send_academy_parameters(
+        self, init_parameters: UnityRLInitializationInputProto
+    ) -> UnityOutputProto:
+        inputs = UnityInputProto()
+        inputs.rl_initialization_input.CopyFrom(init_parameters)
+        return self._communicator.initialize(inputs, self._poll_process)
+
+    @staticmethod
+    def _wrap_unity_input(rl_input: UnityRLInputProto) -> UnityInputProto:
+        result = UnityInputProto()
+        result.rl_input.CopyFrom(rl_input)
+        return result
+
+    @staticmethod
+    def _returncode_to_signal_name(returncode: int) -> Optional[str]:
+        """
+        Try to convert return codes into their corresponding signal name.
+        E.g. returncode_to_signal_name(-2) -> "SIGINT"
+        """
+        try:
+            # A negative value -N indicates that the child was terminated by signal N (POSIX only).
+            s = signal.Signals(-returncode)
+            return s.name
+        except Exception:
+            # Should generally be a ValueError, but catch everything just in case.
+            return None
+
+    @staticmethod
+    def _returncode_to_env_message(returncode: int) -> str:
+        signal_name = UnityEnvironment._returncode_to_signal_name(returncode)
+        signal_name = f" ({signal_name})" if signal_name else ""
+        return f"Environment shut down with return code {returncode}{signal_name}."
diff --git a/ml-agents-envs/mlagents_envs/envs/__init__.py b/ml-agents-envs/mlagents_envs/envs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a4f89414563d505de11853ace56ff470a83ed
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/__init__.py
@@ -0,0 +1,15 @@
+from mlagents_envs.registry import default_registry
+from mlagents_envs.envs.pettingzoo_env_factory import logger, PettingZooEnvFactory
+
+# Register each environment in default_registry as a PettingZooEnv
+for key in default_registry:
+    env_name = key
+    if key[0].isdigit():
+        env_name = key.replace("3", "Three")
+    if not env_name.isidentifier():
+        logger.warning(
+            f"Environment id {env_name} can not be registered since it is"
+            f"not a valid identifier name."
+        )
+        continue
+    locals()[env_name] = PettingZooEnvFactory(key)
diff --git a/ml-agents-envs/mlagents_envs/envs/env_helpers.py b/ml-agents-envs/mlagents_envs/envs/env_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..768e6706038b5cb1030d26f8f0efc40cfe695435
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/env_helpers.py
@@ -0,0 +1,76 @@
+from urllib.parse import urlparse, parse_qs
+
+
+def _behavior_to_agent_id(behavior_name: str, unique_id: int) -> str:
+    return f"{behavior_name}?agent_id={unique_id}"
+
+
+def _agent_id_to_behavior(agent_id: str) -> str:
+    return agent_id.split("?agent_id=")[0]
+
+
+def _unwrap_batch_steps(batch_steps, behavior_name):
+    decision_batch, termination_batch = batch_steps
+    decision_id = [
+        _behavior_to_agent_id(behavior_name, i) for i in decision_batch.agent_id
+    ]
+    termination_id = [
+        _behavior_to_agent_id(behavior_name, i) for i in termination_batch.agent_id
+    ]
+    agents = decision_id + termination_id
+    obs = {
+        agent_id: [batch_obs[i] for batch_obs in termination_batch.obs]
+        for i, agent_id in enumerate(termination_id)
+    }
+    if decision_batch.action_mask is not None:
+        obs.update(
+            {
+                agent_id: {
+                    "observation": [batch_obs[i] for batch_obs in decision_batch.obs],
+                    "action_mask": [mask[i] for mask in decision_batch.action_mask],
+                }
+                for i, agent_id in enumerate(decision_id)
+            }
+        )
+    else:
+        obs.update(
+            {
+                agent_id: [batch_obs[i] for batch_obs in decision_batch.obs]
+                for i, agent_id in enumerate(decision_id)
+            }
+        )
+    obs = {k: v if len(v) > 1 else v[0] for k, v in obs.items()}
+    dones = {agent_id: True for agent_id in termination_id}
+    dones.update({agent_id: False for agent_id in decision_id})
+    rewards = {
+        agent_id: termination_batch.reward[i]
+        for i, agent_id in enumerate(termination_id)
+    }
+    rewards.update(
+        {agent_id: decision_batch.reward[i] for i, agent_id in enumerate(decision_id)}
+    )
+    cumulative_rewards = {k: v for k, v in rewards.items()}
+    infos = {}
+    for i, agent_id in enumerate(decision_id):
+        infos[agent_id] = {}
+        infos[agent_id]["behavior_name"] = behavior_name
+        infos[agent_id]["group_id"] = decision_batch.group_id[i]
+        infos[agent_id]["group_reward"] = decision_batch.group_reward[i]
+    for i, agent_id in enumerate(termination_id):
+        infos[agent_id] = {}
+        infos[agent_id]["behavior_name"] = behavior_name
+        infos[agent_id]["group_id"] = termination_batch.group_id[i]
+        infos[agent_id]["group_reward"] = termination_batch.group_reward[i]
+        infos[agent_id]["interrupted"] = termination_batch.interrupted[i]
+    id_map = {agent_id: i for i, agent_id in enumerate(decision_id)}
+    return agents, obs, dones, rewards, cumulative_rewards, infos, id_map
+
+
+def _parse_behavior(full_behavior):
+    parsed = urlparse(full_behavior)
+    name = parsed.path
+    ids = parse_qs(parsed.query)
+    team_id: int = 0
+    if "team" in ids:
+        team_id = int(ids["team"][0])
+    return name, team_id
diff --git a/ml-agents-envs/mlagents_envs/envs/pettingzoo_env_factory.py b/ml-agents-envs/mlagents_envs/envs/pettingzoo_env_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..aae82d36e81ef1d4e67913b673b63edeaec2dff9
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/pettingzoo_env_factory.py
@@ -0,0 +1,50 @@
+from typing import Optional, Union, List
+
+from mlagents_envs import logging_util
+from mlagents_envs.exception import UnityWorkerInUseException
+from mlagents_envs.registry import default_registry
+from mlagents_envs.side_channel.engine_configuration_channel import (
+    EngineConfigurationChannel,
+)
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+from mlagents_envs.side_channel.stats_side_channel import StatsSideChannel
+from mlagents_envs.envs.unity_aec_env import UnityAECEnv
+
+logger = logging_util.get_logger(__name__)
+
+
+class PettingZooEnvFactory:
+    def __init__(self, env_id: str) -> None:
+        self.env_id = env_id
+
+    def env(
+        self, seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]
+    ) -> UnityAECEnv:
+        """
+        Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper
+        :param seed: The seed for the action spaces of the agents.
+        :param kwargs: Any argument accepted by `UnityEnvironment`class except file_name
+        """
+        # If not side_channels specified, add the followings
+        if "side_channels" not in kwargs:
+            kwargs["side_channels"] = [
+                EngineConfigurationChannel(),
+                EnvironmentParametersChannel(),
+                StatsSideChannel(),
+            ]
+        _env = None
+        # If no base port argument is provided, try ports starting at 6000 until one is free
+        if "base_port" not in kwargs:
+            port = 6000
+            while _env is None:
+                try:
+                    kwargs["base_port"] = port
+                    _env = default_registry[self.env_id].make(**kwargs)
+                except UnityWorkerInUseException:
+                    port += 1
+                    pass
+        else:
+            _env = default_registry[self.env_id].make(**kwargs)
+        return UnityAECEnv(_env, seed)
diff --git a/ml-agents-envs/mlagents_envs/envs/unity_aec_env.py b/ml-agents-envs/mlagents_envs/envs/unity_aec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bb6fdf390998755b9443c6e0d687175cb41f7cc
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/unity_aec_env.py
@@ -0,0 +1,72 @@
+from typing import Any, Optional
+from gym import error
+from mlagents_envs.base_env import BaseEnv
+from pettingzoo import AECEnv
+
+from mlagents_envs.envs.unity_pettingzoo_base_env import UnityPettingzooBaseEnv
+
+
+class UnityAECEnv(UnityPettingzooBaseEnv, AECEnv):
+    """
+    Unity AEC (PettingZoo) environment wrapper.
+    """
+
+    def __init__(self, env: BaseEnv, seed: Optional[int] = None):
+        """
+        Initializes a Unity AEC environment wrapper.
+
+        :param env: The UnityEnvironment that is being wrapped.
+        :param seed: The seed for the action spaces of the agents.
+        """
+        super().__init__(env, seed)
+
+    def step(self, action: Any) -> None:
+        """
+        Sets the action of the active agent and get the observation, reward, done
+        and info of the next agent.
+        :param action: The action for the active agent
+        """
+        self._assert_loaded()
+        if len(self._live_agents) <= 0:
+            raise error.Error(
+                "You must reset the environment before you can perform a step"
+            )
+
+        # Process action
+        current_agent = self._agents[self._agent_index]
+        self._process_action(current_agent, action)
+
+        self._agent_index += 1
+        # Reset reward
+        for k in self._rewards.keys():
+            self._rewards[k] = 0
+
+        if self._agent_index >= len(self._agents) and self.num_agents > 0:
+            # The index is too high, time to set the action for the agents we have
+            self._step()
+            self._live_agents.sort()  # unnecessary, only for passing API test
+
+    def observe(self, agent_id):
+        """
+        Returns the observation an agent currently can make. `last()` calls this function.
+        """
+        return (
+            self._observations[agent_id],
+            self._cumm_rewards[agent_id],
+            self._dones[agent_id],
+            self._infos[agent_id],
+        )
+
+    def last(self, observe=True):
+        """
+        returns observation, cumulative reward, done, info for the current agent (specified by self.agent_selection)
+        """
+        obs, reward, done, info = self.observe(self._agents[self._agent_index])
+        return obs if observe else None, reward, done, info
+
+    @property
+    def agent_selection(self):
+        if not self._live_agents:
+            # If we had an agent finish then return that agent even though it isn't alive.
+            return self._agents[0]
+        return self._agents[self._agent_index]
diff --git a/ml-agents-envs/mlagents_envs/envs/unity_gym_env.py b/ml-agents-envs/mlagents_envs/envs/unity_gym_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..df29a95c9ab79d01389a2e66f9ebfcabc72c77ec
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/unity_gym_env.py
@@ -0,0 +1,360 @@
+import itertools
+
+import numpy as np
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import gym
+from gym import error, spaces
+
+from mlagents_envs.base_env import ActionTuple, BaseEnv
+from mlagents_envs.base_env import DecisionSteps, TerminalSteps
+from mlagents_envs import logging_util
+
+
+class UnityGymException(error.Error):
+    """
+    Any error related to the gym wrapper of ml-agents.
+    """
+
+    pass
+
+
+logger = logging_util.get_logger(__name__)
+GymStepResult = Tuple[np.ndarray, float, bool, Dict]
+
+
+class UnityToGymWrapper(gym.Env):
+    """
+    Provides Gym wrapper for Unity Learning Environments.
+    """
+
+    def __init__(
+        self,
+        unity_env: BaseEnv,
+        uint8_visual: bool = False,
+        flatten_branched: bool = False,
+        allow_multiple_obs: bool = False,
+        action_space_seed: Optional[int] = None,
+    ):
+        """
+        Environment initialization
+        :param unity_env: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes.
+        :param uint8_visual: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).
+        :param flatten_branched: If True, turn branched discrete action spaces into a Discrete space rather than
+            MultiDiscrete.
+        :param allow_multiple_obs: If True, return a list of np.ndarrays as observations with the first elements
+            containing the visual observations and the last element containing the array of vector observations.
+            If False, returns a single np.ndarray containing either only a single visual observation or the array of
+            vector observations.
+        :param action_space_seed: If non-None, will be used to set the random seed on created gym.Space instances.
+        """
+        self._env = unity_env
+
+        # Take a single step so that the brain information will be sent over
+        if not self._env.behavior_specs:
+            self._env.step()
+
+        self.visual_obs = None
+
+        # Save the step result from the last time all Agents requested decisions.
+        self._previous_decision_step: Optional[DecisionSteps] = None
+        self._flattener = None
+        # Hidden flag used by Atari environments to determine if the game is over
+        self.game_over = False
+        self._allow_multiple_obs = allow_multiple_obs
+
+        # Check brain configuration
+        if len(self._env.behavior_specs) != 1:
+            raise UnityGymException(
+                "There can only be one behavior in a UnityEnvironment "
+                "if it is wrapped in a gym."
+            )
+
+        self.name = list(self._env.behavior_specs.keys())[0]
+        self.group_spec = self._env.behavior_specs[self.name]
+
+        if self._get_n_vis_obs() == 0 and self._get_vec_obs_size() == 0:
+            raise UnityGymException(
+                "There are no observations provided by the environment."
+            )
+
+        if not self._get_n_vis_obs() >= 1 and uint8_visual:
+            logger.warning(
+                "uint8_visual was set to true, but visual observations are not in use. "
+                "This setting will not have any effect."
+            )
+        else:
+            self.uint8_visual = uint8_visual
+        if (
+            self._get_n_vis_obs() + self._get_vec_obs_size() >= 2
+            and not self._allow_multiple_obs
+        ):
+            logger.warning(
+                "The environment contains multiple observations. "
+                "You must define allow_multiple_obs=True to receive them all. "
+                "Otherwise, only the first visual observation (or vector observation if"
+                "there are no visual observations) will be provided in the observation."
+            )
+
+        # Check for number of agents in scene.
+        self._env.reset()
+        decision_steps, _ = self._env.get_steps(self.name)
+        self._check_agents(len(decision_steps))
+        self._previous_decision_step = decision_steps
+
+        # Set action spaces
+        if self.group_spec.action_spec.is_discrete():
+            self.action_size = self.group_spec.action_spec.discrete_size
+            branches = self.group_spec.action_spec.discrete_branches
+            if self.group_spec.action_spec.discrete_size == 1:
+                self._action_space = spaces.Discrete(branches[0])
+            else:
+                if flatten_branched:
+                    self._flattener = ActionFlattener(branches)
+                    self._action_space = self._flattener.action_space
+                else:
+                    self._action_space = spaces.MultiDiscrete(branches)
+
+        elif self.group_spec.action_spec.is_continuous():
+            if flatten_branched:
+                logger.warning(
+                    "The environment has a non-discrete action space. It will "
+                    "not be flattened."
+                )
+
+            self.action_size = self.group_spec.action_spec.continuous_size
+            high = np.array([1] * self.group_spec.action_spec.continuous_size)
+            self._action_space = spaces.Box(-high, high, dtype=np.float32)
+        else:
+            raise UnityGymException(
+                "The gym wrapper does not provide explicit support for both discrete "
+                "and continuous actions."
+            )
+
+        if action_space_seed is not None:
+            self._action_space.seed(action_space_seed)
+
+        # Set observations space
+        list_spaces: List[gym.Space] = []
+        shapes = self._get_vis_obs_shape()
+        for shape in shapes:
+            if uint8_visual:
+                list_spaces.append(spaces.Box(0, 255, dtype=np.uint8, shape=shape))
+            else:
+                list_spaces.append(spaces.Box(0, 1, dtype=np.float32, shape=shape))
+        if self._get_vec_obs_size() > 0:
+            # vector observation is last
+            high = np.array([np.inf] * self._get_vec_obs_size())
+            list_spaces.append(spaces.Box(-high, high, dtype=np.float32))
+        if self._allow_multiple_obs:
+            self._observation_space = spaces.Tuple(list_spaces)
+        else:
+            self._observation_space = list_spaces[0]  # only return the first one
+
+    def reset(self) -> Union[List[np.ndarray], np.ndarray]:
+        """Resets the state of the environment and returns an initial observation.
+        Returns: observation (object/list): the initial observation of the
+        space.
+        """
+        self._env.reset()
+        decision_step, _ = self._env.get_steps(self.name)
+        n_agents = len(decision_step)
+        self._check_agents(n_agents)
+        self.game_over = False
+
+        res: GymStepResult = self._single_step(decision_step)
+        return res[0]
+
+    def step(self, action: List[Any]) -> GymStepResult:
+        """Run one timestep of the environment's dynamics. When end of
+        episode is reached, you are responsible for calling `reset()`
+        to reset this environment's state.
+        Accepts an action and returns a tuple (observation, reward, done, info).
+        Args:
+            action (object/list): an action provided by the environment
+        Returns:
+            observation (object/list): agent's observation of the current environment
+            reward (float/list) : amount of reward returned after previous action
+            done (boolean/list): whether the episode has ended.
+            info (dict): contains auxiliary diagnostic information.
+        """
+        if self.game_over:
+            raise UnityGymException(
+                "You are calling 'step()' even though this environment has already "
+                "returned done = True. You must always call 'reset()' once you "
+                "receive 'done = True'."
+            )
+        if self._flattener is not None:
+            # Translate action into list
+            action = self._flattener.lookup_action(action)
+
+        action = np.array(action).reshape((1, self.action_size))
+
+        action_tuple = ActionTuple()
+        if self.group_spec.action_spec.is_continuous():
+            action_tuple.add_continuous(action)
+        else:
+            action_tuple.add_discrete(action)
+        self._env.set_actions(self.name, action_tuple)
+
+        self._env.step()
+        decision_step, terminal_step = self._env.get_steps(self.name)
+        self._check_agents(max(len(decision_step), len(terminal_step)))
+        if len(terminal_step) != 0:
+            # The agent is done
+            self.game_over = True
+            return self._single_step(terminal_step)
+        else:
+            return self._single_step(decision_step)
+
+    def _single_step(self, info: Union[DecisionSteps, TerminalSteps]) -> GymStepResult:
+        if self._allow_multiple_obs:
+            visual_obs = self._get_vis_obs_list(info)
+            visual_obs_list = []
+            for obs in visual_obs:
+                visual_obs_list.append(self._preprocess_single(obs[0]))
+            default_observation = visual_obs_list
+            if self._get_vec_obs_size() >= 1:
+                default_observation.append(self._get_vector_obs(info)[0, :])
+        else:
+            if self._get_n_vis_obs() >= 1:
+                visual_obs = self._get_vis_obs_list(info)
+                default_observation = self._preprocess_single(visual_obs[0][0])
+            else:
+                default_observation = self._get_vector_obs(info)[0, :]
+
+        if self._get_n_vis_obs() >= 1:
+            visual_obs = self._get_vis_obs_list(info)
+            self.visual_obs = self._preprocess_single(visual_obs[0][0])
+
+        done = isinstance(info, TerminalSteps)
+
+        return (default_observation, info.reward[0], done, {"step": info})
+
+    def _preprocess_single(self, single_visual_obs: np.ndarray) -> np.ndarray:
+        if self.uint8_visual:
+            return (255.0 * single_visual_obs).astype(np.uint8)
+        else:
+            return single_visual_obs
+
+    def _get_n_vis_obs(self) -> int:
+        result = 0
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 3:
+                result += 1
+        return result
+
+    def _get_vis_obs_shape(self) -> List[Tuple]:
+        result: List[Tuple] = []
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 3:
+                result.append(obs_spec.shape)
+        return result
+
+    def _get_vis_obs_list(
+        self, step_result: Union[DecisionSteps, TerminalSteps]
+    ) -> List[np.ndarray]:
+        result: List[np.ndarray] = []
+        for obs in step_result.obs:
+            if len(obs.shape) == 4:
+                result.append(obs)
+        return result
+
+    def _get_vector_obs(
+        self, step_result: Union[DecisionSteps, TerminalSteps]
+    ) -> np.ndarray:
+        result: List[np.ndarray] = []
+        for obs in step_result.obs:
+            if len(obs.shape) == 2:
+                result.append(obs)
+        return np.concatenate(result, axis=1)
+
+    def _get_vec_obs_size(self) -> int:
+        result = 0
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 1:
+                result += obs_spec.shape[0]
+        return result
+
+    def render(self, mode="rgb_array"):
+        """
+        Return the latest visual observations.
+        Note that it will not render a new frame of the environment.
+        """
+        return self.visual_obs
+
+    def close(self) -> None:
+        """Override _close in your subclass to perform any necessary cleanup.
+        Environments will automatically close() themselves when
+        garbage collected or when the program exits.
+        """
+        self._env.close()
+
+    def seed(self, seed: Any = None) -> None:
+        """Sets the seed for this env's random number generator(s).
+        Currently not implemented.
+        """
+        logger.warning("Could not seed environment %s", self.name)
+        return
+
+    @staticmethod
+    def _check_agents(n_agents: int) -> None:
+        if n_agents > 1:
+            raise UnityGymException(
+                f"There can only be one Agent in the environment but {n_agents} were detected."
+            )
+
+    @property
+    def metadata(self):
+        return {"render.modes": ["rgb_array"]}
+
+    @property
+    def reward_range(self) -> Tuple[float, float]:
+        return -float("inf"), float("inf")
+
+    @property
+    def action_space(self) -> gym.Space:
+        return self._action_space
+
+    @property
+    def observation_space(self):
+        return self._observation_space
+
+
+class ActionFlattener:
+    """
+    Flattens branched discrete action spaces into single-branch discrete action spaces.
+    """
+
+    def __init__(self, branched_action_space):
+        """
+        Initialize the flattener.
+        :param branched_action_space: A List containing the sizes of each branch of the action
+        space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.
+        """
+        self._action_shape = branched_action_space
+        self.action_lookup = self._create_lookup(self._action_shape)
+        self.action_space = spaces.Discrete(len(self.action_lookup))
+
+    @classmethod
+    def _create_lookup(self, branched_action_space):
+        """
+        Creates a Dict that maps discrete actions (scalars) to branched actions (lists).
+        Each key in the Dict maps to one unique set of branched actions, and each value
+        contains the List of branched actions.
+        """
+        possible_vals = [range(_num) for _num in branched_action_space]
+        all_actions = [list(_action) for _action in itertools.product(*possible_vals)]
+        # Dict should be faster than List for large action spaces
+        action_lookup = {
+            _scalar: _action for (_scalar, _action) in enumerate(all_actions)
+        }
+        return action_lookup
+
+    def lookup_action(self, action):
+        """
+        Convert a scalar discrete action into a unique set of branched actions.
+        :param action: A scalar value representing one of the discrete actions.
+        :returns: The List containing the branched actions.
+        """
+        return self.action_lookup[action]
diff --git a/ml-agents-envs/mlagents_envs/envs/unity_parallel_env.py b/ml-agents-envs/mlagents_envs/envs/unity_parallel_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..09398d27fa8369d3af63629d18b93fca8dc218a7
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/unity_parallel_env.py
@@ -0,0 +1,53 @@
+from typing import Optional, Dict, Any, Tuple
+from gym import error
+from mlagents_envs.base_env import BaseEnv
+from pettingzoo import ParallelEnv
+
+from mlagents_envs.envs.unity_pettingzoo_base_env import UnityPettingzooBaseEnv
+
+
+class UnityParallelEnv(UnityPettingzooBaseEnv, ParallelEnv):
+    """
+    Unity Parallel (PettingZoo) environment wrapper.
+    """
+
+    def __init__(self, env: BaseEnv, seed: Optional[int] = None):
+        """
+        Initializes a Unity Parallel environment wrapper.
+
+        :param env: The UnityEnvironment that is being wrapped.
+        :param seed: The seed for the action spaces of the agents.
+        """
+        super().__init__(env, seed)
+
+    def reset(self) -> Dict[str, Any]:
+        """
+        Resets the environment.
+        """
+        super().reset()
+
+        return self._observations
+
+    def step(self, actions: Dict[str, Any]) -> Tuple:
+        self._assert_loaded()
+        if len(self._live_agents) <= 0 and actions:
+            raise error.Error(
+                "You must reset the environment before you can perform a step."
+            )
+
+        # Process actions
+        for current_agent, action in actions.items():
+            self._process_action(current_agent, action)
+
+        # Reset reward
+        for k in self._rewards.keys():
+            self._rewards[k] = 0
+
+        # Step environment
+        self._step()
+
+        # Agent cleanup and sorting
+        self._cleanup_agents()
+        self._live_agents.sort()  # unnecessary, only for passing API test
+
+        return self._observations, self._rewards, self._dones, self._infos
diff --git a/ml-agents-envs/mlagents_envs/envs/unity_pettingzoo_base_env.py b/ml-agents-envs/mlagents_envs/envs/unity_pettingzoo_base_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..3457f18c882643346c020fc1057a7d0c194e51f9
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/envs/unity_pettingzoo_base_env.py
@@ -0,0 +1,323 @@
+import atexit
+from typing import Optional, List, Set, Dict, Any, Tuple
+import numpy as np
+from gym import error, spaces
+from mlagents_envs.base_env import BaseEnv, ActionTuple
+from mlagents_envs.envs.env_helpers import _agent_id_to_behavior, _unwrap_batch_steps
+
+
+class UnityPettingzooBaseEnv:
+    """
+    Unity Petting Zoo base environment.
+    """
+
+    def __init__(
+        self, env: BaseEnv, seed: Optional[int] = None, metadata: Optional[dict] = None
+    ):
+        super().__init__()
+        atexit.register(self.close)
+        self._env = env
+        self.metadata = metadata
+        self._assert_loaded()
+
+        self._agent_index = 0
+        self._seed = seed
+        self._side_channel_dict = {
+            type(v).__name__: v
+            for v in self._env._side_channel_manager._side_channels_dict.values()  # type: ignore
+        }
+
+        self._live_agents: List[str] = []  # agent id for agents alive
+        self._agents: List[str] = []  # all agent id in current step
+        self._possible_agents: Set[str] = set()  # all agents that have ever appear
+        self._agent_id_to_index: Dict[str, int] = {}  # agent_id: index in decision step
+        self._observations: Dict[str, np.ndarray] = {}  # agent_id: obs
+        self._dones: Dict[str, bool] = {}  # agent_id: done
+        self._rewards: Dict[str, float] = {}  # agent_id: reward
+        self._cumm_rewards: Dict[str, float] = {}  # agent_id: reward
+        self._infos: Dict[str, Dict] = {}  # agent_id: info
+        self._action_spaces: Dict[str, spaces.Space] = {}  # behavior_name: action_space
+        self._observation_spaces: Dict[
+            str, spaces.Space
+        ] = {}  # behavior_name: obs_space
+        self._current_action: Dict[str, ActionTuple] = {}  # behavior_name: ActionTuple
+        # Take a single step so that the brain information will be sent over
+        if not self._env.behavior_specs:
+            self._env.step()
+            for behavior_name in self._env.behavior_specs.keys():
+                _, _, _ = self._batch_update(behavior_name)
+        self._update_observation_spaces()
+        self._update_action_spaces()
+
+    def _assert_loaded(self) -> None:
+        if self._env is None:
+            raise error.Error("No environment loaded")
+
+    @property
+    def observation_spaces(self) -> Dict[str, spaces.Space]:
+        """
+        Return the observation spaces of all the agents.
+        """
+        return {
+            agent_id: self._observation_spaces[_agent_id_to_behavior(agent_id)]
+            for agent_id in self._possible_agents
+        }
+
+    def observation_space(self, agent: str) -> Optional[spaces.Space]:
+        """
+        The observation space of the current agent.
+        """
+        behavior_name = _agent_id_to_behavior(agent)
+        return self._observation_spaces[behavior_name]
+
+    def _update_observation_spaces(self) -> None:
+        self._assert_loaded()
+        for behavior_name in self._env.behavior_specs.keys():
+            if behavior_name not in self._observation_spaces:
+                obs_spec = self._env.behavior_specs[behavior_name].observation_specs
+                obs_spaces = tuple(
+                    spaces.Box(
+                        low=-np.float32(np.inf),
+                        high=np.float32(np.inf),
+                        shape=spec.shape,
+                        dtype=np.float32,
+                    )
+                    for spec in obs_spec
+                )
+                if len(obs_spaces) == 1:
+                    self._observation_spaces[behavior_name] = obs_spaces[0]
+                else:
+                    self._observation_spaces[behavior_name] = spaces.Tuple(obs_spaces)
+
+    @property
+    def action_spaces(self) -> Dict[str, spaces.Space]:
+        """
+        Return the action spaces of all the agents.
+        """
+        return {
+            agent_id: self._action_spaces[_agent_id_to_behavior(agent_id)]
+            for agent_id in self._possible_agents
+        }
+
+    def action_space(self, agent: str) -> Optional[spaces.Space]:
+        """
+        The action space of the current agent.
+        """
+        behavior_name = _agent_id_to_behavior(agent)
+        return self._action_spaces[behavior_name]
+
+    def _update_action_spaces(self) -> None:
+        self._assert_loaded()
+        for behavior_name in self._env.behavior_specs.keys():
+            if behavior_name not in self._action_spaces:
+                act_spec = self._env.behavior_specs[behavior_name].action_spec
+                if (
+                    act_spec.continuous_size == 0
+                    and len(act_spec.discrete_branches) == 0
+                ):
+                    raise error.Error("No actions found")
+                if act_spec.discrete_size == 1:
+                    d_space = spaces.Discrete(act_spec.discrete_branches[0])
+                    if self._seed is not None:
+                        d_space.seed(self._seed)
+                    if act_spec.continuous_size == 0:
+                        self._action_spaces[behavior_name] = d_space
+                        continue
+                if act_spec.discrete_size > 0:
+                    d_space = spaces.MultiDiscrete(act_spec.discrete_branches)
+                    if self._seed is not None:
+                        d_space.seed(self._seed)
+                    if act_spec.continuous_size == 0:
+                        self._action_spaces[behavior_name] = d_space
+                        continue
+                if act_spec.continuous_size > 0:
+                    c_space = spaces.Box(
+                        -1, 1, (act_spec.continuous_size,), dtype=np.int32
+                    )
+                    if self._seed is not None:
+                        c_space.seed(self._seed)
+                    if len(act_spec.discrete_branches) == 0:
+                        self._action_spaces[behavior_name] = c_space
+                        continue
+                self._action_spaces[behavior_name] = spaces.Tuple((c_space, d_space))
+
+    def _process_action(self, current_agent, action):
+        current_action_space = self.action_space(current_agent)
+        # Convert actions
+        if action is not None:
+            if isinstance(action, Tuple):
+                action = tuple(np.array(a) for a in action)
+            else:
+                action = self._action_to_np(current_action_space, action)
+            if not current_action_space.contains(action):  # type: ignore
+                raise error.Error(
+                    f"Invalid action, got {action} but was expecting action from {self.action_space}"
+                )
+            if isinstance(current_action_space, spaces.Tuple):
+                action = ActionTuple(action[0], action[1])
+            elif isinstance(current_action_space, spaces.MultiDiscrete):
+                action = ActionTuple(None, action)
+            elif isinstance(current_action_space, spaces.Discrete):
+                action = ActionTuple(None, np.array(action).reshape(1, 1))
+            else:
+                action = ActionTuple(action, None)
+
+        if not self._dones[current_agent]:
+            current_behavior = _agent_id_to_behavior(current_agent)
+            current_index = self._agent_id_to_index[current_agent]
+            if action.continuous is not None:
+                self._current_action[current_behavior].continuous[
+                    current_index
+                ] = action.continuous[0]
+            if action.discrete is not None:
+                self._current_action[current_behavior].discrete[
+                    current_index
+                ] = action.discrete[0]
+        else:
+            self._live_agents.remove(current_agent)
+            del self._observations[current_agent]
+            del self._dones[current_agent]
+            del self._rewards[current_agent]
+            del self._cumm_rewards[current_agent]
+            del self._infos[current_agent]
+
+    def _step(self):
+        for behavior_name, actions in self._current_action.items():
+            self._env.set_actions(behavior_name, actions)
+        self._env.step()
+        self._reset_states()
+        for behavior_name in self._env.behavior_specs.keys():
+            dones, rewards, cumulative_rewards = self._batch_update(behavior_name)
+            self._dones.update(dones)
+            self._rewards.update(rewards)
+            self._cumm_rewards.update(cumulative_rewards)
+        self._agent_index = 0
+
+    def _cleanup_agents(self):
+        for current_agent, done in self.dones.items():
+            if done:
+                self._live_agents.remove(current_agent)
+
+    @property
+    def side_channel(self) -> Dict[str, Any]:
+        """
+        The side channels of the environment. You can access the side channels
+        of an environment with `env.side_channel[<name-of-channel>]`.
+        """
+        self._assert_loaded()
+        return self._side_channel_dict
+
+    @staticmethod
+    def _action_to_np(current_action_space, action):
+        return np.array(action, dtype=current_action_space.dtype)
+
+    def _create_empty_actions(self, behavior_name, num_agents):
+        a_spec = self._env.behavior_specs[behavior_name].action_spec
+        return ActionTuple(
+            np.zeros((num_agents, a_spec.continuous_size), dtype=np.float32),
+            np.zeros((num_agents, len(a_spec.discrete_branches)), dtype=np.int32),
+        )
+
+    @property
+    def _cumulative_rewards(self):
+        return self._cumm_rewards
+
+    def _reset_states(self):
+        self._live_agents = []
+        self._agents = []
+        self._observations = {}
+        self._dones = {}
+        self._rewards = {}
+        self._cumm_rewards = {}
+        self._infos = {}
+        self._agent_id_to_index = {}
+
+    def reset(self):
+        """
+        Resets the environment.
+        """
+        self._assert_loaded()
+        self._agent_index = 0
+        self._reset_states()
+        self._possible_agents = set()
+        self._env.reset()
+        for behavior_name in self._env.behavior_specs.keys():
+            _, _, _ = self._batch_update(behavior_name)
+        self._live_agents.sort()  # unnecessary, only for passing API test
+        self._dones = {agent: False for agent in self._agents}
+        self._rewards = {agent: 0 for agent in self._agents}
+        self._cumm_rewards = {agent: 0 for agent in self._agents}
+
+    def _batch_update(self, behavior_name):
+        current_batch = self._env.get_steps(behavior_name)
+        self._current_action[behavior_name] = self._create_empty_actions(
+            behavior_name, len(current_batch[0])
+        )
+        (
+            agents,
+            obs,
+            dones,
+            rewards,
+            cumulative_rewards,
+            infos,
+            id_map,
+        ) = _unwrap_batch_steps(current_batch, behavior_name)
+        self._live_agents += agents
+        self._agents += agents
+        self._observations.update(obs)
+        self._infos.update(infos)
+        self._agent_id_to_index.update(id_map)
+        self._possible_agents.update(agents)
+        return dones, rewards, cumulative_rewards
+
+    def seed(self, seed=None):
+        """
+        Reseeds the environment (making the resulting environment deterministic).
+        `reset()` must be called after `seed()`, and before `step()`.
+        """
+        self._seed = seed
+
+    def render(self, mode="human"):
+        """
+        NOT SUPPORTED.
+
+        Displays a rendered frame from the environment, if supported.
+        Alternate render modes in the default environments are `'rgb_array'`
+        which returns a numpy array and is supported by all environments outside of classic,
+        and `'ansi'` which returns the strings printed (specific to classic environments).
+        """
+        pass
+
+    @property
+    def dones(self):
+        return dict(self._dones)
+
+    @property
+    def agents(self):
+        return sorted(self._live_agents)
+
+    @property
+    def rewards(self):
+        return dict(self._rewards)
+
+    @property
+    def infos(self):
+        return dict(self._infos)
+
+    @property
+    def possible_agents(self):
+        return sorted(self._possible_agents)
+
+    def close(self) -> None:
+        """
+        Close the environment.
+        """
+        if self._env is not None:
+            self._env.close()
+            self._env = None  # type: ignore
+
+    def __del__(self) -> None:
+        self.close()
+
+    def state(self):
+        pass
diff --git a/ml-agents-envs/mlagents_envs/exception.py b/ml-agents-envs/mlagents_envs/exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..324cdd0969984da52f6736674edda66ee5a628d8
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/exception.py
@@ -0,0 +1,86 @@
+class UnityException(Exception):
+    """
+    Any error related to ml-agents environment.
+    """
+
+    pass
+
+
+class UnityEnvironmentException(UnityException):
+    """
+    Related to errors starting and closing environment.
+    """
+
+    pass
+
+
+class UnityCommunicationException(UnityException):
+    """
+    Related to errors with the communicator.
+    """
+
+    pass
+
+
+class UnityCommunicatorStoppedException(UnityException):
+    """
+    Raised when communicator has stopped gracefully.
+    """
+
+    pass
+
+
+class UnityObservationException(UnityException):
+    """
+    Related to errors with receiving observations.
+    """
+
+    pass
+
+
+class UnityActionException(UnityException):
+    """
+    Related to errors with sending actions.
+    """
+
+    pass
+
+
+class UnityTimeOutException(UnityException):
+    """
+    Related to errors with communication timeouts.
+    """
+
+    pass
+
+
+class UnitySideChannelException(UnityException):
+    """
+    Related to errors with side channels.
+    """
+
+    pass
+
+
+class UnityWorkerInUseException(UnityException):
+    """
+    This error occurs when the port for a certain worker ID is already reserved.
+    """
+
+    MESSAGE_TEMPLATE = (
+        "Couldn't start socket communication because worker number {} is still in use. "
+        "You may need to manually close a previously opened environment "
+        "or use a different worker number."
+    )
+
+    def __init__(self, worker_id):
+        message = self.MESSAGE_TEMPLATE.format(str(worker_id))
+        super().__init__(message)
+
+
+class UnityPolicyException(UnityException):
+    """
+    Related to errors with the Trainer.
+    """
+
+    pass
diff --git a/ml-agents-envs/mlagents_envs/logging_util.py b/ml-agents-envs/mlagents_envs/logging_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddd4e3b3cf56eed10afe4208b68ce89543e2377b
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/logging_util.py
@@ -0,0 +1,63 @@
+import logging  # noqa I251
+import sys
+
+CRITICAL = logging.CRITICAL
+FATAL = logging.FATAL
+ERROR = logging.ERROR
+WARNING = logging.WARNING
+INFO = logging.INFO
+DEBUG = logging.DEBUG
+NOTSET = logging.NOTSET
+
+_loggers = set()
+_log_level = NOTSET
+DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+DEBUG_LOG_FORMAT = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+LOG_FORMAT = "[%(levelname)s] %(message)s"
+
+
+def get_logger(name: str) -> logging.Logger:
+    """
+    Create a logger with the specified name. The logger will use the log level
+    specified by set_log_level()
+    """
+    logger = logging.getLogger(name=name)
+
+    if _log_level == DEBUG:
+        formatter = logging.Formatter(fmt=DEBUG_LOG_FORMAT, datefmt=DATE_FORMAT)
+    else:
+        formatter = logging.Formatter(fmt=LOG_FORMAT)
+    handler = logging.StreamHandler(stream=sys.stdout)
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+    # If we've already set the log level, make sure new loggers use it
+    if _log_level != NOTSET:
+        logger.setLevel(_log_level)
+
+    # Keep track of this logger so that we can change the log level later
+    _loggers.add(logger)
+    return logger
+
+
+def set_log_level(log_level: int) -> None:
+    """
+    Set the ML-Agents logging level. This will also configure the logging format (if it hasn't already been set).
+    """
+    global _log_level
+    _log_level = log_level
+
+    for logger in _loggers:
+        logger.setLevel(log_level)
+
+    if log_level == DEBUG:
+        formatter = logging.Formatter(fmt=DEBUG_LOG_FORMAT, datefmt=DATE_FORMAT)
+    else:
+        formatter = logging.Formatter(LOG_FORMAT)
+    _set_formatter_for_all_loggers(formatter)
+
+
+def _set_formatter_for_all_loggers(formatter: logging.Formatter) -> None:
+    for logger in _loggers:
+        for handler in logger.handlers[:]:
+            handler.setFormatter(formatter)
diff --git a/ml-agents-envs/mlagents_envs/mock_communicator.py b/ml-agents-envs/mlagents_envs/mock_communicator.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e425e2759e354d80b16f19b350743ea61acb7e8
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/mock_communicator.py
@@ -0,0 +1,111 @@
+from typing import Optional
+
+from .communicator import Communicator, PollCallback
+from .environment import UnityEnvironment
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
+    BrainParametersProto,
+    ActionSpecProto,
+)
+from mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2 import (
+    UnityRLInitializationOutputProto,
+)
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
+    ObservationProto,
+    NONE as COMPRESSION_TYPE_NONE,
+    PNG as COMPRESSION_TYPE_PNG,
+)
+
+
+class MockCommunicator(Communicator):
+    def __init__(
+        self,
+        discrete_action=False,
+        visual_inputs=0,
+        num_agents=3,
+        brain_name="RealFakeBrain",
+        vec_obs_size=3,
+    ):
+        """
+        Python side of the grpc communication. Python is the client and Unity the server
+        """
+        super().__init__()
+        self.is_discrete = discrete_action
+        self.steps = 0
+        self.visual_inputs = visual_inputs
+        self.has_been_closed = False
+        self.num_agents = num_agents
+        self.brain_name = brain_name
+        self.vec_obs_size = vec_obs_size
+
+    def initialize(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> UnityOutputProto:
+        if self.is_discrete:
+            action_spec = ActionSpecProto(
+                num_discrete_actions=2, discrete_branch_sizes=[3, 2]
+            )
+        else:
+            action_spec = ActionSpecProto(num_continuous_actions=2)
+        bp = BrainParametersProto(
+            brain_name=self.brain_name, is_training=True, action_spec=action_spec
+        )
+        rl_init = UnityRLInitializationOutputProto(
+            name="RealFakeAcademy",
+            communication_version=UnityEnvironment.API_VERSION,
+            package_version="mock_package_version",
+            log_path="",
+            brain_parameters=[bp],
+        )
+        output = UnityRLOutputProto(agentInfos=self._get_agent_infos())
+        return UnityOutputProto(rl_initialization_output=rl_init, rl_output=output)
+
+    def _get_agent_infos(self):
+        dict_agent_info = {}
+        list_agent_info = []
+        vector_obs = [1, 2, 3]
+
+        observations = [
+            ObservationProto(
+                compressed_data=None,
+                shape=[30, 40, 3],
+                compression_type=COMPRESSION_TYPE_PNG,
+            )
+            for _ in range(self.visual_inputs)
+        ]
+        vector_obs_proto = ObservationProto(
+            float_data=ObservationProto.FloatData(data=vector_obs),
+            shape=[len(vector_obs)],
+            compression_type=COMPRESSION_TYPE_NONE,
+        )
+        observations.append(vector_obs_proto)
+
+        for i in range(self.num_agents):
+            list_agent_info.append(
+                AgentInfoProto(
+                    reward=1,
+                    done=(i == 2),
+                    max_step_reached=False,
+                    id=i,
+                    observations=observations,
+                )
+            )
+        dict_agent_info["RealFakeBrain"] = UnityRLOutputProto.ListAgentInfoProto(
+            value=list_agent_info
+        )
+        return dict_agent_info
+
+    def exchange(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> UnityOutputProto:
+        result = UnityRLOutputProto(agentInfos=self._get_agent_infos())
+        return UnityOutputProto(rl_output=result)
+
+    def close(self):
+        """
+        Sends a shutdown signal to the unity environment, and closes the grpc connection.
+        """
+        self.has_been_closed = True
diff --git a/ml-agents-envs/mlagents_envs/registry/__init__.py b/ml-agents-envs/mlagents_envs/registry/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..74c0e8a7090fbf045dedf89409459e69263794ef
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/registry/__init__.py
@@ -0,0 +1,4 @@
+from mlagents_envs.registry.unity_env_registry import (  # noqa F401
+    default_registry,
+    UnityEnvRegistry,
+)
diff --git a/ml-agents-envs/mlagents_envs/registry/base_registry_entry.py b/ml-agents-envs/mlagents_envs/registry/base_registry_entry.py
new file mode 100644
index 0000000000000000000000000000000000000000..f72009f2a950749e199cacf800fa7cbce9a95e33
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/registry/base_registry_entry.py
@@ -0,0 +1,56 @@
+from abc import abstractmethod
+from typing import Any, Optional
+from mlagents_envs.base_env import BaseEnv
+
+
+class BaseRegistryEntry:
+    def __init__(
+        self,
+        identifier: str,
+        expected_reward: Optional[float],
+        description: Optional[str],
+    ):
+        """
+        BaseRegistryEntry allows launching a Unity Environment with its make method.
+        :param identifier: The name of the Unity Environment.
+        :param expected_reward: The cumulative reward that an Agent must receive
+        for the task to be considered solved.
+        :param description: A description of the Unity Environment. Contains human
+        readable information about potential special arguments that the make method can
+        take as well as information regarding the observation, reward, actions,
+        behaviors and number of agents in the Environment.
+        """
+        self._identifier = identifier
+        self._expected_reward = expected_reward
+        self._description = description
+
+    @property
+    def identifier(self) -> str:
+        """
+        The unique identifier of the entry
+        """
+        return self._identifier
+
+    @property
+    def expected_reward(self) -> Optional[float]:
+        """
+        The cumulative reward that an Agent must receive for the task to be considered
+        solved.
+        """
+        return self._expected_reward
+
+    @property
+    def description(self) -> Optional[str]:
+        """
+        A description of the Unity Environment the entry can make.
+        """
+        return self._description
+
+    @abstractmethod
+    def make(self, **kwargs: Any) -> BaseEnv:
+        """
+        This method creates a Unity BaseEnv (usually a UnityEnvironment).
+        """
+        raise NotImplementedError(
+            f"The make() method not implemented for entry {self.identifier}"
+        )
diff --git a/ml-agents-envs/mlagents_envs/registry/binary_utils.py b/ml-agents-envs/mlagents_envs/registry/binary_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c397e1628d3b5faa736a594bb81e89f7ff20f1ae
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/registry/binary_utils.py
@@ -0,0 +1,259 @@
+import urllib.request
+import tempfile
+import os
+import uuid
+import shutil
+import glob
+
+import yaml
+import hashlib
+
+from zipfile import ZipFile
+from sys import platform
+from typing import Tuple, Optional, Dict, Any
+
+from filelock import FileLock
+
+from mlagents_envs.env_utils import validate_environment_path
+
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+# The default logical block size is 8192 bytes (8 KB) for UFS file systems.
+BLOCK_SIZE = 8192
+
+
+def get_local_binary_path(name: str, url: str, tmp_dir: Optional[str] = None) -> str:
+    """
+    Returns the path to the executable previously downloaded with the name argument. If
+    None is found, the executable at the url argument will be downloaded and stored
+    under name for future uses.
+    :param name: The name that will be given to the folder containing the extracted data
+    :param url: The URL of the zip file
+    :param: tmp_dir: Optional override for the temporary directory to save binaries and zips in.
+    """
+    NUMBER_ATTEMPTS = 5
+    tmp_dir = tmp_dir or tempfile.gettempdir()
+    lock = FileLock(os.path.join(tmp_dir, name + ".lock"))
+    with lock:
+        path = get_local_binary_path_if_exists(name, url, tmp_dir=tmp_dir)
+        if path is None:
+            logger.debug(
+                f"Local environment {name} not found, downloading environment from {url}"
+            )
+        for attempt in range(
+            NUMBER_ATTEMPTS
+        ):  # Perform 5 attempts at downloading the file
+            if path is not None:
+                break
+            try:
+                download_and_extract_zip(url, name, tmp_dir=tmp_dir)
+            except Exception:
+                if attempt + 1 < NUMBER_ATTEMPTS:
+                    logger.warning(
+                        f"Attempt {attempt + 1} / {NUMBER_ATTEMPTS}"
+                        ": Failed to download and extract binary."
+                    )
+                else:
+                    raise
+            path = get_local_binary_path_if_exists(name, url, tmp_dir=tmp_dir)
+
+    if path is None:
+        raise FileNotFoundError(
+            f"Binary not found, make sure {url} is a valid url to "
+            "a zip folder containing a valid Unity executable"
+        )
+    return path
+
+
+def get_local_binary_path_if_exists(name: str, url: str, tmp_dir: str) -> Optional[str]:
+    """
+    Recursively searches for a Unity executable in the extracted files folders. This is
+    platform dependent : It will only return a Unity executable compatible with the
+    computer's OS. If no executable is found, None will be returned.
+    :param name: The name/identifier of the executable
+    :param url: The url the executable was downloaded from (for verification)
+    :param: tmp_dir: Optional override for the temporary directory to save binaries and zips in.
+    """
+    _, bin_dir = get_tmp_dirs(tmp_dir)
+    extension = None
+
+    if platform == "linux" or platform == "linux2":
+        extension = "*.x86_64"
+    if platform == "darwin":
+        extension = "*.app"
+    if platform == "win32":
+        extension = "*.exe"
+    if extension is None:
+        raise NotImplementedError("No extensions found for this platform.")
+    url_hash = "-" + hashlib.md5(url.encode()).hexdigest()
+    path = os.path.join(bin_dir, name + url_hash, "**", extension)
+    candidates = glob.glob(path, recursive=True)
+    if len(candidates) == 0:
+        return None
+    else:
+        for c in candidates:
+            # Unity sometimes produces another .exe file that we must filter out
+            if "UnityCrashHandler64" not in c:
+                # If the file is not valid, return None and delete faulty directory
+                if validate_environment_path(c) is None:
+                    shutil.rmtree(c)
+                    return None
+                return c
+        return None
+
+
+def _get_tmp_dir_helper(tmp_dir: Optional[str] = None) -> Tuple[str, str]:
+    tmp_dir = tmp_dir or ("/tmp" if platform == "darwin" else tempfile.gettempdir())
+    MLAGENTS = "ml-agents-binaries"
+    TMP_FOLDER_NAME = "tmp"
+    BINARY_FOLDER_NAME = "binaries"
+    mla_directory = os.path.join(tmp_dir, MLAGENTS)
+    if not os.path.exists(mla_directory):
+        os.makedirs(mla_directory)
+        os.chmod(mla_directory, 16877)
+    zip_directory = os.path.join(tmp_dir, MLAGENTS, TMP_FOLDER_NAME)
+    if not os.path.exists(zip_directory):
+        os.makedirs(zip_directory)
+        os.chmod(zip_directory, 16877)
+    bin_directory = os.path.join(tmp_dir, MLAGENTS, BINARY_FOLDER_NAME)
+    if not os.path.exists(bin_directory):
+        os.makedirs(bin_directory)
+        os.chmod(bin_directory, 16877)
+    return zip_directory, bin_directory
+
+
+def get_tmp_dirs(tmp_dir: Optional[str] = None) -> Tuple[str, str]:
+    """
+    Returns the path to the folder containing the downloaded zip files and the extracted
+    binaries. If these folders do not exist, they will be created.
+    :retrun: Tuple containing path to : (zip folder, extracted files folder)
+    """
+    # TODO: Once we don't use python 3.7 we should just use exists_ok=True when creating the dirs to avoid this.
+    # Should only be able to error out 3 times (once for each subdir).
+    for _attempt in range(3):
+        try:
+            return _get_tmp_dir_helper(tmp_dir)
+        except FileExistsError:
+            continue
+    return _get_tmp_dir_helper(tmp_dir)
+
+
+def download_and_extract_zip(
+    url: str, name: str, tmp_dir: Optional[str] = None
+) -> None:
+    """
+    Downloads a zip file under a URL, extracts its contents into a folder with the name
+    argument and gives chmod 755 to all the files it contains. Files are downloaded and
+    extracted into special folders in the temp folder of the machine.
+    :param url: The URL of the zip file
+    :param name: The name that will be given to the folder containing the extracted data
+    :param: tmp_dir: Optional override for the temporary directory to save binaries and zips in.
+    """
+    zip_dir, bin_dir = get_tmp_dirs(tmp_dir)
+    url_hash = "-" + hashlib.md5(url.encode()).hexdigest()
+    binary_path = os.path.join(bin_dir, name + url_hash)
+    if os.path.exists(binary_path):
+        shutil.rmtree(binary_path)
+
+    # Download zip
+    try:
+        request = urllib.request.urlopen(url, timeout=30)
+    except urllib.error.HTTPError as e:  # type: ignore
+        e.reason = f"{e.reason} {url}"  # type: ignore
+        raise
+    zip_size = int(request.headers["content-length"])
+    zip_file_path = os.path.join(zip_dir, str(uuid.uuid4()) + ".zip")
+    with open(zip_file_path, "wb") as zip_file:
+        downloaded = 0
+        while True:
+            buffer = request.read(BLOCK_SIZE)
+            if not buffer:
+                # There is nothing more to read
+                break
+            downloaded += len(buffer)
+            zip_file.write(buffer)
+            downloaded_percent = downloaded / zip_size * 100
+            print_progress(f"  Downloading {name}", downloaded_percent)
+        print("")
+
+    # Extraction
+    with ZipFileWithProgress(zip_file_path, "r") as zip_ref:
+        zip_ref.extract_zip(f"  Extracting  {name}", binary_path)  # type: ignore
+    print("")
+
+    # Clean up zip
+    print_progress(f"  Cleaning up {name}", 0)
+    os.remove(zip_file_path)
+
+    # Give permission
+    for f in glob.glob(binary_path + "/**/*", recursive=True):
+        # 16877 is octal 40755, which denotes a directory with permissions 755
+        os.chmod(f, 16877)
+    print_progress(f"  Cleaning up {name}", 100)
+    print("")
+
+
+def print_progress(prefix: str, percent: float) -> None:
+    """
+    Displays a single progress bar in the terminal with value percent.
+    :param prefix: The string that will precede the progress bar.
+    :param percent: The percent progression of the bar (min is 0, max is 100)
+    """
+    BAR_LEN = 20
+    percent = min(100, max(0, percent))
+    bar_progress = min(int(percent / 100 * BAR_LEN), BAR_LEN)
+    bar = "|" + "\u2588" * bar_progress + " " * (BAR_LEN - bar_progress) + "|"
+    str_percent = "%3.0f%%" % percent
+    print(f"{prefix} : {bar} {str_percent} \r", end="", flush=True)
+
+
+def load_remote_manifest(url: str) -> Dict[str, Any]:
+    """
+    Converts a remote yaml file into a Python dictionary
+    """
+    tmp_dir, _ = get_tmp_dirs()
+    try:
+        request = urllib.request.urlopen(url, timeout=30)
+    except urllib.error.HTTPError as e:  # type: ignore
+        e.reason = f"{e.reason} {url}"  # type: ignore
+        raise
+    manifest_path = os.path.join(tmp_dir, str(uuid.uuid4()) + ".yaml")
+    with open(manifest_path, "wb") as manifest:
+        while True:
+            buffer = request.read(BLOCK_SIZE)
+            if not buffer:
+                # There is nothing more to read
+                break
+            manifest.write(buffer)
+    try:
+        result = load_local_manifest(manifest_path)
+    finally:
+        os.remove(manifest_path)
+    return result
+
+
+def load_local_manifest(path: str) -> Dict[str, Any]:
+    """
+    Converts a local yaml file into a Python dictionary
+    """
+    with open(path) as data_file:
+        return yaml.safe_load(data_file)
+
+
+class ZipFileWithProgress(ZipFile):
+    """
+    This is a helper class inheriting from ZipFile that allows to display a progress
+    bar while the files are being extracted.
+    """
+
+    def extract_zip(self, prefix: str, path: str) -> None:
+        members = self.namelist()
+        path = os.fspath(path)
+        total = len(members)
+        n = 0
+        for zipinfo in members:
+            self.extract(zipinfo, path, None)  # type: ignore
+            n += 1
+            print_progress(prefix, n / total * 100)
diff --git a/ml-agents-envs/mlagents_envs/registry/remote_registry_entry.py b/ml-agents-envs/mlagents_envs/registry/remote_registry_entry.py
new file mode 100644
index 0000000000000000000000000000000000000000..816d7331ba70e0faf26fbd7957ec888670cc123c
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/registry/remote_registry_entry.py
@@ -0,0 +1,75 @@
+from sys import platform
+from typing import Optional, Any, List
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.base_env import BaseEnv
+from mlagents_envs.registry.binary_utils import get_local_binary_path
+from mlagents_envs.registry.base_registry_entry import BaseRegistryEntry
+
+
+class RemoteRegistryEntry(BaseRegistryEntry):
+    def __init__(
+        self,
+        identifier: str,
+        expected_reward: Optional[float],
+        description: Optional[str],
+        linux_url: Optional[str],
+        darwin_url: Optional[str],
+        win_url: Optional[str],
+        additional_args: Optional[List[str]] = None,
+        tmp_dir: Optional[str] = None,
+    ):
+        """
+        A RemoteRegistryEntry is an implementation of BaseRegistryEntry that uses a
+        Unity executable downloaded from the internet to launch a UnityEnvironment.
+        __Note__: The url provided must be a link to a `.zip` file containing a single
+        compressed folder with the executable inside. There can only be one executable
+        in the folder and it must be at the root of the folder.
+        :param identifier: The name of the Unity Environment.
+        :param expected_reward: The cumulative reward that an Agent must receive
+        for the task to be considered solved.
+        :param description: A description of the Unity Environment. Contains human
+        readable information about potential special arguments that the make method can
+        take as well as information regarding the observation, reward, actions,
+        behaviors and number of agents in the Environment.
+        :param linux_url: The url of the Unity executable for the Linux platform
+        :param darwin_url: The url of the Unity executable for the OSX platform
+        :param win_url: The url of the Unity executable for the Windows platform
+        """
+        super().__init__(identifier, expected_reward, description)
+        self._linux_url = linux_url
+        self._darwin_url = darwin_url
+        self._win_url = win_url
+        self._add_args = additional_args
+        self._tmp_dir_override = tmp_dir
+
+    def make(self, **kwargs: Any) -> BaseEnv:
+        """
+        Returns the UnityEnvironment that corresponds to the Unity executable found at
+        the provided url. The arguments passed to this method will be passed to the
+        constructor of the UnityEnvironment (except for the file_name argument)
+        """
+        url = None
+        if platform == "linux" or platform == "linux2":
+            url = self._linux_url
+        if platform == "darwin":
+            url = self._darwin_url
+        if platform == "win32":
+            url = self._win_url
+        if url is None:
+            raise FileNotFoundError(
+                f"The entry {self.identifier} does not contain a valid url for this "
+                "platform"
+            )
+        path = get_local_binary_path(
+            self.identifier, url, tmp_dir=self._tmp_dir_override
+        )
+        if "file_name" in kwargs:
+            kwargs.pop("file_name")
+        args: List[str] = []
+        if "additional_args" in kwargs:
+            if kwargs["additional_args"] is not None:
+                args += kwargs["additional_args"]
+        if self._add_args is not None:
+            args += self._add_args
+        kwargs["additional_args"] = args
+        return UnityEnvironment(file_name=path, **kwargs)
diff --git a/ml-agents-envs/mlagents_envs/registry/unity_env_registry.py b/ml-agents-envs/mlagents_envs/registry/unity_env_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..639f85794774c9271cda215e2aaddbf3a5552852
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/registry/unity_env_registry.py
@@ -0,0 +1,125 @@
+from typing import Dict, Iterator, Any, List
+from collections.abc import Mapping
+from mlagents_envs.registry.base_registry_entry import BaseRegistryEntry
+from mlagents_envs.registry.binary_utils import (
+    load_local_manifest,
+    load_remote_manifest,
+)
+from mlagents_envs.registry.remote_registry_entry import RemoteRegistryEntry
+
+
+class UnityEnvRegistry(Mapping):
+    """
+    ### UnityEnvRegistry
+    Provides a library of Unity environments that can be launched without the need
+    of downloading the Unity Editor.
+    The UnityEnvRegistry implements a Map, to access an entry of the Registry, use:
+    ```python
+    registry = UnityEnvRegistry()
+    entry = registry[<environment_identifyier>]
+    ```
+    An entry has the following properties :
+     * `identifier` : Uniquely identifies this environment
+     * `expected_reward` : Corresponds to the reward an agent must obtained for the task
+     to be considered completed.
+     * `description` : A human readable description of the environment.
+
+    To launch a Unity environment from a registry entry, use the `make` method:
+    ```python
+    registry = UnityEnvRegistry()
+    env = registry[<environment_identifyier>].make()
+    ```
+    """
+
+    def __init__(self):
+        self._REGISTERED_ENVS: Dict[str, BaseRegistryEntry] = {}
+        self._manifests: List[str] = []
+        self._sync = True
+
+    def register(self, new_entry: BaseRegistryEntry) -> None:
+        """
+        Registers a new BaseRegistryEntry to the registry. The
+        BaseRegistryEntry.identifier value will be used as indexing key.
+        If two are more environments are registered under the same key, the most
+        recentry added will replace the others.
+        """
+        self._REGISTERED_ENVS[new_entry.identifier] = new_entry
+
+    def register_from_yaml(self, path_to_yaml: str) -> None:
+        """
+        Registers the environments listed in a yaml file (either local or remote). Note
+        that the entries are registered lazily: the registration will only happen when
+        an environment is accessed.
+        The yaml file must have the following format :
+        ```yaml
+        environments:
+        - <identifier of the first environment>:
+            expected_reward: <expected reward of the environment>
+            description: | <a multi line description of the environment>
+              <continued multi line description>
+            linux_url: <The url for the Linux executable zip file>
+            darwin_url: <The url for the OSX executable zip file>
+            win_url: <The url for the Windows executable zip file>
+
+        - <identifier of the second environment>:
+            expected_reward: <expected reward of the environment>
+            description: | <a multi line description of the environment>
+              <continued multi line description>
+            linux_url: <The url for the Linux executable zip file>
+            darwin_url: <The url for the OSX executable zip file>
+            win_url: <The url for the Windows executable zip file>
+
+        - ...
+        ```
+        :param path_to_yaml: A local path or url to the yaml file
+        """
+        self._manifests.append(path_to_yaml)
+        self._sync = False
+
+    def _load_all_manifests(self) -> None:
+        if not self._sync:
+            for path_to_yaml in self._manifests:
+                if path_to_yaml[:4] == "http":
+                    manifest = load_remote_manifest(path_to_yaml)
+                else:
+                    manifest = load_local_manifest(path_to_yaml)
+                for env in manifest["environments"]:
+                    remote_entry_args = list(env.values())[0]
+                    remote_entry_args["identifier"] = list(env.keys())[0]
+                    self.register(RemoteRegistryEntry(**remote_entry_args))
+            self._manifests = []
+            self._sync = True
+
+    def clear(self) -> None:
+        """
+        Deletes all entries in the registry.
+        """
+        self._REGISTERED_ENVS.clear()
+        self._manifests = []
+        self._sync = True
+
+    def __getitem__(self, identifier: str) -> BaseRegistryEntry:
+        """
+        Returns the BaseRegistryEntry with the provided identifier. BaseRegistryEntry
+        can then be used to make a Unity Environment.
+        :param identifier: The identifier of the BaseRegistryEntry
+        :returns: The associated BaseRegistryEntry
+        """
+        self._load_all_manifests()
+        if identifier not in self._REGISTERED_ENVS:
+            raise KeyError(f"The entry {identifier} is not present in the registry.")
+        return self._REGISTERED_ENVS[identifier]
+
+    def __len__(self) -> int:
+        self._load_all_manifests()
+        return len(self._REGISTERED_ENVS)
+
+    def __iter__(self) -> Iterator[Any]:
+        self._load_all_manifests()
+        yield from self._REGISTERED_ENVS
+
+
+default_registry = UnityEnvRegistry()
+default_registry.register_from_yaml(
+    "https://storage.googleapis.com/mlagents-test-environments/1.0.0/manifest.yaml"
+)  # noqa E501
diff --git a/ml-agents-envs/mlagents_envs/rpc_communicator.py b/ml-agents-envs/mlagents_envs/rpc_communicator.py
new file mode 100644
index 0000000000000000000000000000000000000000..13c0df08b1c36241dd3858c47dc82366746c87e9
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/rpc_communicator.py
@@ -0,0 +1,158 @@
+import grpc
+from typing import Optional
+
+from multiprocessing import Pipe
+from sys import platform
+import socket
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+from .communicator import Communicator, PollCallback
+from mlagents_envs.communicator_objects.unity_to_external_pb2_grpc import (
+    UnityToExternalProtoServicer,
+    add_UnityToExternalProtoServicer_to_server,
+)
+from mlagents_envs.communicator_objects.unity_message_pb2 import UnityMessageProto
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from .exception import UnityTimeOutException, UnityWorkerInUseException
+
+
+class UnityToExternalServicerImplementation(UnityToExternalProtoServicer):
+    def __init__(self):
+        self.parent_conn, self.child_conn = Pipe()
+
+    def Initialize(self, request, context):
+        self.child_conn.send(request)
+        return self.child_conn.recv()
+
+    def Exchange(self, request, context):
+        self.child_conn.send(request)
+        return self.child_conn.recv()
+
+
+class RpcCommunicator(Communicator):
+    def __init__(self, worker_id=0, base_port=5005, timeout_wait=30):
+        """
+        Python side of the grpc communication. Python is the server and Unity the client
+
+
+        :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
+        :int worker_id: Offset from base_port. Used for training multiple environments simultaneously.
+        :int timeout_wait: Timeout (in seconds) to wait for a response before exiting.
+        """
+        super().__init__(worker_id, base_port)
+        self.port = base_port + worker_id
+        self.worker_id = worker_id
+        self.timeout_wait = timeout_wait
+        self.server = None
+        self.unity_to_external = None
+        self.is_open = False
+        self.create_server()
+
+    def create_server(self):
+        """
+        Creates the GRPC server.
+        """
+        self.check_port(self.port)
+
+        try:
+            # Establish communication grpc
+            self.server = grpc.server(
+                thread_pool=ThreadPoolExecutor(max_workers=10),
+                options=(("grpc.so_reuseport", 1),),
+            )
+            self.unity_to_external = UnityToExternalServicerImplementation()
+            add_UnityToExternalProtoServicer_to_server(
+                self.unity_to_external, self.server
+            )
+            # Using unspecified address, which means that grpc is communicating on all IPs
+            # This is so that the docker container can connect.
+            self.server.add_insecure_port("[::]:" + str(self.port))
+            self.server.start()
+            self.is_open = True
+        except Exception:
+            raise UnityWorkerInUseException(self.worker_id)
+
+    def check_port(self, port):
+        """
+        Attempts to bind to the requested communicator port, checking if it is already in use.
+        """
+        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        if platform == "linux" or platform == "linux2":
+            # On linux, the port remains unusable for TIME_WAIT=60 seconds after closing
+            # SO_REUSEADDR frees the port right after closing the environment
+            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        try:
+            s.bind(("localhost", port))
+        except OSError:
+            raise UnityWorkerInUseException(self.worker_id)
+        finally:
+            s.close()
+
+    def poll_for_timeout(self, poll_callback: Optional[PollCallback] = None) -> None:
+        """
+        Polls the GRPC parent connection for data, to be used before calling recv.  This prevents
+        us from hanging indefinitely in the case where the environment process has died or was not
+        launched.
+
+        Additionally, a callback can be passed to periodically check the state of the environment.
+        This is used to detect the case when the environment dies without cleaning up the connection,
+        so that we can stop sooner and raise a more appropriate error.
+        """
+        deadline = time.monotonic() + self.timeout_wait
+        callback_timeout_wait = self.timeout_wait // 10
+        while time.monotonic() < deadline:
+            if self.unity_to_external.parent_conn.poll(callback_timeout_wait):
+                # Got an acknowledgment from the connection
+                return
+            if poll_callback:
+                # Fire the callback - if it detects something wrong, it should raise an exception.
+                poll_callback()
+
+        # Got this far without reading any data from the connection, so it must be dead.
+        raise UnityTimeOutException(
+            "The Unity environment took too long to respond. Make sure that :\n"
+            "\t The environment does not need user interaction to launch\n"
+            '\t The Agents\' Behavior Parameters > Behavior Type is set to "Default"\n'
+            "\t The environment and the Python interface have compatible versions.\n"
+            "\t If you're running on a headless server without graphics support, turn off display "
+            "by either passing --no-graphics option or build your Unity executable as server build."
+        )
+
+    def initialize(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> UnityOutputProto:
+        self.poll_for_timeout(poll_callback)
+        aca_param = self.unity_to_external.parent_conn.recv().unity_output
+        message = UnityMessageProto()
+        message.header.status = 200
+        message.unity_input.CopyFrom(inputs)
+        self.unity_to_external.parent_conn.send(message)
+        self.unity_to_external.parent_conn.recv()
+        return aca_param
+
+    def exchange(
+        self, inputs: UnityInputProto, poll_callback: Optional[PollCallback] = None
+    ) -> Optional[UnityOutputProto]:
+        message = UnityMessageProto()
+        message.header.status = 200
+        message.unity_input.CopyFrom(inputs)
+        self.unity_to_external.parent_conn.send(message)
+        self.poll_for_timeout(poll_callback)
+        output = self.unity_to_external.parent_conn.recv()
+        if output.header.status != 200:
+            return None
+        return output.unity_output
+
+    def close(self):
+        """
+        Sends a shutdown signal to the unity environment, and closes the grpc connection.
+        """
+        if self.is_open:
+            message_input = UnityMessageProto()
+            message_input.header.status = 400
+            self.unity_to_external.parent_conn.send(message_input)
+            self.unity_to_external.parent_conn.close()
+            self.server.stop(False)
+            self.is_open = False
diff --git a/ml-agents-envs/mlagents_envs/rpc_utils.py b/ml-agents-envs/mlagents_envs/rpc_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2e3d1d4684d998434d714092b0010bdd12653cb
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/rpc_utils.py
@@ -0,0 +1,431 @@
+from mlagents_envs.base_env import (
+    ActionSpec,
+    ObservationSpec,
+    DimensionProperty,
+    BehaviorSpec,
+    DecisionSteps,
+    TerminalSteps,
+    ObservationType,
+)
+from mlagents_envs.exception import UnityObservationException
+from mlagents_envs.timers import hierarchical_timer, timed
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
+    ObservationProto,
+    NONE as COMPRESSION_TYPE_NONE,
+)
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+import numpy as np
+import io
+from typing import cast, List, Tuple, Collection, Optional, Iterable
+from PIL import Image
+
+
+PNG_HEADER = b"\x89PNG\r\n\x1a\n"
+
+
+def behavior_spec_from_proto(
+    brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto
+) -> BehaviorSpec:
+    """
+    Converts brain parameter and agent info proto to BehaviorSpec object.
+    :param brain_param_proto: protobuf object.
+    :param agent_info: protobuf object.
+    :return: BehaviorSpec object.
+    """
+    observation_specs = []
+    for obs in agent_info.observations:
+        observation_specs.append(
+            ObservationSpec(
+                name=obs.name,
+                shape=tuple(obs.shape),
+                observation_type=ObservationType(obs.observation_type),
+                dimension_property=tuple(
+                    DimensionProperty(dim) for dim in obs.dimension_properties
+                )
+                if len(obs.dimension_properties) > 0
+                else (DimensionProperty.UNSPECIFIED,) * len(obs.shape),
+            )
+        )
+
+    # proto from communicator < v1.3 does not set action spec, use deprecated fields instead
+    if (
+        brain_param_proto.action_spec.num_continuous_actions == 0
+        and brain_param_proto.action_spec.num_discrete_actions == 0
+    ):
+        if brain_param_proto.vector_action_space_type_deprecated == 1:
+            action_spec = ActionSpec(
+                brain_param_proto.vector_action_size_deprecated[0], ()
+            )
+        else:
+            action_spec = ActionSpec(
+                0, tuple(brain_param_proto.vector_action_size_deprecated)
+            )
+    else:
+        action_spec_proto = brain_param_proto.action_spec
+        action_spec = ActionSpec(
+            action_spec_proto.num_continuous_actions,
+            tuple(branch for branch in action_spec_proto.discrete_branch_sizes),
+        )
+    return BehaviorSpec(observation_specs, action_spec)
+
+
+class OffsetBytesIO:
+    """
+    Simple file-like class that wraps a bytes, and allows moving its "start"
+    position in the bytes. This is only used for reading concatenated PNGs,
+    because Pillow always calls seek(0) at the start of reading.
+    """
+
+    __slots__ = ["fp", "offset"]
+
+    def __init__(self, data: bytes):
+        self.fp = io.BytesIO(data)
+        self.offset = 0
+
+    def seek(self, offset: int, whence: int = io.SEEK_SET) -> int:
+        if whence == io.SEEK_SET:
+            res = self.fp.seek(offset + self.offset)
+            return res - self.offset
+        raise NotImplementedError()
+
+    def tell(self) -> int:
+        return self.fp.tell() - self.offset
+
+    def read(self, size: int = -1) -> bytes:
+        return self.fp.read(size)
+
+    def original_tell(self) -> int:
+        """
+        Returns the offset into the original byte array
+        """
+        return self.fp.tell()
+
+
+@timed
+def process_pixels(
+    image_bytes: bytes, expected_channels: int, mappings: Optional[List[int]] = None
+) -> np.ndarray:
+    """
+    Converts byte array observation image into numpy array, re-sizes it,
+    and optionally converts it to grey scale
+    :param image_bytes: input byte array corresponding to image
+    :param expected_channels: Expected output channels
+    :return: processed numpy array of observation from environment
+    """
+    image_fp = OffsetBytesIO(image_bytes)
+
+    image_arrays = []
+    # Read the images back from the bytes (without knowing the sizes).
+    while True:
+        with hierarchical_timer("image_decompress"):
+            image = Image.open(image_fp)
+            # Normally Image loads lazily, load() forces it to do loading in the timer scope.
+            image.load()
+        image_arrays.append(np.array(image, dtype=np.float32) / 255.0)
+
+        # Look for the next header, starting from the current stream location
+        try:
+            new_offset = image_bytes.index(PNG_HEADER, image_fp.original_tell())
+            image_fp.offset = new_offset
+        except ValueError:
+            # Didn't find the header, so must be at the end.
+            break
+
+    if mappings is not None and len(mappings) > 0:
+        return _process_images_mapping(image_arrays, mappings)
+    else:
+        return _process_images_num_channels(image_arrays, expected_channels)
+
+
+def _process_images_mapping(image_arrays, mappings):
+    """
+    Helper function for processing decompressed images with compressed channel mappings.
+    """
+    image_arrays = np.concatenate(image_arrays, axis=2).transpose((2, 0, 1))
+
+    if len(mappings) != len(image_arrays):
+        raise UnityObservationException(
+            f"Compressed observation and its mapping had different number of channels - "
+            f"observation had {len(image_arrays)} channels but its mapping had {len(mappings)} channels"
+        )
+    if len({m for m in mappings if m > -1}) != max(mappings) + 1:
+        raise UnityObservationException(
+            f"Invalid Compressed Channel Mapping: the mapping {mappings} does not have the correct format."
+        )
+    if max(mappings) >= len(image_arrays):
+        raise UnityObservationException(
+            f"Invalid Compressed Channel Mapping: the mapping has index larger than the total "
+            f"number of channels in observation - mapping index {max(mappings)} is"
+            f"invalid for input observation with {len(image_arrays)} channels."
+        )
+
+    processed_image_arrays: List[np.array] = [[] for _ in range(max(mappings) + 1)]
+    for mapping_idx, img in zip(mappings, image_arrays):
+        if mapping_idx > -1:
+            processed_image_arrays[mapping_idx].append(img)
+
+    for i, img_array in enumerate(processed_image_arrays):
+        processed_image_arrays[i] = np.mean(img_array, axis=0)
+    img = np.stack(processed_image_arrays, axis=2)
+    return img
+
+
+def _process_images_num_channels(image_arrays, expected_channels):
+    """
+    Helper function for processing decompressed images with number of expected channels.
+    This is for old API without mapping provided. Use the first n channel, n=expected_channels.
+    """
+    if expected_channels == 1:
+        # Convert to grayscale
+        img = np.mean(image_arrays[0], axis=2)
+        img = np.reshape(img, [img.shape[0], img.shape[1], 1])
+    else:
+        img = np.concatenate(image_arrays, axis=2)
+        # We can drop additional channels since they may need to be added to include
+        # numbers of observation channels not divisible by 3.
+        actual_channels = list(img.shape)[2]
+        if actual_channels > expected_channels:
+            img = img[..., 0:expected_channels]
+    return img
+
+
+def _check_observations_match_spec(
+    obs_index: int,
+    observation_spec: ObservationSpec,
+    agent_info_list: Collection[AgentInfoProto],
+) -> None:
+    """
+    Check that all the observations match the expected size.
+    This gives a nicer error than a cryptic numpy error later.
+    """
+    expected_obs_shape = tuple(observation_spec.shape)
+    for agent_info in agent_info_list:
+        agent_obs_shape = tuple(agent_info.observations[obs_index].shape)
+        if expected_obs_shape != agent_obs_shape:
+            raise UnityObservationException(
+                f"Observation at index={obs_index} for agent with "
+                f"id={agent_info.id} didn't match the ObservationSpec. "
+                f"Expected shape {expected_obs_shape} but got {agent_obs_shape}."
+            )
+
+
+@timed
+def _observation_to_np_array(
+    obs: ObservationProto, expected_shape: Optional[Iterable[int]] = None
+) -> np.ndarray:
+    """
+    Converts observation proto into numpy array of the appropriate size.
+    :param obs: observation proto to be converted
+    :param expected_shape: optional shape information, used for sanity checks.
+    :return: processed numpy array of observation from environment
+    """
+    if expected_shape is not None:
+        if list(obs.shape) != list(expected_shape):
+            raise UnityObservationException(
+                f"Observation did not have the expected shape - got {obs.shape} but expected {expected_shape}"
+            )
+    expected_channels = obs.shape[2]
+    if obs.compression_type == COMPRESSION_TYPE_NONE:
+        img = np.array(obs.float_data.data, dtype=np.float32)
+        img = np.reshape(img, obs.shape)
+        return img
+    else:
+        img = process_pixels(
+            obs.compressed_data, expected_channels, list(obs.compressed_channel_mapping)
+        )
+        # Compare decompressed image size to observation shape and make sure they match
+        if list(obs.shape) != list(img.shape):
+            raise UnityObservationException(
+                f"Decompressed observation did not have the expected shape - "
+                f"decompressed had {img.shape} but expected {obs.shape}"
+            )
+        return img
+
+
+@timed
+def _process_maybe_compressed_observation(
+    obs_index: int,
+    observation_spec: ObservationSpec,
+    agent_info_list: Collection[AgentInfoProto],
+) -> np.ndarray:
+    shape = cast(Tuple[int, int, int], observation_spec.shape)
+    if len(agent_info_list) == 0:
+        return np.zeros((0, shape[0], shape[1], shape[2]), dtype=np.float32)
+
+    try:
+        batched_visual = [
+            _observation_to_np_array(agent_obs.observations[obs_index], shape)
+            for agent_obs in agent_info_list
+        ]
+    except ValueError:
+        # Try to get a more useful error message
+        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
+        # If that didn't raise anything, raise the original error
+        raise
+    return np.array(batched_visual, dtype=np.float32)
+
+
+def _raise_on_nan_and_inf(data: np.array, source: str) -> np.array:
+    # Check for NaNs or Infinite values in the observation or reward data.
+    # If there's a NaN in the observations, the np.mean() result will be NaN
+    # If there's an Infinite value (either sign) then the result will be Inf
+    # See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background
+    # Note that a very large values (larger than sqrt(float_max)) will result in an Inf value here
+    # Raise a Runtime error in the case that NaNs or Infinite values make it into the data.
+    if data.size == 0:
+        return data
+
+    d = np.mean(data)
+    has_nan = np.isnan(d)
+    has_inf = not np.isfinite(d)
+
+    if has_nan:
+        raise RuntimeError(f"The {source} provided had NaN values.")
+    if has_inf:
+        raise RuntimeError(f"The {source} provided had Infinite values.")
+
+
+@timed
+def _process_rank_one_or_two_observation(
+    obs_index: int,
+    observation_spec: ObservationSpec,
+    agent_info_list: Collection[AgentInfoProto],
+) -> np.ndarray:
+    if len(agent_info_list) == 0:
+        return np.zeros((0,) + observation_spec.shape, dtype=np.float32)
+    try:
+        np_obs = np.array(
+            [
+                agent_obs.observations[obs_index].float_data.data
+                for agent_obs in agent_info_list
+            ],
+            dtype=np.float32,
+        ).reshape((len(agent_info_list),) + observation_spec.shape)
+    except ValueError:
+        # Try to get a more useful error message
+        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
+        # If that didn't raise anything, raise the original error
+        raise
+    _raise_on_nan_and_inf(np_obs, "observations")
+    return np_obs
+
+
+@timed
+def steps_from_proto(
+    agent_info_list: Collection[AgentInfoProto], behavior_spec: BehaviorSpec
+) -> Tuple[DecisionSteps, TerminalSteps]:
+    decision_agent_info_list = [
+        agent_info for agent_info in agent_info_list if not agent_info.done
+    ]
+    terminal_agent_info_list = [
+        agent_info for agent_info in agent_info_list if agent_info.done
+    ]
+    decision_obs_list: List[np.ndarray] = []
+    terminal_obs_list: List[np.ndarray] = []
+    for obs_index, observation_spec in enumerate(behavior_spec.observation_specs):
+        is_visual = len(observation_spec.shape) == 3
+        if is_visual:
+            decision_obs_list.append(
+                _process_maybe_compressed_observation(
+                    obs_index, observation_spec, decision_agent_info_list
+                )
+            )
+            terminal_obs_list.append(
+                _process_maybe_compressed_observation(
+                    obs_index, observation_spec, terminal_agent_info_list
+                )
+            )
+        else:
+            decision_obs_list.append(
+                _process_rank_one_or_two_observation(
+                    obs_index, observation_spec, decision_agent_info_list
+                )
+            )
+            terminal_obs_list.append(
+                _process_rank_one_or_two_observation(
+                    obs_index, observation_spec, terminal_agent_info_list
+                )
+            )
+    decision_rewards = np.array(
+        [agent_info.reward for agent_info in decision_agent_info_list], dtype=np.float32
+    )
+    terminal_rewards = np.array(
+        [agent_info.reward for agent_info in terminal_agent_info_list], dtype=np.float32
+    )
+
+    decision_group_rewards = np.array(
+        [agent_info.group_reward for agent_info in decision_agent_info_list],
+        dtype=np.float32,
+    )
+    terminal_group_rewards = np.array(
+        [agent_info.group_reward for agent_info in terminal_agent_info_list],
+        dtype=np.float32,
+    )
+
+    _raise_on_nan_and_inf(decision_rewards, "rewards")
+    _raise_on_nan_and_inf(terminal_rewards, "rewards")
+    _raise_on_nan_and_inf(decision_group_rewards, "group_rewards")
+    _raise_on_nan_and_inf(terminal_group_rewards, "group_rewards")
+
+    decision_group_id = [agent_info.group_id for agent_info in decision_agent_info_list]
+    terminal_group_id = [agent_info.group_id for agent_info in terminal_agent_info_list]
+
+    max_step = np.array(
+        [agent_info.max_step_reached for agent_info in terminal_agent_info_list],
+        dtype=bool,
+    )
+    decision_agent_id = np.array(
+        [agent_info.id for agent_info in decision_agent_info_list], dtype=np.int32
+    )
+    terminal_agent_id = np.array(
+        [agent_info.id for agent_info in terminal_agent_info_list], dtype=np.int32
+    )
+    action_mask = None
+    if behavior_spec.action_spec.discrete_size > 0:
+        if any(
+            [agent_info.action_mask is not None]
+            for agent_info in decision_agent_info_list
+        ):
+            n_agents = len(decision_agent_info_list)
+            a_size = np.sum(behavior_spec.action_spec.discrete_branches)
+            mask_matrix = np.ones((n_agents, a_size), dtype=bool)
+            for agent_index, agent_info in enumerate(decision_agent_info_list):
+                if agent_info.action_mask is not None:
+                    if len(agent_info.action_mask) == a_size:
+                        mask_matrix[agent_index, :] = [
+                            False if agent_info.action_mask[k] else True
+                            for k in range(a_size)
+                        ]
+            action_mask = (1 - mask_matrix).astype(bool)
+            indices = _generate_split_indices(
+                behavior_spec.action_spec.discrete_branches
+            )
+            action_mask = np.split(action_mask, indices, axis=1)
+    return (
+        DecisionSteps(
+            decision_obs_list,
+            decision_rewards,
+            decision_agent_id,
+            action_mask,
+            decision_group_id,
+            decision_group_rewards,
+        ),
+        TerminalSteps(
+            terminal_obs_list,
+            terminal_rewards,
+            max_step,
+            terminal_agent_id,
+            terminal_group_id,
+            terminal_group_rewards,
+        ),
+    )
+
+
+def _generate_split_indices(dims):
+    if len(dims) <= 1:
+        return ()
+    result = (dims[0],)
+    for i in range(len(dims) - 2):
+        result += (dims[i + 1] + result[i],)
+    return result
diff --git a/ml-agents-envs/mlagents_envs/side_channel/__init__.py b/ml-agents-envs/mlagents_envs/side_channel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9a1f5f0f71d0b0971214921d0514538cb3c3a89
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/__init__.py
@@ -0,0 +1,7 @@
+from mlagents_envs.side_channel.incoming_message import IncomingMessage  # noqa
+from mlagents_envs.side_channel.outgoing_message import OutgoingMessage  # noqa
+
+from mlagents_envs.side_channel.side_channel import SideChannel  # noqa
+from mlagents_envs.side_channel.default_training_analytics_side_channel import (  # noqa
+    DefaultTrainingAnalyticsSideChannel,  # noqa
+)  # noqa
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diff --git a/ml-agents-envs/mlagents_envs/side_channel/default_training_analytics_side_channel.py b/ml-agents-envs/mlagents_envs/side_channel/default_training_analytics_side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..a53e686709e3876bf3444bc1027c552d53dd076b
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/default_training_analytics_side_channel.py
@@ -0,0 +1,49 @@
+import sys
+import uuid
+import mlagents_envs
+
+from mlagents_envs.exception import UnityCommunicationException
+from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage
+from mlagents_envs.communicator_objects.training_analytics_pb2 import (
+    TrainingEnvironmentInitialized,
+)
+from google.protobuf.any_pb2 import Any
+
+
+class DefaultTrainingAnalyticsSideChannel(SideChannel):
+    """
+    Side channel that sends information about the training to the Unity environment so it can be logged.
+    """
+
+    CHANNEL_ID = uuid.UUID("b664a4a9-d86f-5a5f-95cb-e8353a7e8356")
+
+    def __init__(self) -> None:
+        # >>> uuid.uuid5(uuid.NAMESPACE_URL, "com.unity.ml-agents/TrainingAnalyticsSideChannel")
+        # UUID('b664a4a9-d86f-5a5f-95cb-e8353a7e8356')
+        # We purposefully use the SAME side channel as the TrainingAnalyticsSideChannel
+
+        super().__init__(DefaultTrainingAnalyticsSideChannel.CHANNEL_ID)
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        raise UnityCommunicationException(
+            "The DefaultTrainingAnalyticsSideChannel received a message from Unity, "
+            + "this should not have happened."
+        )
+
+    def environment_initialized(self) -> None:
+        # Tuple of (major, minor, patch)
+        vi = sys.version_info
+
+        msg = TrainingEnvironmentInitialized(
+            python_version=f"{vi[0]}.{vi[1]}.{vi[2]}",
+            mlagents_version="Custom",
+            mlagents_envs_version=mlagents_envs.__version__,
+            torch_version="Unknown",
+            torch_device_type="Unknown",
+        )
+        any_message = Any()
+        any_message.Pack(msg)
+
+        env_init_msg = OutgoingMessage()
+        env_init_msg.set_raw_bytes(any_message.SerializeToString())  # type: ignore
+        super().queue_message_to_send(env_init_msg)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py b/ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce1715ba07349fc377fb846dc5e050855b140965
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py
@@ -0,0 +1,127 @@
+from mlagents_envs.side_channel import SideChannel, OutgoingMessage, IncomingMessage
+from mlagents_envs.exception import (
+    UnityCommunicationException,
+    UnitySideChannelException,
+)
+import uuid
+from typing import NamedTuple, Optional
+from enum import IntEnum
+
+
+class EngineConfig(NamedTuple):
+    width: Optional[int]
+    height: Optional[int]
+    quality_level: Optional[int]
+    time_scale: Optional[float]
+    target_frame_rate: Optional[int]
+    capture_frame_rate: Optional[int]
+
+    @staticmethod
+    def default_config():
+        return EngineConfig(80, 80, 1, 20.0, -1, 60)
+
+
+class EngineConfigurationChannel(SideChannel):
+    """
+    This is the SideChannel for engine configuration exchange. The data in the
+    engine configuration is as follows :
+     - int width;
+     - int height;
+     - int qualityLevel;
+     - float timeScale;
+     - int targetFrameRate;
+     - int captureFrameRate;
+    """
+
+    class ConfigurationType(IntEnum):
+        SCREEN_RESOLUTION = 0
+        QUALITY_LEVEL = 1
+        TIME_SCALE = 2
+        TARGET_FRAME_RATE = 3
+        CAPTURE_FRAME_RATE = 4
+
+    def __init__(self) -> None:
+        super().__init__(uuid.UUID("e951342c-4f7e-11ea-b238-784f4387d1f7"))
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Is called by the environment to the side channel. Can be called
+        multiple times per step if multiple messages are meant for that
+        SideChannel.
+        Note that Python should never receive an engine configuration from
+        Unity
+        """
+        raise UnityCommunicationException(
+            "The EngineConfigurationChannel received a message from Unity, "
+            + "this should not have happened."
+        )
+
+    def set_configuration_parameters(
+        self,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        quality_level: Optional[int] = None,
+        time_scale: Optional[float] = None,
+        target_frame_rate: Optional[int] = None,
+        capture_frame_rate: Optional[int] = None,
+    ) -> None:
+        """
+        Sets the engine configuration. Takes as input the configurations of the
+        engine.
+        :param width: Defines the width of the display. (Must be set alongside height)
+        :param height: Defines the height of the display. (Must be set alongside width)
+        :param quality_level: Defines the quality level of the simulation.
+        :param time_scale: Defines the multiplier for the deltatime in the
+        simulation. If set to a higher value, time will pass faster in the
+        simulation but the physics might break.
+        :param target_frame_rate: Instructs simulation to try to render at a
+        specified frame rate.
+        :param capture_frame_rate: Instructs the simulation to consider time between
+        updates to always be constant, regardless of the actual frame rate.
+        """
+
+        if (width is None and height is not None) or (
+            width is not None and height is None
+        ):
+            raise UnitySideChannelException(
+                "You cannot set the width/height of the screen resolution without also setting the height/width"
+            )
+
+        if width is not None and height is not None:
+            screen_msg = OutgoingMessage()
+            screen_msg.write_int32(self.ConfigurationType.SCREEN_RESOLUTION)
+            screen_msg.write_int32(width)
+            screen_msg.write_int32(height)
+            super().queue_message_to_send(screen_msg)
+
+        if quality_level is not None:
+            quality_level_msg = OutgoingMessage()
+            quality_level_msg.write_int32(self.ConfigurationType.QUALITY_LEVEL)
+            quality_level_msg.write_int32(quality_level)
+            super().queue_message_to_send(quality_level_msg)
+
+        if time_scale is not None:
+            time_scale_msg = OutgoingMessage()
+            time_scale_msg.write_int32(self.ConfigurationType.TIME_SCALE)
+            time_scale_msg.write_float32(time_scale)
+            super().queue_message_to_send(time_scale_msg)
+
+        if target_frame_rate is not None:
+            target_frame_rate_msg = OutgoingMessage()
+            target_frame_rate_msg.write_int32(self.ConfigurationType.TARGET_FRAME_RATE)
+            target_frame_rate_msg.write_int32(target_frame_rate)
+            super().queue_message_to_send(target_frame_rate_msg)
+
+        if capture_frame_rate is not None:
+            capture_frame_rate_msg = OutgoingMessage()
+            capture_frame_rate_msg.write_int32(
+                self.ConfigurationType.CAPTURE_FRAME_RATE
+            )
+            capture_frame_rate_msg.write_int32(capture_frame_rate)
+            super().queue_message_to_send(capture_frame_rate_msg)
+
+    def set_configuration(self, config: EngineConfig) -> None:
+        """
+        Sets the engine configuration. Takes as input an EngineConfig.
+        """
+        self.set_configuration_parameters(**config._asdict())
diff --git a/ml-agents-envs/mlagents_envs/side_channel/environment_parameters_channel.py b/ml-agents-envs/mlagents_envs/side_channel/environment_parameters_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff516a5eb586355ee3699d4065e79c5e57340c1d
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/environment_parameters_channel.py
@@ -0,0 +1,100 @@
+from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage
+from mlagents_envs.exception import UnityCommunicationException
+import uuid
+from enum import IntEnum
+from typing import List, Tuple
+
+
+class EnvironmentParametersChannel(SideChannel):
+    """
+    This is the SideChannel for sending environment parameters to Unity.
+    You can send parameters to an environment with the command
+    set_float_parameter.
+    """
+
+    class EnvironmentDataTypes(IntEnum):
+        FLOAT = 0
+        SAMPLER = 1
+
+    class SamplerTypes(IntEnum):
+        UNIFORM = 0
+        GAUSSIAN = 1
+        MULTIRANGEUNIFORM = 2
+
+    def __init__(self) -> None:
+        channel_id = uuid.UUID("534c891e-810f-11ea-a9d0-822485860400")
+        super().__init__(channel_id)
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        raise UnityCommunicationException(
+            "The EnvironmentParametersChannel received a message from Unity, "
+            + "this should not have happened."
+        )
+
+    def set_float_parameter(self, key: str, value: float) -> None:
+        """
+        Sets a float environment parameter in the Unity Environment.
+        :param key: The string identifier of the parameter.
+        :param value: The float value of the parameter.
+        """
+        msg = OutgoingMessage()
+        msg.write_string(key)
+        msg.write_int32(self.EnvironmentDataTypes.FLOAT)
+        msg.write_float32(value)
+        super().queue_message_to_send(msg)
+
+    def set_uniform_sampler_parameters(
+        self, key: str, min_value: float, max_value: float, seed: int
+    ) -> None:
+        """
+        Sets a uniform environment parameter sampler.
+        :param key: The string identifier of the parameter.
+        :param min_value: The minimum of the sampling distribution.
+        :param max_value: The maximum of the sampling distribution.
+        :param seed: The random seed to initialize the sampler.
+        """
+        msg = OutgoingMessage()
+        msg.write_string(key)
+        msg.write_int32(self.EnvironmentDataTypes.SAMPLER)
+        msg.write_int32(seed)
+        msg.write_int32(self.SamplerTypes.UNIFORM)
+        msg.write_float32(min_value)
+        msg.write_float32(max_value)
+        super().queue_message_to_send(msg)
+
+    def set_gaussian_sampler_parameters(
+        self, key: str, mean: float, st_dev: float, seed: int
+    ) -> None:
+        """
+        Sets a gaussian environment parameter sampler.
+        :param key: The string identifier of the parameter.
+        :param mean: The mean of the sampling distribution.
+        :param st_dev: The standard deviation of the sampling distribution.
+        :param seed: The random seed to initialize the sampler.
+        """
+        msg = OutgoingMessage()
+        msg.write_string(key)
+        msg.write_int32(self.EnvironmentDataTypes.SAMPLER)
+        msg.write_int32(seed)
+        msg.write_int32(self.SamplerTypes.GAUSSIAN)
+        msg.write_float32(mean)
+        msg.write_float32(st_dev)
+        super().queue_message_to_send(msg)
+
+    def set_multirangeuniform_sampler_parameters(
+        self, key: str, intervals: List[Tuple[float, float]], seed: int
+    ) -> None:
+        """
+        Sets a multirangeuniform environment parameter sampler.
+        :param key: The string identifier of the parameter.
+        :param intervals: The lists of min and max that define each uniform distribution.
+        :param seed: The random seed to initialize the sampler.
+        """
+        msg = OutgoingMessage()
+        msg.write_string(key)
+        msg.write_int32(self.EnvironmentDataTypes.SAMPLER)
+        msg.write_int32(seed)
+        msg.write_int32(self.SamplerTypes.MULTIRANGEUNIFORM)
+        flattened_intervals = [value for interval in intervals for value in interval]
+        msg.write_float32_list(flattened_intervals)
+        super().queue_message_to_send(msg)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py b/ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c6701129f4f3103db238371552a9d6384ca3ab8
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py
@@ -0,0 +1,62 @@
+from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage
+import uuid
+from typing import Dict, Optional, List
+
+
+class FloatPropertiesChannel(SideChannel):
+    """
+    This is the SideChannel for float properties shared with Unity.
+    You can modify the float properties of an environment with the commands
+    set_property, get_property and list_properties.
+    """
+
+    def __init__(self, channel_id: uuid.UUID = None) -> None:
+        self._float_properties: Dict[str, float] = {}
+        if channel_id is None:
+            channel_id = uuid.UUID("60ccf7d0-4f7e-11ea-b238-784f4387d1f7")
+        super().__init__(channel_id)
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Is called by the environment to the side channel. Can be called
+        multiple times per step if multiple messages are meant for that
+        SideChannel.
+        """
+        k = msg.read_string()
+        v = msg.read_float32()
+        self._float_properties[k] = v
+
+    def set_property(self, key: str, value: float) -> None:
+        """
+        Sets a property in the Unity Environment.
+        :param key: The string identifier of the property.
+        :param value: The float value of the property.
+        """
+        self._float_properties[key] = value
+        msg = OutgoingMessage()
+        msg.write_string(key)
+        msg.write_float32(value)
+        super().queue_message_to_send(msg)
+
+    def get_property(self, key: str) -> Optional[float]:
+        """
+        Gets a property in the Unity Environment. If the property was not
+        found, will return None.
+        :param key: The string identifier of the property.
+        :return: The float value of the property or None.
+        """
+        return self._float_properties.get(key)
+
+    def list_properties(self) -> List[str]:
+        """
+        Returns a list of all the string identifiers of the properties
+        currently present in the Unity Environment.
+        """
+        return list(self._float_properties.keys())
+
+    def get_property_dict_copy(self) -> Dict[str, float]:
+        """
+        Returns a copy of the float properties.
+        :return:
+        """
+        return dict(self._float_properties)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/incoming_message.py b/ml-agents-envs/mlagents_envs/side_channel/incoming_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c00f252868f072f239462581278225c2336b2c8
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/incoming_message.py
@@ -0,0 +1,93 @@
+from typing import List
+import struct
+
+
+class IncomingMessage:
+    """
+    Utility class for reading the message written to a SideChannel.
+    Values must be read in the order they were written.
+    """
+
+    def __init__(self, buffer: bytes, offset: int = 0):
+        """
+        Create a new IncomingMessage from the bytes.
+        """
+        self.buffer = buffer
+        self.offset = offset
+
+    def read_bool(self, default_value: bool = False) -> bool:
+        """
+        Read a boolean value from the message buffer.
+        :param default_value: Default value to use if the end of the message is reached.
+        :return: The value read from the message, or the default value if the end was reached.
+        """
+        if self._at_end_of_buffer():
+            return default_value
+
+        val = struct.unpack_from("<?", self.buffer, self.offset)[0]
+        self.offset += 1
+        return val
+
+    def read_int32(self, default_value: int = 0) -> int:
+        """
+        Read an integer value from the message buffer.
+        :param default_value: Default value to use if the end of the message is reached.
+        :return: The value read from the message, or the default value if the end was reached.
+        """
+        if self._at_end_of_buffer():
+            return default_value
+
+        val = struct.unpack_from("<i", self.buffer, self.offset)[0]
+        self.offset += 4
+        return val
+
+    def read_float32(self, default_value: float = 0.0) -> float:
+        """
+        Read a float value from the message buffer.
+        :param default_value: Default value to use if the end of the message is reached.
+        :return: The value read from the message, or the default value if the end was reached.
+        """
+        if self._at_end_of_buffer():
+            return default_value
+
+        val = struct.unpack_from("<f", self.buffer, self.offset)[0]
+        self.offset += 4
+        return val
+
+    def read_float32_list(self, default_value: List[float] = None) -> List[float]:
+        """
+        Read a list of float values from the message buffer.
+        :param default_value: Default value to use if the end of the message is reached.
+        :return: The value read from the message, or the default value if the end was reached.
+        """
+        if self._at_end_of_buffer():
+            return [] if default_value is None else default_value
+
+        list_len = self.read_int32()
+        output = []
+        for _ in range(list_len):
+            output.append(self.read_float32())
+        return output
+
+    def read_string(self, default_value: str = "") -> str:
+        """
+        Read a string value from the message buffer.
+        :param default_value: Default value to use if the end of the message is reached.
+        :return: The value read from the message, or the default value if the end was reached.
+        """
+        if self._at_end_of_buffer():
+            return default_value
+
+        encoded_str_len = self.read_int32()
+        val = self.buffer[self.offset : self.offset + encoded_str_len].decode("ascii")
+        self.offset += encoded_str_len
+        return val
+
+    def get_raw_bytes(self) -> bytes:
+        """
+        Get a copy of the internal bytes used by the message.
+        """
+        return bytearray(self.buffer)
+
+    def _at_end_of_buffer(self) -> bool:
+        return self.offset >= len(self.buffer)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/outgoing_message.py b/ml-agents-envs/mlagents_envs/side_channel/outgoing_message.py
new file mode 100644
index 0000000000000000000000000000000000000000..83bbe3446cb207e8c9eeffb8146f243eae9e28a2
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/outgoing_message.py
@@ -0,0 +1,66 @@
+from typing import List
+import struct
+
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+
+class OutgoingMessage:
+    """
+    Utility class for forming the message that is written to a SideChannel.
+    All data is written in little-endian format using the struct module.
+    """
+
+    def __init__(self):
+        """
+        Create an OutgoingMessage with an empty buffer.
+        """
+        self.buffer = bytearray()
+
+    def write_bool(self, b: bool) -> None:
+        """
+        Append a boolean value.
+        """
+        self.buffer += struct.pack("<?", b)
+
+    def write_int32(self, i: int) -> None:
+        """
+        Append an integer value.
+        """
+        self.buffer += struct.pack("<i", i)
+
+    def write_float32(self, f: float) -> None:
+        """
+        Append a float value. It will be truncated to 32-bit precision.
+        """
+        self.buffer += struct.pack("<f", f)
+
+    def write_float32_list(self, float_list: List[float]) -> None:
+        """
+        Append a list of float values. They will be truncated to 32-bit precision.
+        """
+        self.write_int32(len(float_list))
+        for f in float_list:
+            self.write_float32(f)
+
+    def write_string(self, s: str) -> None:
+        """
+        Append a string value. Internally, it will be encoded to ascii, and the
+        encoded length will also be written to the message.
+        """
+        encoded_key = s.encode("ascii")
+        self.write_int32(len(encoded_key))
+        self.buffer += encoded_key
+
+    def set_raw_bytes(self, buffer: bytearray) -> None:
+        """
+        Set the internal buffer to a new bytearray. This will overwrite any existing data.
+        :param buffer:
+        :return:
+        """
+        if self.buffer:
+            logger.warning(
+                "Called set_raw_bytes but the message already has been written to. This will overwrite data."
+            )
+        self.buffer = bytearray(buffer)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/raw_bytes_channel.py b/ml-agents-envs/mlagents_envs/side_channel/raw_bytes_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9f748f4fb3bd3360300cfd8810860fffe2ce411
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/raw_bytes_channel.py
@@ -0,0 +1,39 @@
+from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage
+from typing import List
+import uuid
+
+
+class RawBytesChannel(SideChannel):
+    """
+    This is an example of what the SideChannel for raw bytes exchange would
+    look like. Is meant to be used for general research purpose.
+    """
+
+    def __init__(self, channel_id: uuid.UUID):
+        self._received_messages: List[bytes] = []
+        super().__init__(channel_id)
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Is called by the environment to the side channel. Can be called
+        multiple times per step if multiple messages are meant for that
+        SideChannel.
+        """
+        self._received_messages.append(msg.get_raw_bytes())
+
+    def get_and_clear_received_messages(self) -> List[bytes]:
+        """
+        returns a list of bytearray received from the environment.
+        """
+        result = list(self._received_messages)
+        self._received_messages = []
+        return result
+
+    def send_raw_data(self, data: bytearray) -> None:
+        """
+        Queues a message to be sent by the environment at the next call to
+        step.
+        """
+        msg = OutgoingMessage()
+        msg.set_raw_bytes(data)
+        super().queue_message_to_send(msg)
diff --git a/ml-agents-envs/mlagents_envs/side_channel/side_channel.py b/ml-agents-envs/mlagents_envs/side_channel/side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..469cb51eabc684194f26b206e65c6fdafd08a29e
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/side_channel.py
@@ -0,0 +1,46 @@
+from abc import ABC, abstractmethod
+from typing import List
+import uuid
+
+from mlagents_envs.side_channel import IncomingMessage, OutgoingMessage
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+
+class SideChannel(ABC):
+    """
+    The side channel just get access to a bytes buffer that will be shared
+    between C# and Python. For example, We will create a specific side channel
+    for properties that will be a list of string (fixed size) to float number,
+    that can be modified by both C# and Python. All side channels are passed
+    to the Env object at construction.
+    """
+
+    def __init__(self, channel_id: uuid.UUID):
+        self._channel_id: uuid.UUID = channel_id
+        self.message_queue: List[bytearray] = []
+
+    def queue_message_to_send(self, msg: OutgoingMessage) -> None:
+        """
+        Queues a message to be sent by the environment at the next call to
+        step.
+        """
+        self.message_queue.append(msg.buffer)
+
+    @abstractmethod
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Is called by the environment to the side channel. Can be called
+        multiple times per step if multiple messages are meant for that
+        SideChannel.
+        """
+        pass
+
+    @property
+    def channel_id(self) -> uuid.UUID:
+        """
+        :return:The type of side channel used. Will influence how the data is
+        processed in the environment.
+        """
+        return self._channel_id
diff --git a/ml-agents-envs/mlagents_envs/side_channel/side_channel_manager.py b/ml-agents-envs/mlagents_envs/side_channel/side_channel_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5356c26f86a3eaad95d2a2d5a07e0bfab29d70c
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/side_channel_manager.py
@@ -0,0 +1,81 @@
+import uuid
+import struct
+from typing import Dict, Optional, List
+from mlagents_envs.side_channel import SideChannel, IncomingMessage
+from mlagents_envs.exception import UnityEnvironmentException
+from mlagents_envs.logging_util import get_logger
+
+
+class SideChannelManager:
+    def __init__(self, side_channels=Optional[List[SideChannel]]):
+        self._side_channels_dict = self._get_side_channels_dict(side_channels)
+
+    def process_side_channel_message(self, data: bytes) -> None:
+        """
+        Separates the data received from Python into individual messages for each
+        registered side channel and calls on_message_received on them.
+        :param data: The packed message sent by Unity
+        """
+        offset = 0
+        while offset < len(data):
+            try:
+                channel_id = uuid.UUID(bytes_le=bytes(data[offset : offset + 16]))
+                offset += 16
+                (message_len,) = struct.unpack_from("<i", data, offset)
+                offset = offset + 4
+                message_data = data[offset : offset + message_len]
+                offset = offset + message_len
+            except (struct.error, ValueError, IndexError):
+                raise UnityEnvironmentException(
+                    "There was a problem reading a message in a SideChannel. "
+                    "Please make sure the version of MLAgents in Unity is "
+                    "compatible with the Python version."
+                )
+            if len(message_data) != message_len:
+                raise UnityEnvironmentException(
+                    "The message received by the side channel {} was "
+                    "unexpectedly short. Make sure your Unity Environment "
+                    "sending side channel data properly.".format(channel_id)
+                )
+            if channel_id in self._side_channels_dict:
+                incoming_message = IncomingMessage(message_data)
+                self._side_channels_dict[channel_id].on_message_received(
+                    incoming_message
+                )
+            else:
+                get_logger(__name__).warning(
+                    f"Unknown side channel data received. Channel type: {channel_id}."
+                )
+
+    def generate_side_channel_messages(self) -> bytearray:
+        """
+        Gathers the messages that the registered side channels will send to Unity
+        and combines them into a single message ready to be sent.
+        """
+        result = bytearray()
+        for channel_id, channel in self._side_channels_dict.items():
+            for message in channel.message_queue:
+                result += channel_id.bytes_le
+                result += struct.pack("<i", len(message))
+                result += message
+            channel.message_queue = []
+        return result
+
+    @staticmethod
+    def _get_side_channels_dict(
+        side_channels: Optional[List[SideChannel]],
+    ) -> Dict[uuid.UUID, SideChannel]:
+        """
+        Converts a list of side channels into a dictionary of channel_id to SideChannel
+        :param side_channels: The list of side channels.
+        """
+        side_channels_dict: Dict[uuid.UUID, SideChannel] = {}
+        if side_channels is not None:
+            for _sc in side_channels:
+                if _sc.channel_id in side_channels_dict:
+                    raise UnityEnvironmentException(
+                        f"There cannot be two side channels with "
+                        f"the same channel id {_sc.channel_id}."
+                    )
+                side_channels_dict[_sc.channel_id] = _sc
+        return side_channels_dict
diff --git a/ml-agents-envs/mlagents_envs/side_channel/stats_side_channel.py b/ml-agents-envs/mlagents_envs/side_channel/stats_side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fbbfb23d9f650b87892b8cd6317417dcb82e5ea
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/side_channel/stats_side_channel.py
@@ -0,0 +1,62 @@
+import uuid
+from typing import Tuple, List, Mapping
+from enum import Enum
+from collections import defaultdict
+
+from mlagents_envs.side_channel import SideChannel, IncomingMessage
+
+
+# Determines the behavior of how multiple stats within the same summary period are combined.
+class StatsAggregationMethod(Enum):
+    # Values within the summary period are averaged before reporting.
+    AVERAGE = 0
+
+    # Only the most recent value is reported.
+    MOST_RECENT = 1
+
+    # Values within the summary period are summed up before reporting.
+    SUM = 2
+
+    # All values within a summary period are reported as a histogram.
+    HISTOGRAM = 3
+
+
+StatList = List[Tuple[float, StatsAggregationMethod]]
+EnvironmentStats = Mapping[str, StatList]
+
+
+class StatsSideChannel(SideChannel):
+    """
+    Side channel that receives (string, float) pairs from the environment, so that they can eventually
+    be passed to a StatsReporter.
+    """
+
+    def __init__(self) -> None:
+        # >>> uuid.uuid5(uuid.NAMESPACE_URL, "com.unity.ml-agents/StatsSideChannel")
+        # UUID('a1d8f7b7-cec8-50f9-b78b-d3e165a78520')
+        super().__init__(uuid.UUID("a1d8f7b7-cec8-50f9-b78b-d3e165a78520"))
+
+        self.stats: EnvironmentStats = defaultdict(list)
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Receive the message from the environment, and save it for later retrieval.
+
+        :param msg:
+        :return:
+        """
+        key = msg.read_string()
+        val = msg.read_float32()
+        agg_type = StatsAggregationMethod(msg.read_int32())
+
+        self.stats[key].append((val, agg_type))
+
+    def get_and_reset_stats(self) -> EnvironmentStats:
+        """
+        Returns the current stats, and resets the internal storage of the stats.
+
+        :return:
+        """
+        s = self.stats
+        self.stats = defaultdict(list)
+        return s
diff --git a/ml-agents-envs/mlagents_envs/timers.py b/ml-agents-envs/mlagents_envs/timers.py
new file mode 100644
index 0000000000000000000000000000000000000000..32b8602c9ccc1b17926d5eb45dd04fde50bf07bf
--- /dev/null
+++ b/ml-agents-envs/mlagents_envs/timers.py
@@ -0,0 +1,362 @@
+"""
+Lightweight, hierarchical timers for profiling sections of code.
+
+Example:
+
+@timed
+def foo(t):
+    time.sleep(t)
+
+def main():
+    for i in range(3):
+        foo(i + 1)
+    with hierarchical_timer("context"):
+        foo(1)
+
+    print(get_timer_tree())
+
+This would produce a timer tree like
+    (root)
+        "foo"
+        "context"
+            "foo"
+
+The total time and counts are tracked for each block of code; in this example "foo" and "context.foo" are considered
+distinct blocks, and are tracked separately.
+
+The decorator and contextmanager are equivalent; the context manager may be more useful if you want more control
+over the timer name, or are splitting up multiple sections of a large function.
+"""
+
+import math
+import sys
+import time
+import threading
+
+from contextlib import contextmanager
+from typing import Any, Callable, Dict, Generator, Optional, TypeVar
+
+TIMER_FORMAT_VERSION = "0.1.0"
+
+
+class TimerNode:
+    """
+    Represents the time spent in a block of code.
+    """
+
+    __slots__ = ["children", "total", "count", "is_parallel"]
+
+    def __init__(self):
+        # Note that since dictionary keys are the node names, we don't explicitly store the name on the TimerNode.
+        self.children: Dict[str, TimerNode] = {}
+        self.total: float = 0.0
+        self.count: int = 0
+        self.is_parallel = False
+
+    def get_child(self, name: str) -> "TimerNode":
+        """
+        Get the child node corresponding to the name (and create if it doesn't already exist).
+        """
+        child = self.children.get(name)
+        if child is None:
+            child = TimerNode()
+            self.children[name] = child
+        return child
+
+    def add_time(self, elapsed: float) -> None:
+        """
+        Accumulate the time spent in the node (and increment the count).
+        """
+        self.total += elapsed
+        self.count += 1
+
+    def merge(
+        self, other: "TimerNode", root_name: str = None, is_parallel: bool = True
+    ) -> None:
+        """
+        Add the other node to this node, then do the same recursively on its children.
+        :param other: The other node to merge
+        :param root_name: Optional name of the root node being merged.
+        :param is_parallel: Whether or not the code block was executed in parallel.
+        :return:
+        """
+        if root_name:
+            node = self.get_child(root_name)
+        else:
+            node = self
+
+        node.total += other.total
+        node.count += other.count
+        node.is_parallel |= is_parallel
+        for other_child_name, other_child_node in other.children.items():
+            child = node.get_child(other_child_name)
+            child.merge(other_child_node, is_parallel=is_parallel)
+
+
+class GaugeNode:
+    """
+    Tracks the most recent value of a metric. This is analogous to gauges in statsd.
+    """
+
+    __slots__ = ["value", "min_value", "max_value", "count", "_timestamp"]
+
+    def __init__(self, value: float):
+        self.value = value
+        self.min_value = value
+        self.max_value = value
+        self.count = 1
+        # Internal timestamp so we can determine priority.
+        self._timestamp = time.time()
+
+    def update(self, new_value: float) -> None:
+        self.min_value = min(self.min_value, new_value)
+        self.max_value = max(self.max_value, new_value)
+        self.value = new_value
+        self.count += 1
+        self._timestamp = time.time()
+
+    def merge(self, other: "GaugeNode") -> None:
+        if self._timestamp < other._timestamp:
+            # Keep the "later" value
+            self.value = other.value
+            self._timestamp = other._timestamp
+        self.min_value = min(self.min_value, other.min_value)
+        self.max_value = max(self.max_value, other.max_value)
+        self.count += other.count
+
+    def as_dict(self) -> Dict[str, float]:
+        return {
+            "value": self.value,
+            "min": self.min_value,
+            "max": self.max_value,
+            "count": self.count,
+        }
+
+
+class TimerStack:
+    """
+    Tracks all the time spent. Users shouldn't use this directly, they should use the contextmanager below to make
+    sure that pushes and pops are already matched.
+    """
+
+    __slots__ = ["root", "stack", "start_time", "gauges", "metadata"]
+
+    def __init__(self):
+        self.root = TimerNode()
+        self.stack = [self.root]
+        self.start_time = time.perf_counter()
+        self.gauges: Dict[str, GaugeNode] = {}
+        self.metadata: Dict[str, str] = {}
+        self._add_default_metadata()
+
+    def reset(self):
+        self.root = TimerNode()
+        self.stack = [self.root]
+        self.start_time = time.perf_counter()
+        self.gauges: Dict[str, GaugeNode] = {}
+        self.metadata: Dict[str, str] = {}
+        self._add_default_metadata()
+
+    def push(self, name: str) -> TimerNode:
+        """
+        Called when entering a new block of code that is timed (e.g. with a contextmanager).
+        """
+        current_node: TimerNode = self.stack[-1]
+        next_node = current_node.get_child(name)
+        self.stack.append(next_node)
+        return next_node
+
+    def pop(self) -> None:
+        """
+        Called when exiting a new block of code that is timed (e.g. with a contextmanager).
+        """
+        self.stack.pop()
+
+    def get_root(self) -> TimerNode:
+        """
+        Update the total time and count of the root name, and return it.
+        """
+        root = self.root
+        root.total = time.perf_counter() - self.start_time
+        root.count = 1
+        return root
+
+    def get_timing_tree(self, node: TimerNode = None) -> Dict[str, Any]:
+        """
+        Recursively build a tree of timings, suitable for output/archiving.
+        """
+        res: Dict[str, Any] = {}
+        if node is None:
+            # Special case the root - total is time since it was created, and count is 1
+            node = self.get_root()
+            res["name"] = "root"
+
+            # Only output gauges at top level
+            if self.gauges:
+                res["gauges"] = self._get_gauges()
+
+            if self.metadata:
+                self.metadata["end_time_seconds"] = str(int(time.time()))
+                res["metadata"] = self.metadata
+
+        res["total"] = node.total
+        res["count"] = node.count
+
+        if node.is_parallel:
+            # Note when the block ran in parallel, so that it's less confusing that a timer is less that its children.
+            res["is_parallel"] = True
+
+        child_total = 0.0
+        child_dict = {}
+        for child_name, child_node in node.children.items():
+            child_res: Dict[str, Any] = self.get_timing_tree(child_node)
+            child_dict[child_name] = child_res
+            child_total += child_res["total"]
+
+        # "self" time is total time minus all time spent on children
+        res["self"] = max(0.0, node.total - child_total)
+        if child_dict:
+            res["children"] = child_dict
+
+        return res
+
+    def set_gauge(self, name: str, value: float) -> None:
+        if math.isnan(value):
+            return
+        gauge_node = self.gauges.get(name)
+        if gauge_node:
+            gauge_node.update(value)
+        else:
+            self.gauges[name] = GaugeNode(value)
+
+    def add_metadata(self, key: str, value: str) -> None:
+        self.metadata[key] = value
+
+    def _get_gauges(self) -> Dict[str, Dict[str, float]]:
+        gauges = {}
+        for gauge_name, gauge_node in self.gauges.items():
+            gauges[gauge_name] = gauge_node.as_dict()
+        return gauges
+
+    def _add_default_metadata(self):
+        self.metadata["timer_format_version"] = TIMER_FORMAT_VERSION
+        self.metadata["start_time_seconds"] = str(int(time.time()))
+        self.metadata["python_version"] = sys.version
+        self.metadata["command_line_arguments"] = " ".join(sys.argv)
+
+
+# Maintain a separate "global" timer per thread, so that they don't accidentally conflict with each other.
+_thread_timer_stacks: Dict[int, TimerStack] = {}
+
+
+def _get_thread_timer() -> TimerStack:
+    ident = threading.get_ident()
+    if ident not in _thread_timer_stacks:
+        timer_stack = TimerStack()
+        _thread_timer_stacks[ident] = timer_stack
+    return _thread_timer_stacks[ident]
+
+
+def get_timer_stack_for_thread(t: threading.Thread) -> Optional[TimerStack]:
+    if t.ident is None:
+        # Thread hasn't started, shouldn't ever happen
+        return None
+    return _thread_timer_stacks.get(t.ident)
+
+
+@contextmanager
+def hierarchical_timer(name: str, timer_stack: TimerStack = None) -> Generator:
+    """
+    Creates a scoped timer around a block of code. This time spent will automatically be incremented when
+    the context manager exits.
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    timer_node = timer_stack.push(name)
+    start_time = time.perf_counter()
+
+    try:
+        # The wrapped code block will run here.
+        yield timer_node
+    finally:
+        # This will trigger either when the context manager exits, or an exception is raised.
+        # We'll accumulate the time, and the exception (if any) gets raised automatically.
+        elapsed = time.perf_counter() - start_time
+        timer_node.add_time(elapsed)
+        timer_stack.pop()
+
+
+# This is used to ensure the signature of the decorated function is preserved
+# See also https://github.com/python/mypy/issues/3157
+FuncT = TypeVar("FuncT", bound=Callable[..., Any])
+
+
+def timed(func: FuncT) -> FuncT:
+    """
+    Decorator for timing a function or method. The name of the timer will be the qualified name of the function.
+    Usage:
+        @timed
+        def my_func(x, y):
+            return x + y
+    Note that because this doesn't take arguments, the global timer stack is always used.
+    """
+
+    def wrapped(*args, **kwargs):
+        with hierarchical_timer(func.__qualname__):
+            return func(*args, **kwargs)
+
+    return wrapped  # type: ignore
+
+
+def set_gauge(name: str, value: float, timer_stack: TimerStack = None) -> None:
+    """
+    Updates the value of the gauge (or creates it if it hasn't been set before).
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    timer_stack.set_gauge(name, value)
+
+
+def merge_gauges(gauges: Dict[str, GaugeNode], timer_stack: TimerStack = None) -> None:
+    """
+    Merge the gauges from another TimerStack with the provided one (or the
+    current thread's stack if none is provided).
+    :param gauges:
+    :param timer_stack:
+    :return:
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    for n, g in gauges.items():
+        if n in timer_stack.gauges:
+            timer_stack.gauges[n].merge(g)
+        else:
+            timer_stack.gauges[n] = g
+
+
+def add_metadata(key: str, value: str, timer_stack: TimerStack = None) -> None:
+    timer_stack = timer_stack or _get_thread_timer()
+    timer_stack.add_metadata(key, value)
+
+
+def get_timer_tree(timer_stack: TimerStack = None) -> Dict[str, Any]:
+    """
+    Return the tree of timings from the TimerStack as a dictionary (or the
+     current thread's  stack if none is provided)
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    return timer_stack.get_timing_tree()
+
+
+def get_timer_root(timer_stack: TimerStack = None) -> TimerNode:
+    """
+    Get the root TimerNode of the timer_stack (or the current thread's
+    TimerStack if not specified)
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    return timer_stack.get_root()
+
+
+def reset_timers(timer_stack: TimerStack = None) -> None:
+    """
+    Reset the timer_stack (or the current thread's TimerStack if not specified)
+    """
+    timer_stack = timer_stack or _get_thread_timer()
+    timer_stack.reset()
diff --git a/ml-agents-envs/pydoc-config.yaml b/ml-agents-envs/pydoc-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9fec4a895161ef71866bcc4797b7b088f0a8df9c
--- /dev/null
+++ b/ml-agents-envs/pydoc-config.yaml
@@ -0,0 +1,31 @@
+# config to specify which modules will be used to render api docs docs from ml-agents-env package
+folder: docs
+modules:
+    - name: mlagents_envs
+      file_name: Python-Gym-API-Documentation.md
+      submodules:
+        - envs.unity_gym_env
+    - name: mlagents_envs
+      file_name: Python-PettingZoo-API-Documentation.md
+      submodules:
+          - envs.pettingzoo_env_factory
+          - envs.unity_aec_env
+          - envs.unity_parallel_env
+          - envs.unity_pettingzoo_base_env
+    - name: mlagents_envs
+      file_name: Python-LLAPI-Documentation.md
+      submodules:
+        - base_env
+        - environment
+        - registry
+        - registry.unity_env_registry
+        - side_channel
+        - side_channel.raw_bytes_channel
+        - side_channel.outgoing_message
+        - side_channel.engine_configuration_channel
+        - side_channel.side_channel_manager
+        - side_channel.stats_side_channel
+        - side_channel.incoming_message
+        - side_channel.float_properties_channel
+        - side_channel.environment_parameters_channel
+        - side_channel.side_channel
diff --git a/ml-agents-envs/setup.py b/ml-agents-envs/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..f25e2a1924370a5608772fdd4212d4a13fc1a3cc
--- /dev/null
+++ b/ml-agents-envs/setup.py
@@ -0,0 +1,65 @@
+import os
+import sys
+from setuptools import setup, find_packages
+from setuptools.command.install import install
+import mlagents_envs
+
+VERSION = mlagents_envs.__version__
+EXPECTED_TAG = mlagents_envs.__release_tag__
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+
+class VerifyVersionCommand(install):
+    """
+    Custom command to verify that the git tag is the expected one for the release.
+    Originally based on https://circleci.com/blog/continuously-deploying-python-packages-to-pypi-with-circleci/
+    This differs slightly because our tags and versions are different.
+    """
+
+    description = "verify that the git tag matches our version"
+
+    def run(self):
+        tag = os.getenv("GITHUB_REF", "NO GITHUB TAG!").replace("refs/tags/", "")
+
+        if tag != EXPECTED_TAG:
+            info = "Git tag: {} does not match the expected tag of this app: {}".format(
+                tag, EXPECTED_TAG
+            )
+            sys.exit(info)
+
+
+setup(
+    name="mlagents_envs",
+    version=VERSION,
+    description="Unity Machine Learning Agents Interface",
+    url="https://github.com/Unity-Technologies/ml-agents",
+    author="Unity Technologies",
+    author_email="ML-Agents@unity3d.com",
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+    ],
+    packages=find_packages(
+        exclude=["*.tests", "*.tests.*", "tests.*", "tests", "colabs", "*.ipynb"]
+    ),
+    zip_safe=False,
+    install_requires=[
+        "cloudpickle",
+        "grpcio>=1.11.0",
+        "Pillow>=4.2.1",
+        "protobuf>=3.6,<3.20",
+        "pyyaml>=3.1.0",
+        "gym>=0.21.0",
+        "pettingzoo==1.15.0",
+        "numpy==1.21.2",
+        "filelock>=3.4.0",
+    ],
+    python_requires=">=3.8.13,<=3.10.12",
+    # TODO: Remove this once mypy stops having spurious setuptools issues.
+    cmdclass={"verify": VerifyVersionCommand},  # type: ignore
+)
diff --git a/ml-agents-envs/tests/dummy_config.py b/ml-agents-envs/tests/dummy_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..40109e7b395d1bd7f3ab514be952b5cd379d8681
--- /dev/null
+++ b/ml-agents-envs/tests/dummy_config.py
@@ -0,0 +1,113 @@
+from typing import List, Tuple
+from mlagents_envs.base_env import ObservationSpec, DimensionProperty, ObservationType
+import pytest
+import copy
+import os
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    GAILSettings,
+    CuriositySettings,
+    RewardSignalSettings,
+    NetworkSettings,
+    RewardSignalType,
+    ScheduleType,
+)
+from mlagents.trainers.ppo.trainer import PPOSettings, TRAINER_NAME as PPO_TRAINER_NAME
+from mlagents.trainers.sac.trainer import SACSettings, TRAINER_NAME as SAC_TRAINER_NAME
+from mlagents.trainers.poca.trainer import (
+    POCASettings,
+    TRAINER_NAME as POCA_TRAINER_NAME,
+)
+
+CONTINUOUS_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
+DISCRETE_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
+
+_PPO_CONFIG = TrainerSettings(
+    trainer_type=PPO_TRAINER_NAME,
+    hyperparameters=PPOSettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=16,
+        buffer_size=64,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=32),
+    summary_freq=500,
+    max_steps=3000,
+    threaded=False,
+)
+
+_SAC_CONFIG = TrainerSettings(
+    trainer_type=SAC_TRAINER_NAME,
+    hyperparameters=SACSettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=8,
+        buffer_init_steps=100,
+        buffer_size=5000,
+        tau=0.01,
+        init_entcoef=0.01,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=16),
+    summary_freq=100,
+    max_steps=1000,
+    threaded=False,
+)
+
+_POCA_CONFIG = TrainerSettings(
+    trainer_type=POCA_TRAINER_NAME,
+    hyperparameters=POCASettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=16,
+        buffer_size=64,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=32),
+    summary_freq=500,
+    max_steps=3000,
+    threaded=False,
+)
+
+
+def ppo_dummy_config():
+    return copy.deepcopy(_PPO_CONFIG)
+
+
+def sac_dummy_config():
+    return copy.deepcopy(_SAC_CONFIG)
+
+
+def poca_dummy_config():
+    return copy.deepcopy(_POCA_CONFIG)
+
+
+@pytest.fixture
+def gail_dummy_config():
+    return {RewardSignalType.GAIL: GAILSettings(demo_path=CONTINUOUS_DEMO_PATH)}
+
+
+@pytest.fixture
+def curiosity_dummy_config():
+    return {RewardSignalType.CURIOSITY: CuriositySettings()}
+
+
+@pytest.fixture
+def extrinsic_dummy_config():
+    return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
+
+
+def create_observation_specs_with_shapes(
+    shapes: List[Tuple[int, ...]]
+) -> List[ObservationSpec]:
+    obs_specs: List[ObservationSpec] = []
+    for i, shape in enumerate(shapes):
+        dim_prop = (DimensionProperty.UNSPECIFIED,) * len(shape)
+        if len(shape) == 2:
+            dim_prop = (DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)
+        spec = ObservationSpec(
+            name=f"observation {i} with shape {shape}",
+            shape=shape,
+            dimension_property=dim_prop,
+            observation_type=ObservationType.DEFAULT,
+        )
+        obs_specs.append(spec)
+    return obs_specs
diff --git a/ml-agents-envs/tests/simple_test_envs.py b/ml-agents-envs/tests/simple_test_envs.py
new file mode 100644
index 0000000000000000000000000000000000000000..64b720fad37c7da3c09a8e1bae916685b4fb1ba3
--- /dev/null
+++ b/ml-agents-envs/tests/simple_test_envs.py
@@ -0,0 +1,507 @@
+"""
+Copied from ml-agents/mlagents/trainers/tests/simple_test_envs.py
+
+Modified the env so that it doesn't automatically reset and respawn agent in order to pass
+pettingzoo api tests, since current PZ api test doesn't allow spawning new agents.
+"""
+
+import random
+from typing import Dict, List, Any, Tuple
+import numpy as np
+
+from mlagents_envs.base_env import (
+    ActionSpec,
+    ObservationSpec,
+    ObservationType,
+    ActionTuple,
+    BaseEnv,
+    BehaviorSpec,
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorMapping,
+)
+from mlagents_envs.side_channel.side_channel_manager import SideChannelManager
+from dummy_config import create_observation_specs_with_shapes
+
+OBS_SIZE = 1
+VIS_OBS_SIZE = (20, 20, 3)
+VAR_LEN_SIZE = (10, 5)
+STEP_SIZE = 0.2
+
+TIME_PENALTY = 0.01
+MIN_STEPS = int(1.0 / STEP_SIZE) + 1
+SUCCESS_REWARD = 1.0 + MIN_STEPS * TIME_PENALTY
+
+
+def clamp(x, min_val, max_val):
+    return max(min_val, min(x, max_val))
+
+
+class SimpleEnvironment(BaseEnv):
+    """
+    Very simple "game" - the agent has a position on [-1, 1], gets a reward of 1 if it reaches 1, and a reward of -1 if
+    it reaches -1. The position is incremented by the action amount (clamped to [-step_size, step_size]).
+    """
+
+    def __init__(
+        self,
+        brain_names,
+        step_size=STEP_SIZE,
+        num_visual=0,
+        num_vector=1,
+        num_var_len=0,
+        vis_obs_size=VIS_OBS_SIZE,
+        vec_obs_size=OBS_SIZE,
+        var_len_obs_size=VAR_LEN_SIZE,
+        action_sizes=(1, 0),
+        goal_indices=None,
+    ):
+        super().__init__()
+        self.num_visual = num_visual
+        self.num_vector = num_vector
+        self.num_var_len = num_var_len
+        self.vis_obs_size = vis_obs_size
+        self.vec_obs_size = vec_obs_size
+        self.var_len_obs_size = var_len_obs_size
+        self.goal_indices = goal_indices
+        continuous_action_size, discrete_action_size = action_sizes
+        discrete_tuple = tuple(2 for _ in range(discrete_action_size))
+        action_spec = ActionSpec(continuous_action_size, discrete_tuple)
+        self.total_action_size = (
+            continuous_action_size + discrete_action_size
+        )  # to set the goals/positions
+        self.action_spec = action_spec
+        self.behavior_spec = BehaviorSpec(self._make_observation_specs(), action_spec)
+        self.action_spec = action_spec
+        self.names = brain_names
+        self.positions: Dict[str, List[float]] = {}
+        self.step_count: Dict[str, float] = {}
+        self._side_channel_manager = SideChannelManager([])
+
+        # Concatenate the arguments for a consistent random seed
+        seed = (
+            brain_names,
+            step_size,
+            num_visual,
+            num_vector,
+            num_var_len,
+            vis_obs_size,
+            vec_obs_size,
+            var_len_obs_size,
+            action_sizes,
+        )
+        self.random = random.Random(str(seed))
+
+        self.goal: Dict[str, int] = {}
+        self.action = {}
+        self.rewards: Dict[str, float] = {}
+        self.final_rewards: Dict[str, List[float]] = {}
+        self.step_result: Dict[str, Tuple[DecisionSteps, TerminalSteps]] = {}
+        self.agent_id: Dict[str, int] = {}
+        self.step_size = step_size  # defines the difficulty of the test
+        # Allow to be used as a UnityEnvironment during tests
+        self.academy_capabilities = None
+
+        for name in self.names:
+            self.agent_id[name] = 0
+            self.goal[name] = self.random.choice([-1, 1])
+            self.rewards[name] = 0
+            self.final_rewards[name] = []
+            self._reset_agent(name)
+            self.action[name] = None
+            self.step_result[name] = None
+
+    def _make_observation_specs(self) -> List[ObservationSpec]:
+        obs_shape: List[Any] = []
+        for _ in range(self.num_vector):
+            obs_shape.append((self.vec_obs_size,))
+        for _ in range(self.num_visual):
+            obs_shape.append(self.vis_obs_size)
+        for _ in range(self.num_var_len):
+            obs_shape.append(self.var_len_obs_size)
+        obs_spec = create_observation_specs_with_shapes(obs_shape)
+        if self.goal_indices is not None:
+            for i in range(len(obs_spec)):
+                if i in self.goal_indices:
+                    obs_spec[i] = ObservationSpec(
+                        shape=obs_spec[i].shape,
+                        dimension_property=obs_spec[i].dimension_property,
+                        observation_type=ObservationType.GOAL_SIGNAL,
+                        name=obs_spec[i].name,
+                    )
+        return obs_spec
+
+    def _make_obs(self, value: float) -> List[np.ndarray]:
+        obs = []
+        for _ in range(self.num_vector):
+            obs.append(np.ones((1, self.vec_obs_size), dtype=np.float32) * value)
+        for _ in range(self.num_visual):
+            obs.append(np.ones((1,) + self.vis_obs_size, dtype=np.float32) * value)
+        for _ in range(self.num_var_len):
+            obs.append(np.ones((1,) + self.var_len_obs_size, dtype=np.float32) * value)
+        return obs
+
+    @property
+    def behavior_specs(self):
+        behavior_dict = {}
+        for n in self.names:
+            behavior_dict[n] = self.behavior_spec
+        return BehaviorMapping(behavior_dict)
+
+    def set_action_for_agent(self, behavior_name, agent_id, action):
+        pass
+
+    def set_actions(self, behavior_name, action):
+        self.action[behavior_name] = action
+
+    def get_steps(self, behavior_name):
+        return self.step_result[behavior_name]
+
+    def _take_action(self, name: str) -> bool:
+        deltas = []
+        _act = self.action[name]
+        if self.action_spec.continuous_size > 0 and not _act:
+            for _cont in _act.continuous[0]:
+                deltas.append(_cont)
+        if self.action_spec.discrete_size > 0 and not _act:
+            for _disc in _act.discrete[0]:
+                deltas.append(1 if _disc else -1)
+        for i, _delta in enumerate(deltas):
+            _delta = clamp(_delta, -self.step_size, self.step_size)
+            self.positions[name][i] += _delta
+            self.positions[name][i] = clamp(self.positions[name][i], -1, 1)
+            self.step_count[name] += 1
+            # Both must be in 1.0 to be done
+        done = all(pos >= 1.0 or pos <= -1.0 for pos in self.positions[name])
+        return done
+
+    def _generate_mask(self):
+        action_mask = None
+        if self.action_spec.discrete_size > 0:
+            # LL-Python API will return an empty dim if there is only 1 agent.
+            ndmask = np.array(2 * self.action_spec.discrete_size * [False], dtype=bool)
+            ndmask = np.expand_dims(ndmask, axis=0)
+            action_mask = [ndmask]
+        return action_mask
+
+    def _compute_reward(self, name: str, done: bool) -> float:
+        if done:
+            reward = 0.0
+            for _pos in self.positions[name]:
+                reward += (SUCCESS_REWARD * _pos * self.goal[name]) / len(
+                    self.positions[name]
+                )
+        else:
+            reward = -TIME_PENALTY
+        return reward
+
+    def _reset_agent(self, name):
+        self.goal[name] = self.random.choice([-1, 1])
+        self.positions[name] = [0.0 for _ in range(self.total_action_size)]
+        self.step_count[name] = 0
+        self.rewards[name] = 0
+        self.agent_id[name] = self.agent_id[name] + 1
+
+    def _make_batched_step(
+        self, name: str, done: bool, reward: float, group_reward: float
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        m_vector_obs = self._make_obs(self.goal[name])
+        m_reward = np.array([reward], dtype=np.float32)
+        m_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
+        m_group_id = np.array([0], dtype=np.int32)
+        m_group_reward = np.array([group_reward], dtype=np.float32)
+        action_mask = self._generate_mask()
+        decision_step = DecisionSteps(
+            m_vector_obs, m_reward, m_agent_id, action_mask, m_group_id, m_group_reward
+        )
+        terminal_step = TerminalSteps.empty(self.behavior_spec)
+        if done:
+            self.final_rewards[name].append(self.rewards[name])
+            # self._reset_agent(name)
+            # new_vector_obs = self._make_obs(self.goal[name])
+            # (
+            #     new_reward,
+            #     new_done,
+            #     new_agent_id,
+            #     new_action_mask,
+            #     new_group_id,
+            #     new_group_reward,
+            # ) = self._construct_reset_step(name)
+
+            # decision_step = DecisionSteps(
+            #     new_vector_obs,
+            #     new_reward,
+            #     new_agent_id,
+            #     new_action_mask,
+            #     new_group_id,
+            #     new_group_reward,
+            # )
+            decision_step = DecisionSteps([], [], [], [], [], [])
+            terminal_step = TerminalSteps(
+                m_vector_obs,
+                m_reward,
+                np.array([False], dtype=bool),
+                m_agent_id,
+                m_group_id,
+                m_group_reward,
+            )
+        return (decision_step, terminal_step)
+
+    def _construct_reset_step(
+        self, name: str
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+        new_reward = np.array([0.0], dtype=np.float32)
+        new_done = np.array([False], dtype=bool)
+        new_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
+        new_action_mask = self._generate_mask()
+        new_group_id = np.array([0], dtype=np.int32)
+        new_group_reward = np.array([0.0], dtype=np.float32)
+        return (
+            new_reward,
+            new_done,
+            new_agent_id,
+            new_action_mask,
+            new_group_id,
+            new_group_reward,
+        )
+
+    def step(self) -> None:
+        assert all(action is not None for action in self.action.values())
+        for name in self.names:
+            done = self._take_action(name)
+            reward = self._compute_reward(name, done)
+            self.rewards[name] += reward
+            self.step_result[name] = self._make_batched_step(name, done, reward, 0.0)
+
+    def reset(self) -> None:  # type: ignore
+        for name in self.names:
+            self._reset_agent(name)
+            self.step_result[name] = self._make_batched_step(name, False, 0.0, 0.0)
+
+    @property
+    def reset_parameters(self) -> Dict[str, str]:
+        return {}
+
+    def close(self):
+        pass
+
+
+class MultiAgentEnvironment(BaseEnv):
+    """
+    The MultiAgentEnvironment maintains a list of SimpleEnvironment, one for each agent.
+    When sending DecisionSteps and TerminalSteps to the trainers, it first batches the
+    decision steps from the individual environments. When setting actions, it indexes the
+    batched ActionTuple to obtain the ActionTuple for individual agents
+    """
+
+    def __init__(
+        self,
+        brain_names,
+        step_size=STEP_SIZE,
+        num_visual=0,
+        num_vector=1,
+        num_var_len=0,
+        vis_obs_size=VIS_OBS_SIZE,
+        vec_obs_size=OBS_SIZE,
+        var_len_obs_size=VAR_LEN_SIZE,
+        action_sizes=(1, 0),
+        num_agents=2,
+        goal_indices=None,
+    ):
+        super().__init__()
+        self.envs = {}
+        self.dones = {}
+        self.just_died = set()
+        self.names = brain_names
+        self.final_rewards: Dict[str, List[float]] = {}
+        for name in brain_names:
+            self.final_rewards[name] = []
+            for i in range(num_agents):
+                name_and_num = name + str(i)
+                self.envs[name_and_num] = SimpleEnvironment(
+                    [name],
+                    step_size,
+                    num_visual,
+                    num_vector,
+                    num_var_len,
+                    vis_obs_size,
+                    vec_obs_size,
+                    var_len_obs_size,
+                    action_sizes,
+                    goal_indices,
+                )
+                self.dones[name_and_num] = False
+                self.envs[name_and_num].reset()
+        # All envs have the same behavior spec, so just get the last one.
+        self.behavior_spec = self.envs[name_and_num].behavior_spec
+        self.action_spec = self.envs[name_and_num].action_spec
+        self.num_agents = num_agents
+        self._side_channel_manager = SideChannelManager([])
+
+    @property
+    def all_done(self):
+        return all(self.dones.values())
+
+    @property
+    def behavior_specs(self):
+        behavior_dict = {}
+        for n in self.names:
+            behavior_dict[n] = self.behavior_spec
+        return BehaviorMapping(behavior_dict)
+
+    def set_action_for_agent(self, behavior_name, agent_id, action):
+        pass
+
+    def set_actions(self, behavior_name, action):
+        # The ActionTuple contains the actions for all n_agents. This
+        # slices the ActionTuple into an action tuple for each environment
+        # and sets it. The index j is used to ignore agents that have already
+        # reached done.
+        j = 0
+        for i in range(self.num_agents):
+            _act = ActionTuple()
+            name_and_num = behavior_name + str(i)
+            env = self.envs[name_and_num]
+            if not self.dones[name_and_num]:
+                if self.action_spec.continuous_size > 0:
+                    _act.add_continuous(action.continuous[j : j + 1])
+                if self.action_spec.discrete_size > 0:
+                    _disc_list = [action.discrete[j, :]]
+                    _act.add_discrete(np.array(_disc_list))
+                j += 1
+                env.action[behavior_name] = _act
+
+    def get_steps(self, behavior_name):
+        # This gets the individual DecisionSteps and TerminalSteps
+        # from the envs and merges them into a batch to be sent
+        # to the AgentProcessor.
+        dec_vec_obs = []
+        dec_reward = []
+        dec_group_reward = []
+        dec_agent_id = []
+        dec_group_id = []
+        ter_vec_obs = []
+        ter_reward = []
+        ter_group_reward = []
+        ter_agent_id = []
+        ter_group_id = []
+        interrupted = []
+
+        action_mask = None
+        terminal_step = TerminalSteps.empty(self.behavior_spec)
+        decision_step = None
+        for i in range(self.num_agents):
+            name_and_num = behavior_name + str(i)
+            env = self.envs[name_and_num]
+            _dec, _term = env.step_result[behavior_name]
+            if not self.dones[name_and_num]:
+                dec_agent_id.append(i)
+                dec_group_id.append(1)
+                if len(dec_vec_obs) > 0:
+                    for j, obs in enumerate(_dec.obs):
+                        dec_vec_obs[j] = np.concatenate((dec_vec_obs[j], obs), axis=0)
+                else:
+                    for obs in _dec.obs:
+                        dec_vec_obs.append(obs)
+                dec_reward.append(_dec.reward[0])
+                dec_group_reward.append(_dec.group_reward[0])
+                if _dec.action_mask is not None:
+                    if action_mask is None:
+                        action_mask = []
+                    if len(action_mask) > 0:
+                        action_mask[0] = np.concatenate(
+                            (action_mask[0], _dec.action_mask[0]), axis=0
+                        )
+                    else:
+                        action_mask.append(_dec.action_mask[0])
+            if len(_term.reward) > 0 and name_and_num in self.just_died:
+                ter_agent_id.append(i)
+                ter_group_id.append(1)
+                if len(ter_vec_obs) > 0:
+                    for j, obs in enumerate(_term.obs):
+                        ter_vec_obs[j] = np.concatenate((ter_vec_obs[j], obs), axis=0)
+                else:
+                    for obs in _term.obs:
+                        ter_vec_obs.append(obs)
+                ter_reward.append(_term.reward[0])
+                ter_group_reward.append(_term.group_reward[0])
+                interrupted.append(False)
+                self.just_died.remove(name_and_num)
+        decision_step = DecisionSteps(
+            dec_vec_obs,
+            dec_reward,
+            dec_agent_id,
+            action_mask,
+            dec_group_id,
+            dec_group_reward,
+        )
+        terminal_step = TerminalSteps(
+            ter_vec_obs,
+            ter_reward,
+            interrupted,
+            ter_agent_id,
+            ter_group_id,
+            ter_group_reward,
+        )
+        if self.all_done:
+            decision_step = DecisionSteps([], [], [], [], [], [])
+        return (decision_step, terminal_step)
+
+    def step(self) -> None:
+        # Steps all environments and calls reset if all agents are done.
+        for name in self.names:
+            for i in range(self.num_agents):
+                name_and_num = name + str(i)
+                # Does not step the env if done
+                if not self.dones[name_and_num]:
+                    env = self.envs[name_and_num]
+                    # Reproducing part of env step to intercept Dones
+                    assert all(action is not None for action in env.action.values())
+                    done = env._take_action(name)
+                    reward = env._compute_reward(name, done)
+                    self.dones[name_and_num] = done
+                    if done:
+                        self.just_died.add(name_and_num)
+                    if self.all_done:
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, 0.0, reward
+                        )
+                        self.final_rewards[name].append(reward)
+                        # self.reset()
+                    elif done:
+                        # This agent has finished but others are still running.
+                        # This gives a reward of the time penalty if this agent
+                        # is successful and the negative env reward if it fails.
+                        ceil_reward = min(-TIME_PENALTY, reward)
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, ceil_reward, 0.0
+                        )
+                        self.final_rewards[name].append(reward)
+
+                    else:
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, reward, 0.0
+                        )
+
+    def reset(self) -> None:  # type: ignore
+        for name in self.names:
+            for i in range(self.num_agents):
+                name_and_num = name + str(i)
+                self.dones[name_and_num] = False
+
+        self.dones = {}
+        self.just_died = set()
+        self.final_rewards = {}
+        for name in self.names:
+            self.final_rewards[name] = []
+            for i in range(self.num_agents):
+                name_and_num = name + str(i)
+                self.dones[name_and_num] = False
+                self.envs[name_and_num].reset()
+
+    @property
+    def reset_parameters(self) -> Dict[str, str]:
+        return {}
+
+    def close(self):
+        pass
diff --git a/ml-agents-envs/tests/test_env_utils.py b/ml-agents-envs/tests/test_env_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8b783ffe883821e5434750ae1bac38368e300a6
--- /dev/null
+++ b/ml-agents-envs/tests/test_env_utils.py
@@ -0,0 +1,64 @@
+from unittest import mock
+import pytest
+from mlagents_envs.env_utils import validate_environment_path, launch_executable
+from mlagents_envs.exception import UnityEnvironmentException
+from mlagents_envs.logging_util import (
+    set_log_level,
+    get_logger,
+    INFO,
+    ERROR,
+    FATAL,
+    CRITICAL,
+    DEBUG,
+)
+
+
+def mock_glob_method(path):
+    """
+    Given a path input, returns a list of candidates
+    """
+    if ".x86" in path:
+        return ["linux"]
+    if ".app" in path:
+        return ["darwin"]
+    if ".exe" in path:
+        return ["win32"]
+    if "*" in path:
+        return "Any"
+    return []
+
+
+@mock.patch("sys.platform")
+@mock.patch("glob.glob")
+def test_validate_path_empty(glob_mock, platform_mock):
+    glob_mock.return_value = None
+    path = validate_environment_path(" ")
+    assert path is None
+
+
+@mock.patch("mlagents_envs.env_utils.get_platform")
+@mock.patch("glob.glob")
+def test_validate_path(glob_mock, platform_mock):
+    glob_mock.side_effect = mock_glob_method
+    for platform in ["linux", "darwin", "win32"]:
+        platform_mock.return_value = platform
+        path = validate_environment_path(" ")
+        assert path == platform
+
+
+@mock.patch("glob.glob")
+@mock.patch("subprocess.Popen")
+def test_launch_executable(mock_popen, glob_mock):
+    with pytest.raises(UnityEnvironmentException):
+        launch_executable(" ", [])
+    glob_mock.return_value = ["FakeLaunchPath"]
+    launch_executable(" ", [])
+    mock_popen.side_effect = PermissionError("Fake permission error")
+    with pytest.raises(UnityEnvironmentException):
+        launch_executable(" ", [])
+
+
+def test_set_logging_level():
+    for level in [INFO, ERROR, FATAL, CRITICAL, DEBUG]:
+        set_log_level(level)
+        assert get_logger("test").level == level
diff --git a/ml-agents-envs/tests/test_envs.py b/ml-agents-envs/tests/test_envs.py
new file mode 100644
index 0000000000000000000000000000000000000000..138ac6d983b9e71f77eb10f2d7a7b77ff646072f
--- /dev/null
+++ b/ml-agents-envs/tests/test_envs.py
@@ -0,0 +1,170 @@
+from unittest import mock
+import pytest
+
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.base_env import DecisionSteps, TerminalSteps, ActionTuple
+from mlagents_envs.exception import UnityEnvironmentException, UnityActionException
+from mlagents_envs.mock_communicator import MockCommunicator
+
+
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_handles_bad_filename(get_communicator):
+    with pytest.raises(UnityEnvironmentException):
+        UnityEnvironment(" ")
+
+
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_initialization(mock_communicator, mock_launcher):
+    mock_communicator.return_value = MockCommunicator(
+        discrete_action=False, visual_inputs=0
+    )
+    env = UnityEnvironment(" ")
+    assert list(env.behavior_specs.keys()) == ["RealFakeBrain"]
+    env.close()
+
+
+@pytest.mark.parametrize(
+    "base_port,file_name,expected",
+    [
+        # Non-None base port value will always be used
+        (6001, "foo.exe", 6001),
+        # No port specified and environment specified, so use BASE_ENVIRONMENT_PORT
+        (None, "foo.exe", UnityEnvironment.BASE_ENVIRONMENT_PORT),
+        # No port specified and no environment, so use DEFAULT_EDITOR_PORT
+        (None, None, UnityEnvironment.DEFAULT_EDITOR_PORT),
+    ],
+)
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_port_defaults(
+    mock_communicator, mock_launcher, base_port, file_name, expected
+):
+    mock_communicator.return_value = MockCommunicator(
+        discrete_action=False, visual_inputs=0
+    )
+    env = UnityEnvironment(file_name=file_name, worker_id=0, base_port=base_port)
+    assert expected == env._port
+    env.close()
+
+
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_log_file_path_is_set(mock_communicator, mock_launcher):
+    mock_communicator.return_value = MockCommunicator()
+    env = UnityEnvironment(
+        file_name="myfile", worker_id=0, log_folder="./some-log-folder-path"
+    )
+    args = env._executable_args()
+    log_file_index = args.index("-logFile")
+    assert args[log_file_index + 1] == "./some-log-folder-path/Player-0.log"
+    env.close()
+
+
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_reset(mock_communicator, mock_launcher):
+    mock_communicator.return_value = MockCommunicator(
+        discrete_action=False, visual_inputs=0
+    )
+    env = UnityEnvironment(" ")
+    spec = env.behavior_specs["RealFakeBrain"]
+    env.reset()
+    decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
+    env.close()
+    assert isinstance(decision_steps, DecisionSteps)
+    assert isinstance(terminal_steps, TerminalSteps)
+    assert len(spec.observation_specs) == len(decision_steps.obs)
+    assert len(spec.observation_specs) == len(terminal_steps.obs)
+    n_agents = len(decision_steps)
+    for sen_spec, obs in zip(spec.observation_specs, decision_steps.obs):
+        assert (n_agents,) + sen_spec.shape == obs.shape
+    n_agents = len(terminal_steps)
+    for sen_spec, obs in zip(spec.observation_specs, terminal_steps.obs):
+        assert (n_agents,) + sen_spec.shape == obs.shape
+
+
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_step(mock_communicator, mock_launcher):
+    mock_communicator.return_value = MockCommunicator(
+        discrete_action=False, visual_inputs=0
+    )
+    env = UnityEnvironment(" ")
+    spec = env.behavior_specs["RealFakeBrain"]
+    env.step()
+    decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
+    n_agents = len(decision_steps)
+    env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents))
+    env.step()
+    with pytest.raises(UnityActionException):
+        env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents - 1))
+    decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
+    n_agents = len(decision_steps)
+    _empty_act = spec.action_spec.empty_action(n_agents)
+    next_action = ActionTuple(_empty_act.continuous - 1, _empty_act.discrete - 1)
+    env.set_actions("RealFakeBrain", next_action)
+    env.step()
+
+    env.close()
+    assert isinstance(decision_steps, DecisionSteps)
+    assert isinstance(terminal_steps, TerminalSteps)
+    assert len(spec.observation_specs) == len(decision_steps.obs)
+    assert len(spec.observation_specs) == len(terminal_steps.obs)
+    for spec, obs in zip(spec.observation_specs, decision_steps.obs):
+        assert (n_agents,) + spec.shape == obs.shape
+    assert 0 in decision_steps
+    assert 2 in terminal_steps
+
+
+@mock.patch("mlagents_envs.env_utils.launch_executable")
+@mock.patch("mlagents_envs.environment.UnityEnvironment._get_communicator")
+def test_close(mock_communicator, mock_launcher):
+    comm = MockCommunicator(discrete_action=False, visual_inputs=0)
+    mock_communicator.return_value = comm
+    env = UnityEnvironment(" ")
+    assert env._loaded
+    env.close()
+    assert not env._loaded
+    assert comm.has_been_closed
+
+
+def test_check_communication_compatibility():
+    unity_ver = "1.0.0"
+    python_ver = "1.0.0"
+    unity_package_version = "0.15.0"
+    assert UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+    unity_ver = "1.1.0"
+    assert UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+    unity_ver = "2.0.0"
+    assert not UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+
+    unity_ver = "0.16.0"
+    python_ver = "0.16.0"
+    assert UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+    unity_ver = "0.17.0"
+    assert not UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+    unity_ver = "1.16.0"
+    assert not UnityEnvironment._check_communication_compatibility(
+        unity_ver, python_ver, unity_package_version
+    )
+
+
+def test_returncode_to_signal_name():
+    assert UnityEnvironment._returncode_to_signal_name(-2) == "SIGINT"
+    assert UnityEnvironment._returncode_to_signal_name(42) is None
+    assert UnityEnvironment._returncode_to_signal_name("SIGINT") is None
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents-envs/tests/test_gym.py b/ml-agents-envs/tests/test_gym.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fc2bf548ccf74978380dd08bcfd0d98304e9101
--- /dev/null
+++ b/ml-agents-envs/tests/test_gym.py
@@ -0,0 +1,278 @@
+from unittest import mock
+import pytest
+import numpy as np
+
+from gym import spaces
+
+from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
+from mlagents_envs.base_env import (
+    BehaviorSpec,
+    ActionSpec,
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorMapping,
+)
+from dummy_config import create_observation_specs_with_shapes
+
+
+def test_gym_wrapper():
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec()
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(mock_spec)
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+    env = UnityToGymWrapper(mock_env)
+    assert isinstance(env.reset(), np.ndarray)
+    actions = env.action_space.sample()
+    assert actions.shape[0] == 2
+    obs, rew, done, info = env.step(actions)
+    assert env.observation_space.contains(obs)
+    assert isinstance(obs, np.ndarray)
+    assert isinstance(rew, float)
+    assert isinstance(done, (bool, np.bool_))
+    assert isinstance(info, dict)
+
+
+def test_branched_flatten():
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec(
+        vector_action_space_type="discrete", vector_action_space_size=[2, 2, 3]
+    )
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(
+        mock_spec, num_agents=1
+    )
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+
+    env = UnityToGymWrapper(mock_env, flatten_branched=True)
+    assert isinstance(env.action_space, spaces.Discrete)
+    assert env.action_space.n == 12
+    assert env._flattener.lookup_action(0) == [0, 0, 0]
+    assert env._flattener.lookup_action(11) == [1, 1, 2]
+
+    # Check that False produces a MultiDiscrete
+    env = UnityToGymWrapper(mock_env, flatten_branched=False)
+    assert isinstance(env.action_space, spaces.MultiDiscrete)
+
+
+def test_action_space():
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec(
+        vector_action_space_type="discrete", vector_action_space_size=[5]
+    )
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(
+        mock_spec, num_agents=1
+    )
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+
+    env = UnityToGymWrapper(mock_env, flatten_branched=True)
+    assert isinstance(env.action_space, spaces.Discrete)
+    assert env.action_space.n == 5
+
+    env = UnityToGymWrapper(mock_env, flatten_branched=False)
+    assert isinstance(env.action_space, spaces.Discrete)
+    assert env.action_space.n == 5
+
+
+def test_action_space_seed():
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec()
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(mock_spec)
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+    actions = []
+    for _ in range(0, 2):
+        env = UnityToGymWrapper(mock_env, action_space_seed=1337)
+        env.reset()
+        actions.append(env.action_space.sample())
+    assert (actions[0] == actions[1]).all()
+
+
+@pytest.mark.parametrize("use_uint8", [True, False], ids=["float", "uint8"])
+def test_gym_wrapper_visual(use_uint8):
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec(
+        number_visual_observations=1, vector_observation_space_size=0
+    )
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(
+        mock_spec, number_visual_observations=1
+    )
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+
+    env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8)
+    assert isinstance(env.observation_space, spaces.Box)
+    assert isinstance(env.reset(), np.ndarray)
+    actions = env.action_space.sample()
+    assert actions.shape[0] == 2
+    obs, rew, done, info = env.step(actions)
+    assert env.observation_space.contains(obs)
+    assert isinstance(obs, np.ndarray)
+    assert isinstance(rew, float)
+    assert isinstance(done, (bool, np.bool_))
+    assert isinstance(info, dict)
+
+
+@pytest.mark.parametrize("use_uint8", [True, False], ids=["float", "uint8"])
+def test_gym_wrapper_single_visual_and_vector(use_uint8):
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec(
+        number_visual_observations=1,
+        vector_observation_space_size=3,
+        vector_action_space_size=[2],
+    )
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(
+        mock_spec, number_visual_observations=1
+    )
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+
+    env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=True)
+    assert isinstance(env.observation_space, spaces.Tuple)
+    assert len(env.observation_space) == 2
+    reset_obs = env.reset()
+    assert isinstance(reset_obs, list)
+    assert len(reset_obs) == 2
+    assert all(isinstance(ob, np.ndarray) for ob in reset_obs)
+    assert reset_obs[-1].shape == (3,)
+    assert len(reset_obs[0].shape) == 3
+    actions = env.action_space.sample()
+    assert actions.shape == (2,)
+    obs, rew, done, info = env.step(actions)
+    assert isinstance(obs, list)
+    assert len(obs) == 2
+    assert all(isinstance(ob, np.ndarray) for ob in obs)
+    assert reset_obs[-1].shape == (3,)
+    assert isinstance(rew, float)
+    assert isinstance(done, (bool, np.bool_))
+    assert isinstance(info, dict)
+
+    # check behavior for allow_multiple_obs = False
+    env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=False)
+    assert isinstance(env.observation_space, spaces.Box)
+    reset_obs = env.reset()
+    assert isinstance(reset_obs, np.ndarray)
+    assert len(reset_obs.shape) == 3
+    actions = env.action_space.sample()
+    assert actions.shape == (2,)
+    obs, rew, done, info = env.step(actions)
+    assert isinstance(obs, np.ndarray)
+
+
+@pytest.mark.parametrize("use_uint8", [True, False], ids=["float", "uint8"])
+def test_gym_wrapper_multi_visual_and_vector(use_uint8):
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec(
+        number_visual_observations=2,
+        vector_observation_space_size=3,
+        vector_action_space_size=[2],
+    )
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(
+        mock_spec, number_visual_observations=2
+    )
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+
+    env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=True)
+    assert isinstance(env.observation_space, spaces.Tuple)
+    assert len(env.observation_space) == 3
+    reset_obs = env.reset()
+    assert isinstance(reset_obs, list)
+    assert len(reset_obs) == 3
+    assert all(isinstance(ob, np.ndarray) for ob in reset_obs)
+    assert reset_obs[-1].shape == (3,)
+    actions = env.action_space.sample()
+    assert actions.shape == (2,)
+    obs, rew, done, info = env.step(actions)
+    assert all(isinstance(ob, np.ndarray) for ob in obs)
+    assert isinstance(rew, float)
+    assert isinstance(done, (bool, np.bool_))
+    assert isinstance(info, dict)
+
+    # check behavior for allow_multiple_obs = False
+    env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=False)
+    assert isinstance(env.observation_space, spaces.Box)
+    reset_obs = env.reset()
+    assert isinstance(reset_obs, np.ndarray)
+    assert len(reset_obs.shape) == 3
+    actions = env.action_space.sample()
+    assert actions.shape == (2,)
+    obs, rew, done, info = env.step(actions)
+    assert isinstance(obs, np.ndarray)
+
+
+# Helper methods
+
+
+def create_mock_group_spec(
+    number_visual_observations=0,
+    vector_action_space_type="continuous",
+    vector_observation_space_size=3,
+    vector_action_space_size=None,
+):
+    """
+    Creates a mock BrainParameters object with parameters.
+    """
+    # Avoid using mutable object as default param
+    if vector_action_space_type == "continuous":
+        if vector_action_space_size is None:
+            vector_action_space_size = 2
+        else:
+            vector_action_space_size = vector_action_space_size[0]
+        action_spec = ActionSpec.create_continuous(vector_action_space_size)
+    else:
+        if vector_action_space_size is None:
+            vector_action_space_size = (2,)
+        else:
+            vector_action_space_size = tuple(vector_action_space_size)
+        action_spec = ActionSpec.create_discrete(vector_action_space_size)
+    obs_shapes = [(vector_observation_space_size,)]
+    for _ in range(number_visual_observations):
+        obs_shapes += [(8, 8, 3)]
+    obs_spec = create_observation_specs_with_shapes(obs_shapes)
+    return BehaviorSpec(obs_spec, action_spec)
+
+
+def create_mock_vector_steps(specs, num_agents=1, number_visual_observations=0):
+    """
+    Creates a mock BatchedStepResult with vector observations. Imitates constant
+    vector observations, rewards, dones, and agents.
+
+    :BehaviorSpecs specs: The BehaviorSpecs for this mock
+    :int num_agents: Number of "agents" to imitate in your BatchedStepResult values.
+    """
+    obs = [np.array([num_agents * [1, 2, 3]], dtype=np.float32).reshape(num_agents, 3)]
+    if number_visual_observations:
+        obs += [
+            np.zeros(shape=(num_agents, 8, 8, 3), dtype=np.float32)
+        ] * number_visual_observations
+    rewards = np.array(num_agents * [1.0])
+    agents = np.array(range(0, num_agents))
+    group_id = np.array(num_agents * [0])
+    group_rewards = np.array(num_agents * [0.0])
+    return (
+        DecisionSteps(obs, rewards, agents, None, group_id, group_rewards),
+        TerminalSteps.empty(specs),
+    )
+
+
+def setup_mock_unityenvironment(mock_env, mock_spec, mock_decision, mock_termination):
+    """
+    Takes a mock UnityEnvironment and adds the appropriate properties, defined by the mock
+    GroupSpec and BatchedStepResult.
+
+    :Mock mock_env: A mock UnityEnvironment, usually empty.
+    :Mock mock_spec: An AgentGroupSpec object that specifies the params of this environment.
+    :Mock mock_decision: A DecisionSteps object that will be returned at each step and reset.
+    :Mock mock_termination: A TerminationSteps object that will be returned at each step and reset.
+    """
+    mock_env.behavior_specs = BehaviorMapping({"MockBrain": mock_spec})
+    mock_env.get_steps.return_value = (mock_decision, mock_termination)
diff --git a/ml-agents-envs/tests/test_pettingzoo_wrapper.py b/ml-agents-envs/tests/test_pettingzoo_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c978d8f109ed59e094fdf93b5112f98894a5b6
--- /dev/null
+++ b/ml-agents-envs/tests/test_pettingzoo_wrapper.py
@@ -0,0 +1,32 @@
+from mlagents_envs.envs.unity_aec_env import UnityAECEnv
+from mlagents_envs.envs.unity_parallel_env import UnityParallelEnv
+from simple_test_envs import SimpleEnvironment, MultiAgentEnvironment
+from pettingzoo.test import api_test, parallel_api_test
+
+NUM_TEST_CYCLES = 100
+
+
+def test_single_agent_aec():
+    unity_env = SimpleEnvironment(["test_single"])
+    env = UnityAECEnv(unity_env)
+    api_test(env, num_cycles=NUM_TEST_CYCLES, verbose_progress=False)
+
+
+def test_multi_agent_aec():
+    unity_env = MultiAgentEnvironment(["test_multi_1", "test_multi_2"], num_agents=2)
+    env = UnityAECEnv(unity_env)
+    api_test(env, num_cycles=NUM_TEST_CYCLES, verbose_progress=False)
+
+
+def test_single_agent_parallel():
+    unity_env = SimpleEnvironment(["test_single"])
+    env = UnityParallelEnv(unity_env)
+    parallel_api_test(env, num_cycles=NUM_TEST_CYCLES)
+
+
+def test_multi_agent_parallel():
+    unity_env = MultiAgentEnvironment(
+        ["test_multi_1", "test_multi_2", "test_multi_3"], num_agents=3
+    )
+    env = UnityParallelEnv(unity_env)
+    parallel_api_test(env, num_cycles=NUM_TEST_CYCLES)
diff --git a/ml-agents-envs/tests/test_registry.py b/ml-agents-envs/tests/test_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..586831c43f3388d048065c5096404b40eafe4e27
--- /dev/null
+++ b/ml-agents-envs/tests/test_registry.py
@@ -0,0 +1,40 @@
+import os
+from pathlib import Path
+
+import pytest
+
+from mlagents_envs.registry import default_registry, UnityEnvRegistry
+from mlagents_envs.registry.remote_registry_entry import RemoteRegistryEntry
+
+BASIC_ID = "Basic"
+
+
+def create_registry(tmp_dir: str) -> UnityEnvRegistry:
+    reg = UnityEnvRegistry()
+    entry = RemoteRegistryEntry(
+        BASIC_ID,
+        0.0,
+        "Description",
+        "https://storage.googleapis.com/mlagents-test-environments/1.0.0/linux/Basic.zip",
+        "https://storage.googleapis.com/mlagents-test-environments/1.0.0/darwin/Basic.zip",
+        "https://storage.googleapis.com/mlagents-test-environments/1.0.0/windows/Basic.zip",
+        tmp_dir=tmp_dir,
+    )
+    reg.register(entry)
+    return reg
+
+
+@pytest.mark.parametrize("n_ports", [2])
+def test_basic_in_registry(base_port: int, tmp_path: Path) -> None:
+    assert BASIC_ID in default_registry
+    os.environ["TERM"] = "xterm"
+    registry = create_registry(str(tmp_path))
+    for worker_id in range(2):
+        assert BASIC_ID in registry
+        env = registry[BASIC_ID].make(
+            base_port=base_port, worker_id=worker_id, no_graphics=True
+        )
+        env.reset()
+        env.step()
+        assert len(env.behavior_specs) == 1
+        env.close()
diff --git a/ml-agents-envs/tests/test_rpc_communicator.py b/ml-agents-envs/tests/test_rpc_communicator.py
new file mode 100644
index 0000000000000000000000000000000000000000..6435541455878404021856fa89fbd280d155a9ed
--- /dev/null
+++ b/ml-agents-envs/tests/test_rpc_communicator.py
@@ -0,0 +1,96 @@
+from unittest.mock import Mock
+
+import pytest
+from unittest import mock
+
+import grpc
+
+import mlagents_envs.rpc_communicator
+from mlagents_envs.rpc_communicator import RpcCommunicator
+from mlagents_envs.exception import (
+    UnityWorkerInUseException,
+    UnityTimeOutException,
+    UnityEnvironmentException,
+)
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+
+
+@pytest.mark.parametrize("n_ports", [1])
+def test_rpc_communicator_checks_port_on_create(base_port: int) -> None:
+    first_comm = RpcCommunicator(base_port=base_port)
+    with pytest.raises(UnityWorkerInUseException):
+        second_comm = RpcCommunicator(base_port=base_port)
+        second_comm.close()
+    first_comm.close()
+
+
+@pytest.mark.parametrize("n_ports", [2])
+def test_rpc_communicator_close(base_port: int) -> None:
+    # Ensures it is possible to open a new RPC Communicators
+    # after closing one on the same worker_id
+    first_comm = RpcCommunicator(base_port=base_port)
+    first_comm.close()
+    second_comm = RpcCommunicator(base_port=base_port + 1)
+    second_comm.close()
+
+
+@pytest.mark.parametrize("n_ports", [2])
+def test_rpc_communicator_create_multiple_workers(base_port: int) -> None:
+    # Ensures multiple RPC communicators can be created with
+    # different worker_ids without causing an error.
+    first_comm = RpcCommunicator(base_port=base_port)
+    second_comm = RpcCommunicator(base_port=base_port, worker_id=1)
+    first_comm.close()
+    second_comm.close()
+
+
+@pytest.mark.parametrize("n_ports", [1])
+@mock.patch.object(grpc, "server")
+@mock.patch.object(
+    mlagents_envs.rpc_communicator, "UnityToExternalServicerImplementation"
+)
+def test_rpc_communicator_initialize_OK(
+    mock_impl: Mock, mock_grpc_server: Mock, base_port: int
+) -> None:
+    comm = RpcCommunicator(base_port=base_port, timeout_wait=0.25)
+    comm.unity_to_external.parent_conn.poll.return_value = True
+    input = UnityInputProto()
+    comm.initialize(input)
+    comm.unity_to_external.parent_conn.poll.assert_called()
+
+
+@pytest.mark.parametrize("n_ports", [1])
+@mock.patch.object(grpc, "server")
+@mock.patch.object(
+    mlagents_envs.rpc_communicator, "UnityToExternalServicerImplementation"
+)
+def test_rpc_communicator_initialize_timeout(
+    mock_impl: Mock, mock_grpc_server: Mock, base_port: int
+) -> None:
+    comm = RpcCommunicator(timeout_wait=0.25, base_port=base_port)
+    comm.unity_to_external.parent_conn.poll.return_value = None
+    input = UnityInputProto()
+    # Expect a timeout
+    with pytest.raises(UnityTimeOutException):
+        comm.initialize(input)
+    comm.unity_to_external.parent_conn.poll.assert_called()
+
+
+@pytest.mark.parametrize("n_ports", [1])
+@mock.patch.object(grpc, "server")
+@mock.patch.object(
+    mlagents_envs.rpc_communicator, "UnityToExternalServicerImplementation"
+)
+def test_rpc_communicator_initialize_callback(
+    mock_impl: Mock, mock_grpc_server: Mock, base_port: int
+) -> None:
+    def callback():
+        raise UnityEnvironmentException
+
+    comm = RpcCommunicator(base_port=base_port, timeout_wait=0.25)
+    comm.unity_to_external.parent_conn.poll.return_value = None
+    input = UnityInputProto()
+    # Expect a timeout
+    with pytest.raises(UnityEnvironmentException):
+        comm.initialize(input, poll_callback=callback)
+    comm.unity_to_external.parent_conn.poll.assert_called()
diff --git a/ml-agents-envs/tests/test_rpc_utils.py b/ml-agents-envs/tests/test_rpc_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9959aa4a993b52da9f0125726c262b30968e20c5
--- /dev/null
+++ b/ml-agents-envs/tests/test_rpc_utils.py
@@ -0,0 +1,503 @@
+import io
+import numpy as np
+import pytest
+from typing import List, Tuple, Any
+
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
+    ObservationProto,
+    NONE,
+    PNG,
+)
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
+from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
+from mlagents_envs.base_env import (
+    BehaviorSpec,
+    ActionSpec,
+    DecisionSteps,
+    TerminalSteps,
+)
+from mlagents_envs.exception import UnityObservationException
+from mlagents_envs.rpc_utils import (
+    behavior_spec_from_proto,
+    process_pixels,
+    _process_maybe_compressed_observation,
+    _process_rank_one_or_two_observation,
+    steps_from_proto,
+)
+from PIL import Image
+from dummy_config import create_observation_specs_with_shapes
+
+
+def generate_list_agent_proto(
+    n_agent: int,
+    shape: List[Tuple[int]],
+    infinite_rewards: bool = False,
+    nan_observations: bool = False,
+) -> List[AgentInfoProto]:
+    result = []
+    for agent_index in range(n_agent):
+        ap = AgentInfoProto()
+        ap.reward = float("inf") if infinite_rewards else agent_index
+        ap.done = agent_index % 2 == 0
+        ap.max_step_reached = agent_index % 4 == 0
+        ap.id = agent_index
+        ap.action_mask.extend([True, False] * 5)
+        obs_proto_list = []
+        for obs_index in range(len(shape)):
+            obs_proto = ObservationProto()
+            obs_proto.shape.extend(list(shape[obs_index]))
+            obs_proto.compression_type = NONE
+            obs_proto.float_data.data.extend(
+                ([float("nan")] if nan_observations else [0.1])
+                * np.prod(shape[obs_index])
+            )
+            obs_proto_list.append(obs_proto)
+        ap.observations.extend(obs_proto_list)
+        result.append(ap)
+    return result
+
+
+def generate_compressed_data(in_array: np.ndarray) -> bytes:
+    image_arr = (in_array * 255).astype(np.uint8)
+    bytes_out = bytes()
+
+    num_channels = in_array.shape[2]
+    num_images = (num_channels + 2) // 3
+    # Split the input image into batches of 3 channels.
+    for i in range(num_images):
+        sub_image = image_arr[..., 3 * i : 3 * i + 3]
+        if (i == num_images - 1) and (num_channels % 3) != 0:
+            # Pad zeros
+            zero_shape = list(in_array.shape)
+            zero_shape[2] = 3 - (num_channels % 3)
+            z = np.zeros(zero_shape, dtype=np.uint8)
+            sub_image = np.concatenate([sub_image, z], axis=2)
+        im = Image.fromarray(sub_image, "RGB")
+        byteIO = io.BytesIO()
+        im.save(byteIO, format="PNG")
+        bytes_out += byteIO.getvalue()
+    return bytes_out
+
+
+# test helper function for old C# API (no compressed channel mapping)
+def generate_compressed_proto_obs(
+    in_array: np.ndarray, grayscale: bool = False
+) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.compressed_data = generate_compressed_data(in_array)
+    obs_proto.compression_type = PNG
+    if grayscale:
+        # grayscale flag is only used for old API without mapping
+        expected_shape = [in_array.shape[0], in_array.shape[1], 1]
+        obs_proto.shape.extend(expected_shape)
+    else:
+        obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+# test helper function for new C# API (with compressed channel mapping)
+def generate_compressed_proto_obs_with_mapping(
+    in_array: np.ndarray, mapping: List[int]
+) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.compressed_data = generate_compressed_data(in_array)
+    obs_proto.compression_type = PNG
+    if mapping is not None:
+        obs_proto.compressed_channel_mapping.extend(mapping)
+        expected_shape = [
+            in_array.shape[0],
+            in_array.shape[1],
+            len({m for m in mapping if m >= 0}),
+        ]
+        obs_proto.shape.extend(expected_shape)
+    else:
+        obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+def generate_uncompressed_proto_obs(in_array: np.ndarray) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.float_data.data.extend(in_array.flatten().tolist())
+    obs_proto.compression_type = NONE
+    obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+def proto_from_steps(
+    decision_steps: DecisionSteps, terminal_steps: TerminalSteps
+) -> List[AgentInfoProto]:
+    agent_info_protos: List[AgentInfoProto] = []
+    # Take care of the DecisionSteps first
+    for agent_id in decision_steps.agent_id:
+        agent_id_index = decision_steps.agent_id_to_index[agent_id]
+        reward = decision_steps.reward[agent_id_index]
+        done = False
+        max_step_reached = False
+        agent_mask: Any = None
+        if decision_steps.action_mask is not None:
+            agent_mask = []
+            for _branch in decision_steps.action_mask:
+                agent_mask = np.concatenate(
+                    (agent_mask, _branch[agent_id_index, :]), axis=0
+                )
+            agent_mask = agent_mask.astype(bool).tolist()
+        observations: List[ObservationProto] = []
+        for all_observations_of_type in decision_steps.obs:
+            observation = all_observations_of_type[agent_id_index]
+            if len(observation.shape) == 3:
+                observations.append(generate_uncompressed_proto_obs(observation))
+            else:
+                observations.append(
+                    ObservationProto(
+                        float_data=ObservationProto.FloatData(data=observation),
+                        shape=[len(observation)],
+                        compression_type=NONE,
+                    )
+                )
+        agent_info_proto = AgentInfoProto(
+            reward=reward,
+            done=done,
+            id=agent_id,
+            max_step_reached=bool(max_step_reached),
+            action_mask=agent_mask,
+            observations=observations,
+        )
+        agent_info_protos.append(agent_info_proto)
+    # Take care of the TerminalSteps second
+    for agent_id in terminal_steps.agent_id:
+        agent_id_index = terminal_steps.agent_id_to_index[agent_id]
+        reward = terminal_steps.reward[agent_id_index]
+        done = True
+        max_step_reached = terminal_steps.interrupted[agent_id_index]
+
+        final_observations: List[ObservationProto] = []
+        for all_observations_of_type in terminal_steps.obs:
+            observation = all_observations_of_type[agent_id_index]
+            if len(observation.shape) == 3:
+                final_observations.append(generate_uncompressed_proto_obs(observation))
+            else:
+                final_observations.append(
+                    ObservationProto(
+                        float_data=ObservationProto.FloatData(data=observation),
+                        shape=[len(observation)],
+                        compression_type=NONE,
+                    )
+                )
+        agent_info_proto = AgentInfoProto(
+            reward=reward,
+            done=done,
+            id=agent_id,
+            max_step_reached=bool(max_step_reached),
+            action_mask=None,
+            observations=final_observations,
+        )
+        agent_info_protos.append(agent_info_proto)
+
+    return agent_info_protos
+
+
+# The arguments here are the DecisionSteps, TerminalSteps and continuous/discrete actions for a single agent name
+def proto_from_steps_and_action(
+    decision_steps: DecisionSteps,
+    terminal_steps: TerminalSteps,
+    continuous_actions: np.ndarray,
+    discrete_actions: np.ndarray,
+) -> List[AgentInfoActionPairProto]:
+    agent_info_protos = proto_from_steps(decision_steps, terminal_steps)
+    agent_action_protos = []
+    num_agents = (
+        len(continuous_actions)
+        if continuous_actions is not None
+        else len(discrete_actions)
+    )
+    for i in range(num_agents):
+        proto = AgentActionProto()
+        if continuous_actions is not None:
+            proto.continuous_actions.extend(continuous_actions[i])
+            proto.vector_actions_deprecated.extend(continuous_actions[i])
+        if discrete_actions is not None:
+            proto.discrete_actions.extend(discrete_actions[i])
+            proto.vector_actions_deprecated.extend(discrete_actions[i])
+        agent_action_protos.append(proto)
+    agent_info_action_pair_protos = [
+        AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
+        for agent_info_proto, action_proto in zip(
+            agent_info_protos, agent_action_protos
+        )
+    ]
+    return agent_info_action_pair_protos
+
+
+def test_process_pixels():
+    in_array = np.random.rand(128, 64, 3)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, 3)
+    assert out_array.shape == (128, 64, 3)
+    assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
+    assert np.allclose(in_array, out_array, atol=0.01)
+
+
+def test_process_pixels_multi_png():
+    height = 128
+    width = 64
+    num_channels = 7
+    in_array = np.random.rand(height, width, num_channels)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, num_channels)
+    assert out_array.shape == (height, width, num_channels)
+    assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
+    assert np.allclose(in_array, out_array, atol=0.01)
+
+
+def test_process_pixels_gray():
+    in_array = np.random.rand(128, 64, 3)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, 1)
+    assert out_array.shape == (128, 64, 1)
+    assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01
+    assert np.allclose(in_array.mean(axis=2, keepdims=True), out_array, atol=0.01)
+
+
+def test_vector_observation():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    obs_specs = create_observation_specs_with_shapes(shapes)
+    list_proto = generate_list_agent_proto(n_agents, shapes)
+    for obs_index, shape in enumerate(shapes):
+        arr = _process_rank_one_or_two_observation(
+            obs_index, obs_specs[obs_index], list_proto
+        )
+        assert list(arr.shape) == ([n_agents] + list(shape))
+        assert np.allclose(arr, 0.1, atol=0.01)
+
+
+def test_process_visual_observation():
+    shape = (128, 64, 3)
+    in_array_1 = np.random.rand(*shape)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1)
+    in_array_2 = np.random.rand(*shape)
+    in_array_2_mapping = [0, 1, 2]
+    proto_obs_2 = generate_compressed_proto_obs_with_mapping(
+        in_array_2, in_array_2_mapping
+    )
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap2 = AgentInfoProto()
+    ap2.observations.extend([proto_obs_2])
+    ap_list = [ap1, ap2]
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [2, 128, 64, 3]
+    assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
+    assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
+
+
+def test_process_visual_observation_grayscale():
+    in_array_1 = np.random.rand(128, 64, 3)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
+    expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
+    in_array_2 = np.random.rand(128, 64, 3)
+    in_array_2_mapping = [0, 0, 0]
+    proto_obs_2 = generate_compressed_proto_obs_with_mapping(
+        in_array_2, in_array_2_mapping
+    )
+    expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap2 = AgentInfoProto()
+    ap2.observations.extend([proto_obs_2])
+    ap_list = [ap1, ap2]
+    shape = (128, 64, 1)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [2, 128, 64, 1]
+    assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
+    assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
+
+
+def test_process_visual_observation_padded_channels():
+    in_array_1 = np.random.rand(128, 64, 12)
+    in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
+    proto_obs_1 = generate_compressed_proto_obs_with_mapping(
+        in_array_1, in_array_1_mapping
+    )
+    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=2)
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap_list = [ap1]
+    shape = (128, 64, 8)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [1, 128, 64, 8]
+    assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
+
+
+def test_process_visual_observation_bad_shape():
+    in_array_1 = np.random.rand(128, 64, 3)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1)
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap_list = [ap1]
+
+    shape = (128, 42, 3)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+
+    with pytest.raises(UnityObservationException):
+        _process_maybe_compressed_observation(0, obs_spec, ap_list)
+
+
+def test_batched_step_result_from_proto():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, spec)
+    for agent_id in range(n_agents):
+        if agent_id in decision_steps:
+            # we set the reward equal to the agent id in generate_list_agent_proto
+            assert decision_steps[agent_id].reward == agent_id
+        elif agent_id in terminal_steps:
+            assert terminal_steps[agent_id].reward == agent_id
+        else:
+            raise Exception("Missing agent from the steps")
+    # We sort the AgentId since they are split between DecisionSteps and TerminalSteps
+    combined_agent_id = list(decision_steps.agent_id) + list(terminal_steps.agent_id)
+    combined_agent_id.sort()
+    assert combined_agent_id == list(range(n_agents))
+    for agent_id in range(n_agents):
+        assert (agent_id in terminal_steps) == (agent_id % 2 == 0)
+        if agent_id in terminal_steps:
+            assert terminal_steps[agent_id].interrupted == (agent_id % 4 == 0)
+    assert decision_steps.obs[0].shape[1] == shapes[0][0]
+    assert decision_steps.obs[1].shape[1] == shapes[1][0]
+    assert terminal_steps.obs[0].shape[1] == shapes[0][0]
+    assert terminal_steps.obs[1].shape[1] == shapes[1][0]
+
+
+def test_mismatch_observations_raise_in_step_result_from_proto():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    # Hack an observation to be larger, we should get an exception
+    ap_list[0].observations[0].shape[0] += 1
+    ap_list[0].observations[0].float_data.data.append(0.42)
+    with pytest.raises(UnityObservationException):
+        steps_from_proto(ap_list, spec)
+
+
+def test_action_masking_discrete():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_discrete((7, 3))
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 2
+    assert masks[0].shape == (n_agents / 2, 7)  # half agents are done
+    assert masks[1].shape == (n_agents / 2, 3)  # half agents are done
+    assert masks[0][0, 0]
+    assert not masks[1][0, 0]
+    assert masks[1][0, 1]
+
+
+def test_action_masking_discrete_1():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_discrete((10,))
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 1
+    assert masks[0].shape == (n_agents / 2, 10)
+    assert masks[0][0, 0]
+
+
+def test_action_masking_discrete_2():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes),
+        ActionSpec.create_discrete((2, 2, 6)),
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 3
+    assert masks[0].shape == (n_agents / 2, 2)
+    assert masks[1].shape == (n_agents / 2, 2)
+    assert masks[2].shape == (n_agents / 2, 6)
+    assert masks[0][0, 0]
+
+
+def test_action_masking_continuous():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(10)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert masks is None
+
+
+def test_agent_behavior_spec_from_proto():
+    agent_proto = generate_list_agent_proto(1, [(3,), (4,)])[0]
+    bp = BrainParametersProto()
+    bp.vector_action_size_deprecated.extend([5, 4])
+    bp.vector_action_space_type_deprecated = 0
+    behavior_spec = behavior_spec_from_proto(bp, agent_proto)
+    assert behavior_spec.action_spec.is_discrete()
+    assert not behavior_spec.action_spec.is_continuous()
+    assert [spec.shape for spec in behavior_spec.observation_specs] == [(3,), (4,)]
+    assert behavior_spec.action_spec.discrete_branches == (5, 4)
+    assert behavior_spec.action_spec.discrete_size == 2
+    bp = BrainParametersProto()
+    bp.vector_action_size_deprecated.extend([6])
+    bp.vector_action_space_type_deprecated = 1
+    behavior_spec = behavior_spec_from_proto(bp, agent_proto)
+    assert not behavior_spec.action_spec.is_discrete()
+    assert behavior_spec.action_spec.is_continuous()
+    assert behavior_spec.action_spec.continuous_size == 6
+
+
+def test_batched_step_result_from_proto_raises_on_infinite():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes, infinite_rewards=True)
+    with pytest.raises(RuntimeError):
+        steps_from_proto(ap_list, behavior_spec)
+
+
+def test_batched_step_result_from_proto_raises_on_nan():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True)
+    with pytest.raises(RuntimeError):
+        steps_from_proto(ap_list, behavior_spec)
diff --git a/ml-agents-envs/tests/test_set_action.py b/ml-agents-envs/tests/test_set_action.py
new file mode 100644
index 0000000000000000000000000000000000000000..23910090ae7ece84e6fd73df90e95c0a51625e7f
--- /dev/null
+++ b/ml-agents-envs/tests/test_set_action.py
@@ -0,0 +1,59 @@
+import pytest
+
+from mlagents_envs.registry import default_registry
+from mlagents_envs.side_channel.engine_configuration_channel import (
+    EngineConfigurationChannel,
+)
+from mlagents_envs.base_env import ActionTuple
+import numpy as np
+
+BALL_ID = "3DBall"
+
+
+@pytest.mark.parametrize("n_ports", [1])
+def test_set_action_single_agent(base_port: int) -> None:
+    engine_config_channel = EngineConfigurationChannel()
+    env = default_registry[BALL_ID].make(
+        base_port=base_port,
+        worker_id=0,
+        no_graphics=True,
+        side_channels=[engine_config_channel],
+    )
+    engine_config_channel.set_configuration_parameters(time_scale=100)
+    for _ in range(3):
+        env.reset()
+        behavior_name = list(env.behavior_specs.keys())[0]
+        d, t = env.get_steps(behavior_name)
+        for _ in range(50):
+            for agent_id in d.agent_id:
+                action = np.ones((1, 2))
+                action_tuple = ActionTuple()
+                action_tuple.add_continuous(action)
+                env.set_action_for_agent(behavior_name, agent_id, action_tuple)
+            env.step()
+            d, t = env.get_steps(behavior_name)
+    env.close()
+
+
+@pytest.mark.parametrize("n_ports", [1])
+def test_set_action_multi_agent(base_port: int) -> None:
+    engine_config_channel = EngineConfigurationChannel()
+    env = default_registry[BALL_ID].make(
+        base_port=base_port,
+        worker_id=0,
+        no_graphics=True,
+        side_channels=[engine_config_channel],
+    )
+    engine_config_channel.set_configuration_parameters(time_scale=100)
+    for _ in range(3):
+        env.reset()
+        behavior_name = list(env.behavior_specs.keys())[0]
+        d, t = env.get_steps(behavior_name)
+        for _ in range(50):
+            action = np.ones((len(d), 2))
+            action_tuple = ActionTuple()
+            action_tuple.add_continuous(action)
+            env.set_actions(behavior_name, action_tuple)
+            env.step()
+            d, t = env.get_steps(behavior_name)
+    env.close()
diff --git a/ml-agents-envs/tests/test_side_channel.py b/ml-agents-envs/tests/test_side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b1447ffe7ffdac7da5813c50d8dcbbd668e145
--- /dev/null
+++ b/ml-agents-envs/tests/test_side_channel.py
@@ -0,0 +1,258 @@
+import uuid
+import pytest
+from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage
+from mlagents_envs.side_channel.side_channel_manager import SideChannelManager
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.side_channel.raw_bytes_channel import RawBytesChannel
+from mlagents_envs.side_channel.engine_configuration_channel import (
+    EngineConfigurationChannel,
+    EngineConfig,
+)
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+from mlagents_envs.side_channel.stats_side_channel import (
+    StatsSideChannel,
+    StatsAggregationMethod,
+)
+from mlagents_envs.exception import (
+    UnitySideChannelException,
+    UnityCommunicationException,
+)
+
+
+class IntChannel(SideChannel):
+    def __init__(self):
+        self.list_int = []
+        super().__init__(uuid.UUID("a85ba5c0-4f87-11ea-a517-784f4387d1f7"))
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        val = msg.read_int32()
+        self.list_int += [val]
+
+    def send_int(self, value):
+        msg = OutgoingMessage()
+        msg.write_int32(value)
+        super().queue_message_to_send(msg)
+
+
+def test_int_channel():
+    sender = IntChannel()
+    receiver = IntChannel()
+    sender.send_int(5)
+    sender.send_int(6)
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+    assert receiver.list_int[0] == 5
+    assert receiver.list_int[1] == 6
+
+
+def test_float_properties():
+    sender = FloatPropertiesChannel()
+    receiver = FloatPropertiesChannel()
+
+    sender.set_property("prop1", 1.0)
+
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    val = receiver.get_property("prop1")
+    assert val == 1.0
+    val = receiver.get_property("prop2")
+    assert val is None
+    sender.set_property("prop2", 2.0)
+
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    val = receiver.get_property("prop1")
+    assert val == 1.0
+    val = receiver.get_property("prop2")
+    assert val == 2.0
+    assert len(receiver.list_properties()) == 2
+    assert "prop1" in receiver.list_properties()
+    assert "prop2" in receiver.list_properties()
+    val = sender.get_property("prop1")
+    assert val == 1.0
+
+    assert receiver.get_property_dict_copy() == {"prop1": 1.0, "prop2": 2.0}
+    assert receiver.get_property_dict_copy() == sender.get_property_dict_copy()
+
+
+def test_raw_bytes():
+    guid = uuid.uuid4()
+    sender = RawBytesChannel(guid)
+    receiver = RawBytesChannel(guid)
+
+    sender.send_raw_data(b"foo")
+    sender.send_raw_data(b"bar")
+
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    messages = receiver.get_and_clear_received_messages()
+    assert len(messages) == 2
+    assert messages[0].decode("ascii") == "foo"
+    assert messages[1].decode("ascii") == "bar"
+
+    messages = receiver.get_and_clear_received_messages()
+    assert len(messages) == 0
+
+
+def test_message_bool():
+    vals = [True, False]
+    msg_out = OutgoingMessage()
+    for v in vals:
+        msg_out.write_bool(v)
+
+    msg_in = IncomingMessage(msg_out.buffer)
+    read_vals = []
+    for _ in range(len(vals)):
+        read_vals.append(msg_in.read_bool())
+    assert vals == read_vals
+
+    # Test reading with defaults
+    assert msg_in.read_bool() is False
+    assert msg_in.read_bool(default_value=True) is True
+
+
+def test_message_int32():
+    val = 1337
+    msg_out = OutgoingMessage()
+    msg_out.write_int32(val)
+
+    msg_in = IncomingMessage(msg_out.buffer)
+    read_val = msg_in.read_int32()
+    assert val == read_val
+
+    # Test reading with defaults
+    assert 0 == msg_in.read_int32()
+    assert val == msg_in.read_int32(default_value=val)
+
+
+def test_message_float32():
+    val = 42.0
+    msg_out = OutgoingMessage()
+    msg_out.write_float32(val)
+
+    msg_in = IncomingMessage(msg_out.buffer)
+    read_val = msg_in.read_float32()
+    # These won't be exactly equal in general, since python floats are 64-bit.
+    assert val == read_val
+
+    # Test reading with defaults
+    assert 0.0 == msg_in.read_float32()
+    assert val == msg_in.read_float32(default_value=val)
+
+
+def test_message_string():
+    val = "mlagents!"
+    msg_out = OutgoingMessage()
+    msg_out.write_string(val)
+
+    msg_in = IncomingMessage(msg_out.buffer)
+    read_val = msg_in.read_string()
+    assert val == read_val
+
+    # Test reading with defaults
+    assert "" == msg_in.read_string()
+    assert val == msg_in.read_string(default_value=val)
+
+
+def test_message_float_list():
+    val = [1.0, 3.0, 9.0]
+    msg_out = OutgoingMessage()
+    msg_out.write_float32_list(val)
+
+    msg_in = IncomingMessage(msg_out.buffer)
+    read_val = msg_in.read_float32_list()
+    # These won't be exactly equal in general, since python floats are 64-bit.
+    assert val == read_val
+
+    # Test reading with defaults
+    assert [] == msg_in.read_float32_list()
+    assert val == msg_in.read_float32_list(default_value=val)
+
+
+def test_engine_configuration():
+    sender = EngineConfigurationChannel()
+    # We use a raw bytes channel to interpred the data
+    receiver = RawBytesChannel(sender.channel_id)
+
+    config = EngineConfig.default_config()
+    sender.set_configuration(config)
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    received_data = receiver.get_and_clear_received_messages()
+    assert len(received_data) == 5  # 5 different messages one for each setting
+
+    sent_time_scale = 4.5
+    sender.set_configuration_parameters(time_scale=sent_time_scale)
+
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    message = IncomingMessage(receiver.get_and_clear_received_messages()[0])
+    message.read_int32()
+    time_scale = message.read_float32()
+    assert time_scale == sent_time_scale
+
+    with pytest.raises(UnitySideChannelException):
+        sender.set_configuration_parameters(width=None, height=42)
+
+    with pytest.raises(UnityCommunicationException):
+        # try to send data to the EngineConfigurationChannel
+        sender.set_configuration_parameters(time_scale=sent_time_scale)
+        data = SideChannelManager([sender]).generate_side_channel_messages()
+        SideChannelManager([sender]).process_side_channel_message(data)
+
+
+def test_environment_parameters():
+    sender = EnvironmentParametersChannel()
+    # We use a raw bytes channel to interpred the data
+    receiver = RawBytesChannel(sender.channel_id)
+
+    sender.set_float_parameter("param-1", 0.1)
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    message = IncomingMessage(receiver.get_and_clear_received_messages()[0])
+    key = message.read_string()
+    dtype = message.read_int32()
+    value = message.read_float32()
+    assert key == "param-1"
+    assert dtype == EnvironmentParametersChannel.EnvironmentDataTypes.FLOAT
+    assert value - 0.1 < 1e-8
+
+    sender.set_float_parameter("param-1", 0.1)
+    sender.set_float_parameter("param-2", 0.1)
+    sender.set_float_parameter("param-3", 0.1)
+
+    data = SideChannelManager([sender]).generate_side_channel_messages()
+    SideChannelManager([receiver]).process_side_channel_message(data)
+
+    assert len(receiver.get_and_clear_received_messages()) == 3
+
+    with pytest.raises(UnityCommunicationException):
+        # try to send data to the EngineConfigurationChannel
+        sender.set_float_parameter("param-1", 0.1)
+        data = SideChannelManager([sender]).generate_side_channel_messages()
+        SideChannelManager([sender]).process_side_channel_message(data)
+
+
+def test_stats_channel():
+    receiver = StatsSideChannel()
+    message = OutgoingMessage()
+    message.write_string("stats-1")
+    message.write_float32(42.0)
+    message.write_int32(1)  # corresponds to StatsAggregationMethod.MOST_RECENT
+
+    receiver.on_message_received(IncomingMessage(message.buffer))
+
+    stats = receiver.get_and_reset_stats()
+
+    assert len(stats) == 1
+    val, method = stats["stats-1"][0]
+    assert val - 42.0 < 1e-8
+    assert method == StatsAggregationMethod.MOST_RECENT
diff --git a/ml-agents-envs/tests/test_steps.py b/ml-agents-envs/tests/test_steps.py
new file mode 100644
index 0000000000000000000000000000000000000000..01e39f3ea784c2e6ef8918b18be17f6a80470e98
--- /dev/null
+++ b/ml-agents-envs/tests/test_steps.py
@@ -0,0 +1,136 @@
+import pytest
+import numpy as np
+
+from mlagents_envs.base_env import (
+    DecisionSteps,
+    TerminalSteps,
+    ActionSpec,
+    BehaviorSpec,
+)
+from dummy_config import create_observation_specs_with_shapes
+
+
+def test_decision_steps():
+    ds = DecisionSteps(
+        obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
+        reward=np.array(range(3), dtype=np.float32),
+        agent_id=np.array(range(10, 13), dtype=np.int32),
+        action_mask=[np.zeros((3, 4), dtype=bool)],
+        group_id=np.array(range(3), dtype=np.int32),
+        group_reward=np.array(range(3), dtype=np.float32),
+    )
+
+    assert ds.agent_id_to_index[10] == 0
+    assert ds.agent_id_to_index[11] == 1
+    assert ds.agent_id_to_index[12] == 2
+
+    with pytest.raises(KeyError):
+        assert ds.agent_id_to_index[-1] == -1
+
+    mask_agent = ds[10].action_mask
+    assert isinstance(mask_agent, list)
+    assert len(mask_agent) == 1
+    assert np.array_equal(mask_agent[0], np.zeros((4), dtype=bool))
+
+    for agent_id in ds:
+        assert ds.agent_id_to_index[agent_id] in range(3)
+
+
+def test_empty_decision_steps():
+    specs = BehaviorSpec(
+        observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
+        action_spec=ActionSpec.create_continuous(3),
+    )
+    ds = DecisionSteps.empty(specs)
+    assert len(ds.obs) == 2
+    assert ds.obs[0].shape == (0, 3, 2)
+    assert ds.obs[1].shape == (0, 5)
+
+
+def test_terminal_steps():
+    ts = TerminalSteps(
+        obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
+        reward=np.array(range(3), dtype=np.float32),
+        agent_id=np.array(range(10, 13), dtype=np.int32),
+        interrupted=np.array([1, 0, 1], dtype=bool),
+        group_id=np.array(range(3), dtype=np.int32),
+        group_reward=np.array(range(3), dtype=np.float32),
+    )
+
+    assert ts.agent_id_to_index[10] == 0
+    assert ts.agent_id_to_index[11] == 1
+    assert ts.agent_id_to_index[12] == 2
+
+    assert ts[10].interrupted
+    assert not ts[11].interrupted
+    assert ts[12].interrupted
+
+    with pytest.raises(KeyError):
+        assert ts.agent_id_to_index[-1] == -1
+
+    for agent_id in ts:
+        assert ts.agent_id_to_index[agent_id] in range(3)
+
+
+def test_empty_terminal_steps():
+    specs = BehaviorSpec(
+        observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
+        action_spec=ActionSpec.create_continuous(3),
+    )
+    ts = TerminalSteps.empty(specs)
+    assert len(ts.obs) == 2
+    assert ts.obs[0].shape == (0, 3, 2)
+    assert ts.obs[1].shape == (0, 5)
+
+
+def test_specs():
+    specs = ActionSpec.create_continuous(3)
+    assert specs.discrete_branches == ()
+    assert specs.discrete_size == 0
+    assert specs.continuous_size == 3
+    assert specs.empty_action(5).continuous.shape == (5, 3)
+    assert specs.empty_action(5).continuous.dtype == np.float32
+
+    specs = ActionSpec.create_discrete((3,))
+    assert specs.discrete_branches == (3,)
+    assert specs.discrete_size == 1
+    assert specs.continuous_size == 0
+    assert specs.empty_action(5).discrete.shape == (5, 1)
+    assert specs.empty_action(5).discrete.dtype == np.int32
+
+    specs = ActionSpec(3, (3,))
+    assert specs.continuous_size == 3
+    assert specs.discrete_branches == (3,)
+    assert specs.discrete_size == 1
+    assert specs.empty_action(5).continuous.shape == (5, 3)
+    assert specs.empty_action(5).continuous.dtype == np.float32
+    assert specs.empty_action(5).discrete.shape == (5, 1)
+    assert specs.empty_action(5).discrete.dtype == np.int32
+
+
+def test_action_generator():
+    # Continuous
+    action_len = 30
+    specs = ActionSpec.create_continuous(action_len)
+    zero_action = specs.empty_action(4).continuous
+    assert np.array_equal(zero_action, np.zeros((4, action_len), dtype=np.float32))
+    print(specs.random_action(4))
+    random_action = specs.random_action(4).continuous
+    print(random_action)
+    assert random_action.dtype == np.float32
+    assert random_action.shape == (4, action_len)
+    assert np.min(random_action) >= -1
+    assert np.max(random_action) <= 1
+
+    # Discrete
+    action_shape = (10, 20, 30)
+    specs = ActionSpec.create_discrete(action_shape)
+    zero_action = specs.empty_action(4).discrete
+    assert np.array_equal(zero_action, np.zeros((4, len(action_shape)), dtype=np.int32))
+
+    random_action = specs.random_action(4).discrete
+    assert random_action.dtype == np.int32
+    assert random_action.shape == (4, len(action_shape))
+    assert np.min(random_action) >= 0
+    for index, branch_size in enumerate(action_shape):
+        assert np.max(random_action[:, index]) < branch_size
diff --git a/ml-agents-envs/tests/test_timers.py b/ml-agents-envs/tests/test_timers.py
new file mode 100644
index 0000000000000000000000000000000000000000..38de561970db10d36bfed1b0761399ffe3d14a46
--- /dev/null
+++ b/ml-agents-envs/tests/test_timers.py
@@ -0,0 +1,92 @@
+from unittest import mock
+
+from mlagents_envs import timers
+
+
+@timers.timed
+def decorated_func(x: int = 0, y: float = 1.0) -> str:
+    timers.set_gauge("my_gauge", x + y)
+    return f"{x} + {y} = {x + y}"
+
+
+def test_timers() -> None:
+    test_timer = timers.TimerStack()
+    with mock.patch("mlagents_envs.timers._get_thread_timer", return_value=test_timer):
+        # First, run some simple code
+        with timers.hierarchical_timer("top_level"):
+            for i in range(3):
+                with timers.hierarchical_timer("multiple"):
+                    decorated_func(i, i)
+
+            raised = False
+            try:
+                with timers.hierarchical_timer("raises"):
+                    raise RuntimeError("timeout!")
+            except RuntimeError:
+                raised = True
+
+            with timers.hierarchical_timer("post_raise"):
+                assert raised
+                pass
+
+        # We expect the hierarchy to look like
+        #   (root)
+        #       top_level
+        #           multiple
+        #               decorated_func
+        #           raises
+        #           post_raise
+        root = test_timer.root
+        assert root.children.keys() == {"top_level"}
+
+        top_level = root.children["top_level"]
+        assert top_level.children.keys() == {"multiple", "raises", "post_raise"}
+
+        # make sure the scope was closed properly when the exception was raised
+        raises = top_level.children["raises"]
+        assert raises.count == 1
+
+        multiple = top_level.children["multiple"]
+        assert multiple.count == 3
+
+        timer_tree = test_timer.get_timing_tree()
+
+        expected_tree = {
+            "name": "root",
+            "total": mock.ANY,
+            "count": 1,
+            "self": mock.ANY,
+            "children": {
+                "top_level": {
+                    "total": mock.ANY,
+                    "count": 1,
+                    "self": mock.ANY,
+                    "children": {
+                        "multiple": {
+                            "total": mock.ANY,
+                            "count": 3,
+                            "self": mock.ANY,
+                            "children": {
+                                "decorated_func": {
+                                    "total": mock.ANY,
+                                    "count": 3,
+                                    "self": mock.ANY,
+                                }
+                            },
+                        },
+                        "raises": {"total": mock.ANY, "count": 1, "self": mock.ANY},
+                        "post_raise": {"total": mock.ANY, "count": 1, "self": mock.ANY},
+                    },
+                }
+            },
+            "gauges": {"my_gauge": {"value": 4.0, "max": 4.0, "min": 0.0, "count": 3}},
+            "metadata": {
+                "timer_format_version": timers.TIMER_FORMAT_VERSION,
+                "start_time_seconds": mock.ANY,
+                "end_time_seconds": mock.ANY,
+                "python_version": mock.ANY,
+                "command_line_arguments": mock.ANY,
+            },
+        }
+
+        assert timer_tree == expected_tree
diff --git a/ml-agents-plugin-examples/README.md b/ml-agents-plugin-examples/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..db66662d7047ae035523ff84b00673b795e8c1fd
--- /dev/null
+++ b/ml-agents-plugin-examples/README.md
@@ -0,0 +1,3 @@
+# ML-Agents Plugins
+
+See the [Plugins documentation](../docs/Training-Plugins.md) for more information.
diff --git a/ml-agents-plugin-examples/mlagents_plugin_examples/__init__.py b/ml-agents-plugin-examples/mlagents_plugin_examples/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py b/ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..17c4afe0073bc06cce5edf0d33cd0c25526c609b
--- /dev/null
+++ b/ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py
@@ -0,0 +1,27 @@
+from typing import Dict, List
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.stats import StatsWriter, StatsSummary
+
+
+class ExampleStatsWriter(StatsWriter):
+    """
+    Example implementation of the StatsWriter abstract class.
+    This doesn't do anything interesting, just prints the stats that it gets.
+    """
+
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        print(f"ExampleStatsWriter category: {category} values: {values}")
+
+
+def get_example_stats_writer(run_options: RunOptions) -> List[StatsWriter]:
+    """
+    Registration function. This is referenced in setup.py and will
+    be called by mlagents-learn when it starts to determine the
+    list of StatsWriters to use.
+
+    It must return a list of StatsWriters.
+    """
+    print("Creating a new stats writer! This is so exciting!")
+    return [ExampleStatsWriter()]
diff --git a/ml-agents-plugin-examples/mlagents_plugin_examples/tests/__init__.py b/ml-agents-plugin-examples/mlagents_plugin_examples/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents-plugin-examples/mlagents_plugin_examples/tests/test_stats_writer_plugin.py b/ml-agents-plugin-examples/mlagents_plugin_examples/tests/test_stats_writer_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5f76a42af5a20a9cd0f7e66b0aacc16127fb3c5
--- /dev/null
+++ b/ml-agents-plugin-examples/mlagents_plugin_examples/tests/test_stats_writer_plugin.py
@@ -0,0 +1,13 @@
+import pytest
+
+from mlagents.plugins.stats_writer import register_stats_writer_plugins
+from mlagents.trainers.settings import RunOptions
+
+from mlagents_plugin_examples.example_stats_writer import ExampleStatsWriter
+
+
+@pytest.mark.slow
+def test_register_stats_writers():
+    # Make sure that the ExampleStatsWriter gets returned from the list of all StatsWriters
+    stats_writers = register_stats_writer_plugins(RunOptions())
+    assert any(isinstance(sw, ExampleStatsWriter) for sw in stats_writers)
diff --git a/ml-agents-plugin-examples/setup.py b/ml-agents-plugin-examples/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3fb24e0b5196c4fd0ec3993cab74061781ebe49
--- /dev/null
+++ b/ml-agents-plugin-examples/setup.py
@@ -0,0 +1,17 @@
+from setuptools import setup
+from mlagents.plugins import ML_AGENTS_STATS_WRITER
+
+setup(
+    name="mlagents_plugin_examples",
+    version="0.0.1",
+    # Example of how to add your own registration functions that will be called
+    # by mlagents-learn.
+    #
+    # Here, the get_example_stats_writer() function in mlagents_plugin_examples/example_stats_writer.py
+    # will get registered with the ML_AGENTS_STATS_WRITER plugin interface.
+    entry_points={
+        ML_AGENTS_STATS_WRITER: [
+            "example=mlagents_plugin_examples.example_stats_writer:get_example_stats_writer"
+        ]
+    },
+)
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/__init__.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/__init__.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2b0af46070f0dda63d4ac8bf8f030afdbd8a7f96
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml
@@ -0,0 +1,24 @@
+behaviors:
+  3DBall:
+    trainer_type: a2c
+    hyperparameters:
+      batch_size: 1000
+      buffer_size: 1000
+      learning_rate: 0.0003
+      beta: 0.001
+      lambd: 0.99
+      num_epoch: 1
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 1000
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_optimizer.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8af95ee41ef39d56a6c1e85c90302920bc2e1963
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_optimizer.py
@@ -0,0 +1,192 @@
+from typing import Dict, cast
+import attr
+
+from mlagents.torch_utils import torch, default_device
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey, RewardSignalUtil
+
+from mlagents_envs.timers import timed
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    OnPolicyHyperparamSettings,
+    ScheduleType,
+)
+from mlagents.trainers.torch_entities.networks import ValueNetwork
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.trajectory import ObsUtil
+
+from mlagents.trainers.exception import TrainerConfigError
+
+
+@attr.s(auto_attribs=True)
+class A2CSettings(OnPolicyHyperparamSettings):
+    beta: float = 5.0e-3
+    lambd: float = 0.95
+    num_epoch: int = attr.ib(default=1)  # A2C does just one pass
+    shared_critic: bool = False
+
+    @num_epoch.validator
+    def _check_num_epoch_one(self, attribute, value):
+        if value != 1:
+            raise TrainerConfigError("A2C requires num_epoch = 1")
+
+    learning_rate_schedule: ScheduleType = ScheduleType.LINEAR
+    beta_schedule: ScheduleType = ScheduleType.LINEAR
+
+
+class A2COptimizer(TorchOptimizer):
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        """
+        Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
+        The A2C optimizer has a value estimator and a loss function.
+        :param policy: A TorchPolicy object that will be updated by this A2C Optimizer.
+        :param trainer_params: Trainer parameters dictionary that specifies the
+        properties of the trainer.
+        """
+        # Create the graph here to give more granular control of the TF graph to the Optimizer.
+
+        super().__init__(policy, trainer_settings)
+        self.hyperparameters: A2CSettings = cast(
+            A2CSettings, trainer_settings.hyperparameters
+        )
+
+        params = list(self.policy.actor.parameters())
+        if self.hyperparameters.shared_critic:
+            self._critic = policy.actor
+        else:
+
+            self._critic = ValueNetwork(
+                list(self.reward_signals.keys()),
+                policy.behavior_spec.observation_specs,
+                network_settings=trainer_settings.network_settings,
+            )
+        self._critic.to(default_device())
+        params += list(self._critic.parameters())
+
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+
+        self.decay_beta = ModelUtils.DecayedValue(
+            self.hyperparameters.beta_schedule,
+            self.hyperparameters.beta,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+
+        self.optimizer = torch.optim.Adam(
+            params, lr=self.trainer_settings.hyperparameters.learning_rate
+        )
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+
+        self.stream_names = list(self.reward_signals.keys())
+
+    @property
+    def critic(self):
+        return self._critic
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Performs update on model.
+        :param batch: Batch of experiences.
+        :param num_sequences: Number of sequences to process.
+        :return: Results of update.
+        """
+        # Get decayed parameters
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
+        returns = {}
+        for name in self.reward_signals:
+            returns[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.returns_key(name)]
+            )
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        # Convert to tensors
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+
+        act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
+        actions = AgentAction.from_buffer(batch)
+
+        memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i])
+            for i in range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
+        ]
+        if len(memories) > 0:
+            memories = torch.stack(memories).unsqueeze(0)
+
+        # Get value memories
+        value_memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
+            for i in range(
+                0, len(batch[BufferKey.CRITIC_MEMORY]), self.policy.sequence_length
+            )
+        ]
+        if len(value_memories) > 0:
+            value_memories = torch.stack(value_memories).unsqueeze(0)
+
+        run_out = self.policy.actor.get_stats(
+            current_obs,
+            masks=act_masks,
+            actions=actions,
+            memories=memories,
+            sequence_length=self.policy.sequence_length,
+        )
+
+        log_probs = run_out["log_probs"]
+        entropy = run_out["entropy"]
+
+        values, _ = self.critic.critic_pass(
+            current_obs,
+            memories=value_memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        log_probs = log_probs.flatten()
+
+        value_loss_per_head = []
+        for name, head in values.items():
+            returns_tensor = returns[name]
+            be = (returns_tensor - head) ** 2
+            value_loss_per_head.append(be)
+        value_loss = torch.mean(torch.stack(value_loss_per_head))
+
+        advantages = ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES])
+        policy_loss = -1 * torch.mean(torch.sum(log_probs, dim=1) * advantages)
+
+        loss = policy_loss + 0.5 * value_loss - decay_bet * torch.mean(entropy)
+
+        # Set optimizer learning rate
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        update_stats = {
+            # NOTE: abs() is not technically correct, but matches the behavior in TensorFlow.
+            # TODO: After PyTorch is default, change to something more correct.
+            "Losses/Policy Loss": torch.abs(policy_loss).item(),
+            "Losses/Value Loss": value_loss.item(),
+            "Policy/Learning Rate": decay_lr,
+            "Policy/Beta": decay_bet,
+        }
+
+        return update_stats
+
+    def get_modules(self):
+        modules = {
+            "Optimizer:value_optimizer": self.optimizer,
+            "Optimizer:critic": self._critic,
+        }
+        for reward_provider in self.reward_signals.values():
+            modules.update(reward_provider.get_modules())
+        return modules
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_trainer.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..36cf762fb86042b2613ebe6814f17d1fd83ef9d5
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_trainer.py
@@ -0,0 +1,213 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agent Learning (A2C)
+# Contains an implementation of A2C as described in: https://arxiv.org/abs/1707.06347
+
+from typing import cast
+
+import numpy as np
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+from mlagents.trainers.trainer.on_policy_trainer import OnPolicyTrainer
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.trainer.trainer_utils import get_gae
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from .a2c_optimizer import A2COptimizer, A2CSettings
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings
+
+from mlagents.trainers.torch_entities.networks import SimpleActor, SharedActorCritic
+
+logger = get_logger(__name__)
+
+TRAINER_NAME = "a2c"
+
+
+class A2CTrainer(OnPolicyTrainer):
+    """The A2CTrainer is an implementation of the A2C algorithm."""
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training A2C model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            reward_buff_cap,
+            trainer_settings,
+            training,
+            load,
+            seed,
+            artifact_path,
+        )
+        self.hyperparameters: A2CSettings = cast(
+            A2CSettings, self.trainer_settings.hyperparameters
+        )
+        self.shared_critic = self.hyperparameters.shared_critic
+        self.policy: TorchPolicy = None  # type: ignore
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the update buffer.
+        Processing involves calculating value and advantage targets for model updating step.
+        :param trajectory: The Trajectory tuple containing the steps to be processed.
+        """
+        super()._process_trajectory(trajectory)
+        agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+        agent_buffer_trajectory = trajectory.to_agentbuffer()
+        # Check if we used group rewards, warn if so.
+        self._warn_if_group_reward(agent_buffer_trajectory)
+
+        # Update the normalization
+        if self.is_training:
+            self.policy.actor.update_normalization(agent_buffer_trajectory)
+            self.optimizer.critic.update_normalization(agent_buffer_trajectory)
+
+        # Get all value estimates
+        (
+            value_estimates,
+            value_next,
+            value_memories,
+        ) = self.optimizer.get_trajectory_value_estimates(
+            agent_buffer_trajectory,
+            trajectory.next_obs,
+            trajectory.done_reached and not trajectory.interrupted,
+        )
+        if value_memories is not None:
+            agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
+
+        for name, v in value_estimates.items():
+            agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(
+                v
+            )
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
+                np.mean(v),
+            )
+
+        # Evaluate all reward functions
+        self.collected_rewards["environment"][agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+        )
+        for name, reward_signal in self.optimizer.reward_signals.items():
+            evaluate_result = (
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+            )
+            agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(
+                evaluate_result
+            )
+            # Report the reward signals
+            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+        # Compute GAE and returns
+        tmp_advantages = []
+        tmp_returns = []
+        for name in self.optimizer.reward_signals:
+            bootstrap_value = value_next[name]
+
+            local_rewards = agent_buffer_trajectory[
+                RewardSignalUtil.rewards_key(name)
+            ].get_batch()
+            local_value_estimates = agent_buffer_trajectory[
+                RewardSignalUtil.value_estimates_key(name)
+            ].get_batch()
+
+            local_advantage = get_gae(
+                rewards=local_rewards,
+                value_estimates=local_value_estimates,
+                value_next=bootstrap_value,
+                gamma=self.optimizer.reward_signals[name].gamma,
+                lambd=self.hyperparameters.lambd,
+            )
+            local_return = local_advantage + local_value_estimates
+            # This is later use as target for the different value estimates
+            agent_buffer_trajectory[RewardSignalUtil.returns_key(name)].set(
+                local_return
+            )
+            agent_buffer_trajectory[RewardSignalUtil.advantage_key(name)].set(
+                local_advantage
+            )
+            tmp_advantages.append(local_advantage)
+            tmp_returns.append(local_return)
+
+        # Get global advantages
+        global_advantages = list(
+            np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
+        )
+        global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0))
+        agent_buffer_trajectory[BufferKey.ADVANTAGES].set(global_advantages)
+        agent_buffer_trajectory[BufferKey.DISCOUNTED_RETURNS].set(global_returns)
+
+        self._append_to_update_buffer(agent_buffer_trajectory)
+
+        # If this was a terminal trajectory, append stats and reset reward collection
+        if trajectory.done_reached:
+            self._update_end_episode_stats(agent_id, self.optimizer)
+
+    def create_optimizer(self) -> TorchOptimizer:
+        """
+        Creates an Optimizer object
+        """
+        return A2COptimizer(  # type: ignore
+            cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore
+        )  # type: ignore
+
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a policy with a PyTorch backend and PPO hyperparameters
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        actor_cls = SimpleActor
+        actor_kwargs = {"conditional_sigma": False, "tanh_squash": False}
+        if self.shared_critic:
+            reward_signal_configs = self.trainer_settings.reward_signals
+            reward_signal_names = [
+                key.value for key, _ in reward_signal_configs.items()
+            ]
+            actor_cls = SharedActorCritic
+            actor_kwargs.update({"stream_names": reward_signal_names})
+
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings.network_settings,
+            actor_cls,
+            actor_kwargs,
+        )
+        return policy
+
+    @staticmethod
+    def get_settings_type():
+        return A2CSettings
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        return TRAINER_NAME
+
+
+def get_type_and_setting():
+    return {A2CTrainer.get_trainer_name(): A2CTrainer}, {
+        A2CTrainer.get_trainer_name(): A2CSettings
+    }
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/__init__.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_basic.yaml b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_basic.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07237dac4e69ac2f1cf702568729db9ad4a61890
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_basic.yaml
@@ -0,0 +1,27 @@
+behaviors:
+  Basic:
+    trainer_type: dqn
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 64
+      buffer_size: 50000
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      exploration_schedule: linear
+      exploration_initial_eps: 0.8
+      exploration_final_eps: 0.05
+    network_settings:
+      normalize: false
+      hidden_units: 20
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 10
+    summary_freq: 1000
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_optimizer.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e01b6b502059aa280a90ea296104be1e417be779
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_optimizer.py
@@ -0,0 +1,290 @@
+from typing import cast
+from mlagents.torch_utils import torch, nn, default_device
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.buffer import AgentBuffer, BufferKey, RewardSignalUtil
+from mlagents_envs.timers import timed
+from typing import List, Dict, Tuple, Optional, Union, Any
+from mlagents.trainers.torch_entities.networks import ValueNetwork, Actor
+from mlagents_envs.base_env import ActionSpec, ObservationSpec
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.trajectory import ObsUtil
+from mlagents.trainers.settings import TrainerSettings, OffPolicyHyperparamSettings
+from mlagents.trainers.settings import ScheduleType, NetworkSettings
+
+from mlagents.trainers.torch_entities.networks import Critic
+import numpy as np
+import attr
+
+
+# TODO: fix saving to onnx
+
+
+@attr.s(auto_attribs=True)
+class DQNSettings(OffPolicyHyperparamSettings):
+    gamma: float = 0.99
+    exploration_schedule: ScheduleType = ScheduleType.LINEAR
+    exploration_initial_eps: float = 0.1
+    exploration_final_eps: float = 0.05
+    target_update_interval: int = 10000
+    tau: float = 0.005
+    steps_per_update: float = 1
+    save_replay_buffer: bool = False
+    reward_signal_steps_per_update: float = attr.ib()
+
+    @reward_signal_steps_per_update.default
+    def _reward_signal_steps_per_update_default(self):
+        return self.steps_per_update
+
+
+class DQNOptimizer(TorchOptimizer):
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        super().__init__(policy, trainer_settings)
+
+        # initialize hyper parameters
+        params = list(self.policy.actor.parameters())
+        self.optimizer = torch.optim.Adam(
+            params, lr=self.trainer_settings.hyperparameters.learning_rate
+        )
+        self.stream_names = list(self.reward_signals.keys())
+        self.gammas = [_val.gamma for _val in trainer_settings.reward_signals.values()]
+        self.use_dones_in_backup = {
+            name: int(not self.reward_signals[name].ignore_done)
+            for name in self.stream_names
+        }
+
+        self.hyperparameters: DQNSettings = cast(
+            DQNSettings, trainer_settings.hyperparameters
+        )
+        self.tau = self.hyperparameters.tau
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+
+        self.decay_exploration_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.exploration_schedule,
+            self.hyperparameters.exploration_initial_eps,
+            self.hyperparameters.exploration_final_eps,
+            20000,
+        )
+
+        # initialize Target Q_network
+        self.q_net_target = QNetwork(
+            stream_names=self.reward_signals.keys(),
+            observation_specs=policy.behavior_spec.observation_specs,
+            network_settings=policy.network_settings,
+            action_spec=policy.behavior_spec.action_spec,
+        )
+        ModelUtils.soft_update(self.policy.actor, self.q_net_target, 1.0)
+
+        self.q_net_target.to(default_device())
+
+    @property
+    def critic(self):
+        return self.q_net_target
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Performs update on model.
+        :param batch: Batch of experiences.
+        :param num_sequences: Number of sequences to process.
+        :return: Results of update.
+        """
+        # Get decayed parameters
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        exp_rate = self.decay_exploration_rate.get_value(self.policy.get_current_step())
+        self.policy.actor.exploration_rate = exp_rate
+        rewards = {}
+        for name in self.reward_signals:
+            rewards[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.rewards_key(name)]
+            )
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        # Convert to tensors
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+
+        next_obs = ObsUtil.from_buffer_next(batch, n_obs)
+        # Convert to tensors
+        next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
+
+        actions = AgentAction.from_buffer(batch)
+
+        dones = ModelUtils.list_to_tensor(batch[BufferKey.DONE])
+
+        current_q_values, _ = self.policy.actor.critic_pass(
+            current_obs, sequence_length=self.policy.sequence_length
+        )
+
+        qloss = []
+        with torch.no_grad():
+            greedy_actions = self.policy.actor.get_greedy_action(current_q_values)
+            next_q_values_list, _ = self.q_net_target.critic_pass(
+                next_obs, sequence_length=self.policy.sequence_length
+            )
+        for name_i, name in enumerate(rewards.keys()):
+            with torch.no_grad():
+                next_q_values = torch.gather(
+                    next_q_values_list[name], dim=1, index=greedy_actions
+                ).squeeze()
+                target_q_values = rewards[name] + (
+                    (1.0 - self.use_dones_in_backup[name] * dones)
+                    * self.gammas[name_i]
+                    * next_q_values
+                )
+                target_q_values = target_q_values.reshape(-1, 1)
+            curr_q = torch.gather(
+                current_q_values[name], dim=1, index=actions.discrete_tensor
+            )
+            qloss.append(torch.nn.functional.smooth_l1_loss(curr_q, target_q_values))
+
+        loss = torch.mean(torch.stack(qloss))
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        ModelUtils.soft_update(self.policy.actor, self.q_net_target, self.tau)
+        update_stats = {
+            "Losses/Value Loss": loss.item(),
+            "Policy/Learning Rate": decay_lr,
+            "Policy/epsilon": exp_rate,
+        }
+
+        for reward_provider in self.reward_signals.values():
+            update_stats.update(reward_provider.update(batch))
+        return update_stats
+
+    def get_modules(self):
+        modules = {
+            "Optimizer:value_optimizer": self.optimizer,
+            "Optimizer:critic": self.critic,
+        }
+        for reward_provider in self.reward_signals.values():
+            modules.update(reward_provider.get_modules())
+        return modules
+
+
+class QNetwork(nn.Module, Actor, Critic):
+    MODEL_EXPORT_VERSION = 3
+
+    def __init__(
+        self,
+        stream_names: List[str],
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        action_spec: ActionSpec,
+        exploration_initial_eps: float = 1.0,
+    ):
+        self.exploration_rate = exploration_initial_eps
+        nn.Module.__init__(self)
+        output_act_size = max(sum(action_spec.discrete_branches), 1)
+        self.network_body = ValueNetwork(
+            stream_names,
+            observation_specs,
+            network_settings,
+            outputs_per_stream=output_act_size,
+        )
+
+        # extra tensors for exporting to ONNX
+        self.action_spec = action_spec
+        self.version_number = torch.nn.Parameter(
+            torch.Tensor([self.MODEL_EXPORT_VERSION]), requires_grad=False
+        )
+        self.is_continuous_int_deprecated = torch.nn.Parameter(
+            torch.Tensor([int(self.action_spec.is_continuous())]), requires_grad=False
+        )
+        self.continuous_act_size_vector = torch.nn.Parameter(
+            torch.Tensor([int(self.action_spec.continuous_size)]), requires_grad=False
+        )
+        self.discrete_act_size_vector = torch.nn.Parameter(
+            torch.Tensor([self.action_spec.discrete_branches]), requires_grad=False
+        )
+        self.act_size_vector_deprecated = torch.nn.Parameter(
+            torch.Tensor(
+                [
+                    self.action_spec.continuous_size
+                    + sum(self.action_spec.discrete_branches)
+                ]
+            ),
+            requires_grad=False,
+        )
+        self.memory_size_vector = torch.nn.Parameter(
+            torch.Tensor([int(self.network_body.memory_size)]), requires_grad=False
+        )
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        self.network_body.update_normalization(buffer)
+
+    def critic_pass(
+        self,
+        inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        value_outputs, critic_mem_out = self.network_body(
+            inputs, memories=memories, sequence_length=sequence_length
+        )
+        return value_outputs, critic_mem_out
+
+    @property
+    def memory_size(self) -> int:
+        return self.network_body.memory_size
+
+    def forward(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Union[int, torch.Tensor], ...]:
+        out_vals, memories = self.critic_pass(inputs, memories, sequence_length)
+
+        # fixme random action tensor
+        export_out = [self.version_number, self.memory_size_vector]
+
+        disc_action_out = self.get_greedy_action(out_vals)
+        deterministic_disc_action_out = self.get_random_action(out_vals)
+        export_out += [
+            disc_action_out,
+            self.discrete_act_size_vector,
+            deterministic_disc_action_out,
+        ]
+        return tuple(export_out)
+
+    def get_random_action(self, inputs) -> torch.Tensor:
+        action_out = torch.randint(
+            0, self.action_spec.discrete_branches[0], (len(inputs), 1)
+        )
+        return action_out
+
+    @staticmethod
+    def get_greedy_action(q_values) -> torch.Tensor:
+        all_q = torch.cat([val.unsqueeze(0) for val in q_values.values()])
+        return torch.argmax(all_q.sum(dim=0), dim=1, keepdim=True)
+
+    def get_action_and_stats(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+        deterministic=False,
+    ) -> Tuple[AgentAction, Dict[str, Any], torch.Tensor]:
+        run_out = {}
+        if not deterministic and np.random.rand() < self.exploration_rate:
+            action_out = self.get_random_action(inputs)
+            action_out = AgentAction(None, [action_out])
+            run_out["env_action"] = action_out.to_action_tuple()
+        else:
+            out_vals, _ = self.critic_pass(inputs, memories, sequence_length)
+            action_out = self.get_greedy_action(out_vals)
+            action_out = AgentAction(None, [action_out])
+            run_out["env_action"] = action_out.to_action_tuple()
+        return action_out, run_out, torch.Tensor([])
diff --git a/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_trainer.py b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2d8011b47cae6d2eae933311d10b340593a2b69
--- /dev/null
+++ b/ml-agents-trainer-plugin/mlagents_trainer_plugin/dqn/dqn_trainer.py
@@ -0,0 +1,162 @@
+from typing import cast
+
+import numpy as np
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.buffer import BufferKey
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.trainer.off_policy_trainer import OffPolicyTrainer
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.trajectory import Trajectory, ObsUtil
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.settings import TrainerSettings
+from .dqn_optimizer import DQNOptimizer, DQNSettings, QNetwork
+
+logger = get_logger(__name__)
+TRAINER_NAME = "dqn"
+
+
+class DQNTrainer(OffPolicyTrainer):
+    """The DQNTrainer is an implementation of"""
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training SAC model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            reward_buff_cap,
+            trainer_settings,
+            training,
+            load,
+            seed,
+            artifact_path,
+        )
+        self.policy: TorchPolicy = None  # type: ignore
+        self.optimizer: DQNOptimizer = None  # type: ignore
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the replay buffer.
+        """
+        super()._process_trajectory(trajectory)
+        last_step = trajectory.steps[-1]
+        agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+        agent_buffer_trajectory = trajectory.to_agentbuffer()
+        # Check if we used group rewards, warn if so.
+        self._warn_if_group_reward(agent_buffer_trajectory)
+
+        # Update the normalization
+        if self.is_training:
+            self.policy.actor.update_normalization(agent_buffer_trajectory)
+            self.optimizer.critic.update_normalization(agent_buffer_trajectory)
+
+        # Evaluate all reward functions for reporting purposes
+        self.collected_rewards["environment"][agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+        )
+        for name, reward_signal in self.optimizer.reward_signals.items():
+            evaluate_result = (
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+            )
+
+            # Report the reward signals
+            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+        # Get all value estimates for reporting purposes
+        (
+            value_estimates,
+            _,
+            value_memories,
+        ) = self.optimizer.get_trajectory_value_estimates(
+            agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached
+        )
+        if value_memories is not None:
+            agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
+
+        for name, v in value_estimates.items():
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
+                np.mean(v),
+            )
+
+        # Bootstrap using the last step rather than the bootstrap step if max step is reached.
+        # Set last element to duplicate obs and remove dones.
+        if last_step.interrupted:
+            last_step_obs = last_step.obs
+            for i, obs in enumerate(last_step_obs):
+                agent_buffer_trajectory[ObsUtil.get_name_at_next(i)][-1] = obs
+            agent_buffer_trajectory[BufferKey.DONE][-1] = False
+
+        self._append_to_update_buffer(agent_buffer_trajectory)
+
+        if trajectory.done_reached:
+            self._update_end_episode_stats(agent_id, self.optimizer)
+
+    def create_optimizer(self) -> TorchOptimizer:
+        """
+        Creates an Optimizer object
+        """
+        return DQNOptimizer(  # type: ignore
+            cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore
+        )  # type: ignore
+
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a policy with a PyTorch backend and give DQN hyperparameters
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        # initialize online Q-network which works as actor
+        exploration_initial_eps = cast(
+            DQNSettings, self.trainer_settings.hyperparameters
+        ).exploration_initial_eps
+        actor_kwargs = {
+            "exploration_initial_eps": exploration_initial_eps,
+            "stream_names": [
+                signal.value for signal in self.trainer_settings.reward_signals.keys()
+            ],
+        }
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings.network_settings,
+            actor_cls=QNetwork,
+            actor_kwargs=actor_kwargs,
+        )
+        self.maybe_load_replay_buffer()
+        return policy
+
+    @staticmethod
+    def get_settings_type():
+        return DQNSettings
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        return TRAINER_NAME
+
+
+def get_type_and_setting():
+    return {DQNTrainer.get_trainer_name(): DQNTrainer}, {
+        DQNTrainer.get_trainer_name(): DQNTrainer.get_settings_type()
+    }
diff --git a/ml-agents-trainer-plugin/setup.py b/ml-agents-trainer-plugin/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..01f8c66aabfd923c8a6a5ab6af36211ccef199b8
--- /dev/null
+++ b/ml-agents-trainer-plugin/setup.py
@@ -0,0 +1,18 @@
+from setuptools import setup
+from mlagents.plugins import ML_AGENTS_TRAINER_TYPE
+
+setup(
+    name="mlagents_trainer_plugin",
+    version="0.0.1",
+    # Example of how to add your own registration functions that will be called
+    # by mlagents-learn.
+    #
+    # Here, the get_example_stats_writer() function in mlagents_plugin_examples/example_stats_writer.py
+    # will get registered with the ML_AGENTS_STATS_WRITER plugin interface.
+    entry_points={
+        ML_AGENTS_TRAINER_TYPE: [
+            "a2c=mlagents_trainer_plugin.a2c.a2c_trainer:get_type_and_setting",
+            "dqn=mlagents_trainer_plugin.dqn.dqn_trainer:get_type_and_setting",
+        ]
+    },
+)
diff --git a/ml-agents/README.md b/ml-agents/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..470ae4a98e62e741abc2178209c562a6a97c8ab0
--- /dev/null
+++ b/ml-agents/README.md
@@ -0,0 +1,31 @@
+# Unity ML-Agents Trainers
+
+The `mlagents` Python package is part of the
+[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents). `mlagents`
+provides a set of reinforcement and imitation learning algorithms designed to be
+used with Unity environments. The algorithms interface with the Python API
+provided by the `mlagents_envs` package. See [here](../docs/Python-LLAPI.md) for
+more information on `mlagents_envs`.
+
+The algorithms can be accessed using the: `mlagents-learn` access point. See
+[here](../docs/Training-ML-Agents.md) for more information on using this
+package.
+
+## Installation
+
+Install the `mlagents` package with:
+
+```sh
+python -m pip install mlagents==0.30.0
+```
+
+## Usage & More Information
+
+For more information on the ML-Agents Toolkit and how to instrument a Unity
+scene with the ML-Agents SDK, check out the main
+[ML-Agents Toolkit documentation](../docs/Readme.md).
+
+## Limitations
+
+- Resuming self-play from a checkpoint resets the reported ELO to the default
+  value.
diff --git a/ml-agents/mlagents.egg-info/PKG-INFO b/ml-agents/mlagents.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..0550bedf8b617d6a4d4cbfba6faa292e8d469670
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/PKG-INFO
@@ -0,0 +1,47 @@
+Metadata-Version: 2.1
+Name: mlagents
+Version: 0.31.0.dev0
+Summary: Unity Machine Learning Agents
+Home-page: https://github.com/Unity-Technologies/ml-agents
+Author: Unity Technologies
+Author-email: ML-Agents@unity3d.com
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.8.13,<=3.10.12
+Description-Content-Type: text/markdown
+
+# Unity ML-Agents Trainers
+
+The `mlagents` Python package is part of the
+[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents). `mlagents`
+provides a set of reinforcement and imitation learning algorithms designed to be
+used with Unity environments. The algorithms interface with the Python API
+provided by the `mlagents_envs` package. See [here](../docs/Python-LLAPI.md) for
+more information on `mlagents_envs`.
+
+The algorithms can be accessed using the: `mlagents-learn` access point. See
+[here](../docs/Training-ML-Agents.md) for more information on using this
+package.
+
+## Installation
+
+Install the `mlagents` package with:
+
+```sh
+python -m pip install mlagents==0.30.0
+```
+
+## Usage & More Information
+
+For more information on the ML-Agents Toolkit and how to instrument a Unity
+scene with the ML-Agents SDK, check out the main
+[ML-Agents Toolkit documentation](../docs/Readme.md).
+
+## Limitations
+
+- Resuming self-play from a checkpoint resets the reported ELO to the default
+  value.
diff --git a/ml-agents/mlagents.egg-info/SOURCES.txt b/ml-agents/mlagents.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0ac40eb6305def3f0dd2a66ff420a468004dfa80
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/SOURCES.txt
@@ -0,0 +1,95 @@
+README.md
+setup.py
+mlagents/__init__.py
+mlagents.egg-info/PKG-INFO
+mlagents.egg-info/SOURCES.txt
+mlagents.egg-info/dependency_links.txt
+mlagents.egg-info/entry_points.txt
+mlagents.egg-info/not-zip-safe
+mlagents.egg-info/requires.txt
+mlagents.egg-info/top_level.txt
+mlagents/plugins/__init__.py
+mlagents/plugins/stats_writer.py
+mlagents/plugins/trainer_type.py
+mlagents/torch_utils/__init__.py
+mlagents/torch_utils/cpu_utils.py
+mlagents/torch_utils/globals.py
+mlagents/torch_utils/torch.py
+mlagents/trainers/__init__.py
+mlagents/trainers/action_info.py
+mlagents/trainers/agent_processor.py
+mlagents/trainers/behavior_id_utils.py
+mlagents/trainers/buffer.py
+mlagents/trainers/cli_utils.py
+mlagents/trainers/demo_loader.py
+mlagents/trainers/directory_utils.py
+mlagents/trainers/env_manager.py
+mlagents/trainers/environment_parameter_manager.py
+mlagents/trainers/exception.py
+mlagents/trainers/learn.py
+mlagents/trainers/run_experiment.py
+mlagents/trainers/settings.py
+mlagents/trainers/simple_env_manager.py
+mlagents/trainers/stats.py
+mlagents/trainers/subprocess_env_manager.py
+mlagents/trainers/trainer_controller.py
+mlagents/trainers/training_analytics_side_channel.py
+mlagents/trainers/training_status.py
+mlagents/trainers/trajectory.py
+mlagents/trainers/upgrade_config.py
+mlagents/trainers/ghost/__init__.py
+mlagents/trainers/ghost/controller.py
+mlagents/trainers/ghost/trainer.py
+mlagents/trainers/model_saver/__init__.py
+mlagents/trainers/model_saver/model_saver.py
+mlagents/trainers/model_saver/torch_model_saver.py
+mlagents/trainers/optimizer/__init__.py
+mlagents/trainers/optimizer/optimizer.py
+mlagents/trainers/optimizer/torch_optimizer.py
+mlagents/trainers/poca/__init__.py
+mlagents/trainers/poca/optimizer_torch.py
+mlagents/trainers/poca/trainer.py
+mlagents/trainers/policy/__init__.py
+mlagents/trainers/policy/checkpoint_manager.py
+mlagents/trainers/policy/policy.py
+mlagents/trainers/policy/torch_policy.py
+mlagents/trainers/ppo/__init__.py
+mlagents/trainers/ppo/optimizer_torch.py
+mlagents/trainers/ppo/trainer.py
+mlagents/trainers/sac/__init__.py
+mlagents/trainers/sac/optimizer_torch.py
+mlagents/trainers/sac/trainer.py
+mlagents/trainers/torch_entities/__init__.py
+mlagents/trainers/torch_entities/action_flattener.py
+mlagents/trainers/torch_entities/action_log_probs.py
+mlagents/trainers/torch_entities/action_model.py
+mlagents/trainers/torch_entities/agent_action.py
+mlagents/trainers/torch_entities/attention.py
+mlagents/trainers/torch_entities/conditioning.py
+mlagents/trainers/torch_entities/decoders.py
+mlagents/trainers/torch_entities/distributions.py
+mlagents/trainers/torch_entities/encoders.py
+mlagents/trainers/torch_entities/layers.py
+mlagents/trainers/torch_entities/model_serialization.py
+mlagents/trainers/torch_entities/networks.py
+mlagents/trainers/torch_entities/utils.py
+mlagents/trainers/torch_entities/components/__init__.py
+mlagents/trainers/torch_entities/components/bc/__init__.py
+mlagents/trainers/torch_entities/components/bc/module.py
+mlagents/trainers/torch_entities/components/reward_providers/__init__.py
+mlagents/trainers/torch_entities/components/reward_providers/base_reward_provider.py
+mlagents/trainers/torch_entities/components/reward_providers/curiosity_reward_provider.py
+mlagents/trainers/torch_entities/components/reward_providers/extrinsic_reward_provider.py
+mlagents/trainers/torch_entities/components/reward_providers/gail_reward_provider.py
+mlagents/trainers/torch_entities/components/reward_providers/reward_provider_factory.py
+mlagents/trainers/torch_entities/components/reward_providers/rnd_reward_provider.py
+mlagents/trainers/trainer/__init__.py
+mlagents/trainers/trainer/off_policy_trainer.py
+mlagents/trainers/trainer/on_policy_trainer.py
+mlagents/trainers/trainer/rl_trainer.py
+mlagents/trainers/trainer/trainer.py
+mlagents/trainers/trainer/trainer_factory.py
+mlagents/trainers/trainer/trainer_utils.py
+mlagents/utils/__init__.py
+mlagents/utils/load_from_hf.py
+mlagents/utils/push_to_hf.py
\ No newline at end of file
diff --git a/ml-agents/mlagents.egg-info/dependency_links.txt b/ml-agents/mlagents.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/ml-agents/mlagents.egg-info/entry_points.txt b/ml-agents/mlagents.egg-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c0c27e38349c7e52e1543f9cf25e10b5dd048403
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/entry_points.txt
@@ -0,0 +1,11 @@
+[console_scripts]
+mlagents-learn = mlagents.trainers.learn:main
+mlagents-load-from-hf = mlagents.utils.load_from_hf:main
+mlagents-push-to-hf = mlagents.utils.push_to_hf:main
+mlagents-run-experiment = mlagents.trainers.run_experiment:main
+
+[mlagents.stats_writer]
+default = mlagents.plugins.stats_writer:get_default_stats_writers
+
+[mlagents.trainer_type]
+default = mlagents.plugins.trainer_type:get_default_trainer_types
diff --git a/ml-agents/mlagents.egg-info/not-zip-safe b/ml-agents/mlagents.egg-info/not-zip-safe
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/not-zip-safe
@@ -0,0 +1 @@
+
diff --git a/ml-agents/mlagents.egg-info/requires.txt b/ml-agents/mlagents.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c384f95554de964d088af0682a236ed89c3b4c0f
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/requires.txt
@@ -0,0 +1,26 @@
+grpcio>=1.11.0
+h5py>=2.9.0
+mlagents_envs==0.31.0.dev0
+numpy<2.0,>=1.13.3
+Pillow>=4.2.1
+protobuf<3.20,>=3.6
+pyyaml>=3.1.0
+tensorboard>=1.15
+attrs>=19.3.0
+huggingface_hub>=0.14
+
+[:platform_system != "Windows" and python_version < "3.9"]
+torch<1.9.0,>=1.6.0
+
+[:platform_system != "Windows" and python_version >= "3.9"]
+torch<=1.11.0,>=1.8.0
+
+[:platform_system == "Windows"]
+pypiwin32==223
+
+[:python_version < "3.8"]
+cattrs<1.1.0
+importlib_metadata==4.4
+
+[:python_version >= "3.8"]
+cattrs<1.7,>=1.1.0
diff --git a/ml-agents/mlagents.egg-info/top_level.txt b/ml-agents/mlagents.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdf8e207464b3ed4067646e83e568a57a6a38fa1
--- /dev/null
+++ b/ml-agents/mlagents.egg-info/top_level.txt
@@ -0,0 +1 @@
+mlagents
diff --git a/ml-agents/mlagents/__init__.py b/ml-agents/mlagents/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/mlagents/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..03793193ba77ae449326da5b07bbf86542ee33c5
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diff --git a/ml-agents/mlagents/plugins/__init__.py b/ml-agents/mlagents/plugins/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b63a39732d8027e1ab6c893725b3c6912af161d3
--- /dev/null
+++ b/ml-agents/mlagents/plugins/__init__.py
@@ -0,0 +1,8 @@
+from typing import Dict, Any
+
+ML_AGENTS_STATS_WRITER = "mlagents.stats_writer"
+ML_AGENTS_TRAINER_TYPE = "mlagents.trainer_type"
+
+# TODO: the real type is Dict[str, HyperparamSettings]
+all_trainer_types: Dict[str, Any] = {}
+all_trainer_settings: Dict[str, Any] = {}
diff --git a/ml-agents/mlagents/plugins/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/plugins/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4fe62ca497ba24d7b8f73e435f80d24538075522
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diff --git a/ml-agents/mlagents/plugins/__pycache__/stats_writer.cpython-310.pyc b/ml-agents/mlagents/plugins/__pycache__/stats_writer.cpython-310.pyc
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index 0000000000000000000000000000000000000000..221afbc45d14de242dd82ac7001278a27cbf7a73
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diff --git a/ml-agents/mlagents/plugins/__pycache__/trainer_type.cpython-310.pyc b/ml-agents/mlagents/plugins/__pycache__/trainer_type.cpython-310.pyc
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index 0000000000000000000000000000000000000000..f3e97f669eb1ad1ae1ba0e53bdbe279d270aff4e
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diff --git a/ml-agents/mlagents/plugins/stats_writer.py b/ml-agents/mlagents/plugins/stats_writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..17acefd32e25f0a9e49b3f89fc4f90733f8a9495
--- /dev/null
+++ b/ml-agents/mlagents/plugins/stats_writer.py
@@ -0,0 +1,72 @@
+import sys
+from typing import List
+
+# importlib.metadata is new in python3.8
+# We use the backport for older python versions.
+if sys.version_info < (3, 8):
+    import importlib_metadata
+else:
+    import importlib.metadata as importlib_metadata  # pylint: disable=E0611
+
+from mlagents.trainers.stats import StatsWriter
+
+from mlagents_envs import logging_util
+from mlagents.plugins import ML_AGENTS_STATS_WRITER
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.stats import TensorboardWriter, GaugeWriter, ConsoleWriter
+
+
+logger = logging_util.get_logger(__name__)
+
+
+def get_default_stats_writers(run_options: RunOptions) -> List[StatsWriter]:
+    """
+    The StatsWriters that mlagents-learn always uses:
+    * A TensorboardWriter to write information to TensorBoard
+    * A GaugeWriter to record our internal stats
+    * A ConsoleWriter to output to stdout.
+    """
+    checkpoint_settings = run_options.checkpoint_settings
+    return [
+        TensorboardWriter(
+            checkpoint_settings.write_path,
+            clear_past_data=not checkpoint_settings.resume,
+            hidden_keys=["Is Training", "Step"],
+        ),
+        GaugeWriter(),
+        ConsoleWriter(),
+    ]
+
+
+def register_stats_writer_plugins(run_options: RunOptions) -> List[StatsWriter]:
+    """
+    Registers all StatsWriter plugins (including the default one),
+    and evaluates them, and returns the list of all the StatsWriter implementations.
+    """
+    all_stats_writers: List[StatsWriter] = []
+    if ML_AGENTS_STATS_WRITER not in importlib_metadata.entry_points():
+        logger.warning(
+            f"Unable to find any entry points for {ML_AGENTS_STATS_WRITER}, even the default ones. "
+            "Uninstalling and reinstalling ml-agents via pip should resolve. "
+            "Using default plugins for now."
+        )
+        return get_default_stats_writers(run_options)
+
+    entry_points = importlib_metadata.entry_points()[ML_AGENTS_STATS_WRITER]
+
+    for entry_point in entry_points:
+
+        try:
+            logger.debug(f"Initializing StatsWriter plugins: {entry_point.name}")
+            plugin_func = entry_point.load()
+            plugin_stats_writers = plugin_func(run_options)
+            logger.debug(
+                f"Found {len(plugin_stats_writers)} StatsWriters for plugin {entry_point.name}"
+            )
+            all_stats_writers += plugin_stats_writers
+        except BaseException:
+            # Catch all exceptions from setting up the plugin, so that bad user code doesn't break things.
+            logger.exception(
+                f"Error initializing StatsWriter plugins for {entry_point.name}. This plugin will not be used."
+            )
+    return all_stats_writers
diff --git a/ml-agents/mlagents/plugins/trainer_type.py b/ml-agents/mlagents/plugins/trainer_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..2766368863caca5e74182c02a94429f09ba3c148
--- /dev/null
+++ b/ml-agents/mlagents/plugins/trainer_type.py
@@ -0,0 +1,80 @@
+import sys
+from typing import Dict, Tuple, Any
+
+# importlib.metadata is new in python3.8
+# We use the backport for older python versions.
+if sys.version_info < (3, 8):
+    import importlib_metadata
+else:
+    import importlib.metadata as importlib_metadata  # pylint: disable=E0611
+
+
+from mlagents_envs import logging_util
+from mlagents.plugins import ML_AGENTS_TRAINER_TYPE
+from mlagents.trainers.ppo.trainer import PPOTrainer
+from mlagents.trainers.sac.trainer import SACTrainer
+from mlagents.trainers.poca.trainer import POCATrainer
+from mlagents.trainers.ppo.optimizer_torch import PPOSettings
+from mlagents.trainers.sac.optimizer_torch import SACSettings
+from mlagents.trainers.poca.optimizer_torch import POCASettings
+from mlagents import plugins as mla_plugins
+
+logger = logging_util.get_logger(__name__)
+
+
+def get_default_trainer_types() -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    """
+    The Trainers that mlagents-learn always uses:
+    """
+
+    mla_plugins.all_trainer_types.update(
+        {
+            PPOTrainer.get_trainer_name(): PPOTrainer,
+            SACTrainer.get_trainer_name(): SACTrainer,
+            POCATrainer.get_trainer_name(): POCATrainer,
+        }
+    )
+    # global all_trainer_settings
+    mla_plugins.all_trainer_settings.update(
+        {
+            PPOTrainer.get_trainer_name(): PPOSettings,
+            SACTrainer.get_trainer_name(): SACSettings,
+            POCATrainer.get_trainer_name(): POCASettings,
+        }
+    )
+
+    return mla_plugins.all_trainer_types, mla_plugins.all_trainer_settings
+
+
+def register_trainer_plugins() -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    """
+    Registers all Trainer plugins (including the default one),
+    and evaluates them, and returns the list of all the Trainer implementations.
+    """
+    if ML_AGENTS_TRAINER_TYPE not in importlib_metadata.entry_points():
+        logger.warning(
+            f"Unable to find any entry points for {ML_AGENTS_TRAINER_TYPE}, even the default ones. "
+            "Uninstalling and reinstalling ml-agents via pip should resolve. "
+            "Using default plugins for now."
+        )
+        return get_default_trainer_types()
+
+    entry_points = importlib_metadata.entry_points()[ML_AGENTS_TRAINER_TYPE]
+
+    for entry_point in entry_points:
+
+        try:
+            logger.debug(f"Initializing Trainer plugins: {entry_point.name}")
+            plugin_func = entry_point.load()
+            plugin_trainer_types, plugin_trainer_settings = plugin_func()
+            logger.debug(
+                f"Found {len(plugin_trainer_types)} Trainers for plugin {entry_point.name}"
+            )
+            mla_plugins.all_trainer_types.update(plugin_trainer_types)
+            mla_plugins.all_trainer_settings.update(plugin_trainer_settings)
+        except BaseException:
+            # Catch all exceptions from setting up the plugin, so that bad user code doesn't break things.
+            logger.exception(
+                f"Error initializing Trainer plugins for {entry_point.name}. This plugin will not be used."
+            )
+    return mla_plugins.all_trainer_types, mla_plugins.all_trainer_settings
diff --git a/ml-agents/mlagents/torch_utils/__init__.py b/ml-agents/mlagents/torch_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0acc96997d94d8d9968950e8db21c651484b8cf3
--- /dev/null
+++ b/ml-agents/mlagents/torch_utils/__init__.py
@@ -0,0 +1,4 @@
+from mlagents.torch_utils.torch import torch as torch  # noqa
+from mlagents.torch_utils.torch import nn  # noqa
+from mlagents.torch_utils.torch import set_torch_config  # noqa
+from mlagents.torch_utils.torch import default_device  # noqa
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diff --git a/ml-agents/mlagents/torch_utils/cpu_utils.py b/ml-agents/mlagents/torch_utils/cpu_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f577375703e0066d596cef16f5d4660881b55f9
--- /dev/null
+++ b/ml-agents/mlagents/torch_utils/cpu_utils.py
@@ -0,0 +1,41 @@
+from typing import Optional
+
+import os
+
+
+def get_num_threads_to_use() -> Optional[int]:
+    """
+    Gets the number of threads to use. For most problems, 4 is all you
+    need, but for smaller machines, we'd like to scale to less than that.
+    By default, PyTorch uses 1/2 of the available cores.
+    """
+    num_cpus = _get_num_available_cpus()
+    return max(min(num_cpus // 2, 4), 1) if num_cpus is not None else None
+
+
+def _get_num_available_cpus() -> Optional[int]:
+    """
+    Returns number of CPUs using cgroups if possible. This accounts
+    for Docker containers that are limited in cores.
+    """
+    period = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.cfs_period_us")
+    quota = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.cfs_quota_us")
+    share = _read_in_integer_file("/sys/fs/cgroup/cpu/cpu.shares")
+    is_kubernetes = os.getenv("KUBERNETES_SERVICE_HOST") is not None
+
+    if period > 0 and quota > 0:
+        return int(quota // period)
+    elif period > 0 and share > 0 and is_kubernetes:
+        # In kubernetes, each requested CPU is 1024 CPU shares
+        # https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#how-pods-with-resource-limits-are-run
+        return int(share // 1024)
+    else:
+        return os.cpu_count()
+
+
+def _read_in_integer_file(filename: str) -> int:
+    try:
+        with open(filename) as f:
+            return int(f.read().rstrip())
+    except FileNotFoundError:
+        return -1
diff --git a/ml-agents/mlagents/torch_utils/globals.py b/ml-agents/mlagents/torch_utils/globals.py
new file mode 100644
index 0000000000000000000000000000000000000000..99705b1067305890316036cb700845af626f9a59
--- /dev/null
+++ b/ml-agents/mlagents/torch_utils/globals.py
@@ -0,0 +1,13 @@
+from typing import Optional
+
+_rank: Optional[int] = None
+
+
+def get_rank() -> Optional[int]:
+    """
+    Returns the rank (in the MPI sense) of the current node.
+    For local training, this will always be None.
+    If this needs to be used, it should be done from outside ml-agents.
+    :return:
+    """
+    return _rank
diff --git a/ml-agents/mlagents/torch_utils/torch.py b/ml-agents/mlagents/torch_utils/torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..24dc45cca3ff5ca99300fff7d86ffc27c0fc49b9
--- /dev/null
+++ b/ml-agents/mlagents/torch_utils/torch.py
@@ -0,0 +1,68 @@
+import os
+
+from distutils.version import LooseVersion
+import pkg_resources
+from mlagents.torch_utils import cpu_utils
+from mlagents.trainers.settings import TorchSettings
+from mlagents_envs.logging_util import get_logger
+
+
+logger = get_logger(__name__)
+
+
+def assert_torch_installed():
+    # Check that torch version 1.6.0 or later has been installed. If not, refer
+    # user to the PyTorch webpage for install instructions.
+    torch_pkg = None
+    try:
+        torch_pkg = pkg_resources.get_distribution("torch")
+    except pkg_resources.DistributionNotFound:
+        pass
+    assert torch_pkg is not None and LooseVersion(torch_pkg.version) >= LooseVersion(
+        "1.6.0"
+    ), (
+        "A compatible version of PyTorch was not installed. Please visit the PyTorch homepage "
+        + "(https://pytorch.org/get-started/locally/) and follow the instructions to install. "
+        + "Version 1.6.0 and later are supported."
+    )
+
+
+assert_torch_installed()
+
+# This should be the only place that we import torch directly.
+# Everywhere else is caught by the banned-modules setting for flake8
+import torch  # noqa I201
+
+
+torch.set_num_threads(cpu_utils.get_num_threads_to_use())
+os.environ["KMP_BLOCKTIME"] = "0"
+
+
+_device = torch.device("cpu")
+
+
+def set_torch_config(torch_settings: TorchSettings) -> None:
+    global _device
+
+    if torch_settings.device is None:
+        device_str = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device_str = torch_settings.device
+
+    _device = torch.device(device_str)
+
+    if _device.type == "cuda":
+        torch.set_default_tensor_type(torch.cuda.FloatTensor)
+    else:
+        torch.set_default_tensor_type(torch.FloatTensor)
+    logger.debug(f"default Torch device: {_device}")
+
+
+# Initialize to default settings
+set_torch_config(TorchSettings(device=None))
+
+nn = torch.nn
+
+
+def default_device():
+    return _device
diff --git a/ml-agents/mlagents/trainers/__init__.py b/ml-agents/mlagents/trainers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b8c9d285f3d51ce2d20c3ded236dfcd4a428caa
--- /dev/null
+++ b/ml-agents/mlagents/trainers/__init__.py
@@ -0,0 +1,5 @@
+# Version of the library that will be used to upload to pypi
+__version__ = "0.31.0.dev0"
+
+# Git tag that will be checked to determine whether to trigger upload to pypi
+__release_tag__ = None
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diff --git a/ml-agents/mlagents/trainers/action_info.py b/ml-agents/mlagents/trainers/action_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0ec02327116119c3115891c4acea4227d3791f3
--- /dev/null
+++ b/ml-agents/mlagents/trainers/action_info.py
@@ -0,0 +1,25 @@
+from typing import NamedTuple, Any, Dict, List
+import numpy as np
+from mlagents_envs.base_env import AgentId
+
+ActionInfoOutputs = Dict[str, np.ndarray]
+
+
+class ActionInfo(NamedTuple):
+    """
+    A NamedTuple containing actions and related quantities to the policy forward
+    pass. Additionally contains the agent ids in the corresponding DecisionStep
+    :param action: The action output of the policy
+    :param env_action: The possibly clipped action to be executed in the environment
+    :param outputs: Dict of all quantities associated with the policy forward pass
+    :param agent_ids: List of int agent ids in DecisionStep
+    """
+
+    action: Any
+    env_action: Any
+    outputs: ActionInfoOutputs
+    agent_ids: List[AgentId]
+
+    @staticmethod
+    def empty() -> "ActionInfo":
+        return ActionInfo([], [], {}, [])
diff --git a/ml-agents/mlagents/trainers/agent_processor.py b/ml-agents/mlagents/trainers/agent_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..720f3d14bdc9060b8def61b5b8fbead30bbf375e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/agent_processor.py
@@ -0,0 +1,469 @@
+import sys
+import numpy as np
+from typing import List, Dict, TypeVar, Generic, Tuple, Any, Union
+from collections import defaultdict, Counter
+import queue
+from mlagents.torch_utils import torch
+
+from mlagents_envs.base_env import (
+    ActionTuple,
+    DecisionSteps,
+    DecisionStep,
+    TerminalSteps,
+    TerminalStep,
+)
+from mlagents_envs.side_channel.stats_side_channel import (
+    StatsAggregationMethod,
+    EnvironmentStats,
+)
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.trajectory import AgentStatus, Trajectory, AgentExperience
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.action_info import ActionInfo, ActionInfoOutputs
+from mlagents.trainers.stats import StatsReporter
+from mlagents.trainers.behavior_id_utils import (
+    get_global_agent_id,
+    get_global_group_id,
+    GlobalAgentId,
+    GlobalGroupId,
+)
+from mlagents.trainers.torch_entities.action_log_probs import LogProbsTuple
+from mlagents.trainers.torch_entities.utils import ModelUtils
+
+T = TypeVar("T")
+
+
+class AgentProcessor:
+    """
+    AgentProcessor contains a dictionary per-agent trajectory buffers. The buffers are indexed by agent_id.
+    Buffer also contains an update_buffer that corresponds to the buffer used when updating the model.
+    One AgentProcessor should be created per agent group.
+    """
+
+    def __init__(
+        self,
+        policy: Policy,
+        behavior_id: str,
+        stats_reporter: StatsReporter,
+        max_trajectory_length: int = sys.maxsize,
+    ):
+        """
+        Create an AgentProcessor.
+
+        :param trainer: Trainer instance connected to this AgentProcessor. Trainer is given trajectory
+        when it is finished.
+        :param policy: Policy instance associated with this AgentProcessor.
+        :param max_trajectory_length: Maximum length of a trajectory before it is added to the trainer.
+        :param stats_category: The category under which to write the stats. Usually, this comes from the Trainer.
+        """
+        self._experience_buffers: Dict[
+            GlobalAgentId, List[AgentExperience]
+        ] = defaultdict(list)
+        self._last_step_result: Dict[GlobalAgentId, Tuple[DecisionStep, int]] = {}
+        # current_group_obs is used to collect the current (i.e. the most recently seen)
+        # obs of all the agents in the same group, and assemble the group obs.
+        # It is a dictionary of GlobalGroupId to dictionaries of GlobalAgentId to observation.
+        self._current_group_obs: Dict[
+            GlobalGroupId, Dict[GlobalAgentId, List[np.ndarray]]
+        ] = defaultdict(lambda: defaultdict(list))
+        # group_status is used to collect the current, most recently seen
+        # group status of all the agents in the same group, and assemble the group's status.
+        # It is a dictionary of GlobalGroupId to dictionaries of GlobalAgentId to AgentStatus.
+        self._group_status: Dict[
+            GlobalGroupId, Dict[GlobalAgentId, AgentStatus]
+        ] = defaultdict(lambda: defaultdict(None))
+        # last_take_action_outputs stores the action a_t taken before the current observation s_(t+1), while
+        # grabbing previous_action from the policy grabs the action PRIOR to that, a_(t-1).
+        self._last_take_action_outputs: Dict[GlobalAgentId, ActionInfoOutputs] = {}
+
+        self._episode_steps: Counter = Counter()
+        self._episode_rewards: Dict[GlobalAgentId, float] = defaultdict(float)
+        self._stats_reporter = stats_reporter
+        self._max_trajectory_length = max_trajectory_length
+        self._trajectory_queues: List[AgentManagerQueue[Trajectory]] = []
+        self._behavior_id = behavior_id
+
+        # Note: In the future this policy reference will be the policy of the env_manager and not the trainer.
+        # We can in that case just grab the action from the policy rather than having it passed in.
+        self.policy = policy
+
+    def add_experiences(
+        self,
+        decision_steps: DecisionSteps,
+        terminal_steps: TerminalSteps,
+        worker_id: int,
+        previous_action: ActionInfo,
+    ) -> None:
+        """
+        Adds experiences to each agent's experience history.
+        :param decision_steps: current DecisionSteps.
+        :param terminal_steps: current TerminalSteps.
+        :param previous_action: The outputs of the Policy's get_action method.
+        """
+        take_action_outputs = previous_action.outputs
+        if take_action_outputs:
+            try:
+                for _entropy in take_action_outputs["entropy"]:
+                    if isinstance(_entropy, torch.Tensor):
+                        _entropy = ModelUtils.to_numpy(_entropy)
+                    self._stats_reporter.add_stat("Policy/Entropy", _entropy)
+            except KeyError:
+                pass
+
+        # Make unique agent_ids that are global across workers
+        action_global_agent_ids = [
+            get_global_agent_id(worker_id, ag_id) for ag_id in previous_action.agent_ids
+        ]
+        for global_id in action_global_agent_ids:
+            if global_id in self._last_step_result:  # Don't store if agent just reset
+                self._last_take_action_outputs[global_id] = take_action_outputs
+
+        # Iterate over all the terminal steps, first gather all the group obs
+        # and then create the AgentExperiences/Trajectories. _add_to_group_status
+        # stores Group statuses in a common data structure self.group_status
+        for terminal_step in terminal_steps.values():
+            self._add_group_status_and_obs(terminal_step, worker_id)
+        for terminal_step in terminal_steps.values():
+            local_id = terminal_step.agent_id
+            global_id = get_global_agent_id(worker_id, local_id)
+            self._process_step(
+                terminal_step, worker_id, terminal_steps.agent_id_to_index[local_id]
+            )
+
+        # Iterate over all the decision steps, first gather all the group obs
+        # and then create the trajectories. _add_to_group_status
+        # stores Group statuses in a common data structure self.group_status
+        for ongoing_step in decision_steps.values():
+            self._add_group_status_and_obs(ongoing_step, worker_id)
+        for ongoing_step in decision_steps.values():
+            local_id = ongoing_step.agent_id
+            self._process_step(
+                ongoing_step, worker_id, decision_steps.agent_id_to_index[local_id]
+            )
+        # Clear the last seen group obs when agents die, but only after all of the group
+        # statuses were added to the trajectory.
+        for terminal_step in terminal_steps.values():
+            local_id = terminal_step.agent_id
+            global_id = get_global_agent_id(worker_id, local_id)
+            self._clear_group_status_and_obs(global_id)
+
+        for _gid in action_global_agent_ids:
+            # If the ID doesn't have a last step result, the agent just reset,
+            # don't store the action.
+            if _gid in self._last_step_result:
+                if "action" in take_action_outputs:
+                    self.policy.save_previous_action(
+                        [_gid], take_action_outputs["action"]
+                    )
+
+    def _add_group_status_and_obs(
+        self, step: Union[TerminalStep, DecisionStep], worker_id: int
+    ) -> None:
+        """
+        Takes a TerminalStep or DecisionStep and adds the information in it
+        to self.group_status. This information can then be retrieved
+        when constructing trajectories to get the status of group mates. Also stores the current
+        observation into current_group_obs, to be used to get the next group observations
+        for bootstrapping.
+        :param step: TerminalStep or DecisionStep
+        :param worker_id: Worker ID of this particular environment. Used to generate a
+            global group id.
+        """
+        global_agent_id = get_global_agent_id(worker_id, step.agent_id)
+        stored_decision_step, idx = self._last_step_result.get(
+            global_agent_id, (None, None)
+        )
+        stored_take_action_outputs = self._last_take_action_outputs.get(
+            global_agent_id, None
+        )
+        if stored_decision_step is not None and stored_take_action_outputs is not None:
+            # 0, the default group_id, means that the agent doesn't belong to an agent group.
+            # If 0, don't add any groupmate information.
+            if step.group_id > 0:
+                global_group_id = get_global_group_id(worker_id, step.group_id)
+                stored_actions = stored_take_action_outputs["action"]
+                action_tuple = ActionTuple(
+                    continuous=stored_actions.continuous[idx],
+                    discrete=stored_actions.discrete[idx],
+                )
+                group_status = AgentStatus(
+                    obs=stored_decision_step.obs,
+                    reward=step.reward,
+                    action=action_tuple,
+                    done=isinstance(step, TerminalStep),
+                )
+                self._group_status[global_group_id][global_agent_id] = group_status
+                self._current_group_obs[global_group_id][global_agent_id] = step.obs
+
+    def _clear_group_status_and_obs(self, global_id: GlobalAgentId) -> None:
+        """
+        Clears an agent from self._group_status and self._current_group_obs.
+        """
+        self._delete_in_nested_dict(self._current_group_obs, global_id)
+        self._delete_in_nested_dict(self._group_status, global_id)
+
+    def _delete_in_nested_dict(self, nested_dict: Dict[str, Any], key: str) -> None:
+        for _manager_id in list(nested_dict.keys()):
+            _team_group = nested_dict[_manager_id]
+            self._safe_delete(_team_group, key)
+            if not _team_group:  # if dict is empty
+                self._safe_delete(nested_dict, _manager_id)
+
+    def _process_step(
+        self, step: Union[TerminalStep, DecisionStep], worker_id: int, index: int
+    ) -> None:
+        terminated = isinstance(step, TerminalStep)
+        global_agent_id = get_global_agent_id(worker_id, step.agent_id)
+        global_group_id = get_global_group_id(worker_id, step.group_id)
+        stored_decision_step, idx = self._last_step_result.get(
+            global_agent_id, (None, None)
+        )
+        stored_take_action_outputs = self._last_take_action_outputs.get(
+            global_agent_id, None
+        )
+        if not terminated:
+            # Index is needed to grab from last_take_action_outputs
+            self._last_step_result[global_agent_id] = (step, index)
+
+        # This state is the consequence of a past action
+        if stored_decision_step is not None and stored_take_action_outputs is not None:
+            obs = stored_decision_step.obs
+            if self.policy.use_recurrent:
+                memory = self.policy.retrieve_previous_memories([global_agent_id])[0, :]
+            else:
+                memory = None
+            done = terminated  # Since this is an ongoing step
+            interrupted = step.interrupted if terminated else False
+            # Add the outputs of the last eval
+            stored_actions = stored_take_action_outputs["action"]
+            action_tuple = ActionTuple(
+                continuous=stored_actions.continuous[idx],
+                discrete=stored_actions.discrete[idx],
+            )
+            try:
+                stored_action_probs = stored_take_action_outputs["log_probs"]
+                if not isinstance(stored_action_probs, LogProbsTuple):
+                    stored_action_probs = stored_action_probs.to_log_probs_tuple()
+                log_probs_tuple = LogProbsTuple(
+                    continuous=stored_action_probs.continuous[idx],
+                    discrete=stored_action_probs.discrete[idx],
+                )
+            except KeyError:
+                log_probs_tuple = LogProbsTuple.empty_log_probs()
+
+            action_mask = stored_decision_step.action_mask
+            prev_action = self.policy.retrieve_previous_action([global_agent_id])[0, :]
+
+            # Assemble teammate_obs. If none saved, then it will be an empty list.
+            group_statuses = []
+            for _id, _mate_status in self._group_status[global_group_id].items():
+                if _id != global_agent_id:
+                    group_statuses.append(_mate_status)
+
+            experience = AgentExperience(
+                obs=obs,
+                reward=step.reward,
+                done=done,
+                action=action_tuple,
+                action_probs=log_probs_tuple,
+                action_mask=action_mask,
+                prev_action=prev_action,
+                interrupted=interrupted,
+                memory=memory,
+                group_status=group_statuses,
+                group_reward=step.group_reward,
+            )
+            # Add the value outputs if needed
+            self._experience_buffers[global_agent_id].append(experience)
+            self._episode_rewards[global_agent_id] += step.reward
+            if not terminated:
+                self._episode_steps[global_agent_id] += 1
+
+            # Add a trajectory segment to the buffer if terminal or the length has reached the time horizon
+            if (
+                len(self._experience_buffers[global_agent_id])
+                >= self._max_trajectory_length
+                or terminated
+            ):
+                next_obs = step.obs
+                next_group_obs = []
+                for _id, _obs in self._current_group_obs[global_group_id].items():
+                    if _id != global_agent_id:
+                        next_group_obs.append(_obs)
+
+                trajectory = Trajectory(
+                    steps=self._experience_buffers[global_agent_id],
+                    agent_id=global_agent_id,
+                    next_obs=next_obs,
+                    next_group_obs=next_group_obs,
+                    behavior_id=self._behavior_id,
+                )
+                for traj_queue in self._trajectory_queues:
+                    traj_queue.put(trajectory)
+                self._experience_buffers[global_agent_id] = []
+            if terminated:
+                # Record episode length.
+                self._stats_reporter.add_stat(
+                    "Environment/Episode Length",
+                    self._episode_steps.get(global_agent_id, 0),
+                )
+                self._clean_agent_data(global_agent_id)
+
+    def _clean_agent_data(self, global_id: GlobalAgentId) -> None:
+        """
+        Removes the data for an Agent.
+        """
+        self._safe_delete(self._experience_buffers, global_id)
+        self._safe_delete(self._last_take_action_outputs, global_id)
+        self._safe_delete(self._last_step_result, global_id)
+        self._safe_delete(self._episode_steps, global_id)
+        self._safe_delete(self._episode_rewards, global_id)
+        self.policy.remove_previous_action([global_id])
+        self.policy.remove_memories([global_id])
+
+    def _safe_delete(self, my_dictionary: Dict[Any, Any], key: Any) -> None:
+        """
+        Safe removes data from a dictionary. If not found,
+        don't delete.
+        """
+        if key in my_dictionary:
+            del my_dictionary[key]
+
+    def publish_trajectory_queue(
+        self, trajectory_queue: "AgentManagerQueue[Trajectory]"
+    ) -> None:
+        """
+        Adds a trajectory queue to the list of queues to publish to when this AgentProcessor
+        assembles a Trajectory
+        :param trajectory_queue: Trajectory queue to publish to.
+        """
+        self._trajectory_queues.append(trajectory_queue)
+
+    def end_episode(self) -> None:
+        """
+        Ends the episode, terminating the current trajectory and stopping stats collection for that
+        episode. Used for forceful reset (e.g. in curriculum or generalization training.)
+        """
+        all_gids = list(self._experience_buffers.keys())  # Need to make copy
+        for _gid in all_gids:
+            self._clean_agent_data(_gid)
+
+
+class AgentManagerQueue(Generic[T]):
+    """
+    Queue used by the AgentManager. Note that we make our own class here because in most implementations
+    deque is sufficient and faster. However, if we want to switch to multiprocessing, we'll need to change
+    out this implementation.
+    """
+
+    class Empty(Exception):
+        """
+        Exception for when the queue is empty.
+        """
+
+        pass
+
+    def __init__(self, behavior_id: str, maxlen: int = 0):
+        """
+        Initializes an AgentManagerQueue. Note that we can give it a behavior_id so that it can be identified
+        separately from an AgentManager.
+        """
+        self._maxlen: int = maxlen
+        self._queue: queue.Queue = queue.Queue(maxsize=maxlen)
+        self._behavior_id = behavior_id
+
+    @property
+    def maxlen(self):
+        """
+        The maximum length of the queue.
+        :return: Maximum length of the queue.
+        """
+        return self._maxlen
+
+    @property
+    def behavior_id(self):
+        """
+        The Behavior ID of this queue.
+        :return: Behavior ID associated with the queue.
+        """
+        return self._behavior_id
+
+    def qsize(self) -> int:
+        """
+        Returns the approximate size of the queue. Note that values may differ
+        depending on the underlying queue implementation.
+        """
+        return self._queue.qsize()
+
+    def empty(self) -> bool:
+        return self._queue.empty()
+
+    def get_nowait(self) -> T:
+        """
+        Gets the next item from the queue, throwing an AgentManagerQueue.Empty exception
+        if the queue is empty.
+        """
+        try:
+            return self._queue.get_nowait()
+        except queue.Empty:
+            raise self.Empty("The AgentManagerQueue is empty.")
+
+    def put(self, item: T) -> None:
+        self._queue.put(item)
+
+
+class AgentManager(AgentProcessor):
+    """
+    An AgentManager is an AgentProcessor that also holds a single trajectory and policy queue.
+    Note: this leaves room for adding AgentProcessors that publish multiple trajectory queues.
+    """
+
+    def __init__(
+        self,
+        policy: Policy,
+        behavior_id: str,
+        stats_reporter: StatsReporter,
+        max_trajectory_length: int = sys.maxsize,
+        threaded: bool = True,
+    ):
+        super().__init__(policy, behavior_id, stats_reporter, max_trajectory_length)
+        trajectory_queue_len = 20 if threaded else 0
+        self.trajectory_queue: AgentManagerQueue[Trajectory] = AgentManagerQueue(
+            self._behavior_id, maxlen=trajectory_queue_len
+        )
+        # NOTE: we make policy queues of infinite length to avoid lockups of the trainers.
+        # In the environment manager, we make sure to empty the policy queue before continuing to produce steps.
+        self.policy_queue: AgentManagerQueue[Policy] = AgentManagerQueue(
+            self._behavior_id, maxlen=0
+        )
+        self.publish_trajectory_queue(self.trajectory_queue)
+
+    def record_environment_stats(
+        self, env_stats: EnvironmentStats, worker_id: int
+    ) -> None:
+        """
+        Pass stats from the environment to the StatsReporter.
+        Depending on the StatsAggregationMethod, either StatsReporter.add_stat or StatsReporter.set_stat is used.
+        The worker_id is used to determine whether StatsReporter.set_stat should be used.
+
+        :param env_stats:
+        :param worker_id:
+        :return:
+        """
+        for stat_name, value_list in env_stats.items():
+            for val, agg_type in value_list:
+                if agg_type == StatsAggregationMethod.AVERAGE:
+                    self._stats_reporter.add_stat(stat_name, val, agg_type)
+                elif agg_type == StatsAggregationMethod.SUM:
+                    self._stats_reporter.add_stat(stat_name, val, agg_type)
+                elif agg_type == StatsAggregationMethod.HISTOGRAM:
+                    self._stats_reporter.add_stat(stat_name, val, agg_type)
+                elif agg_type == StatsAggregationMethod.MOST_RECENT:
+                    # In order to prevent conflicts between multiple environments,
+                    # only stats from the first environment are recorded.
+                    if worker_id == 0:
+                        self._stats_reporter.set_stat(stat_name, val)
+                else:
+                    raise UnityTrainerException(
+                        f"Unknown StatsAggregationMethod encountered. {agg_type}"
+                    )
diff --git a/ml-agents/mlagents/trainers/behavior_id_utils.py b/ml-agents/mlagents/trainers/behavior_id_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c23069161b8996c2a148b87ca884589cc0c7e8e3
--- /dev/null
+++ b/ml-agents/mlagents/trainers/behavior_id_utils.py
@@ -0,0 +1,64 @@
+from typing import NamedTuple
+from urllib.parse import urlparse, parse_qs
+from mlagents_envs.base_env import AgentId, GroupId
+
+GlobalGroupId = str
+GlobalAgentId = str
+
+
+class BehaviorIdentifiers(NamedTuple):
+    """
+    BehaviorIdentifiers is a named tuple of the identifiers that uniquely distinguish
+    an agent encountered in the trainer_controller. The named tuple consists of the
+    fully qualified behavior name, the name of the brain name (corresponds to trainer
+    in the trainer controller) and the team id.  In the future, this can be extended
+    to support further identifiers.
+    """
+
+    behavior_id: str
+    brain_name: str
+    team_id: int
+
+    @staticmethod
+    def from_name_behavior_id(name_behavior_id: str) -> "BehaviorIdentifiers":
+        """
+        Parses a name_behavior_id of the form name?team=0
+        into a BehaviorIdentifiers NamedTuple.
+        This allows you to access the brain name and team id of an agent
+        :param name_behavior_id: String of behavior params in HTTP format.
+        :returns: A BehaviorIdentifiers object.
+        """
+
+        parsed = urlparse(name_behavior_id)
+        name = parsed.path
+        ids = parse_qs(parsed.query)
+        team_id: int = 0
+        if "team" in ids:
+            team_id = int(ids["team"][0])
+        return BehaviorIdentifiers(
+            behavior_id=name_behavior_id, brain_name=name, team_id=team_id
+        )
+
+
+def create_name_behavior_id(name: str, team_id: int) -> str:
+    """
+    Reconstructs fully qualified behavior name from name and team_id
+    :param name: brain name
+    :param team_id: team ID
+    :return: name_behavior_id
+    """
+    return name + "?team=" + str(team_id)
+
+
+def get_global_agent_id(worker_id: int, agent_id: AgentId) -> GlobalAgentId:
+    """
+    Create an agent id that is unique across environment workers using the worker_id.
+    """
+    return f"agent_{worker_id}-{agent_id}"
+
+
+def get_global_group_id(worker_id: int, group_id: GroupId) -> GlobalGroupId:
+    """
+    Create a group id that is unique across environment workers when using the worker_id.
+    """
+    return f"group_{worker_id}-{group_id}"
diff --git a/ml-agents/mlagents/trainers/buffer.py b/ml-agents/mlagents/trainers/buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea6a2d51114906d9c755f4b7d0a411cdd345846e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/buffer.py
@@ -0,0 +1,521 @@
+from collections import defaultdict
+from collections.abc import MutableMapping
+import enum
+import itertools
+from typing import BinaryIO, DefaultDict, List, Tuple, Union, Optional
+
+import numpy as np
+import h5py
+
+from mlagents_envs.exception import UnityException
+
+# Elements in the buffer can be np.ndarray, or in the case of teammate obs, actions, rewards,
+# a List of np.ndarray. This is done so that we don't have duplicated np.ndarrays, only references.
+BufferEntry = Union[np.ndarray, List[np.ndarray]]
+
+
+class BufferException(UnityException):
+    """
+    Related to errors with the Buffer.
+    """
+
+    pass
+
+
+class BufferKey(enum.Enum):
+    ACTION_MASK = "action_mask"
+    CONTINUOUS_ACTION = "continuous_action"
+    NEXT_CONT_ACTION = "next_continuous_action"
+    CONTINUOUS_LOG_PROBS = "continuous_log_probs"
+    DISCRETE_ACTION = "discrete_action"
+    NEXT_DISC_ACTION = "next_discrete_action"
+    DISCRETE_LOG_PROBS = "discrete_log_probs"
+    DONE = "done"
+    ENVIRONMENT_REWARDS = "environment_rewards"
+    MASKS = "masks"
+    MEMORY = "memory"
+    CRITIC_MEMORY = "critic_memory"
+    BASELINE_MEMORY = "poca_baseline_memory"
+    PREV_ACTION = "prev_action"
+
+    ADVANTAGES = "advantages"
+    DISCOUNTED_RETURNS = "discounted_returns"
+
+    GROUP_DONES = "group_dones"
+    GROUPMATE_REWARDS = "groupmate_reward"
+    GROUP_REWARD = "group_reward"
+    GROUP_CONTINUOUS_ACTION = "group_continuous_action"
+    GROUP_DISCRETE_ACTION = "group_discrete_aaction"
+    GROUP_NEXT_CONT_ACTION = "group_next_cont_action"
+    GROUP_NEXT_DISC_ACTION = "group_next_disc_action"
+
+
+class ObservationKeyPrefix(enum.Enum):
+    OBSERVATION = "obs"
+    NEXT_OBSERVATION = "next_obs"
+
+    GROUP_OBSERVATION = "group_obs"
+    NEXT_GROUP_OBSERVATION = "next_group_obs"
+
+
+class RewardSignalKeyPrefix(enum.Enum):
+    # Reward signals
+    REWARDS = "rewards"
+    VALUE_ESTIMATES = "value_estimates"
+    RETURNS = "returns"
+    ADVANTAGE = "advantage"
+    BASELINES = "baselines"
+
+
+AgentBufferKey = Union[
+    BufferKey, Tuple[ObservationKeyPrefix, int], Tuple[RewardSignalKeyPrefix, str]
+]
+
+
+class RewardSignalUtil:
+    @staticmethod
+    def rewards_key(name: str) -> AgentBufferKey:
+        return RewardSignalKeyPrefix.REWARDS, name
+
+    @staticmethod
+    def value_estimates_key(name: str) -> AgentBufferKey:
+        return RewardSignalKeyPrefix.RETURNS, name
+
+    @staticmethod
+    def returns_key(name: str) -> AgentBufferKey:
+        return RewardSignalKeyPrefix.RETURNS, name
+
+    @staticmethod
+    def advantage_key(name: str) -> AgentBufferKey:
+        return RewardSignalKeyPrefix.ADVANTAGE, name
+
+    @staticmethod
+    def baseline_estimates_key(name: str) -> AgentBufferKey:
+        return RewardSignalKeyPrefix.BASELINES, name
+
+
+class AgentBufferField(list):
+    """
+    AgentBufferField is a list of numpy arrays, or List[np.ndarray] for group entries.
+    When an agent collects a field, you can add it to its AgentBufferField with the append method.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.padding_value = 0
+        super().__init__(*args, **kwargs)
+
+    def __str__(self) -> str:
+        return f"AgentBufferField: {super().__str__()}"
+
+    def __getitem__(self, index):
+        return_data = super().__getitem__(index)
+        if isinstance(return_data, list):
+            return AgentBufferField(return_data)
+        else:
+            return return_data
+
+    @property
+    def contains_lists(self) -> bool:
+        """
+        Checks whether this AgentBufferField contains List[np.ndarray].
+        """
+        return len(self) > 0 and isinstance(self[0], list)
+
+    def append(self, element: BufferEntry, padding_value: float = 0.0) -> None:
+        """
+        Adds an element to this list. Also lets you change the padding
+        type, so that it can be set on append (e.g. action_masks should
+        be padded with 1.)
+        :param element: The element to append to the list.
+        :param padding_value: The value used to pad when get_batch is called.
+        """
+        super().append(element)
+        self.padding_value = padding_value
+
+    def set(self, data: List[BufferEntry]) -> None:
+        """
+        Sets the list of BufferEntry to the input data
+        :param data: The BufferEntry list to be set.
+        """
+        self[:] = data
+
+    def get_batch(
+        self,
+        batch_size: int = None,
+        training_length: Optional[int] = 1,
+        sequential: bool = True,
+    ) -> List[BufferEntry]:
+        """
+        Retrieve the last batch_size elements of length training_length
+        from the list of np.array
+        :param batch_size: The number of elements to retrieve. If None:
+        All elements will be retrieved.
+        :param training_length: The length of the sequence to be retrieved. If
+        None: only takes one element.
+        :param sequential: If true and training_length is not None: the elements
+        will not repeat in the sequence. [a,b,c,d,e] with training_length = 2 and
+        sequential=True gives [[0,a],[b,c],[d,e]]. If sequential=False gives
+        [[a,b],[b,c],[c,d],[d,e]]
+        """
+        if training_length is None:
+            training_length = 1
+        if sequential:
+            # The sequences will not have overlapping elements (this involves padding)
+            leftover = len(self) % training_length
+            # leftover is the number of elements in the first sequence (this sequence might need 0 padding)
+            if batch_size is None:
+                # retrieve the maximum number of elements
+                batch_size = len(self) // training_length + 1 * (leftover != 0)
+            # The maximum number of sequences taken from a list of length len(self) without overlapping
+            # with padding is equal to batch_size
+            if batch_size > (len(self) // training_length + 1 * (leftover != 0)):
+                raise BufferException(
+                    "The batch size and training length requested for get_batch where"
+                    " too large given the current number of data points."
+                )
+            if batch_size * training_length > len(self):
+                if self.contains_lists:
+                    padding = []
+                else:
+                    # We want to duplicate the last value in the array, multiplied by the padding_value.
+                    padding = np.array(self[-1], dtype=np.float32) * self.padding_value
+                return self[:] + [padding] * (training_length - leftover)
+
+            else:
+                return self[len(self) - batch_size * training_length :]
+        else:
+            # The sequences will have overlapping elements
+            if batch_size is None:
+                # retrieve the maximum number of elements
+                batch_size = len(self) - training_length + 1
+            # The number of sequences of length training_length taken from a list of len(self) elements
+            # with overlapping is equal to batch_size
+            if (len(self) - training_length + 1) < batch_size:
+                raise BufferException(
+                    "The batch size and training length requested for get_batch where"
+                    " too large given the current number of data points."
+                )
+            tmp_list: List[np.ndarray] = []
+            for end in range(len(self) - batch_size + 1, len(self) + 1):
+                tmp_list += self[end - training_length : end]
+            return tmp_list
+
+    def reset_field(self) -> None:
+        """
+        Resets the AgentBufferField
+        """
+        self[:] = []
+
+    def padded_to_batch(
+        self, pad_value: np.float = 0, dtype: np.dtype = np.float32
+    ) -> Union[np.ndarray, List[np.ndarray]]:
+        """
+        Converts this AgentBufferField (which is a List[BufferEntry]) into a numpy array
+        with first dimension equal to the length of this AgentBufferField. If this AgentBufferField
+        contains a List[List[BufferEntry]] (i.e., in the case of group observations), return a List
+        containing numpy arrays or tensors, of length equal to the maximum length of an entry. Missing
+        For entries with less than that length, the array will be padded with pad_value.
+        :param pad_value: Value to pad List AgentBufferFields, when there are less than the maximum
+            number of agents present.
+        :param dtype: Dtype of output numpy array.
+        :return: Numpy array or List of numpy arrays representing this AgentBufferField, where the first
+            dimension is equal to the length of the AgentBufferField.
+        """
+        if len(self) > 0 and not isinstance(self[0], list):
+            return np.asanyarray(self, dtype=dtype)
+
+        shape = None
+        for _entry in self:
+            # _entry could be an empty list if there are no group agents in this
+            # step. Find the first non-empty list and use that shape.
+            if _entry:
+                shape = _entry[0].shape
+                break
+        # If there were no groupmate agents in the entire batch, return an empty List.
+        if shape is None:
+            return []
+
+        # Convert to numpy array while padding with 0's
+        new_list = list(
+            map(
+                lambda x: np.asanyarray(x, dtype=dtype),
+                itertools.zip_longest(*self, fillvalue=np.full(shape, pad_value)),
+            )
+        )
+        return new_list
+
+    def to_ndarray(self):
+        """
+        Returns the AgentBufferField which is a list of numpy ndarrays (or List[np.ndarray]) as an ndarray.
+        """
+        return np.array(self)
+
+
+class AgentBuffer(MutableMapping):
+    """
+    AgentBuffer contains a dictionary of AgentBufferFields. Each agent has his own AgentBuffer.
+    The keys correspond to the name of the field. Example: state, action
+    """
+
+    # Whether or not to validate the types of keys at runtime
+    # This should be off for training, but enabled for testing
+    CHECK_KEY_TYPES_AT_RUNTIME = False
+
+    def __init__(self):
+        self.last_brain_info = None
+        self.last_take_action_outputs = None
+        self._fields: DefaultDict[AgentBufferKey, AgentBufferField] = defaultdict(
+            AgentBufferField
+        )
+
+    def __str__(self):
+        return ", ".join([f"'{k}' : {str(self[k])}" for k in self._fields.keys()])
+
+    def reset_agent(self) -> None:
+        """
+        Resets the AgentBuffer
+        """
+        for f in self._fields.values():
+            f.reset_field()
+        self.last_brain_info = None
+        self.last_take_action_outputs = None
+
+    @staticmethod
+    def _check_key(key):
+        if isinstance(key, BufferKey):
+            return
+        if isinstance(key, tuple):
+            key0, key1 = key
+            if isinstance(key0, ObservationKeyPrefix):
+                if isinstance(key1, int):
+                    return
+                raise KeyError(f"{key} has type ({type(key0)}, {type(key1)})")
+            if isinstance(key0, RewardSignalKeyPrefix):
+                if isinstance(key1, str):
+                    return
+                raise KeyError(f"{key} has type ({type(key0)}, {type(key1)})")
+        raise KeyError(f"{key} is a {type(key)}")
+
+    @staticmethod
+    def _encode_key(key: AgentBufferKey) -> str:
+        """
+        Convert the key to a string representation so that it can be used for serialization.
+        """
+        if isinstance(key, BufferKey):
+            return key.value
+        prefix, suffix = key
+        return f"{prefix.value}:{suffix}"
+
+    @staticmethod
+    def _decode_key(encoded_key: str) -> AgentBufferKey:
+        """
+        Convert the string representation back to a key after serialization.
+        """
+        # Simple case: convert the string directly to a BufferKey
+        try:
+            return BufferKey(encoded_key)
+        except ValueError:
+            pass
+
+        # Not a simple key, so split into two parts
+        prefix_str, _, suffix_str = encoded_key.partition(":")
+
+        # See if it's an ObservationKeyPrefix first
+        try:
+            return ObservationKeyPrefix(prefix_str), int(suffix_str)
+        except ValueError:
+            pass
+
+        # If not, it had better be a RewardSignalKeyPrefix
+        try:
+            return RewardSignalKeyPrefix(prefix_str), suffix_str
+        except ValueError:
+            raise ValueError(f"Unable to convert {encoded_key} to an AgentBufferKey")
+
+    def __getitem__(self, key: AgentBufferKey) -> AgentBufferField:
+        if self.CHECK_KEY_TYPES_AT_RUNTIME:
+            self._check_key(key)
+        return self._fields[key]
+
+    def __setitem__(self, key: AgentBufferKey, value: AgentBufferField) -> None:
+        if self.CHECK_KEY_TYPES_AT_RUNTIME:
+            self._check_key(key)
+        self._fields[key] = value
+
+    def __delitem__(self, key: AgentBufferKey) -> None:
+        if self.CHECK_KEY_TYPES_AT_RUNTIME:
+            self._check_key(key)
+        self._fields.__delitem__(key)
+
+    def __iter__(self):
+        return self._fields.__iter__()
+
+    def __len__(self) -> int:
+        return self._fields.__len__()
+
+    def __contains__(self, key):
+        if self.CHECK_KEY_TYPES_AT_RUNTIME:
+            self._check_key(key)
+        return self._fields.__contains__(key)
+
+    def check_length(self, key_list: List[AgentBufferKey]) -> bool:
+        """
+        Some methods will require that some fields have the same length.
+        check_length will return true if the fields in key_list
+        have the same length.
+        :param key_list: The fields which length will be compared
+        """
+        if self.CHECK_KEY_TYPES_AT_RUNTIME:
+            for k in key_list:
+                self._check_key(k)
+
+        if len(key_list) < 2:
+            return True
+        length = None
+        for key in key_list:
+            if key not in self._fields:
+                return False
+            if (length is not None) and (length != len(self[key])):
+                return False
+            length = len(self[key])
+        return True
+
+    def shuffle(
+        self, sequence_length: int, key_list: List[AgentBufferKey] = None
+    ) -> None:
+        """
+        Shuffles the fields in key_list in a consistent way: The reordering will
+        be the same across fields.
+        :param key_list: The fields that must be shuffled.
+        """
+        if key_list is None:
+            key_list = list(self._fields.keys())
+        if not self.check_length(key_list):
+            raise BufferException(
+                "Unable to shuffle if the fields are not of same length"
+            )
+        s = np.arange(len(self[key_list[0]]) // sequence_length)
+        np.random.shuffle(s)
+        for key in key_list:
+            buffer_field = self[key]
+            tmp: List[np.ndarray] = []
+            for i in s:
+                tmp += buffer_field[i * sequence_length : (i + 1) * sequence_length]
+            buffer_field.set(tmp)
+
+    def make_mini_batch(self, start: int, end: int) -> "AgentBuffer":
+        """
+        Creates a mini-batch from buffer.
+        :param start: Starting index of buffer.
+        :param end: Ending index of buffer.
+        :return: Dict of mini batch.
+        """
+        mini_batch = AgentBuffer()
+        for key, field in self._fields.items():
+            # slicing AgentBufferField returns a List[Any}
+            mini_batch[key] = field[start:end]  # type: ignore
+        return mini_batch
+
+    def sample_mini_batch(
+        self, batch_size: int, sequence_length: int = 1
+    ) -> "AgentBuffer":
+        """
+        Creates a mini-batch from a random start and end.
+        :param batch_size: number of elements to withdraw.
+        :param sequence_length: Length of sequences to sample.
+            Number of sequences to sample will be batch_size/sequence_length.
+        """
+        num_seq_to_sample = batch_size // sequence_length
+        mini_batch = AgentBuffer()
+        buff_len = self.num_experiences
+        num_sequences_in_buffer = buff_len // sequence_length
+        start_idxes = (
+            np.random.randint(num_sequences_in_buffer, size=num_seq_to_sample)
+            * sequence_length
+        )  # Sample random sequence starts
+        for key in self:
+            buffer_field = self[key]
+            mb_list = (buffer_field[i : i + sequence_length] for i in start_idxes)
+            # See comparison of ways to make a list from a list of lists here:
+            # https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-list-of-lists
+            mini_batch[key].set(list(itertools.chain.from_iterable(mb_list)))
+        return mini_batch
+
+    def save_to_file(self, file_object: BinaryIO) -> None:
+        """
+        Saves the AgentBuffer to a file-like object.
+        """
+        with h5py.File(file_object, "w") as write_file:
+            for key, data in self.items():
+                write_file.create_dataset(
+                    self._encode_key(key), data=data, dtype="f", compression="gzip"
+                )
+
+    def load_from_file(self, file_object: BinaryIO) -> None:
+        """
+        Loads the AgentBuffer from a file-like object.
+        """
+        with h5py.File(file_object, "r") as read_file:
+            for key in list(read_file.keys()):
+                decoded_key = self._decode_key(key)
+                self[decoded_key] = AgentBufferField()
+                # extend() will convert the numpy array's first dimension into list
+                self[decoded_key].extend(read_file[key][()])
+
+    def truncate(self, max_length: int, sequence_length: int = 1) -> None:
+        """
+        Truncates the buffer to a certain length.
+
+        This can be slow for large buffers. We compensate by cutting further than we need to, so that
+        we're not truncating at each update. Note that we must truncate an integer number of sequence_lengths
+        param: max_length: The length at which to truncate the buffer.
+        """
+        current_length = self.num_experiences
+        # make max_length an integer number of sequence_lengths
+        max_length -= max_length % sequence_length
+        if current_length > max_length:
+            for _key in self.keys():
+                self[_key][:] = self[_key][current_length - max_length :]
+
+    def resequence_and_append(
+        self,
+        target_buffer: "AgentBuffer",
+        key_list: List[AgentBufferKey] = None,
+        batch_size: int = None,
+        training_length: int = None,
+    ) -> None:
+        """
+        Takes in a batch size and training length (sequence length), and appends this AgentBuffer to target_buffer
+        properly padded for LSTM use. Optionally, use key_list to restrict which fields are inserted into the new
+        buffer.
+        :param target_buffer: The buffer which to append the samples to.
+        :param key_list: The fields that must be added. If None: all fields will be appended.
+        :param batch_size: The number of elements that must be appended. If None: All of them will be.
+        :param training_length: The length of the samples that must be appended. If None: only takes one element.
+        """
+        if key_list is None:
+            key_list = list(self.keys())
+        if not self.check_length(key_list):
+            raise BufferException(
+                f"The length of the fields {key_list} were not of same length"
+            )
+        for field_key in key_list:
+            target_buffer[field_key].extend(
+                self[field_key].get_batch(
+                    batch_size=batch_size, training_length=training_length
+                )
+            )
+
+    @property
+    def num_experiences(self) -> int:
+        """
+        The number of agent experiences in the AgentBuffer, i.e. the length of the buffer.
+
+        An experience consists of one element across all of the fields of this AgentBuffer.
+        Note that these all have to be the same length, otherwise shuffle and append_to_update_buffer
+        will fail.
+        """
+        if self.values():
+            return len(next(iter(self.values())))
+        else:
+            return 0
diff --git a/ml-agents/mlagents/trainers/cli_utils.py b/ml-agents/mlagents/trainers/cli_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..de420c42a42c5519f196c502f77ad36f80346abb
--- /dev/null
+++ b/ml-agents/mlagents/trainers/cli_utils.py
@@ -0,0 +1,331 @@
+from typing import Set, Dict, Any, TextIO
+import os
+import yaml
+from mlagents.trainers.exception import TrainerConfigError
+from mlagents_envs.environment import UnityEnvironment
+import argparse
+from mlagents_envs import logging_util
+
+logger = logging_util.get_logger(__name__)
+
+
+class RaiseRemovedWarning(argparse.Action):
+    """
+    Internal custom Action to raise warning when argument is called.
+    """
+
+    def __init__(self, nargs=0, **kwargs):
+        super().__init__(nargs=nargs, **kwargs)
+
+    def __call__(self, arg_parser, namespace, values, option_string=None):
+        logger.warning(f"The command line argument {option_string} was removed.")
+
+
+class DetectDefault(argparse.Action):
+    """
+    Internal custom Action to help detect arguments that aren't default.
+    """
+
+    non_default_args: Set[str] = set()
+
+    def __call__(self, arg_parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, values)
+        DetectDefault.non_default_args.add(self.dest)
+
+
+class DetectDefaultStoreTrue(DetectDefault):
+    """
+    Internal class to help detect arguments that aren't default.
+    Used for store_true arguments.
+    """
+
+    def __init__(self, nargs=0, **kwargs):
+        super().__init__(nargs=nargs, **kwargs)
+
+    def __call__(self, arg_parser, namespace, values, option_string=None):
+        super().__call__(arg_parser, namespace, True, option_string)
+
+
+class StoreConfigFile(argparse.Action):
+    """
+    Custom Action to store the config file location not as part of the CLI args.
+    This is because we want to maintain an equivalence between the config file's
+    contents and the args themselves.
+    """
+
+    trainer_config_path: str
+
+    def __call__(self, arg_parser, namespace, values, option_string=None):
+        delattr(namespace, self.dest)
+        StoreConfigFile.trainer_config_path = values
+
+
+def _create_parser() -> argparse.ArgumentParser:
+    argparser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    argparser.add_argument(
+        "trainer_config_path", action=StoreConfigFile, nargs="?", default=None
+    )
+    argparser.add_argument(
+        "--env",
+        default=None,
+        dest="env_path",
+        help="Path to the Unity executable to train",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--load",
+        default=False,
+        dest="load_model",
+        action=DetectDefaultStoreTrue,
+        help=argparse.SUPPRESS,  # Deprecated but still usable for now.
+    )
+    argparser.add_argument(
+        "--resume",
+        default=False,
+        dest="resume",
+        action=DetectDefaultStoreTrue,
+        help="Whether to resume training from a checkpoint. Specify a --run-id to use this option. "
+        "If set, the training code loads an already trained model to initialize the neural network "
+        "before resuming training. This option is only valid when the models exist, and have the same "
+        "behavior names as the current agents in your scene.",
+    )
+    argparser.add_argument(
+        "--deterministic",
+        default=False,
+        dest="deterministic",
+        action=DetectDefaultStoreTrue,
+        help="Whether to select actions deterministically in policy. `dist.mean` for continuous action "
+        "space, and `dist.argmax` for deterministic action space ",
+    )
+    argparser.add_argument(
+        "--force",
+        default=False,
+        dest="force",
+        action=DetectDefaultStoreTrue,
+        help="Whether to force-overwrite this run-id's existing summary and model data. (Without "
+        "this flag, attempting to train a model with a run-id that has been used before will throw "
+        "an error.",
+    )
+    argparser.add_argument(
+        "--run-id",
+        default="ppo",
+        help="The identifier for the training run. This identifier is used to name the "
+        "subdirectories in which the trained model and summary statistics are saved as well "
+        "as the saved model itself. If you use TensorBoard to view the training statistics, "
+        "always set a unique run-id for each training run. (The statistics for all runs with the "
+        "same id are combined as if they were produced by a the same session.)",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--initialize-from",
+        metavar="RUN_ID",
+        default=None,
+        help="Specify a previously saved run ID from which to initialize the model from. "
+        "This can be used, for instance, to fine-tune an existing model on a new environment. "
+        "Note that the previously saved models must have the same behavior parameters as your "
+        "current environment.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--seed",
+        default=-1,
+        type=int,
+        help="A number to use as a seed for the random number generator used by the training code",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--train",
+        default=False,
+        dest="train_model",
+        action=DetectDefaultStoreTrue,
+        help=argparse.SUPPRESS,
+    )
+    argparser.add_argument(
+        "--inference",
+        default=False,
+        dest="inference",
+        action=DetectDefaultStoreTrue,
+        help="Whether to run in Python inference mode (i.e. no training). Use with --resume to load "
+        "a model trained with an existing run ID.",
+    )
+    argparser.add_argument(
+        "--base-port",
+        default=UnityEnvironment.BASE_ENVIRONMENT_PORT,
+        type=int,
+        help="The starting port for environment communication. Each concurrent Unity environment "
+        "instance will get assigned a port sequentially, starting from the base-port. Each instance "
+        "will use the port (base_port + worker_id), where the worker_id is sequential IDs given to "
+        "each instance from 0 to (num_envs - 1). Note that when training using the Editor rather "
+        "than an executable, the base port will be ignored.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--num-envs",
+        default=1,
+        type=int,
+        help="The number of concurrent Unity environment instances to collect experiences "
+        "from when training",
+        action=DetectDefault,
+    )
+
+    argparser.add_argument(
+        "--num-areas",
+        default=1,
+        type=int,
+        help="The number of parallel training areas in each Unity environment instance.",
+        action=DetectDefault,
+    )
+
+    argparser.add_argument(
+        "--debug",
+        default=False,
+        action=DetectDefaultStoreTrue,
+        help="Whether to enable debug-level logging for some parts of the code",
+    )
+    argparser.add_argument(
+        "--env-args",
+        default=None,
+        nargs=argparse.REMAINDER,
+        help="Arguments passed to the Unity executable. Be aware that the standalone build will also "
+        "process these as Unity Command Line Arguments. You should choose different argument names if "
+        "you want to create environment-specific arguments. All arguments after this flag will be "
+        "passed to the executable.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--max-lifetime-restarts",
+        default=10,
+        help="The max number of times a single Unity executable can crash over its lifetime before ml-agents exits. "
+        "Can be set to -1 if no limit is desired.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--restarts-rate-limit-n",
+        default=1,
+        help="The maximum number of times a single Unity executable can crash over a period of time (period set in "
+        "restarts-rate-limit-period-s). Can be set to -1 to not use rate limiting with restarts.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--restarts-rate-limit-period-s",
+        default=60,
+        help="The period of time --restarts-rate-limit-n applies to.",
+        action=DetectDefault,
+    )
+    argparser.add_argument(
+        "--torch",
+        default=False,
+        action=RaiseRemovedWarning,
+        help="(Removed) Use the PyTorch framework.",
+    )
+    argparser.add_argument(
+        "--tensorflow",
+        default=False,
+        action=RaiseRemovedWarning,
+        help="(Removed) Use the TensorFlow framework.",
+    )
+    argparser.add_argument(
+        "--results-dir",
+        default="results",
+        action=DetectDefault,
+        help="Results base directory",
+    )
+
+    eng_conf = argparser.add_argument_group(title="Engine Configuration")
+    eng_conf.add_argument(
+        "--width",
+        default=84,
+        type=int,
+        help="The width of the executable window of the environment(s) in pixels "
+        "(ignored for editor training).",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--height",
+        default=84,
+        type=int,
+        help="The height of the executable window of the environment(s) in pixels "
+        "(ignored for editor training)",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--quality-level",
+        default=5,
+        type=int,
+        help="The quality level of the environment(s). Equivalent to calling "
+        "QualitySettings.SetQualityLevel in Unity.",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--time-scale",
+        default=20,
+        type=float,
+        help="The time scale of the Unity environment(s). Equivalent to setting "
+        "Time.timeScale in Unity.",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--target-frame-rate",
+        default=-1,
+        type=int,
+        help="The target frame rate of the Unity environment(s). Equivalent to setting "
+        "Application.targetFrameRate in Unity.",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--capture-frame-rate",
+        default=60,
+        type=int,
+        help="The capture frame rate of the Unity environment(s). Equivalent to setting "
+        "Time.captureFramerate in Unity.",
+        action=DetectDefault,
+    )
+    eng_conf.add_argument(
+        "--no-graphics",
+        default=False,
+        action=DetectDefaultStoreTrue,
+        help="Whether to run the Unity executable in no-graphics mode (i.e. without initializing "
+        "the graphics driver. Use this only if your agents don't use visual observations.",
+    )
+
+    torch_conf = argparser.add_argument_group(title="Torch Configuration")
+    torch_conf.add_argument(
+        "--torch-device",
+        default=None,
+        dest="device",
+        action=DetectDefault,
+        help='Settings for the default torch.device used in training, for example, "cpu", "cuda", or "cuda:0"',
+    )
+    return argparser
+
+
+def load_config(config_path: str) -> Dict[str, Any]:
+    try:
+        with open(config_path) as data_file:
+            return _load_config(data_file)
+    except OSError:
+        abs_path = os.path.abspath(config_path)
+        raise TrainerConfigError(f"Config file could not be found at {abs_path}.")
+    except UnicodeDecodeError:
+        raise TrainerConfigError(
+            f"There was an error decoding Config file from {config_path}. "
+            f"Make sure your file is save using UTF-8"
+        )
+
+
+def _load_config(fp: TextIO) -> Dict[str, Any]:
+    """
+    Load the yaml config from the file-like object.
+    """
+    try:
+        return yaml.safe_load(fp)
+    except yaml.parser.ParserError as e:
+        raise TrainerConfigError(
+            "Error parsing yaml file. Please check for formatting errors. "
+            "A tool such as http://www.yamllint.com/ can be helpful with this."
+        ) from e
+
+
+parser = _create_parser()
diff --git a/ml-agents/mlagents/trainers/demo_loader.py b/ml-agents/mlagents/trainers/demo_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..eba0200b8830ce884ec08e674c3be26f8e0e3ea7
--- /dev/null
+++ b/ml-agents/mlagents/trainers/demo_loader.py
@@ -0,0 +1,246 @@
+import os
+from typing import List, Tuple
+import numpy as np
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
+from mlagents.trainers.trajectory import ObsUtil
+from mlagents_envs.rpc_utils import behavior_spec_from_proto, steps_from_proto
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
+    DemonstrationMetaProto,
+)
+from mlagents_envs.timers import timed, hierarchical_timer
+from google.protobuf.internal.decoder import _DecodeVarint32  # type: ignore
+from google.protobuf.internal.encoder import _EncodeVarint  # type: ignore
+
+
+INITIAL_POS = 33
+SUPPORTED_DEMONSTRATION_VERSIONS = frozenset([0, 1])
+
+
+@timed
+def make_demo_buffer(
+    pair_infos: List[AgentInfoActionPairProto],
+    behavior_spec: BehaviorSpec,
+    sequence_length: int,
+) -> AgentBuffer:
+    # Create and populate buffer using experiences
+    demo_raw_buffer = AgentBuffer()
+    demo_processed_buffer = AgentBuffer()
+    for idx, current_pair_info in enumerate(pair_infos):
+        if idx > len(pair_infos) - 2:
+            break
+        next_pair_info = pair_infos[idx + 1]
+        current_decision_step, current_terminal_step = steps_from_proto(
+            [current_pair_info.agent_info], behavior_spec
+        )
+        next_decision_step, next_terminal_step = steps_from_proto(
+            [next_pair_info.agent_info], behavior_spec
+        )
+        previous_action = (
+            np.array(
+                pair_infos[idx].action_info.vector_actions_deprecated, dtype=np.float32
+            )
+            * 0
+        )
+        if idx > 0:
+            previous_action = np.array(
+                pair_infos[idx - 1].action_info.vector_actions_deprecated,
+                dtype=np.float32,
+            )
+
+        next_done = len(next_terminal_step) == 1
+        next_reward = 0
+        if len(next_terminal_step) == 1:
+            next_reward = next_terminal_step.reward[0]
+        else:
+            next_reward = next_decision_step.reward[0]
+        current_obs = None
+        if len(current_terminal_step) == 1:
+            current_obs = list(current_terminal_step.values())[0].obs
+        else:
+            current_obs = list(current_decision_step.values())[0].obs
+
+        demo_raw_buffer[BufferKey.DONE].append(next_done)
+        demo_raw_buffer[BufferKey.ENVIRONMENT_REWARDS].append(next_reward)
+        for i, obs in enumerate(current_obs):
+            demo_raw_buffer[ObsUtil.get_name_at(i)].append(obs)
+        if (
+            len(current_pair_info.action_info.continuous_actions) == 0
+            and len(current_pair_info.action_info.discrete_actions) == 0
+        ):
+            if behavior_spec.action_spec.continuous_size > 0:
+                demo_raw_buffer[BufferKey.CONTINUOUS_ACTION].append(
+                    current_pair_info.action_info.vector_actions_deprecated
+                )
+            else:
+                demo_raw_buffer[BufferKey.DISCRETE_ACTION].append(
+                    current_pair_info.action_info.vector_actions_deprecated
+                )
+        else:
+            if behavior_spec.action_spec.continuous_size > 0:
+                demo_raw_buffer[BufferKey.CONTINUOUS_ACTION].append(
+                    current_pair_info.action_info.continuous_actions
+                )
+            if behavior_spec.action_spec.discrete_size > 0:
+                demo_raw_buffer[BufferKey.DISCRETE_ACTION].append(
+                    current_pair_info.action_info.discrete_actions
+                )
+        demo_raw_buffer[BufferKey.PREV_ACTION].append(previous_action)
+        if next_done:
+            demo_raw_buffer.resequence_and_append(
+                demo_processed_buffer, batch_size=None, training_length=sequence_length
+            )
+            demo_raw_buffer.reset_agent()
+    demo_raw_buffer.resequence_and_append(
+        demo_processed_buffer, batch_size=None, training_length=sequence_length
+    )
+    return demo_processed_buffer
+
+
+@timed
+def demo_to_buffer(
+    file_path: str, sequence_length: int, expected_behavior_spec: BehaviorSpec = None
+) -> Tuple[BehaviorSpec, AgentBuffer]:
+    """
+    Loads demonstration file and uses it to fill training buffer.
+    :param file_path: Location of demonstration file (.demo).
+    :param sequence_length: Length of trajectories to fill buffer.
+    :return:
+    """
+    behavior_spec, info_action_pair, _ = load_demonstration(file_path)
+    demo_buffer = make_demo_buffer(info_action_pair, behavior_spec, sequence_length)
+    if expected_behavior_spec:
+        # check action dimensions in demonstration match
+        if behavior_spec.action_spec != expected_behavior_spec.action_spec:
+            raise RuntimeError(
+                "The actions {} in demonstration do not match the policy's {}.".format(
+                    behavior_spec.action_spec, expected_behavior_spec.action_spec
+                )
+            )
+        # check observations match
+        if len(behavior_spec.observation_specs) != len(
+            expected_behavior_spec.observation_specs
+        ):
+            raise RuntimeError(
+                "The demonstrations do not have the same number of observations as the policy."
+            )
+        else:
+            for i, (demo_obs, policy_obs) in enumerate(
+                zip(
+                    behavior_spec.observation_specs,
+                    expected_behavior_spec.observation_specs,
+                )
+            ):
+                if demo_obs.shape != policy_obs.shape:
+                    raise RuntimeError(
+                        f"The shape {demo_obs} for observation {i} in demonstration \
+                        do not match the policy's {policy_obs}."
+                    )
+    return behavior_spec, demo_buffer
+
+
+def get_demo_files(path: str) -> List[str]:
+    """
+    Retrieves the demonstration file(s) from a path.
+    :param path: Path of demonstration file or directory.
+    :return: List of demonstration files
+
+    Raises errors if |path| is invalid.
+    """
+    if os.path.isfile(path):
+        if not path.endswith(".demo"):
+            raise ValueError("The path provided is not a '.demo' file.")
+        return [path]
+    elif os.path.isdir(path):
+        paths = [
+            os.path.join(path, name)
+            for name in os.listdir(path)
+            if name.endswith(".demo")
+        ]
+        if not paths:
+            raise ValueError("There are no '.demo' files in the provided directory.")
+        return paths
+    else:
+        raise FileNotFoundError(
+            f"The demonstration file or directory {path} does not exist."
+        )
+
+
+@timed
+def load_demonstration(
+    file_path: str,
+) -> Tuple[BehaviorSpec, List[AgentInfoActionPairProto], int]:
+    """
+    Loads and parses a demonstration file.
+    :param file_path: Location of demonstration file (.demo).
+    :return: BrainParameter and list of AgentInfoActionPairProto containing demonstration data.
+    """
+
+    # First 32 bytes of file dedicated to meta-data.
+    file_paths = get_demo_files(file_path)
+    behavior_spec = None
+    brain_param_proto = None
+    info_action_pairs = []
+    total_expected = 0
+    for _file_path in file_paths:
+        with open(_file_path, "rb") as fp:
+            with hierarchical_timer("read_file"):
+                data = fp.read()
+            next_pos, pos, obs_decoded = 0, 0, 0
+            while pos < len(data):
+                next_pos, pos = _DecodeVarint32(data, pos)
+                if obs_decoded == 0:
+                    meta_data_proto = DemonstrationMetaProto()
+                    meta_data_proto.ParseFromString(data[pos : pos + next_pos])
+                    if (
+                        meta_data_proto.api_version
+                        not in SUPPORTED_DEMONSTRATION_VERSIONS
+                    ):
+                        raise RuntimeError(
+                            f"Can't load Demonstration data from an unsupported version ({meta_data_proto.api_version})"
+                        )
+                    total_expected += meta_data_proto.number_steps
+                    pos = INITIAL_POS
+                if obs_decoded == 1:
+                    brain_param_proto = BrainParametersProto()
+                    brain_param_proto.ParseFromString(data[pos : pos + next_pos])
+                    pos += next_pos
+                if obs_decoded > 1:
+                    agent_info_action = AgentInfoActionPairProto()
+                    agent_info_action.ParseFromString(data[pos : pos + next_pos])
+                    if behavior_spec is None:
+                        behavior_spec = behavior_spec_from_proto(
+                            brain_param_proto, agent_info_action.agent_info
+                        )
+                    info_action_pairs.append(agent_info_action)
+                    if len(info_action_pairs) == total_expected:
+                        break
+                    pos += next_pos
+                obs_decoded += 1
+    if not behavior_spec:
+        raise RuntimeError(
+            f"No BrainParameters found in demonstration file at {file_path}."
+        )
+    return behavior_spec, info_action_pairs, total_expected
+
+
+def write_delimited(f, message):
+    msg_string = message.SerializeToString()
+    msg_size = len(msg_string)
+    _EncodeVarint(f.write, msg_size)
+    f.write(msg_string)
+
+
+def write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos):
+    with open(demo_path, "wb") as f:
+        # write metadata
+        write_delimited(f, meta_data_proto)
+        f.seek(INITIAL_POS)
+        write_delimited(f, brain_param_proto)
+
+        for agent in agent_info_protos:
+            write_delimited(f, agent)
diff --git a/ml-agents/mlagents/trainers/directory_utils.py b/ml-agents/mlagents/trainers/directory_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..80379d81e998743d6bc5e3759f8f7a3b71e3c970
--- /dev/null
+++ b/ml-agents/mlagents/trainers/directory_utils.py
@@ -0,0 +1,76 @@
+import os
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.settings import TrainerSettings
+from mlagents.trainers.model_saver.torch_model_saver import DEFAULT_CHECKPOINT_NAME
+
+
+def validate_existing_directories(
+    output_path: str, resume: bool, force: bool, init_path: str = None
+) -> None:
+    """
+    Validates that if the run_id model exists, we do not overwrite it unless --force is specified.
+    Throws an exception if resume isn't specified and run_id exists. Throws an exception
+    if --resume is specified and run-id was not found.
+    :param model_path: The model path specified.
+    :param summary_path: The summary path to be used.
+    :param resume: Whether or not the --resume flag was passed.
+    :param force: Whether or not the --force flag was passed.
+    :param init_path: Path to run-id dir to initialize from
+    """
+
+    output_path_exists = os.path.isdir(output_path)
+
+    if output_path_exists:
+        if not resume and not force:
+            raise UnityTrainerException(
+                "Previous data from this run ID was found. "
+                "Either specify a new run ID, use --resume to resume this run, "
+                "or use the --force parameter to overwrite existing data."
+            )
+    else:
+        if resume:
+            raise UnityTrainerException(
+                "Previous data from this run ID was not found. "
+                "Train a new run by removing the --resume flag."
+            )
+
+    # Verify init path if specified.
+    if init_path is not None:
+        if not os.path.isdir(init_path):
+            raise UnityTrainerException(
+                "Could not initialize from {}. "
+                "Make sure models have already been saved with that run ID.".format(
+                    init_path
+                )
+            )
+
+
+def setup_init_path(
+    behaviors: TrainerSettings.DefaultTrainerDict, init_dir: str
+) -> None:
+    """
+    For each behavior, setup full init_path to checkpoint file to initialize policy from
+    :param behaviors: mapping from behavior_name to TrainerSettings
+    :param init_dir: Path to run-id dir to initialize from
+    """
+    for behavior_name, ts in behaviors.items():
+        if ts.init_path is None:
+            # set default if None
+            ts.init_path = os.path.join(
+                init_dir, behavior_name, DEFAULT_CHECKPOINT_NAME
+            )
+        elif not os.path.dirname(ts.init_path):
+            # update to full path if just the file name
+            ts.init_path = os.path.join(init_dir, behavior_name, ts.init_path)
+        _validate_init_full_path(ts.init_path)
+
+
+def _validate_init_full_path(init_file: str) -> None:
+    """
+    Validate initialization path to be a .pt file
+    :param init_file: full path to initialization checkpoint file
+    """
+    if not (os.path.isfile(init_file) and init_file.endswith(".pt")):
+        raise UnityTrainerException(
+            f"Could not initialize from {init_file}. file does not exists or is not a `.pt` file"
+        )
diff --git a/ml-agents/mlagents/trainers/env_manager.py b/ml-agents/mlagents/trainers/env_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4a90fdc709d4829112a11d9dce1ee5dd428ff0e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/env_manager.py
@@ -0,0 +1,157 @@
+from abc import ABC, abstractmethod
+
+from typing import List, Dict, NamedTuple, Iterable, Tuple
+from mlagents_envs.base_env import (
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorSpec,
+    BehaviorName,
+)
+from mlagents_envs.side_channel.stats_side_channel import EnvironmentStats
+
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.agent_processor import AgentManager, AgentManagerQueue
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.settings import TrainerSettings
+from mlagents_envs.logging_util import get_logger
+
+AllStepResult = Dict[BehaviorName, Tuple[DecisionSteps, TerminalSteps]]
+AllGroupSpec = Dict[BehaviorName, BehaviorSpec]
+
+logger = get_logger(__name__)
+
+
+class EnvironmentStep(NamedTuple):
+    current_all_step_result: AllStepResult
+    worker_id: int
+    brain_name_to_action_info: Dict[BehaviorName, ActionInfo]
+    environment_stats: EnvironmentStats
+
+    @property
+    def name_behavior_ids(self) -> Iterable[BehaviorName]:
+        return self.current_all_step_result.keys()
+
+    @staticmethod
+    def empty(worker_id: int) -> "EnvironmentStep":
+        return EnvironmentStep({}, worker_id, {}, {})
+
+
+class EnvManager(ABC):
+    def __init__(self):
+        self.policies: Dict[BehaviorName, Policy] = {}
+        self.agent_managers: Dict[BehaviorName, AgentManager] = {}
+        self.first_step_infos: List[EnvironmentStep] = []
+
+    def set_policy(self, brain_name: BehaviorName, policy: Policy) -> None:
+        self.policies[brain_name] = policy
+        if brain_name in self.agent_managers:
+            self.agent_managers[brain_name].policy = policy
+
+    def set_agent_manager(
+        self, brain_name: BehaviorName, manager: AgentManager
+    ) -> None:
+        self.agent_managers[brain_name] = manager
+
+    @abstractmethod
+    def _step(self) -> List[EnvironmentStep]:
+        pass
+
+    @abstractmethod
+    def _reset_env(self, config: Dict = None) -> List[EnvironmentStep]:
+        pass
+
+    def reset(self, config: Dict = None) -> int:
+        for manager in self.agent_managers.values():
+            manager.end_episode()
+        # Save the first step infos, after the reset.
+        # They will be processed on the first advance().
+        self.first_step_infos = self._reset_env(config)
+        return len(self.first_step_infos)
+
+    @abstractmethod
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSideChannel.
+        :param config: Dict of environment parameter keys and values
+        """
+        pass
+
+    def on_training_started(
+        self, behavior_name: str, trainer_settings: TrainerSettings
+    ) -> None:
+        """
+        Handle traing starting for a new behavior type. Generally nothing is necessary here.
+        :param behavior_name:
+        :param trainer_settings:
+        :return:
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def training_behaviors(self) -> Dict[BehaviorName, BehaviorSpec]:
+        pass
+
+    @abstractmethod
+    def close(self):
+        pass
+
+    def get_steps(self) -> List[EnvironmentStep]:
+        """
+        Updates the policies, steps the environments, and returns the step information from the environments.
+        Calling code should pass the returned EnvironmentSteps to process_steps() after calling this.
+        :return: The list of EnvironmentSteps
+        """
+        # If we had just reset, process the first EnvironmentSteps.
+        # Note that we do it here instead of in reset() so that on the very first reset(),
+        # we can create the needed AgentManagers before calling advance() and processing the EnvironmentSteps.
+        if self.first_step_infos:
+            self._process_step_infos(self.first_step_infos)
+            self.first_step_infos = []
+        # Get new policies if found. Always get the latest policy.
+        for brain_name in self.agent_managers.keys():
+            _policy = None
+            try:
+                # We make sure to empty the policy queue before continuing to produce steps.
+                # This halts the trainers until the policy queue is empty.
+                while True:
+                    _policy = self.agent_managers[brain_name].policy_queue.get_nowait()
+            except AgentManagerQueue.Empty:
+                if _policy is not None:
+                    self.set_policy(brain_name, _policy)
+        # Step the environments
+        new_step_infos = self._step()
+        return new_step_infos
+
+    def process_steps(self, new_step_infos: List[EnvironmentStep]) -> int:
+        # Add to AgentProcessor
+        num_step_infos = self._process_step_infos(new_step_infos)
+        return num_step_infos
+
+    def _process_step_infos(self, step_infos: List[EnvironmentStep]) -> int:
+        for step_info in step_infos:
+            for name_behavior_id in step_info.name_behavior_ids:
+                if name_behavior_id not in self.agent_managers:
+                    logger.warning(
+                        "Agent manager was not created for behavior id {}.".format(
+                            name_behavior_id
+                        )
+                    )
+                    continue
+                decision_steps, terminal_steps = step_info.current_all_step_result[
+                    name_behavior_id
+                ]
+                self.agent_managers[name_behavior_id].add_experiences(
+                    decision_steps,
+                    terminal_steps,
+                    step_info.worker_id,
+                    step_info.brain_name_to_action_info.get(
+                        name_behavior_id, ActionInfo.empty()
+                    ),
+                )
+
+                self.agent_managers[name_behavior_id].record_environment_stats(
+                    step_info.environment_stats, step_info.worker_id
+                )
+        return len(step_infos)
diff --git a/ml-agents/mlagents/trainers/environment_parameter_manager.py b/ml-agents/mlagents/trainers/environment_parameter_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dd00f98a04a29067a1cd4f5a83c319a237add38
--- /dev/null
+++ b/ml-agents/mlagents/trainers/environment_parameter_manager.py
@@ -0,0 +1,186 @@
+from typing import Dict, List, Tuple, Optional
+from mlagents.trainers.settings import (
+    EnvironmentParameterSettings,
+    ParameterRandomizationSettings,
+)
+from collections import defaultdict
+from mlagents.trainers.training_status import GlobalTrainingStatus, StatusType
+
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+
+class EnvironmentParameterManager:
+    def __init__(
+        self,
+        settings: Optional[Dict[str, EnvironmentParameterSettings]] = None,
+        run_seed: int = -1,
+        restore: bool = False,
+    ):
+        """
+        EnvironmentParameterManager manages all the environment parameters of a training
+        session. It determines when parameters should change and gives access to the
+        current sampler of each parameter.
+        :param settings: A dictionary from environment parameter to
+        EnvironmentParameterSettings.
+        :param run_seed: When the seed is not provided for an environment parameter,
+        this seed will be used instead.
+        :param restore: If true, the EnvironmentParameterManager will use the
+        GlobalTrainingStatus to try and reload the lesson status of each environment
+        parameter.
+        """
+        if settings is None:
+            settings = {}
+        self._dict_settings = settings
+        for parameter_name in self._dict_settings.keys():
+            initial_lesson = GlobalTrainingStatus.get_parameter_state(
+                parameter_name, StatusType.LESSON_NUM
+            )
+            if initial_lesson is None or not restore:
+                GlobalTrainingStatus.set_parameter_state(
+                    parameter_name, StatusType.LESSON_NUM, 0
+                )
+        self._smoothed_values: Dict[str, float] = defaultdict(float)
+        for key in self._dict_settings.keys():
+            self._smoothed_values[key] = 0.0
+        # Update the seeds of the samplers
+        self._set_sampler_seeds(run_seed)
+
+    def _set_sampler_seeds(self, seed):
+        """
+        Sets the seeds for the samplers (if no seed was already present). Note that
+        using the provided seed.
+        """
+        offset = 0
+        for settings in self._dict_settings.values():
+            for lesson in settings.curriculum:
+                if lesson.value.seed == -1:
+                    lesson.value.seed = seed + offset
+                    offset += 1
+
+    def get_minimum_reward_buffer_size(self, behavior_name: str) -> int:
+        """
+        Calculates the minimum size of the reward buffer a behavior must use. This
+        method uses the 'min_lesson_length' sampler_parameter to determine this value.
+        :param behavior_name: The name of the behavior the minimum reward buffer
+        size corresponds to.
+        """
+        result = 1
+        for settings in self._dict_settings.values():
+            for lesson in settings.curriculum:
+                if lesson.completion_criteria is not None:
+                    if lesson.completion_criteria.behavior == behavior_name:
+                        result = max(
+                            result, lesson.completion_criteria.min_lesson_length
+                        )
+        return result
+
+    def get_current_samplers(self) -> Dict[str, ParameterRandomizationSettings]:
+        """
+        Creates a dictionary from environment parameter name to their corresponding
+        ParameterRandomizationSettings. If curriculum is used, the
+        ParameterRandomizationSettings corresponds to the sampler of the current lesson.
+        """
+        samplers: Dict[str, ParameterRandomizationSettings] = {}
+        for param_name, settings in self._dict_settings.items():
+            lesson_num = GlobalTrainingStatus.get_parameter_state(
+                param_name, StatusType.LESSON_NUM
+            )
+            lesson = settings.curriculum[lesson_num]
+            samplers[param_name] = lesson.value
+        return samplers
+
+    def get_current_lesson_number(self) -> Dict[str, int]:
+        """
+        Creates a dictionary from environment parameter to the current lesson number.
+        If not using curriculum, this number is always 0 for that environment parameter.
+        """
+        result: Dict[str, int] = {}
+        for parameter_name in self._dict_settings.keys():
+            result[parameter_name] = GlobalTrainingStatus.get_parameter_state(
+                parameter_name, StatusType.LESSON_NUM
+            )
+        return result
+
+    def log_current_lesson(self, parameter_name: Optional[str] = None) -> None:
+        """
+        Logs the current lesson number and sampler value of the parameter with name
+        parameter_name. If no parameter_name is provided, the values and lesson
+        numbers of all parameters will be displayed.
+        """
+        if parameter_name is not None:
+            settings = self._dict_settings[parameter_name]
+            lesson_number = GlobalTrainingStatus.get_parameter_state(
+                parameter_name, StatusType.LESSON_NUM
+            )
+            lesson_name = settings.curriculum[lesson_number].name
+            lesson_value = settings.curriculum[lesson_number].value
+            logger.info(
+                f"Parameter '{parameter_name}' is in lesson '{lesson_name}' "
+                f"and has value '{lesson_value}'."
+            )
+        else:
+            for parameter_name, settings in self._dict_settings.items():
+                lesson_number = GlobalTrainingStatus.get_parameter_state(
+                    parameter_name, StatusType.LESSON_NUM
+                )
+                lesson_name = settings.curriculum[lesson_number].name
+                lesson_value = settings.curriculum[lesson_number].value
+                logger.info(
+                    f"Parameter '{parameter_name}' is in lesson '{lesson_name}' "
+                    f"and has value '{lesson_value}'."
+                )
+
+    def update_lessons(
+        self,
+        trainer_steps: Dict[str, int],
+        trainer_max_steps: Dict[str, int],
+        trainer_reward_buffer: Dict[str, List[float]],
+    ) -> Tuple[bool, bool]:
+        """
+        Given progress metrics, calculates if at least one environment parameter is
+        in a new lesson and if at least one environment parameter requires the env
+        to reset.
+        :param trainer_steps: A dictionary from behavior_name to the number of training
+        steps this behavior's trainer has performed.
+        :param trainer_max_steps: A dictionary from behavior_name to the maximum number
+        of training steps this behavior's trainer has performed.
+        :param trainer_reward_buffer: A dictionary from behavior_name to the list of
+        the most recent episode returns for this behavior's trainer.
+        :returns: A tuple of two booleans : (True if any lesson has changed, True if
+        environment needs to reset)
+        """
+        must_reset = False
+        updated = False
+        for param_name, settings in self._dict_settings.items():
+            lesson_num = GlobalTrainingStatus.get_parameter_state(
+                param_name, StatusType.LESSON_NUM
+            )
+            next_lesson_num = lesson_num + 1
+            lesson = settings.curriculum[lesson_num]
+            if (
+                lesson.completion_criteria is not None
+                and len(settings.curriculum) > next_lesson_num
+            ):
+                behavior_to_consider = lesson.completion_criteria.behavior
+                if behavior_to_consider in trainer_steps:
+                    (
+                        must_increment,
+                        new_smoothing,
+                    ) = lesson.completion_criteria.need_increment(
+                        float(trainer_steps[behavior_to_consider])
+                        / float(trainer_max_steps[behavior_to_consider]),
+                        trainer_reward_buffer[behavior_to_consider],
+                        self._smoothed_values[param_name],
+                    )
+                    self._smoothed_values[param_name] = new_smoothing
+                    if must_increment:
+                        GlobalTrainingStatus.set_parameter_state(
+                            param_name, StatusType.LESSON_NUM, next_lesson_num
+                        )
+                        self.log_current_lesson(param_name)
+                        updated = True
+                        if lesson.completion_criteria.require_reset:
+                            must_reset = True
+        return updated, must_reset
diff --git a/ml-agents/mlagents/trainers/exception.py b/ml-agents/mlagents/trainers/exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c0742bcec7a45f96a7ccb4c320010f845320d4c
--- /dev/null
+++ b/ml-agents/mlagents/trainers/exception.py
@@ -0,0 +1,75 @@
+"""
+Contains exceptions for the trainers package.
+"""
+
+
+class TrainerError(Exception):
+    """
+    Any error related to the trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
+class TrainerConfigError(Exception):
+    """
+    Any error related to the configuration of trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
+class TrainerConfigWarning(Warning):
+    """
+    Any warning related to the configuration of trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
+class CurriculumError(TrainerError):
+    """
+    Any error related to training with a curriculum.
+    """
+
+    pass
+
+
+class CurriculumLoadingError(CurriculumError):
+    """
+    Any error related to loading the Curriculum config file.
+    """
+
+    pass
+
+
+class CurriculumConfigError(CurriculumError):
+    """
+    Any error related to processing the Curriculum config file.
+    """
+
+    pass
+
+
+class MetaCurriculumError(TrainerError):
+    """
+    Any error related to the configuration of a metacurriculum.
+    """
+
+    pass
+
+
+class SamplerException(TrainerError):
+    """
+    Related to errors with the sampler actions.
+    """
+
+    pass
+
+
+class UnityTrainerException(TrainerError):
+    """
+    Related to errors with the Trainer.
+    """
+
+    pass
diff --git a/ml-agents/mlagents/trainers/ghost/__init__.py b/ml-agents/mlagents/trainers/ghost/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/mlagents/trainers/ghost/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/trainers/ghost/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..993986534feb1a2715edd8995ed4d4f6da24ebe7
Binary files /dev/null and b/ml-agents/mlagents/trainers/ghost/__pycache__/__init__.cpython-310.pyc differ
diff --git a/ml-agents/mlagents/trainers/ghost/__pycache__/controller.cpython-310.pyc b/ml-agents/mlagents/trainers/ghost/__pycache__/controller.cpython-310.pyc
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diff --git a/ml-agents/mlagents/trainers/ghost/controller.py b/ml-agents/mlagents/trainers/ghost/controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..84901e14f6e57993317a1a5a3f94c7571c7c39d9
--- /dev/null
+++ b/ml-agents/mlagents/trainers/ghost/controller.py
@@ -0,0 +1,103 @@
+from mlagents_envs.logging_util import get_logger
+from typing import Deque, Dict
+from collections import deque
+from mlagents.trainers.ghost.trainer import GhostTrainer
+
+logger = get_logger(__name__)
+
+
+class GhostController:
+    """
+    GhostController contains a queue of team ids. GhostTrainers subscribe to the GhostController and query
+    it to get the current learning team.  The GhostController cycles through team ids every 'swap_interval'
+    which corresponds to the number of trainer steps between changing learning teams.
+    The GhostController is a unique object and there can only be one per training run.
+    """
+
+    def __init__(self, maxlen: int = 10):
+        """
+        Create a GhostController.
+        :param maxlen: Maximum number of GhostTrainers allowed in this GhostController
+        """
+
+        # Tracks last swap step for  each learning team because trainer
+        # steps of all GhostTrainers do not increment together
+        self._queue: Deque[int] = deque(maxlen=maxlen)
+        self._learning_team: int = -1
+        # Dict from team id to GhostTrainer for ELO calculation
+        self._ghost_trainers: Dict[int, GhostTrainer] = {}
+        # Signals to the trainer control to perform a hard change_training_team
+        self._changed_training_team = False
+
+    @property
+    def get_learning_team(self) -> int:
+        """
+        Returns the current learning team.
+        :return: The learning team id
+        """
+        return self._learning_team
+
+    def should_reset(self) -> bool:
+        """
+        Whether or not team change occurred. Causes full reset in trainer_controller
+        :return: The truth value of the team changing
+        """
+        changed_team = self._changed_training_team
+        if self._changed_training_team:
+            self._changed_training_team = False
+        return changed_team
+
+    def subscribe_team_id(self, team_id: int, trainer: GhostTrainer) -> None:
+        """
+        Given a team_id and trainer, add to queue and trainers if not already.
+        The GhostTrainer is used later by the controller to get ELO ratings of agents.
+        :param team_id: The team_id of an agent managed by this GhostTrainer
+        :param trainer: A GhostTrainer that manages this team_id.
+        """
+        if team_id not in self._ghost_trainers:
+            self._ghost_trainers[team_id] = trainer
+            if self._learning_team < 0:
+                self._learning_team = team_id
+            else:
+                self._queue.append(team_id)
+
+    def change_training_team(self, step: int) -> None:
+        """
+        The current learning team is added to the end of the queue and then updated with the
+        next in line.
+        :param step: The step of the trainer for debugging
+        """
+        self._queue.append(self._learning_team)
+        self._learning_team = self._queue.popleft()
+        logger.debug(f"Learning team {self._learning_team} swapped on step {step}")
+        self._changed_training_team = True
+
+    # Adapted from https://github.com/Unity-Technologies/ml-agents/pull/1975 and
+    # https://metinmediamath.wordpress.com/2013/11/27/how-to-calculate-the-elo-rating-including-example/
+    # ELO calculation
+    # TODO : Generalize this to more than two teams
+    def compute_elo_rating_changes(self, rating: float, result: float) -> float:
+        """
+        Calculates ELO. Given the rating of the learning team and result.  The GhostController
+        queries the other GhostTrainers for the ELO of their agent that is currently being deployed.
+        Note, this could be the current agent or a past snapshot.
+        :param rating: Rating of the learning team.
+        :param result: Win, loss, or draw from the perspective of the learning team.
+        :return: The change in ELO.
+        """
+        opponent_rating: float = 0.0
+        for team_id, trainer in self._ghost_trainers.items():
+            if team_id != self._learning_team:
+                opponent_rating = trainer.get_opponent_elo()
+        r1 = pow(10, rating / 400)
+        r2 = pow(10, opponent_rating / 400)
+
+        summed = r1 + r2
+        e1 = r1 / summed
+
+        change = result - e1
+        for team_id, trainer in self._ghost_trainers.items():
+            if team_id != self._learning_team:
+                trainer.change_opponent_elo(change)
+
+        return change
diff --git a/ml-agents/mlagents/trainers/ghost/trainer.py b/ml-agents/mlagents/trainers/ghost/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f49a643574b8eef7b1f64c28acd9c1a48b0ec0d7
--- /dev/null
+++ b/ml-agents/mlagents/trainers/ghost/trainer.py
@@ -0,0 +1,480 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agent Learning (Ghost Trainer)
+
+from collections import defaultdict
+from typing import Deque, Dict, DefaultDict, List
+
+import numpy as np
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.policy import Policy
+
+from mlagents.trainers.trainer import Trainer
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.stats import StatsPropertyType
+from mlagents.trainers.behavior_id_utils import (
+    BehaviorIdentifiers,
+    create_name_behavior_id,
+)
+from mlagents.trainers.training_status import GlobalTrainingStatus, StatusType
+
+
+logger = get_logger(__name__)
+
+
+class GhostTrainer(Trainer):
+    """
+    The GhostTrainer trains agents in adversarial games (there are teams in opposition) using a self-play mechanism.
+    In adversarial settings with self-play, at any time, there is only a single learning team. The other team(s) is
+    "ghosted" which means that its agents are executing fixed policies and not learning. The GhostTrainer wraps
+    a standard RL trainer which trains the learning team and ensures that only the trajectories collected
+    by the learning team are used for training.  The GhostTrainer also maintains past policy snapshots to be used
+    as the fixed policies when the team is not learning. The GhostTrainer is 1:1 with brain_names as the other
+    trainers, and is responsible for one or more teams. Note, a GhostTrainer can have only one team in
+    asymmetric games where there is only one team with a particular behavior i.e. Hide and Seek.
+    The GhostController manages high level coordination between multiple ghost trainers. The learning team id
+    is cycled throughout a training run.
+    """
+
+    def __init__(
+        self,
+        trainer,
+        brain_name,
+        controller,
+        reward_buff_cap,
+        trainer_settings,
+        training,
+        artifact_path,
+    ):
+        """
+        Creates a GhostTrainer.
+        :param trainer: The trainer of the policy/policies being trained with self_play
+        :param brain_name: The name of the brain associated with trainer config
+        :param controller: GhostController that coordinates all ghost trainers and calculates ELO
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param artifact_path: Path to store artifacts from this trainer.
+        """
+
+        super().__init__(
+            brain_name, trainer_settings, training, artifact_path, reward_buff_cap
+        )
+
+        self.trainer = trainer
+        self.controller = controller
+
+        self._internal_trajectory_queues: Dict[str, AgentManagerQueue[Trajectory]] = {}
+        self._internal_policy_queues: Dict[str, AgentManagerQueue[Policy]] = {}
+
+        self._team_to_name_to_policy_queue: DefaultDict[
+            int, Dict[str, AgentManagerQueue[Policy]]
+        ] = defaultdict(dict)
+
+        self._name_to_parsed_behavior_id: Dict[str, BehaviorIdentifiers] = {}
+
+        # assign ghost's stats collection to wrapped trainer's
+        self._stats_reporter = self.trainer.stats_reporter
+        # Set the logging to print ELO in the console
+        self._stats_reporter.add_property(StatsPropertyType.SELF_PLAY, True)
+
+        self_play_parameters = trainer_settings.self_play
+        self.window = self_play_parameters.window
+        self.play_against_latest_model_ratio = (
+            self_play_parameters.play_against_latest_model_ratio
+        )
+        if (
+            self.play_against_latest_model_ratio > 1.0
+            or self.play_against_latest_model_ratio < 0.0
+        ):
+            logger.warning(
+                "The play_against_latest_model_ratio is not between 0 and 1."
+            )
+
+        self.steps_between_save = self_play_parameters.save_steps
+        self.steps_between_swap = self_play_parameters.swap_steps
+        self.steps_to_train_team = self_play_parameters.team_change
+        if self.steps_to_train_team > self.get_max_steps:
+            logger.warning(
+                "The max steps of the GhostTrainer for behavior name {} is less than team change. This team will not face \
+                opposition that has been trained if the opposition is managed by a different GhostTrainer as in an \
+                asymmetric game.".format(
+                    self.brain_name
+                )
+            )
+
+        # Counts the number of steps of the ghost policies. Snapshot swapping
+        # depends on this counter whereas snapshot saving and team switching depends
+        # on the wrapped. This ensures that all teams train for the same number of trainer
+        # steps.
+        self.ghost_step: int = 0
+
+        # A list of dicts from brain name to a single snapshot for this trainer's policies
+        self.policy_snapshots: List[Dict[str, List[float]]] = []
+
+        # A dict from brain name to the current snapshot of this trainer's policies
+        self.current_policy_snapshot: Dict[str, List[float]] = {}
+
+        self.snapshot_counter: int = 0
+
+        # wrapped_training_team and learning team need to be separate
+        # in the situation where new agents are created destroyed
+        # after learning team switches. These agents need to be added
+        # to trainers properly.
+        self._learning_team: int = None
+        self.wrapped_trainer_team: int = None
+        self.last_save: int = 0
+        self.last_swap: int = 0
+        self.last_team_change: int = 0
+
+        self.initial_elo = GlobalTrainingStatus.get_parameter_state(
+            self.brain_name, StatusType.ELO
+        )
+        if self.initial_elo is None:
+            self.initial_elo = self_play_parameters.initial_elo
+        self.policy_elos: List[float] = [self.initial_elo] * (
+            self.window + 1
+        )  # for learning policy
+        self.current_opponent: int = 0
+
+    @property
+    def get_step(self) -> int:
+        """
+        Returns the number of steps the wrapped trainer has performed
+        :return: the step count of the wrapped trainer
+        """
+        return self.trainer.get_step
+
+    @property
+    def reward_buffer(self) -> Deque[float]:
+        """
+        Returns the reward buffer. The reward buffer contains the cumulative
+        rewards of the most recent episodes completed by agents using this
+        trainer.
+        :return: the reward buffer.
+        """
+        return self.trainer.reward_buffer
+
+    @property
+    def current_elo(self) -> float:
+        """
+        Gets ELO of current policy which is always last in the list
+        :return: ELO of current policy
+        """
+        return self.policy_elos[-1]
+
+    def change_current_elo(self, change: float) -> None:
+        """
+        Changes elo of current policy which is always last in the list
+        :param change: Amount to change current elo by
+        """
+        self.policy_elos[-1] += change
+
+    def get_opponent_elo(self) -> float:
+        """
+        Get elo of current opponent policy
+        :return: ELO of current opponent policy
+        """
+        return self.policy_elos[self.current_opponent]
+
+    def change_opponent_elo(self, change: float) -> None:
+        """
+        Changes elo of current opponent policy
+        :param change: Amount to change current opponent elo by
+        """
+        self.policy_elos[self.current_opponent] -= change
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Determines the final result of an episode and asks the GhostController
+        to calculate the ELO change. The GhostController changes the ELO
+        of the opponent policy since this may be in a different GhostTrainer
+        i.e. in asymmetric games. We assume the last reward determines the winner.
+        :param trajectory: Trajectory.
+        """
+        if (
+            trajectory.done_reached
+            and trajectory.all_group_dones_reached
+            and not trajectory.interrupted
+        ):
+            # Assumption is that final reward is >0/0/<0 for win/draw/loss
+            final_reward = (
+                trajectory.steps[-1].reward + trajectory.steps[-1].group_reward
+            )
+            result = 0.5
+            if final_reward > 0:
+                result = 1.0
+            elif final_reward < 0:
+                result = 0.0
+
+            change = self.controller.compute_elo_rating_changes(
+                self.current_elo, result
+            )
+            self.change_current_elo(change)
+            self._stats_reporter.add_stat("Self-play/ELO", self.current_elo)
+
+    def advance(self) -> None:
+        """
+        Steps the trainer, passing trajectories to wrapped trainer and calling trainer advance
+        """
+        for trajectory_queue in self.trajectory_queues:
+            parsed_behavior_id = self._name_to_parsed_behavior_id[
+                trajectory_queue.behavior_id
+            ]
+            if parsed_behavior_id.team_id == self._learning_team:
+                # With a future multiagent trainer, this will be indexed by 'role'
+                internal_trajectory_queue = self._internal_trajectory_queues[
+                    parsed_behavior_id.brain_name
+                ]
+                try:
+                    # We grab at most the maximum length of the queue.
+                    # This ensures that even if the queue is being filled faster than it is
+                    # being emptied, the trajectories in the queue are on-policy.
+                    for _ in range(trajectory_queue.qsize()):
+                        t = trajectory_queue.get_nowait()
+                        # adds to wrapped trainers queue
+                        internal_trajectory_queue.put(t)
+                        self._process_trajectory(t)
+                except AgentManagerQueue.Empty:
+                    pass
+            else:
+                # Dump trajectories from non-learning policy
+                try:
+                    for _ in range(trajectory_queue.qsize()):
+                        t = trajectory_queue.get_nowait()
+                        # count ghost steps
+                        self.ghost_step += len(t.steps)
+                except AgentManagerQueue.Empty:
+                    pass
+
+        self._next_summary_step = self.trainer._next_summary_step
+        self.trainer.advance()
+        if self.get_step - self.last_team_change > self.steps_to_train_team:
+            self.controller.change_training_team(self.get_step)
+            self.last_team_change = self.get_step
+
+        next_learning_team = self.controller.get_learning_team
+
+        # Case 1: No team change. The if statement just continues to push the policy
+        # into the correct queue (or not if not learning team).
+        for brain_name in self._internal_policy_queues:
+            internal_policy_queue = self._internal_policy_queues[brain_name]
+            try:
+                policy = internal_policy_queue.get_nowait()
+                self.current_policy_snapshot[brain_name] = policy.get_weights()
+            except AgentManagerQueue.Empty:
+                continue
+            if (
+                self._learning_team == next_learning_team
+                and next_learning_team in self._team_to_name_to_policy_queue
+            ):
+                name_to_policy_queue = self._team_to_name_to_policy_queue[
+                    next_learning_team
+                ]
+                if brain_name in name_to_policy_queue:
+                    behavior_id = create_name_behavior_id(
+                        brain_name, next_learning_team
+                    )
+                    policy = self.get_policy(behavior_id)
+                    policy.load_weights(self.current_policy_snapshot[brain_name])
+                    name_to_policy_queue[brain_name].put(policy)
+
+        # CASE 2: Current learning team is managed by this GhostTrainer.
+        # If the learning team changes, the following loop over queues will push the
+        # new policy into the policy queue for the new learning agent if
+        # that policy is managed by this GhostTrainer. Otherwise, it will save the current snapshot.
+        # CASE 3: Current learning team is managed by a different GhostTrainer.
+        # If the learning team changes to a team managed by this GhostTrainer, this loop
+        # will push the current_snapshot into the correct queue.  Otherwise,
+        # it will continue skipping and swap_snapshot will continue to handle
+        # pushing fixed snapshots
+        if (
+            self._learning_team != next_learning_team
+            and next_learning_team in self._team_to_name_to_policy_queue
+        ):
+            name_to_policy_queue = self._team_to_name_to_policy_queue[
+                next_learning_team
+            ]
+            for brain_name in name_to_policy_queue:
+                behavior_id = create_name_behavior_id(brain_name, next_learning_team)
+                policy = self.get_policy(behavior_id)
+                policy.load_weights(self.current_policy_snapshot[brain_name])
+                name_to_policy_queue[brain_name].put(policy)
+
+        # Note save and swap should be on different step counters.
+        # We don't want to save unless the policy is learning.
+        if self.get_step - self.last_save > self.steps_between_save:
+            self._save_snapshot()
+            self.last_save = self.get_step
+
+        if (
+            self._learning_team != next_learning_team
+            or self.ghost_step - self.last_swap > self.steps_between_swap
+        ):
+            self._learning_team = next_learning_team
+            self._swap_snapshots()
+            self.last_swap = self.ghost_step
+
+    def end_episode(self):
+        """
+        Forwarding call to wrapped trainers end_episode
+        """
+        self.trainer.end_episode()
+
+    def save_model(self) -> None:
+        """
+        Forwarding call to wrapped trainers save_model.
+        """
+        GlobalTrainingStatus.set_parameter_state(
+            self.brain_name, StatusType.ELO, self.current_elo
+        )
+        self.trainer.save_model()
+
+    def create_policy(
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+    ) -> Policy:
+        """
+        Creates policy with the wrapped trainer's create_policy function
+        The first policy encountered sets the wrapped
+        trainer team.  This is to ensure that all agents from the same multi-agent
+        team are grouped. All policies associated with this team are added to the
+        wrapped trainer to be trained.
+        """
+        policy = self.trainer.create_policy(parsed_behavior_id, behavior_spec)
+        team_id = parsed_behavior_id.team_id
+        self.controller.subscribe_team_id(team_id, self)
+
+        # First policy or a new agent on the same team encountered
+        if self.wrapped_trainer_team is None or team_id == self.wrapped_trainer_team:
+            internal_trainer_policy = self.trainer.create_policy(
+                parsed_behavior_id, behavior_spec
+            )
+            self.trainer.add_policy(parsed_behavior_id, internal_trainer_policy)
+            self.current_policy_snapshot[
+                parsed_behavior_id.brain_name
+            ] = internal_trainer_policy.get_weights()
+
+            policy.load_weights(internal_trainer_policy.get_weights())
+            self._save_snapshot()  # Need to save after trainer initializes policy
+            self._learning_team = self.controller.get_learning_team
+            self.wrapped_trainer_team = team_id
+        else:
+            # Load the weights of the ghost policy from the wrapped one
+            policy.load_weights(
+                self.trainer.get_policy(parsed_behavior_id).get_weights()
+            )
+        return policy
+
+    def create_optimizer(self) -> TorchOptimizer:
+        pass
+
+    def add_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
+    ) -> None:
+        """
+        Adds policy to GhostTrainer.
+        :param parsed_behavior_id: Behavior ID that the policy should belong to.
+        :param policy: Policy to associate with name_behavior_id.
+        """
+        name_behavior_id = parsed_behavior_id.behavior_id
+        self._name_to_parsed_behavior_id[name_behavior_id] = parsed_behavior_id
+        self.policies[name_behavior_id] = policy
+
+    def _save_snapshot(self) -> None:
+        """
+        Saves a snapshot of the current weights of the policy and maintains the policy_snapshots
+        according to the window size
+        """
+        for brain_name in self.current_policy_snapshot:
+            current_snapshot_for_brain_name = self.current_policy_snapshot[brain_name]
+
+            try:
+                self.policy_snapshots[self.snapshot_counter][
+                    brain_name
+                ] = current_snapshot_for_brain_name
+            except IndexError:
+                self.policy_snapshots.append(
+                    {brain_name: current_snapshot_for_brain_name}
+                )
+        self.policy_elos[self.snapshot_counter] = self.current_elo
+        self.snapshot_counter = (self.snapshot_counter + 1) % self.window
+
+    def _swap_snapshots(self) -> None:
+        """
+        Swaps the appropriate weight to the policy and pushes it to respective policy queues
+        """
+
+        for team_id in self._team_to_name_to_policy_queue:
+            if team_id == self._learning_team:
+                continue
+            elif np.random.uniform() < (1 - self.play_against_latest_model_ratio):
+                x = np.random.randint(len(self.policy_snapshots))
+                snapshot = self.policy_snapshots[x]
+            else:
+                snapshot = self.current_policy_snapshot
+                x = "current"
+
+            self.current_opponent = -1 if x == "current" else x
+            name_to_policy_queue = self._team_to_name_to_policy_queue[team_id]
+            for brain_name in self._team_to_name_to_policy_queue[team_id]:
+                behavior_id = create_name_behavior_id(brain_name, team_id)
+                policy = self.get_policy(behavior_id)
+                policy.load_weights(snapshot[brain_name])
+                name_to_policy_queue[brain_name].put(policy)
+                logger.debug(
+                    "Step {}: Swapping snapshot {} to id {} with team {} learning".format(
+                        self.ghost_step, x, behavior_id, self._learning_team
+                    )
+                )
+
+    def publish_policy_queue(self, policy_queue: AgentManagerQueue[Policy]) -> None:
+        """
+        Adds a policy queue for every member of the team to the list of queues to publish to when this Trainer
+        makes a policy update.  Creates an internal policy queue for the wrapped
+        trainer to push to.  The GhostTrainer pushes all policies to the env.
+        :param queue: Policy queue to publish to.
+        """
+        super().publish_policy_queue(policy_queue)
+        parsed_behavior_id = self._name_to_parsed_behavior_id[policy_queue.behavior_id]
+        self._team_to_name_to_policy_queue[parsed_behavior_id.team_id][
+            parsed_behavior_id.brain_name
+        ] = policy_queue
+        if parsed_behavior_id.team_id == self.wrapped_trainer_team:
+            # With a future multiagent trainer, this will be indexed by 'role'
+            internal_policy_queue: AgentManagerQueue[Policy] = AgentManagerQueue(
+                parsed_behavior_id.brain_name
+            )
+
+            self._internal_policy_queues[
+                parsed_behavior_id.brain_name
+            ] = internal_policy_queue
+            self.trainer.publish_policy_queue(internal_policy_queue)
+
+    def subscribe_trajectory_queue(
+        self, trajectory_queue: AgentManagerQueue[Trajectory]
+    ) -> None:
+        """
+        Adds a trajectory queue for every member of the team to the list of queues for the trainer
+        to ingest Trajectories from. Creates an internal trajectory queue to push trajectories from
+        the learning team.  The wrapped trainer subscribes to this queue.
+        :param queue: Trajectory queue to publish to.
+        """
+        super().subscribe_trajectory_queue(trajectory_queue)
+        parsed_behavior_id = self._name_to_parsed_behavior_id[
+            trajectory_queue.behavior_id
+        ]
+        if parsed_behavior_id.team_id == self.wrapped_trainer_team:
+            # With a future multiagent trainer, this will be indexed by 'role'
+            internal_trajectory_queue: AgentManagerQueue[
+                Trajectory
+            ] = AgentManagerQueue(parsed_behavior_id.brain_name)
+
+            self._internal_trajectory_queues[
+                parsed_behavior_id.brain_name
+            ] = internal_trajectory_queue
+            self.trainer.subscribe_trajectory_queue(internal_trajectory_queue)
diff --git a/ml-agents/mlagents/trainers/learn.py b/ml-agents/mlagents/trainers/learn.py
new file mode 100644
index 0000000000000000000000000000000000000000..69320920a585d8782c3596cea5e4b2fbefc24f69
--- /dev/null
+++ b/ml-agents/mlagents/trainers/learn.py
@@ -0,0 +1,269 @@
+# # Unity ML-Agents Toolkit
+from mlagents import torch_utils
+import yaml
+
+import os
+import numpy as np
+import json
+
+from typing import Callable, Optional, List
+
+import mlagents.trainers
+import mlagents_envs
+from mlagents.trainers.trainer_controller import TrainerController
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.trainer import TrainerFactory
+from mlagents.trainers.directory_utils import (
+    validate_existing_directories,
+    setup_init_path,
+)
+from mlagents.trainers.stats import StatsReporter
+from mlagents.trainers.cli_utils import parser
+from mlagents_envs.environment import UnityEnvironment
+from mlagents.trainers.settings import RunOptions
+
+from mlagents.trainers.training_status import GlobalTrainingStatus
+from mlagents_envs.base_env import BaseEnv
+from mlagents.trainers.subprocess_env_manager import SubprocessEnvManager
+from mlagents_envs.side_channel.side_channel import SideChannel
+from mlagents_envs.timers import (
+    hierarchical_timer,
+    get_timer_tree,
+    add_metadata as add_timer_metadata,
+)
+from mlagents_envs import logging_util
+from mlagents.plugins.stats_writer import register_stats_writer_plugins
+from mlagents.plugins.trainer_type import register_trainer_plugins
+
+logger = logging_util.get_logger(__name__)
+
+TRAINING_STATUS_FILE_NAME = "training_status.json"
+
+
+def get_version_string() -> str:
+    return f""" Version information:
+  ml-agents: {mlagents.trainers.__version__},
+  ml-agents-envs: {mlagents_envs.__version__},
+  Communicator API: {UnityEnvironment.API_VERSION},
+  PyTorch: {torch_utils.torch.__version__}"""
+
+
+def parse_command_line(
+    argv: Optional[List[str]] = None,
+) -> RunOptions:
+    _, _ = register_trainer_plugins()
+    args = parser.parse_args(argv)
+    return RunOptions.from_argparse(args)
+
+
+def run_training(run_seed: int, options: RunOptions, num_areas: int) -> None:
+    """
+    Launches training session.
+    :param run_seed: Random seed used for training.
+    :param num_areas: Number of training areas to instantiate
+    :param options: parsed command line arguments
+    """
+    with hierarchical_timer("run_training.setup"):
+        torch_utils.set_torch_config(options.torch_settings)
+        checkpoint_settings = options.checkpoint_settings
+        env_settings = options.env_settings
+        engine_settings = options.engine_settings
+
+        run_logs_dir = checkpoint_settings.run_logs_dir
+        port: Optional[int] = env_settings.base_port
+        # Check if directory exists
+        validate_existing_directories(
+            checkpoint_settings.write_path,
+            checkpoint_settings.resume,
+            checkpoint_settings.force,
+            checkpoint_settings.maybe_init_path,
+        )
+        # Make run logs directory
+        os.makedirs(run_logs_dir, exist_ok=True)
+        # Load any needed states in case of resume
+        if checkpoint_settings.resume:
+            GlobalTrainingStatus.load_state(
+                os.path.join(run_logs_dir, "training_status.json")
+            )
+        # In case of initialization, set full init_path for all behaviors
+        elif checkpoint_settings.maybe_init_path is not None:
+            setup_init_path(options.behaviors, checkpoint_settings.maybe_init_path)
+
+        # Configure Tensorboard Writers and StatsReporter
+        stats_writers = register_stats_writer_plugins(options)
+        for sw in stats_writers:
+            StatsReporter.add_writer(sw)
+
+        if env_settings.env_path is None:
+            port = None
+        env_factory = create_environment_factory(
+            env_settings.env_path,
+            engine_settings.no_graphics,
+            run_seed,
+            num_areas,
+            port,
+            env_settings.env_args,
+            os.path.abspath(run_logs_dir),  # Unity environment requires absolute path
+        )
+
+        env_manager = SubprocessEnvManager(env_factory, options, env_settings.num_envs)
+        env_parameter_manager = EnvironmentParameterManager(
+            options.environment_parameters, run_seed, restore=checkpoint_settings.resume
+        )
+
+        trainer_factory = TrainerFactory(
+            trainer_config=options.behaviors,
+            output_path=checkpoint_settings.write_path,
+            train_model=not checkpoint_settings.inference,
+            load_model=checkpoint_settings.resume,
+            seed=run_seed,
+            param_manager=env_parameter_manager,
+            init_path=checkpoint_settings.maybe_init_path,
+            multi_gpu=False,
+        )
+        # Create controller and begin training.
+        tc = TrainerController(
+            trainer_factory,
+            checkpoint_settings.write_path,
+            checkpoint_settings.run_id,
+            env_parameter_manager,
+            not checkpoint_settings.inference,
+            run_seed,
+        )
+
+    # Begin training
+    try:
+        tc.start_learning(env_manager)
+    finally:
+        env_manager.close()
+        write_run_options(checkpoint_settings.write_path, options)
+        write_timing_tree(run_logs_dir)
+        write_training_status(run_logs_dir)
+
+
+def write_run_options(output_dir: str, run_options: RunOptions) -> None:
+    run_options_path = os.path.join(output_dir, "configuration.yaml")
+    try:
+        with open(run_options_path, "w") as f:
+            try:
+                yaml.dump(run_options.as_dict(), f, sort_keys=False)
+            except TypeError:  # Older versions of pyyaml don't support sort_keys
+                yaml.dump(run_options.as_dict(), f)
+    except FileNotFoundError:
+        logger.warning(
+            f"Unable to save configuration to {run_options_path}. Make sure the directory exists"
+        )
+
+
+def write_training_status(output_dir: str) -> None:
+    GlobalTrainingStatus.save_state(os.path.join(output_dir, TRAINING_STATUS_FILE_NAME))
+
+
+def write_timing_tree(output_dir: str) -> None:
+    timing_path = os.path.join(output_dir, "timers.json")
+    try:
+        with open(timing_path, "w") as f:
+            json.dump(get_timer_tree(), f, indent=4)
+    except FileNotFoundError:
+        logger.warning(
+            f"Unable to save to {timing_path}. Make sure the directory exists"
+        )
+
+
+def create_environment_factory(
+    env_path: Optional[str],
+    no_graphics: bool,
+    seed: int,
+    num_areas: int,
+    start_port: Optional[int],
+    env_args: Optional[List[str]],
+    log_folder: str,
+) -> Callable[[int, List[SideChannel]], BaseEnv]:
+    def create_unity_environment(
+        worker_id: int, side_channels: List[SideChannel]
+    ) -> UnityEnvironment:
+        # Make sure that each environment gets a different seed
+        env_seed = seed + worker_id
+        return UnityEnvironment(
+            file_name=env_path,
+            worker_id=worker_id,
+            seed=env_seed,
+            num_areas=num_areas,
+            no_graphics=no_graphics,
+            base_port=start_port,
+            additional_args=env_args,
+            side_channels=side_channels,
+            log_folder=log_folder,
+        )
+
+    return create_unity_environment
+
+
+def run_cli(options: RunOptions) -> None:
+    try:
+        print(
+            """
+            �  ╖
+        ╓╖╬│╡  ││╬╖╖
+    ╓╖╬│││││┘  ╬│││││╬╖
+ ╖╬│││││╬╜        ╙╬│││││╖╖                               ╗╗╗
+ ╬╬╬╬╖││╦╖        ╖╬││╗╣╣╣╬      ╟╣╣╬    ╟╣╣╣             ╜╜╜  ╟╣╣
+ ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╒╣╣╖╗╣╣╣╗   ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖   ╣╣╣
+ ╬╬╬╬�  ╙╬╬╬╬│╓╣╣╣�╜  ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣  ╣╣╣ ╙╟╣╣╜╙  ╫╣╣  ╟╣╣
+ ╬╬╬╬�     ╙╬╬╣╣      ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣     ╣╣╣┌╣╣╜
+ ╬╬╬╜       ╬╬╣╣      ╙�╣╣╬      ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣╦╓    ╣╣╣╣╣
+ ╙   ╓╦╖    ╬╬╣╣   ╓╗╗╖            ╙�╣╣╣╣�╜   ╘��╜   ���  ���   ╙╣╣╣    ╟╣╣╣
+   ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣�                                             ╫╣╣╣╣
+      ╙╬╬╬╬╬╬╬╣╣╣╣╣╣�╜
+          ╙╬╬╬╣╣╣╜
+             â•™
+        """
+        )
+    except Exception:
+        print("\n\n\tUnity Technologies\n")
+    print(get_version_string())
+
+    if options.debug:
+        log_level = logging_util.DEBUG
+    else:
+        log_level = logging_util.INFO
+
+    logging_util.set_log_level(log_level)
+
+    logger.debug("Configuration for this run:")
+    logger.debug(json.dumps(options.as_dict(), indent=4))
+
+    # Options deprecation warnings
+    if options.checkpoint_settings.load_model:
+        logger.warning(
+            "The --load option has been deprecated. Please use the --resume option instead."
+        )
+    if options.checkpoint_settings.train_model:
+        logger.warning(
+            "The --train option has been deprecated. Train mode is now the default. Use "
+            "--inference to run in inference mode."
+        )
+
+    run_seed = options.env_settings.seed
+    num_areas = options.env_settings.num_areas
+
+    # Add some timer metadata
+    add_timer_metadata("mlagents_version", mlagents.trainers.__version__)
+    add_timer_metadata("mlagents_envs_version", mlagents_envs.__version__)
+    add_timer_metadata("communication_protocol_version", UnityEnvironment.API_VERSION)
+    add_timer_metadata("pytorch_version", torch_utils.torch.__version__)
+    add_timer_metadata("numpy_version", np.__version__)
+
+    if options.env_settings.seed == -1:
+        run_seed = np.random.randint(0, 10000)
+        logger.debug(f"run_seed set to {run_seed}")
+    run_training(run_seed, options, num_areas)
+
+
+def main():
+    run_cli(parse_command_line())
+
+
+# For python debugger to directly run this script
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/mlagents/trainers/model_saver/__init__.py b/ml-agents/mlagents/trainers/model_saver/__init__.py
new file mode 100644
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diff --git a/ml-agents/mlagents/trainers/model_saver/__pycache__/torch_model_saver.cpython-310.pyc b/ml-agents/mlagents/trainers/model_saver/__pycache__/torch_model_saver.cpython-310.pyc
new file mode 100644
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diff --git a/ml-agents/mlagents/trainers/model_saver/model_saver.py b/ml-agents/mlagents/trainers/model_saver/model_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1594ff08e79e251a8cb7ce6c074d929819787ff
--- /dev/null
+++ b/ml-agents/mlagents/trainers/model_saver/model_saver.py
@@ -0,0 +1,69 @@
+# # Unity ML-Agents Toolkit
+import abc
+from typing import Any, Tuple, List
+
+
+class BaseModelSaver(abc.ABC):
+    """This class is the base class for the ModelSaver"""
+
+    def __init__(self):
+        pass
+
+    @abc.abstractmethod
+    def register(self, module: Any) -> None:
+        """
+        Register the modules to the ModelSaver.
+        The ModelSaver will store the module and include it in the saved files
+        when saving checkpoint/exporting graph.
+        :param module: the module to be registered
+        """
+        pass
+
+    def _register_policy(self, policy):
+        """
+        Helper function for registering policy to the ModelSaver.
+        :param policy: the policy to be registered
+        """
+        pass
+
+    def _register_optimizer(self, optimizer):
+        """
+        Helper function for registering optimizer to the ModelSaver.
+        :param optimizer: the optimizer to be registered
+        """
+        pass
+
+    @abc.abstractmethod
+    def save_checkpoint(self, behavior_name: str, step: int) -> Tuple[str, List[str]]:
+        """
+        Checkpoints the policy on disk.
+        :param checkpoint_path: filepath to write the checkpoint
+        :param behavior_name: Behavior name of bevavior to be trained
+        :return: A Tuple of the path to the exported file, as well as a List of any
+            auxillary files that were returned. For instance, an exported file would be Model.onnx,
+            and the auxillary files would be [Model.pt] for PyTorch
+        """
+        pass
+
+    @abc.abstractmethod
+    def export(self, output_filepath: str, behavior_name: str) -> None:
+        """
+        Saves the serialized model, given a path and behavior name.
+        This method will save the policy graph to the given filepath.  The path
+        should be provided without an extension as multiple serialized model formats
+        may be generated as a result.
+        :param output_filepath: path (without suffix) for the model file(s)
+        :param behavior_name: Behavior name of behavior to be trained.
+        """
+        pass
+
+    @abc.abstractmethod
+    def initialize_or_load(self, policy):
+        """
+        Initialize/Load registered modules by default.
+        If given input argument policy, do with the input policy instead.
+        This argument is mainly for the initialization of the ghost trainer's fixed policy.
+        :param policy (optional): if given, perform the initializing/loading on this input policy.
+                                  Otherwise, do with the registered policy
+        """
+        pass
diff --git a/ml-agents/mlagents/trainers/model_saver/torch_model_saver.py b/ml-agents/mlagents/trainers/model_saver/torch_model_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..70c3f19e431d6cdd9b346a311e79745326238ff5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/model_saver/torch_model_saver.py
@@ -0,0 +1,153 @@
+import os
+import shutil
+from mlagents.torch_utils import torch
+from typing import Dict, Union, Optional, cast, Tuple, List
+from mlagents_envs.exception import UnityPolicyException
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.model_saver.model_saver import BaseModelSaver
+from mlagents.trainers.settings import TrainerSettings, SerializationSettings
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.torch_entities.model_serialization import ModelSerializer
+
+
+logger = get_logger(__name__)
+DEFAULT_CHECKPOINT_NAME = "checkpoint.pt"
+
+
+class TorchModelSaver(BaseModelSaver):
+    """
+    ModelSaver class for PyTorch
+    """
+
+    def __init__(
+        self, trainer_settings: TrainerSettings, model_path: str, load: bool = False
+    ):
+        super().__init__()
+        self.model_path = model_path
+        self.initialize_path = trainer_settings.init_path
+        self._keep_checkpoints = trainer_settings.keep_checkpoints
+        self.load = load
+
+        self.policy: Optional[TorchPolicy] = None
+        self.exporter: Optional[ModelSerializer] = None
+        self.modules: Dict[str, torch.nn.Modules] = {}
+
+    def register(self, module: Union[TorchPolicy, TorchOptimizer]) -> None:
+        if isinstance(module, TorchPolicy) or isinstance(module, TorchOptimizer):
+            self.modules.update(module.get_modules())  # type: ignore
+        else:
+            raise UnityPolicyException(
+                "Registering Object of unsupported type {} to ModelSaver ".format(
+                    type(module)
+                )
+            )
+        if self.policy is None and isinstance(module, TorchPolicy):
+            self.policy = module
+            self.exporter = ModelSerializer(self.policy)
+
+    def save_checkpoint(self, behavior_name: str, step: int) -> Tuple[str, List[str]]:
+        if not os.path.exists(self.model_path):
+            os.makedirs(self.model_path)
+        checkpoint_path = os.path.join(self.model_path, f"{behavior_name}-{step}")
+        state_dict = {
+            name: module.state_dict() for name, module in self.modules.items()
+        }
+        pytorch_ckpt_path = f"{checkpoint_path}.pt"
+        export_ckpt_path = f"{checkpoint_path}.onnx"
+        torch.save(state_dict, f"{checkpoint_path}.pt")
+        torch.save(state_dict, os.path.join(self.model_path, DEFAULT_CHECKPOINT_NAME))
+        self.export(checkpoint_path, behavior_name)
+        return export_ckpt_path, [pytorch_ckpt_path]
+
+    def export(self, output_filepath: str, behavior_name: str) -> None:
+        if self.exporter is not None:
+            self.exporter.export_policy_model(output_filepath)
+
+    def initialize_or_load(self, policy: Optional[TorchPolicy] = None) -> None:
+        # Initialize/Load registered self.policy by default.
+        # If given input argument policy, use the input policy instead.
+        # This argument is mainly for initialization of the ghost trainer's fixed policy.
+        reset_steps = not self.load
+        if self.initialize_path is not None:
+            logger.info(f"Initializing from {self.initialize_path}.")
+            self._load_model(
+                self.initialize_path, policy, reset_global_steps=reset_steps
+            )
+        elif self.load:
+            logger.info(f"Resuming from {self.model_path}.")
+            self._load_model(
+                os.path.join(self.model_path, DEFAULT_CHECKPOINT_NAME),
+                policy,
+                reset_global_steps=reset_steps,
+            )
+
+    def _load_model(
+        self,
+        load_path: str,
+        policy: Optional[TorchPolicy] = None,
+        reset_global_steps: bool = False,
+    ) -> None:
+        saved_state_dict = torch.load(load_path)
+        if policy is None:
+            modules = self.modules
+            policy = self.policy
+        else:
+            modules = policy.get_modules()
+        policy = cast(TorchPolicy, policy)
+
+        for name, mod in modules.items():
+            try:
+                if isinstance(mod, torch.nn.Module):
+                    missing_keys, unexpected_keys = mod.load_state_dict(
+                        saved_state_dict[name], strict=False
+                    )
+                    if missing_keys:
+                        logger.warning(
+                            f"Did not find these keys {missing_keys} in checkpoint. Initializing."
+                        )
+                    if unexpected_keys:
+                        logger.warning(
+                            f"Did not expect these keys {unexpected_keys} in checkpoint. Ignoring."
+                        )
+                else:
+                    # If module is not an nn.Module, try to load as one piece
+                    mod.load_state_dict(saved_state_dict[name])
+
+            # KeyError is raised if the module was not present in the last run but is being
+            # accessed in the saved_state_dict.
+            # ValueError is raised by the optimizer's load_state_dict if the parameters have
+            # have changed. Note, the optimizer uses a completely different load_state_dict
+            # function because it is not an nn.Module.
+            # RuntimeError is raised by PyTorch if there is a size mismatch between modules
+            # of the same name. This will still partially assign values to those layers that
+            # have not changed shape.
+            except (KeyError, ValueError, RuntimeError) as err:
+                logger.warning(f"Failed to load for module {name}. Initializing")
+                logger.debug(f"Module loading error : {err}")
+
+        if reset_global_steps:
+            policy.set_step(0)
+            logger.info(
+                "Starting training from step 0 and saving to {}.".format(
+                    self.model_path
+                )
+            )
+        else:
+            logger.info(f"Resuming training from step {policy.get_current_step()}.")
+
+    def copy_final_model(self, source_nn_path: str) -> None:
+        """
+        Copy the .nn file at the given source to the destination.
+        Also copies the corresponding .onnx file if it exists.
+        """
+        final_model_name = os.path.splitext(source_nn_path)[0]
+
+        if SerializationSettings.convert_to_onnx:
+            try:
+                source_path = f"{final_model_name}.onnx"
+                destination_path = f"{self.model_path}.onnx"
+                shutil.copyfile(source_path, destination_path)
+                logger.info(f"Copied {source_path} to {destination_path}.")
+            except OSError:
+                pass
diff --git a/ml-agents/mlagents/trainers/optimizer/__init__.py b/ml-agents/mlagents/trainers/optimizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba7b9a71313c198fad246562c0e0f796053eb562
--- /dev/null
+++ b/ml-agents/mlagents/trainers/optimizer/__init__.py
@@ -0,0 +1 @@
+from mlagents.trainers.optimizer.optimizer import Optimizer  # noqa
diff --git a/ml-agents/mlagents/trainers/optimizer/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/trainers/optimizer/__pycache__/__init__.cpython-310.pyc
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diff --git a/ml-agents/mlagents/trainers/optimizer/optimizer.py b/ml-agents/mlagents/trainers/optimizer/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d18e3c60ec292084127f28e77d144723804c4e9a
--- /dev/null
+++ b/ml-agents/mlagents/trainers/optimizer/optimizer.py
@@ -0,0 +1,24 @@
+import abc
+from typing import Dict
+
+from mlagents.trainers.buffer import AgentBuffer
+
+
+class Optimizer(abc.ABC):
+    """
+    Creates loss functions and auxillary networks (e.g. Q or Value) needed for training.
+    Provides methods to update the Policy.
+    """
+
+    def __init__(self):
+        self.reward_signals = {}
+
+    @abc.abstractmethod
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Update the Policy based on the batch that was passed in.
+        :param batch: AgentBuffer that contains the minibatch of data used for this update.
+        :param num_sequences: Number of recurrent sequences found in the minibatch.
+        :return: A Dict containing statistics (name, value) from the update (e.g. loss)
+        """
+        pass
diff --git a/ml-agents/mlagents/trainers/optimizer/torch_optimizer.py b/ml-agents/mlagents/trainers/optimizer/torch_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cb0a6ee8c0809ccbf7a15cdd474e92059575efc
--- /dev/null
+++ b/ml-agents/mlagents/trainers/optimizer/torch_optimizer.py
@@ -0,0 +1,211 @@
+from typing import Dict, Optional, Tuple, List
+from mlagents.torch_utils import torch
+import numpy as np
+from collections import defaultdict
+
+from mlagents.trainers.buffer import AgentBuffer, AgentBufferField
+from mlagents.trainers.trajectory import ObsUtil
+from mlagents.trainers.torch_entities.components.bc.module import BCModule
+from mlagents.trainers.torch_entities.components.reward_providers import (
+    create_reward_provider,
+)
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer import Optimizer
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    RewardSignalSettings,
+    RewardSignalType,
+)
+from mlagents.trainers.torch_entities.utils import ModelUtils
+
+
+class TorchOptimizer(Optimizer):
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        super().__init__()
+        self.policy = policy
+        self.trainer_settings = trainer_settings
+        self.update_dict: Dict[str, torch.Tensor] = {}
+        self.value_heads: Dict[str, torch.Tensor] = {}
+        self.memory_in: torch.Tensor = None
+        self.memory_out: torch.Tensor = None
+        self.m_size: int = 0
+        self.global_step = torch.tensor(0)
+        self.bc_module: Optional[BCModule] = None
+        self.create_reward_signals(trainer_settings.reward_signals)
+        self.critic_memory_dict: Dict[str, torch.Tensor] = {}
+        if trainer_settings.behavioral_cloning is not None:
+            self.bc_module = BCModule(
+                self.policy,
+                trainer_settings.behavioral_cloning,
+                policy_learning_rate=trainer_settings.hyperparameters.learning_rate,
+                default_batch_size=trainer_settings.hyperparameters.batch_size,
+                default_num_epoch=3,
+            )
+
+    @property
+    def critic(self):
+        raise NotImplementedError
+
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        pass
+
+    def create_reward_signals(
+        self, reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]
+    ) -> None:
+        """
+        Create reward signals
+        :param reward_signal_configs: Reward signal config.
+        """
+        for reward_signal, settings in reward_signal_configs.items():
+            # Name reward signals by string in case we have duplicates later
+            self.reward_signals[reward_signal.value] = create_reward_provider(
+                reward_signal, self.policy.behavior_spec, settings
+            )
+
+    def _evaluate_by_sequence(
+        self, tensor_obs: List[torch.Tensor], initial_memory: torch.Tensor
+    ) -> Tuple[Dict[str, torch.Tensor], AgentBufferField, torch.Tensor]:
+        """
+        Evaluate a trajectory sequence-by-sequence, assembling the result. This enables us to get the
+        intermediate memories for the critic.
+        :param tensor_obs: A List of tensors of shape (trajectory_len, <obs_dim>) that are the agent's
+            observations for this trajectory.
+        :param initial_memory: The memory that preceeds this trajectory. Of shape (1,1,<mem_size>), i.e.
+            what is returned as the output of a MemoryModules.
+        :return: A Tuple of the value estimates as a Dict of [name, tensor], an AgentBufferField of the initial
+            memories to be used during value function update, and the final memory at the end of the trajectory.
+        """
+        num_experiences = tensor_obs[0].shape[0]
+        all_next_memories = AgentBufferField()
+        # When using LSTM, we need to divide the trajectory into sequences of equal length. Sometimes,
+        # that division isn't even, and we must pad the leftover sequence.
+        # When it is added to the buffer, the last sequence will be padded. So if seq_len = 3 and
+        # trajectory is of length 10, the last sequence is [obs,pad,pad] once it is added to the buffer.
+        # Compute the number of elements in this sequence that will end up being padded.
+        leftover_seq_len = num_experiences % self.policy.sequence_length
+
+        all_values: Dict[str, List[np.ndarray]] = defaultdict(list)
+        _mem = initial_memory
+        # Evaluate other trajectories, carrying over _mem after each
+        # trajectory
+        for seq_num in range(num_experiences // self.policy.sequence_length):
+            seq_obs = []
+            for _ in range(self.policy.sequence_length):
+                all_next_memories.append(ModelUtils.to_numpy(_mem.squeeze()))
+            start = seq_num * self.policy.sequence_length
+            end = (seq_num + 1) * self.policy.sequence_length
+
+            for _obs in tensor_obs:
+                seq_obs.append(_obs[start:end])
+            values, _mem = self.critic.critic_pass(
+                seq_obs, _mem, sequence_length=self.policy.sequence_length
+            )
+            for signal_name, _val in values.items():
+                all_values[signal_name].append(_val)
+
+        # Compute values for the potentially truncated last sequence. Note that this
+        # sequence isn't padded yet, but will be.
+        seq_obs = []
+
+        if leftover_seq_len > 0:
+            for _obs in tensor_obs:
+                last_seq_obs = _obs[-leftover_seq_len:]
+                seq_obs.append(last_seq_obs)
+
+            # For the last sequence, the initial memory should be the one at the
+            # end of this trajectory.
+            for _ in range(leftover_seq_len):
+                all_next_memories.append(ModelUtils.to_numpy(_mem.squeeze()))
+
+            last_values, _mem = self.critic.critic_pass(
+                seq_obs, _mem, sequence_length=leftover_seq_len
+            )
+            for signal_name, _val in last_values.items():
+                all_values[signal_name].append(_val)
+
+        # Create one tensor per reward signal
+        all_value_tensors = {
+            signal_name: torch.cat(value_list, dim=0)
+            for signal_name, value_list in all_values.items()
+        }
+        next_mem = _mem
+        return all_value_tensors, all_next_memories, next_mem
+
+    def update_reward_signals(self, batch: AgentBuffer) -> Dict[str, float]:
+        update_stats: Dict[str, float] = {}
+        for reward_provider in self.reward_signals.values():
+            update_stats.update(reward_provider.update(batch))
+        return update_stats
+
+    def get_trajectory_value_estimates(
+        self,
+        batch: AgentBuffer,
+        next_obs: List[np.ndarray],
+        done: bool,
+        agent_id: str = "",
+    ) -> Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]]:
+        """
+        Get value estimates and memories for a trajectory, in batch form.
+        :param batch: An AgentBuffer that consists of a trajectory.
+        :param next_obs: the next observation (after the trajectory). Used for boostrapping
+            if this is not a termiinal trajectory.
+        :param done: Set true if this is a terminal trajectory.
+        :param agent_id: Agent ID of the agent that this trajectory belongs to.
+        :returns: A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)],
+            the final value estimate as a Dict of [name, float], and optionally (if using memories)
+            an AgentBufferField of initial critic memories to be used during update.
+        """
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+
+        if agent_id in self.critic_memory_dict:
+            memory = self.critic_memory_dict[agent_id]
+        else:
+            memory = (
+                torch.zeros((1, 1, self.critic.memory_size))
+                if self.policy.use_recurrent
+                else None
+            )
+
+        # Convert to tensors
+        current_obs = [
+            ModelUtils.list_to_tensor(obs) for obs in ObsUtil.from_buffer(batch, n_obs)
+        ]
+        next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
+
+        next_obs = [obs.unsqueeze(0) for obs in next_obs]
+
+        # If we're using LSTM, we want to get all the intermediate memories.
+        all_next_memories: Optional[AgentBufferField] = None
+
+        # To prevent memory leak and improve performance, evaluate with no_grad.
+        with torch.no_grad():
+            if self.policy.use_recurrent:
+                (
+                    value_estimates,
+                    all_next_memories,
+                    next_memory,
+                ) = self._evaluate_by_sequence(current_obs, memory)
+            else:
+                value_estimates, next_memory = self.critic.critic_pass(
+                    current_obs, memory, sequence_length=batch.num_experiences
+                )
+
+        # Store the memory for the next trajectory. This should NOT have a gradient.
+        self.critic_memory_dict[agent_id] = next_memory
+
+        next_value_estimate, _ = self.critic.critic_pass(
+            next_obs, next_memory, sequence_length=1
+        )
+
+        for name, estimate in value_estimates.items():
+            value_estimates[name] = ModelUtils.to_numpy(estimate)
+            next_value_estimate[name] = ModelUtils.to_numpy(next_value_estimate[name])
+
+        if done:
+            for k in next_value_estimate:
+                if not self.reward_signals[k].ignore_done:
+                    next_value_estimate[k] = 0.0
+            if agent_id in self.critic_memory_dict:
+                self.critic_memory_dict.pop(agent_id)
+        return value_estimates, next_value_estimate, all_next_memories
diff --git a/ml-agents/mlagents/trainers/poca/__init__.py b/ml-agents/mlagents/trainers/poca/__init__.py
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diff --git a/ml-agents/mlagents/trainers/poca/optimizer_torch.py b/ml-agents/mlagents/trainers/poca/optimizer_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f77de4ebbf3f005fb7e2f4eb5ccda0f1bb640e0
--- /dev/null
+++ b/ml-agents/mlagents/trainers/poca/optimizer_torch.py
@@ -0,0 +1,690 @@
+from typing import Dict, cast, List, Tuple, Optional
+from collections import defaultdict
+import attr
+
+from mlagents.trainers.torch_entities.components.reward_providers.extrinsic_reward_provider import (
+    ExtrinsicRewardProvider,
+)
+import numpy as np
+from mlagents.torch_utils import torch, default_device
+
+from mlagents.trainers.buffer import (
+    AgentBuffer,
+    BufferKey,
+    RewardSignalUtil,
+    AgentBufferField,
+)
+
+from mlagents_envs.timers import timed
+from mlagents_envs.base_env import ObservationSpec, ActionSpec
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.settings import (
+    RewardSignalSettings,
+    RewardSignalType,
+    TrainerSettings,
+    NetworkSettings,
+    OnPolicyHyperparamSettings,
+    ScheduleType,
+)
+from mlagents.trainers.torch_entities.networks import Critic, MultiAgentNetworkBody
+from mlagents.trainers.torch_entities.decoders import ValueHeads
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_log_probs import ActionLogProbs
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.trajectory import ObsUtil, GroupObsUtil
+
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+
+@attr.s(auto_attribs=True)
+class POCASettings(OnPolicyHyperparamSettings):
+    beta: float = 5.0e-3
+    epsilon: float = 0.2
+    lambd: float = 0.95
+    num_epoch: int = 3
+    learning_rate_schedule: ScheduleType = ScheduleType.LINEAR
+    beta_schedule: ScheduleType = ScheduleType.LINEAR
+    epsilon_schedule: ScheduleType = ScheduleType.LINEAR
+
+
+class TorchPOCAOptimizer(TorchOptimizer):
+    class POCAValueNetwork(torch.nn.Module, Critic):
+        """
+        The POCAValueNetwork uses the MultiAgentNetworkBody to compute the value
+        and POCA baseline for a variable number of agents in a group that all
+        share the same observation and action space.
+        """
+
+        def __init__(
+            self,
+            stream_names: List[str],
+            observation_specs: List[ObservationSpec],
+            network_settings: NetworkSettings,
+            action_spec: ActionSpec,
+        ):
+            torch.nn.Module.__init__(self)
+            self.network_body = MultiAgentNetworkBody(
+                observation_specs, network_settings, action_spec
+            )
+            if network_settings.memory is not None:
+                encoding_size = network_settings.memory.memory_size // 2
+            else:
+                encoding_size = network_settings.hidden_units
+
+            self.value_heads = ValueHeads(stream_names, encoding_size + 1, 1)
+            # The + 1 is for the normalized number of agents
+
+        @property
+        def memory_size(self) -> int:
+            return self.network_body.memory_size
+
+        def update_normalization(self, buffer: AgentBuffer) -> None:
+            self.network_body.update_normalization(buffer)
+
+        def baseline(
+            self,
+            obs_without_actions: List[torch.Tensor],
+            obs_with_actions: Tuple[List[List[torch.Tensor]], List[AgentAction]],
+            memories: Optional[torch.Tensor] = None,
+            sequence_length: int = 1,
+        ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+            """
+            The POCA baseline marginalizes the action of the agent associated with self_obs.
+            It calls the forward pass of the MultiAgentNetworkBody with the state action
+            pairs of groupmates but just the state of the agent in question.
+            :param obs_without_actions: The obs of the agent for which to compute the baseline.
+            :param obs_with_actions: Tuple of observations and actions for all groupmates.
+            :param memories: If using memory, a Tensor of initial memories.
+            :param sequence_length: If using memory, the sequence length.
+
+            :return: A Tuple of Dict of reward stream to tensor and critic memories.
+            """
+            (obs, actions) = obs_with_actions
+            encoding, memories = self.network_body(
+                obs_only=[obs_without_actions],
+                obs=obs,
+                actions=actions,
+                memories=memories,
+                sequence_length=sequence_length,
+            )
+
+            value_outputs, critic_mem_out = self.forward(
+                encoding, memories, sequence_length
+            )
+            return value_outputs, critic_mem_out
+
+        def critic_pass(
+            self,
+            obs: List[List[torch.Tensor]],
+            memories: Optional[torch.Tensor] = None,
+            sequence_length: int = 1,
+        ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+            """
+            A centralized value function. It calls the forward pass of MultiAgentNetworkBody
+            with just the states of all agents.
+            :param obs: List of observations for all agents in group
+            :param memories: If using memory, a Tensor of initial memories.
+            :param sequence_length: If using memory, the sequence length.
+            :return: A Tuple of Dict of reward stream to tensor and critic memories.
+            """
+            encoding, memories = self.network_body(
+                obs_only=obs,
+                obs=[],
+                actions=[],
+                memories=memories,
+                sequence_length=sequence_length,
+            )
+
+            value_outputs, critic_mem_out = self.forward(
+                encoding, memories, sequence_length
+            )
+            return value_outputs, critic_mem_out
+
+        def forward(
+            self,
+            encoding: torch.Tensor,
+            memories: Optional[torch.Tensor] = None,
+            sequence_length: int = 1,
+        ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+            output = self.value_heads(encoding)
+            return output, memories
+
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        """
+        Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
+        :param policy: A TorchPolicy object that will be updated by this POCA Optimizer.
+        :param trainer_params: Trainer parameters dictionary that specifies the
+        properties of the trainer.
+        """
+        # Create the graph here to give more granular control of the TF graph to the Optimizer.
+
+        super().__init__(policy, trainer_settings)
+        reward_signal_configs = trainer_settings.reward_signals
+        reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
+
+        self._critic = TorchPOCAOptimizer.POCAValueNetwork(
+            reward_signal_names,
+            policy.behavior_spec.observation_specs,
+            network_settings=trainer_settings.network_settings,
+            action_spec=policy.behavior_spec.action_spec,
+        )
+        # Move to GPU if needed
+        self._critic.to(default_device())
+
+        params = list(self.policy.actor.parameters()) + list(self.critic.parameters())
+
+        self.hyperparameters: POCASettings = cast(
+            POCASettings, trainer_settings.hyperparameters
+        )
+
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_epsilon = ModelUtils.DecayedValue(
+            self.hyperparameters.epsilon_schedule,
+            self.hyperparameters.epsilon,
+            0.1,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_beta = ModelUtils.DecayedValue(
+            self.hyperparameters.beta_schedule,
+            self.hyperparameters.beta,
+            1e-5,
+            self.trainer_settings.max_steps,
+        )
+
+        self.optimizer = torch.optim.Adam(
+            params, lr=self.trainer_settings.hyperparameters.learning_rate
+        )
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+
+        self.stream_names = list(self.reward_signals.keys())
+        self.value_memory_dict: Dict[str, torch.Tensor] = {}
+        self.baseline_memory_dict: Dict[str, torch.Tensor] = {}
+
+    def create_reward_signals(
+        self, reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]
+    ) -> None:
+        """
+        Create reward signals. Override default to provide warnings for Curiosity and
+        GAIL, and make sure Extrinsic adds team rewards.
+        :param reward_signal_configs: Reward signal config.
+        """
+        for reward_signal in reward_signal_configs.keys():
+            if reward_signal != RewardSignalType.EXTRINSIC:
+                logger.warning(
+                    f"Reward signal {reward_signal.value.capitalize()} is not supported with the POCA trainer; "
+                    "results may be unexpected."
+                )
+        super().create_reward_signals(reward_signal_configs)
+        # Make sure we add the groupmate rewards in POCA, so agents learn how to help each
+        # other achieve individual rewards as well
+        for reward_provider in self.reward_signals.values():
+            if isinstance(reward_provider, ExtrinsicRewardProvider):
+                reward_provider.add_groupmate_rewards = True
+
+    @property
+    def critic(self):
+        return self._critic
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Performs update on model.
+        :param batch: Batch of experiences.
+        :param num_sequences: Number of sequences to process.
+        :return: Results of update.
+        """
+        # Get decayed parameters
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        decay_eps = self.decay_epsilon.get_value(self.policy.get_current_step())
+        decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
+        returns = {}
+        old_values = {}
+        old_baseline_values = {}
+        for name in self.reward_signals:
+            old_values[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.value_estimates_key(name)]
+            )
+            returns[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.returns_key(name)]
+            )
+            old_baseline_values[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.baseline_estimates_key(name)]
+            )
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        # Convert to tensors
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+        groupmate_obs = GroupObsUtil.from_buffer(batch, n_obs)
+        groupmate_obs = [
+            [ModelUtils.list_to_tensor(obs) for obs in _groupmate_obs]
+            for _groupmate_obs in groupmate_obs
+        ]
+
+        act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
+        actions = AgentAction.from_buffer(batch)
+        groupmate_actions = AgentAction.group_from_buffer(batch)
+
+        memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i])
+            for i in range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
+        ]
+        if len(memories) > 0:
+            memories = torch.stack(memories).unsqueeze(0)
+        value_memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
+            for i in range(
+                0, len(batch[BufferKey.CRITIC_MEMORY]), self.policy.sequence_length
+            )
+        ]
+
+        baseline_memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.BASELINE_MEMORY][i])
+            for i in range(
+                0, len(batch[BufferKey.BASELINE_MEMORY]), self.policy.sequence_length
+            )
+        ]
+
+        if len(value_memories) > 0:
+            value_memories = torch.stack(value_memories).unsqueeze(0)
+            baseline_memories = torch.stack(baseline_memories).unsqueeze(0)
+
+        run_out = self.policy.actor.get_stats(
+            current_obs,
+            actions,
+            masks=act_masks,
+            memories=memories,
+            sequence_length=self.policy.sequence_length,
+        )
+
+        log_probs = run_out["log_probs"]
+        entropy = run_out["entropy"]
+
+        all_obs = [current_obs] + groupmate_obs
+        values, _ = self.critic.critic_pass(
+            all_obs,
+            memories=value_memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        groupmate_obs_and_actions = (groupmate_obs, groupmate_actions)
+        baselines, _ = self.critic.baseline(
+            current_obs,
+            groupmate_obs_and_actions,
+            memories=baseline_memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        old_log_probs = ActionLogProbs.from_buffer(batch).flatten()
+        log_probs = log_probs.flatten()
+        loss_masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS], dtype=torch.bool)
+
+        baseline_loss = ModelUtils.trust_region_value_loss(
+            baselines, old_baseline_values, returns, decay_eps, loss_masks
+        )
+        value_loss = ModelUtils.trust_region_value_loss(
+            values, old_values, returns, decay_eps, loss_masks
+        )
+        policy_loss = ModelUtils.trust_region_policy_loss(
+            ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]),
+            log_probs,
+            old_log_probs,
+            loss_masks,
+            decay_eps,
+        )
+
+        loss = (
+            policy_loss
+            + 0.5 * (value_loss + 0.5 * baseline_loss)
+            - decay_bet * ModelUtils.masked_mean(entropy, loss_masks)
+        )
+
+        # Set optimizer learning rate
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.optimizer.zero_grad()
+        loss.backward()
+
+        self.optimizer.step()
+        update_stats = {
+            # NOTE: abs() is not technically correct, but matches the behavior in TensorFlow.
+            # TODO: After PyTorch is default, change to something more correct.
+            "Losses/Policy Loss": torch.abs(policy_loss).item(),
+            "Losses/Value Loss": value_loss.item(),
+            "Losses/Baseline Loss": baseline_loss.item(),
+            "Policy/Learning Rate": decay_lr,
+            "Policy/Epsilon": decay_eps,
+            "Policy/Beta": decay_bet,
+        }
+
+        return update_stats
+
+    def get_modules(self):
+        modules = {"Optimizer:adam": self.optimizer, "Optimizer:critic": self._critic}
+        for reward_provider in self.reward_signals.values():
+            modules.update(reward_provider.get_modules())
+        return modules
+
+    def _evaluate_by_sequence_team(
+        self,
+        self_obs: List[torch.Tensor],
+        obs: List[List[torch.Tensor]],
+        actions: List[AgentAction],
+        init_value_mem: torch.Tensor,
+        init_baseline_mem: torch.Tensor,
+    ) -> Tuple[
+        Dict[str, torch.Tensor],
+        Dict[str, torch.Tensor],
+        AgentBufferField,
+        AgentBufferField,
+        torch.Tensor,
+        torch.Tensor,
+    ]:
+        """
+        Evaluate a trajectory sequence-by-sequence, assembling the result. This enables us to get the
+        intermediate memories for the critic.
+        :param tensor_obs: A List of tensors of shape (trajectory_len, <obs_dim>) that are the agent's
+            observations for this trajectory.
+        :param initial_memory: The memory that preceeds this trajectory. Of shape (1,1,<mem_size>), i.e.
+            what is returned as the output of a MemoryModules.
+        :return: A Tuple of the value estimates as a Dict of [name, tensor], an AgentBufferField of the initial
+            memories to be used during value function update, and the final memory at the end of the trajectory.
+        """
+        num_experiences = self_obs[0].shape[0]
+        all_next_value_mem = AgentBufferField()
+        all_next_baseline_mem = AgentBufferField()
+
+        # When using LSTM, we need to divide the trajectory into sequences of equal length. Sometimes,
+        # that division isn't even, and we must pad the leftover sequence.
+        # In the buffer, the last sequence are the ones that are padded. So if seq_len = 3 and
+        # trajectory is of length 10, the last sequence is [obs,pad,pad].
+        # Compute the number of elements in this padded seq.
+        leftover_seq_len = num_experiences % self.policy.sequence_length
+
+        all_values: Dict[str, List[np.ndarray]] = defaultdict(list)
+        all_baseline: Dict[str, List[np.ndarray]] = defaultdict(list)
+        _baseline_mem = init_baseline_mem
+        _value_mem = init_value_mem
+
+        # Evaluate other trajectories, carrying over _mem after each
+        # trajectory
+        for seq_num in range(num_experiences // self.policy.sequence_length):
+            for _ in range(self.policy.sequence_length):
+                all_next_value_mem.append(ModelUtils.to_numpy(_value_mem.squeeze()))
+                all_next_baseline_mem.append(
+                    ModelUtils.to_numpy(_baseline_mem.squeeze())
+                )
+
+            start = seq_num * self.policy.sequence_length
+            end = (seq_num + 1) * self.policy.sequence_length
+
+            self_seq_obs = []
+            groupmate_seq_obs = []
+            groupmate_seq_act = []
+            seq_obs = []
+            for _self_obs in self_obs:
+                seq_obs.append(_self_obs[start:end])
+            self_seq_obs.append(seq_obs)
+
+            for groupmate_obs, groupmate_action in zip(obs, actions):
+                seq_obs = []
+                for _obs in groupmate_obs:
+                    sliced_seq_obs = _obs[start:end]
+                    seq_obs.append(sliced_seq_obs)
+                groupmate_seq_obs.append(seq_obs)
+                _act = groupmate_action.slice(start, end)
+                groupmate_seq_act.append(_act)
+
+            all_seq_obs = self_seq_obs + groupmate_seq_obs
+            values, _value_mem = self.critic.critic_pass(
+                all_seq_obs, _value_mem, sequence_length=self.policy.sequence_length
+            )
+            for signal_name, _val in values.items():
+                all_values[signal_name].append(_val)
+
+            groupmate_obs_and_actions = (groupmate_seq_obs, groupmate_seq_act)
+            baselines, _baseline_mem = self.critic.baseline(
+                self_seq_obs[0],
+                groupmate_obs_and_actions,
+                _baseline_mem,
+                sequence_length=self.policy.sequence_length,
+            )
+            for signal_name, _val in baselines.items():
+                all_baseline[signal_name].append(_val)
+
+        # Compute values for the potentially truncated initial sequence
+        if leftover_seq_len > 0:
+            self_seq_obs = []
+            groupmate_seq_obs = []
+            groupmate_seq_act = []
+            seq_obs = []
+            for _self_obs in self_obs:
+                last_seq_obs = _self_obs[-leftover_seq_len:]
+                seq_obs.append(last_seq_obs)
+            self_seq_obs.append(seq_obs)
+
+            for groupmate_obs, groupmate_action in zip(obs, actions):
+                seq_obs = []
+                for _obs in groupmate_obs:
+                    last_seq_obs = _obs[-leftover_seq_len:]
+                    seq_obs.append(last_seq_obs)
+                groupmate_seq_obs.append(seq_obs)
+                _act = groupmate_action.slice(len(_obs) - leftover_seq_len, len(_obs))
+                groupmate_seq_act.append(_act)
+
+            # For the last sequence, the initial memory should be the one at the
+            # beginning of this trajectory.
+            seq_obs = []
+            for _ in range(leftover_seq_len):
+                all_next_value_mem.append(ModelUtils.to_numpy(_value_mem.squeeze()))
+                all_next_baseline_mem.append(
+                    ModelUtils.to_numpy(_baseline_mem.squeeze())
+                )
+
+            all_seq_obs = self_seq_obs + groupmate_seq_obs
+            last_values, _value_mem = self.critic.critic_pass(
+                all_seq_obs, _value_mem, sequence_length=leftover_seq_len
+            )
+            for signal_name, _val in last_values.items():
+                all_values[signal_name].append(_val)
+            groupmate_obs_and_actions = (groupmate_seq_obs, groupmate_seq_act)
+            last_baseline, _baseline_mem = self.critic.baseline(
+                self_seq_obs[0],
+                groupmate_obs_and_actions,
+                _baseline_mem,
+                sequence_length=leftover_seq_len,
+            )
+            for signal_name, _val in last_baseline.items():
+                all_baseline[signal_name].append(_val)
+        # Create one tensor per reward signal
+        all_value_tensors = {
+            signal_name: torch.cat(value_list, dim=0)
+            for signal_name, value_list in all_values.items()
+        }
+        all_baseline_tensors = {
+            signal_name: torch.cat(baseline_list, dim=0)
+            for signal_name, baseline_list in all_baseline.items()
+        }
+        next_value_mem = _value_mem
+        next_baseline_mem = _baseline_mem
+        return (
+            all_value_tensors,
+            all_baseline_tensors,
+            all_next_value_mem,
+            all_next_baseline_mem,
+            next_value_mem,
+            next_baseline_mem,
+        )
+
+    def get_trajectory_value_estimates(
+        self,
+        batch: AgentBuffer,
+        next_obs: List[np.ndarray],
+        done: bool,
+        agent_id: str = "",
+    ) -> Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]]:
+        """
+        Override base class method. Unused in the trainer, but needed to make sure class heirarchy is maintained.
+        Assume that there are no group obs.
+        """
+        (
+            value_estimates,
+            _,
+            next_value_estimates,
+            all_next_value_mem,
+            _,
+        ) = self.get_trajectory_and_baseline_value_estimates(
+            batch, next_obs, [], done, agent_id
+        )
+
+        return value_estimates, next_value_estimates, all_next_value_mem
+
+    def get_trajectory_and_baseline_value_estimates(
+        self,
+        batch: AgentBuffer,
+        next_obs: List[np.ndarray],
+        next_groupmate_obs: List[List[np.ndarray]],
+        done: bool,
+        agent_id: str = "",
+    ) -> Tuple[
+        Dict[str, np.ndarray],
+        Dict[str, np.ndarray],
+        Dict[str, float],
+        Optional[AgentBufferField],
+        Optional[AgentBufferField],
+    ]:
+        """
+        Get value estimates, baseline estimates, and memories for a trajectory, in batch form.
+        :param batch: An AgentBuffer that consists of a trajectory.
+        :param next_obs: the next observation (after the trajectory). Used for boostrapping
+            if this is not a termiinal trajectory.
+        :param next_groupmate_obs: the next observations from other members of the group.
+        :param done: Set true if this is a terminal trajectory.
+        :param agent_id: Agent ID of the agent that this trajectory belongs to.
+        :returns: A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)],
+            the baseline estimates as a Dict, the final value estimate as a Dict of [name, float], and
+            optionally (if using memories) an AgentBufferField of initial critic and baseline memories to be used
+            during update.
+        """
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        groupmate_obs = GroupObsUtil.from_buffer(batch, n_obs)
+
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+        groupmate_obs = [
+            [ModelUtils.list_to_tensor(obs) for obs in _groupmate_obs]
+            for _groupmate_obs in groupmate_obs
+        ]
+
+        groupmate_actions = AgentAction.group_from_buffer(batch)
+
+        next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
+        next_obs = [obs.unsqueeze(0) for obs in next_obs]
+
+        next_groupmate_obs = [
+            ModelUtils.list_to_tensor_list(_list_obs)
+            for _list_obs in next_groupmate_obs
+        ]
+        # Expand dimensions of next critic obs
+        next_groupmate_obs = [
+            [_obs.unsqueeze(0) for _obs in _list_obs]
+            for _list_obs in next_groupmate_obs
+        ]
+
+        if agent_id in self.value_memory_dict:
+            # The agent_id should always be in both since they are added together
+            _init_value_mem = self.value_memory_dict[agent_id]
+            _init_baseline_mem = self.baseline_memory_dict[agent_id]
+        else:
+            _init_value_mem = (
+                torch.zeros((1, 1, self.critic.memory_size))
+                if self.policy.use_recurrent
+                else None
+            )
+            _init_baseline_mem = (
+                torch.zeros((1, 1, self.critic.memory_size))
+                if self.policy.use_recurrent
+                else None
+            )
+
+        all_obs = (
+            [current_obs] + groupmate_obs
+            if groupmate_obs is not None
+            else [current_obs]
+        )
+        all_next_value_mem: Optional[AgentBufferField] = None
+        all_next_baseline_mem: Optional[AgentBufferField] = None
+        with torch.no_grad():
+            if self.policy.use_recurrent:
+                (
+                    value_estimates,
+                    baseline_estimates,
+                    all_next_value_mem,
+                    all_next_baseline_mem,
+                    next_value_mem,
+                    next_baseline_mem,
+                ) = self._evaluate_by_sequence_team(
+                    current_obs,
+                    groupmate_obs,
+                    groupmate_actions,
+                    _init_value_mem,
+                    _init_baseline_mem,
+                )
+            else:
+                value_estimates, next_value_mem = self.critic.critic_pass(
+                    all_obs, _init_value_mem, sequence_length=batch.num_experiences
+                )
+                groupmate_obs_and_actions = (groupmate_obs, groupmate_actions)
+                baseline_estimates, next_baseline_mem = self.critic.baseline(
+                    current_obs,
+                    groupmate_obs_and_actions,
+                    _init_baseline_mem,
+                    sequence_length=batch.num_experiences,
+                )
+        # Store the memory for the next trajectory
+        self.value_memory_dict[agent_id] = next_value_mem
+        self.baseline_memory_dict[agent_id] = next_baseline_mem
+
+        all_next_obs = (
+            [next_obs] + next_groupmate_obs
+            if next_groupmate_obs is not None
+            else [next_obs]
+        )
+
+        next_value_estimates, _ = self.critic.critic_pass(
+            all_next_obs, next_value_mem, sequence_length=1
+        )
+
+        for name, estimate in baseline_estimates.items():
+            baseline_estimates[name] = ModelUtils.to_numpy(estimate)
+
+        for name, estimate in value_estimates.items():
+            value_estimates[name] = ModelUtils.to_numpy(estimate)
+
+        # the base line and V shpuld  not be on the same done flag
+        for name, estimate in next_value_estimates.items():
+            next_value_estimates[name] = ModelUtils.to_numpy(estimate)
+
+        if done:
+            for k in next_value_estimates:
+                if not self.reward_signals[k].ignore_done:
+                    next_value_estimates[k][-1] = 0.0
+
+        return (
+            value_estimates,
+            baseline_estimates,
+            next_value_estimates,
+            all_next_value_mem,
+            all_next_baseline_mem,
+        )
diff --git a/ml-agents/mlagents/trainers/poca/trainer.py b/ml-agents/mlagents/trainers/poca/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..266a14932154fc7413c9949bef221a1c268c1d4f
--- /dev/null
+++ b/ml-agents/mlagents/trainers/poca/trainer.py
@@ -0,0 +1,249 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agents Learning (POCA)
+# Contains an implementation of MA-POCA.
+
+from collections import defaultdict
+from typing import cast, Dict, Union, Any, Type
+
+import numpy as np
+
+from mlagents_envs.side_channel.stats_side_channel import StatsAggregationMethod
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+from mlagents.trainers.trainer.on_policy_trainer import OnPolicyTrainer
+from mlagents.trainers.trainer.trainer_utils import lambda_return
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.poca.optimizer_torch import TorchPOCAOptimizer, POCASettings
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings
+
+from mlagents.trainers.torch_entities.networks import SimpleActor, SharedActorCritic
+
+logger = get_logger(__name__)
+
+TRAINER_NAME = "poca"
+
+
+class POCATrainer(OnPolicyTrainer):
+    """The POCATrainer is an implementation of the MA-POCA algorithm."""
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training POCA model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            reward_buff_cap,
+            trainer_settings,
+            training,
+            load,
+            seed,
+            artifact_path,
+        )
+        self.hyperparameters: POCASettings = cast(
+            POCASettings, self.trainer_settings.hyperparameters
+        )
+        self.seed = seed
+        self.policy: TorchPolicy = None  # type: ignore
+        self.optimizer: TorchPOCAOptimizer = None  # type: ignore
+        self.collected_group_rewards: Dict[str, int] = defaultdict(lambda: 0)
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the update buffer.
+        Processing involves calculating value and advantage targets for model updating step.
+        :param trajectory: The Trajectory tuple containing the steps to be processed.
+        """
+        super()._process_trajectory(trajectory)
+        agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+        agent_buffer_trajectory = trajectory.to_agentbuffer()
+        # Update the normalization
+        if self.is_training:
+            self.policy.actor.update_normalization(agent_buffer_trajectory)
+            self.optimizer.critic.update_normalization(agent_buffer_trajectory)
+
+        # Get all value estimates
+        (
+            value_estimates,
+            baseline_estimates,
+            value_next,
+            value_memories,
+            baseline_memories,
+        ) = self.optimizer.get_trajectory_and_baseline_value_estimates(
+            agent_buffer_trajectory,
+            trajectory.next_obs,
+            trajectory.next_group_obs,
+            trajectory.all_group_dones_reached
+            and trajectory.done_reached
+            and not trajectory.interrupted,
+        )
+
+        if value_memories is not None and baseline_memories is not None:
+            agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
+            agent_buffer_trajectory[BufferKey.BASELINE_MEMORY].set(baseline_memories)
+
+        for name, v in value_estimates.items():
+            agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(
+                v
+            )
+            agent_buffer_trajectory[
+                RewardSignalUtil.baseline_estimates_key(name)
+            ].extend(baseline_estimates[name])
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Baseline Estimate",
+                np.mean(baseline_estimates[name]),
+            )
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
+                np.mean(value_estimates[name]),
+            )
+
+        self.collected_rewards["environment"][agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+        )
+        self.collected_group_rewards[agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.GROUP_REWARD]
+        )
+        for name, reward_signal in self.optimizer.reward_signals.items():
+            evaluate_result = (
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+            )
+            agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(
+                evaluate_result
+            )
+            # Report the reward signals
+            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+        # Compute lambda returns and advantage
+        tmp_advantages = []
+        for name in self.optimizer.reward_signals:
+
+            local_rewards = np.array(
+                agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].get_batch(),
+                dtype=np.float32,
+            )
+
+            baseline_estimate = agent_buffer_trajectory[
+                RewardSignalUtil.baseline_estimates_key(name)
+            ].get_batch()
+            v_estimates = agent_buffer_trajectory[
+                RewardSignalUtil.value_estimates_key(name)
+            ].get_batch()
+
+            lambd_returns = lambda_return(
+                r=local_rewards,
+                value_estimates=v_estimates,
+                gamma=self.optimizer.reward_signals[name].gamma,
+                lambd=self.hyperparameters.lambd,
+                value_next=value_next[name],
+            )
+
+            local_advantage = np.array(lambd_returns) - np.array(baseline_estimate)
+
+            agent_buffer_trajectory[RewardSignalUtil.returns_key(name)].set(
+                lambd_returns
+            )
+            agent_buffer_trajectory[RewardSignalUtil.advantage_key(name)].set(
+                local_advantage
+            )
+            tmp_advantages.append(local_advantage)
+
+        # Get global advantages
+        global_advantages = list(
+            np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
+        )
+        agent_buffer_trajectory[BufferKey.ADVANTAGES].set(global_advantages)
+
+        self._append_to_update_buffer(agent_buffer_trajectory)
+
+        # If this was a terminal trajectory, append stats and reset reward collection
+        if trajectory.done_reached:
+            self._update_end_episode_stats(agent_id, self.optimizer)
+            # Remove dead agents from group reward recording
+            if not trajectory.all_group_dones_reached:
+                self.collected_group_rewards.pop(agent_id)
+
+        # If the whole team is done, average the remaining group rewards.
+        if trajectory.all_group_dones_reached and trajectory.done_reached:
+            self.stats_reporter.add_stat(
+                "Environment/Group Cumulative Reward",
+                self.collected_group_rewards.get(agent_id, 0),
+                aggregation=StatsAggregationMethod.HISTOGRAM,
+            )
+            self.collected_group_rewards.pop(agent_id)
+
+    def _is_ready_update(self):
+        """
+        Returns whether or not the trainer has enough elements to run update model
+        :return: A boolean corresponding to whether or not update_model() can be run
+        """
+        size_of_buffer = self.update_buffer.num_experiences
+        return size_of_buffer > self.hyperparameters.buffer_size
+
+    def end_episode(self) -> None:
+        """
+        A signal that the Episode has ended. The buffer must be reset.
+        Get only called when the academy resets. For POCA, we should
+        also zero out the group rewards.
+        """
+        super().end_episode()
+        self.collected_group_rewards.clear()
+
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a policy with a PyTorch backend and POCA hyperparameters
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor
+        actor_kwargs: Dict[str, Any] = {
+            "conditional_sigma": False,
+            "tanh_squash": False,
+        }
+
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings.network_settings,
+            actor_cls,
+            actor_kwargs,
+        )
+        return policy
+
+    def create_optimizer(self) -> TorchPOCAOptimizer:
+        return TorchPOCAOptimizer(self.policy, self.trainer_settings)
+
+    def get_policy(self, name_behavior_id: str) -> Policy:
+        """
+        Gets policy from trainer associated with name_behavior_id
+        :param name_behavior_id: full identifier of policy
+        """
+
+        return self.policy
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        return TRAINER_NAME
diff --git a/ml-agents/mlagents/trainers/policy/__init__.py b/ml-agents/mlagents/trainers/policy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8348d8738b939b384661966836d61d4509b19f87
--- /dev/null
+++ b/ml-agents/mlagents/trainers/policy/__init__.py
@@ -0,0 +1 @@
+from mlagents.trainers.policy.policy import Policy  # noqa
diff --git a/ml-agents/mlagents/trainers/policy/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/trainers/policy/__pycache__/__init__.cpython-310.pyc
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diff --git a/ml-agents/mlagents/trainers/policy/checkpoint_manager.py b/ml-agents/mlagents/trainers/policy/checkpoint_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f3e2762b36357c97c6114d969a627d3b874a661
--- /dev/null
+++ b/ml-agents/mlagents/trainers/policy/checkpoint_manager.py
@@ -0,0 +1,101 @@
+# # Unity ML-Agents Toolkit
+from typing import Dict, Any, Optional, List
+import os
+import attr
+from mlagents.trainers.training_status import GlobalTrainingStatus, StatusType
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+
+
+@attr.s(auto_attribs=True)
+class ModelCheckpoint:
+    steps: int
+    file_path: str
+    reward: Optional[float]
+    creation_time: float
+    auxillary_file_paths: List[str] = attr.ib(factory=list)
+
+
+class ModelCheckpointManager:
+    @staticmethod
+    def get_checkpoints(behavior_name: str) -> List[Dict[str, Any]]:
+        checkpoint_list = GlobalTrainingStatus.get_parameter_state(
+            behavior_name, StatusType.CHECKPOINTS
+        )
+        if not checkpoint_list:
+            checkpoint_list = []
+            GlobalTrainingStatus.set_parameter_state(
+                behavior_name, StatusType.CHECKPOINTS, checkpoint_list
+            )
+        return checkpoint_list
+
+    @staticmethod
+    def remove_checkpoint(checkpoint: Dict[str, Any]) -> None:
+        """
+        Removes a checkpoint stored in checkpoint_list.
+        If checkpoint cannot be found, no action is done.
+
+        :param checkpoint: A checkpoint stored in checkpoint_list
+        """
+        file_paths: List[str] = [checkpoint["file_path"]]
+        file_paths.extend(checkpoint["auxillary_file_paths"])
+        for file_path in file_paths:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+                logger.debug(f"Removed checkpoint model {file_path}.")
+            else:
+                logger.debug(f"Checkpoint at {file_path} could not be found.")
+        return
+
+    @classmethod
+    def _cleanup_extra_checkpoints(
+        cls, checkpoints: List[Dict], keep_checkpoints: int
+    ) -> List[Dict]:
+        """
+        Ensures that the number of checkpoints stored are within the number
+        of checkpoints the user defines. If the limit is hit, checkpoints are
+        removed to create room for the next checkpoint to be inserted.
+
+        :param behavior_name: The behavior name whose checkpoints we will mange.
+        :param keep_checkpoints: Number of checkpoints to record (user-defined).
+        """
+        while len(checkpoints) > keep_checkpoints:
+            if keep_checkpoints <= 0 or len(checkpoints) == 0:
+                break
+            ModelCheckpointManager.remove_checkpoint(checkpoints.pop(0))
+        return checkpoints
+
+    @classmethod
+    def add_checkpoint(
+        cls, behavior_name: str, new_checkpoint: ModelCheckpoint, keep_checkpoints: int
+    ) -> None:
+        """
+        Make room for new checkpoint if needed and insert new checkpoint information.
+        :param behavior_name: Behavior name for the checkpoint.
+        :param new_checkpoint: The new checkpoint to be recorded.
+        :param keep_checkpoints: Number of checkpoints to record (user-defined).
+        """
+        new_checkpoint_dict = attr.asdict(new_checkpoint)
+        checkpoints = cls.get_checkpoints(behavior_name)
+        checkpoints.append(new_checkpoint_dict)
+        cls._cleanup_extra_checkpoints(checkpoints, keep_checkpoints)
+        GlobalTrainingStatus.set_parameter_state(
+            behavior_name, StatusType.CHECKPOINTS, checkpoints
+        )
+
+    @classmethod
+    def track_final_checkpoint(
+        cls, behavior_name: str, final_checkpoint: ModelCheckpoint
+    ) -> None:
+        """
+        Ensures number of checkpoints stored is within the max number of checkpoints
+        defined by the user and finally stores the information about the final
+        model (or intermediate model if training is interrupted).
+        :param behavior_name: Behavior name of the model.
+        :param final_checkpoint: Checkpoint information for the final model.
+        """
+        final_model_dict = attr.asdict(final_checkpoint)
+        GlobalTrainingStatus.set_parameter_state(
+            behavior_name, StatusType.FINAL_CHECKPOINT, final_model_dict
+        )
diff --git a/ml-agents/mlagents/trainers/policy/policy.py b/ml-agents/mlagents/trainers/policy/policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c5e9f72472d071676b9fbaa9ba845d0f31ab40b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/policy/policy.py
@@ -0,0 +1,146 @@
+from abc import abstractmethod
+from typing import Dict, List, Optional
+import numpy as np
+
+from mlagents_envs.base_env import ActionTuple, BehaviorSpec, DecisionSteps
+from mlagents_envs.exception import UnityException
+
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.behavior_id_utils import GlobalAgentId
+
+
+class UnityPolicyException(UnityException):
+    """
+    Related to errors with the Trainer.
+    """
+
+    pass
+
+
+class Policy:
+    def __init__(
+        self,
+        seed: int,
+        behavior_spec: BehaviorSpec,
+        network_settings: NetworkSettings,
+    ):
+        self.behavior_spec = behavior_spec
+        self.network_settings: NetworkSettings = network_settings
+        self.seed = seed
+        self.previous_action_dict: Dict[str, np.ndarray] = {}
+        self.previous_memory_dict: Dict[str, np.ndarray] = {}
+        self.memory_dict: Dict[str, np.ndarray] = {}
+        self.normalize = network_settings.normalize
+        self.use_recurrent = self.network_settings.memory is not None
+        self.m_size = 0
+        self.sequence_length = 1
+        if self.use_recurrent:
+            self.m_size = self.network_settings.memory.memory_size
+            self.sequence_length = self.network_settings.memory.sequence_length
+
+    def make_empty_memory(self, num_agents):
+        """
+        Creates empty memory for use with RNNs
+        :param num_agents: Number of agents.
+        :return: Numpy array of zeros.
+        """
+        return np.zeros((num_agents, self.m_size), dtype=np.float32)
+
+    def save_memories(
+        self, agent_ids: List[GlobalAgentId], memory_matrix: Optional[np.ndarray]
+    ) -> None:
+        if memory_matrix is None:
+            return
+
+        # Pass old memories into previous_memory_dict
+        for agent_id in agent_ids:
+            if agent_id in self.memory_dict:
+                self.previous_memory_dict[agent_id] = self.memory_dict[agent_id]
+
+        for index, agent_id in enumerate(agent_ids):
+            self.memory_dict[agent_id] = memory_matrix[index, :]
+
+    def retrieve_memories(self, agent_ids: List[GlobalAgentId]) -> np.ndarray:
+        memory_matrix = np.zeros((len(agent_ids), self.m_size), dtype=np.float32)
+        for index, agent_id in enumerate(agent_ids):
+            if agent_id in self.memory_dict:
+                memory_matrix[index, :] = self.memory_dict[agent_id]
+        return memory_matrix
+
+    def retrieve_previous_memories(self, agent_ids: List[GlobalAgentId]) -> np.ndarray:
+        memory_matrix = np.zeros((len(agent_ids), self.m_size), dtype=np.float32)
+        for index, agent_id in enumerate(agent_ids):
+            if agent_id in self.previous_memory_dict:
+                memory_matrix[index, :] = self.previous_memory_dict[agent_id]
+        return memory_matrix
+
+    def remove_memories(self, agent_ids: List[GlobalAgentId]) -> None:
+        for agent_id in agent_ids:
+            if agent_id in self.memory_dict:
+                self.memory_dict.pop(agent_id)
+            if agent_id in self.previous_memory_dict:
+                self.previous_memory_dict.pop(agent_id)
+
+    def make_empty_previous_action(self, num_agents: int) -> np.ndarray:
+        """
+        Creates empty previous action for use with RNNs and discrete control
+        :param num_agents: Number of agents.
+        :return: Numpy array of zeros.
+        """
+        return np.zeros(
+            (num_agents, self.behavior_spec.action_spec.discrete_size), dtype=np.int32
+        )
+
+    def save_previous_action(
+        self, agent_ids: List[GlobalAgentId], action_tuple: ActionTuple
+    ) -> None:
+        for index, agent_id in enumerate(agent_ids):
+            self.previous_action_dict[agent_id] = action_tuple.discrete[index, :]
+
+    def retrieve_previous_action(self, agent_ids: List[GlobalAgentId]) -> np.ndarray:
+        action_matrix = self.make_empty_previous_action(len(agent_ids))
+        for index, agent_id in enumerate(agent_ids):
+            if agent_id in self.previous_action_dict:
+                action_matrix[index, :] = self.previous_action_dict[agent_id]
+        return action_matrix
+
+    def remove_previous_action(self, agent_ids: List[GlobalAgentId]) -> None:
+        for agent_id in agent_ids:
+            if agent_id in self.previous_action_dict:
+                self.previous_action_dict.pop(agent_id)
+
+    def get_action(
+        self, decision_requests: DecisionSteps, worker_id: int = 0
+    ) -> ActionInfo:
+        raise NotImplementedError
+
+    @staticmethod
+    def check_nan_action(action: Optional[ActionTuple]) -> None:
+        # Fast NaN check on the action
+        # See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background.
+        if action is not None:
+            d = np.sum(action.continuous)
+            has_nan = np.isnan(d)
+            if has_nan:
+                raise RuntimeError("Continuous NaN action detected.")
+
+    @abstractmethod
+    def increment_step(self, n_steps):
+        pass
+
+    @abstractmethod
+    def get_current_step(self):
+        pass
+
+    @abstractmethod
+    def load_weights(self, values: List[np.ndarray]) -> None:
+        pass
+
+    @abstractmethod
+    def get_weights(self) -> List[np.ndarray]:
+        return []
+
+    @abstractmethod
+    def init_load_weights(self) -> None:
+        pass
diff --git a/ml-agents/mlagents/trainers/policy/torch_policy.py b/ml-agents/mlagents/trainers/policy/torch_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..fceacda6e95c8fb31a7dbeb837a65992af94b41f
--- /dev/null
+++ b/ml-agents/mlagents/trainers/policy/torch_policy.py
@@ -0,0 +1,173 @@
+from typing import Any, Dict, List
+import numpy as np
+from mlagents.torch_utils import torch, default_device
+import copy
+
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.behavior_id_utils import get_global_agent_id
+from mlagents.trainers.policy import Policy
+from mlagents_envs.base_env import DecisionSteps, BehaviorSpec
+from mlagents_envs.timers import timed
+
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.torch_entities.networks import GlobalSteps
+
+from mlagents.trainers.torch_entities.utils import ModelUtils
+
+EPSILON = 1e-7  # Small value to avoid divide by zero
+
+
+class TorchPolicy(Policy):
+    def __init__(
+        self,
+        seed: int,
+        behavior_spec: BehaviorSpec,
+        network_settings: NetworkSettings,
+        actor_cls: type,
+        actor_kwargs: Dict[str, Any],
+    ):
+        """
+        Policy that uses a multilayer perceptron to map the observations to actions. Could
+        also use a CNN to encode visual input prior to the MLP. Supports discrete and
+        continuous actions, as well as recurrent networks.
+        :param seed: Random seed.
+        :param behavior_spec: Assigned BehaviorSpec object.
+        :param network_settings: Defined network parameters.
+        :param actor_cls: The type of Actor
+        :param actor_kwargs: Keyword args for the Actor class
+        """
+        super().__init__(seed, behavior_spec, network_settings)
+        self.global_step = (
+            GlobalSteps()
+        )  # could be much simpler if TorchPolicy is nn.Module
+
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+
+        self.actor = actor_cls(
+            observation_specs=self.behavior_spec.observation_specs,
+            network_settings=network_settings,
+            action_spec=behavior_spec.action_spec,
+            **actor_kwargs,
+        )
+
+        # Save the m_size needed for export
+        self._export_m_size = self.m_size
+        # m_size needed for training is determined by network, not trainer settings
+        self.m_size = self.actor.memory_size
+
+        self.actor.to(default_device())
+
+    @property
+    def export_memory_size(self) -> int:
+        """
+        Returns the memory size of the exported ONNX policy. This only includes the memory
+        of the Actor and not any auxillary networks.
+        """
+        return self._export_m_size
+
+    def _extract_masks(self, decision_requests: DecisionSteps) -> np.ndarray:
+        mask = None
+        if self.behavior_spec.action_spec.discrete_size > 0:
+            num_discrete_flat = np.sum(self.behavior_spec.action_spec.discrete_branches)
+            mask = torch.ones([len(decision_requests), num_discrete_flat])
+            if decision_requests.action_mask is not None:
+                mask = torch.as_tensor(
+                    1 - np.concatenate(decision_requests.action_mask, axis=1)
+                )
+        return mask
+
+    @timed
+    def evaluate(
+        self, decision_requests: DecisionSteps, global_agent_ids: List[str]
+    ) -> Dict[str, Any]:
+        """
+        Evaluates policy for the agent experiences provided.
+        :param global_agent_ids:
+        :param decision_requests: DecisionStep object containing inputs.
+        :return: Outputs from network as defined by self.inference_dict.
+        """
+        obs = decision_requests.obs
+        masks = self._extract_masks(decision_requests)
+        tensor_obs = [torch.as_tensor(np_ob) for np_ob in obs]
+
+        memories = torch.as_tensor(self.retrieve_memories(global_agent_ids)).unsqueeze(
+            0
+        )
+        with torch.no_grad():
+            action, run_out, memories = self.actor.get_action_and_stats(
+                tensor_obs, masks=masks, memories=memories
+            )
+        run_out["action"] = action.to_action_tuple()
+        if "log_probs" in run_out:
+            run_out["log_probs"] = run_out["log_probs"].to_log_probs_tuple()
+        if "entropy" in run_out:
+            run_out["entropy"] = ModelUtils.to_numpy(run_out["entropy"])
+        if self.use_recurrent:
+            run_out["memory_out"] = ModelUtils.to_numpy(memories).squeeze(0)
+        return run_out
+
+    def get_action(
+        self, decision_requests: DecisionSteps, worker_id: int = 0
+    ) -> ActionInfo:
+        """
+        Decides actions given observations information, and takes them in environment.
+        :param worker_id:
+        :param decision_requests: A dictionary of behavior names and DecisionSteps from environment.
+        :return: an ActionInfo containing action, memories, values and an object
+        to be passed to add experiences
+        """
+        if len(decision_requests) == 0:
+            return ActionInfo.empty()
+
+        global_agent_ids = [
+            get_global_agent_id(worker_id, int(agent_id))
+            for agent_id in decision_requests.agent_id
+        ]  # For 1-D array, the iterator order is correct.
+
+        run_out = self.evaluate(decision_requests, global_agent_ids)
+        self.save_memories(global_agent_ids, run_out.get("memory_out"))
+        self.check_nan_action(run_out.get("action"))
+        return ActionInfo(
+            action=run_out.get("action"),
+            env_action=run_out.get("env_action"),
+            outputs=run_out,
+            agent_ids=list(decision_requests.agent_id),
+        )
+
+    def get_current_step(self):
+        """
+        Gets current model step.
+        :return: current model step.
+        """
+        return self.global_step.current_step
+
+    def set_step(self, step: int) -> int:
+        """
+        Sets current model step to step without creating additional ops.
+        :param step: Step to set the current model step to.
+        :return: The step the model was set to.
+        """
+        self.global_step.current_step = step
+        return step
+
+    def increment_step(self, n_steps):
+        """
+        Increments model step.
+        """
+        self.global_step.increment(n_steps)
+        return self.get_current_step()
+
+    def load_weights(self, values: List[np.ndarray]) -> None:
+        self.actor.load_state_dict(values)
+
+    def init_load_weights(self) -> None:
+        pass
+
+    def get_weights(self) -> List[np.ndarray]:
+        return copy.deepcopy(self.actor.state_dict())
+
+    def get_modules(self):
+        return {"Policy": self.actor, "global_step": self.global_step}
diff --git a/ml-agents/mlagents/trainers/ppo/__init__.py b/ml-agents/mlagents/trainers/ppo/__init__.py
new file mode 100644
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diff --git a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..41a452c65cb114e81767d92ac90d198aa44ef6e9
--- /dev/null
+++ b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
@@ -0,0 +1,207 @@
+from typing import Dict, cast
+import attr
+
+from mlagents.torch_utils import torch, default_device
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey, RewardSignalUtil
+
+from mlagents_envs.timers import timed
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    OnPolicyHyperparamSettings,
+    ScheduleType,
+)
+from mlagents.trainers.torch_entities.networks import ValueNetwork
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_log_probs import ActionLogProbs
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.trajectory import ObsUtil
+
+
+@attr.s(auto_attribs=True)
+class PPOSettings(OnPolicyHyperparamSettings):
+    beta: float = 5.0e-3
+    epsilon: float = 0.2
+    lambd: float = 0.95
+    num_epoch: int = 3
+    shared_critic: bool = False
+    learning_rate_schedule: ScheduleType = ScheduleType.LINEAR
+    beta_schedule: ScheduleType = ScheduleType.LINEAR
+    epsilon_schedule: ScheduleType = ScheduleType.LINEAR
+
+
+class TorchPPOOptimizer(TorchOptimizer):
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        """
+        Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
+        The PPO optimizer has a value estimator and a loss function.
+        :param policy: A TorchPolicy object that will be updated by this PPO Optimizer.
+        :param trainer_params: Trainer parameters dictionary that specifies the
+        properties of the trainer.
+        """
+        # Create the graph here to give more granular control of the TF graph to the Optimizer.
+
+        super().__init__(policy, trainer_settings)
+        reward_signal_configs = trainer_settings.reward_signals
+        reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
+
+        self.hyperparameters: PPOSettings = cast(
+            PPOSettings, trainer_settings.hyperparameters
+        )
+
+        params = list(self.policy.actor.parameters())
+        if self.hyperparameters.shared_critic:
+            self._critic = policy.actor
+        else:
+            self._critic = ValueNetwork(
+                reward_signal_names,
+                policy.behavior_spec.observation_specs,
+                network_settings=trainer_settings.network_settings,
+            )
+            self._critic.to(default_device())
+            params += list(self._critic.parameters())
+
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_epsilon = ModelUtils.DecayedValue(
+            self.hyperparameters.epsilon_schedule,
+            self.hyperparameters.epsilon,
+            0.1,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_beta = ModelUtils.DecayedValue(
+            self.hyperparameters.beta_schedule,
+            self.hyperparameters.beta,
+            1e-5,
+            self.trainer_settings.max_steps,
+        )
+
+        self.optimizer = torch.optim.Adam(
+            params, lr=self.trainer_settings.hyperparameters.learning_rate
+        )
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+
+        self.stream_names = list(self.reward_signals.keys())
+
+    @property
+    def critic(self):
+        return self._critic
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Performs update on model.
+        :param batch: Batch of experiences.
+        :param num_sequences: Number of sequences to process.
+        :return: Results of update.
+        """
+        # Get decayed parameters
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        decay_eps = self.decay_epsilon.get_value(self.policy.get_current_step())
+        decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
+        returns = {}
+        old_values = {}
+        for name in self.reward_signals:
+            old_values[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.value_estimates_key(name)]
+            )
+            returns[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.returns_key(name)]
+            )
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        # Convert to tensors
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+
+        act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
+        actions = AgentAction.from_buffer(batch)
+
+        memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i])
+            for i in range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
+        ]
+        if len(memories) > 0:
+            memories = torch.stack(memories).unsqueeze(0)
+
+        # Get value memories
+        value_memories = [
+            ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
+            for i in range(
+                0, len(batch[BufferKey.CRITIC_MEMORY]), self.policy.sequence_length
+            )
+        ]
+        if len(value_memories) > 0:
+            value_memories = torch.stack(value_memories).unsqueeze(0)
+
+        run_out = self.policy.actor.get_stats(
+            current_obs,
+            actions,
+            masks=act_masks,
+            memories=memories,
+            sequence_length=self.policy.sequence_length,
+        )
+
+        log_probs = run_out["log_probs"]
+        entropy = run_out["entropy"]
+
+        values, _ = self.critic.critic_pass(
+            current_obs,
+            memories=value_memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        old_log_probs = ActionLogProbs.from_buffer(batch).flatten()
+        log_probs = log_probs.flatten()
+        loss_masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS], dtype=torch.bool)
+        value_loss = ModelUtils.trust_region_value_loss(
+            values, old_values, returns, decay_eps, loss_masks
+        )
+        policy_loss = ModelUtils.trust_region_policy_loss(
+            ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]),
+            log_probs,
+            old_log_probs,
+            loss_masks,
+            decay_eps,
+        )
+        loss = (
+            policy_loss
+            + 0.5 * value_loss
+            - decay_bet * ModelUtils.masked_mean(entropy, loss_masks)
+        )
+
+        # Set optimizer learning rate
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.optimizer.zero_grad()
+        loss.backward()
+
+        self.optimizer.step()
+        update_stats = {
+            # NOTE: abs() is not technically correct, but matches the behavior in TensorFlow.
+            # TODO: After PyTorch is default, change to something more correct.
+            "Losses/Policy Loss": torch.abs(policy_loss).item(),
+            "Losses/Value Loss": value_loss.item(),
+            "Policy/Learning Rate": decay_lr,
+            "Policy/Epsilon": decay_eps,
+            "Policy/Beta": decay_bet,
+        }
+
+        return update_stats
+
+    # TODO move module update into TorchOptimizer for reward_provider
+    def get_modules(self):
+        modules = {
+            "Optimizer:value_optimizer": self.optimizer,
+            "Optimizer:critic": self._critic,
+        }
+        for reward_provider in self.reward_signals.values():
+            modules.update(reward_provider.get_modules())
+        return modules
diff --git a/ml-agents/mlagents/trainers/ppo/trainer.py b/ml-agents/mlagents/trainers/ppo/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7421f0da155244058ac234edc7b49fedc3f046e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/ppo/trainer.py
@@ -0,0 +1,213 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agent Learning (PPO)
+# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347
+
+from typing import cast, Type, Union, Dict, Any
+
+import numpy as np
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+from mlagents.trainers.trainer.on_policy_trainer import OnPolicyTrainer
+from mlagents.trainers.policy.policy import Policy
+from mlagents.trainers.trainer.trainer_utils import get_gae
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer, PPOSettings
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings
+
+from mlagents.trainers.torch_entities.networks import SimpleActor, SharedActorCritic
+
+logger = get_logger(__name__)
+
+TRAINER_NAME = "ppo"
+
+
+class PPOTrainer(OnPolicyTrainer):
+    """The PPOTrainer is an implementation of the PPO algorithm."""
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training PPO model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            reward_buff_cap,
+            trainer_settings,
+            training,
+            load,
+            seed,
+            artifact_path,
+        )
+        self.hyperparameters: PPOSettings = cast(
+            PPOSettings, self.trainer_settings.hyperparameters
+        )
+        self.seed = seed
+        self.shared_critic = self.hyperparameters.shared_critic
+        self.policy: TorchPolicy = None  # type: ignore
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the update buffer.
+        Processing involves calculating value and advantage targets for model updating step.
+        :param trajectory: The Trajectory tuple containing the steps to be processed.
+        """
+        super()._process_trajectory(trajectory)
+        agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+        agent_buffer_trajectory = trajectory.to_agentbuffer()
+        # Check if we used group rewards, warn if so.
+        self._warn_if_group_reward(agent_buffer_trajectory)
+
+        # Update the normalization
+        if self.is_training:
+            self.policy.actor.update_normalization(agent_buffer_trajectory)
+            self.optimizer.critic.update_normalization(agent_buffer_trajectory)
+
+        # Get all value estimates
+        (
+            value_estimates,
+            value_next,
+            value_memories,
+        ) = self.optimizer.get_trajectory_value_estimates(
+            agent_buffer_trajectory,
+            trajectory.next_obs,
+            trajectory.done_reached and not trajectory.interrupted,
+        )
+        if value_memories is not None:
+            agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
+
+        for name, v in value_estimates.items():
+            agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(
+                v
+            )
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
+                np.mean(v),
+            )
+
+        # Evaluate all reward functions
+        self.collected_rewards["environment"][agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+        )
+        for name, reward_signal in self.optimizer.reward_signals.items():
+            evaluate_result = (
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+            )
+            agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(
+                evaluate_result
+            )
+            # Report the reward signals
+            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+        # Compute GAE and returns
+        tmp_advantages = []
+        tmp_returns = []
+        for name in self.optimizer.reward_signals:
+            bootstrap_value = value_next[name]
+
+            local_rewards = agent_buffer_trajectory[
+                RewardSignalUtil.rewards_key(name)
+            ].get_batch()
+            local_value_estimates = agent_buffer_trajectory[
+                RewardSignalUtil.value_estimates_key(name)
+            ].get_batch()
+
+            local_advantage = get_gae(
+                rewards=local_rewards,
+                value_estimates=local_value_estimates,
+                value_next=bootstrap_value,
+                gamma=self.optimizer.reward_signals[name].gamma,
+                lambd=self.hyperparameters.lambd,
+            )
+            local_return = local_advantage + local_value_estimates
+            # This is later use as target for the different value estimates
+            agent_buffer_trajectory[RewardSignalUtil.returns_key(name)].set(
+                local_return
+            )
+            agent_buffer_trajectory[RewardSignalUtil.advantage_key(name)].set(
+                local_advantage
+            )
+            tmp_advantages.append(local_advantage)
+            tmp_returns.append(local_return)
+
+        # Get global advantages
+        global_advantages = list(
+            np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
+        )
+        global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0))
+        agent_buffer_trajectory[BufferKey.ADVANTAGES].set(global_advantages)
+        agent_buffer_trajectory[BufferKey.DISCOUNTED_RETURNS].set(global_returns)
+
+        self._append_to_update_buffer(agent_buffer_trajectory)
+
+        # If this was a terminal trajectory, append stats and reset reward collection
+        if trajectory.done_reached:
+            self._update_end_episode_stats(agent_id, self.optimizer)
+
+    def create_optimizer(self) -> TorchOptimizer:
+        return TorchPPOOptimizer(  # type: ignore
+            cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore
+        )  # type: ignore
+
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a policy with a PyTorch backend and PPO hyperparameters
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor
+        actor_kwargs: Dict[str, Any] = {
+            "conditional_sigma": False,
+            "tanh_squash": False,
+        }
+        if self.shared_critic:
+            reward_signal_configs = self.trainer_settings.reward_signals
+            reward_signal_names = [
+                key.value for key, _ in reward_signal_configs.items()
+            ]
+            actor_cls = SharedActorCritic
+            actor_kwargs.update({"stream_names": reward_signal_names})
+
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings.network_settings,
+            actor_cls,
+            actor_kwargs,
+        )
+        return policy
+
+    def get_policy(self, name_behavior_id: str) -> Policy:
+        """
+        Gets policy from trainer associated with name_behavior_id
+        :param name_behavior_id: full identifier of policy
+        """
+
+        return self.policy
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        return TRAINER_NAME
diff --git a/ml-agents/mlagents/trainers/run_experiment.py b/ml-agents/mlagents/trainers/run_experiment.py
new file mode 100644
index 0000000000000000000000000000000000000000..8544b673bcf209ac1c42a4967788dec5ef45c94b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/run_experiment.py
@@ -0,0 +1,31 @@
+import argparse
+from typing import Optional, List
+from mlagents.trainers.learn import run_cli
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.cli_utils import load_config
+
+from mlagents.plugins.trainer_type import register_trainer_plugins
+
+
+def parse_command_line(argv: Optional[List[str]] = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("experiment_config_path")
+    return parser.parse_args(argv)
+
+
+def main():
+    """
+    Provides an alternative CLI interface to mlagents-learn, 'mlagents-run-experiment'.
+    Accepts a JSON/YAML formatted mlagents.trainers.learn.RunOptions object, and executes
+    the run loop as defined in mlagents.trainers.learn.run_cli.
+    """
+    args = parse_command_line()
+    expt_config = load_config(args.experiment_config_path)
+    _, _ = register_trainer_plugins()
+    run_cli(RunOptions.from_dict(expt_config))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/mlagents/trainers/sac/__init__.py b/ml-agents/mlagents/trainers/sac/__init__.py
new file mode 100644
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diff --git a/ml-agents/mlagents/trainers/sac/optimizer_torch.py b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7d566859d07937c36d0b1cc7389c84b91155b9d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
@@ -0,0 +1,655 @@
+import numpy as np
+from typing import Dict, List, NamedTuple, cast, Tuple, Optional
+import attr
+
+from mlagents.torch_utils import torch, nn, default_device
+
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.torch_entities.networks import ValueNetwork, SharedActorCritic
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_log_probs import ActionLogProbs
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.buffer import AgentBuffer, BufferKey, RewardSignalUtil
+from mlagents_envs.timers import timed
+from mlagents_envs.base_env import ActionSpec, ObservationSpec
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.settings import TrainerSettings, OffPolicyHyperparamSettings
+from contextlib import ExitStack
+from mlagents.trainers.trajectory import ObsUtil
+
+EPSILON = 1e-6  # Small value to avoid divide by zero
+
+logger = get_logger(__name__)
+
+
+@attr.s(auto_attribs=True)
+class SACSettings(OffPolicyHyperparamSettings):
+    batch_size: int = 128
+    buffer_size: int = 50000
+    buffer_init_steps: int = 0
+    tau: float = 0.005
+    steps_per_update: float = 1
+    save_replay_buffer: bool = False
+    init_entcoef: float = 1.0
+    reward_signal_steps_per_update: float = attr.ib()
+
+    @reward_signal_steps_per_update.default
+    def _reward_signal_steps_per_update_default(self):
+        return self.steps_per_update
+
+
+class TorchSACOptimizer(TorchOptimizer):
+    class PolicyValueNetwork(nn.Module):
+        def __init__(
+            self,
+            stream_names: List[str],
+            observation_specs: List[ObservationSpec],
+            network_settings: NetworkSettings,
+            action_spec: ActionSpec,
+        ):
+            super().__init__()
+            num_value_outs = max(sum(action_spec.discrete_branches), 1)
+            num_action_ins = int(action_spec.continuous_size)
+
+            self.q1_network = ValueNetwork(
+                stream_names,
+                observation_specs,
+                network_settings,
+                num_action_ins,
+                num_value_outs,
+            )
+            self.q2_network = ValueNetwork(
+                stream_names,
+                observation_specs,
+                network_settings,
+                num_action_ins,
+                num_value_outs,
+            )
+
+        def forward(
+            self,
+            inputs: List[torch.Tensor],
+            actions: Optional[torch.Tensor] = None,
+            memories: Optional[torch.Tensor] = None,
+            sequence_length: int = 1,
+            q1_grad: bool = True,
+            q2_grad: bool = True,
+        ) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
+            """
+            Performs a forward pass on the value network, which consists of a Q1 and Q2
+            network. Optionally does not evaluate gradients for either the Q1, Q2, or both.
+            :param inputs: List of observation tensors.
+            :param actions: For a continuous Q function (has actions), tensor of actions.
+                Otherwise, None.
+            :param memories: Initial memories if using memory. Otherwise, None.
+            :param sequence_length: Sequence length if using memory.
+            :param q1_grad: Whether or not to compute gradients for the Q1 network.
+            :param q2_grad: Whether or not to compute gradients for the Q2 network.
+            :return: Tuple of two dictionaries, which both map {reward_signal: Q} for Q1 and Q2,
+                respectively.
+            """
+            # ExitStack allows us to enter the torch.no_grad() context conditionally
+            with ExitStack() as stack:
+                if not q1_grad:
+                    stack.enter_context(torch.no_grad())
+                q1_out, _ = self.q1_network(
+                    inputs,
+                    actions=actions,
+                    memories=memories,
+                    sequence_length=sequence_length,
+                )
+            with ExitStack() as stack:
+                if not q2_grad:
+                    stack.enter_context(torch.no_grad())
+                q2_out, _ = self.q2_network(
+                    inputs,
+                    actions=actions,
+                    memories=memories,
+                    sequence_length=sequence_length,
+                )
+            return q1_out, q2_out
+
+    class TargetEntropy(NamedTuple):
+
+        discrete: List[float] = []  # One per branch
+        continuous: float = 0.0
+
+    class LogEntCoef(nn.Module):
+        def __init__(self, discrete, continuous):
+            super().__init__()
+            self.discrete = discrete
+            self.continuous = continuous
+
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        super().__init__(policy, trainer_settings)
+        reward_signal_configs = trainer_settings.reward_signals
+        reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
+        if isinstance(policy.actor, SharedActorCritic):
+            raise UnityTrainerException("SAC does not support SharedActorCritic")
+        self._critic = ValueNetwork(
+            reward_signal_names,
+            policy.behavior_spec.observation_specs,
+            policy.network_settings,
+        )
+        hyperparameters: SACSettings = cast(
+            SACSettings, trainer_settings.hyperparameters
+        )
+
+        self.tau = hyperparameters.tau
+        self.init_entcoef = hyperparameters.init_entcoef
+
+        self.policy = policy
+        policy_network_settings = policy.network_settings
+
+        self.tau = hyperparameters.tau
+        self.burn_in_ratio = 0.0
+
+        # Non-exposed SAC parameters
+        self.discrete_target_entropy_scale = 0.2  # Roughly equal to e-greedy 0.05
+        self.continuous_target_entropy_scale = 1.0
+
+        self.stream_names = list(self.reward_signals.keys())
+        # Use to reduce "survivor bonus" when using Curiosity or GAIL.
+        self.gammas = [_val.gamma for _val in trainer_settings.reward_signals.values()]
+        self.use_dones_in_backup = {
+            name: int(not self.reward_signals[name].ignore_done)
+            for name in self.stream_names
+        }
+        self._action_spec = self.policy.behavior_spec.action_spec
+
+        self.q_network = TorchSACOptimizer.PolicyValueNetwork(
+            self.stream_names,
+            self.policy.behavior_spec.observation_specs,
+            policy_network_settings,
+            self._action_spec,
+        )
+
+        self.target_network = ValueNetwork(
+            self.stream_names,
+            self.policy.behavior_spec.observation_specs,
+            policy_network_settings,
+        )
+        ModelUtils.soft_update(self._critic, self.target_network, 1.0)
+
+        # We create one entropy coefficient per action, whether discrete or continuous.
+        _disc_log_ent_coef = torch.nn.Parameter(
+            torch.log(
+                torch.as_tensor(
+                    [self.init_entcoef] * len(self._action_spec.discrete_branches)
+                )
+            ),
+            requires_grad=True,
+        )
+        _cont_log_ent_coef = torch.nn.Parameter(
+            torch.log(torch.as_tensor([self.init_entcoef])), requires_grad=True
+        )
+        self._log_ent_coef = TorchSACOptimizer.LogEntCoef(
+            discrete=_disc_log_ent_coef, continuous=_cont_log_ent_coef
+        )
+        _cont_target = (
+            -1
+            * self.continuous_target_entropy_scale
+            * np.prod(self._action_spec.continuous_size).astype(np.float32)
+        )
+        _disc_target = [
+            self.discrete_target_entropy_scale * np.log(i).astype(np.float32)
+            for i in self._action_spec.discrete_branches
+        ]
+        self.target_entropy = TorchSACOptimizer.TargetEntropy(
+            continuous=_cont_target, discrete=_disc_target
+        )
+        policy_params = list(self.policy.actor.parameters())
+        value_params = list(self.q_network.parameters()) + list(
+            self._critic.parameters()
+        )
+
+        logger.debug("value_vars")
+        for param in value_params:
+            logger.debug(param.shape)
+        logger.debug("policy_vars")
+        for param in policy_params:
+            logger.debug(param.shape)
+
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            hyperparameters.learning_rate_schedule,
+            hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+        self.policy_optimizer = torch.optim.Adam(
+            policy_params, lr=hyperparameters.learning_rate
+        )
+        self.value_optimizer = torch.optim.Adam(
+            value_params, lr=hyperparameters.learning_rate
+        )
+        self.entropy_optimizer = torch.optim.Adam(
+            self._log_ent_coef.parameters(), lr=hyperparameters.learning_rate
+        )
+        self._move_to_device(default_device())
+
+    @property
+    def critic(self):
+        return self._critic
+
+    def _move_to_device(self, device: torch.device) -> None:
+        self._log_ent_coef.to(device)
+        self.target_network.to(device)
+        self._critic.to(device)
+        self.q_network.to(device)
+
+    def sac_q_loss(
+        self,
+        q1_out: Dict[str, torch.Tensor],
+        q2_out: Dict[str, torch.Tensor],
+        target_values: Dict[str, torch.Tensor],
+        dones: torch.Tensor,
+        rewards: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        q1_losses = []
+        q2_losses = []
+        # Multiple q losses per stream
+        for i, name in enumerate(q1_out.keys()):
+            q1_stream = q1_out[name].squeeze()
+            q2_stream = q2_out[name].squeeze()
+            with torch.no_grad():
+                q_backup = rewards[name] + (
+                    (1.0 - self.use_dones_in_backup[name] * dones)
+                    * self.gammas[i]
+                    * target_values[name]
+                )
+            _q1_loss = 0.5 * ModelUtils.masked_mean(
+                torch.nn.functional.mse_loss(q_backup, q1_stream), loss_masks
+            )
+            _q2_loss = 0.5 * ModelUtils.masked_mean(
+                torch.nn.functional.mse_loss(q_backup, q2_stream), loss_masks
+            )
+
+            q1_losses.append(_q1_loss)
+            q2_losses.append(_q2_loss)
+        q1_loss = torch.mean(torch.stack(q1_losses))
+        q2_loss = torch.mean(torch.stack(q2_losses))
+        return q1_loss, q2_loss
+
+    def sac_value_loss(
+        self,
+        log_probs: ActionLogProbs,
+        values: Dict[str, torch.Tensor],
+        q1p_out: Dict[str, torch.Tensor],
+        q2p_out: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+    ) -> torch.Tensor:
+        min_policy_qs = {}
+        with torch.no_grad():
+            _cont_ent_coef = self._log_ent_coef.continuous.exp()
+            _disc_ent_coef = self._log_ent_coef.discrete.exp()
+            for name in values.keys():
+                if self._action_spec.discrete_size <= 0:
+                    min_policy_qs[name] = torch.min(q1p_out[name], q2p_out[name])
+                else:
+                    disc_action_probs = log_probs.all_discrete_tensor.exp()
+                    _branched_q1p = ModelUtils.break_into_branches(
+                        q1p_out[name] * disc_action_probs,
+                        self._action_spec.discrete_branches,
+                    )
+                    _branched_q2p = ModelUtils.break_into_branches(
+                        q2p_out[name] * disc_action_probs,
+                        self._action_spec.discrete_branches,
+                    )
+                    _q1p_mean = torch.mean(
+                        torch.stack(
+                            [
+                                torch.sum(_br, dim=1, keepdim=True)
+                                for _br in _branched_q1p
+                            ]
+                        ),
+                        dim=0,
+                    )
+                    _q2p_mean = torch.mean(
+                        torch.stack(
+                            [
+                                torch.sum(_br, dim=1, keepdim=True)
+                                for _br in _branched_q2p
+                            ]
+                        ),
+                        dim=0,
+                    )
+
+                    min_policy_qs[name] = torch.min(_q1p_mean, _q2p_mean)
+
+        value_losses = []
+        if self._action_spec.discrete_size <= 0:
+            for name in values.keys():
+                with torch.no_grad():
+                    v_backup = min_policy_qs[name] - torch.sum(
+                        _cont_ent_coef * log_probs.continuous_tensor, dim=1
+                    )
+                value_loss = 0.5 * ModelUtils.masked_mean(
+                    torch.nn.functional.mse_loss(values[name], v_backup), loss_masks
+                )
+                value_losses.append(value_loss)
+        else:
+            disc_log_probs = log_probs.all_discrete_tensor
+            branched_per_action_ent = ModelUtils.break_into_branches(
+                disc_log_probs * disc_log_probs.exp(),
+                self._action_spec.discrete_branches,
+            )
+            # We have to do entropy bonus per action branch
+            branched_ent_bonus = torch.stack(
+                [
+                    torch.sum(_disc_ent_coef[i] * _lp, dim=1, keepdim=True)
+                    for i, _lp in enumerate(branched_per_action_ent)
+                ]
+            )
+            for name in values.keys():
+                with torch.no_grad():
+                    v_backup = min_policy_qs[name] - torch.mean(
+                        branched_ent_bonus, axis=0
+                    )
+                    # Add continuous entropy bonus to minimum Q
+                    if self._action_spec.continuous_size > 0:
+                        v_backup += torch.sum(
+                            _cont_ent_coef * log_probs.continuous_tensor,
+                            dim=1,
+                            keepdim=True,
+                        )
+                value_loss = 0.5 * ModelUtils.masked_mean(
+                    torch.nn.functional.mse_loss(values[name], v_backup.squeeze()),
+                    loss_masks,
+                )
+                value_losses.append(value_loss)
+        value_loss = torch.mean(torch.stack(value_losses))
+        if torch.isinf(value_loss).any() or torch.isnan(value_loss).any():
+            raise UnityTrainerException("Inf found")
+        return value_loss
+
+    def sac_policy_loss(
+        self,
+        log_probs: ActionLogProbs,
+        q1p_outs: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+    ) -> torch.Tensor:
+        _cont_ent_coef, _disc_ent_coef = (
+            self._log_ent_coef.continuous,
+            self._log_ent_coef.discrete,
+        )
+        _cont_ent_coef = _cont_ent_coef.exp()
+        _disc_ent_coef = _disc_ent_coef.exp()
+
+        mean_q1 = torch.mean(torch.stack(list(q1p_outs.values())), axis=0)
+        batch_policy_loss = 0
+        if self._action_spec.discrete_size > 0:
+            disc_log_probs = log_probs.all_discrete_tensor
+            disc_action_probs = disc_log_probs.exp()
+            branched_per_action_ent = ModelUtils.break_into_branches(
+                disc_log_probs * disc_action_probs, self._action_spec.discrete_branches
+            )
+            branched_q_term = ModelUtils.break_into_branches(
+                mean_q1 * disc_action_probs, self._action_spec.discrete_branches
+            )
+            branched_policy_loss = torch.stack(
+                [
+                    torch.sum(_disc_ent_coef[i] * _lp - _qt, dim=1, keepdim=False)
+                    for i, (_lp, _qt) in enumerate(
+                        zip(branched_per_action_ent, branched_q_term)
+                    )
+                ],
+                dim=1,
+            )
+            batch_policy_loss += torch.sum(branched_policy_loss, dim=1)
+            all_mean_q1 = torch.sum(disc_action_probs * mean_q1, dim=1)
+        else:
+            all_mean_q1 = mean_q1
+        if self._action_spec.continuous_size > 0:
+            cont_log_probs = log_probs.continuous_tensor
+            batch_policy_loss += (
+                _cont_ent_coef * torch.sum(cont_log_probs, dim=1) - all_mean_q1
+            )
+        policy_loss = ModelUtils.masked_mean(batch_policy_loss, loss_masks)
+
+        return policy_loss
+
+    def sac_entropy_loss(
+        self, log_probs: ActionLogProbs, loss_masks: torch.Tensor
+    ) -> torch.Tensor:
+        _cont_ent_coef, _disc_ent_coef = (
+            self._log_ent_coef.continuous,
+            self._log_ent_coef.discrete,
+        )
+        entropy_loss = 0
+        if self._action_spec.discrete_size > 0:
+            with torch.no_grad():
+                # Break continuous into separate branch
+                disc_log_probs = log_probs.all_discrete_tensor
+                branched_per_action_ent = ModelUtils.break_into_branches(
+                    disc_log_probs * disc_log_probs.exp(),
+                    self._action_spec.discrete_branches,
+                )
+                target_current_diff_branched = torch.stack(
+                    [
+                        torch.sum(_lp, axis=1, keepdim=True) + _te
+                        for _lp, _te in zip(
+                            branched_per_action_ent, self.target_entropy.discrete
+                        )
+                    ],
+                    axis=1,
+                )
+                target_current_diff = torch.squeeze(
+                    target_current_diff_branched, axis=2
+                )
+            entropy_loss += -1 * ModelUtils.masked_mean(
+                torch.mean(_disc_ent_coef * target_current_diff, axis=1), loss_masks
+            )
+        if self._action_spec.continuous_size > 0:
+            with torch.no_grad():
+                cont_log_probs = log_probs.continuous_tensor
+                target_current_diff = (
+                    torch.sum(cont_log_probs, dim=1) + self.target_entropy.continuous
+                )
+            # We update all the _cont_ent_coef as one block
+            entropy_loss += -1 * ModelUtils.masked_mean(
+                _cont_ent_coef * target_current_diff, loss_masks
+            )
+
+        return entropy_loss
+
+    def _condense_q_streams(
+        self, q_output: Dict[str, torch.Tensor], discrete_actions: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        condensed_q_output = {}
+        onehot_actions = ModelUtils.actions_to_onehot(
+            discrete_actions, self._action_spec.discrete_branches
+        )
+        for key, item in q_output.items():
+            branched_q = ModelUtils.break_into_branches(
+                item, self._action_spec.discrete_branches
+            )
+            only_action_qs = torch.stack(
+                [
+                    torch.sum(_act * _q, dim=1, keepdim=True)
+                    for _act, _q in zip(onehot_actions, branched_q)
+                ]
+            )
+
+            condensed_q_output[key] = torch.mean(only_action_qs, dim=0)
+        return condensed_q_output
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Updates model using buffer.
+        :param num_sequences: Number of trajectories in batch.
+        :param batch: Experience mini-batch.
+        :param update_target: Whether or not to update target value network
+        :param reward_signal_batches: Minibatches to use for updating the reward signals,
+            indexed by name. If none, don't update the reward signals.
+        :return: Output from update process.
+        """
+        rewards = {}
+        for name in self.reward_signals:
+            rewards[name] = ModelUtils.list_to_tensor(
+                batch[RewardSignalUtil.rewards_key(name)]
+            )
+
+        n_obs = len(self.policy.behavior_spec.observation_specs)
+        current_obs = ObsUtil.from_buffer(batch, n_obs)
+        # Convert to tensors
+        current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
+
+        next_obs = ObsUtil.from_buffer_next(batch, n_obs)
+        # Convert to tensors
+        next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
+
+        act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
+        actions = AgentAction.from_buffer(batch)
+
+        memories_list = [
+            ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i])
+            for i in range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
+        ]
+        # LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
+        value_memories_list = [
+            ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
+            for i in range(
+                0, len(batch[BufferKey.CRITIC_MEMORY]), self.policy.sequence_length
+            )
+        ]
+
+        if len(memories_list) > 0:
+            memories = torch.stack(memories_list).unsqueeze(0)
+            value_memories = torch.stack(value_memories_list).unsqueeze(0)
+        else:
+            memories = None
+            value_memories = None
+
+        # Q and V network memories are 0'ed out, since we don't have them during inference.
+        q_memories = (
+            torch.zeros_like(value_memories) if value_memories is not None else None
+        )
+
+        # Copy normalizers from policy
+        self.q_network.q1_network.network_body.copy_normalization(
+            self.policy.actor.network_body
+        )
+        self.q_network.q2_network.network_body.copy_normalization(
+            self.policy.actor.network_body
+        )
+        self.target_network.network_body.copy_normalization(
+            self.policy.actor.network_body
+        )
+        self._critic.network_body.copy_normalization(self.policy.actor.network_body)
+        sampled_actions, run_out, _, = self.policy.actor.get_action_and_stats(
+            current_obs,
+            masks=act_masks,
+            memories=memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        log_probs = run_out["log_probs"]
+        value_estimates, _ = self._critic.critic_pass(
+            current_obs, value_memories, sequence_length=self.policy.sequence_length
+        )
+
+        cont_sampled_actions = sampled_actions.continuous_tensor
+        cont_actions = actions.continuous_tensor
+        q1p_out, q2p_out = self.q_network(
+            current_obs,
+            cont_sampled_actions,
+            memories=q_memories,
+            sequence_length=self.policy.sequence_length,
+            q2_grad=False,
+        )
+        q1_out, q2_out = self.q_network(
+            current_obs,
+            cont_actions,
+            memories=q_memories,
+            sequence_length=self.policy.sequence_length,
+        )
+
+        if self._action_spec.discrete_size > 0:
+            disc_actions = actions.discrete_tensor
+            q1_stream = self._condense_q_streams(q1_out, disc_actions)
+            q2_stream = self._condense_q_streams(q2_out, disc_actions)
+        else:
+            q1_stream, q2_stream = q1_out, q2_out
+
+        with torch.no_grad():
+            # Since we didn't record the next value memories, evaluate one step in the critic to
+            # get them.
+            if value_memories is not None:
+                # Get the first observation in each sequence
+                just_first_obs = [
+                    _obs[:: self.policy.sequence_length] for _obs in current_obs
+                ]
+                _, next_value_memories = self._critic.critic_pass(
+                    just_first_obs, value_memories, sequence_length=1
+                )
+            else:
+                next_value_memories = None
+            target_values, _ = self.target_network(
+                next_obs,
+                memories=next_value_memories,
+                sequence_length=self.policy.sequence_length,
+            )
+        masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS], dtype=torch.bool)
+        dones = ModelUtils.list_to_tensor(batch[BufferKey.DONE])
+
+        q1_loss, q2_loss = self.sac_q_loss(
+            q1_stream, q2_stream, target_values, dones, rewards, masks
+        )
+        value_loss = self.sac_value_loss(
+            log_probs, value_estimates, q1p_out, q2p_out, masks
+        )
+        policy_loss = self.sac_policy_loss(log_probs, q1p_out, masks)
+        entropy_loss = self.sac_entropy_loss(log_probs, masks)
+
+        total_value_loss = q1_loss + q2_loss + value_loss
+
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        ModelUtils.update_learning_rate(self.policy_optimizer, decay_lr)
+        self.policy_optimizer.zero_grad()
+        policy_loss.backward()
+        self.policy_optimizer.step()
+
+        ModelUtils.update_learning_rate(self.value_optimizer, decay_lr)
+        self.value_optimizer.zero_grad()
+        total_value_loss.backward()
+        self.value_optimizer.step()
+
+        ModelUtils.update_learning_rate(self.entropy_optimizer, decay_lr)
+        self.entropy_optimizer.zero_grad()
+        entropy_loss.backward()
+        self.entropy_optimizer.step()
+
+        # Update target network
+        ModelUtils.soft_update(self._critic, self.target_network, self.tau)
+        update_stats = {
+            "Losses/Policy Loss": policy_loss.item(),
+            "Losses/Value Loss": value_loss.item(),
+            "Losses/Q1 Loss": q1_loss.item(),
+            "Losses/Q2 Loss": q2_loss.item(),
+            "Policy/Discrete Entropy Coeff": torch.mean(
+                torch.exp(self._log_ent_coef.discrete)
+            ).item(),
+            "Policy/Continuous Entropy Coeff": torch.mean(
+                torch.exp(self._log_ent_coef.continuous)
+            ).item(),
+            "Policy/Learning Rate": decay_lr,
+        }
+
+        return update_stats
+
+    def get_modules(self):
+        modules = {
+            "Optimizer:q_network": self.q_network,
+            "Optimizer:value_network": self._critic,
+            "Optimizer:target_network": self.target_network,
+            "Optimizer:policy_optimizer": self.policy_optimizer,
+            "Optimizer:value_optimizer": self.value_optimizer,
+            "Optimizer:entropy_optimizer": self.entropy_optimizer,
+        }
+        for reward_provider in self.reward_signals.values():
+            modules.update(reward_provider.get_modules())
+        return modules
diff --git a/ml-agents/mlagents/trainers/sac/trainer.py b/ml-agents/mlagents/trainers/sac/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..56860c7381849dfb4ec4edab2813972c76f568ed
--- /dev/null
+++ b/ml-agents/mlagents/trainers/sac/trainer.py
@@ -0,0 +1,181 @@
+# ## ML-Agent Learning (SAC)
+# Contains an implementation of SAC as described in https://arxiv.org/abs/1801.01290
+# and implemented in https://github.com/hill-a/stable-baselines
+
+from typing import cast
+
+import numpy as np
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.buffer import BufferKey
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.trainer.off_policy_trainer import OffPolicyTrainer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.policy.policy import Policy
+from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer, SACSettings
+from mlagents.trainers.trajectory import Trajectory, ObsUtil
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings
+
+from mlagents.trainers.torch_entities.networks import SimpleActor
+
+logger = get_logger(__name__)
+
+BUFFER_TRUNCATE_PERCENT = 0.8
+
+TRAINER_NAME = "sac"
+
+
+class SACTrainer(OffPolicyTrainer):
+    """
+    The SACTrainer is an implementation of the SAC algorithm, with support
+    for discrete actions and recurrent networks.
+    """
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training SAC model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            reward_buff_cap,
+            trainer_settings,
+            training,
+            load,
+            seed,
+            artifact_path,
+        )
+
+        self.seed = seed
+        self.policy: TorchPolicy = None  # type: ignore
+        self.optimizer: TorchSACOptimizer = None  # type: ignore
+        self.hyperparameters: SACSettings = cast(
+            SACSettings, trainer_settings.hyperparameters
+        )
+        self._step = 0
+
+        # Don't divide by zero
+        self.update_steps = 1
+        self.reward_signal_update_steps = 1
+
+        self.steps_per_update = self.hyperparameters.steps_per_update
+        self.reward_signal_steps_per_update = (
+            self.hyperparameters.reward_signal_steps_per_update
+        )
+
+        self.checkpoint_replay_buffer = self.hyperparameters.save_replay_buffer
+
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the replay buffer.
+        """
+        super()._process_trajectory(trajectory)
+        last_step = trajectory.steps[-1]
+        agent_id = trajectory.agent_id  # All the agents should have the same ID
+
+        agent_buffer_trajectory = trajectory.to_agentbuffer()
+        # Check if we used group rewards, warn if so.
+        self._warn_if_group_reward(agent_buffer_trajectory)
+
+        # Update the normalization
+        if self.is_training:
+            self.policy.actor.update_normalization(agent_buffer_trajectory)
+            self.optimizer.critic.update_normalization(agent_buffer_trajectory)
+
+        # Evaluate all reward functions for reporting purposes
+        self.collected_rewards["environment"][agent_id] += np.sum(
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
+        )
+        for name, reward_signal in self.optimizer.reward_signals.items():
+            evaluate_result = (
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
+            )
+
+            # Report the reward signals
+            self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
+
+        # Get all value estimates for reporting purposes
+        (
+            value_estimates,
+            _,
+            value_memories,
+        ) = self.optimizer.get_trajectory_value_estimates(
+            agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached
+        )
+        if value_memories is not None:
+            agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
+
+        for name, v in value_estimates.items():
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
+                np.mean(v),
+            )
+
+        # Bootstrap using the last step rather than the bootstrap step if max step is reached.
+        # Set last element to duplicate obs and remove dones.
+        if last_step.interrupted:
+            last_step_obs = last_step.obs
+            for i, obs in enumerate(last_step_obs):
+                agent_buffer_trajectory[ObsUtil.get_name_at_next(i)][-1] = obs
+            agent_buffer_trajectory[BufferKey.DONE][-1] = False
+
+        self._append_to_update_buffer(agent_buffer_trajectory)
+
+        if trajectory.done_reached:
+            self._update_end_episode_stats(agent_id, self.optimizer)
+
+    def create_optimizer(self) -> TorchOptimizer:
+        return TorchSACOptimizer(  # type: ignore
+            cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore
+        )  # type: ignore
+
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a policy with a PyTorch backend and SAC hyperparameters
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        actor_cls = SimpleActor
+        actor_kwargs = {"conditional_sigma": True, "tanh_squash": True}
+
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings.network_settings,
+            actor_cls,
+            actor_kwargs,
+        )
+        self.maybe_load_replay_buffer()
+        return policy
+
+    def get_policy(self, name_behavior_id: str) -> Policy:
+        """
+        Gets policy from trainer associated with name_behavior_id
+        :param name_behavior_id: full identifier of policy
+        """
+
+        return self.policy
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        return TRAINER_NAME
diff --git a/ml-agents/mlagents/trainers/settings.py b/ml-agents/mlagents/trainers/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..2aca4fdb13a131a618641906d1fcf1066c3e99b3
--- /dev/null
+++ b/ml-agents/mlagents/trainers/settings.py
@@ -0,0 +1,974 @@
+import os.path
+import warnings
+
+import attr
+import cattr
+from typing import (
+    Dict,
+    Optional,
+    List,
+    Any,
+    DefaultDict,
+    Mapping,
+    Tuple,
+    Union,
+    ClassVar,
+)
+from enum import Enum
+import collections
+import argparse
+import abc
+import numpy as np
+import math
+import copy
+
+from mlagents.trainers.cli_utils import StoreConfigFile, DetectDefault, parser
+from mlagents.trainers.cli_utils import load_config
+from mlagents.trainers.exception import TrainerConfigError, TrainerConfigWarning
+
+from mlagents_envs import logging_util
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+from mlagents.plugins import all_trainer_settings, all_trainer_types
+
+logger = logging_util.get_logger(__name__)
+
+
+def check_and_structure(key: str, value: Any, class_type: type) -> Any:
+    attr_fields_dict = attr.fields_dict(class_type)
+    if key not in attr_fields_dict:
+        raise TrainerConfigError(
+            f"The option {key} was specified in your YAML file for {class_type.__name__}, but is invalid."
+        )
+    # Apply cattr structure to the values
+    return cattr.structure(value, attr_fields_dict[key].type)
+
+
+def check_hyperparam_schedules(val: Dict, trainer_type: str) -> Dict:
+    # Check if beta and epsilon are set. If not, set to match learning rate schedule.
+    if trainer_type == "ppo" or trainer_type == "poca":
+        if "beta_schedule" not in val.keys() and "learning_rate_schedule" in val.keys():
+            val["beta_schedule"] = val["learning_rate_schedule"]
+        if (
+            "epsilon_schedule" not in val.keys()
+            and "learning_rate_schedule" in val.keys()
+        ):
+            val["epsilon_schedule"] = val["learning_rate_schedule"]
+    return val
+
+
+def strict_to_cls(d: Mapping, t: type) -> Any:
+    if not isinstance(d, Mapping):
+        raise TrainerConfigError(f"Unsupported config {d} for {t.__name__}.")
+    d_copy: Dict[str, Any] = {}
+    d_copy.update(d)
+    for key, val in d_copy.items():
+        d_copy[key] = check_and_structure(key, val, t)
+    return t(**d_copy)
+
+
+def defaultdict_to_dict(d: DefaultDict) -> Dict:
+    return {key: cattr.unstructure(val) for key, val in d.items()}
+
+
+def deep_update_dict(d: Dict, update_d: Mapping) -> None:
+    """
+    Similar to dict.update(), but works for nested dicts of dicts as well.
+    """
+    for key, val in update_d.items():
+        if key in d and isinstance(d[key], Mapping) and isinstance(val, Mapping):
+            deep_update_dict(d[key], val)
+        else:
+            d[key] = val
+
+
+class SerializationSettings:
+    convert_to_onnx = True
+    onnx_opset = 9
+
+
+@attr.s(auto_attribs=True)
+class ExportableSettings:
+    def as_dict(self):
+        return cattr.unstructure(self)
+
+
+class EncoderType(Enum):
+    FULLY_CONNECTED = "fully_connected"
+    MATCH3 = "match3"
+    SIMPLE = "simple"
+    NATURE_CNN = "nature_cnn"
+    RESNET = "resnet"
+
+
+class ScheduleType(Enum):
+    CONSTANT = "constant"
+    LINEAR = "linear"
+    # TODO add support for lesson based scheduling
+    # LESSON = "lesson"
+
+
+class ConditioningType(Enum):
+    HYPER = "hyper"
+    NONE = "none"
+
+
+@attr.s(auto_attribs=True)
+class NetworkSettings:
+    @attr.s
+    class MemorySettings:
+        sequence_length: int = attr.ib(default=64)
+        memory_size: int = attr.ib(default=128)
+
+        @memory_size.validator
+        def _check_valid_memory_size(self, attribute, value):
+            if value <= 0:
+                raise TrainerConfigError(
+                    "When using a recurrent network, memory size must be greater than 0."
+                )
+            elif value % 2 != 0:
+                raise TrainerConfigError(
+                    "When using a recurrent network, memory size must be divisible by 2."
+                )
+
+    normalize: bool = False
+    hidden_units: int = 128
+    num_layers: int = 2
+    vis_encode_type: EncoderType = EncoderType.SIMPLE
+    memory: Optional[MemorySettings] = None
+    goal_conditioning_type: ConditioningType = ConditioningType.HYPER
+    deterministic: bool = parser.get_default("deterministic")
+
+
+@attr.s(auto_attribs=True)
+class BehavioralCloningSettings:
+    demo_path: str
+    steps: int = 0
+    strength: float = 1.0
+    samples_per_update: int = 0
+    # Setting either of these to None will allow the Optimizer
+    # to decide these parameters, based on Trainer hyperparams
+    num_epoch: Optional[int] = None
+    batch_size: Optional[int] = None
+
+
+@attr.s(auto_attribs=True)
+class HyperparamSettings:
+    batch_size: int = 1024
+    buffer_size: int = 10240
+    learning_rate: float = 3.0e-4
+    learning_rate_schedule: ScheduleType = ScheduleType.CONSTANT
+
+
+@attr.s(auto_attribs=True)
+class OnPolicyHyperparamSettings(HyperparamSettings):
+    num_epoch: int = 3
+
+
+@attr.s(auto_attribs=True)
+class OffPolicyHyperparamSettings(HyperparamSettings):
+    batch_size: int = 128
+    buffer_size: int = 50000
+    buffer_init_steps: int = 0
+    steps_per_update: float = 1
+    save_replay_buffer: bool = False
+    reward_signal_steps_per_update: float = 4
+
+
+# INTRINSIC REWARD SIGNALS #############################################################
+class RewardSignalType(Enum):
+    EXTRINSIC: str = "extrinsic"
+    GAIL: str = "gail"
+    CURIOSITY: str = "curiosity"
+    RND: str = "rnd"
+
+    def to_settings(self) -> type:
+        _mapping = {
+            RewardSignalType.EXTRINSIC: RewardSignalSettings,
+            RewardSignalType.GAIL: GAILSettings,
+            RewardSignalType.CURIOSITY: CuriositySettings,
+            RewardSignalType.RND: RNDSettings,
+        }
+        return _mapping[self]
+
+
+@attr.s(auto_attribs=True)
+class RewardSignalSettings:
+    gamma: float = 0.99
+    strength: float = 1.0
+    network_settings: NetworkSettings = attr.ib(factory=NetworkSettings)
+
+    @staticmethod
+    def structure(d: Mapping, t: type) -> Any:
+        """
+        Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with
+        cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
+        the special Enum selection of RewardSignalSettings classes.
+        """
+        if not isinstance(d, Mapping):
+            raise TrainerConfigError(f"Unsupported reward signal configuration {d}.")
+        d_final: Dict[RewardSignalType, RewardSignalSettings] = {}
+        for key, val in d.items():
+            enum_key = RewardSignalType(key)
+            t = enum_key.to_settings()
+            d_final[enum_key] = strict_to_cls(val, t)
+            # Checks to see if user specifying deprecated encoding_size for RewardSignals.
+            # If network_settings is not specified, this updates the default hidden_units
+            # to the value of encoding size. If specified, this ignores encoding size and
+            # uses network_settings values.
+            if "encoding_size" in val:
+                logger.warning(
+                    "'encoding_size' was deprecated for RewardSignals. Please use network_settings."
+                )
+                # If network settings was not specified, use the encoding size. Otherwise, use hidden_units
+                if "network_settings" not in val:
+                    d_final[enum_key].network_settings.hidden_units = val[
+                        "encoding_size"
+                    ]
+        return d_final
+
+
+@attr.s(auto_attribs=True)
+class GAILSettings(RewardSignalSettings):
+    learning_rate: float = 3e-4
+    encoding_size: Optional[int] = None
+    use_actions: bool = False
+    use_vail: bool = False
+    demo_path: str = attr.ib(kw_only=True)
+
+
+@attr.s(auto_attribs=True)
+class CuriositySettings(RewardSignalSettings):
+    learning_rate: float = 3e-4
+    encoding_size: Optional[int] = None
+
+
+@attr.s(auto_attribs=True)
+class RNDSettings(RewardSignalSettings):
+    learning_rate: float = 1e-4
+    encoding_size: Optional[int] = None
+
+
+# SAMPLERS #############################################################################
+class ParameterRandomizationType(Enum):
+    UNIFORM: str = "uniform"
+    GAUSSIAN: str = "gaussian"
+    MULTIRANGEUNIFORM: str = "multirangeuniform"
+    CONSTANT: str = "constant"
+
+    def to_settings(self) -> type:
+        _mapping = {
+            ParameterRandomizationType.UNIFORM: UniformSettings,
+            ParameterRandomizationType.GAUSSIAN: GaussianSettings,
+            ParameterRandomizationType.MULTIRANGEUNIFORM: MultiRangeUniformSettings,
+            ParameterRandomizationType.CONSTANT: ConstantSettings
+            # Constant type is handled if a float is provided instead of a config
+        }
+        return _mapping[self]
+
+
+@attr.s(auto_attribs=True)
+class ParameterRandomizationSettings(abc.ABC):
+    seed: int = parser.get_default("seed")
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        raise TrainerConfigError(f"__str__ not implemented for type {self.__class__}.")
+
+    @staticmethod
+    def structure(
+        d: Union[Mapping, float], t: type
+    ) -> "ParameterRandomizationSettings":
+        """
+        Helper method to a ParameterRandomizationSettings class. Meant to be registered with
+        cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
+        the special Enum selection of ParameterRandomizationSettings classes.
+        """
+        if isinstance(d, (float, int)):
+            return ConstantSettings(value=d)
+        if not isinstance(d, Mapping):
+            raise TrainerConfigError(
+                f"Unsupported parameter randomization configuration {d}."
+            )
+        if "sampler_type" not in d:
+            raise TrainerConfigError(
+                f"Sampler configuration does not contain sampler_type : {d}."
+            )
+        if "sampler_parameters" not in d:
+            raise TrainerConfigError(
+                f"Sampler configuration does not contain sampler_parameters : {d}."
+            )
+        enum_key = ParameterRandomizationType(d["sampler_type"])
+        t = enum_key.to_settings()
+        return strict_to_cls(d["sampler_parameters"], t)
+
+    @staticmethod
+    def unstructure(d: "ParameterRandomizationSettings") -> Mapping:
+        """
+        Helper method to a ParameterRandomizationSettings class. Meant to be registered with
+        cattr.register_unstructure_hook() and called with cattr.unstructure().
+        """
+        _reversed_mapping = {
+            UniformSettings: ParameterRandomizationType.UNIFORM,
+            GaussianSettings: ParameterRandomizationType.GAUSSIAN,
+            MultiRangeUniformSettings: ParameterRandomizationType.MULTIRANGEUNIFORM,
+            ConstantSettings: ParameterRandomizationType.CONSTANT,
+        }
+        sampler_type: Optional[str] = None
+        for t, name in _reversed_mapping.items():
+            if isinstance(d, t):
+                sampler_type = name.value
+        sampler_parameters = attr.asdict(d)
+        return {"sampler_type": sampler_type, "sampler_parameters": sampler_parameters}
+
+    @abc.abstractmethod
+    def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
+        """
+        Helper method to send sampler settings over EnvironmentParametersChannel
+        Calls the appropriate sampler type set method.
+        :param key: environment parameter to be sampled
+        :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
+        """
+        pass
+
+
+@attr.s(auto_attribs=True)
+class ConstantSettings(ParameterRandomizationSettings):
+    value: float = 0.0
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Float: value={self.value}"
+
+    def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
+        """
+        Helper method to send sampler settings over EnvironmentParametersChannel
+        Calls the constant sampler type set method.
+        :param key: environment parameter to be sampled
+        :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
+        """
+        env_channel.set_float_parameter(key, self.value)
+
+
+@attr.s(auto_attribs=True)
+class UniformSettings(ParameterRandomizationSettings):
+    min_value: float = attr.ib()
+    max_value: float = 1.0
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Uniform sampler: min={self.min_value}, max={self.max_value}"
+
+    @min_value.default
+    def _min_value_default(self):
+        return 0.0
+
+    @min_value.validator
+    def _check_min_value(self, attribute, value):
+        if self.min_value > self.max_value:
+            raise TrainerConfigError(
+                "Minimum value is greater than maximum value in uniform sampler."
+            )
+
+    def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
+        """
+        Helper method to send sampler settings over EnvironmentParametersChannel
+        Calls the uniform sampler type set method.
+        :param key: environment parameter to be sampled
+        :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
+        """
+        env_channel.set_uniform_sampler_parameters(
+            key, self.min_value, self.max_value, self.seed
+        )
+
+
+@attr.s(auto_attribs=True)
+class GaussianSettings(ParameterRandomizationSettings):
+    mean: float = 1.0
+    st_dev: float = 1.0
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Gaussian sampler: mean={self.mean}, stddev={self.st_dev}"
+
+    def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
+        """
+        Helper method to send sampler settings over EnvironmentParametersChannel
+        Calls the gaussian sampler type set method.
+        :param key: environment parameter to be sampled
+        :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
+        """
+        env_channel.set_gaussian_sampler_parameters(
+            key, self.mean, self.st_dev, self.seed
+        )
+
+
+@attr.s(auto_attribs=True)
+class MultiRangeUniformSettings(ParameterRandomizationSettings):
+    intervals: List[Tuple[float, float]] = attr.ib()
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"MultiRangeUniform sampler: intervals={self.intervals}"
+
+    @intervals.default
+    def _intervals_default(self):
+        return [[0.0, 1.0]]
+
+    @intervals.validator
+    def _check_intervals(self, attribute, value):
+        for interval in self.intervals:
+            if len(interval) != 2:
+                raise TrainerConfigError(
+                    f"The sampling interval {interval} must contain exactly two values."
+                )
+            min_value, max_value = interval
+            if min_value > max_value:
+                raise TrainerConfigError(
+                    f"Minimum value is greater than maximum value in interval {interval}."
+                )
+
+    def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
+        """
+        Helper method to send sampler settings over EnvironmentParametersChannel
+        Calls the multirangeuniform sampler type set method.
+        :param key: environment parameter to be sampled
+        :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
+        """
+        env_channel.set_multirangeuniform_sampler_parameters(
+            key, self.intervals, self.seed
+        )
+
+
+# ENVIRONMENT PARAMETERS ###############################################################
+@attr.s(auto_attribs=True)
+class CompletionCriteriaSettings:
+    """
+    CompletionCriteriaSettings contains the information needed to figure out if the next
+    lesson must start.
+    """
+
+    class MeasureType(Enum):
+        PROGRESS: str = "progress"
+        REWARD: str = "reward"
+
+    behavior: str
+    measure: MeasureType = attr.ib(default=MeasureType.REWARD)
+    min_lesson_length: int = 0
+    signal_smoothing: bool = True
+    threshold: float = attr.ib(default=0.0)
+    require_reset: bool = False
+
+    @threshold.validator
+    def _check_threshold_value(self, attribute, value):
+        """
+        Verify that the threshold has a value between 0 and 1 when the measure is
+        PROGRESS
+        """
+        if self.measure == self.MeasureType.PROGRESS:
+            if self.threshold > 1.0:
+                raise TrainerConfigError(
+                    "Threshold for next lesson cannot be greater than 1 when the measure is progress."
+                )
+            if self.threshold < 0.0:
+                raise TrainerConfigError(
+                    "Threshold for next lesson cannot be negative when the measure is progress."
+                )
+
+    def need_increment(
+        self, progress: float, reward_buffer: List[float], smoothing: float
+    ) -> Tuple[bool, float]:
+        """
+        Given measures, this method returns a boolean indicating if the lesson
+        needs to change now, and a float corresponding to the new smoothed value.
+        """
+        # Is the min number of episodes reached
+        if len(reward_buffer) < self.min_lesson_length:
+            return False, smoothing
+        if self.measure == CompletionCriteriaSettings.MeasureType.PROGRESS:
+            if progress > self.threshold:
+                return True, smoothing
+        if self.measure == CompletionCriteriaSettings.MeasureType.REWARD:
+            if len(reward_buffer) < 1:
+                return False, smoothing
+            measure = np.mean(reward_buffer)
+            if math.isnan(measure):
+                return False, smoothing
+            if self.signal_smoothing:
+                measure = 0.25 * smoothing + 0.75 * measure
+                smoothing = measure
+            if measure > self.threshold:
+                return True, smoothing
+        return False, smoothing
+
+
+@attr.s(auto_attribs=True)
+class Lesson:
+    """
+    Gathers the data of one lesson for one environment parameter including its name,
+    the condition that must be fullfiled for the lesson to be completed and a sampler
+    for the environment parameter. If the completion_criteria is None, then this is
+    the last lesson in the curriculum.
+    """
+
+    value: ParameterRandomizationSettings
+    name: str
+    completion_criteria: Optional[CompletionCriteriaSettings] = attr.ib(default=None)
+
+
+@attr.s(auto_attribs=True)
+class EnvironmentParameterSettings:
+    """
+    EnvironmentParameterSettings is an ordered list of lessons for one environment
+    parameter.
+    """
+
+    curriculum: List[Lesson]
+
+    @staticmethod
+    def _check_lesson_chain(lessons, parameter_name):
+        """
+        Ensures that when using curriculum, all non-terminal lessons have a valid
+        CompletionCriteria, and that the terminal lesson does not contain a CompletionCriteria.
+        """
+        num_lessons = len(lessons)
+        for index, lesson in enumerate(lessons):
+            if index < num_lessons - 1 and lesson.completion_criteria is None:
+                raise TrainerConfigError(
+                    f"A non-terminal lesson does not have a completion_criteria for {parameter_name}."
+                )
+            if index == num_lessons - 1 and lesson.completion_criteria is not None:
+                warnings.warn(
+                    f"Your final lesson definition contains completion_criteria for {parameter_name}."
+                    f"It will be ignored.",
+                    TrainerConfigWarning,
+                )
+
+    @staticmethod
+    def structure(d: Mapping, t: type) -> Dict[str, "EnvironmentParameterSettings"]:
+        """
+        Helper method to structure a Dict of EnvironmentParameterSettings class. Meant
+        to be registered with cattr.register_structure_hook() and called with
+        cattr.structure().
+        """
+        if not isinstance(d, Mapping):
+            raise TrainerConfigError(
+                f"Unsupported parameter environment parameter settings {d}."
+            )
+        d_final: Dict[str, EnvironmentParameterSettings] = {}
+        for environment_parameter, environment_parameter_config in d.items():
+            if (
+                isinstance(environment_parameter_config, Mapping)
+                and "curriculum" in environment_parameter_config
+            ):
+                d_final[environment_parameter] = strict_to_cls(
+                    environment_parameter_config, EnvironmentParameterSettings
+                )
+                EnvironmentParameterSettings._check_lesson_chain(
+                    d_final[environment_parameter].curriculum, environment_parameter
+                )
+            else:
+                sampler = ParameterRandomizationSettings.structure(
+                    environment_parameter_config, ParameterRandomizationSettings
+                )
+                d_final[environment_parameter] = EnvironmentParameterSettings(
+                    curriculum=[
+                        Lesson(
+                            completion_criteria=None,
+                            value=sampler,
+                            name=environment_parameter,
+                        )
+                    ]
+                )
+        return d_final
+
+
+# TRAINERS #############################################################################
+@attr.s(auto_attribs=True)
+class SelfPlaySettings:
+    save_steps: int = 20000
+    team_change: int = attr.ib()
+
+    @team_change.default
+    def _team_change_default(self):
+        # Assign team_change to about 4x save_steps
+        return self.save_steps * 5
+
+    swap_steps: int = 2000
+    window: int = 10
+    play_against_latest_model_ratio: float = 0.5
+    initial_elo: float = 1200.0
+
+
+@attr.s(auto_attribs=True)
+class TrainerSettings(ExportableSettings):
+    default_override: ClassVar[Optional["TrainerSettings"]] = None
+    trainer_type: str = "ppo"
+    hyperparameters: HyperparamSettings = attr.ib()
+    checkpoint_interval: int = attr.ib()
+
+    @hyperparameters.default
+    def _set_default_hyperparameters(self):
+        return all_trainer_settings[self.trainer_type]()
+
+    @checkpoint_interval.default
+    def _set_default_checkpoint_interval(self):
+        return 500000
+
+    network_settings: NetworkSettings = attr.ib(factory=NetworkSettings)
+    reward_signals: Dict[RewardSignalType, RewardSignalSettings] = attr.ib(
+        factory=lambda: {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
+    )
+    init_path: Optional[str] = None
+    keep_checkpoints: int = 5
+    even_checkpoints: bool = False
+    max_steps: int = 500000
+    time_horizon: int = 64
+    summary_freq: int = 50000
+    threaded: bool = False
+    self_play: Optional[SelfPlaySettings] = None
+    behavioral_cloning: Optional[BehavioralCloningSettings] = None
+
+    cattr.register_structure_hook_func(
+        lambda t: t == Dict[RewardSignalType, RewardSignalSettings],
+        RewardSignalSettings.structure,
+    )
+
+    @network_settings.validator
+    def _check_batch_size_seq_length(self, attribute, value):
+        if self.network_settings.memory is not None:
+            if (
+                self.network_settings.memory.sequence_length
+                > self.hyperparameters.batch_size
+            ):
+                raise TrainerConfigError(
+                    "When using memory, sequence length must be less than or equal to batch size. "
+                )
+
+    @checkpoint_interval.validator
+    def _set_checkpoint_interval(self, attribute, value):
+        if self.even_checkpoints:
+            self.checkpoint_interval = int(self.max_steps / self.keep_checkpoints)
+
+    @staticmethod
+    def dict_to_trainerdict(d: Dict, t: type) -> "TrainerSettings.DefaultTrainerDict":
+        return TrainerSettings.DefaultTrainerDict(
+            cattr.structure(d, Dict[str, TrainerSettings])
+        )
+
+    @staticmethod
+    def structure(d: Mapping, t: type) -> Any:
+        """
+        Helper method to structure a TrainerSettings class. Meant to be registered with
+        cattr.register_structure_hook() and called with cattr.structure().
+        """
+
+        if not isinstance(d, Mapping):
+            raise TrainerConfigError(f"Unsupported config {d} for {t.__name__}.")
+
+        d_copy: Dict[str, Any] = {}
+
+        # Check if a default_settings was specified. If so, used those as the default
+        # rather than an empty dict.
+        if TrainerSettings.default_override is not None:
+            d_copy.update(cattr.unstructure(TrainerSettings.default_override))
+
+        deep_update_dict(d_copy, d)
+
+        if "framework" in d_copy:
+            logger.warning("Framework option was deprecated but was specified")
+            d_copy.pop("framework", None)
+
+        for key, val in d_copy.items():
+            if attr.has(type(val)):
+                # Don't convert already-converted attrs classes.
+                continue
+            if key == "hyperparameters":
+                if "trainer_type" not in d_copy:
+                    raise TrainerConfigError(
+                        "Hyperparameters were specified but no trainer_type was given."
+                    )
+                else:
+                    d_copy[key] = check_hyperparam_schedules(
+                        val, d_copy["trainer_type"]
+                    )
+                    try:
+                        d_copy[key] = strict_to_cls(
+                            d_copy[key], all_trainer_settings[d_copy["trainer_type"]]
+                        )
+                    except KeyError:
+                        raise TrainerConfigError(
+                            f"Settings for trainer type {d_copy['trainer_type']} were not found"
+                        )
+            elif key == "max_steps":
+                d_copy[key] = int(float(val))
+                # In some legacy configs, max steps was specified as a float
+            # elif key == "even_checkpoints":
+            #     if val:
+            #         d_copy["checkpoint_interval"] = int(d_copy["max_steps"] / d_copy["keep_checkpoints"])
+            elif key == "trainer_type":
+                if val not in all_trainer_types.keys():
+                    raise TrainerConfigError(f"Invalid trainer type {val} was found")
+            else:
+                d_copy[key] = check_and_structure(key, val, t)
+        return t(**d_copy)
+
+    class DefaultTrainerDict(collections.defaultdict):
+        def __init__(self, *args):
+            # Depending on how this is called, args may have the defaultdict
+            # callable at the start of the list or not. In particular, unpickling
+            # will pass [TrainerSettings].
+            if args and args[0] == TrainerSettings:
+                super().__init__(*args)
+            else:
+                super().__init__(TrainerSettings, *args)
+            self._config_specified = True
+
+        def set_config_specified(self, require_config_specified: bool) -> None:
+            self._config_specified = require_config_specified
+
+        def __missing__(self, key: Any) -> "TrainerSettings":
+            if TrainerSettings.default_override is not None:
+                self[key] = copy.deepcopy(TrainerSettings.default_override)
+            elif self._config_specified:
+                raise TrainerConfigError(
+                    f"The behavior name {key} has not been specified in the trainer configuration. "
+                    f"Please add an entry in the configuration file for {key}, or set default_settings."
+                )
+            else:
+                logger.warning(
+                    f"Behavior name {key} does not match any behaviors specified "
+                    f"in the trainer configuration file. A default configuration will be used."
+                )
+                self[key] = TrainerSettings()
+            return self[key]
+
+
+# COMMAND LINE #########################################################################
+@attr.s(auto_attribs=True)
+class CheckpointSettings:
+    run_id: str = parser.get_default("run_id")
+    initialize_from: Optional[str] = parser.get_default("initialize_from")
+    load_model: bool = parser.get_default("load_model")
+    resume: bool = parser.get_default("resume")
+    force: bool = parser.get_default("force")
+    train_model: bool = parser.get_default("train_model")
+    inference: bool = parser.get_default("inference")
+    results_dir: str = parser.get_default("results_dir")
+
+    @property
+    def write_path(self) -> str:
+        return os.path.join(self.results_dir, self.run_id)
+
+    @property
+    def maybe_init_path(self) -> Optional[str]:
+        return (
+            os.path.join(self.results_dir, self.initialize_from)
+            if self.initialize_from is not None
+            else None
+        )
+
+    @property
+    def run_logs_dir(self) -> str:
+        return os.path.join(self.write_path, "run_logs")
+
+    def prioritize_resume_init(self) -> None:
+        """Prioritize explicit command line resume/init over conflicting yaml options.
+        if both resume/init are set at one place use resume"""
+        _non_default_args = DetectDefault.non_default_args
+        if "resume" in _non_default_args:
+            if self.initialize_from is not None:
+                logger.warning(
+                    f"Both 'resume' and 'initialize_from={self.initialize_from}' are set!"
+                    f" Current run will be resumed ignoring initialization."
+                )
+                self.initialize_from = parser.get_default("initialize_from")
+        elif "initialize_from" in _non_default_args:
+            if self.resume:
+                logger.warning(
+                    f"Both 'resume' and 'initialize_from={self.initialize_from}' are set!"
+                    f" {self.run_id} is initialized_from {self.initialize_from} and resume will be ignored."
+                )
+                self.resume = parser.get_default("resume")
+        elif self.resume and self.initialize_from is not None:
+            # no cli args but both are set in yaml file
+            logger.warning(
+                f"Both 'resume' and 'initialize_from={self.initialize_from}' are set in yaml file!"
+                f" Current run will be resumed ignoring initialization."
+            )
+            self.initialize_from = parser.get_default("initialize_from")
+
+
+@attr.s(auto_attribs=True)
+class EnvironmentSettings:
+    env_path: Optional[str] = parser.get_default("env_path")
+    env_args: Optional[List[str]] = parser.get_default("env_args")
+    base_port: int = parser.get_default("base_port")
+    num_envs: int = attr.ib(default=parser.get_default("num_envs"))
+    num_areas: int = attr.ib(default=parser.get_default("num_areas"))
+    seed: int = parser.get_default("seed")
+    max_lifetime_restarts: int = parser.get_default("max_lifetime_restarts")
+    restarts_rate_limit_n: int = parser.get_default("restarts_rate_limit_n")
+    restarts_rate_limit_period_s: int = parser.get_default(
+        "restarts_rate_limit_period_s"
+    )
+
+    @num_envs.validator
+    def validate_num_envs(self, attribute, value):
+        if value > 1 and self.env_path is None:
+            raise ValueError("num_envs must be 1 if env_path is not set.")
+
+    @num_areas.validator
+    def validate_num_area(self, attribute, value):
+        if value <= 0:
+            raise ValueError("num_areas must be set to a positive number >= 1.")
+
+
+@attr.s(auto_attribs=True)
+class EngineSettings:
+    width: int = parser.get_default("width")
+    height: int = parser.get_default("height")
+    quality_level: int = parser.get_default("quality_level")
+    time_scale: float = parser.get_default("time_scale")
+    target_frame_rate: int = parser.get_default("target_frame_rate")
+    capture_frame_rate: int = parser.get_default("capture_frame_rate")
+    no_graphics: bool = parser.get_default("no_graphics")
+
+
+@attr.s(auto_attribs=True)
+class TorchSettings:
+    device: Optional[str] = parser.get_default("device")
+
+
+@attr.s(auto_attribs=True)
+class RunOptions(ExportableSettings):
+    default_settings: Optional[TrainerSettings] = None
+    behaviors: TrainerSettings.DefaultTrainerDict = attr.ib(
+        factory=TrainerSettings.DefaultTrainerDict
+    )
+    env_settings: EnvironmentSettings = attr.ib(factory=EnvironmentSettings)
+    engine_settings: EngineSettings = attr.ib(factory=EngineSettings)
+    environment_parameters: Optional[Dict[str, EnvironmentParameterSettings]] = None
+    checkpoint_settings: CheckpointSettings = attr.ib(factory=CheckpointSettings)
+    torch_settings: TorchSettings = attr.ib(factory=TorchSettings)
+
+    # These are options that are relevant to the run itself, and not the engine or environment.
+    # They will be left here.
+    debug: bool = parser.get_default("debug")
+
+    # Convert to settings while making sure all fields are valid
+    cattr.register_structure_hook(EnvironmentSettings, strict_to_cls)
+    cattr.register_structure_hook(EngineSettings, strict_to_cls)
+    cattr.register_structure_hook(CheckpointSettings, strict_to_cls)
+    cattr.register_structure_hook_func(
+        lambda t: t == Dict[str, EnvironmentParameterSettings],
+        EnvironmentParameterSettings.structure,
+    )
+    cattr.register_structure_hook(Lesson, strict_to_cls)
+    cattr.register_structure_hook(
+        ParameterRandomizationSettings, ParameterRandomizationSettings.structure
+    )
+    cattr.register_unstructure_hook(
+        ParameterRandomizationSettings, ParameterRandomizationSettings.unstructure
+    )
+    cattr.register_structure_hook(TrainerSettings, TrainerSettings.structure)
+    cattr.register_structure_hook(
+        TrainerSettings.DefaultTrainerDict, TrainerSettings.dict_to_trainerdict
+    )
+    cattr.register_unstructure_hook(collections.defaultdict, defaultdict_to_dict)
+
+    @staticmethod
+    def from_argparse(args: argparse.Namespace) -> "RunOptions":
+        """
+        Takes an argparse.Namespace as specified in `parse_command_line`, loads input configuration files
+        from file paths, and converts to a RunOptions instance.
+        :param args: collection of command-line parameters passed to mlagents-learn
+        :return: RunOptions representing the passed in arguments, with trainer config, curriculum and sampler
+          configs loaded from files.
+        """
+        argparse_args = vars(args)
+        config_path = StoreConfigFile.trainer_config_path
+
+        # Load YAML
+        configured_dict: Dict[str, Any] = {
+            "checkpoint_settings": {},
+            "env_settings": {},
+            "engine_settings": {},
+            "torch_settings": {},
+        }
+        _require_all_behaviors = True
+        if config_path is not None:
+            configured_dict.update(load_config(config_path))
+        else:
+            # If we're not loading from a file, we don't require all behavior names to be specified.
+            _require_all_behaviors = False
+
+        # Use the YAML file values for all values not specified in the CLI.
+        for key in configured_dict.keys():
+            # Detect bad config options
+            if key not in attr.fields_dict(RunOptions):
+                raise TrainerConfigError(
+                    "The option {} was specified in your YAML file, but is invalid.".format(
+                        key
+                    )
+                )
+
+        # Override with CLI args
+        # Keep deprecated --load working, TODO: remove
+        argparse_args["resume"] = argparse_args["resume"] or argparse_args["load_model"]
+
+        for key, val in argparse_args.items():
+            if key in DetectDefault.non_default_args:
+                if key in attr.fields_dict(CheckpointSettings):
+                    configured_dict["checkpoint_settings"][key] = val
+                elif key in attr.fields_dict(EnvironmentSettings):
+                    configured_dict["env_settings"][key] = val
+                elif key in attr.fields_dict(EngineSettings):
+                    configured_dict["engine_settings"][key] = val
+                elif key in attr.fields_dict(TorchSettings):
+                    configured_dict["torch_settings"][key] = val
+                else:  # Base options
+                    configured_dict[key] = val
+
+        final_runoptions = RunOptions.from_dict(configured_dict)
+        final_runoptions.checkpoint_settings.prioritize_resume_init()
+        # Need check to bypass type checking but keep structure on dict working
+        if isinstance(final_runoptions.behaviors, TrainerSettings.DefaultTrainerDict):
+            # configure whether or not we should require all behavior names to be found in the config YAML
+            final_runoptions.behaviors.set_config_specified(_require_all_behaviors)
+
+        _non_default_args = DetectDefault.non_default_args
+
+        # Prioritize the deterministic mode from the cli for deterministic actions.
+        if "deterministic" in _non_default_args:
+            for behaviour in final_runoptions.behaviors.keys():
+                final_runoptions.behaviors[
+                    behaviour
+                ].network_settings.deterministic = argparse_args["deterministic"]
+
+        return final_runoptions
+
+    @staticmethod
+    def from_dict(
+        options_dict: Dict[str, Any],
+    ) -> "RunOptions":
+        # If a default settings was specified, set the TrainerSettings class override
+        if (
+            "default_settings" in options_dict.keys()
+            and options_dict["default_settings"] is not None
+        ):
+            TrainerSettings.default_override = cattr.structure(
+                options_dict["default_settings"], TrainerSettings
+            )
+        return cattr.structure(options_dict, RunOptions)
diff --git a/ml-agents/mlagents/trainers/simple_env_manager.py b/ml-agents/mlagents/trainers/simple_env_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..b29fd1ad3b969e33512171090f45f57f0037e305
--- /dev/null
+++ b/ml-agents/mlagents/trainers/simple_env_manager.py
@@ -0,0 +1,84 @@
+from typing import Dict, List
+
+from mlagents_envs.base_env import BaseEnv, BehaviorName, BehaviorSpec
+from mlagents.trainers.env_manager import EnvManager, EnvironmentStep, AllStepResult
+from mlagents_envs.timers import timed
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.settings import ParameterRandomizationSettings
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+
+
+class SimpleEnvManager(EnvManager):
+    """
+    Simple implementation of the EnvManager interface that only handles one BaseEnv at a time.
+    This is generally only useful for testing; see SubprocessEnvManager for a production-quality implementation.
+    """
+
+    def __init__(self, env: BaseEnv, env_params: EnvironmentParametersChannel):
+        super().__init__()
+        self.env_params = env_params
+        self.env = env
+        self.previous_step: EnvironmentStep = EnvironmentStep.empty(0)
+        self.previous_all_action_info: Dict[str, ActionInfo] = {}
+
+    def _step(self) -> List[EnvironmentStep]:
+        all_action_info = self._take_step(self.previous_step)
+        self.previous_all_action_info = all_action_info
+
+        for brain_name, action_info in all_action_info.items():
+            self.env.set_actions(brain_name, action_info.env_action)
+        self.env.step()
+        all_step_result = self._generate_all_results()
+
+        step_info = EnvironmentStep(
+            all_step_result, 0, self.previous_all_action_info, {}
+        )
+        self.previous_step = step_info
+        return [step_info]
+
+    def _reset_env(
+        self, config: Dict[BehaviorName, float] = None
+    ) -> List[EnvironmentStep]:  # type: ignore
+        self.set_env_parameters(config)
+        self.env.reset()
+        all_step_result = self._generate_all_results()
+        self.previous_step = EnvironmentStep(all_step_result, 0, {}, {})
+        return [self.previous_step]
+
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSidehannel.
+        :param config: Dict of environment parameter keys and values
+        """
+        if config is not None:
+            for k, v in config.items():
+                if isinstance(v, float):
+                    self.env_params.set_float_parameter(k, v)
+                elif isinstance(v, ParameterRandomizationSettings):
+                    v.apply(k, self.env_params)
+
+    @property
+    def training_behaviors(self) -> Dict[BehaviorName, BehaviorSpec]:
+        return self.env.behavior_specs
+
+    def close(self):
+        self.env.close()
+
+    @timed
+    def _take_step(self, last_step: EnvironmentStep) -> Dict[BehaviorName, ActionInfo]:
+        all_action_info: Dict[str, ActionInfo] = {}
+        for brain_name, step_tuple in last_step.current_all_step_result.items():
+            all_action_info[brain_name] = self.policies[brain_name].get_action(
+                step_tuple[0],
+                0,  # As there is only one worker, we assign the worker_id to 0.
+            )
+        return all_action_info
+
+    def _generate_all_results(self) -> AllStepResult:
+        all_step_result: AllStepResult = {}
+        for brain_name in self.env.behavior_specs:
+            all_step_result[brain_name] = self.env.get_steps(brain_name)
+        return all_step_result
diff --git a/ml-agents/mlagents/trainers/stats.py b/ml-agents/mlagents/trainers/stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..78c86116e2abe2724a0d1ce563b828dec5c5c527
--- /dev/null
+++ b/ml-agents/mlagents/trainers/stats.py
@@ -0,0 +1,393 @@
+from collections import defaultdict
+from enum import Enum
+from typing import List, Dict, NamedTuple, Any, Optional
+import numpy as np
+import abc
+import os
+import time
+from threading import RLock
+
+from mlagents_envs.side_channel.stats_side_channel import StatsAggregationMethod
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.timers import set_gauge
+from torch.utils.tensorboard import SummaryWriter
+from mlagents.torch_utils.globals import get_rank
+
+logger = get_logger(__name__)
+
+
+def _dict_to_str(param_dict: Dict[str, Any], num_tabs: int) -> str:
+    """
+    Takes a parameter dictionary and converts it to a human-readable string.
+    Recurses if there are multiple levels of dict. Used to print out hyperparameters.
+
+    :param param_dict: A Dictionary of key, value parameters.
+    :return: A string version of this dictionary.
+    """
+    if not isinstance(param_dict, dict):
+        return str(param_dict)
+    else:
+        append_newline = "\n" if num_tabs > 0 else ""
+        return append_newline + "\n".join(
+            [
+                "\t"
+                + "  " * num_tabs
+                + f"{x}:\t{_dict_to_str(param_dict[x], num_tabs + 1)}"
+                for x in param_dict
+            ]
+        )
+
+
+class StatsSummary(NamedTuple):
+    full_dist: List[float]
+    aggregation_method: StatsAggregationMethod
+
+    @staticmethod
+    def empty() -> "StatsSummary":
+        return StatsSummary([], StatsAggregationMethod.AVERAGE)
+
+    @property
+    def aggregated_value(self):
+        if self.aggregation_method == StatsAggregationMethod.SUM:
+            return self.sum
+        else:
+            return self.mean
+
+    @property
+    def mean(self):
+        return np.mean(self.full_dist)
+
+    @property
+    def std(self):
+        return np.std(self.full_dist)
+
+    @property
+    def num(self):
+        return len(self.full_dist)
+
+    @property
+    def sum(self):
+        return np.sum(self.full_dist)
+
+
+class StatsPropertyType(Enum):
+    HYPERPARAMETERS = "hyperparameters"
+    SELF_PLAY = "selfplay"
+
+
+class StatsWriter(abc.ABC):
+    """
+    A StatsWriter abstract class. A StatsWriter takes in a category, key, scalar value, and step
+    and writes it out by some method.
+    """
+
+    def on_add_stat(
+        self,
+        category: str,
+        key: str,
+        value: float,
+        aggregation: StatsAggregationMethod = StatsAggregationMethod.AVERAGE,
+    ) -> None:
+        """
+        Callback method for handling an individual stat value as reported to the StatsReporter add_stat
+        or set_stat methods.
+
+        :param category: Category of the statistics. Usually this is the behavior name.
+        :param key: The type of statistic, e.g. Environment/Reward.
+        :param value: The value of the statistic.
+        :param aggregation: The aggregation method for the statistic, default StatsAggregationMethod.AVERAGE.
+        """
+        pass
+
+    @abc.abstractmethod
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        """
+        Callback to record training information
+        :param category: Category of the statistics. Usually this is the behavior name.
+        :param values: Dictionary of statistics.
+        :param step: The current training step.
+        :return:
+        """
+        pass
+
+    def add_property(
+        self, category: str, property_type: StatsPropertyType, value: Any
+    ) -> None:
+        """
+        Add a generic property to the StatsWriter. This could be e.g. a Dict of hyperparameters,
+        a max step count, a trainer type, etc. Note that not all StatsWriters need to be compatible
+        with all types of properties. For instance, a TB writer doesn't need a max step.
+
+        :param category: The category that the property belongs to.
+        :param property_type: The type of property.
+        :param value: The property itself.
+        """
+        pass
+
+
+class GaugeWriter(StatsWriter):
+    """
+    Write all stats that we receive to the timer gauges, so we can track them offline easily
+    """
+
+    @staticmethod
+    def sanitize_string(s: str) -> str:
+        """
+        Clean up special characters in the category and value names.
+        """
+        return s.replace("/", ".").replace(" ", "")
+
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        for val, stats_summary in values.items():
+            set_gauge(
+                GaugeWriter.sanitize_string(f"{category}.{val}.mean"),
+                float(stats_summary.mean),
+            )
+            set_gauge(
+                GaugeWriter.sanitize_string(f"{category}.{val}.sum"),
+                float(stats_summary.sum),
+            )
+
+
+class ConsoleWriter(StatsWriter):
+    def __init__(self):
+        self.training_start_time = time.time()
+        # If self-play, we want to print ELO as well as reward
+        self.self_play = False
+        self.self_play_team = -1
+        self.rank = get_rank()
+
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        is_training = "Not Training"
+        if "Is Training" in values:
+            stats_summary = values["Is Training"]
+            if stats_summary.aggregated_value > 0.0:
+                is_training = "Training"
+
+        elapsed_time = time.time() - self.training_start_time
+        log_info: List[str] = [category]
+        log_info.append(f"Step: {step}")
+        log_info.append(f"Time Elapsed: {elapsed_time:0.3f} s")
+        if "Environment/Cumulative Reward" in values:
+            stats_summary = values["Environment/Cumulative Reward"]
+            if self.rank is not None:
+                log_info.append(f"Rank: {self.rank}")
+
+            log_info.append(f"Mean Reward: {stats_summary.mean:0.3f}")
+            if "Environment/Group Cumulative Reward" in values:
+                group_stats_summary = values["Environment/Group Cumulative Reward"]
+                log_info.append(f"Mean Group Reward: {group_stats_summary.mean:0.3f}")
+            else:
+                log_info.append(f"Std of Reward: {stats_summary.std:0.3f}")
+            log_info.append(is_training)
+
+            if self.self_play and "Self-play/ELO" in values:
+                elo_stats = values["Self-play/ELO"]
+                log_info.append(f"ELO: {elo_stats.mean:0.3f}")
+        else:
+            log_info.append("No episode was completed since last summary")
+            log_info.append(is_training)
+        logger.info(". ".join(log_info) + ".")
+
+    def add_property(
+        self, category: str, property_type: StatsPropertyType, value: Any
+    ) -> None:
+        if property_type == StatsPropertyType.HYPERPARAMETERS:
+            logger.info(
+                """Hyperparameters for behavior name {}: \n{}""".format(
+                    category, _dict_to_str(value, 0)
+                )
+            )
+        elif property_type == StatsPropertyType.SELF_PLAY:
+            assert isinstance(value, bool)
+            self.self_play = value
+
+
+class TensorboardWriter(StatsWriter):
+    def __init__(
+        self,
+        base_dir: str,
+        clear_past_data: bool = False,
+        hidden_keys: Optional[List[str]] = None,
+    ):
+        """
+        A StatsWriter that writes to a Tensorboard summary.
+
+        :param base_dir: The directory within which to place all the summaries. Tensorboard files will be written to a
+        {base_dir}/{category} directory.
+        :param clear_past_data: Whether or not to clean up existing Tensorboard files associated with the base_dir and
+        category.
+        :param hidden_keys: If provided, Tensorboard Writer won't write statistics identified with these Keys in
+        Tensorboard summary.
+        """
+        self.summary_writers: Dict[str, SummaryWriter] = {}
+        self.base_dir: str = base_dir
+        self._clear_past_data = clear_past_data
+        self.hidden_keys: List[str] = hidden_keys if hidden_keys is not None else []
+
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        self._maybe_create_summary_writer(category)
+        for key, value in values.items():
+            if key in self.hidden_keys:
+                continue
+            self.summary_writers[category].add_scalar(
+                f"{key}", value.aggregated_value, step
+            )
+            if value.aggregation_method == StatsAggregationMethod.HISTOGRAM:
+                self.summary_writers[category].add_histogram(
+                    f"{key}_hist", np.array(value.full_dist), step
+                )
+            self.summary_writers[category].flush()
+
+    def _maybe_create_summary_writer(self, category: str) -> None:
+        if category not in self.summary_writers:
+            filewriter_dir = "{basedir}/{category}".format(
+                basedir=self.base_dir, category=category
+            )
+            os.makedirs(filewriter_dir, exist_ok=True)
+            if self._clear_past_data:
+                self._delete_all_events_files(filewriter_dir)
+            self.summary_writers[category] = SummaryWriter(filewriter_dir)
+
+    def _delete_all_events_files(self, directory_name: str) -> None:
+        for file_name in os.listdir(directory_name):
+            if file_name.startswith("events.out"):
+                logger.warning(
+                    f"Deleting TensorBoard data {file_name} that was left over from a "
+                    "previous run."
+                )
+                full_fname = os.path.join(directory_name, file_name)
+                try:
+                    os.remove(full_fname)
+                except OSError:
+                    logger.error(
+                        "{} was left over from a previous run and "
+                        "not deleted.".format(full_fname)
+                    )
+
+    def add_property(
+        self, category: str, property_type: StatsPropertyType, value: Any
+    ) -> None:
+        if property_type == StatsPropertyType.HYPERPARAMETERS:
+            assert isinstance(value, dict)
+            summary = _dict_to_str(value, 0)
+            self._maybe_create_summary_writer(category)
+            if summary is not None:
+                self.summary_writers[category].add_text("Hyperparameters", summary)
+                self.summary_writers[category].flush()
+
+
+class StatsReporter:
+    writers: List[StatsWriter] = []
+    stats_dict: Dict[str, Dict[str, List]] = defaultdict(lambda: defaultdict(list))
+    lock = RLock()
+    stats_aggregation: Dict[str, Dict[str, StatsAggregationMethod]] = defaultdict(
+        lambda: defaultdict(lambda: StatsAggregationMethod.AVERAGE)
+    )
+
+    def __init__(self, category: str):
+        """
+        Generic StatsReporter. A category is the broadest type of storage (would
+        correspond the run name and trainer name, e.g. 3DBalltest_3DBall. A key is the
+        type of stat it is (e.g. Environment/Reward). Finally the Value is the float value
+        attached to this stat.
+        """
+        self.category: str = category
+
+    @staticmethod
+    def add_writer(writer: StatsWriter) -> None:
+        with StatsReporter.lock:
+            StatsReporter.writers.append(writer)
+
+    def add_property(self, property_type: StatsPropertyType, value: Any) -> None:
+        """
+        Add a generic property to the StatsReporter. This could be e.g. a Dict of hyperparameters,
+        a max step count, a trainer type, etc. Note that not all StatsWriters need to be compatible
+        with all types of properties. For instance, a TB writer doesn't need a max step.
+
+        :param property_type: The type of property.
+        :param value: The property itself.
+        """
+        with StatsReporter.lock:
+            for writer in StatsReporter.writers:
+                writer.add_property(self.category, property_type, value)
+
+    def add_stat(
+        self,
+        key: str,
+        value: float,
+        aggregation: StatsAggregationMethod = StatsAggregationMethod.AVERAGE,
+    ) -> None:
+        """
+        Add a float value stat to the StatsReporter.
+
+        :param key: The type of statistic, e.g. Environment/Reward.
+        :param value: the value of the statistic.
+        :param aggregation: the aggregation method for the statistic, default StatsAggregationMethod.AVERAGE.
+        """
+        with StatsReporter.lock:
+            StatsReporter.stats_dict[self.category][key].append(value)
+            StatsReporter.stats_aggregation[self.category][key] = aggregation
+            for writer in StatsReporter.writers:
+                writer.on_add_stat(self.category, key, value, aggregation)
+
+    def set_stat(self, key: str, value: float) -> None:
+        """
+        Sets a stat value to a float. This is for values that we don't want to average, and just
+        want the latest.
+
+        :param key: The type of statistic, e.g. Environment/Reward.
+        :param value: the value of the statistic.
+        """
+        with StatsReporter.lock:
+            StatsReporter.stats_dict[self.category][key] = [value]
+            StatsReporter.stats_aggregation[self.category][
+                key
+            ] = StatsAggregationMethod.MOST_RECENT
+            for writer in StatsReporter.writers:
+                writer.on_add_stat(
+                    self.category, key, value, StatsAggregationMethod.MOST_RECENT
+                )
+
+    def write_stats(self, step: int) -> None:
+        """
+        Write out all stored statistics that fall under the category specified.
+        The currently stored values will be averaged, written out as a single value,
+        and the buffer cleared.
+
+        :param step: Training step which to write these stats as.
+        """
+        with StatsReporter.lock:
+            values: Dict[str, StatsSummary] = {}
+            for key in StatsReporter.stats_dict[self.category]:
+                if len(StatsReporter.stats_dict[self.category][key]) > 0:
+                    stat_summary = self.get_stats_summaries(key)
+                    values[key] = stat_summary
+            for writer in StatsReporter.writers:
+                writer.write_stats(self.category, values, step)
+            del StatsReporter.stats_dict[self.category]
+
+    def get_stats_summaries(self, key: str) -> StatsSummary:
+        """
+        Get the mean, std, count, sum and aggregation method of a particular statistic, since last write.
+
+        :param key: The type of statistic, e.g. Environment/Reward.
+        :returns: A StatsSummary containing summary statistics.
+        """
+        stat_values = StatsReporter.stats_dict[self.category][key]
+        if len(stat_values) == 0:
+            return StatsSummary.empty()
+
+        return StatsSummary(
+            full_dist=stat_values,
+            aggregation_method=StatsReporter.stats_aggregation[self.category][key],
+        )
diff --git a/ml-agents/mlagents/trainers/subprocess_env_manager.py b/ml-agents/mlagents/trainers/subprocess_env_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..43d468f2bc338549d5fe95659804d31ae7602000
--- /dev/null
+++ b/ml-agents/mlagents/trainers/subprocess_env_manager.py
@@ -0,0 +1,546 @@
+import datetime
+from typing import Dict, NamedTuple, List, Any, Optional, Callable, Set
+import cloudpickle
+import enum
+import time
+
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.exception import (
+    UnityCommunicationException,
+    UnityTimeOutException,
+    UnityEnvironmentException,
+    UnityCommunicatorStoppedException,
+)
+from multiprocessing import Process, Pipe, Queue
+from multiprocessing.connection import Connection
+from queue import Empty as EmptyQueueException
+from mlagents_envs.base_env import BaseEnv, BehaviorName, BehaviorSpec
+from mlagents_envs import logging_util
+from mlagents.trainers.env_manager import EnvManager, EnvironmentStep, AllStepResult
+from mlagents.trainers.settings import TrainerSettings
+from mlagents_envs.timers import (
+    TimerNode,
+    timed,
+    hierarchical_timer,
+    reset_timers,
+    get_timer_root,
+)
+from mlagents.trainers.settings import ParameterRandomizationSettings, RunOptions
+from mlagents.trainers.action_info import ActionInfo
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+from mlagents_envs.side_channel.engine_configuration_channel import (
+    EngineConfigurationChannel,
+    EngineConfig,
+)
+from mlagents_envs.side_channel.stats_side_channel import (
+    EnvironmentStats,
+    StatsSideChannel,
+)
+from mlagents.trainers.training_analytics_side_channel import (
+    TrainingAnalyticsSideChannel,
+)
+from mlagents_envs.side_channel.side_channel import SideChannel
+
+
+logger = logging_util.get_logger(__name__)
+WORKER_SHUTDOWN_TIMEOUT_S = 10
+
+
+class EnvironmentCommand(enum.Enum):
+    STEP = 1
+    BEHAVIOR_SPECS = 2
+    ENVIRONMENT_PARAMETERS = 3
+    RESET = 4
+    CLOSE = 5
+    ENV_EXITED = 6
+    CLOSED = 7
+    TRAINING_STARTED = 8
+
+
+class EnvironmentRequest(NamedTuple):
+    cmd: EnvironmentCommand
+    payload: Any = None
+
+
+class EnvironmentResponse(NamedTuple):
+    cmd: EnvironmentCommand
+    worker_id: int
+    payload: Any
+
+
+class StepResponse(NamedTuple):
+    all_step_result: AllStepResult
+    timer_root: Optional[TimerNode]
+    environment_stats: EnvironmentStats
+
+
+class UnityEnvWorker:
+    def __init__(self, process: Process, worker_id: int, conn: Connection):
+        self.process = process
+        self.worker_id = worker_id
+        self.conn = conn
+        self.previous_step: EnvironmentStep = EnvironmentStep.empty(worker_id)
+        self.previous_all_action_info: Dict[str, ActionInfo] = {}
+        self.waiting = False
+        self.closed = False
+
+    def send(self, cmd: EnvironmentCommand, payload: Any = None) -> None:
+        try:
+            req = EnvironmentRequest(cmd, payload)
+            self.conn.send(req)
+        except (BrokenPipeError, EOFError):
+            raise UnityCommunicationException("UnityEnvironment worker: send failed.")
+
+    def recv(self) -> EnvironmentResponse:
+        try:
+            response: EnvironmentResponse = self.conn.recv()
+            if response.cmd == EnvironmentCommand.ENV_EXITED:
+                env_exception: Exception = response.payload
+                raise env_exception
+            return response
+        except (BrokenPipeError, EOFError):
+            raise UnityCommunicationException("UnityEnvironment worker: recv failed.")
+
+    def request_close(self):
+        try:
+            self.conn.send(EnvironmentRequest(EnvironmentCommand.CLOSE))
+        except (BrokenPipeError, EOFError):
+            logger.debug(
+                f"UnityEnvWorker {self.worker_id} got exception trying to close."
+            )
+            pass
+
+
+def worker(
+    parent_conn: Connection,
+    step_queue: Queue,
+    pickled_env_factory: str,
+    worker_id: int,
+    run_options: RunOptions,
+    log_level: int = logging_util.INFO,
+) -> None:
+    env_factory: Callable[
+        [int, List[SideChannel]], UnityEnvironment
+    ] = cloudpickle.loads(pickled_env_factory)
+    env_parameters = EnvironmentParametersChannel()
+
+    engine_config = EngineConfig(
+        width=run_options.engine_settings.width,
+        height=run_options.engine_settings.height,
+        quality_level=run_options.engine_settings.quality_level,
+        time_scale=run_options.engine_settings.time_scale,
+        target_frame_rate=run_options.engine_settings.target_frame_rate,
+        capture_frame_rate=run_options.engine_settings.capture_frame_rate,
+    )
+    engine_configuration_channel = EngineConfigurationChannel()
+    engine_configuration_channel.set_configuration(engine_config)
+
+    stats_channel = StatsSideChannel()
+    training_analytics_channel: Optional[TrainingAnalyticsSideChannel] = None
+    if worker_id == 0:
+        training_analytics_channel = TrainingAnalyticsSideChannel()
+    env: UnityEnvironment = None
+    # Set log level. On some platforms, the logger isn't common with the
+    # main process, so we need to set it again.
+    logging_util.set_log_level(log_level)
+
+    def _send_response(cmd_name: EnvironmentCommand, payload: Any) -> None:
+        parent_conn.send(EnvironmentResponse(cmd_name, worker_id, payload))
+
+    def _generate_all_results() -> AllStepResult:
+        all_step_result: AllStepResult = {}
+        for brain_name in env.behavior_specs:
+            all_step_result[brain_name] = env.get_steps(brain_name)
+        return all_step_result
+
+    try:
+        side_channels = [env_parameters, engine_configuration_channel, stats_channel]
+        if training_analytics_channel is not None:
+            side_channels.append(training_analytics_channel)
+
+        env = env_factory(worker_id, side_channels)
+        if (
+            not env.academy_capabilities
+            or not env.academy_capabilities.trainingAnalytics
+        ):
+            # Make sure we don't try to send training analytics if the environment doesn't know how to process
+            # them. This wouldn't be catastrophic, but would result in unknown SideChannel UUIDs being used.
+            training_analytics_channel = None
+        if training_analytics_channel:
+            training_analytics_channel.environment_initialized(run_options)
+
+        while True:
+            req: EnvironmentRequest = parent_conn.recv()
+            if req.cmd == EnvironmentCommand.STEP:
+                all_action_info = req.payload
+                for brain_name, action_info in all_action_info.items():
+                    if len(action_info.agent_ids) > 0:
+                        env.set_actions(brain_name, action_info.env_action)
+                env.step()
+                all_step_result = _generate_all_results()
+                # The timers in this process are independent from all the processes and the "main" process
+                # So after we send back the root timer, we can safely clear them.
+                # Note that we could randomly return timers a fraction of the time if we wanted to reduce
+                # the data transferred.
+                # TODO get gauges from the workers and merge them in the main process too.
+                env_stats = stats_channel.get_and_reset_stats()
+                step_response = StepResponse(
+                    all_step_result, get_timer_root(), env_stats
+                )
+                step_queue.put(
+                    EnvironmentResponse(
+                        EnvironmentCommand.STEP, worker_id, step_response
+                    )
+                )
+                reset_timers()
+            elif req.cmd == EnvironmentCommand.BEHAVIOR_SPECS:
+                _send_response(EnvironmentCommand.BEHAVIOR_SPECS, env.behavior_specs)
+            elif req.cmd == EnvironmentCommand.ENVIRONMENT_PARAMETERS:
+                for k, v in req.payload.items():
+                    if isinstance(v, ParameterRandomizationSettings):
+                        v.apply(k, env_parameters)
+            elif req.cmd == EnvironmentCommand.TRAINING_STARTED:
+                behavior_name, trainer_config = req.payload
+                if training_analytics_channel:
+                    training_analytics_channel.training_started(
+                        behavior_name, trainer_config
+                    )
+            elif req.cmd == EnvironmentCommand.RESET:
+                env.reset()
+                all_step_result = _generate_all_results()
+                _send_response(EnvironmentCommand.RESET, all_step_result)
+            elif req.cmd == EnvironmentCommand.CLOSE:
+                break
+    except (
+        KeyboardInterrupt,
+        UnityCommunicationException,
+        UnityTimeOutException,
+        UnityEnvironmentException,
+        UnityCommunicatorStoppedException,
+    ) as ex:
+        logger.debug(f"UnityEnvironment worker {worker_id}: environment stopping.")
+        step_queue.put(
+            EnvironmentResponse(EnvironmentCommand.ENV_EXITED, worker_id, ex)
+        )
+        _send_response(EnvironmentCommand.ENV_EXITED, ex)
+    except Exception as ex:
+        logger.exception(
+            f"UnityEnvironment worker {worker_id}: environment raised an unexpected exception."
+        )
+        step_queue.put(
+            EnvironmentResponse(EnvironmentCommand.ENV_EXITED, worker_id, ex)
+        )
+        _send_response(EnvironmentCommand.ENV_EXITED, ex)
+    finally:
+        logger.debug(f"UnityEnvironment worker {worker_id} closing.")
+        if env is not None:
+            env.close()
+        logger.debug(f"UnityEnvironment worker {worker_id} done.")
+        parent_conn.close()
+        step_queue.put(EnvironmentResponse(EnvironmentCommand.CLOSED, worker_id, None))
+        step_queue.close()
+
+
+class SubprocessEnvManager(EnvManager):
+    def __init__(
+        self,
+        env_factory: Callable[[int, List[SideChannel]], BaseEnv],
+        run_options: RunOptions,
+        n_env: int = 1,
+    ):
+        super().__init__()
+        self.env_workers: List[UnityEnvWorker] = []
+        self.step_queue: Queue = Queue()
+        self.workers_alive = 0
+        self.env_factory = env_factory
+        self.run_options = run_options
+        self.env_parameters: Optional[Dict] = None
+        # Each worker is correlated with a list of times they restarted within the last time period.
+        self.recent_restart_timestamps: List[List[datetime.datetime]] = [
+            [] for _ in range(n_env)
+        ]
+        self.restart_counts: List[int] = [0] * n_env
+        for worker_idx in range(n_env):
+            self.env_workers.append(
+                self.create_worker(
+                    worker_idx, self.step_queue, env_factory, run_options
+                )
+            )
+            self.workers_alive += 1
+
+    @staticmethod
+    def create_worker(
+        worker_id: int,
+        step_queue: Queue,
+        env_factory: Callable[[int, List[SideChannel]], BaseEnv],
+        run_options: RunOptions,
+    ) -> UnityEnvWorker:
+        parent_conn, child_conn = Pipe()
+
+        # Need to use cloudpickle for the env factory function since function objects aren't picklable
+        # on Windows as of Python 3.6.
+        pickled_env_factory = cloudpickle.dumps(env_factory)
+        child_process = Process(
+            target=worker,
+            args=(
+                child_conn,
+                step_queue,
+                pickled_env_factory,
+                worker_id,
+                run_options,
+                logger.level,
+            ),
+        )
+        child_process.start()
+        return UnityEnvWorker(child_process, worker_id, parent_conn)
+
+    def _queue_steps(self) -> None:
+        for env_worker in self.env_workers:
+            if not env_worker.waiting:
+                env_action_info = self._take_step(env_worker.previous_step)
+                env_worker.previous_all_action_info = env_action_info
+                env_worker.send(EnvironmentCommand.STEP, env_action_info)
+                env_worker.waiting = True
+
+    def _restart_failed_workers(self, first_failure: EnvironmentResponse) -> None:
+        if first_failure.cmd != EnvironmentCommand.ENV_EXITED:
+            return
+        # Drain the step queue to make sure all workers are paused and we have found all concurrent errors.
+        # Pausing all training is needed since we need to reset all pending training steps as they could be corrupted.
+        other_failures: Dict[int, Exception] = self._drain_step_queue()
+        # TODO: Once we use python 3.9 switch to using the | operator to combine dicts.
+        failures: Dict[int, Exception] = {
+            **{first_failure.worker_id: first_failure.payload},
+            **other_failures,
+        }
+        for worker_id, ex in failures.items():
+            self._assert_worker_can_restart(worker_id, ex)
+            logger.warning(f"Restarting worker[{worker_id}] after '{ex}'")
+            self.recent_restart_timestamps[worker_id].append(datetime.datetime.now())
+            self.restart_counts[worker_id] += 1
+            self.env_workers[worker_id] = self.create_worker(
+                worker_id, self.step_queue, self.env_factory, self.run_options
+            )
+        # The restarts were successful, clear all the existing training trajectories so we don't use corrupted or
+        # outdated data.
+        self.reset(self.env_parameters)
+
+    def _drain_step_queue(self) -> Dict[int, Exception]:
+        """
+        Drains all steps out of the step queue and returns all exceptions from crashed workers.
+        This will effectively pause all workers so that they won't do anything until _queue_steps is called.
+        """
+        all_failures = {}
+        workers_still_pending = {w.worker_id for w in self.env_workers if w.waiting}
+        deadline = datetime.datetime.now() + datetime.timedelta(minutes=1)
+        while workers_still_pending and deadline > datetime.datetime.now():
+            try:
+                while True:
+                    step: EnvironmentResponse = self.step_queue.get_nowait()
+                    if step.cmd == EnvironmentCommand.ENV_EXITED:
+                        workers_still_pending.add(step.worker_id)
+                        all_failures[step.worker_id] = step.payload
+                    else:
+                        workers_still_pending.remove(step.worker_id)
+                        self.env_workers[step.worker_id].waiting = False
+            except EmptyQueueException:
+                pass
+        if deadline < datetime.datetime.now():
+            still_waiting = {w.worker_id for w in self.env_workers if w.waiting}
+            raise TimeoutError(f"Workers {still_waiting} stuck in waiting state")
+        return all_failures
+
+    def _assert_worker_can_restart(self, worker_id: int, exception: Exception) -> None:
+        """
+        Checks if we can recover from an exception from a worker.
+        If the restart limit is exceeded it will raise a UnityCommunicationException.
+        If the exception is not recoverable it re-raises the exception.
+        """
+        if (
+            isinstance(exception, UnityCommunicationException)
+            or isinstance(exception, UnityTimeOutException)
+            or isinstance(exception, UnityEnvironmentException)
+            or isinstance(exception, UnityCommunicatorStoppedException)
+        ):
+            if self._worker_has_restart_quota(worker_id):
+                return
+            else:
+                logger.error(
+                    f"Worker {worker_id} exceeded the allowed number of restarts."
+                )
+                raise exception
+        raise exception
+
+    def _worker_has_restart_quota(self, worker_id: int) -> bool:
+        self._drop_old_restart_timestamps(worker_id)
+        max_lifetime_restarts = self.run_options.env_settings.max_lifetime_restarts
+        max_limit_check = (
+            max_lifetime_restarts == -1
+            or self.restart_counts[worker_id] < max_lifetime_restarts
+        )
+
+        rate_limit_n = self.run_options.env_settings.restarts_rate_limit_n
+        rate_limit_check = (
+            rate_limit_n == -1
+            or len(self.recent_restart_timestamps[worker_id]) < rate_limit_n
+        )
+
+        return rate_limit_check and max_limit_check
+
+    def _drop_old_restart_timestamps(self, worker_id: int) -> None:
+        """
+        Drops environment restart timestamps that are outside of the current window.
+        """
+
+        def _filter(t: datetime.datetime) -> bool:
+            return t > datetime.datetime.now() - datetime.timedelta(
+                seconds=self.run_options.env_settings.restarts_rate_limit_period_s
+            )
+
+        self.recent_restart_timestamps[worker_id] = list(
+            filter(_filter, self.recent_restart_timestamps[worker_id])
+        )
+
+    def _step(self) -> List[EnvironmentStep]:
+        # Queue steps for any workers which aren't in the "waiting" state.
+        self._queue_steps()
+
+        worker_steps: List[EnvironmentResponse] = []
+        step_workers: Set[int] = set()
+        # Poll the step queue for completed steps from environment workers until we retrieve
+        # 1 or more, which we will then return as StepInfos
+        while len(worker_steps) < 1:
+            try:
+                while True:
+                    step: EnvironmentResponse = self.step_queue.get_nowait()
+                    if step.cmd == EnvironmentCommand.ENV_EXITED:
+                        # If even one env exits try to restart all envs that failed.
+                        self._restart_failed_workers(step)
+                        # Clear state and restart this function.
+                        worker_steps.clear()
+                        step_workers.clear()
+                        self._queue_steps()
+                    elif step.worker_id not in step_workers:
+                        self.env_workers[step.worker_id].waiting = False
+                        worker_steps.append(step)
+                        step_workers.add(step.worker_id)
+            except EmptyQueueException:
+                pass
+        step_infos = self._postprocess_steps(worker_steps)
+        return step_infos
+
+    def _reset_env(self, config: Optional[Dict] = None) -> List[EnvironmentStep]:
+        while any(ew.waiting for ew in self.env_workers):
+            if not self.step_queue.empty():
+                step = self.step_queue.get_nowait()
+                self.env_workers[step.worker_id].waiting = False
+        # Send config to environment
+        self.set_env_parameters(config)
+        # First enqueue reset commands for all workers so that they reset in parallel
+        for ew in self.env_workers:
+            ew.send(EnvironmentCommand.RESET, config)
+        # Next (synchronously) collect the reset observations from each worker in sequence
+        for ew in self.env_workers:
+            ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
+        return list(map(lambda ew: ew.previous_step, self.env_workers))
+
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSidehannel for each worker.
+        :param config: Dict of environment parameter keys and values
+        """
+        self.env_parameters = config
+        for ew in self.env_workers:
+            ew.send(EnvironmentCommand.ENVIRONMENT_PARAMETERS, config)
+
+    def on_training_started(
+        self, behavior_name: str, trainer_settings: TrainerSettings
+    ) -> None:
+        """
+        Handle traing starting for a new behavior type. Generally nothing is necessary here.
+        :param behavior_name:
+        :param trainer_settings:
+        :return:
+        """
+        for ew in self.env_workers:
+            ew.send(
+                EnvironmentCommand.TRAINING_STARTED, (behavior_name, trainer_settings)
+            )
+
+    @property
+    def training_behaviors(self) -> Dict[BehaviorName, BehaviorSpec]:
+        result: Dict[BehaviorName, BehaviorSpec] = {}
+        for worker in self.env_workers:
+            worker.send(EnvironmentCommand.BEHAVIOR_SPECS)
+            result.update(worker.recv().payload)
+        return result
+
+    def close(self) -> None:
+        logger.debug("SubprocessEnvManager closing.")
+        for env_worker in self.env_workers:
+            env_worker.request_close()
+        # Pull messages out of the queue until every worker has CLOSED or we time out.
+        deadline = time.time() + WORKER_SHUTDOWN_TIMEOUT_S
+        while self.workers_alive > 0 and time.time() < deadline:
+            try:
+                step: EnvironmentResponse = self.step_queue.get_nowait()
+                env_worker = self.env_workers[step.worker_id]
+                if step.cmd == EnvironmentCommand.CLOSED and not env_worker.closed:
+                    env_worker.closed = True
+                    self.workers_alive -= 1
+                # Discard all other messages.
+            except EmptyQueueException:
+                pass
+        self.step_queue.close()
+        # Sanity check to kill zombie workers and report an issue if they occur.
+        if self.workers_alive > 0:
+            logger.error("SubprocessEnvManager had workers that didn't signal shutdown")
+            for env_worker in self.env_workers:
+                if not env_worker.closed and env_worker.process.is_alive():
+                    env_worker.process.terminate()
+                    logger.error(
+                        "A SubprocessEnvManager worker did not shut down correctly so it was forcefully terminated."
+                    )
+        self.step_queue.join_thread()
+
+    def _postprocess_steps(
+        self, env_steps: List[EnvironmentResponse]
+    ) -> List[EnvironmentStep]:
+        step_infos = []
+        timer_nodes = []
+        for step in env_steps:
+            payload: StepResponse = step.payload
+            env_worker = self.env_workers[step.worker_id]
+            new_step = EnvironmentStep(
+                payload.all_step_result,
+                step.worker_id,
+                env_worker.previous_all_action_info,
+                payload.environment_stats,
+            )
+            step_infos.append(new_step)
+            env_worker.previous_step = new_step
+
+            if payload.timer_root:
+                timer_nodes.append(payload.timer_root)
+
+        if timer_nodes:
+            with hierarchical_timer("workers") as main_timer_node:
+                for worker_timer_node in timer_nodes:
+                    main_timer_node.merge(
+                        worker_timer_node, root_name="worker_root", is_parallel=True
+                    )
+
+        return step_infos
+
+    @timed
+    def _take_step(self, last_step: EnvironmentStep) -> Dict[BehaviorName, ActionInfo]:
+        all_action_info: Dict[str, ActionInfo] = {}
+        for brain_name, step_tuple in last_step.current_all_step_result.items():
+            if brain_name in self.policies:
+                all_action_info[brain_name] = self.policies[brain_name].get_action(
+                    step_tuple[0], last_step.worker_id
+                )
+        return all_action_info
diff --git a/ml-agents/mlagents/trainers/tests/__init__.py b/ml-agents/mlagents/trainers/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..af16e0d7e74e1484ee8bc43243e36c4c27ca9024
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/__init__.py
@@ -0,0 +1,55 @@
+import os
+
+# Opt-in checking mode to ensure that we always create numpy arrays using float32
+if os.getenv("TEST_ENFORCE_NUMPY_FLOAT32"):
+    # This file is importer by pytest multiple times, but this breaks the patching
+    # Removing the env variable seems the easiest way to prevent this.
+    del os.environ["TEST_ENFORCE_NUMPY_FLOAT32"]
+    import numpy as np
+    import traceback
+
+    __old_np_array = np.array
+    __old_np_zeros = np.zeros
+    __old_np_ones = np.ones
+
+    def _check_no_float64(arr, kwargs_dtype):
+        if arr.dtype == np.float64:
+            tb = traceback.extract_stack()
+            # tb[-1] in the stack is this function.
+            # tb[-2] is the wrapper function, e.g. np_array_no_float64
+            # we want the calling function, so use tb[-3]
+            filename = tb[-3].filename
+            # Only raise if this came from mlagents code
+            if (
+                "ml-agents/mlagents" in filename
+                or "ml-agents-envs/mlagents" in filename
+            ):
+                raise ValueError(
+                    f"float64 array created. Set dtype=np.float32 instead of current dtype={kwargs_dtype}. "
+                    f"Run pytest with TEST_ENFORCE_NUMPY_FLOAT32=1 to confirm fix."
+                )
+
+    def np_array_no_float64(*args, **kwargs):
+        res = __old_np_array(*args, **kwargs)
+        _check_no_float64(res, kwargs.get("dtype"))
+        return res
+
+    def np_zeros_no_float64(*args, **kwargs):
+        res = __old_np_zeros(*args, **kwargs)
+        _check_no_float64(res, kwargs.get("dtype"))
+        return res
+
+    def np_ones_no_float64(*args, **kwargs):
+        res = __old_np_ones(*args, **kwargs)
+        _check_no_float64(res, kwargs.get("dtype"))
+        return res
+
+    np.array = np_array_no_float64
+    np.zeros = np_zeros_no_float64
+    np.ones = np_ones_no_float64
+
+
+if os.getenv("TEST_ENFORCE_BUFFER_KEY_TYPES"):
+    from mlagents.trainers.buffer import AgentBuffer
+
+    AgentBuffer.CHECK_KEY_TYPES_AT_RUNTIME = True
diff --git a/ml-agents/mlagents/trainers/tests/check_env_trains.py b/ml-agents/mlagents/trainers/tests/check_env_trains.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5e98f142120d52d8b75b5954eb74325182e43d2
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/check_env_trains.py
@@ -0,0 +1,99 @@
+import math
+import tempfile
+import numpy as np
+from typing import Dict
+from mlagents.trainers.trainer_controller import TrainerController
+from mlagents.trainers.trainer import TrainerFactory
+from mlagents.trainers.simple_env_manager import SimpleEnvManager
+from mlagents.trainers.stats import StatsReporter, StatsWriter, StatsSummary
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents_envs.side_channel.environment_parameters_channel import (
+    EnvironmentParametersChannel,
+)
+
+
+class DebugWriter(StatsWriter):
+    """
+    Print to stdout so stats can be viewed in pytest
+    """
+
+    def __init__(self):
+        self._last_reward_summary: Dict[str, float] = {}
+
+    def get_last_rewards(self):
+        return self._last_reward_summary
+
+    def write_stats(
+        self, category: str, values: Dict[str, StatsSummary], step: int
+    ) -> None:
+        for val, stats_summary in values.items():
+            if (
+                val == "Environment/Cumulative Reward"
+                or val == "Environment/Group Cumulative Reward"
+            ):
+
+                print(step, val, stats_summary.aggregated_value)
+                self._last_reward_summary[category] = stats_summary.aggregated_value
+
+
+# The reward processor is passed as an argument to _check_environment_trains.
+# It is applied to the list of all final rewards for each brain individually.
+# This is so that we can process all final rewards in different ways for different algorithms.
+# Custom reward processors should be built within the test function and passed to _check_environment_trains
+# Default is average over the last 5 final rewards
+def default_reward_processor(rewards, last_n_rewards=5):
+    rewards_to_use = rewards[-last_n_rewards:]
+    # For debugging tests
+    print(f"Last {last_n_rewards} rewards:", rewards_to_use)
+    return np.array(rewards[-last_n_rewards:], dtype=np.float32).mean()
+
+
+def check_environment_trains(
+    env,
+    trainer_config,
+    reward_processor=default_reward_processor,
+    env_parameter_manager=None,
+    success_threshold=0.9,
+    env_manager=None,
+    training_seed=None,
+):
+    if env_parameter_manager is None:
+        env_parameter_manager = EnvironmentParameterManager()
+    # Create controller and begin training.
+    with tempfile.TemporaryDirectory() as dir:
+        run_id = "id"
+        seed = 1337 if training_seed is None else training_seed
+        StatsReporter.writers.clear()  # Clear StatsReporters so we don't write to file
+        debug_writer = DebugWriter()
+        StatsReporter.add_writer(debug_writer)
+        if env_manager is None:
+            env_manager = SimpleEnvManager(env, EnvironmentParametersChannel())
+        trainer_factory = TrainerFactory(
+            trainer_config=trainer_config,
+            output_path=dir,
+            train_model=True,
+            load_model=False,
+            seed=seed,
+            param_manager=env_parameter_manager,
+            multi_gpu=False,
+        )
+
+        tc = TrainerController(
+            trainer_factory=trainer_factory,
+            output_path=dir,
+            run_id=run_id,
+            param_manager=env_parameter_manager,
+            train=True,
+            training_seed=seed,
+        )
+
+        # Begin training
+        tc.start_learning(env_manager)
+        if (
+            success_threshold is not None
+        ):  # For tests where we are just checking setup and not reward
+            processed_rewards = [
+                reward_processor(rewards) for rewards in env.final_rewards.values()
+            ]
+            assert all(not math.isnan(reward) for reward in processed_rewards)
+            assert all(reward > success_threshold for reward in processed_rewards)
diff --git a/ml-agents/mlagents/trainers/tests/dummy_config.py b/ml-agents/mlagents/trainers/tests/dummy_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4a71b0ee19a306cefe6eddc3b169b49af173dde
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/dummy_config.py
@@ -0,0 +1,111 @@
+from typing import List, Tuple
+from mlagents_envs.base_env import ObservationSpec, DimensionProperty, ObservationType
+import pytest
+import copy
+import os
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    GAILSettings,
+    CuriositySettings,
+    RewardSignalSettings,
+    NetworkSettings,
+    RewardSignalType,
+    ScheduleType,
+)
+
+from mlagents.trainers.ppo.optimizer_torch import PPOSettings
+from mlagents.trainers.sac.optimizer_torch import SACSettings
+from mlagents.trainers.poca.optimizer_torch import POCASettings
+
+CONTINUOUS_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
+DISCRETE_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
+
+_PPO_CONFIG = TrainerSettings(
+    trainer_type="ppo",
+    hyperparameters=PPOSettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=16,
+        buffer_size=64,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=32),
+    summary_freq=500,
+    max_steps=3000,
+    threaded=False,
+)
+
+_SAC_CONFIG = TrainerSettings(
+    trainer_type="sac",
+    hyperparameters=SACSettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=8,
+        buffer_init_steps=100,
+        buffer_size=5000,
+        tau=0.01,
+        init_entcoef=0.01,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=16),
+    summary_freq=100,
+    max_steps=1000,
+    threaded=False,
+)
+
+_POCA_CONFIG = TrainerSettings(
+    trainer_type="poca",
+    hyperparameters=POCASettings(
+        learning_rate=5.0e-3,
+        learning_rate_schedule=ScheduleType.CONSTANT,
+        batch_size=16,
+        buffer_size=64,
+    ),
+    network_settings=NetworkSettings(num_layers=1, hidden_units=32),
+    summary_freq=500,
+    max_steps=3000,
+    threaded=False,
+)
+
+
+def ppo_dummy_config():
+    return copy.deepcopy(_PPO_CONFIG)
+
+
+def sac_dummy_config():
+    return copy.deepcopy(_SAC_CONFIG)
+
+
+def poca_dummy_config():
+    return copy.deepcopy(_POCA_CONFIG)
+
+
+@pytest.fixture
+def gail_dummy_config():
+    return {RewardSignalType.GAIL: GAILSettings(demo_path=CONTINUOUS_DEMO_PATH)}
+
+
+@pytest.fixture
+def curiosity_dummy_config():
+    return {RewardSignalType.CURIOSITY: CuriositySettings()}
+
+
+@pytest.fixture
+def extrinsic_dummy_config():
+    return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
+
+
+def create_observation_specs_with_shapes(
+    shapes: List[Tuple[int, ...]]
+) -> List[ObservationSpec]:
+    obs_specs: List[ObservationSpec] = []
+    for i, shape in enumerate(shapes):
+        dim_prop = (DimensionProperty.UNSPECIFIED,) * len(shape)
+        if len(shape) == 2:
+            dim_prop = (DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)
+        spec = ObservationSpec(
+            name=f"observation {i} with shape {shape}",
+            shape=shape,
+            dimension_property=dim_prop,
+            observation_type=ObservationType.DEFAULT,
+        )
+        obs_specs.append(spec)
+    return obs_specs
diff --git a/ml-agents/mlagents/trainers/tests/mock_brain.py b/ml-agents/mlagents/trainers/tests/mock_brain.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3857429de16b30d3961e19c33d86cfc8a176ecf
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/mock_brain.py
@@ -0,0 +1,236 @@
+from typing import List, Optional, Tuple
+import numpy as np
+
+from mlagents.trainers.buffer import AgentBuffer, AgentBufferKey
+from mlagents.trainers.torch_entities.action_log_probs import LogProbsTuple
+from mlagents.trainers.trajectory import AgentStatus, Trajectory, AgentExperience
+from mlagents_envs.base_env import (
+    DecisionSteps,
+    TerminalSteps,
+    ObservationSpec,
+    BehaviorSpec,
+    ActionSpec,
+    ActionTuple,
+)
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+def create_mock_steps(
+    num_agents: int,
+    observation_specs: List[ObservationSpec],
+    action_spec: ActionSpec,
+    done: bool = False,
+    grouped: bool = False,
+    agent_ids: Optional[List[int]] = None,
+) -> Tuple[DecisionSteps, TerminalSteps]:
+    """
+    Creates a mock Tuple[DecisionSteps, TerminalSteps] with observations.
+    Imitates constant vector/visual observations, rewards, dones, and agents.
+
+    :int num_agents: Number of "agents" to imitate.
+    :List observation_specs: A List of the observation specs in your steps
+    :int action_spec: ActionSpec for the agent
+    :bool done: Whether all the agents in the batch are done
+    """
+    obs_list = []
+    for obs_spec in observation_specs:
+        obs_list.append(np.ones((num_agents,) + obs_spec.shape, dtype=np.float32))
+    action_mask = None
+    if action_spec.is_discrete():
+        action_mask = [
+            np.array(num_agents * [action_size * [False]])
+            for action_size in action_spec.discrete_branches  # type: ignore
+        ]  # type: ignore
+
+    reward = np.array(num_agents * [1.0], dtype=np.float32)
+    interrupted = np.array(num_agents * [False], dtype=np.bool)
+    if agent_ids is not None:
+        agent_id = np.array(agent_ids, dtype=np.int32)
+    else:
+        agent_id = np.arange(num_agents, dtype=np.int32)
+    _gid = 1 if grouped else 0
+    group_id = np.array(num_agents * [_gid], dtype=np.int32)
+    group_reward = np.array(num_agents * [0.0], dtype=np.float32)
+    behavior_spec = BehaviorSpec(observation_specs, action_spec)
+    if done:
+        return (
+            DecisionSteps.empty(behavior_spec),
+            TerminalSteps(
+                obs_list, reward, interrupted, agent_id, group_id, group_reward
+            ),
+        )
+    else:
+        return (
+            DecisionSteps(
+                obs_list, reward, agent_id, action_mask, group_id, group_reward
+            ),
+            TerminalSteps.empty(behavior_spec),
+        )
+
+
+def create_steps_from_behavior_spec(
+    behavior_spec: BehaviorSpec, num_agents: int = 1
+) -> Tuple[DecisionSteps, TerminalSteps]:
+    return create_mock_steps(
+        num_agents=num_agents,
+        observation_specs=behavior_spec.observation_specs,
+        action_spec=behavior_spec.action_spec,
+    )
+
+
+def make_fake_trajectory(
+    length: int,
+    observation_specs: List[ObservationSpec],
+    action_spec: ActionSpec,
+    max_step_complete: bool = False,
+    memory_size: int = 10,
+    num_other_agents_in_group: int = 0,
+    group_reward: float = 0.0,
+    is_terminal: bool = True,
+    team_id: int = 0,
+) -> Trajectory:
+    """
+    Makes a fake trajectory of length length. If max_step_complete,
+    the trajectory is terminated by a max step rather than a done.
+    """
+    steps_list = []
+
+    action_size = action_spec.discrete_size + action_spec.continuous_size
+    for _i in range(length - 1):
+        obs = []
+        for obs_spec in observation_specs:
+            obs.append(np.ones(obs_spec.shape, dtype=np.float32))
+        reward = 1.0
+        done = False
+        action = ActionTuple(
+            continuous=np.zeros(action_spec.continuous_size, dtype=np.float32),
+            discrete=np.zeros(action_spec.discrete_size, dtype=np.int32),
+        )
+        action_probs = LogProbsTuple(
+            continuous=np.ones(action_spec.continuous_size, dtype=np.float32),
+            discrete=np.ones(action_spec.discrete_size, dtype=np.float32),
+        )
+        action_mask = (
+            [
+                [False for _ in range(branch)]
+                for branch in action_spec.discrete_branches
+            ]  # type: ignore
+            if action_spec.is_discrete()
+            else None
+        )
+        if action_spec.is_discrete():
+            prev_action = np.ones(action_size, dtype=np.int32)
+        else:
+            prev_action = np.ones(action_size, dtype=np.float32)
+
+        max_step = False
+        memory = np.ones(memory_size, dtype=np.float32)
+        agent_id = "test_agent"
+        behavior_id = "test_brain?team=" + str(team_id)
+        group_status = []
+        for _ in range(num_other_agents_in_group):
+            group_status.append(AgentStatus(obs, reward, action, done))
+        experience = AgentExperience(
+            obs=obs,
+            reward=reward,
+            done=done,
+            action=action,
+            action_probs=action_probs,
+            action_mask=action_mask,
+            prev_action=prev_action,
+            interrupted=max_step,
+            memory=memory,
+            group_status=group_status,
+            group_reward=group_reward,
+        )
+        steps_list.append(experience)
+    obs = []
+    for obs_spec in observation_specs:
+        obs.append(np.ones(obs_spec.shape, dtype=np.float32))
+    last_group_status = []
+    for _ in range(num_other_agents_in_group):
+        last_group_status.append(
+            AgentStatus(obs, reward, action, not max_step_complete and is_terminal)
+        )
+    last_experience = AgentExperience(
+        obs=obs,
+        reward=reward,
+        done=not max_step_complete and is_terminal,
+        action=action,
+        action_probs=action_probs,
+        action_mask=action_mask,
+        prev_action=prev_action,
+        interrupted=max_step_complete,
+        memory=memory,
+        group_status=last_group_status,
+        group_reward=group_reward,
+    )
+    steps_list.append(last_experience)
+    return Trajectory(
+        steps=steps_list,
+        agent_id=agent_id,
+        behavior_id=behavior_id,
+        next_obs=obs,
+        next_group_obs=[obs] * num_other_agents_in_group,
+    )
+
+
+def copy_buffer_fields(
+    buffer: AgentBuffer, src_key: AgentBufferKey, dst_keys: List[AgentBufferKey]
+) -> None:
+    for dst_key in dst_keys:
+        buffer[dst_key] = buffer[src_key]
+
+
+def simulate_rollout(
+    length: int,
+    behavior_spec: BehaviorSpec,
+    memory_size: int = 10,
+    exclude_key_list: List[str] = None,
+    num_other_agents_in_group: int = 0,
+) -> AgentBuffer:
+    trajectory = make_fake_trajectory(
+        length,
+        behavior_spec.observation_specs,
+        action_spec=behavior_spec.action_spec,
+        memory_size=memory_size,
+        num_other_agents_in_group=num_other_agents_in_group,
+    )
+    buffer = trajectory.to_agentbuffer()
+    # If a key_list was given, remove those keys
+    if exclude_key_list:
+        for key in exclude_key_list:
+            if key in buffer:
+                buffer.pop(key)
+    return buffer
+
+
+def setup_test_behavior_specs(
+    use_discrete=True, use_visual=False, vector_action_space=2, vector_obs_space=8
+):
+    if use_discrete:
+        action_spec = ActionSpec.create_discrete(tuple(vector_action_space))
+    else:
+        action_spec = ActionSpec.create_continuous(vector_action_space)
+    observation_shapes = [(84, 84, 3)] * int(use_visual) + [(vector_obs_space,)]
+    obs_spec = create_observation_specs_with_shapes(observation_shapes)
+    behavior_spec = BehaviorSpec(obs_spec, action_spec)
+    return behavior_spec
+
+
+def create_mock_3dball_behavior_specs():
+    return setup_test_behavior_specs(
+        False, False, vector_action_space=2, vector_obs_space=8
+    )
+
+
+def create_mock_pushblock_behavior_specs():
+    return setup_test_behavior_specs(
+        True, False, vector_action_space=7, vector_obs_space=70
+    )
+
+
+def create_mock_banana_behavior_specs():
+    return setup_test_behavior_specs(
+        True, True, vector_action_space=[3, 3, 3, 2], vector_obs_space=0
+    )
diff --git a/ml-agents/mlagents/trainers/tests/results/ppo/run_logs/training_status.json b/ml-agents/mlagents/trainers/tests/results/ppo/run_logs/training_status.json
new file mode 100644
index 0000000000000000000000000000000000000000..04eaa876ae88310640cde7a2d243a6d50f887c0f
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/results/ppo/run_logs/training_status.json
@@ -0,0 +1,16 @@
+{
+    "param_1": {
+        "lesson_num": 2
+    },
+    "param_2": {
+        "lesson_num": 0
+    },
+    "param_3": {
+        "lesson_num": 0
+    },
+    "metadata": {
+        "stats_format_version": "0.3.0",
+        "mlagents_version": "0.29.0",
+        "torch_version": "1.8.1"
+    }
+}
diff --git a/ml-agents/mlagents/trainers/tests/simple_test_envs.py b/ml-agents/mlagents/trainers/tests/simple_test_envs.py
new file mode 100644
index 0000000000000000000000000000000000000000..79c792fcffd9682ef64ab13f30ad1e3917e8b632
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/simple_test_envs.py
@@ -0,0 +1,621 @@
+import random
+from typing import Dict, List, Any, Tuple
+import numpy as np
+
+from mlagents_envs.base_env import (
+    ActionSpec,
+    ObservationSpec,
+    ObservationType,
+    ActionTuple,
+    BaseEnv,
+    BehaviorSpec,
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorMapping,
+)
+from .test_rpc_utils import proto_from_steps_and_action
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+OBS_SIZE = 1
+VIS_OBS_SIZE = (20, 20, 3)
+VAR_LEN_SIZE = (10, 5)
+STEP_SIZE = 0.2
+
+TIME_PENALTY = 0.01
+MIN_STEPS = int(1.0 / STEP_SIZE) + 1
+SUCCESS_REWARD = 1.0 + MIN_STEPS * TIME_PENALTY
+
+
+def clamp(x, min_val, max_val):
+    return max(min_val, min(x, max_val))
+
+
+class SimpleEnvironment(BaseEnv):
+    """
+    Very simple "game" - the agent has a position on [-1, 1], gets a reward of 1 if it reaches 1, and a reward of -1 if
+    it reaches -1. The position is incremented by the action amount (clamped to [-step_size, step_size]).
+    """
+
+    def __init__(
+        self,
+        brain_names,
+        step_size=STEP_SIZE,
+        num_visual=0,
+        num_vector=1,
+        num_var_len=0,
+        vis_obs_size=VIS_OBS_SIZE,
+        vec_obs_size=OBS_SIZE,
+        var_len_obs_size=VAR_LEN_SIZE,
+        action_sizes=(1, 0),
+        goal_indices=None,
+    ):
+        super().__init__()
+        self.num_visual = num_visual
+        self.num_vector = num_vector
+        self.num_var_len = num_var_len
+        self.vis_obs_size = vis_obs_size
+        self.vec_obs_size = vec_obs_size
+        self.var_len_obs_size = var_len_obs_size
+        self.goal_indices = goal_indices
+        continuous_action_size, discrete_action_size = action_sizes
+        discrete_tuple = tuple(2 for _ in range(discrete_action_size))
+        action_spec = ActionSpec(continuous_action_size, discrete_tuple)
+        self.total_action_size = (
+            continuous_action_size + discrete_action_size
+        )  # to set the goals/positions
+        self.action_spec = action_spec
+        self.behavior_spec = BehaviorSpec(self._make_observation_specs(), action_spec)
+        self.action_spec = action_spec
+        self.names = brain_names
+        self.positions: Dict[str, List[float]] = {}
+        self.step_count: Dict[str, float] = {}
+
+        # Concatenate the arguments for a consistent random seed
+        seed = (
+            brain_names,
+            step_size,
+            num_visual,
+            num_vector,
+            num_var_len,
+            vis_obs_size,
+            vec_obs_size,
+            var_len_obs_size,
+            action_sizes,
+        )
+        self.random = random.Random(str(seed))
+
+        self.goal: Dict[str, int] = {}
+        self.action = {}
+        self.rewards: Dict[str, float] = {}
+        self.final_rewards: Dict[str, List[float]] = {}
+        self.step_result: Dict[str, Tuple[DecisionSteps, TerminalSteps]] = {}
+        self.agent_id: Dict[str, int] = {}
+        self.step_size = step_size  # defines the difficulty of the test
+        # Allow to be used as a UnityEnvironment during tests
+        self.academy_capabilities = None
+
+        for name in self.names:
+            self.agent_id[name] = 0
+            self.goal[name] = self.random.choice([-1, 1])
+            self.rewards[name] = 0
+            self.final_rewards[name] = []
+            self._reset_agent(name)
+            self.action[name] = None
+            self.step_result[name] = None
+
+    def _make_observation_specs(self) -> List[ObservationSpec]:
+        obs_shape: List[Any] = []
+        for _ in range(self.num_vector):
+            obs_shape.append((self.vec_obs_size,))
+        for _ in range(self.num_visual):
+            obs_shape.append(self.vis_obs_size)
+        for _ in range(self.num_var_len):
+            obs_shape.append(self.var_len_obs_size)
+        obs_spec = create_observation_specs_with_shapes(obs_shape)
+        if self.goal_indices is not None:
+            for i in range(len(obs_spec)):
+                if i in self.goal_indices:
+                    obs_spec[i] = ObservationSpec(
+                        shape=obs_spec[i].shape,
+                        dimension_property=obs_spec[i].dimension_property,
+                        observation_type=ObservationType.GOAL_SIGNAL,
+                        name=obs_spec[i].name,
+                    )
+        return obs_spec
+
+    def _make_obs(self, value: float) -> List[np.ndarray]:
+        obs = []
+        for _ in range(self.num_vector):
+            obs.append(np.ones((1, self.vec_obs_size), dtype=np.float32) * value)
+        for _ in range(self.num_visual):
+            obs.append(np.ones((1,) + self.vis_obs_size, dtype=np.float32) * value)
+        for _ in range(self.num_var_len):
+            obs.append(np.ones((1,) + self.var_len_obs_size, dtype=np.float32) * value)
+        return obs
+
+    @property
+    def behavior_specs(self):
+        behavior_dict = {}
+        for n in self.names:
+            behavior_dict[n] = self.behavior_spec
+        return BehaviorMapping(behavior_dict)
+
+    def set_action_for_agent(self, behavior_name, agent_id, action):
+        pass
+
+    def set_actions(self, behavior_name, action):
+        self.action[behavior_name] = action
+
+    def get_steps(self, behavior_name):
+        return self.step_result[behavior_name]
+
+    def _take_action(self, name: str) -> bool:
+        deltas = []
+        _act = self.action[name]
+        if self.action_spec.continuous_size > 0:
+            for _cont in _act.continuous[0]:
+                deltas.append(_cont)
+        if self.action_spec.discrete_size > 0:
+            for _disc in _act.discrete[0]:
+                deltas.append(1 if _disc else -1)
+        for i, _delta in enumerate(deltas):
+            _delta = clamp(_delta, -self.step_size, self.step_size)
+            self.positions[name][i] += _delta
+            self.positions[name][i] = clamp(self.positions[name][i], -1, 1)
+            self.step_count[name] += 1
+            # Both must be in 1.0 to be done
+        done = all(pos >= 1.0 or pos <= -1.0 for pos in self.positions[name])
+        return done
+
+    def _generate_mask(self):
+        action_mask = None
+        if self.action_spec.discrete_size > 0:
+            # LL-Python API will return an empty dim if there is only 1 agent.
+            ndmask = np.array(
+                2 * self.action_spec.discrete_size * [False], dtype=np.bool
+            )
+            ndmask = np.expand_dims(ndmask, axis=0)
+            action_mask = [ndmask]
+        return action_mask
+
+    def _compute_reward(self, name: str, done: bool) -> float:
+        if done:
+            reward = 0.0
+            for _pos in self.positions[name]:
+                reward += (SUCCESS_REWARD * _pos * self.goal[name]) / len(
+                    self.positions[name]
+                )
+        else:
+            reward = -TIME_PENALTY
+        return reward
+
+    def _reset_agent(self, name):
+        self.goal[name] = self.random.choice([-1, 1])
+        self.positions[name] = [0.0 for _ in range(self.total_action_size)]
+        self.step_count[name] = 0
+        self.rewards[name] = 0
+        self.agent_id[name] = self.agent_id[name] + 1
+
+    def _make_batched_step(
+        self, name: str, done: bool, reward: float, group_reward: float
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        m_vector_obs = self._make_obs(self.goal[name])
+        m_reward = np.array([reward], dtype=np.float32)
+        m_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
+        m_group_id = np.array([0], dtype=np.int32)
+        m_group_reward = np.array([group_reward], dtype=np.float32)
+        action_mask = self._generate_mask()
+        decision_step = DecisionSteps(
+            m_vector_obs, m_reward, m_agent_id, action_mask, m_group_id, m_group_reward
+        )
+        terminal_step = TerminalSteps.empty(self.behavior_spec)
+        if done:
+            self.final_rewards[name].append(self.rewards[name])
+            self._reset_agent(name)
+            new_vector_obs = self._make_obs(self.goal[name])
+            (
+                new_reward,
+                new_done,
+                new_agent_id,
+                new_action_mask,
+                new_group_id,
+                new_group_reward,
+            ) = self._construct_reset_step(name)
+
+            decision_step = DecisionSteps(
+                new_vector_obs,
+                new_reward,
+                new_agent_id,
+                new_action_mask,
+                new_group_id,
+                new_group_reward,
+            )
+            terminal_step = TerminalSteps(
+                m_vector_obs,
+                m_reward,
+                np.array([False], dtype=np.bool),
+                m_agent_id,
+                m_group_id,
+                m_group_reward,
+            )
+        return (decision_step, terminal_step)
+
+    def _construct_reset_step(
+        self, name: str
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+        new_reward = np.array([0.0], dtype=np.float32)
+        new_done = np.array([False], dtype=np.bool)
+        new_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
+        new_action_mask = self._generate_mask()
+        new_group_id = np.array([0], dtype=np.int32)
+        new_group_reward = np.array([0.0], dtype=np.float32)
+        return (
+            new_reward,
+            new_done,
+            new_agent_id,
+            new_action_mask,
+            new_group_id,
+            new_group_reward,
+        )
+
+    def step(self) -> None:
+        assert all(action is not None for action in self.action.values())
+        for name in self.names:
+
+            done = self._take_action(name)
+            reward = self._compute_reward(name, done)
+            self.rewards[name] += reward
+            self.step_result[name] = self._make_batched_step(name, done, reward, 0.0)
+
+    def reset(self) -> None:  # type: ignore
+        for name in self.names:
+            self._reset_agent(name)
+            self.step_result[name] = self._make_batched_step(name, False, 0.0, 0.0)
+
+    @property
+    def reset_parameters(self) -> Dict[str, str]:
+        return {}
+
+    def close(self):
+        pass
+
+
+class MemoryEnvironment(SimpleEnvironment):
+    def __init__(self, brain_names, action_sizes=(1, 0), step_size=0.2):
+        super().__init__(brain_names, action_sizes=action_sizes, step_size=step_size)
+        # Number of steps to reveal the goal for. Lower is harder. Should be
+        # less than 1/step_size to force agent to use memory
+        self.num_show_steps = 2
+
+    def _make_batched_step(
+        self, name: str, done: bool, reward: float, group_reward: float
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        recurrent_obs_val = (
+            self.goal[name] if self.step_count[name] <= self.num_show_steps else 0
+        )
+        m_vector_obs = self._make_obs(recurrent_obs_val)
+        m_reward = np.array([reward], dtype=np.float32)
+        m_agent_id = np.array([self.agent_id[name]], dtype=np.int32)
+        m_group_id = np.array([0], dtype=np.int32)
+        m_group_reward = np.array([group_reward], dtype=np.float32)
+        action_mask = self._generate_mask()
+        decision_step = DecisionSteps(
+            m_vector_obs, m_reward, m_agent_id, action_mask, m_group_id, m_group_reward
+        )
+        terminal_step = TerminalSteps.empty(self.behavior_spec)
+        if done:
+            self.final_rewards[name].append(self.rewards[name])
+            self._reset_agent(name)
+            recurrent_obs_val = (
+                self.goal[name] if self.step_count[name] <= self.num_show_steps else 0
+            )
+            new_vector_obs = self._make_obs(recurrent_obs_val)
+            (
+                new_reward,
+                new_done,
+                new_agent_id,
+                new_action_mask,
+                new_group_id,
+                new_group_reward,
+            ) = self._construct_reset_step(name)
+            decision_step = DecisionSteps(
+                new_vector_obs,
+                new_reward,
+                new_agent_id,
+                new_action_mask,
+                new_group_id,
+                new_group_reward,
+            )
+            terminal_step = TerminalSteps(
+                m_vector_obs,
+                m_reward,
+                np.array([False], dtype=np.bool),
+                m_agent_id,
+                m_group_id,
+                m_group_reward,
+            )
+        return (decision_step, terminal_step)
+
+
+class MultiAgentEnvironment(BaseEnv):
+    """
+    The MultiAgentEnvironment maintains a list of SimpleEnvironment, one for each agent.
+    When sending DecisionSteps and TerminalSteps to the trainers, it first batches the
+    decision steps from the individual environments. When setting actions, it indexes the
+    batched ActionTuple to obtain the ActionTuple for individual agents
+    """
+
+    def __init__(
+        self,
+        brain_names,
+        step_size=STEP_SIZE,
+        num_visual=0,
+        num_vector=1,
+        num_var_len=0,
+        vis_obs_size=VIS_OBS_SIZE,
+        vec_obs_size=OBS_SIZE,
+        var_len_obs_size=VAR_LEN_SIZE,
+        action_sizes=(1, 0),
+        num_agents=2,
+        goal_indices=None,
+    ):
+        super().__init__()
+        self.envs = {}
+        self.dones = {}
+        self.just_died = set()
+        self.names = brain_names
+        self.final_rewards: Dict[str, List[float]] = {}
+        for name in brain_names:
+            self.final_rewards[name] = []
+            for i in range(num_agents):
+                name_and_num = name + str(i)
+                self.envs[name_and_num] = SimpleEnvironment(
+                    [name],
+                    step_size,
+                    num_visual,
+                    num_vector,
+                    num_var_len,
+                    vis_obs_size,
+                    vec_obs_size,
+                    var_len_obs_size,
+                    action_sizes,
+                    goal_indices,
+                )
+                self.dones[name_and_num] = False
+                self.envs[name_and_num].reset()
+        # All envs have the same behavior spec, so just get the last one.
+        self.behavior_spec = self.envs[name_and_num].behavior_spec
+        self.action_spec = self.envs[name_and_num].action_spec
+        self.num_agents = num_agents
+
+    @property
+    def all_done(self):
+        return all(self.dones.values())
+
+    @property
+    def behavior_specs(self):
+        behavior_dict = {}
+        for n in self.names:
+            behavior_dict[n] = self.behavior_spec
+        return BehaviorMapping(behavior_dict)
+
+    def set_action_for_agent(self, behavior_name, agent_id, action):
+        pass
+
+    def set_actions(self, behavior_name, action):
+        # The ActionTuple contains the actions for all n_agents. This
+        # slices the ActionTuple into an action tuple for each environment
+        # and sets it. The index j is used to ignore agents that have already
+        # reached done.
+        j = 0
+        for i in range(self.num_agents):
+            _act = ActionTuple()
+            name_and_num = behavior_name + str(i)
+            env = self.envs[name_and_num]
+            if not self.dones[name_and_num]:
+                if self.action_spec.continuous_size > 0:
+                    _act.add_continuous(action.continuous[j : j + 1])
+                if self.action_spec.discrete_size > 0:
+                    _disc_list = [action.discrete[j, :]]
+                    _act.add_discrete(np.array(_disc_list))
+                j += 1
+                env.action[behavior_name] = _act
+
+    def get_steps(self, behavior_name):
+        # This gets the individual DecisionSteps and TerminalSteps
+        # from the envs and merges them into a batch to be sent
+        # to the AgentProcessor.
+        dec_vec_obs = []
+        dec_reward = []
+        dec_group_reward = []
+        dec_agent_id = []
+        dec_group_id = []
+        ter_vec_obs = []
+        ter_reward = []
+        ter_group_reward = []
+        ter_agent_id = []
+        ter_group_id = []
+        interrupted = []
+
+        action_mask = None
+        terminal_step = TerminalSteps.empty(self.behavior_spec)
+        decision_step = None
+        for i in range(self.num_agents):
+            name_and_num = behavior_name + str(i)
+            env = self.envs[name_and_num]
+            _dec, _term = env.step_result[behavior_name]
+            if not self.dones[name_and_num]:
+                dec_agent_id.append(i)
+                dec_group_id.append(1)
+                if len(dec_vec_obs) > 0:
+                    for j, obs in enumerate(_dec.obs):
+                        dec_vec_obs[j] = np.concatenate((dec_vec_obs[j], obs), axis=0)
+                else:
+                    for obs in _dec.obs:
+                        dec_vec_obs.append(obs)
+                dec_reward.append(_dec.reward[0])
+                dec_group_reward.append(_dec.group_reward[0])
+                if _dec.action_mask is not None:
+                    if action_mask is None:
+                        action_mask = []
+                    if len(action_mask) > 0:
+                        action_mask[0] = np.concatenate(
+                            (action_mask[0], _dec.action_mask[0]), axis=0
+                        )
+                    else:
+                        action_mask.append(_dec.action_mask[0])
+            if len(_term.reward) > 0 and name_and_num in self.just_died:
+                ter_agent_id.append(i)
+                ter_group_id.append(1)
+                if len(ter_vec_obs) > 0:
+                    for j, obs in enumerate(_term.obs):
+                        ter_vec_obs[j] = np.concatenate((ter_vec_obs[j], obs), axis=0)
+                else:
+                    for obs in _term.obs:
+                        ter_vec_obs.append(obs)
+                ter_reward.append(_term.reward[0])
+                ter_group_reward.append(_term.group_reward[0])
+                interrupted.append(False)
+                self.just_died.remove(name_and_num)
+        decision_step = DecisionSteps(
+            dec_vec_obs,
+            dec_reward,
+            dec_agent_id,
+            action_mask,
+            dec_group_id,
+            dec_group_reward,
+        )
+        terminal_step = TerminalSteps(
+            ter_vec_obs,
+            ter_reward,
+            interrupted,
+            ter_agent_id,
+            ter_group_id,
+            ter_group_reward,
+        )
+        return (decision_step, terminal_step)
+
+    def step(self) -> None:
+        # Steps all environments and calls reset if all agents are done.
+        for name in self.names:
+            for i in range(self.num_agents):
+                name_and_num = name + str(i)
+                # Does not step the env if done
+                if not self.dones[name_and_num]:
+                    env = self.envs[name_and_num]
+                    # Reproducing part of env step to intercept Dones
+                    assert all(action is not None for action in env.action.values())
+                    done = env._take_action(name)
+                    reward = env._compute_reward(name, done)
+                    self.dones[name_and_num] = done
+                    if done:
+                        self.just_died.add(name_and_num)
+                    if self.all_done:
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, 0.0, reward
+                        )
+                        self.final_rewards[name].append(reward)
+                        self.reset()
+                    elif done:
+                        # This agent has finished but others are still running.
+                        # This gives a reward of the time penalty if this agent
+                        # is successful and the negative env reward if it fails.
+                        ceil_reward = min(-TIME_PENALTY, reward)
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, ceil_reward, 0.0
+                        )
+                        self.final_rewards[name].append(reward)
+
+                    else:
+                        env.step_result[name] = env._make_batched_step(
+                            name, done, reward, 0.0
+                        )
+
+    def reset(self) -> None:  # type: ignore
+        for name in self.names:
+            for i in range(self.num_agents):
+                name_and_num = name + str(i)
+                self.dones[name_and_num] = False
+
+    @property
+    def reset_parameters(self) -> Dict[str, str]:
+        return {}
+
+    def close(self):
+        pass
+
+
+class RecordEnvironment(SimpleEnvironment):
+    def __init__(
+        self,
+        brain_names,
+        step_size=0.2,
+        num_visual=0,
+        num_vector=1,
+        action_sizes=(1, 0),
+        n_demos=30,
+    ):
+        super().__init__(
+            brain_names,
+            step_size=step_size,
+            num_visual=num_visual,
+            num_vector=num_vector,
+            action_sizes=action_sizes,
+        )
+        self.demonstration_protos: Dict[str, List[AgentInfoActionPairProto]] = {}
+        self.n_demos = n_demos
+        for name in self.names:
+            self.demonstration_protos[name] = []
+
+    def step(self) -> None:
+        super().step()
+        for name in self.names:
+            discrete_actions = (
+                self.action[name].discrete
+                if self.action_spec.discrete_size > 0
+                else None
+            )
+            continuous_actions = (
+                self.action[name].continuous
+                if self.action_spec.continuous_size > 0
+                else None
+            )
+            self.demonstration_protos[name] += proto_from_steps_and_action(
+                self.step_result[name][0],
+                self.step_result[name][1],
+                continuous_actions,
+                discrete_actions,
+            )
+            self.demonstration_protos[name] = self.demonstration_protos[name][
+                -self.n_demos :
+            ]
+
+    def solve(self) -> None:
+        self.reset()
+        for _ in range(self.n_demos):
+            for name in self.names:
+                if self.action_spec.discrete_size > 0:
+                    self.action[name] = ActionTuple(
+                        np.array([], dtype=np.float32),
+                        np.array(
+                            [[1]] if self.goal[name] > 0 else [[0]], dtype=np.int32
+                        ),
+                    )
+                else:
+                    self.action[name] = ActionTuple(
+                        np.array([[float(self.goal[name])]], dtype=np.float32),
+                        np.array([], dtype=np.int32),
+                    )
+            self.step()
+
+
+class UnexpectedExceptionEnvironment(SimpleEnvironment):
+    def __init__(self, brain_names, use_discrete, to_raise):
+        super().__init__(brain_names, use_discrete)
+        self.to_raise = to_raise
+
+    def step(self) -> None:
+        raise self.to_raise()
diff --git a/ml-agents/mlagents/trainers/tests/test.demo b/ml-agents/mlagents/trainers/tests/test.demo
new file mode 100644
index 0000000000000000000000000000000000000000..8f188ddd58f2d6daa6f4703466db51e2ea3ce714
Binary files /dev/null and b/ml-agents/mlagents/trainers/tests/test.demo differ
diff --git a/ml-agents/mlagents/trainers/tests/test_agent_processor.py b/ml-agents/mlagents/trainers/tests/test_agent_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..43e1008f02a922c1a4188bbd3bf0f91fd258058b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_agent_processor.py
@@ -0,0 +1,386 @@
+from unittest import mock
+import pytest
+from typing import List
+import mlagents.trainers.tests.mock_brain as mb
+import numpy as np
+from mlagents.trainers.agent_processor import (
+    AgentProcessor,
+    AgentManager,
+    AgentManagerQueue,
+)
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.torch_entities.action_log_probs import LogProbsTuple
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.stats import StatsReporter, StatsSummary
+from mlagents.trainers.behavior_id_utils import get_global_agent_id
+from mlagents_envs.side_channel.stats_side_channel import StatsAggregationMethod
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+from mlagents_envs.base_env import ActionSpec, ActionTuple
+
+
+def create_mock_policy():
+    mock_policy = mock.Mock()
+    mock_policy.reward_signals = {}
+    mock_policy.retrieve_previous_memories.return_value = np.zeros(
+        (1, 1), dtype=np.float32
+    )
+    mock_policy.retrieve_previous_action.return_value = np.zeros((1, 1), dtype=np.int32)
+    return mock_policy
+
+
+def _create_action_info(num_agents: int, agent_ids: List[str]) -> ActionInfo:
+    fake_action_outputs = {
+        "action": ActionTuple(
+            continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
+        ),
+        "entropy": np.array([1.0], dtype=np.float32),
+        "learning_rate": 1.0,
+        "log_probs": LogProbsTuple(
+            continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
+        ),
+    }
+    fake_action_info = ActionInfo(
+        action=ActionTuple(continuous=np.array([[0.1]] * num_agents, dtype=np.float32)),
+        env_action=ActionTuple(
+            continuous=np.array([[0.1]] * num_agents, dtype=np.float32)
+        ),
+        outputs=fake_action_outputs,
+        agent_ids=agent_ids,
+    )
+    return fake_action_info
+
+
+@pytest.mark.parametrize("num_vis_obs", [0, 1, 2], ids=["vec", "1 viz", "2 viz"])
+def test_agentprocessor(num_vis_obs):
+    policy = create_mock_policy()
+    tqueue = mock.Mock()
+    name_behavior_id = "test_brain_name"
+    processor = AgentProcessor(
+        policy,
+        name_behavior_id,
+        max_trajectory_length=5,
+        stats_reporter=StatsReporter("testcat"),
+    )
+
+    mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
+        num_agents=2,
+        observation_specs=create_observation_specs_with_shapes(
+            [(8,)] + num_vis_obs * [(84, 84, 3)]
+        ),
+        action_spec=ActionSpec.create_continuous(2),
+    )
+    fake_action_info = _create_action_info(2, mock_decision_steps.agent_id)
+    processor.publish_trajectory_queue(tqueue)
+    # This is like the initial state after the env reset
+    processor.add_experiences(
+        mock_decision_steps, mock_terminal_steps, 0, ActionInfo.empty()
+    )
+    for _ in range(5):
+        processor.add_experiences(
+            mock_decision_steps, mock_terminal_steps, 0, fake_action_info
+        )
+
+    # Assert that two trajectories have been added to the Trainer
+    assert len(tqueue.put.call_args_list) == 2
+
+    # Assert that the trajectory is of length 5
+    trajectory = tqueue.put.call_args_list[0][0][0]
+    assert len(trajectory.steps) == 5
+    # Make sure ungrouped agents don't have team obs
+    for step in trajectory.steps:
+        assert len(step.group_status) == 0
+
+    # Assert that the AgentProcessor is empty
+    assert len(processor._experience_buffers[0]) == 0
+
+    # Test empty steps
+    mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
+        num_agents=0,
+        observation_specs=create_observation_specs_with_shapes(
+            [(8,)] + num_vis_obs * [(84, 84, 3)]
+        ),
+        action_spec=ActionSpec.create_continuous(2),
+    )
+    processor.add_experiences(
+        mock_decision_steps, mock_terminal_steps, 0, ActionInfo.empty()
+    )
+    # Assert that the AgentProcessor is still empty
+    assert len(processor._experience_buffers[0]) == 0
+
+
+def test_group_statuses():
+    policy = create_mock_policy()
+    tqueue = mock.Mock()
+    name_behavior_id = "test_brain_name"
+    processor = AgentProcessor(
+        policy,
+        name_behavior_id,
+        max_trajectory_length=5,
+        stats_reporter=StatsReporter("testcat"),
+    )
+
+    mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
+        num_agents=4,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+        grouped=True,
+    )
+    fake_action_info = _create_action_info(4, mock_decision_steps.agent_id)
+    processor.publish_trajectory_queue(tqueue)
+    # This is like the initial state after the env reset
+    processor.add_experiences(
+        mock_decision_steps, mock_terminal_steps, 0, ActionInfo.empty()
+    )
+    for _ in range(2):
+        processor.add_experiences(
+            mock_decision_steps, mock_terminal_steps, 0, fake_action_info
+        )
+
+    # Make terminal steps for some dead agents
+    _, mock_terminal_steps_2 = mb.create_mock_steps(
+        num_agents=2,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+        done=True,
+        grouped=True,
+        agent_ids=[2, 3],
+    )
+    # Make decision steps continue for other agents
+    mock_decision_steps_2, _ = mb.create_mock_steps(
+        num_agents=2,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+        done=False,
+        grouped=True,
+        agent_ids=[0, 1],
+    )
+
+    processor.add_experiences(
+        mock_decision_steps_2, mock_terminal_steps_2, 0, fake_action_info
+    )
+    # Continue to add for remaining live agents
+    fake_action_info = _create_action_info(4, mock_decision_steps_2.agent_id)
+    for _ in range(3):
+        processor.add_experiences(
+            mock_decision_steps_2, mock_terminal_steps, 0, fake_action_info
+        )
+
+    # Assert that four trajectories have been added to the Trainer
+    assert len(tqueue.put.call_args_list) == 4
+
+    # Get the first trajectory, which should have been agent 2 (one of the killed agents)
+    trajectory = tqueue.put.call_args_list[0][0][-1]
+    assert len(trajectory.steps) == 3
+    # Make sure trajectory has the right Groupmate Experiences.
+    # All three steps should contain all agents
+    for step in trajectory.steps:
+        assert len(step.group_status) == 3
+
+    # Last trajectory should be the longest. It should be that of agent 1, one of the surviving agents.
+    trajectory = tqueue.put.call_args_list[-1][0][-1]
+    assert len(trajectory.steps) == 5
+
+    # Make sure trajectory has the right Groupmate Experiences.
+    # THe first 3 steps should contain all of the obs (that 3rd step is also the terminal step of 2 of the agents)
+    for step in trajectory.steps[0:3]:
+        assert len(step.group_status) == 3
+    # After 2 agents has died, there should only be 1 group status.
+    for step in trajectory.steps[3:]:
+        assert len(step.group_status) == 1
+
+
+def test_agent_deletion():
+    policy = create_mock_policy()
+    tqueue = mock.Mock()
+    name_behavior_id = "test_brain_name"
+    processor = AgentProcessor(
+        policy,
+        name_behavior_id,
+        max_trajectory_length=5,
+        stats_reporter=StatsReporter("testcat"),
+    )
+    fake_action_outputs = {
+        "action": ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        "entropy": np.array([1.0], dtype=np.float32),
+        "learning_rate": 1.0,
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+    }
+
+    mock_decision_step, mock_terminal_step = mb.create_mock_steps(
+        num_agents=1,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+    )
+    mock_done_decision_step, mock_done_terminal_step = mb.create_mock_steps(
+        num_agents=1,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+        done=True,
+    )
+    fake_action_info = ActionInfo(
+        action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        env_action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        outputs=fake_action_outputs,
+        agent_ids=mock_decision_step.agent_id,
+    )
+
+    processor.publish_trajectory_queue(tqueue)
+    # This is like the initial state after the env reset
+    processor.add_experiences(
+        mock_decision_step, mock_terminal_step, 0, ActionInfo.empty()
+    )
+
+    # Run 3 trajectories, with different workers (to simulate different agents)
+    add_calls = []
+    remove_calls = []
+    for _ep in range(3):
+        for _ in range(5):
+            processor.add_experiences(
+                mock_decision_step, mock_terminal_step, _ep, fake_action_info
+            )
+            add_calls.append(
+                mock.call([get_global_agent_id(_ep, 0)], fake_action_outputs["action"])
+            )
+        processor.add_experiences(
+            mock_done_decision_step, mock_done_terminal_step, _ep, fake_action_info
+        )
+        # Make sure we don't add experiences from the prior agents after the done
+        remove_calls.append(mock.call([get_global_agent_id(_ep, 0)]))
+
+    policy.save_previous_action.assert_has_calls(add_calls)
+    policy.remove_previous_action.assert_has_calls(remove_calls)
+    # Check that there are no experiences left
+    assert len(processor._experience_buffers.keys()) == 0
+    assert len(processor._last_take_action_outputs.keys()) == 0
+    assert len(processor._episode_steps.keys()) == 0
+    assert len(processor._episode_rewards.keys()) == 0
+    assert len(processor._last_step_result.keys()) == 0
+
+    # check that steps with immediate dones don't add to dicts
+    processor.add_experiences(
+        mock_done_decision_step, mock_done_terminal_step, 0, ActionInfo.empty()
+    )
+    assert len(processor._experience_buffers.keys()) == 0
+    assert len(processor._last_take_action_outputs.keys()) == 0
+    assert len(processor._episode_steps.keys()) == 0
+    assert len(processor._episode_rewards.keys()) == 0
+    assert len(processor._last_step_result.keys()) == 0
+
+
+def test_end_episode():
+    policy = create_mock_policy()
+    tqueue = mock.Mock()
+    name_behavior_id = "test_brain_name"
+    processor = AgentProcessor(
+        policy,
+        name_behavior_id,
+        max_trajectory_length=5,
+        stats_reporter=StatsReporter("testcat"),
+    )
+    fake_action_outputs = {
+        "action": ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        "entropy": np.array([1.0], dtype=np.float32),
+        "learning_rate": 1.0,
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+    }
+
+    mock_decision_step, mock_terminal_step = mb.create_mock_steps(
+        num_agents=1,
+        observation_specs=create_observation_specs_with_shapes([(8,)]),
+        action_spec=ActionSpec.create_continuous(2),
+    )
+    fake_action_info = ActionInfo(
+        action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        env_action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        outputs=fake_action_outputs,
+        agent_ids=mock_decision_step.agent_id,
+    )
+
+    processor.publish_trajectory_queue(tqueue)
+    # This is like the initial state after the env reset
+    processor.add_experiences(
+        mock_decision_step, mock_terminal_step, 0, ActionInfo.empty()
+    )
+    # Run 3 trajectories, with different workers (to simulate different agents)
+    remove_calls = []
+    for _ep in range(3):
+        remove_calls.append(mock.call([get_global_agent_id(_ep, 0)]))
+        for _ in range(5):
+            processor.add_experiences(
+                mock_decision_step, mock_terminal_step, _ep, fake_action_info
+            )
+            # Make sure we don't add experiences from the prior agents after the done
+
+    # Call end episode
+    processor.end_episode()
+    # Check that we removed every agent
+    policy.remove_previous_action.assert_has_calls(remove_calls)
+    # Check that there are no experiences left
+    assert len(processor._experience_buffers.keys()) == 0
+    assert len(processor._last_take_action_outputs.keys()) == 0
+    assert len(processor._episode_steps.keys()) == 0
+    assert len(processor._episode_rewards.keys()) == 0
+
+
+def test_agent_manager():
+    policy = create_mock_policy()
+    name_behavior_id = "test_brain_name"
+    manager = AgentManager(
+        policy,
+        name_behavior_id,
+        max_trajectory_length=5,
+        stats_reporter=StatsReporter("testcat"),
+    )
+    assert len(manager._trajectory_queues) == 1
+    assert isinstance(manager._trajectory_queues[0], AgentManagerQueue)
+
+
+def test_agent_manager_queue():
+    queue = AgentManagerQueue(behavior_id="testbehavior")
+    trajectory = mock.Mock(spec=Trajectory)
+    assert queue.empty()
+    queue.put(trajectory)
+    assert not queue.empty()
+    queue_traj = queue.get_nowait()
+    assert isinstance(queue_traj, Trajectory)
+    assert queue.empty()
+
+
+def test_agent_manager_stats():
+    policy = mock.Mock()
+    stats_reporter = StatsReporter("FakeCategory")
+    writer = mock.Mock()
+    stats_reporter.add_writer(writer)
+    manager = AgentManager(policy, "MyBehavior", stats_reporter)
+
+    all_env_stats = [
+        {
+            "averaged": [(1.0, StatsAggregationMethod.AVERAGE)],
+            "most_recent": [(2.0, StatsAggregationMethod.MOST_RECENT)],
+            "summed": [(3.1, StatsAggregationMethod.SUM)],
+        },
+        {
+            "averaged": [(3.0, StatsAggregationMethod.AVERAGE)],
+            "most_recent": [(4.0, StatsAggregationMethod.MOST_RECENT)],
+            "summed": [(1.1, StatsAggregationMethod.SUM)],
+        },
+    ]
+    for env_stats in all_env_stats:
+        manager.record_environment_stats(env_stats, worker_id=0)
+
+    expected_stats = {
+        "averaged": StatsSummary(
+            full_dist=[1.0, 3.0], aggregation_method=StatsAggregationMethod.AVERAGE
+        ),
+        "most_recent": StatsSummary(
+            full_dist=[4.0], aggregation_method=StatsAggregationMethod.MOST_RECENT
+        ),
+        "summed": StatsSummary(
+            full_dist=[3.1, 1.1], aggregation_method=StatsAggregationMethod.SUM
+        ),
+    }
+    stats_reporter.write_stats(123)
+    writer.write_stats.assert_any_call("FakeCategory", expected_stats, 123)
+
+    # clean up our Mock from the global list
+    StatsReporter.writers.remove(writer)
diff --git a/ml-agents/mlagents/trainers/tests/test_buffer.py b/ml-agents/mlagents/trainers/tests/test_buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e831253c572dfe8c6b760e5c61879b4315de27d8
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_buffer.py
@@ -0,0 +1,282 @@
+import numpy as np
+from mlagents.trainers.buffer import (
+    AgentBuffer,
+    AgentBufferField,
+    BufferKey,
+    ObservationKeyPrefix,
+    RewardSignalKeyPrefix,
+)
+from mlagents.trainers.trajectory import ObsUtil
+
+
+def assert_array(a, b):
+    assert a.shape == b.shape
+    la = list(a.flatten())
+    lb = list(b.flatten())
+    for i in range(len(la)):
+        assert la[i] == lb[i]
+
+
+def construct_fake_buffer(fake_agent_id):
+    b = AgentBuffer()
+    for step in range(9):
+        b[ObsUtil.get_name_at(0)].append(
+            np.array(
+                [
+                    100 * fake_agent_id + 10 * step + 1,
+                    100 * fake_agent_id + 10 * step + 2,
+                    100 * fake_agent_id + 10 * step + 3,
+                ],
+                dtype=np.float32,
+            )
+        )
+        b[BufferKey.CONTINUOUS_ACTION].append(
+            np.array(
+                [
+                    100 * fake_agent_id + 10 * step + 4,
+                    100 * fake_agent_id + 10 * step + 5,
+                ],
+                dtype=np.float32,
+            )
+        )
+        b[BufferKey.GROUP_CONTINUOUS_ACTION].append(
+            [
+                np.array(
+                    [
+                        100 * fake_agent_id + 10 * step + 4,
+                        100 * fake_agent_id + 10 * step + 5,
+                    ],
+                    dtype=np.float32,
+                )
+            ]
+            * 3
+        )
+    return b
+
+
+def test_buffer():
+    agent_1_buffer = construct_fake_buffer(1)
+    agent_2_buffer = construct_fake_buffer(2)
+    agent_3_buffer = construct_fake_buffer(3)
+
+    # Test get_batch
+    a = agent_1_buffer[ObsUtil.get_name_at(0)].get_batch(
+        batch_size=2, training_length=1, sequential=True
+    )
+    assert_array(
+        np.array(a), np.array([[171, 172, 173], [181, 182, 183]], dtype=np.float32)
+    )
+
+    # Test get_batch
+    a = agent_2_buffer[ObsUtil.get_name_at(0)].get_batch(
+        batch_size=2, training_length=3, sequential=True
+    )
+    assert_array(
+        np.array(a),
+        np.array(
+            [
+                [231, 232, 233],
+                [241, 242, 243],
+                [251, 252, 253],
+                [261, 262, 263],
+                [271, 272, 273],
+                [281, 282, 283],
+            ],
+            dtype=np.float32,
+        ),
+    )
+    a = agent_2_buffer[ObsUtil.get_name_at(0)].get_batch(
+        batch_size=2, training_length=3, sequential=False
+    )
+    assert_array(
+        np.array(a),
+        np.array(
+            [
+                [251, 252, 253],
+                [261, 262, 263],
+                [271, 272, 273],
+                [261, 262, 263],
+                [271, 272, 273],
+                [281, 282, 283],
+            ]
+        ),
+    )
+
+    # Test padding
+    a = agent_2_buffer[ObsUtil.get_name_at(0)].get_batch(
+        batch_size=None, training_length=4, sequential=True
+    )
+    assert_array(
+        np.array(a),
+        np.array(
+            [
+                [201, 202, 203],
+                [211, 212, 213],
+                [221, 222, 223],
+                [231, 232, 233],
+                [241, 242, 243],
+                [251, 252, 253],
+                [261, 262, 263],
+                [271, 272, 273],
+                [281, 282, 283],
+                [0, 0, 0],
+                [0, 0, 0],
+                [0, 0, 0],
+            ]
+        ),
+    )
+    # Test group entries return Lists of Lists. Make sure to pad properly!
+    a = agent_2_buffer[BufferKey.GROUP_CONTINUOUS_ACTION].get_batch(
+        batch_size=None, training_length=4, sequential=True
+    )
+    for _group_entry in a[:-3]:
+        assert len(_group_entry) == 3
+    for _group_entry in a[-3:]:
+        assert len(_group_entry) == 0
+
+    agent_1_buffer.reset_agent()
+    assert agent_1_buffer.num_experiences == 0
+    update_buffer = AgentBuffer()
+    agent_2_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    agent_3_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    assert len(update_buffer[BufferKey.CONTINUOUS_ACTION]) == 20
+
+    assert np.array(update_buffer[BufferKey.CONTINUOUS_ACTION]).shape == (20, 2)
+
+    c = update_buffer.make_mini_batch(start=0, end=1)
+    assert c.keys() == update_buffer.keys()
+    # Make sure the values of c are AgentBufferField
+    for val in c.values():
+        assert isinstance(val, AgentBufferField)
+    assert np.array(c[BufferKey.CONTINUOUS_ACTION]).shape == (1, 2)
+
+
+def test_agentbufferfield():
+    # Test constructor
+    a = AgentBufferField([0, 1, 2])
+    for i, num in enumerate(a):
+        assert num == i
+        # Test indexing
+        assert a[i] == num
+
+    # Test slicing
+    b = a[1:3]
+    assert b == [1, 2]
+    assert isinstance(b, AgentBufferField)
+
+    # Test padding
+    c = AgentBufferField()
+    for _ in range(2):
+        c.append([np.array(1), np.array(2)])
+
+    for _ in range(2):
+        c.append([np.array(1)])
+
+    padded = c.padded_to_batch(pad_value=3)
+    assert np.array_equal(padded[0], np.array([1, 1, 1, 1]))
+    assert np.array_equal(padded[1], np.array([2, 2, 3, 3]))
+
+    # Make sure it doesn't fail when the field isn't a list
+    padded_a = a.padded_to_batch()
+    assert np.array_equal(padded_a, a)
+
+
+def fakerandint(values):
+    return 19
+
+
+def test_buffer_sample():
+    agent_1_buffer = construct_fake_buffer(1)
+    agent_2_buffer = construct_fake_buffer(2)
+    update_buffer = AgentBuffer()
+    agent_1_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    agent_2_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    # Test non-LSTM
+    mb = update_buffer.sample_mini_batch(batch_size=4, sequence_length=1)
+    assert mb.keys() == update_buffer.keys()
+    assert np.array(mb[BufferKey.CONTINUOUS_ACTION]).shape == (4, 2)
+
+    # Test LSTM
+    # We need to check if we ever get a breaking start - this will maximize the probability
+    mb = update_buffer.sample_mini_batch(batch_size=20, sequence_length=19)
+    assert mb.keys() == update_buffer.keys()
+    # Should only return one sequence
+    assert np.array(mb[BufferKey.CONTINUOUS_ACTION]).shape == (19, 2)
+
+
+def test_num_experiences():
+    agent_1_buffer = construct_fake_buffer(1)
+    agent_2_buffer = construct_fake_buffer(2)
+    update_buffer = AgentBuffer()
+
+    assert len(update_buffer[BufferKey.CONTINUOUS_ACTION]) == 0
+    assert update_buffer.num_experiences == 0
+    agent_1_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    agent_2_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+
+    assert len(update_buffer[BufferKey.CONTINUOUS_ACTION]) == 20
+    assert update_buffer.num_experiences == 20
+
+
+def test_buffer_truncate():
+    agent_1_buffer = construct_fake_buffer(1)
+    agent_2_buffer = construct_fake_buffer(2)
+    update_buffer = AgentBuffer()
+    agent_1_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    agent_2_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    # Test non-LSTM
+    update_buffer.truncate(2)
+    assert update_buffer.num_experiences == 2
+
+    agent_1_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    agent_2_buffer.resequence_and_append(
+        update_buffer, batch_size=None, training_length=2
+    )
+    # Test LSTM, truncate should be some multiple of sequence_length
+    update_buffer.truncate(4, sequence_length=3)
+    assert update_buffer.num_experiences == 3
+    for buffer_field in update_buffer.values():
+        assert isinstance(buffer_field, AgentBufferField)
+
+
+def test_key_encode_decode():
+    keys = (
+        list(BufferKey)
+        + [(k, 42) for k in ObservationKeyPrefix]
+        + [(k, "gail") for k in RewardSignalKeyPrefix]
+    )
+    for k in keys:
+        assert k == AgentBuffer._decode_key(AgentBuffer._encode_key(k))
+
+
+def test_buffer_save_load():
+    original = construct_fake_buffer(3)
+    import io
+
+    write_buffer = io.BytesIO()
+    original.save_to_file(write_buffer)
+
+    loaded = AgentBuffer()
+    loaded.load_from_file(write_buffer)
+
+    assert len(original) == len(loaded)
+    for k in original.keys():
+        assert np.allclose(original[k], loaded[k])
diff --git a/ml-agents/mlagents/trainers/tests/test_config_conversion.py b/ml-agents/mlagents/trainers/tests/test_config_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9a49f6cb2b60007d7b535d9c4e7c53c0d936e29
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_config_conversion.py
@@ -0,0 +1,219 @@
+import yaml
+import pytest
+
+from mlagents.trainers.upgrade_config import convert_behaviors, remove_nones, convert
+from mlagents.trainers.settings import RewardSignalType
+from mlagents.trainers.ppo.trainer import PPOSettings, TRAINER_NAME as PPO_TRAINER_NAME
+from mlagents.trainers.sac.trainer import SACSettings, TRAINER_NAME as SAC_TRAINER_NAME
+
+
+BRAIN_NAME = "testbehavior"
+
+# Check one per category
+BATCH_SIZE = 256
+HIDDEN_UNITS = 32
+SUMMARY_FREQ = 500
+
+PPO_CONFIG = f"""
+    default:
+        trainer: ppo
+        batch_size: 1024
+        beta: 5.0e-3
+        buffer_size: 10240
+        epsilon: 0.2
+        hidden_units: 128
+        lambd: 0.95
+        learning_rate: 3.0e-4
+        learning_rate_schedule: linear
+        beta_schedule: constant
+        epsilon_schedule: linear
+        max_steps: 5.0e5
+        memory_size: 256
+        normalize: false
+        num_epoch: 3
+        num_layers: 2
+        time_horizon: 64
+        sequence_length: 64
+        summary_freq: 10000
+        use_recurrent: false
+        vis_encode_type: simple
+        reward_signals:
+            extrinsic:
+                strength: 1.0
+                gamma: 0.99
+
+    {BRAIN_NAME}:
+        trainer: ppo
+        batch_size: {BATCH_SIZE}
+        beta: 5.0e-3
+        buffer_size: 64
+        epsilon: 0.2
+        hidden_units: {HIDDEN_UNITS}
+        lambd: 0.95
+        learning_rate: 5.0e-3
+        max_steps: 2500
+        memory_size: 256
+        normalize: false
+        num_epoch: 3
+        num_layers: 2
+        time_horizon: 64
+        sequence_length: 64
+        summary_freq: {SUMMARY_FREQ}
+        use_recurrent: false
+        reward_signals:
+            curiosity:
+                strength: 1.0
+                gamma: 0.99
+                encoding_size: 128
+    """
+
+SAC_CONFIG = f"""
+    default:
+        trainer: sac
+        batch_size: 128
+        buffer_size: 50000
+        buffer_init_steps: 0
+        hidden_units: 128
+        init_entcoef: 1.0
+        learning_rate: 3.0e-4
+        learning_rate_schedule: constant
+        max_steps: 5.0e5
+        memory_size: 256
+        normalize: false
+        num_update: 1
+        train_interval: 1
+        num_layers: 2
+        time_horizon: 64
+        sequence_length: 64
+        summary_freq: 10000
+        tau: 0.005
+        use_recurrent: false
+        vis_encode_type: simple
+        reward_signals:
+            extrinsic:
+                strength: 1.0
+                gamma: 0.99
+
+    {BRAIN_NAME}:
+        trainer: sac
+        batch_size: {BATCH_SIZE}
+        buffer_size: 64
+        buffer_init_steps: 100
+        hidden_units: {HIDDEN_UNITS}
+        init_entcoef: 0.01
+        learning_rate: 3.0e-4
+        max_steps: 1000
+        memory_size: 256
+        normalize: false
+        num_update: 1
+        train_interval: 1
+        num_layers: 1
+        time_horizon: 64
+        sequence_length: 64
+        summary_freq: {SUMMARY_FREQ}
+        tau: 0.005
+        use_recurrent: false
+        curiosity_enc_size: 128
+        demo_path: None
+        vis_encode_type: simple
+        reward_signals:
+            curiosity:
+                strength: 1.0
+                gamma: 0.99
+                encoding_size: 128
+    """
+
+CURRICULUM = """
+
+  BigWallJump:
+    measure: progress
+    thresholds: [0.1, 0.3, 0.5]
+    min_lesson_length: 200
+    signal_smoothing: true
+    parameters:
+      big_wall_min_height: [0.0, 4.0, 6.0, 8.0]
+      big_wall_max_height: [4.0, 7.0, 8.0, 8.0]
+  SmallWallJump:
+    measure: progress
+    thresholds: [0.1, 0.3, 0.5]
+    min_lesson_length: 100
+    signal_smoothing: true
+    parameters:
+      small_wall_height: [1.5, 2.0, 2.5, 4.0]
+      """
+
+RANDOMIZATION = """
+  resampling-interval: 5000
+  mass:
+    sampler-type: uniform
+    min_value: 0.5
+    max_value: 10
+  gravity:
+    sampler-type: uniform
+    min_value: 7
+    max_value: 12
+  scale:
+    sampler-type: uniform
+    min_value: 0.75
+    max_value: 3
+    """
+
+
+@pytest.mark.parametrize("use_recurrent", [True, False])
+@pytest.mark.parametrize("trainer_type", [PPO_TRAINER_NAME, SAC_TRAINER_NAME])
+def test_convert_behaviors(trainer_type, use_recurrent):
+    if trainer_type == PPO_TRAINER_NAME:
+        trainer_config = PPO_CONFIG
+        trainer_settings_type = PPOSettings
+    else:
+        trainer_config = SAC_CONFIG
+        trainer_settings_type = SACSettings
+
+    old_config = yaml.safe_load(trainer_config)
+    old_config[BRAIN_NAME]["use_recurrent"] = use_recurrent
+    new_config = convert_behaviors(old_config)
+
+    # Test that the new config can be converted to TrainerSettings w/o exceptions
+    trainer_settings = new_config[BRAIN_NAME]
+
+    # Test that the trainer_settings contains the settings for BRAIN_NAME and
+    # the defaults where specified
+    assert trainer_settings.trainer_type == trainer_type
+    assert isinstance(trainer_settings.hyperparameters, trainer_settings_type)
+    assert trainer_settings.hyperparameters.batch_size == BATCH_SIZE
+    assert trainer_settings.network_settings.hidden_units == HIDDEN_UNITS
+    assert RewardSignalType.CURIOSITY in trainer_settings.reward_signals
+
+
+def test_convert():
+    old_behaviors = yaml.safe_load(PPO_CONFIG)
+    old_curriculum = yaml.safe_load(CURRICULUM)
+    old_sampler = yaml.safe_load(RANDOMIZATION)
+    config = convert(old_behaviors, old_curriculum, old_sampler)
+    assert BRAIN_NAME in config["behaviors"]
+    assert "big_wall_min_height" in config["environment_parameters"]
+
+    curriculum = config["environment_parameters"]["big_wall_min_height"]["curriculum"]
+    assert len(curriculum) == 4
+    for i, expected_value in enumerate([0.0, 4.0, 6.0, 8.0]):
+        assert curriculum[i][f"Lesson{i}"]["value"] == expected_value
+    for i, threshold in enumerate([0.1, 0.3, 0.5]):
+        criteria = curriculum[i][f"Lesson{i}"]["completion_criteria"]
+        assert criteria["threshold"] == threshold
+        assert criteria["behavior"] == "BigWallJump"
+        assert criteria["signal_smoothing"]
+        assert criteria["min_lesson_length"] == 200
+        assert criteria["measure"] == "progress"
+
+    assert "gravity" in config["environment_parameters"]
+    gravity = config["environment_parameters"]["gravity"]
+    assert gravity["sampler_type"] == "uniform"
+    assert gravity["sampler_parameters"]["min_value"] == 7
+    assert gravity["sampler_parameters"]["max_value"] == 12
+
+
+def test_remove_nones():
+    dict_with_nones = {"hello": {"hello2": 2, "hello3": None}, "hello4": None}
+    dict_without_nones = {"hello": {"hello2": 2}}
+    output = remove_nones(dict_with_nones)
+    assert output == dict_without_nones
diff --git a/ml-agents/mlagents/trainers/tests/test_demo_dir/test4.demo b/ml-agents/mlagents/trainers/tests/test_demo_dir/test4.demo
new file mode 100644
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diff --git a/ml-agents/mlagents/trainers/tests/test_demo_dir/test5.demo b/ml-agents/mlagents/trainers/tests/test_demo_dir/test5.demo
new file mode 100644
index 0000000000000000000000000000000000000000..2bb34b31e28f8eac88db409e6618d499e2da1a1f
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diff --git a/ml-agents/mlagents/trainers/tests/test_demo_dir/test6.demo b/ml-agents/mlagents/trainers/tests/test_demo_dir/test6.demo
new file mode 100644
index 0000000000000000000000000000000000000000..2bb34b31e28f8eac88db409e6618d499e2da1a1f
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diff --git a/ml-agents/mlagents/trainers/tests/test_demo_loader.py b/ml-agents/mlagents/trainers/tests/test_demo_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b2bb549c3489248332f7606533f38f31e9b41c1
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_demo_loader.py
@@ -0,0 +1,132 @@
+import io
+import os
+from unittest import mock
+import numpy as np
+import pytest
+import tempfile
+
+from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
+    DemonstrationMetaProto,
+)
+from mlagents.trainers.tests.mock_brain import (
+    create_mock_3dball_behavior_specs,
+    setup_test_behavior_specs,
+)
+from mlagents.trainers.demo_loader import (
+    load_demonstration,
+    demo_to_buffer,
+    get_demo_files,
+    write_delimited,
+)
+from mlagents.trainers.buffer import BufferKey
+
+
+BEHAVIOR_SPEC = create_mock_3dball_behavior_specs()
+
+
+def test_load_demo():
+    path_prefix = os.path.dirname(os.path.abspath(__file__))
+    behavior_spec, pair_infos, total_expected = load_demonstration(
+        path_prefix + "/test.demo"
+    )
+    assert np.sum(behavior_spec.observation_specs[0].shape) == 8
+    assert len(pair_infos) == total_expected
+
+    _, demo_buffer = demo_to_buffer(path_prefix + "/test.demo", 1, BEHAVIOR_SPEC)
+    assert (
+        len(demo_buffer[BufferKey.CONTINUOUS_ACTION]) == total_expected - 1
+        or len(demo_buffer[BufferKey.DISCRETE_ACTION]) == total_expected - 1
+    )
+
+
+def test_load_demo_dir():
+    path_prefix = os.path.dirname(os.path.abspath(__file__))
+    behavior_spec, pair_infos, total_expected = load_demonstration(
+        path_prefix + "/test_demo_dir"
+    )
+    assert np.sum(behavior_spec.observation_specs[0].shape) == 8
+    assert len(pair_infos) == total_expected
+
+    _, demo_buffer = demo_to_buffer(path_prefix + "/test_demo_dir", 1, BEHAVIOR_SPEC)
+    assert (
+        len(demo_buffer[BufferKey.CONTINUOUS_ACTION]) == total_expected - 1
+        or len(demo_buffer[BufferKey.DISCRETE_ACTION]) == total_expected - 1
+    )
+
+
+def test_demo_mismatch():
+    path_prefix = os.path.dirname(os.path.abspath(__file__))
+    # observation size mismatch
+    with pytest.raises(RuntimeError):
+        mismatch_obs = setup_test_behavior_specs(
+            False, False, vector_action_space=2, vector_obs_space=9
+        )
+        _, demo_buffer = demo_to_buffer(path_prefix + "/test.demo", 1, mismatch_obs)
+    # action mismatch
+    with pytest.raises(RuntimeError):
+        mismatch_act = setup_test_behavior_specs(
+            False, False, vector_action_space=3, vector_obs_space=9
+        )
+        _, demo_buffer = demo_to_buffer(path_prefix + "/test.demo", 1, mismatch_act)
+    # action type mismatch
+    with pytest.raises(RuntimeError):
+        mismatch_act_type = setup_test_behavior_specs(
+            True, False, vector_action_space=[2], vector_obs_space=9
+        )
+        _, demo_buffer = demo_to_buffer(
+            path_prefix + "/test.demo", 1, mismatch_act_type
+        )
+    # number obs mismatch
+    with pytest.raises(RuntimeError):
+        mismatch_obs_number = setup_test_behavior_specs(
+            False, True, vector_action_space=2, vector_obs_space=9
+        )
+        _, demo_buffer = demo_to_buffer(
+            path_prefix + "/test.demo", 1, mismatch_obs_number
+        )
+
+
+def test_edge_cases():
+    path_prefix = os.path.dirname(os.path.abspath(__file__))
+    # nonexistent file and directory
+    with pytest.raises(FileNotFoundError):
+        get_demo_files(os.path.join(path_prefix, "nonexistent_file.demo"))
+    with pytest.raises(FileNotFoundError):
+        get_demo_files(os.path.join(path_prefix, "nonexistent_directory"))
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        # empty directory
+        with pytest.raises(ValueError):
+            get_demo_files(tmpdirname)
+        # invalid file
+        invalid_fname = os.path.join(tmpdirname, "mydemo.notademo")
+        with open(invalid_fname, "w") as f:
+            f.write("I'm not a demo")
+        with pytest.raises(ValueError):
+            get_demo_files(invalid_fname)
+        # invalid directory
+        with pytest.raises(ValueError):
+            get_demo_files(tmpdirname)
+        # valid file
+        valid_fname = os.path.join(tmpdirname, "mydemo.demo")
+        with open(valid_fname, "w") as f:
+            f.write("I'm a demo file")
+        assert get_demo_files(valid_fname) == [valid_fname]
+        # valid directory
+        assert get_demo_files(tmpdirname) == [valid_fname]
+
+
+@mock.patch("mlagents.trainers.demo_loader.get_demo_files", return_value=["foo.demo"])
+def test_unsupported_version_raises_error(mock_get_demo_files):
+    # Create a metadata proto with an unsupported version
+    bad_metadata = DemonstrationMetaProto()
+    bad_metadata.api_version = 1337
+
+    # Write the metadata to a temporary buffer, which will get returned by open()
+    buffer = io.BytesIO()
+    write_delimited(buffer, bad_metadata)
+    m = mock.mock_open(read_data=buffer.getvalue())
+
+    # Make sure that we get a RuntimeError when trying to load this.
+    with mock.patch("builtins.open", m):
+        with pytest.raises(RuntimeError):
+            load_demonstration("foo")
diff --git a/ml-agents/mlagents/trainers/tests/test_env_param_manager.py b/ml-agents/mlagents/trainers/tests/test_env_param_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5ac2e4f0002aa8adc3e9515f8177e8ee02c58e0
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_env_param_manager.py
@@ -0,0 +1,342 @@
+import pytest
+import yaml
+
+
+from mlagents.trainers.exception import TrainerConfigError, TrainerConfigWarning
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.settings import (
+    RunOptions,
+    UniformSettings,
+    GaussianSettings,
+    ConstantSettings,
+    CompletionCriteriaSettings,
+)
+
+
+test_sampler_config_yaml = """
+environment_parameters:
+  param_1:
+    sampler_type: uniform
+    sampler_parameters:
+      min_value: 0.5
+      max_value: 10
+"""
+
+
+def test_sampler_conversion():
+    run_options = RunOptions.from_dict(yaml.safe_load(test_sampler_config_yaml))
+    assert run_options.environment_parameters is not None
+    assert "param_1" in run_options.environment_parameters
+    lessons = run_options.environment_parameters["param_1"].curriculum
+    assert len(lessons) == 1
+    assert lessons[0].completion_criteria is None
+    assert isinstance(lessons[0].value, UniformSettings)
+    assert lessons[0].value.min_value == 0.5
+    assert lessons[0].value.max_value == 10
+
+
+test_sampler_and_constant_config_yaml = """
+environment_parameters:
+  param_1:
+    sampler_type: gaussian
+    sampler_parameters:
+      mean: 4
+      st_dev: 5
+  param_2: 20
+"""
+
+
+def test_sampler_and_constant_conversion():
+    run_options = RunOptions.from_dict(
+        yaml.safe_load(test_sampler_and_constant_config_yaml)
+    )
+    assert "param_1" in run_options.environment_parameters
+    assert "param_2" in run_options.environment_parameters
+    lessons_1 = run_options.environment_parameters["param_1"].curriculum
+    lessons_2 = run_options.environment_parameters["param_2"].curriculum
+    # gaussian
+    assert isinstance(lessons_1[0].value, GaussianSettings)
+    assert lessons_1[0].value.mean == 4
+    assert lessons_1[0].value.st_dev == 5
+    # constant
+    assert isinstance(lessons_2[0].value, ConstantSettings)
+    assert lessons_2[0].value.value == 20
+
+
+test_curriculum_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 60
+                min_lesson_length: 100
+                require_reset: false
+            value: 2
+          - name: Lesson3
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+"""
+
+
+def test_curriculum_conversion():
+    run_options = RunOptions.from_dict(yaml.safe_load(test_curriculum_config_yaml))
+    assert "param_1" in run_options.environment_parameters
+    lessons = run_options.environment_parameters["param_1"].curriculum
+    assert len(lessons) == 3
+    # First lesson
+    lesson = lessons[0]
+    assert lesson.completion_criteria is not None
+    assert (
+        lesson.completion_criteria.measure
+        == CompletionCriteriaSettings.MeasureType.REWARD
+    )
+    assert lesson.completion_criteria.behavior == "fake_behavior"
+    assert lesson.completion_criteria.threshold == 30.0
+    assert lesson.completion_criteria.min_lesson_length == 100
+    assert lesson.completion_criteria.require_reset
+    assert isinstance(lesson.value, ConstantSettings)
+    assert lesson.value.value == 1
+    # Second lesson
+    lesson = lessons[1]
+    assert lesson.completion_criteria is not None
+    assert (
+        lesson.completion_criteria.measure
+        == CompletionCriteriaSettings.MeasureType.REWARD
+    )
+    assert lesson.completion_criteria.behavior == "fake_behavior"
+    assert lesson.completion_criteria.threshold == 60.0
+    assert lesson.completion_criteria.min_lesson_length == 100
+    assert not lesson.completion_criteria.require_reset
+    assert isinstance(lesson.value, ConstantSettings)
+    assert lesson.value.value == 2
+    # Last lesson
+    lesson = lessons[2]
+    assert lesson.completion_criteria is None
+    assert isinstance(lesson.value, UniformSettings)
+    assert lesson.value.min_value == 1
+    assert lesson.value.max_value == 3
+
+
+test_bad_curriculum_no_competion_criteria_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            value: 2
+          - name: Lesson3
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+"""
+
+
+test_bad_curriculum_all_competion_criteria_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 2
+          - name: Lesson3
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+"""
+
+
+def test_curriculum_raises_no_completion_criteria_conversion():
+    with pytest.raises(TrainerConfigError):
+        RunOptions.from_dict(
+            yaml.safe_load(test_bad_curriculum_no_competion_criteria_config_yaml)
+        )
+
+
+def test_curriculum_raises_all_completion_criteria_conversion():
+    with pytest.warns(TrainerConfigWarning):
+        run_options = RunOptions.from_dict(
+            yaml.safe_load(test_bad_curriculum_all_competion_criteria_config_yaml)
+        )
+
+    param_manager = EnvironmentParameterManager(
+        run_options.environment_parameters, 1337, False
+    )
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1000] * 101},
+    ) == (True, True)
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1000] * 101},
+    ) == (True, True)
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1000] * 101},
+    ) == (False, False)
+    assert param_manager.get_current_lesson_number() == {"param_1": 2}
+
+
+test_everything_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            completion_criteria:
+                measure: progress
+                behavior: fake_behavior
+                threshold: 0.5
+                min_lesson_length: 100
+                require_reset: false
+            value: 2
+          - name: Lesson3
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+    param_2:
+        sampler_type: gaussian
+        sampler_parameters:
+            mean: 4
+            st_dev: 5
+    param_3: 20
+"""
+
+
+def test_create_manager():
+    run_options = RunOptions.from_dict(yaml.safe_load(test_everything_config_yaml))
+    param_manager = EnvironmentParameterManager(
+        run_options.environment_parameters, 1337, False
+    )
+    assert param_manager.get_minimum_reward_buffer_size("fake_behavior") == 100
+    assert param_manager.get_current_lesson_number() == {
+        "param_1": 0,
+        "param_2": 0,
+        "param_3": 0,
+    }
+    assert param_manager.get_current_samplers() == {
+        "param_1": ConstantSettings(seed=1337, value=1),
+        "param_2": GaussianSettings(seed=1337 + 3, mean=4, st_dev=5),
+        "param_3": ConstantSettings(seed=1337 + 3 + 1, value=20),
+    }
+    # Not enough episodes completed
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1000] * 99},
+    ) == (False, False)
+    # Not enough episodes reward
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1] * 101},
+    ) == (False, False)
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 500},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [1000] * 101},
+    ) == (True, True)
+    assert param_manager.get_current_lesson_number() == {
+        "param_1": 1,
+        "param_2": 0,
+        "param_3": 0,
+    }
+    param_manager_2 = EnvironmentParameterManager(
+        run_options.environment_parameters, 1337, restore=True
+    )
+    # The use of global status should make it so that the lesson numbers are maintained
+    assert param_manager_2.get_current_lesson_number() == {
+        "param_1": 1,
+        "param_2": 0,
+        "param_3": 0,
+    }
+    # No reset required
+    assert param_manager.update_lessons(
+        trainer_steps={"fake_behavior": 700},
+        trainer_max_steps={"fake_behavior": 1000},
+        trainer_reward_buffer={"fake_behavior": [0] * 101},
+    ) == (True, False)
+    assert param_manager.get_current_samplers() == {
+        "param_1": UniformSettings(seed=1337 + 2, min_value=1, max_value=3),
+        "param_2": GaussianSettings(seed=1337 + 3, mean=4, st_dev=5),
+        "param_3": ConstantSettings(seed=1337 + 3 + 1, value=20),
+    }
+
+
+test_curriculum_no_behavior_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            value: 2
+"""
+
+
+def test_curriculum_no_behavior():
+    with pytest.raises(TypeError):
+        run_options = RunOptions.from_dict(
+            yaml.safe_load(test_curriculum_no_behavior_yaml)
+        )
+        EnvironmentParameterManager(run_options.environment_parameters, 1337, False)
diff --git a/ml-agents/mlagents/trainers/tests/test_learn.py b/ml-agents/mlagents/trainers/tests/test_learn.py
new file mode 100644
index 0000000000000000000000000000000000000000..7837d59eb25279b9c1d34871c4a4337f63f8202d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_learn.py
@@ -0,0 +1,233 @@
+import pytest
+import yaml
+from unittest.mock import MagicMock, patch, mock_open
+from mlagents.trainers import learn
+from mlagents.trainers.trainer_controller import TrainerController
+from mlagents.trainers.learn import parse_command_line
+from mlagents.trainers.cli_utils import DetectDefault
+from mlagents_envs.exception import UnityEnvironmentException
+from mlagents.trainers.stats import StatsReporter
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+import os.path
+
+
+def basic_options(extra_args=None):
+    extra_args = extra_args or {}
+    args = ["basic_path"]
+    if extra_args:
+        args += [f"{k}={v}" for k, v in extra_args.items()]
+    return parse_command_line(args)
+
+
+MOCK_YAML = """
+    behaviors:
+        {}
+    """
+
+MOCK_INITIALIZE_YAML = """
+    behaviors:
+        {}
+    checkpoint_settings:
+        initialize_from: notuselessrun
+    """
+
+MOCK_PARAMETER_YAML = """
+    behaviors:
+        {}
+    env_settings:
+        env_path: "./oldenvfile"
+        num_envs: 4
+        num_areas: 4
+        base_port: 4001
+        seed: 9870
+    checkpoint_settings:
+        run_id: uselessrun
+        initialize_from: notuselessrun
+    debug: false
+    """
+
+
+@patch("mlagents.trainers.learn.write_timing_tree")
+@patch("mlagents.trainers.learn.write_run_options")
+@patch("mlagents.trainers.learn.validate_existing_directories")
+@patch("mlagents.trainers.learn.TrainerFactory")
+@patch("mlagents.trainers.learn.SubprocessEnvManager")
+@patch("mlagents.trainers.learn.create_environment_factory")
+@patch("mlagents.trainers.settings.load_config")
+def test_run_training(
+    load_config,
+    create_environment_factory,
+    subproc_env_mock,
+    trainer_factory_mock,
+    handle_dir_mock,
+    write_run_options_mock,
+    write_timing_tree_mock,
+):
+    mock_env = MagicMock()
+    mock_env.external_brain_names = []
+    mock_env.academy_name = "TestAcademyName"
+    create_environment_factory.return_value = mock_env
+    load_config.return_value = yaml.safe_load(MOCK_INITIALIZE_YAML)
+    mock_param_manager = MagicMock(return_value="mock_param_manager")
+    mock_init = MagicMock(return_value=None)
+    with patch.object(EnvironmentParameterManager, "__new__", mock_param_manager):
+        with patch.object(TrainerController, "__init__", mock_init):
+            with patch.object(TrainerController, "start_learning", MagicMock()):
+                options = basic_options()
+                learn.run_training(0, options, 1)
+                mock_init.assert_called_once_with(
+                    trainer_factory_mock.return_value,
+                    os.path.join("results", "ppo"),
+                    "ppo",
+                    "mock_param_manager",
+                    True,
+                    0,
+                )
+                handle_dir_mock.assert_called_once_with(
+                    os.path.join("results", "ppo"),
+                    False,
+                    False,
+                    os.path.join("results", "notuselessrun"),
+                )
+                write_timing_tree_mock.assert_called_once_with(
+                    os.path.join("results", "ppo", "run_logs")
+                )
+                write_run_options_mock.assert_called_once_with(
+                    os.path.join("results", "ppo"), options
+                )
+    StatsReporter.writers.clear()  # make sure there aren't any writers as added by learn.py
+
+
+def test_bad_env_path():
+    with pytest.raises(UnityEnvironmentException):
+        factory = learn.create_environment_factory(
+            env_path="/foo/bar",
+            no_graphics=True,
+            seed=-1,
+            num_areas=1,
+            start_port=8000,
+            env_args=None,
+            log_folder="results/log_folder",
+        )
+        factory(worker_id=-1, side_channels=[])
+
+
+@patch("builtins.open", new_callable=mock_open, read_data=MOCK_YAML)
+def test_commandline_args(mock_file):
+    # No args raises
+    # with pytest.raises(SystemExit):
+    #     parse_command_line([])
+    # Test with defaults
+    opt = parse_command_line(["mytrainerpath"])
+    assert opt.behaviors == {}
+    assert opt.env_settings.env_path is None
+    assert opt.checkpoint_settings.resume is False
+    assert opt.checkpoint_settings.inference is False
+    assert opt.checkpoint_settings.run_id == "ppo"
+    assert opt.checkpoint_settings.initialize_from is None
+    assert opt.env_settings.seed == -1
+    assert opt.env_settings.base_port == 5005
+    assert opt.env_settings.num_envs == 1
+    assert opt.env_settings.num_areas == 1
+    assert opt.engine_settings.no_graphics is False
+    assert opt.debug is False
+    assert opt.env_settings.env_args is None
+
+    full_args = [
+        "mytrainerpath",
+        "--env=./myenvfile",
+        "--inference",
+        "--run-id=myawesomerun",
+        "--seed=7890",
+        "--train",
+        "--base-port=4004",
+        "--initialize-from=testdir",
+        "--num-envs=2",
+        "--num-areas=2",
+        "--no-graphics",
+        "--debug",
+    ]
+
+    opt = parse_command_line(full_args)
+    assert opt.behaviors == {}
+    assert opt.env_settings.env_path == "./myenvfile"
+    assert opt.checkpoint_settings.run_id == "myawesomerun"
+    assert opt.checkpoint_settings.initialize_from == "testdir"
+    assert opt.env_settings.seed == 7890
+    assert opt.env_settings.base_port == 4004
+    assert opt.env_settings.num_envs == 2
+    assert opt.env_settings.num_areas == 2
+    assert opt.engine_settings.no_graphics is True
+    assert opt.debug is True
+    assert opt.checkpoint_settings.inference is True
+    assert opt.checkpoint_settings.resume is False
+
+    # ignore init if resume is set
+    full_args.append("--resume")
+    opt = parse_command_line(full_args)
+    assert opt.checkpoint_settings.initialize_from is None  # ignore init if resume set
+    assert opt.checkpoint_settings.resume is True
+
+
+@patch("builtins.open", new_callable=mock_open, read_data=MOCK_PARAMETER_YAML)
+def test_yaml_args(mock_file):
+    # Test with opts loaded from YAML
+    DetectDefault.non_default_args.clear()
+    opt = parse_command_line(["mytrainerpath"])
+    assert opt.behaviors == {}
+    assert opt.env_settings.env_path == "./oldenvfile"
+    assert opt.checkpoint_settings.run_id == "uselessrun"
+    assert opt.checkpoint_settings.initialize_from == "notuselessrun"
+    assert opt.env_settings.seed == 9870
+    assert opt.env_settings.base_port == 4001
+    assert opt.env_settings.num_envs == 4
+    assert opt.env_settings.num_areas == 4
+    assert opt.engine_settings.no_graphics is False
+    assert opt.debug is False
+    assert opt.env_settings.env_args is None
+    # Test that CLI overrides YAML
+    full_args = [
+        "mytrainerpath",
+        "--env=./myenvfile",
+        "--resume",
+        "--inference",
+        "--run-id=myawesomerun",
+        "--seed=7890",
+        "--train",
+        "--base-port=4004",
+        "--num-envs=2",
+        "--num-areas=2",
+        "--no-graphics",
+        "--debug",
+        "--results-dir=myresults",
+    ]
+
+    opt = parse_command_line(full_args)
+    assert opt.behaviors == {}
+    assert opt.env_settings.env_path == "./myenvfile"
+    assert opt.checkpoint_settings.run_id == "myawesomerun"
+    assert opt.env_settings.seed == 7890
+    assert opt.env_settings.base_port == 4004
+    assert opt.env_settings.num_envs == 2
+    assert opt.env_settings.num_areas == 2
+    assert opt.engine_settings.no_graphics is True
+    assert opt.debug is True
+    assert opt.checkpoint_settings.inference is True
+    assert opt.checkpoint_settings.resume is True
+    assert opt.checkpoint_settings.results_dir == "myresults"
+
+
+@patch("builtins.open", new_callable=mock_open, read_data=MOCK_YAML)
+def test_env_args(mock_file):
+    full_args = [
+        "mytrainerpath",
+        "--env=./myenvfile",
+        "--env-args",  # Everything after here will be grouped in a list
+        "--foo=bar",
+        "--blah",
+        "baz",
+        "100",
+    ]
+
+    opt = parse_command_line(full_args)
+    assert opt.env_settings.env_args == ["--foo=bar", "--blah", "baz", "100"]
diff --git a/ml-agents/mlagents/trainers/tests/test_rl_trainer.py b/ml-agents/mlagents/trainers/tests/test_rl_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..89b5ae9810504031a01511cc997155edf7ea8349
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_rl_trainer.py
@@ -0,0 +1,263 @@
+import os
+import unittest
+from unittest import mock
+import pytest
+import mlagents.trainers.tests.mock_brain as mb
+from mlagents.trainers.policy.checkpoint_manager import ModelCheckpoint
+from mlagents.trainers.trainer.rl_trainer import RLTrainer
+from mlagents.trainers.tests.test_buffer import construct_fake_buffer
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.settings import TrainerSettings
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+from mlagents_envs.base_env import ActionSpec
+import os.path
+
+
+# Add concrete implementations of abstract methods
+class FakeTrainer(RLTrainer):
+    def set_is_policy_updating(self, is_updating):
+        self.update_policy = is_updating
+
+    def get_policy(self, name_behavior_id):
+        return mock.Mock()
+
+    def _is_ready_update(self):
+        return True
+
+    def _update_policy(self):
+        return self.update_policy
+
+    def add_policy(self, mock_behavior_id, mock_policy):
+        def checkpoint_path(brain_name, step):
+            onnx_file_path = os.path.join(
+                self.model_saver.model_path, f"{brain_name}-{step}.onnx"
+            )
+            other_file_paths = [
+                os.path.join(self.model_saver.model_path, f"{brain_name}-{step}.pt")
+            ]
+            return onnx_file_path, other_file_paths
+
+        self.policies[mock_behavior_id] = mock_policy
+        mock_model_saver = mock.Mock()
+        mock_model_saver.model_path = self.artifact_path
+        mock_model_saver.save_checkpoint.side_effect = checkpoint_path
+        self.model_saver = mock_model_saver
+
+    def create_optimizer(self) -> TorchOptimizer:
+        return mock.Mock()
+
+    def create_policy(
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+    ) -> Policy:
+        return mock.Mock()
+
+    def _process_trajectory(self, trajectory):
+        super()._process_trajectory(trajectory)
+
+
+def create_rl_trainer():
+    trainer = FakeTrainer(
+        "test_trainer",
+        TrainerSettings(max_steps=100, checkpoint_interval=10, summary_freq=20),
+        True,
+        False,
+        "mock_model_path",
+        0,
+    )
+    trainer.set_is_policy_updating(True)
+    return trainer
+
+
+def test_rl_trainer():
+    trainer = create_rl_trainer()
+    agent_id = "0"
+    trainer.collected_rewards["extrinsic"] = {agent_id: 3}
+    # Test end episode
+    trainer.end_episode()
+    for rewards in trainer.collected_rewards.values():
+        for agent_id in rewards:
+            assert rewards[agent_id] == 0
+
+
+def test_clear_update_buffer():
+    trainer = create_rl_trainer()
+    trainer.update_buffer = construct_fake_buffer(0)
+    trainer._clear_update_buffer()
+    for _, arr in trainer.update_buffer.items():
+        assert len(arr) == 0
+
+
+@mock.patch("mlagents.trainers.trainer.trainer.Trainer.save_model")
+def test_advance(mocked_save_model):
+    trainer = create_rl_trainer()
+    mock_policy = mock.Mock()
+    trainer.add_policy("TestBrain", mock_policy)
+    trajectory_queue = AgentManagerQueue("testbrain")
+    policy_queue = AgentManagerQueue("testbrain")
+    trainer.subscribe_trajectory_queue(trajectory_queue)
+    trainer.publish_policy_queue(policy_queue)
+    time_horizon = 10
+    trajectory = mb.make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        max_step_complete=True,
+        action_spec=ActionSpec.create_discrete((2,)),
+    )
+    trajectory_queue.put(trajectory)
+
+    trainer.advance()
+    policy_queue.get_nowait()
+    # Check that get_step is correct
+    assert trainer.get_step == time_horizon
+    # Check that we can turn off the trainer and that the buffer is cleared
+    for _ in range(0, 5):
+        trajectory_queue.put(trajectory)
+        trainer.advance()
+        # Check that there is stuff in the policy queue
+        policy_queue.get_nowait()
+
+    # Check that if the policy doesn't update, we don't push it to the queue
+    trainer.set_is_policy_updating(False)
+    for _ in range(0, 10):
+        trajectory_queue.put(trajectory)
+        trainer.advance()
+        # Check that there nothing  in the policy queue
+        with pytest.raises(AgentManagerQueue.Empty):
+            policy_queue.get_nowait()
+
+    # Check that no model has been saved
+    assert not trainer.should_still_train
+    assert mocked_save_model.call_count == 0
+
+
+@mock.patch("mlagents.trainers.trainer.trainer.StatsReporter.write_stats")
+@mock.patch(
+    "mlagents.trainers.trainer.rl_trainer.ModelCheckpointManager.add_checkpoint"
+)
+def test_summary_checkpoint(mock_add_checkpoint, mock_write_summary):
+    trainer = create_rl_trainer()
+    mock_policy = mock.Mock()
+    trainer.add_policy("TestBrain", mock_policy)
+    trajectory_queue = AgentManagerQueue("testbrain")
+    policy_queue = AgentManagerQueue("testbrain")
+    trainer.subscribe_trajectory_queue(trajectory_queue)
+    trainer.publish_policy_queue(policy_queue)
+    time_horizon = 10
+    summary_freq = trainer.trainer_settings.summary_freq
+    checkpoint_interval = trainer.trainer_settings.checkpoint_interval
+    trajectory = mb.make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        max_step_complete=True,
+        action_spec=ActionSpec.create_discrete((2,)),
+    )
+    # Check that we can turn off the trainer and that the buffer is cleared
+    num_trajectories = 5
+    for _ in range(0, num_trajectories):
+        trajectory_queue.put(trajectory)
+        trainer.advance()
+        # Check that there is stuff in the policy queue
+        policy_queue.get_nowait()
+
+    # Check that we have called write_summary the appropriate number of times
+    calls = [
+        mock.call(step)
+        for step in range(summary_freq, num_trajectories * time_horizon, summary_freq)
+    ]
+    mock_write_summary.assert_has_calls(calls, any_order=True)
+
+    checkpoint_range = range(
+        checkpoint_interval, num_trajectories * time_horizon, checkpoint_interval
+    )
+    calls = [mock.call(trainer.brain_name, step) for step in checkpoint_range]
+
+    trainer.model_saver.save_checkpoint.assert_has_calls(calls, any_order=True)
+    export_ext = "onnx"
+
+    add_checkpoint_calls = [
+        mock.call(
+            trainer.brain_name,
+            ModelCheckpoint(
+                step,
+                f"{trainer.model_saver.model_path}{os.path.sep}{trainer.brain_name}-{step}.{export_ext}",
+                None,
+                mock.ANY,
+                [
+                    f"{trainer.model_saver.model_path}{os.path.sep}{trainer.brain_name}-{step}.pt"
+                ],
+            ),
+            trainer.trainer_settings.keep_checkpoints,
+        )
+        for step in checkpoint_range
+    ]
+    mock_add_checkpoint.assert_has_calls(add_checkpoint_calls)
+
+
+def test_update_buffer_append():
+    trainer = create_rl_trainer()
+    mock_policy = mock.Mock()
+    trainer.add_policy("TestBrain", mock_policy)
+    trajectory_queue = AgentManagerQueue("testbrain")
+    policy_queue = AgentManagerQueue("testbrain")
+    trainer.subscribe_trajectory_queue(trajectory_queue)
+    trainer.publish_policy_queue(policy_queue)
+    time_horizon = 10
+    trajectory = mb.make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        max_step_complete=True,
+        action_spec=ActionSpec.create_discrete((2,)),
+    )
+    agentbuffer_trajectory = trajectory.to_agentbuffer()
+    assert trainer.update_buffer.num_experiences == 0
+
+    # Check that if we append, our update buffer gets longer.
+    # max_steps = 100
+    for i in range(10):
+        trainer._process_trajectory(trajectory)
+        trainer._append_to_update_buffer(agentbuffer_trajectory)
+        assert trainer.update_buffer.num_experiences == (i + 1) * time_horizon
+
+    # Check that if we append after stopping training, nothing happens.
+    # We process enough trajectories to hit max steps
+    trainer.set_is_policy_updating(False)
+    trainer._process_trajectory(trajectory)
+    trainer._append_to_update_buffer(agentbuffer_trajectory)
+    assert trainer.update_buffer.num_experiences == (i + 1) * time_horizon
+
+
+class RLTrainerWarningTest(unittest.TestCase):
+    def test_warning_group_reward(self):
+        with self.assertLogs("mlagents.trainers", level="WARN") as cm:
+            rl_trainer = create_rl_trainer()
+            # This one should warn
+            trajectory = mb.make_fake_trajectory(
+                length=10,
+                observation_specs=create_observation_specs_with_shapes([(1,)]),
+                max_step_complete=True,
+                action_spec=ActionSpec.create_discrete((2,)),
+                group_reward=1.0,
+            )
+            buff = trajectory.to_agentbuffer()
+            rl_trainer._warn_if_group_reward(buff)
+            assert len(cm.output) > 0
+            len_of_first_warning = len(cm.output)
+
+            rl_trainer = create_rl_trainer()
+            # This one shouldn't
+            trajectory = mb.make_fake_trajectory(
+                length=10,
+                observation_specs=create_observation_specs_with_shapes([(1,)]),
+                max_step_complete=True,
+                action_spec=ActionSpec.create_discrete((2,)),
+            )
+            buff = trajectory.to_agentbuffer()
+            rl_trainer._warn_if_group_reward(buff)
+            # Make sure warnings don't get bigger
+            assert len(cm.output) == len_of_first_warning
diff --git a/ml-agents/mlagents/trainers/tests/test_rpc_utils.py b/ml-agents/mlagents/trainers/tests/test_rpc_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d03b21f411ecc466dd28ba33d5120527aeb2db4
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_rpc_utils.py
@@ -0,0 +1,503 @@
+import io
+import numpy as np
+import pytest
+from typing import List, Tuple, Any
+
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
+    ObservationProto,
+    NONE,
+    PNG,
+)
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
+from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
+from mlagents_envs.base_env import (
+    BehaviorSpec,
+    ActionSpec,
+    DecisionSteps,
+    TerminalSteps,
+)
+from mlagents_envs.exception import UnityObservationException
+from mlagents_envs.rpc_utils import (
+    behavior_spec_from_proto,
+    process_pixels,
+    _process_maybe_compressed_observation,
+    _process_rank_one_or_two_observation,
+    steps_from_proto,
+)
+from PIL import Image
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+def generate_list_agent_proto(
+    n_agent: int,
+    shape: List[Tuple[int]],
+    infinite_rewards: bool = False,
+    nan_observations: bool = False,
+) -> List[AgentInfoProto]:
+    result = []
+    for agent_index in range(n_agent):
+        ap = AgentInfoProto()
+        ap.reward = float("inf") if infinite_rewards else agent_index
+        ap.done = agent_index % 2 == 0
+        ap.max_step_reached = agent_index % 4 == 0
+        ap.id = agent_index
+        ap.action_mask.extend([True, False] * 5)
+        obs_proto_list = []
+        for obs_index in range(len(shape)):
+            obs_proto = ObservationProto()
+            obs_proto.shape.extend(list(shape[obs_index]))
+            obs_proto.compression_type = NONE
+            obs_proto.float_data.data.extend(
+                ([float("nan")] if nan_observations else [0.1])
+                * np.prod(shape[obs_index])
+            )
+            obs_proto_list.append(obs_proto)
+        ap.observations.extend(obs_proto_list)
+        result.append(ap)
+    return result
+
+
+def generate_compressed_data(in_array: np.ndarray) -> bytes:
+    image_arr = (in_array * 255).astype(np.uint8)
+    bytes_out = bytes()
+
+    num_channels = in_array.shape[2]
+    num_images = (num_channels + 2) // 3
+    # Split the input image into batches of 3 channels.
+    for i in range(num_images):
+        sub_image = image_arr[..., 3 * i : 3 * i + 3]
+        if (i == num_images - 1) and (num_channels % 3) != 0:
+            # Pad zeros
+            zero_shape = list(in_array.shape)
+            zero_shape[2] = 3 - (num_channels % 3)
+            z = np.zeros(zero_shape, dtype=np.uint8)
+            sub_image = np.concatenate([sub_image, z], axis=2)
+        im = Image.fromarray(sub_image, "RGB")
+        byteIO = io.BytesIO()
+        im.save(byteIO, format="PNG")
+        bytes_out += byteIO.getvalue()
+    return bytes_out
+
+
+# test helper function for old C# API (no compressed channel mapping)
+def generate_compressed_proto_obs(
+    in_array: np.ndarray, grayscale: bool = False
+) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.compressed_data = generate_compressed_data(in_array)
+    obs_proto.compression_type = PNG
+    if grayscale:
+        # grayscale flag is only used for old API without mapping
+        expected_shape = [in_array.shape[0], in_array.shape[1], 1]
+        obs_proto.shape.extend(expected_shape)
+    else:
+        obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+# test helper function for new C# API (with compressed channel mapping)
+def generate_compressed_proto_obs_with_mapping(
+    in_array: np.ndarray, mapping: List[int]
+) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.compressed_data = generate_compressed_data(in_array)
+    obs_proto.compression_type = PNG
+    if mapping is not None:
+        obs_proto.compressed_channel_mapping.extend(mapping)
+        expected_shape = [
+            in_array.shape[0],
+            in_array.shape[1],
+            len({m for m in mapping if m >= 0}),
+        ]
+        obs_proto.shape.extend(expected_shape)
+    else:
+        obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+def generate_uncompressed_proto_obs(in_array: np.ndarray) -> ObservationProto:
+    obs_proto = ObservationProto()
+    obs_proto.float_data.data.extend(in_array.flatten().tolist())
+    obs_proto.compression_type = NONE
+    obs_proto.shape.extend(in_array.shape)
+    return obs_proto
+
+
+def proto_from_steps(
+    decision_steps: DecisionSteps, terminal_steps: TerminalSteps
+) -> List[AgentInfoProto]:
+    agent_info_protos: List[AgentInfoProto] = []
+    # Take care of the DecisionSteps first
+    for agent_id in decision_steps.agent_id:
+        agent_id_index = decision_steps.agent_id_to_index[agent_id]
+        reward = decision_steps.reward[agent_id_index]
+        done = False
+        max_step_reached = False
+        agent_mask: Any = None
+        if decision_steps.action_mask is not None:
+            agent_mask = []
+            for _branch in decision_steps.action_mask:
+                agent_mask = np.concatenate(
+                    (agent_mask, _branch[agent_id_index, :]), axis=0
+                )
+            agent_mask = agent_mask.astype(np.bool).tolist()
+        observations: List[ObservationProto] = []
+        for all_observations_of_type in decision_steps.obs:
+            observation = all_observations_of_type[agent_id_index]
+            if len(observation.shape) == 3:
+                observations.append(generate_uncompressed_proto_obs(observation))
+            else:
+                observations.append(
+                    ObservationProto(
+                        float_data=ObservationProto.FloatData(data=observation),
+                        shape=[len(observation)],
+                        compression_type=NONE,
+                    )
+                )
+        agent_info_proto = AgentInfoProto(
+            reward=reward,
+            done=done,
+            id=agent_id,
+            max_step_reached=bool(max_step_reached),
+            action_mask=agent_mask,
+            observations=observations,
+        )
+        agent_info_protos.append(agent_info_proto)
+    # Take care of the TerminalSteps second
+    for agent_id in terminal_steps.agent_id:
+        agent_id_index = terminal_steps.agent_id_to_index[agent_id]
+        reward = terminal_steps.reward[agent_id_index]
+        done = True
+        max_step_reached = terminal_steps.interrupted[agent_id_index]
+
+        final_observations: List[ObservationProto] = []
+        for all_observations_of_type in terminal_steps.obs:
+            observation = all_observations_of_type[agent_id_index]
+            if len(observation.shape) == 3:
+                final_observations.append(generate_uncompressed_proto_obs(observation))
+            else:
+                final_observations.append(
+                    ObservationProto(
+                        float_data=ObservationProto.FloatData(data=observation),
+                        shape=[len(observation)],
+                        compression_type=NONE,
+                    )
+                )
+        agent_info_proto = AgentInfoProto(
+            reward=reward,
+            done=done,
+            id=agent_id,
+            max_step_reached=bool(max_step_reached),
+            action_mask=None,
+            observations=final_observations,
+        )
+        agent_info_protos.append(agent_info_proto)
+
+    return agent_info_protos
+
+
+# The arguments here are the DecisionSteps, TerminalSteps and continuous/discrete actions for a single agent name
+def proto_from_steps_and_action(
+    decision_steps: DecisionSteps,
+    terminal_steps: TerminalSteps,
+    continuous_actions: np.ndarray,
+    discrete_actions: np.ndarray,
+) -> List[AgentInfoActionPairProto]:
+    agent_info_protos = proto_from_steps(decision_steps, terminal_steps)
+    agent_action_protos = []
+    num_agents = (
+        len(continuous_actions)
+        if continuous_actions is not None
+        else len(discrete_actions)
+    )
+    for i in range(num_agents):
+        proto = AgentActionProto()
+        if continuous_actions is not None:
+            proto.continuous_actions.extend(continuous_actions[i])
+            proto.vector_actions_deprecated.extend(continuous_actions[i])
+        if discrete_actions is not None:
+            proto.discrete_actions.extend(discrete_actions[i])
+            proto.vector_actions_deprecated.extend(discrete_actions[i])
+        agent_action_protos.append(proto)
+    agent_info_action_pair_protos = [
+        AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
+        for agent_info_proto, action_proto in zip(
+            agent_info_protos, agent_action_protos
+        )
+    ]
+    return agent_info_action_pair_protos
+
+
+def test_process_pixels():
+    in_array = np.random.rand(128, 64, 3)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, 3)
+    assert out_array.shape == (128, 64, 3)
+    assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
+    assert np.allclose(in_array, out_array, atol=0.01)
+
+
+def test_process_pixels_multi_png():
+    height = 128
+    width = 64
+    num_channels = 7
+    in_array = np.random.rand(height, width, num_channels)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, num_channels)
+    assert out_array.shape == (height, width, num_channels)
+    assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
+    assert np.allclose(in_array, out_array, atol=0.01)
+
+
+def test_process_pixels_gray():
+    in_array = np.random.rand(128, 64, 3)
+    byte_arr = generate_compressed_data(in_array)
+    out_array = process_pixels(byte_arr, 1)
+    assert out_array.shape == (128, 64, 1)
+    assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01
+    assert np.allclose(in_array.mean(axis=2, keepdims=True), out_array, atol=0.01)
+
+
+def test_vector_observation():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    obs_specs = create_observation_specs_with_shapes(shapes)
+    list_proto = generate_list_agent_proto(n_agents, shapes)
+    for obs_index, shape in enumerate(shapes):
+        arr = _process_rank_one_or_two_observation(
+            obs_index, obs_specs[obs_index], list_proto
+        )
+        assert list(arr.shape) == ([n_agents] + list(shape))
+        assert np.allclose(arr, 0.1, atol=0.01)
+
+
+def test_process_visual_observation():
+    shape = (128, 64, 3)
+    in_array_1 = np.random.rand(*shape)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1)
+    in_array_2 = np.random.rand(*shape)
+    in_array_2_mapping = [0, 1, 2]
+    proto_obs_2 = generate_compressed_proto_obs_with_mapping(
+        in_array_2, in_array_2_mapping
+    )
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap2 = AgentInfoProto()
+    ap2.observations.extend([proto_obs_2])
+    ap_list = [ap1, ap2]
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [2, 128, 64, 3]
+    assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
+    assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
+
+
+def test_process_visual_observation_grayscale():
+    in_array_1 = np.random.rand(128, 64, 3)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
+    expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
+    in_array_2 = np.random.rand(128, 64, 3)
+    in_array_2_mapping = [0, 0, 0]
+    proto_obs_2 = generate_compressed_proto_obs_with_mapping(
+        in_array_2, in_array_2_mapping
+    )
+    expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap2 = AgentInfoProto()
+    ap2.observations.extend([proto_obs_2])
+    ap_list = [ap1, ap2]
+    shape = (128, 64, 1)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [2, 128, 64, 1]
+    assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
+    assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
+
+
+def test_process_visual_observation_padded_channels():
+    in_array_1 = np.random.rand(128, 64, 12)
+    in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
+    proto_obs_1 = generate_compressed_proto_obs_with_mapping(
+        in_array_1, in_array_1_mapping
+    )
+    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=2)
+
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap_list = [ap1]
+    shape = (128, 64, 8)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+
+    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    assert list(arr.shape) == [1, 128, 64, 8]
+    assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
+
+
+def test_process_visual_observation_bad_shape():
+    in_array_1 = np.random.rand(128, 64, 3)
+    proto_obs_1 = generate_compressed_proto_obs(in_array_1)
+    ap1 = AgentInfoProto()
+    ap1.observations.extend([proto_obs_1])
+    ap_list = [ap1]
+
+    shape = (128, 42, 3)
+    obs_spec = create_observation_specs_with_shapes([shape])[0]
+
+    with pytest.raises(UnityObservationException):
+        _process_maybe_compressed_observation(0, obs_spec, ap_list)
+
+
+def test_batched_step_result_from_proto():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, spec)
+    for agent_id in range(n_agents):
+        if agent_id in decision_steps:
+            # we set the reward equal to the agent id in generate_list_agent_proto
+            assert decision_steps[agent_id].reward == agent_id
+        elif agent_id in terminal_steps:
+            assert terminal_steps[agent_id].reward == agent_id
+        else:
+            raise Exception("Missing agent from the steps")
+    # We sort the AgentId since they are split between DecisionSteps and TerminalSteps
+    combined_agent_id = list(decision_steps.agent_id) + list(terminal_steps.agent_id)
+    combined_agent_id.sort()
+    assert combined_agent_id == list(range(n_agents))
+    for agent_id in range(n_agents):
+        assert (agent_id in terminal_steps) == (agent_id % 2 == 0)
+        if agent_id in terminal_steps:
+            assert terminal_steps[agent_id].interrupted == (agent_id % 4 == 0)
+    assert decision_steps.obs[0].shape[1] == shapes[0][0]
+    assert decision_steps.obs[1].shape[1] == shapes[1][0]
+    assert terminal_steps.obs[0].shape[1] == shapes[0][0]
+    assert terminal_steps.obs[1].shape[1] == shapes[1][0]
+
+
+def test_mismatch_observations_raise_in_step_result_from_proto():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    # Hack an observation to be larger, we should get an exception
+    ap_list[0].observations[0].shape[0] += 1
+    ap_list[0].observations[0].float_data.data.append(0.42)
+    with pytest.raises(UnityObservationException):
+        steps_from_proto(ap_list, spec)
+
+
+def test_action_masking_discrete():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_discrete((7, 3))
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 2
+    assert masks[0].shape == (n_agents / 2, 7)  # half agents are done
+    assert masks[1].shape == (n_agents / 2, 3)  # half agents are done
+    assert masks[0][0, 0]
+    assert not masks[1][0, 0]
+    assert masks[1][0, 1]
+
+
+def test_action_masking_discrete_1():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_discrete((10,))
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 1
+    assert masks[0].shape == (n_agents / 2, 10)
+    assert masks[0][0, 0]
+
+
+def test_action_masking_discrete_2():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes),
+        ActionSpec.create_discrete((2, 2, 6)),
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert isinstance(masks, list)
+    assert len(masks) == 3
+    assert masks[0].shape == (n_agents / 2, 2)
+    assert masks[1].shape == (n_agents / 2, 2)
+    assert masks[2].shape == (n_agents / 2, 6)
+    assert masks[0][0, 0]
+
+
+def test_action_masking_continuous():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(10)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes)
+    decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
+    masks = decision_steps.action_mask
+    assert masks is None
+
+
+def test_agent_behavior_spec_from_proto():
+    agent_proto = generate_list_agent_proto(1, [(3,), (4,)])[0]
+    bp = BrainParametersProto()
+    bp.vector_action_size_deprecated.extend([5, 4])
+    bp.vector_action_space_type_deprecated = 0
+    behavior_spec = behavior_spec_from_proto(bp, agent_proto)
+    assert behavior_spec.action_spec.is_discrete()
+    assert not behavior_spec.action_spec.is_continuous()
+    assert [spec.shape for spec in behavior_spec.observation_specs] == [(3,), (4,)]
+    assert behavior_spec.action_spec.discrete_branches == (5, 4)
+    assert behavior_spec.action_spec.discrete_size == 2
+    bp = BrainParametersProto()
+    bp.vector_action_size_deprecated.extend([6])
+    bp.vector_action_space_type_deprecated = 1
+    behavior_spec = behavior_spec_from_proto(bp, agent_proto)
+    assert not behavior_spec.action_spec.is_discrete()
+    assert behavior_spec.action_spec.is_continuous()
+    assert behavior_spec.action_spec.continuous_size == 6
+
+
+def test_batched_step_result_from_proto_raises_on_infinite():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes, infinite_rewards=True)
+    with pytest.raises(RuntimeError):
+        steps_from_proto(ap_list, behavior_spec)
+
+
+def test_batched_step_result_from_proto_raises_on_nan():
+    n_agents = 10
+    shapes = [(3,), (4,)]
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
+    )
+    ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True)
+    with pytest.raises(RuntimeError):
+        steps_from_proto(ap_list, behavior_spec)
diff --git a/ml-agents/mlagents/trainers/tests/test_settings.py b/ml-agents/mlagents/trainers/tests/test_settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..568dd4c2d324e929ccd8f726d0cc32c6ce1b0110
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_settings.py
@@ -0,0 +1,630 @@
+import attr
+import cattr
+import pickle
+import pytest
+import yaml
+
+from typing import Dict, List, Optional
+
+from mlagents.trainers.settings import (
+    RunOptions,
+    TrainerSettings,
+    NetworkSettings,
+    RewardSignalType,
+    RewardSignalSettings,
+    CuriositySettings,
+    EnvironmentSettings,
+    EnvironmentParameterSettings,
+    ConstantSettings,
+    UniformSettings,
+    GaussianSettings,
+    MultiRangeUniformSettings,
+    deep_update_dict,
+    strict_to_cls,
+    ScheduleType,
+)
+from mlagents.trainers.ppo.trainer import PPOSettings, TRAINER_NAME as PPO_TRAINER_NAME
+from mlagents.trainers.sac.trainer import SACSettings, TRAINER_NAME as SAC_TRAINER_NAME
+
+from mlagents.trainers.exception import TrainerConfigError
+
+TRAINER_SETTING_TYPES = {"ppo": PPOSettings, "sac": SACSettings}
+
+
+def check_if_different(testobj1: object, testobj2: object) -> None:
+    assert testobj1 is not testobj2
+    if attr.has(testobj1.__class__) and attr.has(testobj2.__class__):
+        for key, val in attr.asdict(testobj1, recurse=False).items():
+            if (
+                isinstance(val, dict) or isinstance(val, list) or attr.has(val)
+            ) and val != {}:
+                # Note: this check doesn't check the contents of mutables.
+                check_if_different(val, attr.asdict(testobj2, recurse=False)[key])
+
+
+def check_dict_is_at_least(
+    testdict1: Dict, testdict2: Dict, exceptions: Optional[List[str]] = None
+) -> None:
+    """
+    Check if everything present in the 1st dict is the same in the second dict.
+    Excludes things that the second dict has but is not present in the heirarchy of the
+    1st dict. Used to compare an underspecified config dict structure (e.g. as
+    would be provided by a user) with a complete one (e.g. as exported by RunOptions).
+    """
+    for key, val in testdict1.items():
+        if exceptions is not None and key in exceptions:
+            continue
+        assert key in testdict2
+        if isinstance(val, dict):
+            check_dict_is_at_least(val, testdict2[key])
+        elif isinstance(val, list):
+            assert isinstance(testdict2[key], list)
+            for _el0, _el1 in zip(val, testdict2[key]):
+                if isinstance(_el0, dict):
+                    check_dict_is_at_least(_el0, _el1)
+                else:
+                    assert val == testdict2[key]
+        else:  # If not a dict, don't recurse into it
+            assert val == testdict2[key]
+
+
+def test_is_new_instance():
+    """
+    Verify that every instance of RunOptions() and its subclasses
+    is a new instance (i.e. all factory methods are used properly.)
+    """
+    check_if_different(RunOptions(), RunOptions())
+    check_if_different(TrainerSettings(), TrainerSettings())
+
+
+def test_no_configuration():
+    """
+    Verify that a new config will have a PPO trainer with extrinsic rewards.
+    """
+    blank_runoptions = RunOptions()
+    blank_runoptions.behaviors.set_config_specified(False)
+    assert isinstance(blank_runoptions.behaviors["test"], TrainerSettings)
+    assert isinstance(blank_runoptions.behaviors["test"].hyperparameters, PPOSettings)
+    assert (
+        RewardSignalType.EXTRINSIC in blank_runoptions.behaviors["test"].reward_signals
+    )
+
+
+def test_strict_to_cls():
+    """
+    Test strict structuring method.
+    """
+
+    @attr.s(auto_attribs=True)
+    class TestAttrsClass:
+        field1: int = 0
+        field2: str = "test"
+
+    correct_dict = {"field1": 1, "field2": "test2"}
+    assert strict_to_cls(correct_dict, TestAttrsClass) == TestAttrsClass(**correct_dict)
+
+    incorrect_dict = {"field3": 1, "field2": "test2"}
+
+    with pytest.raises(TrainerConfigError):
+        strict_to_cls(incorrect_dict, TestAttrsClass)
+
+    with pytest.raises(TrainerConfigError):
+        strict_to_cls("non_dict_input", TestAttrsClass)
+
+
+def test_deep_update_dict():
+    dict1 = {"a": 1, "b": 2, "c": {"d": 3}}
+    dict2 = {"a": 2, "c": {"d": 4, "e": 5}}
+
+    deep_update_dict(dict1, dict2)
+    assert dict1 == {"a": 2, "b": 2, "c": {"d": 4, "e": 5}}
+
+
+def test_trainersettings_structure():
+    """
+    Test structuring method for TrainerSettings
+    """
+    trainersettings_dict = {
+        "trainer_type": SAC_TRAINER_NAME,
+        "hyperparameters": {"batch_size": 1024},
+        "max_steps": 1.0,
+        "reward_signals": {"curiosity": {"encoding_size": 64}},
+    }
+    trainer_settings = TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+    # check_trainer_setting_types([trainer_settings], TRAINER_SETTING_TYPES)
+    assert isinstance(trainer_settings.hyperparameters, SACSettings)
+    assert trainer_settings.trainer_type == SAC_TRAINER_NAME
+    assert isinstance(trainer_settings.max_steps, int)
+    assert RewardSignalType.CURIOSITY in trainer_settings.reward_signals
+
+    # Check invalid trainer type
+    with pytest.raises(TrainerConfigError):
+        trainersettings_dict = {
+            "trainer_type": "puppo",
+            "hyperparameters": {"batch_size": 1024},
+            "max_steps": 1.0,
+        }
+        TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+
+    # Check invalid hyperparameter
+    with pytest.raises(TrainerConfigError):
+        trainersettings_dict = {
+            "trainer_type": PPO_TRAINER_NAME,
+            "hyperparameters": {"notahyperparam": 1024},
+            "max_steps": 1.0,
+        }
+        TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+
+    # Check non-dict
+    with pytest.raises(TrainerConfigError):
+        TrainerSettings.structure("notadict", TrainerSettings)
+
+    # Check hyperparameters specified but trainer type left as default.
+    # This shouldn't work as you could specify non-PPO hyperparameters.
+    with pytest.raises(TrainerConfigError):
+        trainersettings_dict = {"hyperparameters": {"batch_size": 1024}}
+        TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+
+
+def test_trainersettingsschedules_structure():
+    """
+    Test structuring method for Trainer Settings Schedule
+    """
+    trainersettings_dict = {
+        "trainer_type": PPO_TRAINER_NAME,
+        "hyperparameters": {
+            "learning_rate_schedule": "linear",
+            "beta_schedule": "constant",
+        },
+    }
+    trainer_settings = TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+    assert isinstance(trainer_settings.hyperparameters, PPOSettings)
+    assert (
+        trainer_settings.hyperparameters.learning_rate_schedule == ScheduleType.LINEAR
+    )
+    assert trainer_settings.hyperparameters.beta_schedule == ScheduleType.CONSTANT
+    assert trainer_settings.hyperparameters.epsilon_schedule == ScheduleType.LINEAR
+
+
+def test_even_checkpoints_structure():
+    """
+    Test structuring for even checkpoints
+    """
+    trainersettings_dict = {
+        "trainer_type": PPO_TRAINER_NAME,
+        "keep_checkpoints": 2,
+        "even_checkpoints": True,
+        "max_steps": 100.0,
+    }
+
+    trainer_settings = TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+    assert isinstance(trainer_settings.hyperparameters, PPOSettings)
+    assert trainer_settings.checkpoint_interval == 50
+
+
+def test_default_checkpoint_interval_structure():
+    """
+    Test structuring for even checkpoints
+    """
+    trainersettings_dict = {
+        "trainer_type": PPO_TRAINER_NAME,
+        "keep_checkpoints": 2,
+        "max_steps": 100.0,
+    }
+
+    trainer_settings = TrainerSettings.structure(trainersettings_dict, TrainerSettings)
+    assert isinstance(trainer_settings.hyperparameters, PPOSettings)
+    assert trainer_settings.checkpoint_interval == 500000
+
+
+def test_reward_signal_structure():
+    """
+    Tests the RewardSignalSettings structure method. This one is special b/c
+    it takes in a Dict[RewardSignalType, RewardSignalSettings].
+    """
+    reward_signals_dict = {
+        "extrinsic": {"strength": 1.0},
+        "curiosity": {"strength": 1.0},
+    }
+    reward_signals = RewardSignalSettings.structure(
+        reward_signals_dict, Dict[RewardSignalType, RewardSignalSettings]
+    )
+    assert isinstance(reward_signals[RewardSignalType.EXTRINSIC], RewardSignalSettings)
+    assert isinstance(reward_signals[RewardSignalType.CURIOSITY], CuriositySettings)
+
+    # Check invalid reward signal type
+    reward_signals_dict = {"puppo": {"strength": 1.0}}
+    with pytest.raises(ValueError):
+        RewardSignalSettings.structure(
+            reward_signals_dict, Dict[RewardSignalType, RewardSignalSettings]
+        )
+
+    # Check missing GAIL demo path
+    reward_signals_dict = {"gail": {"strength": 1.0}}
+    with pytest.raises(TypeError):
+        RewardSignalSettings.structure(
+            reward_signals_dict, Dict[RewardSignalType, RewardSignalSettings]
+        )
+
+    # Check non-Dict input
+    with pytest.raises(TrainerConfigError):
+        RewardSignalSettings.structure(
+            "notadict", Dict[RewardSignalType, RewardSignalSettings]
+        )
+
+
+def test_memory_settings_validation():
+    with pytest.raises(TrainerConfigError):
+        NetworkSettings.MemorySettings(sequence_length=128, memory_size=63)
+
+    with pytest.raises(TrainerConfigError):
+        NetworkSettings.MemorySettings(sequence_length=128, memory_size=0)
+
+
+def test_env_parameter_structure():
+    """
+    Tests the EnvironmentParameterSettings structure method and all validators.
+    """
+    env_params_dict = {
+        "mass": {
+            "sampler_type": "uniform",
+            "sampler_parameters": {"min_value": 1.0, "max_value": 2.0},
+        },
+        "scale": {
+            "sampler_type": "gaussian",
+            "sampler_parameters": {"mean": 1.0, "st_dev": 2.0},
+        },
+        "length": {
+            "sampler_type": "multirangeuniform",
+            "sampler_parameters": {"intervals": [[1.0, 2.0], [3.0, 4.0]]},
+        },
+        "gravity": 1,
+        "wall_height": {
+            "curriculum": [
+                {
+                    "name": "Lesson1",
+                    "completion_criteria": {
+                        "measure": "reward",
+                        "behavior": "fake_behavior",
+                        "threshold": 10,
+                    },
+                    "value": 1,
+                },
+                {"value": 4, "name": "Lesson2"},
+            ]
+        },
+    }
+    env_param_settings = EnvironmentParameterSettings.structure(
+        env_params_dict, Dict[str, EnvironmentParameterSettings]
+    )
+    assert isinstance(env_param_settings["mass"].curriculum[0].value, UniformSettings)
+    assert isinstance(env_param_settings["scale"].curriculum[0].value, GaussianSettings)
+    assert isinstance(
+        env_param_settings["length"].curriculum[0].value, MultiRangeUniformSettings
+    )
+
+    # Check __str__ is correct
+    assert (
+        str(env_param_settings["mass"].curriculum[0].value)
+        == "Uniform sampler: min=1.0, max=2.0"
+    )
+    assert (
+        str(env_param_settings["scale"].curriculum[0].value)
+        == "Gaussian sampler: mean=1.0, stddev=2.0"
+    )
+    assert (
+        str(env_param_settings["length"].curriculum[0].value)
+        == "MultiRangeUniform sampler: intervals=[(1.0, 2.0), (3.0, 4.0)]"
+    )
+    assert str(env_param_settings["gravity"].curriculum[0].value) == "Float: value=1"
+
+    assert isinstance(
+        env_param_settings["wall_height"].curriculum[0].value, ConstantSettings
+    )
+    assert isinstance(
+        env_param_settings["wall_height"].curriculum[1].value, ConstantSettings
+    )
+
+    # Check invalid distribution type
+    invalid_distribution_dict = {
+        "mass": {
+            "sampler_type": "beta",
+            "sampler_parameters": {"alpha": 1.0, "beta": 2.0},
+        }
+    }
+    with pytest.raises(ValueError):
+        EnvironmentParameterSettings.structure(
+            invalid_distribution_dict, Dict[str, EnvironmentParameterSettings]
+        )
+
+    # Check min less than max in uniform
+    invalid_distribution_dict = {
+        "mass": {
+            "sampler_type": "uniform",
+            "sampler_parameters": {"min_value": 2.0, "max_value": 1.0},
+        }
+    }
+    with pytest.raises(TrainerConfigError):
+        EnvironmentParameterSettings.structure(
+            invalid_distribution_dict, Dict[str, EnvironmentParameterSettings]
+        )
+
+    # Check min less than max in multirange
+    invalid_distribution_dict = {
+        "mass": {
+            "sampler_type": "multirangeuniform",
+            "sampler_parameters": {"intervals": [[2.0, 1.0]]},
+        }
+    }
+    with pytest.raises(TrainerConfigError):
+        EnvironmentParameterSettings.structure(
+            invalid_distribution_dict, Dict[str, EnvironmentParameterSettings]
+        )
+
+    # Check multirange has valid intervals
+    invalid_distribution_dict = {
+        "mass": {
+            "sampler_type": "multirangeuniform",
+            "sampler_parameters": {"intervals": [[1.0, 2.0], [3.0]]},
+        }
+    }
+    with pytest.raises(TrainerConfigError):
+        EnvironmentParameterSettings.structure(
+            invalid_distribution_dict, Dict[str, EnvironmentParameterSettings]
+        )
+
+    # Check non-Dict input
+    with pytest.raises(TrainerConfigError):
+        EnvironmentParameterSettings.structure(
+            "notadict", Dict[str, EnvironmentParameterSettings]
+        )
+
+    invalid_curriculum_dict = {
+        "wall_height": {
+            "curriculum": [
+                {
+                    "name": "Lesson1",
+                    "completion_criteria": {
+                        "measure": "progress",
+                        "behavior": "fake_behavior",
+                        "threshold": 10,
+                    },  # > 1 is too large
+                    "value": 1,
+                },
+                {"value": 4, "name": "Lesson2"},
+            ]
+        }
+    }
+    with pytest.raises(TrainerConfigError):
+        EnvironmentParameterSettings.structure(
+            invalid_curriculum_dict, Dict[str, EnvironmentParameterSettings]
+        )
+
+
+@pytest.mark.parametrize("use_defaults", [True, False])
+def test_exportable_settings(use_defaults):
+    """
+    Test that structuring and unstructuring a RunOptions object results in the same
+    configuration representation.
+    """
+    # Try to enable as many features as possible in this test YAML to hit all the
+    # edge cases. Set as much as possible as non-default values to ensure no flukes.
+    test_yaml = """
+    behaviors:
+        3DBall:
+            trainer_type: sac
+            hyperparameters:
+                learning_rate: 0.0004
+                learning_rate_schedule: constant
+                batch_size: 64
+                buffer_size: 200000
+                buffer_init_steps: 100
+                tau: 0.006
+                steps_per_update: 10.0
+                save_replay_buffer: true
+                init_entcoef: 0.5
+                reward_signal_steps_per_update: 10.0
+            network_settings:
+                deterministic: true
+                normalize: false
+                hidden_units: 256
+                num_layers: 3
+                vis_encode_type: nature_cnn
+                memory:
+                    memory_size: 1288
+                    sequence_length: 12
+            reward_signals:
+                extrinsic:
+                    gamma: 0.999
+                    strength: 1.0
+                curiosity:
+                    gamma: 0.999
+                    strength: 1.0
+            keep_checkpoints: 5
+            max_steps: 500000
+            time_horizon: 1000
+            summary_freq: 12000
+            checkpoint_interval: 1
+            threaded: true
+    env_settings:
+        env_path: test_env_path
+        env_args:
+            - test_env_args1
+            - test_env_args2
+        base_port: 12345
+        num_envs: 8
+        num_areas: 8
+        seed: 12345
+    engine_settings:
+        width: 12345
+        height: 12345
+        quality_level: 12345
+        time_scale: 12345
+        target_frame_rate: 12345
+        capture_frame_rate: 12345
+        no_graphics: true
+    checkpoint_settings:
+        run_id: test_run_id
+        initialize_from: test_directory
+        load_model: false
+        resume: true
+        force: true
+        train_model: false
+        inference: false
+    debug: true
+    environment_parameters:
+        big_wall_height:
+            curriculum:
+              - name: Lesson0
+                completion_criteria:
+                    measure: progress
+                    behavior: BigWallJump
+                    signal_smoothing: true
+                    min_lesson_length: 100
+                    threshold: 0.1
+                value:
+                    sampler_type: uniform
+                    sampler_parameters:
+                        min_value: 0.0
+                        max_value: 4.0
+              - name: Lesson1
+                completion_criteria:
+                    measure: reward
+                    behavior: BigWallJump
+                    signal_smoothing: true
+                    min_lesson_length: 100
+                    threshold: 0.2
+                value:
+                    sampler_type: gaussian
+                    sampler_parameters:
+                        mean: 4.0
+                        st_dev: 7.0
+              - name: Lesson2
+                completion_criteria:
+                    measure: progress
+                    behavior: BigWallJump
+                    signal_smoothing: true
+                    min_lesson_length: 20
+                    threshold: 0.3
+                value:
+                    sampler_type: multirangeuniform
+                    sampler_parameters:
+                        intervals: [[1.0, 2.0],[4.0, 5.0]]
+              - name: Lesson3
+                value: 8.0
+        small_wall_height: 42.0
+        other_wall_height:
+            sampler_type: multirangeuniform
+            sampler_parameters:
+                intervals: [[1.0, 2.0],[4.0, 5.0]]
+    """
+    if not use_defaults:
+        loaded_yaml = yaml.safe_load(test_yaml)
+        run_options = RunOptions.from_dict(yaml.safe_load(test_yaml))
+    else:
+        run_options = RunOptions()
+    dict_export = run_options.as_dict()
+
+    if not use_defaults:  # Don't need to check if no yaml
+        check_dict_is_at_least(
+            loaded_yaml, dict_export, exceptions=["environment_parameters"]
+        )
+    # Re-import and verify has same elements
+    run_options2 = RunOptions.from_dict(dict_export)
+    second_export = run_options2.as_dict()
+
+    check_dict_is_at_least(dict_export, second_export)
+    # Should be able to use equality instead of back-and-forth once environment_parameters
+    # is working
+    check_dict_is_at_least(second_export, dict_export)
+    # Check that the two exports are the same
+    assert dict_export == second_export
+
+    # check if cehckpoint_settings priorotizes resume over initialize from
+    run_options2.checkpoint_settings.prioritize_resume_init()
+    assert run_options2.checkpoint_settings.initialize_from is None
+
+
+def test_environment_settings():
+    # default args
+    EnvironmentSettings()
+
+    # 1 env is OK if no env_path
+    EnvironmentSettings(num_envs=1)
+
+    # 2 areas are OK
+    EnvironmentSettings(num_areas=2)
+
+    # multiple envs is OK if env_path is set
+    EnvironmentSettings(num_envs=42, env_path="/foo/bar.exe")
+
+    # Multiple environments with no env_path is an error
+    with pytest.raises(ValueError):
+        EnvironmentSettings(num_envs=2)
+
+
+def test_default_settings():
+    # Make default settings, one nested and one not.
+    default_settings = {
+        "max_steps": 1,
+        "network_settings": {"num_layers": 1000, "deterministic": True},
+    }
+    behaviors = {"test1": {"max_steps": 2, "network_settings": {"hidden_units": 2000}}}
+    run_options_dict = {"default_settings": default_settings, "behaviors": behaviors}
+    run_options = RunOptions.from_dict(run_options_dict)
+
+    # Check that a new behavior has the default settings
+    default_settings_cls = cattr.structure(default_settings, TrainerSettings)
+    check_if_different(default_settings_cls, run_options.behaviors["test2"])
+
+    # Check that an existing behavior overrides the defaults in specified fields
+    test1_settings = run_options.behaviors["test1"]
+    assert test1_settings.max_steps == 2
+    assert test1_settings.network_settings.hidden_units == 2000
+    assert test1_settings.network_settings.deterministic is True
+    assert test1_settings.network_settings.num_layers == 1000
+
+    # Change the overridden fields back, and check if the rest are equal.
+    test1_settings.max_steps = 1
+    test1_settings.network_settings.hidden_units = (
+        default_settings_cls.network_settings.hidden_units
+    )
+    check_if_different(test1_settings, default_settings_cls)
+
+
+def test_config_specified():
+    # Test require all behavior names to be specified (or not)
+    # Remove any pre-set defaults
+    TrainerSettings.default_override = None
+    behaviors = {"test1": {"max_steps": 2, "network_settings": {"hidden_units": 2000}}}
+    run_options_dict = {"behaviors": behaviors}
+    ro = RunOptions.from_dict(run_options_dict)
+    # Don't require all behavior names
+    ro.behaviors.set_config_specified(False)
+    # Test that we can grab an entry that is not in the dict.
+    assert isinstance(ro.behaviors["test2"], TrainerSettings)
+
+    # Create strict RunOptions with no defualt_settings
+    run_options_dict = {"behaviors": behaviors}
+    ro = RunOptions.from_dict(run_options_dict)
+    # Require all behavior names
+    ro.behaviors.set_config_specified(True)
+    with pytest.raises(TrainerConfigError):
+        # Variable must be accessed otherwise Python won't query the dict
+        print(ro.behaviors["test2"])
+
+    # Create strict RunOptions with default settings
+    default_settings = {"max_steps": 1, "network_settings": {"num_layers": 1000}}
+    run_options_dict = {"default_settings": default_settings, "behaviors": behaviors}
+    ro = RunOptions.from_dict(run_options_dict)
+    # Require all behavior names
+    ro.behaviors.set_config_specified(True)
+    # Test that we can grab an entry that is not in the dict.
+    assert isinstance(ro.behaviors["test2"], TrainerSettings)
+
+
+def test_pickle():
+    # Make sure RunOptions is pickle-able.
+    run_options = RunOptions()
+    p = pickle.dumps(run_options)
+    pickle.loads(p)
diff --git a/ml-agents/mlagents/trainers/tests/test_stats.py b/ml-agents/mlagents/trainers/tests/test_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..e89b9348691bef620c43e38c5db96262a0e2749e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_stats.py
@@ -0,0 +1,250 @@
+from unittest import mock
+import os
+import pytest
+import tempfile
+import unittest
+import time
+
+
+from mlagents.trainers.stats import (
+    StatsReporter,
+    TensorboardWriter,
+    StatsSummary,
+    GaugeWriter,
+    ConsoleWriter,
+    StatsPropertyType,
+    StatsAggregationMethod,
+)
+
+from mlagents.trainers.env_manager import AgentManager
+
+
+def test_stat_reporter_add_summary_write():
+    # Test add_writer
+    StatsReporter.writers.clear()
+    mock_writer1 = mock.Mock()
+    mock_writer2 = mock.Mock()
+    StatsReporter.add_writer(mock_writer1)
+    StatsReporter.add_writer(mock_writer2)
+    assert len(StatsReporter.writers) == 2
+
+    # Test add_stats and summaries
+    statsreporter1 = StatsReporter("category1")
+    statsreporter2 = StatsReporter("category2")
+    for i in range(10):
+        statsreporter1.add_stat("key1", float(i))
+        statsreporter2.add_stat("key2", float(i))
+
+    statsreportercalls = [
+        mock.call(f"category{j}", f"key{j}", float(i), StatsAggregationMethod.AVERAGE)
+        for i in range(10)
+        for j in [1, 2]
+    ]
+
+    mock_writer1.on_add_stat.assert_has_calls(statsreportercalls)
+    mock_writer2.on_add_stat.assert_has_calls(statsreportercalls)
+
+    statssummary1 = statsreporter1.get_stats_summaries("key1")
+    statssummary2 = statsreporter2.get_stats_summaries("key2")
+
+    assert statssummary1.num == 10
+    assert statssummary2.num == 10
+    assert statssummary1.mean == 4.5
+    assert statssummary2.mean == 4.5
+    assert statssummary1.std == pytest.approx(2.9, abs=0.1)
+    assert statssummary2.std == pytest.approx(2.9, abs=0.1)
+
+    # Test write_stats
+    step = 10
+    statsreporter1.write_stats(step)
+    mock_writer1.write_stats.assert_called_once_with(
+        "category1", {"key1": statssummary1}, step
+    )
+    mock_writer2.write_stats.assert_called_once_with(
+        "category1", {"key1": statssummary1}, step
+    )
+
+
+def test_stat_reporter_property():
+    # Test add_writer
+    mock_writer = mock.Mock()
+    StatsReporter.writers.clear()
+    StatsReporter.add_writer(mock_writer)
+    assert len(StatsReporter.writers) == 1
+
+    statsreporter1 = StatsReporter("category1")
+
+    # Test add_property
+    statsreporter1.add_property("key", "this is a text")
+    mock_writer.add_property.assert_called_once_with(
+        "category1", "key", "this is a text"
+    )
+
+
+@mock.patch("mlagents.trainers.stats.SummaryWriter")
+def test_tensorboard_writer(mock_summary):
+    # Test write_stats
+    category = "category1"
+    with tempfile.TemporaryDirectory(prefix="unittest-") as base_dir:
+        tb_writer = TensorboardWriter(base_dir, clear_past_data=False)
+        statssummary1 = StatsSummary(
+            full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
+        )
+        tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
+
+        # Test that the filewriter has been created and the directory has been created.
+        filewriter_dir = "{basedir}/{category}".format(
+            basedir=base_dir, category=category
+        )
+        assert os.path.exists(filewriter_dir)
+        mock_summary.assert_called_once_with(filewriter_dir)
+
+        # Test that the filewriter was written to and the summary was added.
+        mock_summary.return_value.add_scalar.assert_called_once_with("key1", 1.0, 10)
+        mock_summary.return_value.flush.assert_called_once()
+
+        # Test hyperparameter writing - no good way to parse the TB string though.
+        tb_writer.add_property(
+            "category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
+        )
+        assert mock_summary.return_value.add_text.call_count >= 1
+
+
+@pytest.mark.parametrize("aggregation_type", list(StatsAggregationMethod))
+def test_agent_manager_stats_report(aggregation_type):
+    stats_reporter = StatsReporter("recorder_name")
+    manager = AgentManager(None, "behaviorName", stats_reporter)
+
+    values = range(5)
+
+    env_stats = {"stat": [(i, aggregation_type) for i in values]}
+    manager.record_environment_stats(env_stats, 0)
+    summary = stats_reporter.get_stats_summaries("stat")
+    aggregation_result = {
+        StatsAggregationMethod.AVERAGE: sum(values) / len(values),
+        StatsAggregationMethod.MOST_RECENT: values[-1],
+        StatsAggregationMethod.SUM: sum(values),
+        StatsAggregationMethod.HISTOGRAM: sum(values) / len(values),
+    }
+
+    assert summary.aggregated_value == aggregation_result[aggregation_type]
+    stats_reporter.write_stats(0)
+
+
+def test_tensorboard_writer_clear(tmp_path):
+    tb_writer = TensorboardWriter(tmp_path, clear_past_data=False)
+    statssummary1 = StatsSummary(
+        full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
+    )
+    tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
+    # TB has some sort of timeout before making a new file
+    time.sleep(1.0)
+    assert len(os.listdir(os.path.join(tmp_path, "category1"))) > 0
+
+    # See if creating a new one doesn't delete it
+    tb_writer = TensorboardWriter(tmp_path, clear_past_data=False)
+    tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
+    assert len(os.listdir(os.path.join(tmp_path, "category1"))) > 1
+    time.sleep(1.0)
+
+    # See if creating a new one deletes old ones
+    tb_writer = TensorboardWriter(tmp_path, clear_past_data=True)
+    tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
+    assert len(os.listdir(os.path.join(tmp_path, "category1"))) == 1
+
+
+@mock.patch("mlagents.trainers.stats.SummaryWriter")
+def test_tensorboard_writer_hidden_keys(mock_summary):
+    # Test write_stats
+    category = "category1"
+    with tempfile.TemporaryDirectory(prefix="unittest-") as base_dir:
+        tb_writer = TensorboardWriter(
+            base_dir, clear_past_data=False, hidden_keys="hiddenKey"
+        )
+        statssummary1 = StatsSummary(
+            full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
+        )
+        tb_writer.write_stats("category1", {"hiddenKey": statssummary1}, 10)
+
+        # Test that the filewriter has been created and the directory has been created.
+        filewriter_dir = "{basedir}/{category}".format(
+            basedir=base_dir, category=category
+        )
+        assert os.path.exists(filewriter_dir)
+        mock_summary.assert_called_once_with(filewriter_dir)
+
+        # Test that the filewriter was not written to since we used the hidden key.
+        mock_summary.return_value.add_scalar.assert_not_called()
+        mock_summary.return_value.flush.assert_not_called()
+
+
+def test_gauge_stat_writer_sanitize():
+    assert GaugeWriter.sanitize_string("Policy/Learning Rate") == "Policy.LearningRate"
+    assert (
+        GaugeWriter.sanitize_string("Very/Very/Very Nested Stat")
+        == "Very.Very.VeryNestedStat"
+    )
+
+
+class ConsoleWriterTest(unittest.TestCase):
+    def test_console_writer(self):
+        # Test write_stats
+        with self.assertLogs("mlagents.trainers", level="INFO") as cm:
+            category = "category1"
+            console_writer = ConsoleWriter()
+            statssummary1 = StatsSummary(
+                full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
+            )
+            console_writer.write_stats(
+                category,
+                {
+                    "Environment/Cumulative Reward": statssummary1,
+                    "Is Training": statssummary1,
+                },
+                10,
+            )
+            statssummary2 = StatsSummary(
+                full_dist=[0.0], aggregation_method=StatsAggregationMethod.AVERAGE
+            )
+            console_writer.write_stats(
+                category,
+                {
+                    "Environment/Cumulative Reward": statssummary2,
+                    "Is Training": statssummary2,
+                },
+                10,
+            )
+            # Test hyperparameter writing
+            console_writer.add_property(
+                "category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
+            )
+
+        self.assertIn(
+            "Mean Reward: 1.000. Std of Reward: 0.000. Training.", cm.output[0]
+        )
+        self.assertIn("Not Training.", cm.output[1])
+
+        self.assertIn("Hyperparameters for behavior name", cm.output[2])
+        self.assertIn("example:\t1.0", cm.output[2])
+
+    def test_selfplay_console_writer(self):
+        with self.assertLogs("mlagents.trainers", level="INFO") as cm:
+            category = "category1"
+            console_writer = ConsoleWriter()
+            console_writer.add_property(category, StatsPropertyType.SELF_PLAY, True)
+            statssummary1 = StatsSummary(
+                full_dist=[1.0], aggregation_method=StatsAggregationMethod.AVERAGE
+            )
+            console_writer.write_stats(
+                category,
+                {
+                    "Environment/Cumulative Reward": statssummary1,
+                    "Is Training": statssummary1,
+                    "Self-play/ELO": statssummary1,
+                },
+                10,
+            )
+
+        self.assertIn(
+            "Mean Reward: 1.000. Std of Reward: 0.000. Training.", cm.output[0]
+        )
diff --git a/ml-agents/mlagents/trainers/tests/test_subprocess_env_manager.py b/ml-agents/mlagents/trainers/tests/test_subprocess_env_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..553b0c11b3aad5e6d02afa0bb1f2a4c790728beb
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_subprocess_env_manager.py
@@ -0,0 +1,325 @@
+from unittest import mock
+from unittest.mock import Mock, MagicMock, call, ANY
+import unittest
+import pytest
+from queue import Empty as EmptyQueue
+
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.subprocess_env_manager import (
+    SubprocessEnvManager,
+    EnvironmentResponse,
+    StepResponse,
+    EnvironmentCommand,
+)
+from mlagents.trainers.env_manager import EnvironmentStep
+from mlagents_envs.base_env import BaseEnv
+from mlagents_envs.side_channel.stats_side_channel import StatsAggregationMethod
+from mlagents_envs.exception import (
+    UnityEnvironmentException,
+    UnityCommunicationException,
+)
+from mlagents.trainers.tests.simple_test_envs import (
+    SimpleEnvironment,
+    UnexpectedExceptionEnvironment,
+)
+from mlagents.trainers.stats import StatsReporter
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.tests.check_env_trains import (
+    check_environment_trains,
+    DebugWriter,
+)
+from mlagents.trainers.tests.dummy_config import ppo_dummy_config
+
+
+def mock_env_factory(worker_id):
+    return mock.create_autospec(spec=BaseEnv)
+
+
+class MockEnvWorker:
+    def __init__(self, worker_id, resp=None):
+        self.worker_id = worker_id
+        self.process = None
+        self.conn = None
+        self.send = Mock()
+        self.recv = Mock(return_value=resp)
+        self.waiting = False
+
+
+def create_worker_mock(worker_id, step_queue, env_factor, engine_c):
+    return MockEnvWorker(
+        worker_id, EnvironmentResponse(EnvironmentCommand.RESET, worker_id, worker_id)
+    )
+
+
+class SubprocessEnvManagerTest(unittest.TestCase):
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_environments_are_created(self, mock_create_worker):
+        mock_create_worker.side_effect = create_worker_mock
+        run_options = RunOptions()
+        env = SubprocessEnvManager(mock_env_factory, run_options, 2)
+        # Creates two processes
+        env.create_worker.assert_has_calls(
+            [
+                mock.call(0, env.step_queue, mock_env_factory, run_options),
+                mock.call(1, env.step_queue, mock_env_factory, run_options),
+            ]
+        )
+        self.assertEqual(len(env.env_workers), 2)
+
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_reset_passes_reset_params(self, mock_create_worker):
+        mock_create_worker.side_effect = create_worker_mock
+        manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 1)
+        params = {"test": "params"}
+        manager._reset_env(params)
+        manager.env_workers[0].send.assert_called_with(
+            EnvironmentCommand.RESET, (params)
+        )
+
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_reset_collects_results_from_all_envs(self, mock_create_worker):
+        mock_create_worker.side_effect = create_worker_mock
+        manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 4)
+
+        params = {"test": "params"}
+        res = manager._reset_env(params)
+        for i, env in enumerate(manager.env_workers):
+            env.send.assert_called_with(EnvironmentCommand.RESET, (params))
+            env.recv.assert_called()
+            # Check that the "last steps" are set to the value returned for each step
+            self.assertEqual(
+                manager.env_workers[i].previous_step.current_all_step_result, i
+            )
+        assert res == list(map(lambda ew: ew.previous_step, manager.env_workers))
+
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_training_behaviors_collects_results_from_all_envs(
+        self, mock_create_worker
+    ):
+        def create_worker_mock(worker_id, step_queue, env_factor, engine_c):
+            return MockEnvWorker(
+                worker_id,
+                EnvironmentResponse(
+                    EnvironmentCommand.RESET, worker_id, {f"key{worker_id}": worker_id}
+                ),
+            )
+
+        mock_create_worker.side_effect = create_worker_mock
+        manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 4)
+
+        res = manager.training_behaviors
+        for env in manager.env_workers:
+            env.send.assert_called_with(EnvironmentCommand.BEHAVIOR_SPECS)
+            env.recv.assert_called()
+        for worker_id in range(4):
+            assert f"key{worker_id}" in res
+            assert res[f"key{worker_id}"] == worker_id
+
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_step_takes_steps_for_all_non_waiting_envs(self, mock_create_worker):
+        mock_create_worker.side_effect = create_worker_mock
+        manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 3)
+        manager.step_queue = Mock()
+        manager.step_queue.get_nowait.side_effect = [
+            EnvironmentResponse(EnvironmentCommand.STEP, 0, StepResponse(0, None, {})),
+            EnvironmentResponse(EnvironmentCommand.STEP, 1, StepResponse(1, None, {})),
+            EmptyQueue(),
+        ]
+        step_mock = Mock()
+        last_steps = [Mock(), Mock(), Mock()]
+        manager.env_workers[0].previous_step = last_steps[0]
+        manager.env_workers[1].previous_step = last_steps[1]
+        manager.env_workers[2].previous_step = last_steps[2]
+        manager.env_workers[2].waiting = True
+        manager._take_step = Mock(return_value=step_mock)
+        res = manager._step()
+        for i, env in enumerate(manager.env_workers):
+            if i < 2:
+                env.send.assert_called_with(EnvironmentCommand.STEP, step_mock)
+                manager.step_queue.get_nowait.assert_called()
+                # Check that the "last steps" are set to the value returned for each step
+                self.assertEqual(
+                    manager.env_workers[i].previous_step.current_all_step_result, i
+                )
+        assert res == [
+            manager.env_workers[0].previous_step,
+            manager.env_workers[1].previous_step,
+        ]
+
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_crashed_env_restarts(self, mock_create_worker):
+        crashing_worker = MockEnvWorker(
+            0, EnvironmentResponse(EnvironmentCommand.RESET, 0, 0)
+        )
+        restarting_worker = MockEnvWorker(
+            0, EnvironmentResponse(EnvironmentCommand.RESET, 0, 0)
+        )
+        healthy_worker = MockEnvWorker(
+            1, EnvironmentResponse(EnvironmentCommand.RESET, 1, 1)
+        )
+        mock_create_worker.side_effect = [
+            crashing_worker,
+            healthy_worker,
+            restarting_worker,
+        ]
+        manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 2)
+        manager.step_queue = Mock()
+        manager.step_queue.get_nowait.side_effect = [
+            EnvironmentResponse(
+                EnvironmentCommand.ENV_EXITED,
+                0,
+                UnityCommunicationException("Test msg"),
+            ),
+            EnvironmentResponse(EnvironmentCommand.CLOSED, 0, None),
+            EnvironmentResponse(EnvironmentCommand.STEP, 1, StepResponse(0, None, {})),
+            EmptyQueue(),
+            EnvironmentResponse(EnvironmentCommand.STEP, 0, StepResponse(1, None, {})),
+            EnvironmentResponse(EnvironmentCommand.STEP, 1, StepResponse(2, None, {})),
+            EmptyQueue(),
+        ]
+        step_mock = Mock()
+        last_steps = [Mock(), Mock(), Mock()]
+        assert crashing_worker is manager.env_workers[0]
+        assert healthy_worker is manager.env_workers[1]
+        crashing_worker.previous_step = last_steps[0]
+        crashing_worker.waiting = True
+        healthy_worker.previous_step = last_steps[1]
+        healthy_worker.waiting = True
+        manager._take_step = Mock(return_value=step_mock)
+        manager._step()
+        healthy_worker.send.assert_has_calls(
+            [
+                call(EnvironmentCommand.ENVIRONMENT_PARAMETERS, ANY),
+                call(EnvironmentCommand.RESET, ANY),
+                call(EnvironmentCommand.STEP, ANY),
+            ]
+        )
+        restarting_worker.send.assert_has_calls(
+            [
+                call(EnvironmentCommand.ENVIRONMENT_PARAMETERS, ANY),
+                call(EnvironmentCommand.RESET, ANY),
+                call(EnvironmentCommand.STEP, ANY),
+            ]
+        )
+
+    @mock.patch("mlagents.trainers.subprocess_env_manager.SubprocessEnvManager._step")
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.training_behaviors",
+        new_callable=mock.PropertyMock,
+    )
+    @mock.patch(
+        "mlagents.trainers.subprocess_env_manager.SubprocessEnvManager.create_worker"
+    )
+    def test_advance(self, mock_create_worker, training_behaviors_mock, step_mock):
+        brain_name = "testbrain"
+        action_info_dict = {brain_name: MagicMock()}
+        mock_create_worker.side_effect = create_worker_mock
+        env_manager = SubprocessEnvManager(mock_env_factory, RunOptions(), 3)
+        training_behaviors_mock.return_value = [brain_name]
+        agent_manager_mock = mock.Mock()
+        mock_policy = mock.Mock()
+        agent_manager_mock.policy_queue.get_nowait.side_effect = [
+            mock_policy,
+            mock_policy,
+            AgentManagerQueue.Empty(),
+        ]
+        env_manager.set_agent_manager(brain_name, agent_manager_mock)
+
+        step_info_dict = {brain_name: (Mock(), Mock())}
+        env_stats = {
+            "averaged": (1.0, StatsAggregationMethod.AVERAGE),
+            "most_recent": (2.0, StatsAggregationMethod.MOST_RECENT),
+        }
+        step_info = EnvironmentStep(step_info_dict, 0, action_info_dict, env_stats)
+        step_mock.return_value = [step_info]
+        env_manager.process_steps(env_manager.get_steps())
+
+        # Test add_experiences
+        env_manager._step.assert_called_once()
+
+        agent_manager_mock.add_experiences.assert_called_once_with(
+            step_info.current_all_step_result[brain_name][0],
+            step_info.current_all_step_result[brain_name][1],
+            0,
+            step_info.brain_name_to_action_info[brain_name],
+        )
+
+        # Test policy queue
+        assert env_manager.policies[brain_name] == mock_policy
+        assert agent_manager_mock.policy == mock_policy
+
+
+@pytest.mark.parametrize("num_envs", [1, 4])
+def test_subprocess_env_endtoend(num_envs):
+    def simple_env_factory(worker_id, config):
+        env = SimpleEnvironment(["1D"], action_sizes=(0, 1))
+        return env
+
+    env_manager = SubprocessEnvManager(simple_env_factory, RunOptions(), num_envs)
+    # Run PPO using env_manager
+    check_environment_trains(
+        simple_env_factory(0, []),
+        {"1D": ppo_dummy_config()},
+        env_manager=env_manager,
+        success_threshold=None,
+    )
+    # Note we can't check the env's rewards directly (since they're in separate processes) so we
+    # check the StatsReporter's debug stat writer's last reward.
+    assert isinstance(StatsReporter.writers[0], DebugWriter)
+    assert all(
+        val > 0.7 for val in StatsReporter.writers[0].get_last_rewards().values()
+    )
+    env_manager.close()
+
+
+class CustomTestOnlyException(Exception):
+    pass
+
+
+@pytest.mark.parametrize("num_envs", [1, 4])
+def test_subprocess_failing_step(num_envs):
+    def failing_step_env_factory(_worker_id, _config):
+        env = UnexpectedExceptionEnvironment(
+            ["1D"], use_discrete=True, to_raise=CustomTestOnlyException
+        )
+        return env
+
+    env_manager = SubprocessEnvManager(failing_step_env_factory, RunOptions())
+    # Expect the exception raised to be routed back up to the top level.
+    with pytest.raises(CustomTestOnlyException):
+        check_environment_trains(
+            failing_step_env_factory(0, []),
+            {"1D": ppo_dummy_config()},
+            env_manager=env_manager,
+            success_threshold=None,
+        )
+    env_manager.close()
+
+
+@pytest.mark.parametrize("num_envs", [1, 4])
+def test_subprocess_env_raises_errors(num_envs):
+    def failing_env_factory(worker_id, config):
+        import time
+
+        # Sleep momentarily to allow time for the EnvManager to be waiting for the
+        # subprocess response.  We won't be able to capture failures from the subprocess
+        # that cause it to close the pipe before we can send the first message.
+        time.sleep(0.5)
+        raise UnityEnvironmentException()
+
+    env_manager = SubprocessEnvManager(failing_env_factory, RunOptions(), num_envs)
+    with pytest.raises(UnityEnvironmentException):
+        env_manager.reset()
+    env_manager.close()
diff --git a/ml-agents/mlagents/trainers/tests/test_torch_utils.py b/ml-agents/mlagents/trainers/tests/test_torch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..71468313198cb5949fa307d324537ac63084147f
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_torch_utils.py
@@ -0,0 +1,41 @@
+import pytest
+from unittest import mock
+
+import torch  # noqa I201
+
+from mlagents.torch_utils import set_torch_config, default_device
+from mlagents.trainers.settings import TorchSettings
+
+
+@pytest.mark.parametrize(
+    "device_str, expected_type, expected_index, expected_tensor_type",
+    [
+        ("cpu", "cpu", None, torch.FloatTensor),
+        ("cuda", "cuda", None, torch.cuda.FloatTensor),
+        ("cuda:42", "cuda", 42, torch.cuda.FloatTensor),
+        ("opengl", "opengl", None, torch.FloatTensor),
+    ],
+)
+@mock.patch.object(torch, "set_default_tensor_type")
+def test_set_torch_device(
+    mock_set_default_tensor_type,
+    device_str,
+    expected_type,
+    expected_index,
+    expected_tensor_type,
+):
+    try:
+        torch_settings = TorchSettings(device=device_str)
+        set_torch_config(torch_settings)
+        assert default_device().type == expected_type
+        if expected_index is None:
+            assert default_device().index is None
+        else:
+            assert default_device().index == expected_index
+        mock_set_default_tensor_type.assert_called_once_with(expected_tensor_type)
+    except Exception:
+        raise
+    finally:
+        # restore the defaults
+        torch_settings = TorchSettings(device=None)
+        set_torch_config(torch_settings)
diff --git a/ml-agents/mlagents/trainers/tests/test_trainer_controller.py b/ml-agents/mlagents/trainers/tests/test_trainer_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0d5d793317fc23360aedc62965643fd0c592b23
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_trainer_controller.py
@@ -0,0 +1,139 @@
+from unittest.mock import MagicMock, patch
+import pytest
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.trainer_controller import TrainerController
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.ghost.controller import GhostController
+
+
+@pytest.fixture
+def basic_trainer_controller():
+    trainer_factory_mock = MagicMock()
+    trainer_factory_mock.ghost_controller = GhostController()
+    return TrainerController(
+        trainer_factory=trainer_factory_mock,
+        output_path="test_model_path",
+        run_id="test_run_id",
+        param_manager=EnvironmentParameterManager(),
+        train=True,
+        training_seed=99,
+    )
+
+
+@patch("numpy.random.seed")
+@patch.object(torch, "manual_seed")
+def test_initialization_seed(numpy_random_seed, torch_set_seed):
+    seed = 27
+    trainer_factory_mock = MagicMock()
+    trainer_factory_mock.ghost_controller = GhostController()
+    TrainerController(
+        trainer_factory=trainer_factory_mock,
+        output_path="",
+        run_id="1",
+        param_manager=None,
+        train=True,
+        training_seed=seed,
+    )
+    numpy_random_seed.assert_called_with(seed)
+    torch_set_seed.assert_called_with(seed)
+
+
+@pytest.fixture
+def trainer_controller_with_start_learning_mocks(basic_trainer_controller):
+    trainer_mock = MagicMock()
+    trainer_mock.get_step = 0
+    trainer_mock.get_max_steps = 5
+    trainer_mock.should_still_train = True
+    trainer_mock.parameters = {"some": "parameter"}
+    trainer_mock.write_tensorboard_text = MagicMock()
+
+    tc = basic_trainer_controller
+    tc.trainers = {"testbrain": trainer_mock}
+    tc.advance = MagicMock()
+    tc.trainers["testbrain"].get_step = 0
+
+    def take_step_sideeffect(env):
+        tc.trainers["testbrain"].get_step += 1
+        if (
+            not tc.trainers["testbrain"].get_step
+            <= tc.trainers["testbrain"].get_max_steps
+        ):
+            tc.trainers["testbrain"].should_still_train = False
+        if tc.trainers["testbrain"].get_step > 10:
+            raise KeyboardInterrupt
+        return 1
+
+    tc.advance.side_effect = take_step_sideeffect
+
+    tc._save_models = MagicMock()
+    return tc, trainer_mock
+
+
+def test_start_learning_trains_forever_if_no_train_model(
+    trainer_controller_with_start_learning_mocks,
+):
+    tc, trainer_mock = trainer_controller_with_start_learning_mocks
+    tc.train_model = False
+
+    env_mock = MagicMock()
+    env_mock.close = MagicMock()
+    env_mock.reset = MagicMock()
+    env_mock.training_behaviors = MagicMock()
+
+    tc.start_learning(env_mock)
+    env_mock.reset.assert_called_once()
+    assert tc.advance.call_count == 11
+    tc._save_models.assert_not_called()
+
+
+def test_start_learning_trains_until_max_steps_then_saves(
+    trainer_controller_with_start_learning_mocks,
+):
+    tc, trainer_mock = trainer_controller_with_start_learning_mocks
+
+    brain_info_mock = MagicMock()
+    env_mock = MagicMock()
+    env_mock.close = MagicMock()
+    env_mock.reset = MagicMock(return_value=brain_info_mock)
+    env_mock.training_behaviors = MagicMock()
+
+    tc.start_learning(env_mock)
+    env_mock.reset.assert_called_once()
+    assert tc.advance.call_count == trainer_mock.get_max_steps + 1
+    tc._save_models.assert_called_once()
+
+
+@pytest.fixture
+def trainer_controller_with_take_step_mocks(basic_trainer_controller):
+    trainer_mock = MagicMock()
+    trainer_mock.get_step = 0
+    trainer_mock.get_max_steps = 5
+    trainer_mock.parameters = {"some": "parameter"}
+    trainer_mock.write_tensorboard_text = MagicMock()
+
+    tc = basic_trainer_controller
+    tc.trainers = {"testbrain": trainer_mock}
+    tc.managers = {"testbrain": MagicMock()}
+
+    return tc, trainer_mock
+
+
+def test_advance_adds_experiences_to_trainer_and_trains(
+    trainer_controller_with_take_step_mocks,
+):
+    tc, trainer_mock = trainer_controller_with_take_step_mocks
+
+    brain_name = "testbrain"
+
+    env_mock = MagicMock()
+
+    tc.brain_name_to_identifier[brain_name].add(brain_name)
+
+    tc.advance(env_mock)
+
+    env_mock.reset.assert_not_called()
+    env_mock.get_steps.assert_called_once()
+    env_mock.process_steps.assert_called_once()
+    # May have been called many times due to thread
+    # assert trainer_mock.advance.call_count > 0
diff --git a/ml-agents/mlagents/trainers/tests/test_trainer_util.py b/ml-agents/mlagents/trainers/tests/test_trainer_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d90e46fb061f65afc196b0aa2d9d9a554be345a
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_trainer_util.py
@@ -0,0 +1,187 @@
+import pytest
+import io
+import os
+import yaml
+from unittest.mock import patch
+
+from mlagents.trainers.trainer import TrainerFactory
+from mlagents.trainers.cli_utils import load_config, _load_config
+from mlagents.trainers.ppo.trainer import PPOTrainer
+from mlagents.trainers.exception import TrainerConfigError, UnityTrainerException
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.tests.dummy_config import ppo_dummy_config
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.directory_utils import (
+    validate_existing_directories,
+    setup_init_path,
+)
+
+
+@pytest.fixture
+def dummy_config():
+    return RunOptions(behaviors={"testbrain": ppo_dummy_config()})
+
+
+@patch("mlagents_envs.base_env.BehaviorSpec")
+def test_initialize_ppo_trainer(BehaviorSpecMock, dummy_config):
+    brain_name = "testbrain"
+    training_behaviors = {"testbrain": BehaviorSpecMock()}
+    output_path = "results_dir"
+    train_model = True
+    load_model = False
+    seed = 11
+    expected_reward_buff_cap = 1
+
+    base_config = dummy_config.behaviors
+    expected_config = ppo_dummy_config()
+
+    def mock_constructor(
+        self,
+        brain,
+        reward_buff_cap,
+        trainer_settings,
+        training,
+        load,
+        p_seed,
+        artifact_path,
+    ):
+        assert brain == brain_name
+        assert trainer_settings == expected_config
+        assert reward_buff_cap == expected_reward_buff_cap
+        assert training == train_model
+        assert load == load_model
+        assert p_seed == seed
+        assert artifact_path == os.path.join(output_path, brain_name)
+
+    with patch.object(PPOTrainer, "__init__", mock_constructor):
+        trainer_factory = TrainerFactory(
+            trainer_config=base_config,
+            output_path=output_path,
+            train_model=train_model,
+            load_model=load_model,
+            seed=seed,
+            param_manager=EnvironmentParameterManager(),
+        )
+        trainers = {}
+        for brain_name in training_behaviors.keys():
+            trainers[brain_name] = trainer_factory.generate(brain_name)
+        assert "testbrain" in trainers
+        assert isinstance(trainers["testbrain"], PPOTrainer)
+
+
+def test_handles_no_config_provided():
+    """
+    Make sure the trainer setup handles no configs provided at all.
+    """
+    brain_name = "testbrain"
+    no_default_config = RunOptions().behaviors
+    # Pretend this was created without a YAML file
+    no_default_config.set_config_specified(False)
+
+    trainer_factory = TrainerFactory(
+        trainer_config=no_default_config,
+        output_path="output_path",
+        train_model=True,
+        load_model=False,
+        seed=42,
+        param_manager=EnvironmentParameterManager(),
+    )
+    trainer_factory.generate(brain_name)
+
+
+def test_load_config_missing_file():
+    with pytest.raises(TrainerConfigError):
+        load_config("thisFileDefinitelyDoesNotExist.yaml")
+
+
+def test_load_config_valid_yaml():
+    file_contents = """
+this:
+  - is fine
+    """
+    fp = io.StringIO(file_contents)
+    res = _load_config(fp)
+    assert res == {"this": ["is fine"]}
+
+
+def test_load_config_invalid_yaml():
+    file_contents = """
+you:
+  - will
+- not
+  - parse
+    """
+    with pytest.raises(TrainerConfigError):
+        fp = io.StringIO(file_contents)
+        _load_config(fp)
+
+
+def test_existing_directories(tmp_path):
+    output_path = os.path.join(tmp_path, "runid")
+    # Test fresh new unused path - should do nothing.
+    validate_existing_directories(output_path, False, False)
+    # Test resume with fresh path - should throw an exception.
+    with pytest.raises(UnityTrainerException):
+        validate_existing_directories(output_path, True, False)
+
+    # make a directory
+    os.mkdir(output_path)
+    # Test try to train w.o. force, should complain
+    with pytest.raises(UnityTrainerException):
+        validate_existing_directories(output_path, False, False)
+    # Test try to train w/ resume - should work
+    validate_existing_directories(output_path, True, False)
+    # Test try to train w/ force - should work
+    validate_existing_directories(output_path, False, True)
+
+    # Test initialize option
+    init_path = os.path.join(tmp_path, "runid2")
+    with pytest.raises(UnityTrainerException):
+        validate_existing_directories(output_path, False, True, init_path)
+    os.mkdir(init_path)
+    # Should pass since the directory exists now.
+    validate_existing_directories(output_path, False, True, init_path)
+
+
+@pytest.mark.parametrize("dir_exists", [True, False])
+def test_setup_init_path(tmpdir, dir_exists):
+    """
+
+    :return:
+    """
+    test_yaml = """
+    behaviors:
+        BigWallJump:
+            init_path: BigWallJump-6540981.pt #full path
+            trainer_type: ppo
+        MediumWallJump:
+            init_path: {}/test_setup_init_path_results/test_run_id/MediumWallJump/checkpoint.pt
+            trainer_type: ppo
+        SmallWallJump:
+            trainer_type: ppo
+    checkpoint_settings:
+        run_id: test_run_id
+        initialize_from: test_run_id
+    """.format(
+        tmpdir
+    )
+    run_options = RunOptions.from_dict(yaml.safe_load(test_yaml))
+    if dir_exists:
+        init_path = tmpdir.mkdir("test_setup_init_path_results").mkdir("test_run_id")
+        big = init_path.mkdir("BigWallJump").join("BigWallJump-6540981.pt")
+        big.write("content")
+        med = init_path.mkdir("MediumWallJump").join("checkpoint.pt")
+        med.write("content")
+        small = init_path.mkdir("SmallWallJump").join("checkpoint.pt")
+        small.write("content")
+
+        setup_init_path(run_options.behaviors, init_path)
+        assert run_options.behaviors["BigWallJump"].init_path == big
+        assert run_options.behaviors["MediumWallJump"].init_path == med
+        assert run_options.behaviors["SmallWallJump"].init_path == small
+    else:
+        # don't make dirs and fail
+        with pytest.raises(UnityTrainerException):
+            setup_init_path(
+                run_options.behaviors, run_options.checkpoint_settings.maybe_init_path
+            )
diff --git a/ml-agents/mlagents/trainers/tests/test_trainers.py b/ml-agents/mlagents/trainers/tests/test_trainers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b64b0973b2402ccb4c4603e7212f62c76f2022e0
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_trainers.py
@@ -0,0 +1,164 @@
+from unittest.mock import patch
+
+import pytest
+
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.dummy_config import (
+    create_observation_specs_with_shapes,
+    ppo_dummy_config,
+    poca_dummy_config,
+    sac_dummy_config,
+)
+from mlagents.trainers.tests.mock_brain import make_fake_trajectory
+from mlagents.trainers.trainer import TrainerFactory
+
+
+@pytest.fixture
+def ppo_config():
+    return RunOptions(behaviors={"test_brain": ppo_dummy_config()})
+
+
+@pytest.fixture
+def sac_config():
+    return RunOptions(behaviors={"test_brain": sac_dummy_config()})
+
+
+@pytest.fixture
+def poca_config():
+    return RunOptions(behaviors={"test_brain": poca_dummy_config()})
+
+
+def test_ppo_trainer_update_normalization(ppo_config):
+    behavior_id_team0 = "test_brain?team=0"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    base_config = ppo_config.behaviors
+    output_path = "results_dir"
+    train_model = True
+    load_model = False
+    seed = 42
+    trainer_factory = TrainerFactory(
+        trainer_config=base_config,
+        output_path=output_path,
+        train_model=train_model,
+        load_model=load_model,
+        seed=seed,
+        param_manager=EnvironmentParameterManager(),
+    )
+    ppo_trainer = trainer_factory.generate(brain_name)
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = ppo_trainer.create_policy(parsed_behavior_id0, mock_specs)
+    ppo_trainer.add_policy(parsed_behavior_id0, policy)
+    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
+    ppo_trainer.subscribe_trajectory_queue(trajectory_queue0)
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        action_spec=mock_specs.action_spec,
+    )
+    trajectory_queue0.put(trajectory)
+    # mocking out update_normalization in both the policy and critic
+    with patch(
+        "mlagents.trainers.torch_entities.networks.ValueNetwork.update_normalization"
+    ) as optimizer_update_normalization_mock, patch(
+        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
+    ) as policy_update_normalization_mock:
+        ppo_trainer.advance()
+        optimizer_update_normalization_mock.assert_called_once()
+        policy_update_normalization_mock.assert_called_once()
+
+
+def test_sac_trainer_update_normalization(sac_config):
+    behavior_id_team0 = "test_brain?team=0"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    base_config = sac_config.behaviors
+    output_path = "results_dir"
+    train_model = True
+    load_model = False
+    seed = 42
+    trainer_factory = TrainerFactory(
+        trainer_config=base_config,
+        output_path=output_path,
+        train_model=train_model,
+        load_model=load_model,
+        seed=seed,
+        param_manager=EnvironmentParameterManager(),
+    )
+    sac_trainer = trainer_factory.generate(brain_name)
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = sac_trainer.create_policy(parsed_behavior_id0, mock_specs)
+    sac_trainer.add_policy(parsed_behavior_id0, policy)
+    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
+    sac_trainer.subscribe_trajectory_queue(trajectory_queue0)
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        action_spec=mock_specs.action_spec,
+    )
+    trajectory_queue0.put(trajectory)
+    # mocking out update_normalization in both the policy and critic
+    with patch(
+        "mlagents.trainers.torch_entities.networks.ValueNetwork.update_normalization"
+    ) as optimizer_update_normalization_mock, patch(
+        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
+    ) as policy_update_normalization_mock:
+        sac_trainer.advance()
+        optimizer_update_normalization_mock.assert_called_once()
+        policy_update_normalization_mock.assert_called_once()
+
+
+def test_poca_trainer_update_normalization(poca_config):
+    behavior_id_team0 = "test_brain?team=0"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    base_config = poca_config.behaviors
+    output_path = "results_dir"
+    train_model = True
+    load_model = False
+    seed = 42
+    trainer_factory = TrainerFactory(
+        trainer_config=base_config,
+        output_path=output_path,
+        train_model=train_model,
+        load_model=load_model,
+        seed=seed,
+        param_manager=EnvironmentParameterManager(),
+    )
+    poca_trainer = trainer_factory.generate(brain_name)
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = poca_trainer.create_policy(parsed_behavior_id0, mock_specs)
+    poca_trainer.add_policy(parsed_behavior_id0, policy)
+    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
+    poca_trainer.subscribe_trajectory_queue(trajectory_queue0)
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        action_spec=mock_specs.action_spec,
+    )
+    trajectory_queue0.put(trajectory)
+    # mocking out update_normalization in both the policy and critic
+    with patch(
+        "mlagents.trainers.poca.optimizer_torch.TorchPOCAOptimizer.POCAValueNetwork.update_normalization"
+    ) as optimizer_update_normalization_mock, patch(
+        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
+    ) as policy_update_normalization_mock:
+        poca_trainer.advance()
+        optimizer_update_normalization_mock.assert_called_once()
+        policy_update_normalization_mock.assert_called_once()
diff --git a/ml-agents/mlagents/trainers/tests/test_training_analytics_side_channel.py b/ml-agents/mlagents/trainers/tests/test_training_analytics_side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dcba85d1f5cb1854b1695d2c9b81fb8dab87523
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_training_analytics_side_channel.py
@@ -0,0 +1,48 @@
+import yaml
+from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.training_analytics_side_channel import (
+    TrainingAnalyticsSideChannel,
+)
+
+test_curriculum_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 60
+                min_lesson_length: 100
+                require_reset: false
+            value: 2
+          - name: Lesson3
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+"""
+
+
+def test_sanitize_run_options():
+    run_options = RunOptions.from_dict(yaml.safe_load(test_curriculum_config_yaml))
+    sanitized = TrainingAnalyticsSideChannel._sanitize_run_options(run_options)
+    assert "param_1" not in sanitized["environment_parameters"]
+    assert "fake_behavior" not in sanitized["environment_parameters"]
+    assert (
+        TrainingAnalyticsSideChannel._hash("param_1")
+        in sanitized["environment_parameters"]
+    )
+    level1 = TrainingAnalyticsSideChannel._hash("param_1")
+    assert sanitized["environment_parameters"][level1]["curriculum"][0][
+        "completion_criteria"
+    ]["behavior"] == TrainingAnalyticsSideChannel._hash("fake_behavior")
diff --git a/ml-agents/mlagents/trainers/tests/test_training_status.py b/ml-agents/mlagents/trainers/tests/test_training_status.py
new file mode 100644
index 0000000000000000000000000000000000000000..88dde1c5cd5791b7419a1e9281867c50879159e2
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_training_status.py
@@ -0,0 +1,127 @@
+import os
+import unittest
+import json
+from enum import Enum
+import time
+from mlagents.trainers.training_status import (
+    StatusType,
+    StatusMetaData,
+    GlobalTrainingStatus,
+)
+from mlagents.trainers.policy.checkpoint_manager import (
+    ModelCheckpointManager,
+    ModelCheckpoint,
+)
+
+
+def test_globaltrainingstatus(tmpdir):
+    path_dir = os.path.join(tmpdir, "test.json")
+
+    GlobalTrainingStatus.set_parameter_state("Category1", StatusType.LESSON_NUM, 3)
+    GlobalTrainingStatus.save_state(path_dir)
+
+    with open(path_dir) as fp:
+        test_json = json.load(fp)
+
+    assert "Category1" in test_json
+    assert StatusType.LESSON_NUM.value in test_json["Category1"]
+    assert test_json["Category1"][StatusType.LESSON_NUM.value] == 3
+    assert "metadata" in test_json
+
+    GlobalTrainingStatus.load_state(path_dir)
+    restored_val = GlobalTrainingStatus.get_parameter_state(
+        "Category1", StatusType.LESSON_NUM
+    )
+    assert restored_val == 3
+
+    # Test unknown categories and status types (keys)
+    unknown_category = GlobalTrainingStatus.get_parameter_state(
+        "Category3", StatusType.LESSON_NUM
+    )
+
+    class FakeStatusType(Enum):
+        NOTAREALKEY = "notarealkey"
+
+    unknown_key = GlobalTrainingStatus.get_parameter_state(
+        "Category1", FakeStatusType.NOTAREALKEY
+    )
+    assert unknown_category is None
+    assert unknown_key is None
+
+
+def test_model_management(tmpdir):
+
+    results_path = os.path.join(tmpdir, "results")
+    brain_name = "Mock_brain"
+    final_model_path = os.path.join(results_path, brain_name)
+    test_checkpoint_list = [
+        {
+            "steps": 1,
+            "file_path": os.path.join(final_model_path, f"{brain_name}-1.nn"),
+            "reward": 1.312,
+            "creation_time": time.time(),
+            "auxillary_file_paths": [],
+        },
+        {
+            "steps": 2,
+            "file_path": os.path.join(final_model_path, f"{brain_name}-2.nn"),
+            "reward": 1.912,
+            "creation_time": time.time(),
+            "auxillary_file_paths": [],
+        },
+        {
+            "steps": 3,
+            "file_path": os.path.join(final_model_path, f"{brain_name}-3.nn"),
+            "reward": 2.312,
+            "creation_time": time.time(),
+            "auxillary_file_paths": [],
+        },
+    ]
+    GlobalTrainingStatus.set_parameter_state(
+        brain_name, StatusType.CHECKPOINTS, test_checkpoint_list
+    )
+
+    new_checkpoint_4 = ModelCheckpoint(
+        4, os.path.join(final_model_path, f"{brain_name}-4.nn"), 2.678, time.time()
+    )
+    ModelCheckpointManager.add_checkpoint(brain_name, new_checkpoint_4, 4)
+    assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
+
+    new_checkpoint_5 = ModelCheckpoint(
+        5, os.path.join(final_model_path, f"{brain_name}-5.nn"), 3.122, time.time()
+    )
+    ModelCheckpointManager.add_checkpoint(brain_name, new_checkpoint_5, 4)
+    assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
+
+    final_model_path = f"{final_model_path}.nn"
+    final_model_time = time.time()
+    current_step = 6
+    final_model = ModelCheckpoint(
+        current_step, final_model_path, 3.294, final_model_time
+    )
+
+    ModelCheckpointManager.track_final_checkpoint(brain_name, final_model)
+    assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
+
+    check_checkpoints = GlobalTrainingStatus.saved_state[brain_name][
+        StatusType.CHECKPOINTS.value
+    ]
+    assert check_checkpoints is not None
+
+    final_model = GlobalTrainingStatus.saved_state[StatusType.FINAL_CHECKPOINT.value]
+    assert final_model is not None
+
+
+class StatsMetaDataTest(unittest.TestCase):
+    def test_metadata_compare(self):
+        # Test write_stats
+        with self.assertLogs("mlagents.trainers", level="WARNING") as cm:
+            default_metadata = StatusMetaData()
+            version_statsmetadata = StatusMetaData(mlagents_version="test")
+            default_metadata.check_compatibility(version_statsmetadata)
+
+            torch_version_statsmetadata = StatusMetaData(torch_version="test")
+            default_metadata.check_compatibility(torch_version_statsmetadata)
+
+        # Assert that 2 warnings have been thrown
+        assert len(cm.output) == 2
diff --git a/ml-agents/mlagents/trainers/tests/test_trajectory.py b/ml-agents/mlagents/trainers/tests/test_trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d39478a9e84a56af1d5a8c461575ca9db4923e5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/test_trajectory.py
@@ -0,0 +1,90 @@
+import numpy as np
+
+from mlagents.trainers.tests.mock_brain import make_fake_trajectory
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+from mlagents.trainers.trajectory import GroupObsUtil
+from mlagents_envs.base_env import ActionSpec
+from mlagents.trainers.buffer import AgentBuffer, BufferKey, ObservationKeyPrefix
+
+VEC_OBS_SIZE = 6
+ACTION_SIZE = 4
+
+
+def test_trajectory_to_agentbuffer():
+    length = 15
+    # These keys should be of type np.ndarray
+    wanted_keys = [
+        (ObservationKeyPrefix.OBSERVATION, 0),
+        (ObservationKeyPrefix.OBSERVATION, 1),
+        (ObservationKeyPrefix.NEXT_OBSERVATION, 0),
+        (ObservationKeyPrefix.NEXT_OBSERVATION, 1),
+        BufferKey.MEMORY,
+        BufferKey.MASKS,
+        BufferKey.DONE,
+        BufferKey.CONTINUOUS_ACTION,
+        BufferKey.DISCRETE_ACTION,
+        BufferKey.CONTINUOUS_LOG_PROBS,
+        BufferKey.DISCRETE_LOG_PROBS,
+        BufferKey.ACTION_MASK,
+        BufferKey.PREV_ACTION,
+        BufferKey.ENVIRONMENT_REWARDS,
+        BufferKey.GROUP_REWARD,
+    ]
+    # These keys should be of type List
+    wanted_group_keys = [
+        BufferKey.GROUPMATE_REWARDS,
+        BufferKey.GROUP_CONTINUOUS_ACTION,
+        BufferKey.GROUP_DISCRETE_ACTION,
+        BufferKey.GROUP_DONES,
+        BufferKey.GROUP_NEXT_CONT_ACTION,
+        BufferKey.GROUP_NEXT_DISC_ACTION,
+    ]
+    wanted_keys = set(wanted_keys + wanted_group_keys)
+    trajectory = make_fake_trajectory(
+        length=length,
+        observation_specs=create_observation_specs_with_shapes(
+            [(VEC_OBS_SIZE,), (84, 84, 3)]
+        ),
+        action_spec=ActionSpec.create_continuous(ACTION_SIZE),
+        num_other_agents_in_group=4,
+    )
+    agentbuffer = trajectory.to_agentbuffer()
+    seen_keys = set()
+    for key, field in agentbuffer.items():
+        assert len(field) == length
+        seen_keys.add(key)
+
+    assert seen_keys.issuperset(wanted_keys)
+
+    for _key in wanted_group_keys:
+        for step in agentbuffer[_key]:
+            assert len(step) == 4
+
+
+def test_obsutil_group_from_buffer():
+    buff = AgentBuffer()
+    # Create some obs
+    for _ in range(3):
+        buff[GroupObsUtil.get_name_at(0)].append(3 * [np.ones((5,), dtype=np.float32)])
+    # Some agents have died
+    for _ in range(2):
+        buff[GroupObsUtil.get_name_at(0)].append(1 * [np.ones((5,), dtype=np.float32)])
+
+    # Get the group obs, which will be a List of Lists of np.ndarray, where each element is the same
+    # length as the AgentBuffer but contains only one agent's obs. Dead agents are padded by
+    # NaNs.
+    gobs = GroupObsUtil.from_buffer(buff, 1)
+    # Agent 0 is full
+    agent_0_obs = gobs[0]
+    for obs in agent_0_obs:
+        assert obs.shape == (buff.num_experiences, 5)
+        assert not np.isnan(obs).any()
+
+    agent_1_obs = gobs[1]
+    for obs in agent_1_obs:
+        assert obs.shape == (buff.num_experiences, 5)
+        for i, _exp_obs in enumerate(obs):
+            if i >= 3:
+                assert np.isnan(_exp_obs).all()
+            else:
+                assert not np.isnan(_exp_obs).any()
diff --git a/ml-agents/mlagents/trainers/tests/testdcvis.demo b/ml-agents/mlagents/trainers/tests/testdcvis.demo
new file mode 100644
index 0000000000000000000000000000000000000000..bb9c48dfca6aec46449f4e6c61ac18409d7d0e75
Binary files /dev/null and b/ml-agents/mlagents/trainers/tests/testdcvis.demo differ
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/__init__.py b/ml-agents/mlagents/trainers/tests/torch_entities/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver.py b/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef7eebe56d0ac96208a74355c82865bbad15fa6a
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver.py
@@ -0,0 +1,229 @@
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+import pytest
+from unittest import mock
+import os
+
+import numpy as np
+from mlagents.torch_utils import torch, default_device
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer, PPOSettings
+from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer, SACSettings
+from mlagents.trainers.poca.optimizer_torch import TorchPOCAOptimizer, POCASettings
+from mlagents.trainers.model_saver.torch_model_saver import (
+    TorchModelSaver,
+    DEFAULT_CHECKPOINT_NAME,
+)
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    NetworkSettings,
+    EncoderType,
+)
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.torch_entities.test_policy import create_policy_mock
+from mlagents.trainers.torch_entities.utils import ModelUtils
+
+
+def test_register(tmp_path):
+    trainer_params = TrainerSettings()
+    model_saver = TorchModelSaver(trainer_params, tmp_path)
+
+    opt = mock.Mock(spec=TorchPPOOptimizer)
+    opt.get_modules = mock.Mock(return_value={})
+    model_saver.register(opt)
+    assert model_saver.policy is None
+
+    trainer_params = TrainerSettings()
+    policy = create_policy_mock(trainer_params.network_settings)
+    opt.get_modules = mock.Mock(return_value={})
+    model_saver.register(policy)
+    assert model_saver.policy is not None
+
+
+def test_load_save_policy(tmp_path):
+    path1 = os.path.join(tmp_path, "runid1")
+    path2 = os.path.join(tmp_path, "runid2")
+    trainer_params = TrainerSettings()
+    policy = create_policy_mock(trainer_params.network_settings)
+    model_saver = TorchModelSaver(trainer_params, path1)
+    model_saver.register(policy)
+    model_saver.initialize_or_load(policy)
+    policy.set_step(2000)
+
+    mock_brain_name = "MockBrain"
+    model_saver.save_checkpoint(mock_brain_name, 2000)
+    assert len(os.listdir(tmp_path)) > 0
+
+    # Try load from this path
+    model_saver2 = TorchModelSaver(trainer_params, path1, load=True)
+    policy2 = create_policy_mock(trainer_params.network_settings)
+    model_saver2.register(policy2)
+    model_saver2.initialize_or_load(policy2)
+    _compare_two_policies(policy, policy2)
+    assert policy2.get_current_step() == 2000
+
+    # Try initialize from path 1
+    trainer_params.init_path = os.path.join(path1, DEFAULT_CHECKPOINT_NAME)
+    model_saver3 = TorchModelSaver(trainer_params, path2)
+    policy3 = create_policy_mock(trainer_params.network_settings)
+    model_saver3.register(policy3)
+    model_saver3.initialize_or_load(policy3)
+    _compare_two_policies(policy2, policy3)
+    # Assert that the steps are 0.
+    assert policy3.get_current_step() == 0
+
+
+@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
+def test_load_policy_different_hidden_units(tmp_path, vis_encode_type):
+    path1 = os.path.join(tmp_path, "runid1")
+    trainer_params = TrainerSettings()
+    trainer_params.network_settings = NetworkSettings(
+        hidden_units=12, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    policy = create_policy_mock(trainer_params.network_settings, use_visual=True)
+    conv_params = [mod for mod in policy.actor.parameters() if len(mod.shape) > 2]
+
+    model_saver = TorchModelSaver(trainer_params, path1)
+    model_saver.register(policy)
+    model_saver.initialize_or_load(policy)
+    policy.set_step(2000)
+
+    mock_brain_name = "MockBrain"
+    model_saver.save_checkpoint(mock_brain_name, 2000)
+
+    # Try load from this path
+    trainer_params2 = TrainerSettings()
+    trainer_params2.network_settings = NetworkSettings(
+        hidden_units=10, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    model_saver2 = TorchModelSaver(trainer_params2, path1, load=True)
+    policy2 = create_policy_mock(trainer_params2.network_settings, use_visual=True)
+    conv_params2 = [mod for mod in policy2.actor.parameters() if len(mod.shape) > 2]
+    # asserts convolutions have different parameters before load
+    for conv1, conv2 in zip(conv_params, conv_params2):
+        assert not torch.equal(conv1, conv2)
+    # asserts layers still have different dimensions
+    for mod1, mod2 in zip(policy.actor.parameters(), policy2.actor.parameters()):
+        if mod1.shape[0] == 12:
+            assert mod2.shape[0] == 10
+    model_saver2.register(policy2)
+    model_saver2.initialize_or_load(policy2)
+    # asserts convolutions have same parameters after load
+    for conv1, conv2 in zip(conv_params, conv_params2):
+        assert torch.equal(conv1, conv2)
+    # asserts layers still have different dimensions
+    for mod1, mod2 in zip(policy.actor.parameters(), policy2.actor.parameters()):
+        if mod1.shape[0] == 12:
+            assert mod2.shape[0] == 10
+
+
+@pytest.mark.parametrize(
+    "optimizer",
+    [
+        (TorchPPOOptimizer, PPOSettings),
+        (TorchSACOptimizer, SACSettings),
+        (TorchPOCAOptimizer, POCASettings),
+    ],
+    ids=["ppo", "sac", "poca"],
+)
+def test_load_save_optimizer(tmp_path, optimizer):
+    OptimizerClass, HyperparametersClass = optimizer
+
+    trainer_settings = TrainerSettings()
+    trainer_settings.hyperparameters = HyperparametersClass()
+    policy = create_policy_mock(trainer_settings.network_settings, use_discrete=False)
+    optimizer = OptimizerClass(policy, trainer_settings)
+
+    # save at path 1
+    path1 = os.path.join(tmp_path, "runid1")
+    model_saver = TorchModelSaver(trainer_settings, path1)
+    model_saver.register(policy)
+    model_saver.register(optimizer)
+    model_saver.initialize_or_load()
+    policy.set_step(2000)
+    model_saver.save_checkpoint("MockBrain", 2000)
+
+    # create a new optimizer and policy
+    policy2 = create_policy_mock(trainer_settings.network_settings, use_discrete=False)
+    optimizer2 = OptimizerClass(policy2, trainer_settings)
+
+    # load weights
+    model_saver2 = TorchModelSaver(trainer_settings, path1, load=True)
+    model_saver2.register(policy2)
+    model_saver2.register(optimizer2)
+    model_saver2.initialize_or_load()  # This is to load the optimizers
+
+    # Compare the two optimizers
+    _compare_two_optimizers(optimizer, optimizer2)
+
+
+# TorchPolicy.evalute() returns log_probs instead of all_log_probs like tf does.
+# resulting in indeterministic results for testing.
+# So here use sample_actions instead.
+def _compare_two_policies(policy1: TorchPolicy, policy2: TorchPolicy) -> None:
+    """
+    Make sure two policies have the same output for the same input.
+    """
+    policy1.actor = policy1.actor.to(default_device())
+    policy2.actor = policy2.actor.to(default_device())
+
+    decision_step, _ = mb.create_steps_from_behavior_spec(
+        policy1.behavior_spec, num_agents=1
+    )
+    np_obs = decision_step.obs
+    masks = policy1._extract_masks(decision_step)
+    memories = torch.as_tensor(
+        policy1.retrieve_memories(list(decision_step.agent_id))
+    ).unsqueeze(0)
+    tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+
+    with torch.no_grad():
+        _, stat_dict1, _ = policy1.actor.get_action_and_stats(
+            tensor_obs, masks=masks, memories=memories
+        )
+        _, stat_dict2, _ = policy2.actor.get_action_and_stats(
+            tensor_obs, masks=masks, memories=memories
+        )
+        log_probs1 = stat_dict1["log_probs"]
+        log_probs2 = stat_dict2["log_probs"]
+    np.testing.assert_array_equal(
+        ModelUtils.to_numpy(log_probs1.all_discrete_tensor),
+        ModelUtils.to_numpy(log_probs2.all_discrete_tensor),
+    )
+
+
+def _compare_two_optimizers(opt1: TorchOptimizer, opt2: TorchOptimizer) -> None:
+    trajectory = mb.make_fake_trajectory(
+        length=10,
+        observation_specs=opt1.policy.behavior_spec.observation_specs,
+        action_spec=opt1.policy.behavior_spec.action_spec,
+        max_step_complete=True,
+    )
+    with torch.no_grad():
+        _, opt1_val_out, _ = opt1.get_trajectory_value_estimates(
+            trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+        )
+        _, opt2_val_out, _ = opt2.get_trajectory_value_estimates(
+            trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+        )
+
+    for opt1_val, opt2_val in zip(opt1_val_out.values(), opt2_val_out.values()):
+        np.testing.assert_array_equal(opt1_val, opt2_val)
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_checkpoint_conversion(tmpdir, rnn, visual, discrete):
+    dummy_config = TrainerSettings()
+    model_path = os.path.join(tmpdir, "Mock_Brain")
+    policy = create_policy_mock(
+        dummy_config.network_settings,
+        use_rnn=rnn,
+        use_discrete=discrete,
+        use_visual=visual,
+    )
+    trainer_params = TrainerSettings()
+    model_saver = TorchModelSaver(trainer_params, model_path)
+    model_saver.register(policy)
+    model_saver.save_checkpoint("Mock_Brain", 100)
+    assert os.path.isfile(model_path + "/Mock_Brain-100.onnx")
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver_reward_providers.py b/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver_reward_providers.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ffe61d153fddefbf4c783804586b011ed08f754
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/saver/test_saver_reward_providers.py
@@ -0,0 +1,146 @@
+import pytest
+import os
+
+import numpy as np
+
+from mlagents_envs.logging_util import WARNING
+from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer, PPOSettings
+from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer, SACSettings
+from mlagents.trainers.poca.optimizer_torch import TorchPOCAOptimizer, POCASettings
+from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    RewardSignalType,
+    CuriositySettings,
+    GAILSettings,
+    RNDSettings,
+)
+from mlagents.trainers.tests.torch_entities.test_policy import create_policy_mock
+from mlagents.trainers.tests.torch_entities.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+
+
+DEMO_PATH = (
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)
+    + "/test.demo"
+)
+
+
+@pytest.mark.parametrize(
+    "optimizer",
+    [
+        (TorchPPOOptimizer, PPOSettings),
+        (TorchSACOptimizer, SACSettings),
+        (TorchPOCAOptimizer, POCASettings),
+    ],
+    ids=["ppo", "sac", "poca"],
+)
+def test_reward_provider_save(tmp_path, optimizer):
+    OptimizerClass, HyperparametersClass = optimizer
+
+    trainer_settings = TrainerSettings()
+    trainer_settings.hyperparameters = HyperparametersClass()
+    trainer_settings.reward_signals = {
+        RewardSignalType.CURIOSITY: CuriositySettings(),
+        RewardSignalType.GAIL: GAILSettings(demo_path=DEMO_PATH),
+        RewardSignalType.RND: RNDSettings(),
+    }
+    policy = create_policy_mock(trainer_settings.network_settings, use_discrete=False)
+    optimizer = OptimizerClass(policy, trainer_settings)
+
+    # save at path 1
+    path1 = os.path.join(tmp_path, "runid1")
+    model_saver = TorchModelSaver(trainer_settings, path1)
+    model_saver.register(policy)
+    model_saver.register(optimizer)
+    model_saver.initialize_or_load()
+    policy.set_step(2000)
+    model_saver.save_checkpoint("MockBrain", 2000)
+
+    # create a new optimizer and policy
+    optimizer2 = OptimizerClass(policy, trainer_settings)
+    policy2 = create_policy_mock(trainer_settings.network_settings, use_discrete=False)
+
+    # load weights
+    model_saver2 = TorchModelSaver(trainer_settings, path1, load=True)
+    model_saver2.register(policy2)
+    model_saver2.register(optimizer2)
+    model_saver2.initialize_or_load()  # This is to load the optimizers
+
+    # assert the models have the same weights
+    module_dict_1 = optimizer.get_modules()
+    module_dict_2 = optimizer2.get_modules()
+    assert "Module:GAIL" in module_dict_1
+    assert "Module:GAIL" in module_dict_2
+    assert "Module:Curiosity" in module_dict_1
+    assert "Module:Curiosity" in module_dict_2
+    assert "Module:RND-pred" in module_dict_1
+    assert "Module:RND-pred" in module_dict_2
+    assert "Module:RND-target" in module_dict_1
+    assert "Module:RND-target" in module_dict_2
+    for name, module1 in module_dict_1.items():
+        assert name in module_dict_2
+        module2 = module_dict_2[name]
+        if hasattr(module1, "parameters"):
+            for param1, param2 in zip(module1.parameters(), module2.parameters()):
+                assert param1.data.ne(param2.data).sum() == 0
+
+    # Run some rewards
+    data = create_agent_buffer(policy.behavior_spec, 1)
+    for reward_name in optimizer.reward_signals.keys():
+        rp_1 = optimizer.reward_signals[reward_name]
+        rp_2 = optimizer2.reward_signals[reward_name]
+        assert np.array_equal(rp_1.evaluate(data), rp_2.evaluate(data))
+
+
+@pytest.mark.parametrize(
+    "optimizer",
+    [
+        (TorchPPOOptimizer, PPOSettings),
+        (TorchSACOptimizer, SACSettings),
+        (TorchPOCAOptimizer, POCASettings),
+    ],
+    ids=["ppo", "sac", "poca"],
+)
+def test_load_different_reward_provider(caplog, tmp_path, optimizer):
+    OptimizerClass, HyperparametersClass = optimizer
+
+    trainer_settings = TrainerSettings()
+    trainer_settings.hyperparameters = HyperparametersClass()
+    trainer_settings.reward_signals = {
+        RewardSignalType.CURIOSITY: CuriositySettings(),
+        RewardSignalType.RND: RNDSettings(),
+    }
+
+    policy = create_policy_mock(trainer_settings.network_settings, use_discrete=False)
+    optimizer = OptimizerClass(policy, trainer_settings)
+
+    # save at path 1
+    path1 = os.path.join(tmp_path, "runid1")
+    model_saver = TorchModelSaver(trainer_settings, path1)
+    model_saver.register(policy)
+    model_saver.register(optimizer)
+    model_saver.initialize_or_load()
+    assert len(optimizer.critic.value_heads.stream_names) == 2
+    policy.set_step(2000)
+    model_saver.save_checkpoint("MockBrain", 2000)
+
+    trainer_settings2 = TrainerSettings()
+    trainer_settings2.hyperparameters = HyperparametersClass()
+    trainer_settings2.reward_signals = {
+        RewardSignalType.GAIL: GAILSettings(demo_path=DEMO_PATH)
+    }
+
+    # create a new optimizer and policy
+    policy2 = create_policy_mock(trainer_settings2.network_settings, use_discrete=False)
+    optimizer2 = OptimizerClass(policy2, trainer_settings2)
+
+    # load weights
+    model_saver2 = TorchModelSaver(trainer_settings2, path1, load=True)
+    model_saver2.register(policy2)
+    model_saver2.register(optimizer2)
+    assert len(optimizer2.critic.value_heads.stream_names) == 1
+    model_saver2.initialize_or_load()  # This is to load the optimizers
+    messages = [rec.message for rec in caplog.records if rec.levelno == WARNING]
+    assert len(messages) > 0
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test.demo b/ml-agents/mlagents/trainers/tests/torch_entities/test.demo
new file mode 100644
index 0000000000000000000000000000000000000000..8f188ddd58f2d6daa6f4703466db51e2ea3ce714
Binary files /dev/null and b/ml-agents/mlagents/trainers/tests/torch_entities/test.demo differ
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_action_model.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_action_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfcaf6a06743852c99e4db20cc2c0e7ef6ef1dff
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_action_model.py
@@ -0,0 +1,155 @@
+import pytest
+
+from mlagents.torch_utils import torch
+from mlagents.trainers.torch_entities.action_model import ActionModel, DistInstances
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.distributions import (
+    GaussianDistInstance,
+    CategoricalDistInstance,
+)
+
+from mlagents_envs.base_env import ActionSpec
+
+
+def create_action_model(inp_size, act_size, deterministic=False):
+    mask = torch.ones([1, act_size**2])
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    action_model = ActionModel(inp_size, action_spec, deterministic=deterministic)
+    return action_model, mask
+
+
+def test_get_dists():
+    inp_size = 4
+    act_size = 2
+    action_model, masks = create_action_model(inp_size, act_size)
+    sample_inp = torch.ones((1, inp_size))
+    dists = action_model._get_dists(sample_inp, masks=masks)
+    assert isinstance(dists.continuous, GaussianDistInstance)
+    assert len(dists.discrete) == 2
+    for _dist in dists.discrete:
+        assert isinstance(_dist, CategoricalDistInstance)
+
+
+def test_sample_action():
+    inp_size = 4
+    act_size = 2
+    action_model, masks = create_action_model(inp_size, act_size)
+    sample_inp = torch.ones((1, inp_size))
+    dists = action_model._get_dists(sample_inp, masks=masks)
+    agent_action = action_model._sample_action(dists)
+    assert agent_action.continuous_tensor.shape == (1, 2)
+    assert len(agent_action.discrete_list) == 2
+    for _disc in agent_action.discrete_list:
+        assert _disc.shape == (1, 1)
+
+
+def test_deterministic_sample_action():
+    inp_size = 4
+    act_size = 8
+    action_model, masks = create_action_model(inp_size, act_size, deterministic=True)
+    sample_inp = torch.ones((1, inp_size))
+    dists = action_model._get_dists(sample_inp, masks=masks)
+    agent_action1 = action_model._sample_action(dists)
+    agent_action2 = action_model._sample_action(dists)
+    agent_action3 = action_model._sample_action(dists)
+
+    assert torch.equal(agent_action1.continuous_tensor, agent_action2.continuous_tensor)
+    assert torch.equal(agent_action1.continuous_tensor, agent_action3.continuous_tensor)
+    assert torch.equal(agent_action1.discrete_tensor, agent_action2.discrete_tensor)
+    assert torch.equal(agent_action1.discrete_tensor, agent_action3.discrete_tensor)
+
+    action_model, masks = create_action_model(inp_size, act_size, deterministic=False)
+    sample_inp = torch.ones((1, inp_size))
+    dists = action_model._get_dists(sample_inp, masks=masks)
+    agent_action1 = action_model._sample_action(dists)
+    agent_action2 = action_model._sample_action(dists)
+    agent_action3 = action_model._sample_action(dists)
+
+    assert not torch.equal(
+        agent_action1.continuous_tensor, agent_action2.continuous_tensor
+    )
+
+    assert not torch.equal(
+        agent_action1.continuous_tensor, agent_action3.continuous_tensor
+    )
+
+    chance_counter = 0
+    if not torch.equal(agent_action1.discrete_tensor, agent_action2.discrete_tensor):
+        chance_counter += 1
+    if not torch.equal(agent_action1.discrete_tensor, agent_action3.discrete_tensor):
+        chance_counter += 1
+    if not torch.equal(agent_action2.discrete_tensor, agent_action3.discrete_tensor):
+        chance_counter += 1
+
+    assert chance_counter > 1
+
+
+def test_get_probs_and_entropy():
+    inp_size = 4
+    act_size = 2
+    action_model, masks = create_action_model(inp_size, act_size)
+
+    _continuous_dist = GaussianDistInstance(torch.zeros((1, 2)), torch.ones((1, 2)))
+    act_size = 2
+    test_prob = torch.tensor([[1.0 - 0.1 * (act_size - 1)] + [0.1] * (act_size - 1)])
+    _discrete_dist_list = [
+        CategoricalDistInstance(test_prob),
+        CategoricalDistInstance(test_prob),
+    ]
+    dist_tuple = DistInstances(_continuous_dist, _discrete_dist_list)
+
+    agent_action = AgentAction(
+        torch.zeros((1, 2)), [torch.tensor([0]), torch.tensor([1])]
+    )
+
+    log_probs, entropies = action_model._get_probs_and_entropy(agent_action, dist_tuple)
+
+    assert log_probs.continuous_tensor.shape == (1, 2)
+    assert len(log_probs.discrete_list) == 2
+    for _disc in log_probs.discrete_list:
+        assert _disc.shape == (1,)
+    assert len(log_probs.all_discrete_list) == 2
+    for _disc in log_probs.all_discrete_list:
+        assert _disc.shape == (1, 2)
+
+    for clp in log_probs.continuous_tensor[0].tolist():
+        # Log prob of standard normal at 0
+        assert clp == pytest.approx(-0.919, abs=0.01)
+
+    assert log_probs.discrete_list[0] > log_probs.discrete_list[1]
+
+    for ent, val in zip(entropies[0].tolist(), [1.4189, 0.6191, 0.6191]):
+        assert ent == pytest.approx(val, abs=0.01)
+
+
+def test_get_onnx_deterministic_tensors():
+    inp_size = 4
+    act_size = 2
+    action_model, masks = create_action_model(inp_size, act_size)
+    sample_inp = torch.ones((1, inp_size))
+    out_tensors = action_model.get_action_out(sample_inp, masks=masks)
+    (
+        continuous_out,
+        discrete_out,
+        action_out_deprecated,
+        deterministic_continuous_out,
+        deterministic_discrete_out,
+    ) = out_tensors
+    assert continuous_out.shape == (1, 2)
+    assert discrete_out.shape == (1, 2)
+    assert deterministic_discrete_out.shape == (1, 2)
+    assert deterministic_continuous_out.shape == (1, 2)
+
+    # Second sampling from same distribution
+    out_tensors2 = action_model.get_action_out(sample_inp, masks=masks)
+    (
+        continuous_out_2,
+        discrete_out_2,
+        action_out_2_deprecated,
+        deterministic_continuous_out_2,
+        deterministic_discrete_out_2,
+    ) = out_tensors2
+    assert ~torch.all(torch.eq(continuous_out, continuous_out_2))
+    assert torch.all(
+        torch.eq(deterministic_continuous_out, deterministic_continuous_out_2)
+    )
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_agent_action.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_agent_action.py
new file mode 100644
index 0000000000000000000000000000000000000000..771872d7b1a5e0876c3ce90fc0ed98c1647da945
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_agent_action.py
@@ -0,0 +1,74 @@
+import numpy as np
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+
+
+def test_agent_action_group_from_buffer():
+    buff = AgentBuffer()
+    # Create some actions
+    for _ in range(3):
+        buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
+            3 * [np.ones((5,), dtype=np.float32)]
+        )
+        buff[BufferKey.GROUP_DISCRETE_ACTION].append(
+            3 * [np.ones((4,), dtype=np.float32)]
+        )
+    # Some agents have died
+    for _ in range(2):
+        buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
+            1 * [np.ones((5,), dtype=np.float32)]
+        )
+        buff[BufferKey.GROUP_DISCRETE_ACTION].append(
+            1 * [np.ones((4,), dtype=np.float32)]
+        )
+
+    # Get the group actions, which will be a List of Lists of AgentAction, where each element is the same
+    # length as the AgentBuffer but contains only one agent's obs. Dead agents are padded by
+    # NaNs.
+    gact = AgentAction.group_from_buffer(buff)
+    # Agent 0 is full
+    agent_0_act = gact[0]
+    assert agent_0_act.continuous_tensor.shape == (buff.num_experiences, 5)
+    assert agent_0_act.discrete_tensor.shape == (buff.num_experiences, 4)
+
+    agent_1_act = gact[1]
+    assert agent_1_act.continuous_tensor.shape == (buff.num_experiences, 5)
+    assert agent_1_act.discrete_tensor.shape == (buff.num_experiences, 4)
+    assert (agent_1_act.continuous_tensor[0:3] > 0).all()
+    assert (agent_1_act.continuous_tensor[3:] == 0).all()
+    assert (agent_1_act.discrete_tensor[0:3] > 0).all()
+    assert (agent_1_act.discrete_tensor[3:] == 0).all()
+
+
+def test_slice():
+    # Both continuous and discrete
+    aa = AgentAction(
+        torch.tensor([[1.0], [1.0], [1.0]]),
+        [torch.tensor([2, 1, 0]), torch.tensor([1, 2, 0])],
+    )
+    saa = aa.slice(0, 2)
+    assert saa.continuous_tensor.shape == (2, 1)
+    assert saa.discrete_tensor.shape == (2, 2)
+
+
+def test_to_flat():
+    # Both continuous and discrete
+    aa = AgentAction(
+        torch.tensor([[1.0, 1.0, 1.0]]), [torch.tensor([2]), torch.tensor([1])]
+    )
+    flattened_actions = aa.to_flat([3, 3])
+    assert torch.eq(
+        flattened_actions, torch.tensor([[1, 1, 1, 0, 0, 1, 0, 1, 0]])
+    ).all()
+
+    # Just continuous
+    aa = AgentAction(torch.tensor([[1.0, 1.0, 1.0]]), None)
+    flattened_actions = aa.to_flat([])
+    assert torch.eq(flattened_actions, torch.tensor([1, 1, 1])).all()
+
+    # Just discrete
+    aa = AgentAction(torch.tensor([]), [torch.tensor([2]), torch.tensor([1])])
+    flattened_actions = aa.to_flat([3, 3])
+    assert torch.eq(flattened_actions, torch.tensor([0, 0, 1, 0, 1, 0])).all()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_attention.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7344a647b82fb0911c0095261583fc49cc159f5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_attention.py
@@ -0,0 +1,232 @@
+import pytest
+from mlagents.torch_utils import torch
+import numpy as np
+
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.torch_entities.layers import linear_layer, LinearEncoder
+from mlagents.trainers.torch_entities.attention import (
+    MultiHeadAttention,
+    EntityEmbedding,
+    ResidualSelfAttention,
+    get_zero_entities_mask,
+)
+
+
+def test_multi_head_attention_initialization():
+    n_h, emb_size = 4, 12
+    n_k, n_q, b = 13, 14, 15
+    mha = MultiHeadAttention(emb_size, n_h)
+
+    query = torch.ones((b, n_q, emb_size))
+    key = torch.ones((b, n_k, emb_size))
+    value = torch.ones((b, n_k, emb_size))
+
+    output, attention = mha.forward(query, key, value, n_q, n_k)
+
+    assert output.shape == (b, n_q, emb_size)
+    assert attention.shape == (b, n_h, n_q, n_k)
+
+
+def test_multi_head_attention_masking():
+    epsilon = 0.0001
+    n_h, emb_size = 4, 12
+    n_k, n_q, b = 13, 14, 15
+    mha = MultiHeadAttention(emb_size, n_h)
+    # create a key input with some keys all 0
+    query = torch.ones((b, n_q, emb_size))
+    key = torch.ones((b, n_k, emb_size))
+    value = torch.ones((b, n_k, emb_size))
+
+    mask = torch.zeros((b, n_k))
+    for i in range(n_k):
+        if i % 3 == 0:
+            key[:, i, :] = 0
+            mask[:, i] = 1
+
+    _, attention = mha.forward(query, key, value, n_q, n_k, mask)
+
+    for i in range(n_k):
+        if i % 3 == 0:
+            assert torch.sum(attention[:, :, :, i] ** 2) < epsilon
+        else:
+            assert torch.sum(attention[:, :, :, i] ** 2) > epsilon
+
+
+def test_zero_mask_layer():
+    batch_size, size = 10, 30
+
+    def generate_input_helper(pattern):
+        _input = torch.zeros((batch_size, 0, size))
+        for i in range(len(pattern)):
+            if i % 2 == 0:
+                _input = torch.cat(
+                    [_input, torch.rand((batch_size, pattern[i], size))], dim=1
+                )
+            else:
+                _input = torch.cat(
+                    [_input, torch.zeros((batch_size, pattern[i], size))], dim=1
+                )
+        return _input
+
+    masking_pattern_1 = [3, 2, 3, 4]
+    masking_pattern_2 = [5, 7, 8, 2]
+    input_1 = generate_input_helper(masking_pattern_1)
+    input_2 = generate_input_helper(masking_pattern_2)
+
+    masks = get_zero_entities_mask([input_1, input_2])
+    assert len(masks) == 2
+    masks_1 = masks[0]
+    masks_2 = masks[1]
+    assert masks_1.shape == (batch_size, sum(masking_pattern_1))
+    assert masks_2.shape == (batch_size, sum(masking_pattern_2))
+    for i in masking_pattern_1:
+        assert masks_1[0, 1] == 0 if i % 2 == 0 else 1
+    for i in masking_pattern_2:
+        assert masks_2[0, 1] == 0 if i % 2 == 0 else 1
+
+
+@pytest.mark.parametrize("mask_value", [0, 1])
+def test_all_masking(mask_value):
+    # We make sure that a mask of all zeros or all ones will not trigger an error
+    np.random.seed(1336)
+    torch.manual_seed(1336)
+    size, n_k, = (
+        3,
+        5,
+    )
+    embedding_size = 64
+    entity_embeddings = EntityEmbedding(size, n_k, embedding_size)
+    entity_embeddings.add_self_embedding(size)
+    transformer = ResidualSelfAttention(embedding_size, n_k)
+    l_layer = linear_layer(embedding_size, size)
+    optimizer = torch.optim.Adam(
+        list(entity_embeddings.parameters())
+        + list(transformer.parameters())
+        + list(l_layer.parameters()),
+        lr=0.001,
+        weight_decay=1e-6,
+    )
+    batch_size = 20
+    for _ in range(5):
+        center = torch.rand((batch_size, size))
+        key = torch.rand((batch_size, n_k, size))
+        with torch.no_grad():
+            # create the target : The key closest to the query in euclidean distance
+            distance = torch.sum(
+                (center.reshape((batch_size, 1, size)) - key) ** 2, dim=2
+            )
+            argmin = torch.argmin(distance, dim=1)
+            target = []
+            for i in range(batch_size):
+                target += [key[i, argmin[i], :]]
+            target = torch.stack(target, dim=0)
+            target = target.detach()
+
+        embeddings = entity_embeddings(center, key)
+        masks = [torch.ones_like(key[:, :, 0]) * mask_value]
+        prediction = transformer.forward(embeddings, masks)
+        prediction = l_layer(prediction)
+        prediction = prediction.reshape((batch_size, size))
+        error = torch.mean((prediction - target) ** 2, dim=1)
+        error = torch.mean(error) / 2
+        optimizer.zero_grad()
+        error.backward()
+        optimizer.step()
+
+
+def test_predict_closest_training():
+    np.random.seed(1336)
+    torch.manual_seed(1336)
+    size, n_k, = (
+        3,
+        5,
+    )
+    embedding_size = 64
+    entity_embeddings = EntityEmbedding(size, n_k, embedding_size)
+    entity_embeddings.add_self_embedding(size)
+    transformer = ResidualSelfAttention(embedding_size, n_k)
+    l_layer = linear_layer(embedding_size, size)
+    optimizer = torch.optim.Adam(
+        list(entity_embeddings.parameters())
+        + list(transformer.parameters())
+        + list(l_layer.parameters()),
+        lr=0.001,
+        weight_decay=1e-6,
+    )
+    batch_size = 200
+    for _ in range(200):
+        center = torch.rand((batch_size, size))
+        key = torch.rand((batch_size, n_k, size))
+        with torch.no_grad():
+            # create the target : The key closest to the query in euclidean distance
+            distance = torch.sum(
+                (center.reshape((batch_size, 1, size)) - key) ** 2, dim=2
+            )
+            argmin = torch.argmin(distance, dim=1)
+            target = []
+            for i in range(batch_size):
+                target += [key[i, argmin[i], :]]
+            target = torch.stack(target, dim=0)
+            target = target.detach()
+
+        embeddings = entity_embeddings(center, key)
+        masks = get_zero_entities_mask([key])
+        prediction = transformer.forward(embeddings, masks)
+        prediction = l_layer(prediction)
+        prediction = prediction.reshape((batch_size, size))
+        error = torch.mean((prediction - target) ** 2, dim=1)
+        error = torch.mean(error) / 2
+        print(error.item())
+        optimizer.zero_grad()
+        error.backward()
+        optimizer.step()
+    assert error.item() < 0.02
+
+
+def test_predict_minimum_training():
+    # of 5 numbers, predict index of min
+    np.random.seed(1336)
+    torch.manual_seed(1336)
+    n_k = 5
+    size = n_k + 1
+    embedding_size = 64
+    entity_embedding = EntityEmbedding(size, n_k, embedding_size)  # no self
+    transformer = ResidualSelfAttention(embedding_size)
+    l_layer = LinearEncoder(embedding_size, 2, n_k)
+    loss = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(
+        list(entity_embedding.parameters())
+        + list(transformer.parameters())
+        + list(l_layer.parameters()),
+        lr=0.001,
+        weight_decay=1e-6,
+    )
+
+    batch_size = 200
+    onehots = ModelUtils.actions_to_onehot(torch.range(0, n_k - 1).unsqueeze(1), [n_k])[
+        0
+    ]
+    onehots = onehots.expand((batch_size, -1, -1))
+    losses = []
+    for _ in range(400):
+        num = np.random.randint(0, n_k)
+        inp = torch.rand((batch_size, num + 1, 1))
+        with torch.no_grad():
+            # create the target : The minimum
+            argmin = torch.argmin(inp, dim=1)
+            argmin = argmin.squeeze()
+            argmin = argmin.detach()
+        sliced_oh = onehots[:, : num + 1]
+        inp = torch.cat([inp, sliced_oh], dim=2)
+
+        embeddings = entity_embedding(inp, inp)
+        masks = get_zero_entities_mask([inp])
+        prediction = transformer(embeddings, masks)
+        prediction = l_layer(prediction)
+        ce = loss(prediction, argmin)
+        losses.append(ce.item())
+        print(ce.item())
+        optimizer.zero_grad()
+        ce.backward()
+        optimizer.step()
+    assert np.array(losses[-20:]).mean() < 0.1
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_bcmodule.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_bcmodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..0364ddd47820fa9caf6650924881d358aa488858
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_bcmodule.py
@@ -0,0 +1,153 @@
+import os
+from typing import Dict, Any
+from unittest.mock import MagicMock
+import pytest
+import mlagents.trainers.tests.mock_brain as mb
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.torch_entities.components.bc.module import BCModule
+from mlagents.trainers.torch_entities.networks import SimpleActor
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    BehavioralCloningSettings,
+    NetworkSettings,
+)
+
+
+def create_bc_module(mock_behavior_specs, bc_settings, use_rnn, tanhresample):
+    # model_path = env.external_brain_names[0]
+    trainer_config = TrainerSettings()
+    trainer_config.network_settings.memory = (
+        NetworkSettings.MemorySettings() if use_rnn else None
+    )
+    actor_kwargs: Dict[str, Any] = {
+        "conditional_sigma": False,
+        "tanh_squash": tanhresample,
+    }
+    policy = TorchPolicy(
+        0,
+        mock_behavior_specs,
+        trainer_config.network_settings,
+        SimpleActor,
+        actor_kwargs,
+    )
+    bc_module = BCModule(
+        policy,
+        settings=bc_settings,
+        policy_learning_rate=trainer_config.hyperparameters.learning_rate,
+        default_batch_size=trainer_config.hyperparameters.batch_size,
+        default_num_epoch=3,
+    )
+    return bc_module
+
+
+def assert_stats_are_float(stats):
+    for _, item in stats.items():
+        assert isinstance(item, float)
+
+
+# Test default values
+def test_bcmodule_defaults():
+    # See if default values match
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, False)
+    assert bc_module.num_epoch == 3
+    assert bc_module.batch_size == TrainerSettings().hyperparameters.batch_size
+    # Assign strange values and see if it overrides properly
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        num_epoch=100,
+        batch_size=10000,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, False)
+    assert bc_module.num_epoch == 100
+    assert bc_module.batch_size == 10000
+
+
+# Test with continuous control env and vector actions
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    assert_stats_are_float(stats)
+
+
+# Test with constant pretraining learning rate
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_constant_lr_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        steps=0,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    assert_stats_are_float(stats)
+    old_learning_rate = bc_module.current_lr
+
+    _ = bc_module.update()
+    assert old_learning_rate == bc_module.current_lr
+
+
+# Test with constant pretraining learning rate
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_linear_lr_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        steps=100,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    # Should decay by 10/100 * 0.0003 = 0.00003
+    bc_module.policy.get_current_step = MagicMock(return_value=10)
+    old_learning_rate = bc_module.current_lr
+    _ = bc_module.update()
+    assert old_learning_rate - 0.00003 == pytest.approx(bc_module.current_lr, abs=0.01)
+
+
+# Test with RNN
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_rnn_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
+    stats = bc_module.update()
+    assert_stats_are_float(stats)
+
+
+# Test with discrete control and visual observations
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_dc_visual_update(is_sac):
+    mock_specs = mb.create_mock_banana_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "testdcvis.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    assert_stats_are_float(stats)
+
+
+# Test with discrete control, visual observations and RNN
+
+
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_rnn_dc_update(is_sac):
+    mock_specs = mb.create_mock_banana_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "testdcvis.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
+    stats = bc_module.update()
+    assert_stats_are_float(stats)
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_conditioning.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_conditioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cfab0251e38f155590cf5d4e98368fbccd14374
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_conditioning.py
@@ -0,0 +1,52 @@
+import pytest
+from mlagents.torch_utils import torch
+import numpy as np
+
+from mlagents.trainers.torch_entities.layers import linear_layer
+from mlagents.trainers.torch_entities.conditioning import ConditionalEncoder
+
+
+def test_conditional_layer_initialization():
+    b, input_size, goal_size, h, num_cond_layers, num_normal_layers = 7, 10, 8, 16, 2, 1
+    conditional_enc = ConditionalEncoder(
+        input_size, goal_size, h, num_normal_layers + num_cond_layers, num_cond_layers
+    )
+
+    input_tensor = torch.ones(b, input_size)
+    goal_tensor = torch.ones(b, goal_size)
+
+    output = conditional_enc.forward(input_tensor, goal_tensor)
+
+    assert output.shape == (b, h)
+
+
+@pytest.mark.parametrize("num_cond_layers", [1, 2, 3])
+def test_predict_with_condition(num_cond_layers):
+    np.random.seed(1336)
+    torch.manual_seed(1336)
+    input_size, goal_size, h, num_normal_layers = 10, 1, 16, 1
+
+    conditional_enc = ConditionalEncoder(
+        input_size, goal_size, h, num_normal_layers + num_cond_layers, num_cond_layers
+    )
+    l_layer = linear_layer(h, 1)
+
+    optimizer = torch.optim.Adam(
+        list(conditional_enc.parameters()) + list(l_layer.parameters()), lr=0.001
+    )
+    batch_size = 200
+    for _ in range(300):
+        input_tensor = torch.rand((batch_size, input_size))
+        goal_tensor = (torch.rand((batch_size, goal_size)) > 0.5).float()
+        # If the goal is 1: do the sum of the inputs, else, return 0
+        target = torch.sum(input_tensor, dim=1, keepdim=True) * goal_tensor
+        target.detach()
+        prediction = l_layer(conditional_enc(input_tensor, goal_tensor))
+        error = torch.mean((prediction - target) ** 2, dim=1)
+        error = torch.mean(error) / 2
+
+        print(error.item())
+        optimizer.zero_grad()
+        error.backward()
+        optimizer.step()
+    assert error.item() < 0.02
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_decoders.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_decoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ff54c561fef37e9123e033767b5c8bb0851aa7e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_decoders.py
@@ -0,0 +1,31 @@
+import pytest
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.torch_entities.decoders import ValueHeads
+
+
+def test_valueheads():
+    stream_names = [f"reward_signal_{num}" for num in range(5)]
+    input_size = 5
+    batch_size = 4
+
+    # Test default 1 value per head
+    value_heads = ValueHeads(stream_names, input_size)
+    input_data = torch.ones((batch_size, input_size))
+    value_out = value_heads(input_data)  # Note: mean value will be removed shortly
+
+    for stream_name in stream_names:
+        assert value_out[stream_name].shape == (batch_size,)
+
+    # Test that inputting the wrong size input will throw an error
+    with pytest.raises(Exception):
+        value_out = value_heads(torch.ones((batch_size, input_size + 2)))
+
+    # Test multiple values per head (e.g. discrete Q function)
+    output_size = 4
+    value_heads = ValueHeads(stream_names, input_size, output_size)
+    input_data = torch.ones((batch_size, input_size))
+    value_out = value_heads(input_data)
+
+    for stream_name in stream_names:
+        assert value_out[stream_name].shape == (batch_size, output_size)
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_distributions.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe1f0e888445c6b47b6c5aa348857c9f6db5747
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_distributions.py
@@ -0,0 +1,143 @@
+import pytest
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.torch_entities.distributions import (
+    GaussianDistribution,
+    MultiCategoricalDistribution,
+    GaussianDistInstance,
+    TanhGaussianDistInstance,
+    CategoricalDistInstance,
+)
+
+
+@pytest.mark.parametrize("tanh_squash", [True, False])
+@pytest.mark.parametrize("conditional_sigma", [True, False])
+def test_gaussian_distribution(conditional_sigma, tanh_squash):
+    torch.manual_seed(0)
+    hidden_size = 16
+    act_size = 4
+    sample_embedding = torch.ones((1, 16))
+    gauss_dist = GaussianDistribution(
+        hidden_size,
+        act_size,
+        conditional_sigma=conditional_sigma,
+        tanh_squash=tanh_squash,
+    )
+
+    # Make sure backprop works
+    force_action = torch.zeros((1, act_size))
+    optimizer = torch.optim.Adam(gauss_dist.parameters(), lr=3e-3)
+
+    for _ in range(50):
+        dist_inst = gauss_dist(sample_embedding)
+        if tanh_squash:
+            assert isinstance(dist_inst, TanhGaussianDistInstance)
+        else:
+            assert isinstance(dist_inst, GaussianDistInstance)
+        log_prob = dist_inst.log_prob(force_action)
+        loss = torch.nn.functional.mse_loss(log_prob, -2 * torch.ones(log_prob.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    for prob in log_prob.flatten().tolist():
+        assert prob == pytest.approx(-2, abs=0.1)
+
+
+def test_multi_categorical_distribution():
+    torch.manual_seed(0)
+    hidden_size = 16
+    act_size = [3, 3, 4]
+    sample_embedding = torch.ones((1, 16))
+    gauss_dist = MultiCategoricalDistribution(hidden_size, act_size)
+
+    # Make sure backprop works
+    optimizer = torch.optim.Adam(gauss_dist.parameters(), lr=3e-3)
+
+    def create_test_prob(size: int) -> torch.Tensor:
+        test_prob = torch.tensor(
+            [[1.0 - 0.01 * (size - 1)] + [0.01] * (size - 1)]
+        )  # High prob for first action
+        return test_prob.log()
+
+    for _ in range(100):
+        dist_insts = gauss_dist(sample_embedding, masks=torch.ones((1, sum(act_size))))
+        loss = 0
+        for i, dist_inst in enumerate(dist_insts):
+            assert isinstance(dist_inst, CategoricalDistInstance)
+            log_prob = dist_inst.all_log_prob()
+            test_log_prob = create_test_prob(act_size[i])
+            # Force log_probs to match the high probability for the first action generated by
+            # create_test_prob
+            loss += torch.nn.functional.mse_loss(log_prob, test_log_prob)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    for dist_inst, size in zip(dist_insts, act_size):
+        # Check that the log probs are close to the fake ones that we generated.
+        test_log_probs = create_test_prob(size)
+        for _prob, _test_prob in zip(
+            dist_inst.all_log_prob().flatten().tolist(),
+            test_log_probs.flatten().tolist(),
+        ):
+            assert _prob == pytest.approx(_test_prob, abs=0.1)
+
+    # Test masks
+    masks = []
+    for branch in act_size:
+        masks += [0] * (branch - 1) + [1]
+    masks = torch.tensor([masks])
+    dist_insts = gauss_dist(sample_embedding, masks=masks)
+    for dist_inst in dist_insts:
+        log_prob = dist_inst.all_log_prob()
+        assert log_prob.flatten()[-1].tolist() == pytest.approx(0, abs=0.001)
+
+
+def test_gaussian_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    dist_instance = GaussianDistInstance(
+        torch.zeros(1, act_size), torch.ones(1, act_size)
+    )
+    action = dist_instance.sample()
+    assert action.shape == (1, act_size)
+    for log_prob in (
+        dist_instance.log_prob(torch.zeros((1, act_size))).flatten().tolist()
+    ):
+        # Log prob of standard normal at 0
+        assert log_prob == pytest.approx(-0.919, abs=0.01)
+
+    for ent in dist_instance.entropy().flatten().tolist():
+        # entropy of standard normal at 0, based on 1/2 + ln(sqrt(2pi)sigma)
+        assert ent == pytest.approx(1.42, abs=0.01)
+
+
+def test_tanh_gaussian_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    dist_instance = TanhGaussianDistInstance(
+        torch.zeros(1, act_size), torch.ones(1, act_size)
+    )
+    for _ in range(10):
+        action = dist_instance.sample()
+        assert action.shape == (1, act_size)
+        assert torch.max(action) < 1.0 and torch.min(action) > -1.0
+
+
+def test_categorical_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    test_prob = torch.tensor(
+        [[1.0 - 0.1 * (act_size - 1)] + [0.1] * (act_size - 1)]
+    )  # High prob for first action
+    dist_instance = CategoricalDistInstance(test_prob)
+
+    for _ in range(10):
+        action = dist_instance.sample()
+        assert action.shape == (1, 1)
+        assert action < act_size
+
+    # Make sure the first action as higher probability than the others.
+    prob_first_action = dist_instance.log_prob(torch.tensor([0]))
+
+    for i in range(1, act_size):
+        assert dist_instance.log_prob(torch.tensor([i])) < prob_first_action
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_encoders.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_encoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3c1ebb33e364ccf50a9402a30d61ed9ec92d385
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_encoders.py
@@ -0,0 +1,125 @@
+from mlagents.torch_utils import torch
+from unittest import mock
+import pytest
+
+from mlagents.trainers.torch_entities.encoders import (
+    VectorInput,
+    Normalizer,
+    SmallVisualEncoder,
+    FullyConnectedVisualEncoder,
+    SimpleVisualEncoder,
+    ResNetVisualEncoder,
+    NatureVisualEncoder,
+)
+
+
+# This test will also reveal issues with states not being saved in the state_dict.
+def compare_models(module_1, module_2):
+    is_same = True
+    for key_item_1, key_item_2 in zip(
+        module_1.state_dict().items(), module_2.state_dict().items()
+    ):
+        # Compare tensors in state_dict and not the keys.
+        is_same = torch.equal(key_item_1[1], key_item_2[1]) and is_same
+    return is_same
+
+
+def test_normalizer():
+    input_size = 2
+    norm = Normalizer(input_size)
+
+    # These three inputs should mean to 0.5, and variance 2
+    # with the steps starting at 1
+    vec_input1 = torch.tensor([[1, 1]])
+    vec_input2 = torch.tensor([[1, 1]])
+    vec_input3 = torch.tensor([[0, 0]])
+    norm.update(vec_input1)
+    norm.update(vec_input2)
+    norm.update(vec_input3)
+
+    # Test normalization
+    for val in norm(vec_input1)[0].tolist():
+        assert val == pytest.approx(0.707, abs=0.001)
+
+    # Test copy normalization
+    norm2 = Normalizer(input_size)
+    assert not compare_models(norm, norm2)
+    norm2.copy_from(norm)
+    assert compare_models(norm, norm2)
+    for val in norm2(vec_input1)[0].tolist():
+        assert val == pytest.approx(0.707, abs=0.001)
+
+
+@mock.patch("mlagents.trainers.torch_entities.encoders.Normalizer")
+def test_vector_encoder(mock_normalizer):
+    mock_normalizer_inst = mock.Mock()
+    mock_normalizer.return_value = mock_normalizer_inst
+    input_size = 64
+    normalize = False
+    vector_encoder = VectorInput(input_size, normalize)
+    output = vector_encoder(torch.ones((1, input_size)))
+    assert output.shape == (1, input_size)
+
+    normalize = True
+    vector_encoder = VectorInput(input_size, normalize)
+    new_vec = torch.ones((1, input_size))
+    vector_encoder.update_normalization(new_vec)
+
+    mock_normalizer.assert_called_with(input_size)
+    mock_normalizer_inst.update.assert_called_with(new_vec)
+
+    vector_encoder2 = VectorInput(input_size, normalize)
+    vector_encoder.copy_normalization(vector_encoder2)
+    mock_normalizer_inst.copy_from.assert_called_with(mock_normalizer_inst)
+
+
+@pytest.mark.parametrize("image_size", [(36, 36, 3), (84, 84, 4), (256, 256, 5)])
+@pytest.mark.parametrize(
+    "vis_class",
+    [
+        SimpleVisualEncoder,
+        ResNetVisualEncoder,
+        NatureVisualEncoder,
+        SmallVisualEncoder,
+        FullyConnectedVisualEncoder,
+    ],
+)
+def test_visual_encoder(vis_class, image_size):
+    num_outputs = 128
+    enc = vis_class(image_size[0], image_size[1], image_size[2], num_outputs)
+    # Note: NCHW not NHWC
+    sample_input = torch.ones((1, image_size[0], image_size[1], image_size[2]))
+    encoding = enc(sample_input)
+    assert encoding.shape == (1, num_outputs)
+
+
+@pytest.mark.parametrize(
+    "vis_class, size",
+    [
+        (SimpleVisualEncoder, 36),
+        (ResNetVisualEncoder, 36),
+        (NatureVisualEncoder, 36),
+        (SmallVisualEncoder, 10),
+        (FullyConnectedVisualEncoder, 36),
+    ],
+)
+@pytest.mark.slow
+def test_visual_encoder_trains(vis_class, size):
+    torch.manual_seed(0)
+    image_size = (size, size, 1)
+    batch = 100
+
+    inputs = torch.cat(
+        [torch.zeros((batch,) + image_size), torch.ones((batch,) + image_size)], dim=0
+    )
+    target = torch.cat([torch.zeros((batch,)), torch.ones((batch,))], dim=0)
+    enc = vis_class(image_size[0], image_size[1], image_size[2], 1)
+    optimizer = torch.optim.Adam(enc.parameters(), lr=0.001)
+
+    for _ in range(15):
+        prediction = enc(inputs)[:, 0]
+        loss = torch.mean((target - prediction) ** 2)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    assert loss.item() < 0.05
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_ghost.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_ghost.py
new file mode 100644
index 0000000000000000000000000000000000000000..61a4485e2fcc4d2d9ba837164813ef5517a157ba
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_ghost.py
@@ -0,0 +1,222 @@
+import pytest
+
+import numpy as np
+
+from mlagents.trainers.ghost.trainer import GhostTrainer
+from mlagents.trainers.ghost.controller import GhostController
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.ppo.trainer import PPOTrainer
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.mock_brain import copy_buffer_fields
+from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
+from mlagents.trainers.settings import TrainerSettings, SelfPlaySettings
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+@pytest.fixture
+def dummy_config():
+    return TrainerSettings(self_play=SelfPlaySettings())
+
+
+VECTOR_ACTION_SPACE = 1
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 10241
+NUM_AGENTS = 12
+
+
+@pytest.mark.parametrize("use_discrete", [True, False])
+def test_load_and_set(dummy_config, use_discrete):
+    mock_specs = mb.setup_test_behavior_specs(
+        use_discrete,
+        False,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    trainer_params = dummy_config
+    trainer = PPOTrainer("test", 0, trainer_params, True, False, 0, "0")
+    trainer.seed = 1
+    policy = trainer.create_policy("test", mock_specs)
+    trainer.seed = 20  # otherwise graphs are the same
+    to_load_policy = trainer.create_policy("test", mock_specs)
+
+    weights = policy.get_weights()
+    load_weights = to_load_policy.get_weights()
+    try:
+        for w, lw in zip(weights, load_weights):
+            np.testing.assert_array_equal(w, lw)
+    except AssertionError:
+        pass
+
+    to_load_policy.load_weights(weights)
+    load_weights = to_load_policy.get_weights()
+
+    for w, lw in zip(weights, load_weights):
+        np.testing.assert_array_equal(w, lw)
+
+
+def test_resume(dummy_config, tmp_path):
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    behavior_id_team0 = "test_brain?team=0"
+    behavior_id_team1 = "test_brain?team=1"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+    tmp_path = tmp_path.as_posix()
+    ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, tmp_path)
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, tmp_path
+    )
+
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = trainer.create_policy(parsed_behavior_id0, mock_specs)
+    trainer.add_policy(parsed_behavior_id0, policy)
+
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team1)
+    policy = trainer.create_policy(parsed_behavior_id1, mock_specs)
+    trainer.add_policy(parsed_behavior_id1, policy)
+
+    trainer.save_model()
+
+    # Make a new trainer, check that the policies are the same
+    ppo_trainer2 = PPOTrainer(brain_name, 0, dummy_config, True, True, 0, tmp_path)
+    trainer2 = GhostTrainer(
+        ppo_trainer2, brain_name, controller, 0, dummy_config, True, tmp_path
+    )
+    policy = trainer2.create_policy(parsed_behavior_id0, mock_specs)
+    trainer2.add_policy(parsed_behavior_id0, policy)
+
+    policy = trainer2.create_policy(parsed_behavior_id1, mock_specs)
+    trainer2.add_policy(parsed_behavior_id1, policy)
+
+    trainer1_policy = trainer.get_policy(parsed_behavior_id1.behavior_id)
+    trainer2_policy = trainer2.get_policy(parsed_behavior_id1.behavior_id)
+    weights = trainer1_policy.get_weights()
+    weights2 = trainer2_policy.get_weights()
+
+    for w, lw in zip(weights, weights2):
+        np.testing.assert_array_equal(w, lw)
+
+
+def test_process_trajectory(dummy_config):
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    behavior_id_team0 = "test_brain?team=0"
+    behavior_id_team1 = "test_brain?team=1"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+
+    ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
+
+    # first policy encountered becomes policy trained by wrapped PPO
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = trainer.create_policy(parsed_behavior_id0, mock_specs)
+    trainer.add_policy(parsed_behavior_id0, policy)
+    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
+    trainer.subscribe_trajectory_queue(trajectory_queue0)
+
+    # Ghost trainer should ignore this queue because off policy
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team1)
+    policy = trainer.create_policy(parsed_behavior_id1, mock_specs)
+    trainer.add_policy(parsed_behavior_id1, policy)
+    trajectory_queue1 = AgentManagerQueue(behavior_id_team1)
+    trainer.subscribe_trajectory_queue(trajectory_queue1)
+
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
+        action_spec=mock_specs.action_spec,
+    )
+    trajectory_queue0.put(trajectory)
+    trainer.advance()
+
+    # Check that trainer put trajectory in update buffer
+    assert trainer.trainer.update_buffer.num_experiences == 15
+
+    trajectory_queue1.put(trajectory)
+    trainer.advance()
+
+    # Check that ghost trainer ignored off policy queue
+    assert trainer.trainer.update_buffer.num_experiences == 15
+    # Check that it emptied the queue
+    assert trajectory_queue1.empty()
+
+
+def test_publish_queue(dummy_config):
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[1], vector_obs_space=8
+    )
+
+    behavior_id_team0 = "test_brain?team=0"
+    behavior_id_team1 = "test_brain?team=1"
+
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+
+    brain_name = parsed_behavior_id0.brain_name
+
+    ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
+
+    # First policy encountered becomes policy trained by wrapped PPO
+    # This queue should remain empty after swap snapshot
+    policy = trainer.create_policy(parsed_behavior_id0, mock_specs)
+    trainer.add_policy(parsed_behavior_id0, policy)
+    policy_queue0 = AgentManagerQueue(behavior_id_team0)
+    trainer.publish_policy_queue(policy_queue0)
+
+    # Ghost trainer should use this queue for ghost policy swap
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team1)
+    policy = trainer.create_policy(parsed_behavior_id1, mock_specs)
+    trainer.add_policy(parsed_behavior_id1, policy)
+    policy_queue1 = AgentManagerQueue(behavior_id_team1)
+    trainer.publish_policy_queue(policy_queue1)
+
+    # check ghost trainer swap pushes to ghost queue and not trainer
+    assert policy_queue0.empty() and policy_queue1.empty()
+    trainer._swap_snapshots()
+    assert policy_queue0.empty() and not policy_queue1.empty()
+    # clear
+    policy_queue1.get_nowait()
+
+    buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_specs)
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.rewards_key("extrinsic"),
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.rewards_key("curiosity"),
+            RewardSignalUtil.returns_key("curiosity"),
+            RewardSignalUtil.value_estimates_key("curiosity"),
+        ],
+    )
+
+    trainer.trainer.update_buffer = buffer
+
+    # when ghost trainer advance and wrapped trainer buffers full
+    # the wrapped trainer pushes updated policy to correct queue
+    assert policy_queue0.empty() and policy_queue1.empty()
+    trainer.advance()
+    assert not policy_queue0.empty() and policy_queue1.empty()
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_hybrid.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_hybrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..749ae430eaeaa309f534cbbaa48dae6ff3e34662
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_hybrid.py
@@ -0,0 +1,136 @@
+import attr
+import pytest
+
+
+from mlagents.trainers.tests.simple_test_envs import (
+    SimpleEnvironment,
+    MemoryEnvironment,
+)
+
+from mlagents.trainers.settings import NetworkSettings
+
+from mlagents.trainers.tests.dummy_config import ppo_dummy_config, sac_dummy_config
+from mlagents.trainers.tests.check_env_trains import check_environment_trains
+
+BRAIN_NAME = "1D"
+
+PPO_TORCH_CONFIG = ppo_dummy_config()
+SAC_TORCH_CONFIG = sac_dummy_config()
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("action_size", [(1, 1), (2, 2), (1, 2), (2, 1)])
+def test_hybrid_ppo(action_size):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_size, step_size=0.8)
+    new_network_settings = attr.evolve(PPO_TORCH_CONFIG.network_settings)
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters,
+        batch_size=64,
+        buffer_size=1024,
+        learning_rate=1e-3,
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_network_settings,
+        max_steps=10000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("num_visual,training_seed", [(1, 1336), (2, 1338)])
+def test_hybrid_visual_ppo(num_visual, training_seed):
+    env = SimpleEnvironment(
+        [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters,
+        batch_size=64,
+        buffer_size=1024,
+        learning_rate=1e-4,
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=8000
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=training_seed)
+
+
+@pytest.mark.slow
+def test_hybrid_recurrent_ppo():
+    env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
+    new_network_settings = attr.evolve(
+        PPO_TORCH_CONFIG.network_settings,
+        memory=NetworkSettings.MemorySettings(memory_size=16),
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters,
+        learning_rate=1.0e-3,
+        batch_size=64,
+        buffer_size=512,
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_network_settings,
+        max_steps=5000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("action_size", [(1, 1), (2, 2), (1, 2), (2, 1)])
+def test_hybrid_sac(action_size):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_size, step_size=0.8)
+
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters,
+        buffer_size=50000,
+        batch_size=256,
+        buffer_init_steps=0,
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=4000
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("num_visual,training_seed", [(1, 1337), (2, 1338)])
+def test_hybrid_visual_sac(num_visual, training_seed):
+    env = SimpleEnvironment(
+        [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters,
+        buffer_size=50000,
+        batch_size=128,
+        learning_rate=3.0e-4,
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=3000
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=training_seed)
+
+
+@pytest.mark.slow
+def test_hybrid_recurrent_sac():
+    env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
+    new_networksettings = attr.evolve(
+        SAC_TORCH_CONFIG.network_settings,
+        memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters,
+        batch_size=256,
+        learning_rate=3e-4,
+        buffer_init_steps=1000,
+        steps_per_update=2,
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_networksettings,
+        max_steps=4000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1212)
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_layers.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0dafd4881fa5dac9a19f2440eddfee7519a6bdf
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_layers.py
@@ -0,0 +1,85 @@
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.torch_entities.layers import (
+    Swish,
+    linear_layer,
+    lstm_layer,
+    Initialization,
+    LSTM,
+    LayerNorm,
+)
+
+
+def test_swish():
+    layer = Swish()
+    input_tensor = torch.Tensor([[1, 2, 3], [4, 5, 6]])
+    target_tensor = torch.mul(input_tensor, torch.sigmoid(input_tensor))
+    assert torch.all(torch.eq(layer(input_tensor), target_tensor))
+
+
+def test_initialization_layer():
+    torch.manual_seed(0)
+    # Test Zero
+    layer = linear_layer(
+        3, 4, kernel_init=Initialization.Zero, bias_init=Initialization.Zero
+    )
+    assert torch.all(torch.eq(layer.weight.data, torch.zeros_like(layer.weight.data)))
+    assert torch.all(torch.eq(layer.bias.data, torch.zeros_like(layer.bias.data)))
+
+
+def test_lstm_layer():
+    torch.manual_seed(0)
+    # Test zero for LSTM
+    layer = lstm_layer(
+        4, 4, kernel_init=Initialization.Zero, bias_init=Initialization.Zero
+    )
+    for name, param in layer.named_parameters():
+        if "weight" in name:
+            assert torch.all(torch.eq(param.data, torch.zeros_like(param.data)))
+        elif "bias" in name:
+            assert torch.all(
+                torch.eq(param.data[4:8], torch.ones_like(param.data[4:8]))
+            )
+
+
+def test_lstm_class():
+    torch.manual_seed(0)
+    input_size = 12
+    memory_size = 64
+    batch_size = 8
+    seq_len = 16
+    lstm = LSTM(input_size, memory_size)
+
+    assert lstm.memory_size == memory_size
+
+    sample_input = torch.ones((batch_size, seq_len, input_size))
+    sample_memories = torch.ones((1, batch_size, memory_size))
+    out, mem = lstm(sample_input, sample_memories)
+    # Hidden size should be half of memory_size
+    assert out.shape == (batch_size, seq_len, memory_size // 2)
+    assert mem.shape == (1, batch_size, memory_size)
+
+
+def test_layer_norm():
+    torch.manual_seed(0)
+    torch_ln = torch.nn.LayerNorm(10, elementwise_affine=False)
+    cust_ln = LayerNorm()
+
+    sample_input = torch.rand(10)
+    assert torch.all(
+        torch.isclose(
+            torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
+        )
+    )
+    sample_input = torch.rand((4, 10))
+    assert torch.all(
+        torch.isclose(
+            torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
+        )
+    )
+    sample_input = torch.rand((7, 6, 10))
+    assert torch.all(
+        torch.isclose(
+            torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
+        )
+    )
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_networks.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cf81208062588ce14157463b424737e55fa9c9d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_networks.py
@@ -0,0 +1,389 @@
+import pytest
+
+from mlagents.torch_utils import torch
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.networks import (
+    NetworkBody,
+    MultiAgentNetworkBody,
+    ValueNetwork,
+    SimpleActor,
+    SharedActorCritic,
+)
+from mlagents.trainers.settings import NetworkSettings
+from mlagents_envs.base_env import ActionSpec
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+def test_networkbody_vector():
+    torch.manual_seed(0)
+    obs_size = 4
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+
+    networkbody = NetworkBody(
+        create_observation_specs_with_shapes(obs_shapes),
+        network_settings,
+        encoded_act_size=2,
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = 0.1 * torch.ones((1, obs_size))
+    sample_act = 0.1 * torch.ones((1, 2))
+
+    for _ in range(300):
+        encoded, _ = networkbody([sample_obs], sample_act)
+        assert encoded.shape == (1, network_settings.hidden_units)
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_networkbody_lstm():
+    torch.manual_seed(0)
+    obs_size = 4
+    seq_len = 6
+    network_settings = NetworkSettings(
+        memory=NetworkSettings.MemorySettings(sequence_length=seq_len, memory_size=12)
+    )
+    obs_shapes = [(obs_size,)]
+
+    networkbody = NetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-4)
+    sample_obs = torch.ones((seq_len, obs_size))
+
+    for _ in range(300):
+        encoded, _ = networkbody(
+            [sample_obs], memories=torch.ones(1, 1, 12), sequence_length=seq_len
+        )
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_networkbody_visual():
+    torch.manual_seed(1)
+    vec_obs_size = 4
+    obs_size = (84, 84, 3)
+    network_settings = NetworkSettings()
+    obs_shapes = [(vec_obs_size,), obs_size]
+
+    networkbody = NetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = 0.1 * torch.ones((1, 84, 84, 3), dtype=torch.float32)
+    sample_vec_obs = torch.ones((1, vec_obs_size), dtype=torch.float32)
+    obs = [sample_vec_obs] + [sample_obs]
+    loss = 1
+    step = 0
+    while loss > 1e-6 and step < 1e3:
+        encoded, _ = networkbody(obs)
+        assert encoded.shape == (1, network_settings.hidden_units)
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        step += 1
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
+def test_multinetworkbody_vector(with_actions):
+    torch.manual_seed(0)
+    obs_size = 4
+    act_size = 2
+    n_agents = 3
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    networkbody = MultiAgentNetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings, action_spec
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = [[0.1 * torch.ones((1, obs_size))] for _ in range(n_agents)]
+    # simulate baseline in POCA
+    sample_act = [
+        AgentAction(
+            0.1 * torch.ones((1, 2)), [0.1 * torch.ones(1) for _ in range(act_size)]
+        )
+        for _ in range(n_agents - 1)
+    ]
+
+    for _ in range(300):
+        if with_actions:
+            encoded, _ = networkbody(
+                obs_only=sample_obs[:1], obs=sample_obs[1:], actions=sample_act
+            )
+        else:
+            encoded, _ = networkbody(obs_only=sample_obs, obs=[], actions=[])
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
+def test_multinetworkbody_lstm(with_actions):
+    torch.manual_seed(0)
+    obs_size = 4
+    act_size = 2
+    seq_len = 16
+    n_agents = 3
+    network_settings = NetworkSettings(
+        memory=NetworkSettings.MemorySettings(sequence_length=seq_len, memory_size=12)
+    )
+
+    obs_shapes = [(obs_size,)]
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    networkbody = MultiAgentNetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings, action_spec
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-4)
+    sample_obs = [[0.1 * torch.ones((seq_len, obs_size))] for _ in range(n_agents)]
+    # simulate baseline in POCA
+    sample_act = [
+        AgentAction(
+            0.1 * torch.ones((seq_len, 2)),
+            [0.1 * torch.ones(seq_len) for _ in range(act_size)],
+        )
+        for _ in range(n_agents - 1)
+    ]
+
+    for _ in range(300):
+        if with_actions:
+            encoded, _ = networkbody(
+                obs_only=sample_obs[:1],
+                obs=sample_obs[1:],
+                actions=sample_act,
+                memories=torch.ones(1, 1, 12),
+                sequence_length=seq_len,
+            )
+        else:
+            encoded, _ = networkbody(
+                obs_only=sample_obs,
+                obs=[],
+                actions=[],
+                memories=torch.ones(1, 1, 12),
+                sequence_length=seq_len,
+            )
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
+def test_multinetworkbody_visual(with_actions):
+    torch.manual_seed(0)
+    act_size = 2
+    n_agents = 3
+    obs_size = 4
+    vis_obs_size = (84, 84, 3)
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,), vis_obs_size]
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    networkbody = MultiAgentNetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings, action_spec
+    )
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = [
+        [0.1 * torch.ones((1, obs_size))] + [0.1 * torch.ones((1, 84, 84, 3))]
+        for _ in range(n_agents)
+    ]
+    # simulate baseline in POCA
+    sample_act = [
+        AgentAction(
+            0.1 * torch.ones((1, 2)), [0.1 * torch.ones(1) for _ in range(act_size)]
+        )
+        for _ in range(n_agents - 1)
+    ]
+    for _ in range(300):
+        if with_actions:
+            encoded, _ = networkbody(
+                obs_only=sample_obs[:1], obs=sample_obs[1:], actions=sample_act
+            )
+        else:
+            encoded, _ = networkbody(obs_only=sample_obs, obs=[], actions=[])
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten().tolist():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_valuenetwork():
+    torch.manual_seed(0)
+    obs_size = 4
+    num_outputs = 2
+    network_settings = NetworkSettings()
+    obs_spec = create_observation_specs_with_shapes([(obs_size,)])
+
+    stream_names = [f"stream_name{n}" for n in range(4)]
+    value_net = ValueNetwork(
+        stream_names, obs_spec, network_settings, outputs_per_stream=num_outputs
+    )
+    optimizer = torch.optim.Adam(value_net.parameters(), lr=3e-3)
+
+    for _ in range(50):
+        sample_obs = torch.ones((1, obs_size))
+        values, _ = value_net([sample_obs])
+        loss = 0
+        for s_name in stream_names:
+            assert values[s_name].shape == (1, num_outputs)
+            # Try to force output to 1
+            loss += torch.nn.functional.mse_loss(
+                values[s_name], torch.ones((1, num_outputs))
+            )
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for value in values.values():
+        for _out in value.tolist():
+            assert _out[0] == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("shared", [True, False])
+@pytest.mark.parametrize("lstm", [True, False])
+def test_actor_critic(lstm, shared):
+    obs_size = 4
+    vis_obs_size = (84, 84, 3)
+    network_settings = NetworkSettings(
+        memory=NetworkSettings.MemorySettings() if lstm else None, normalize=True
+    )
+    obs_spec = create_observation_specs_with_shapes([(obs_size,), vis_obs_size])
+    act_size = 2
+    mask = torch.ones([1, act_size * 2])
+    stream_names = [f"stream_name{n}" for n in range(4)]
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    if shared:
+        actor = critic = SharedActorCritic(
+            obs_spec, network_settings, action_spec, stream_names, network_settings
+        )
+    else:
+        actor = SimpleActor(obs_spec, network_settings, action_spec)
+        critic = ValueNetwork(stream_names, obs_spec, network_settings)
+    if lstm:
+        sample_vis_obs = torch.ones(
+            (network_settings.memory.sequence_length, 84, 84, 3), dtype=torch.float32
+        )
+        sample_obs = torch.ones((network_settings.memory.sequence_length, obs_size))
+        memories = torch.ones(
+            (1, network_settings.memory.sequence_length, actor.memory_size)
+        )
+    else:
+        sample_vis_obs = 0.1 * torch.ones((1, 84, 84, 3), dtype=torch.float32)
+        sample_obs = torch.ones((1, obs_size))
+        memories = torch.tensor([])
+        # memories isn't always set to None, the network should be able to
+        # deal with that.
+    # Test critic pass
+    value_out, memories_out = critic.critic_pass(
+        [sample_obs] + [sample_vis_obs], memories=memories
+    )
+    for stream in stream_names:
+        if lstm:
+            assert value_out[stream].shape == (network_settings.memory.sequence_length,)
+            assert memories_out.shape == memories.shape
+        else:
+            assert value_out[stream].shape == (1,)
+
+    # Test get action stats and_value
+    action, run_out, mem_out = actor.get_action_and_stats(
+        [sample_obs] + [sample_vis_obs], memories=memories, masks=mask
+    )
+    log_probs = run_out["log_probs"]
+    entropy = run_out["entropy"]
+
+    eval_run_out = actor.get_stats(
+        [sample_obs] + [sample_vis_obs], action, memories=memories, masks=mask
+    )
+    eval_log_probs = eval_run_out["log_probs"]
+    eval_entropy = eval_run_out["entropy"]
+
+    if lstm:
+        assert action.continuous_tensor.shape == (64, 2)
+        assert log_probs.continuous_tensor.shape == (64, 2)
+        assert entropy.shape == (64,)
+        assert eval_log_probs.continuous_tensor.shape == (64, 2)
+        assert eval_entropy.shape == (64,)
+
+    else:
+        assert action.continuous_tensor.shape == (1, 2)
+        assert log_probs.continuous_tensor.shape == (1, 2)
+        assert entropy.shape == (1,)
+        assert eval_log_probs.continuous_tensor.shape == (1, 2)
+        assert eval_entropy.shape == (1,)
+
+    assert len(action.discrete_list) == 2
+    for _disc, _disc_prob, _eval_disc_prob in zip(
+        action.discrete_list, log_probs.discrete_list, eval_log_probs.discrete_list
+    ):
+        if lstm:
+            assert _disc.shape == (64, 1)
+            assert _eval_disc_prob.shape == (64,)
+        else:
+            assert _disc.shape == (1, 1)
+            assert _disc_prob.shape == (1,)
+            assert _eval_disc_prob.shape == (1,)
+
+    if mem_out is not None:
+        assert mem_out.shape == memories.shape
+
+
+@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
+def test_multinetworkbody_num_agents(with_actions):
+    torch.manual_seed(0)
+    act_size = 2
+    obs_size = 4
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+    action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
+    networkbody = MultiAgentNetworkBody(
+        create_observation_specs_with_shapes(obs_shapes), network_settings, action_spec
+    )
+    sample_obs = [[0.1 * torch.ones((1, obs_size))]]
+    # simulate baseline in POCA
+    sample_act = [
+        AgentAction(
+            0.1 * torch.ones((1, 2)), [0.1 * torch.ones(1) for _ in range(act_size)]
+        )
+    ]
+    for n_agent, max_so_far in [(1, 1), (5, 5), (4, 5), (10, 10), (5, 10), (1, 10)]:
+        if with_actions:
+            encoded, _ = networkbody(
+                obs_only=sample_obs * (n_agent - 1), obs=sample_obs, actions=sample_act
+            )
+        else:
+            encoded, _ = networkbody(obs_only=sample_obs * n_agent, obs=[], actions=[])
+        # look at the last value of the hidden units (the number of agents)
+        target = (n_agent * 1.0 / max_so_far) * 2 - 1
+        assert abs(encoded[0, -1].item() - target) < 1e-6
+        assert encoded[0, -1].item() <= 1
+        assert encoded[0, -1].item() >= -1
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_poca.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_poca.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce0e59a3eab579a6a99942ba5a1cacb59c10d94
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_poca.py
@@ -0,0 +1,345 @@
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+import pytest
+from typing import Dict, Any
+import numpy as np
+import attr
+
+# Import to avoid circular import
+from mlagents.trainers.trainer.trainer_factory import TrainerFactory  # noqa F401
+
+from mlagents.trainers.poca.optimizer_torch import TorchPOCAOptimizer
+from mlagents.trainers.poca.trainer import POCATrainer
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.mock_brain import copy_buffer_fields
+from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.tests.dummy_config import (  # noqa: F401
+    create_observation_specs_with_shapes,
+    poca_dummy_config,
+    curiosity_dummy_config,
+    gail_dummy_config,
+)
+from mlagents.trainers.torch_entities.networks import SimpleActor
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.settings import TrainerSettings
+
+from mlagents_envs.base_env import ActionSpec, BehaviorSpec
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+
+
+@pytest.fixture
+def dummy_config():
+    return poca_dummy_config()
+
+
+VECTOR_ACTION_SPACE = 2
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 64
+NUM_AGENTS = 4
+
+CONTINUOUS_ACTION_SPEC = ActionSpec.create_continuous(VECTOR_ACTION_SPACE)
+DISCRETE_ACTION_SPEC = ActionSpec.create_discrete(tuple(DISCRETE_ACTION_SPACE))
+
+
+def create_test_poca_optimizer(dummy_config, use_rnn, use_discrete, use_visual):
+    mock_specs = mb.setup_test_behavior_specs(
+        use_discrete,
+        use_visual,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    trainer_settings = attr.evolve(dummy_config)
+    trainer_settings.reward_signals = {
+        RewardSignalType.EXTRINSIC: RewardSignalSettings(strength=1.0, gamma=0.99)
+    }
+
+    trainer_settings.network_settings.memory = (
+        NetworkSettings.MemorySettings(sequence_length=8, memory_size=10)
+        if use_rnn
+        else None
+    )
+    actor_kwargs: Dict[str, Any] = {
+        "conditional_sigma": False,
+        "tanh_squash": False,
+    }
+    policy = TorchPolicy(
+        0, mock_specs, trainer_settings.network_settings, SimpleActor, actor_kwargs
+    )
+    optimizer = TorchPOCAOptimizer(policy, trainer_settings)
+    return optimizer
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_poca_optimizer_update(dummy_config, rnn, visual, discrete):
+    # Test evaluate
+    optimizer = create_test_poca_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+        num_other_agents_in_group=NUM_AGENTS,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        BufferKey.ENVIRONMENT_REWARDS,
+        [
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.baseline_estimates_key("extrinsic"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(
+        update_buffer,
+        BufferKey.MEMORY,
+        [BufferKey.CRITIC_MEMORY, BufferKey.BASELINE_MEMORY],
+    )
+
+    return_stats = optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+    # Make sure we have the right stats
+    required_stats = [
+        "Losses/Policy Loss",
+        "Losses/Value Loss",
+        "Policy/Learning Rate",
+        "Policy/Epsilon",
+        "Policy/Beta",
+    ]
+    for stat in required_stats:
+        assert stat in return_stats.keys()
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_poca_get_value_estimates(dummy_config, rnn, visual, discrete):
+    optimizer = create_test_poca_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    time_horizon = 30
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=optimizer.policy.behavior_spec.observation_specs,
+        action_spec=DISCRETE_ACTION_SPEC if discrete else CONTINUOUS_ACTION_SPEC,
+        max_step_complete=True,
+        num_other_agents_in_group=NUM_AGENTS,
+    )
+    (
+        value_estimates,
+        baseline_estimates,
+        value_next,
+        value_memories,
+        baseline_memories,
+    ) = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(),
+        trajectory.next_obs,
+        trajectory.next_group_obs,
+        done=False,
+    )
+    for key, val in value_estimates.items():
+        assert type(key) is str
+        assert len(val) == time_horizon
+    for key, val in baseline_estimates.items():
+        assert type(key) is str
+        assert len(val) == time_horizon
+
+    if value_memories is not None:
+        assert len(value_memories) == time_horizon
+        assert len(baseline_memories) == time_horizon
+
+    (
+        value_estimates,
+        baseline_estimates,
+        value_next,
+        value_memories,
+        baseline_memories,
+    ) = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(),
+        trajectory.next_obs,
+        trajectory.next_group_obs,
+        done=True,
+    )
+    for key, val in value_next.items():
+        assert type(key) is str
+        assert val == 0.0
+
+    # Check if we ignore terminal states properly
+    optimizer.reward_signals["extrinsic"].use_terminal_states = False
+    (
+        value_estimates,
+        baseline_estimates,
+        value_next,
+        value_memories,
+        baseline_memories,
+    ) = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(),
+        trajectory.next_obs,
+        trajectory.next_group_obs,
+        done=False,
+    )
+    for key, val in value_next.items():
+        assert type(key) is str
+        assert val != 0.0
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_poca_optimizer_update_curiosity(
+    dummy_config, curiosity_dummy_config, rnn, visual, discrete  # noqa: F811
+):
+    # Test evaluate
+    dummy_config.reward_signals = curiosity_dummy_config
+    optimizer = create_test_poca_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.baseline_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("curiosity"),
+            RewardSignalUtil.value_estimates_key("curiosity"),
+            RewardSignalUtil.baseline_estimates_key("curiosity"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(
+        update_buffer,
+        BufferKey.MEMORY,
+        [BufferKey.CRITIC_MEMORY, BufferKey.BASELINE_MEMORY],
+    )
+
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_poca_optimizer_update_gail(gail_dummy_config, dummy_config):  # noqa: F811
+    # Test evaluate
+    dummy_config.reward_signals = gail_dummy_config
+    config = poca_dummy_config()
+    optimizer = create_test_poca_optimizer(
+        config, use_rnn=False, use_discrete=False, use_visual=False
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.baseline_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+            RewardSignalUtil.baseline_estimates_key("gail"),
+        ],
+    )
+
+    update_buffer[BufferKey.CONTINUOUS_LOG_PROBS] = np.ones_like(
+        update_buffer[BufferKey.CONTINUOUS_ACTION]
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+    # Check if buffer size is too big
+    update_buffer = mb.simulate_rollout(3000, optimizer.policy.behavior_spec)
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.baseline_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+            RewardSignalUtil.baseline_estimates_key("gail"),
+        ],
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+def test_poca_end_episode():
+    name_behavior_id = "test_brain?team=0"
+    trainer = POCATrainer(
+        name_behavior_id,
+        10,
+        TrainerSettings(max_steps=100, checkpoint_interval=10, summary_freq=20),
+        True,
+        False,
+        0,
+        "mock_model_path",
+    )
+    behavior_spec = BehaviorSpec(
+        create_observation_specs_with_shapes([(1,)]), ActionSpec.create_discrete((2,))
+    )
+    parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id)
+    mock_policy = trainer.create_policy(parsed_behavior_id, behavior_spec)
+    trainer.add_policy(parsed_behavior_id, mock_policy)
+    trajectory_queue = AgentManagerQueue("test_brain?team=0")
+    policy_queue = AgentManagerQueue("test_brain?team=0")
+    trainer.subscribe_trajectory_queue(trajectory_queue)
+    trainer.publish_policy_queue(policy_queue)
+    time_horizon = 10
+    trajectory = mb.make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=behavior_spec.observation_specs,
+        max_step_complete=False,
+        action_spec=behavior_spec.action_spec,
+        num_other_agents_in_group=2,
+        group_reward=1.0,
+        is_terminal=False,
+    )
+    trajectory_queue.put(trajectory)
+    trainer.advance()
+    # Test that some trajectoories have been injested
+    for reward in trainer.collected_group_rewards.values():
+        assert reward == 10
+    # Test end episode
+    trainer.end_episode()
+    assert len(trainer.collected_group_rewards.keys()) == 0
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_policy.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..4358c8ed751ae40b43cf959da460b5b36325e5ac
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_policy.py
@@ -0,0 +1,66 @@
+import pytest
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.torch_entities.networks import SimpleActor
+
+VECTOR_ACTION_SPACE = 2
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 32
+NUM_AGENTS = 12
+EPSILON = 1e-7
+
+
+def create_policy_mock(
+    dummy_config: NetworkSettings,
+    use_rnn: bool = False,
+    use_discrete: bool = True,
+    use_visual: bool = False,
+    seed: int = 0,
+) -> TorchPolicy:
+    mock_spec = mb.setup_test_behavior_specs(
+        use_discrete,
+        use_visual,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    network_settings = dummy_config
+    network_settings.memory = NetworkSettings.MemorySettings() if use_rnn else None
+    actor_kwargs = {
+        "conditional_sigma": False,
+        "tanh_squash": False,
+    }
+    policy = TorchPolicy(seed, mock_spec, network_settings, SimpleActor, actor_kwargs)
+    return policy
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_policy_evaluate(rnn, visual, discrete):
+    # Test evaluate
+    policy = create_policy_mock(
+        NetworkSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    decision_step, terminal_step = mb.create_steps_from_behavior_spec(
+        policy.behavior_spec, num_agents=NUM_AGENTS
+    )
+
+    run_out = policy.evaluate(decision_step, list(decision_step.agent_id))
+    if discrete:
+        run_out["action"].discrete.shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE))
+    else:
+        assert run_out["action"].continuous.shape == (NUM_AGENTS, VECTOR_ACTION_SPACE)
+
+
+def test_step_overflow():
+    policy = create_policy_mock(NetworkSettings())
+    policy.set_step(2**31 - 1)
+    assert policy.get_current_step() == 2**31 - 1  # step = 2147483647
+    policy.increment_step(3)
+    assert policy.get_current_step() == 2**31 + 2  # step = 2147483650
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_ppo.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4e71d4e1523b9fff6a3083f86fb3a194b02882a
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_ppo.py
@@ -0,0 +1,248 @@
+import pytest
+
+import numpy as np
+import attr
+
+from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.torch_entities.networks import SimpleActor
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.mock_brain import copy_buffer_fields
+from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.tests.dummy_config import (  # noqa: F401
+    ppo_dummy_config,
+    curiosity_dummy_config,
+    gail_dummy_config,
+)
+
+from mlagents_envs.base_env import ActionSpec
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+
+
+@pytest.fixture
+def dummy_config():
+    return ppo_dummy_config()
+
+
+VECTOR_ACTION_SPACE = 2
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 64
+NUM_AGENTS = 12
+
+CONTINUOUS_ACTION_SPEC = ActionSpec.create_continuous(VECTOR_ACTION_SPACE)
+DISCRETE_ACTION_SPEC = ActionSpec.create_discrete(tuple(DISCRETE_ACTION_SPACE))
+
+
+def create_test_ppo_optimizer(dummy_config, use_rnn, use_discrete, use_visual):
+    mock_specs = mb.setup_test_behavior_specs(
+        use_discrete,
+        use_visual,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    trainer_settings = attr.evolve(dummy_config)
+    trainer_settings.network_settings.memory = (
+        NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
+        if use_rnn
+        else None
+    )
+    actor_kwargs = {
+        "conditional_sigma": False,
+        "tanh_squash": False,
+    }
+    policy = TorchPolicy(
+        0, mock_specs, trainer_settings.network_settings, SimpleActor, actor_kwargs
+    )
+    optimizer = TorchPPOOptimizer(policy, trainer_settings)
+    return optimizer
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_ppo_optimizer_update(dummy_config, rnn, visual, discrete):
+    # Test evaluate
+    optimizer = create_test_ppo_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        BufferKey.ENVIRONMENT_REWARDS,
+        [
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
+
+    return_stats = optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+    # Make sure we have the right stats
+    required_stats = [
+        "Losses/Policy Loss",
+        "Losses/Value Loss",
+        "Policy/Learning Rate",
+        "Policy/Epsilon",
+        "Policy/Beta",
+    ]
+    for stat in required_stats:
+        assert stat in return_stats.keys()
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_ppo_optimizer_update_curiosity(
+    dummy_config, curiosity_dummy_config, rnn, visual, discrete  # noqa: F811
+):
+    # Test evaluate
+    dummy_config.reward_signals = curiosity_dummy_config
+    optimizer = create_test_ppo_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("curiosity"),
+            RewardSignalUtil.value_estimates_key("curiosity"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
+
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_ppo_optimizer_update_gail(gail_dummy_config, dummy_config):  # noqa: F811
+    # Test evaluate
+    dummy_config.reward_signals = gail_dummy_config
+    config = ppo_dummy_config()
+    optimizer = create_test_ppo_optimizer(
+        config, use_rnn=False, use_discrete=False, use_visual=False
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+        ],
+    )
+
+    update_buffer[BufferKey.CONTINUOUS_LOG_PROBS] = np.ones_like(
+        update_buffer[BufferKey.CONTINUOUS_ACTION]
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+    # Check if buffer size is too big
+    update_buffer = mb.simulate_rollout(3000, optimizer.policy.behavior_spec)
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+        ],
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_ppo_get_value_estimates(dummy_config, rnn, visual, discrete):
+    optimizer = create_test_ppo_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Time horizon is longer than sequence length, make sure to test
+    # process trajectory on multiple sequences in trajectory + some padding
+    time_horizon = 30
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=optimizer.policy.behavior_spec.observation_specs,
+        action_spec=DISCRETE_ACTION_SPEC if discrete else CONTINUOUS_ACTION_SPEC,
+        max_step_complete=True,
+    )
+    run_out, final_value_out, all_memories = optimizer.get_trajectory_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+    )
+    if rnn:
+        # Check that memories don't have a Torch gradient
+        for mem in optimizer.critic_memory_dict.values():
+            assert not mem.requires_grad
+
+    for key, val in run_out.items():
+        assert type(key) is str
+        assert len(val) == time_horizon
+    if all_memories is not None:
+        assert len(all_memories) == time_horizon
+
+    run_out, final_value_out, _ = optimizer.get_trajectory_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=True
+    )
+    for key, val in final_value_out.items():
+        assert type(key) is str
+        assert val == 0.0
+
+    # Check if we ignore terminal states properly
+    optimizer.reward_signals["extrinsic"].use_terminal_states = False
+    run_out, final_value_out, _ = optimizer.get_trajectory_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+    )
+    for key, val in final_value_out.items():
+        assert type(key) is str
+        assert val != 0.0
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_curiosity.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_curiosity.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc219d03208953affb0a09f1e728d1ec1a3edcc2
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_curiosity.py
@@ -0,0 +1,150 @@
+import numpy as np
+import pytest
+from mlagents.torch_utils import torch
+from mlagents.trainers.buffer import BufferKey
+from mlagents.trainers.torch_entities.components.reward_providers import (
+    CuriosityRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionSpec
+from mlagents.trainers.settings import CuriositySettings, RewardSignalType
+from mlagents.trainers.tests.torch_entities.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+SEED = [42]
+
+ACTIONSPEC_CONTINUOUS = ActionSpec.create_continuous(5)
+ACTIONSPEC_TWODISCRETE = ActionSpec.create_discrete((2, 3))
+ACTIONSPEC_DISCRETE = ActionSpec.create_discrete((2,))
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_settings.strength = 0.1
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    assert curiosity_rp.strength == 0.1
+    assert curiosity_rp.name == "Curiosity"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 3), (84, 86, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 1)]),
+            ACTIONSPEC_TWODISCRETE,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_rp = create_reward_provider(
+        RewardSignalType.CURIOSITY, behavior_spec, curiosity_settings
+    )
+    assert curiosity_rp.name == "Curiosity"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    curiosity_rp.update(buffer)
+    reward_old = curiosity_rp.evaluate(buffer)[0]
+    for _ in range(20):
+        curiosity_rp.update(buffer)
+        reward_new = curiosity_rp.evaluate(buffer)[0]
+    assert reward_new < reward_old
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        )
+    ],
+)
+def test_continuous_action_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.1)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    for _ in range(200):
+        curiosity_rp.update(buffer)
+    prediction = curiosity_rp._network.predict_action(buffer)[0]
+    target = torch.tensor(buffer[BufferKey.CONTINUOUS_ACTION][0])
+    error = torch.mean((prediction - target) ** 2).item()
+    assert error < 0.001
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+def test_next_state_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.1)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    for _ in range(100):
+        curiosity_rp.update(buffer)
+    prediction = curiosity_rp._network.predict_next_state(buffer)[0]
+    target = curiosity_rp._network.get_next_state(buffer)[0]
+    error = float(ModelUtils.to_numpy(torch.mean((prediction - target) ** 2)))
+    assert error < 0.001
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_extrinsic.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_extrinsic.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b5fb8a5483c20913f8b767066057b752577cdec
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_extrinsic.py
@@ -0,0 +1,93 @@
+from mlagents.trainers.buffer import BufferKey
+import pytest
+import numpy as np
+from mlagents.trainers.torch_entities.components.reward_providers import (
+    ExtrinsicRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionSpec
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+from mlagents.trainers.tests.torch_entities.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+ACTIONSPEC_CONTINUOUS = ActionSpec.create_continuous(5)
+ACTIONSPEC_TWODISCRETE = ActionSpec.create_discrete((2, 3))
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    settings = RewardSignalSettings()
+    settings.gamma = 0.2
+    extrinsic_rp = ExtrinsicRewardProvider(behavior_spec, settings)
+    assert extrinsic_rp.gamma == 0.2
+    assert extrinsic_rp.name == "Extrinsic"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    settings = RewardSignalSettings()
+    extrinsic_rp = create_reward_provider(
+        RewardSignalType.EXTRINSIC, behavior_spec, settings
+    )
+    assert extrinsic_rp.name == "Extrinsic"
+
+
+@pytest.mark.parametrize("reward", [2.0, 3.0, 4.0])
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+    ],
+)
+def test_reward(behavior_spec: BehaviorSpec, reward: float) -> None:
+    buffer = create_agent_buffer(behavior_spec, 1000, reward)
+    settings = RewardSignalSettings()
+    extrinsic_rp = ExtrinsicRewardProvider(behavior_spec, settings)
+    generated_rewards = extrinsic_rp.evaluate(buffer)
+    assert (generated_rewards == reward).all()
+
+    # Test group rewards. Rewards should be double of the environment rewards, but shouldn't count
+    # the groupmate rewards.
+    buffer[BufferKey.GROUP_REWARD] = buffer[BufferKey.ENVIRONMENT_REWARDS]
+    # 2 agents with identical rewards
+    data = [
+        [np.ones(1, dtype=np.float32) * reward] * 2
+        for _ in range(buffer.num_experiences)
+    ]
+    buffer[BufferKey.GROUPMATE_REWARDS].set(data)
+    generated_rewards = extrinsic_rp.evaluate(buffer)
+    assert (generated_rewards == 2 * reward).all()
+
+    # Test groupmate rewards. Total reward should be indiv_reward + 2 * teammate_reward + group_reward
+    extrinsic_rp = ExtrinsicRewardProvider(behavior_spec, settings)
+    extrinsic_rp.add_groupmate_rewards = True
+    generated_rewards = extrinsic_rp.evaluate(buffer)
+    assert (generated_rewards == 4 * reward).all()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_gail.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_gail.py
new file mode 100644
index 0000000000000000000000000000000000000000..d14c8a8439546a2d7b911300378ffe2a37ec5b36
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_gail.py
@@ -0,0 +1,160 @@
+from typing import Any
+import numpy as np
+import pytest
+from unittest.mock import patch
+from mlagents.torch_utils import torch
+import os
+from mlagents.trainers.torch_entities.components.reward_providers import (
+    GAILRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionSpec
+from mlagents.trainers.settings import GAILSettings, RewardSignalType
+from mlagents.trainers.tests.torch_entities.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.gail_reward_provider import (
+    DiscriminatorNetwork,
+)
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+CONTINUOUS_PATH = (
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)
+    + "/test.demo"
+)
+DISCRETE_PATH = (
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)
+    + "/testdcvis.demo"
+)
+SEED = [42]
+
+ACTIONSPEC_CONTINUOUS = ActionSpec.create_continuous(2)
+ACTIONSPEC_FOURDISCRETE = ActionSpec.create_discrete((2, 3, 3, 3))
+ACTIONSPEC_DISCRETE = ActionSpec.create_discrete((20,))
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [BehaviorSpec(create_observation_specs_with_shapes([(8,)]), ACTIONSPEC_CONTINUOUS)],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    gail_settings = GAILSettings(demo_path=CONTINUOUS_PATH)
+    gail_rp = GAILRewardProvider(behavior_spec, gail_settings)
+    assert gail_rp.name == "GAIL"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [BehaviorSpec(create_observation_specs_with_shapes([(8,)]), ACTIONSPEC_CONTINUOUS)],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    gail_settings = GAILSettings(demo_path=CONTINUOUS_PATH)
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+    assert gail_rp.name == "GAIL"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(8,), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_actions", [False, True])
+@patch(
+    "mlagents.trainers.torch_entities.components.reward_providers.gail_reward_provider.demo_to_buffer"
+)
+def test_reward_decreases(
+    demo_to_buffer: Any, use_actions: bool, behavior_spec: BehaviorSpec, seed: int
+) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    buffer_expert = create_agent_buffer(behavior_spec, 1000)
+    buffer_policy = create_agent_buffer(behavior_spec, 1000)
+    demo_to_buffer.return_value = None, buffer_expert
+    gail_settings = GAILSettings(
+        demo_path="", learning_rate=0.005, use_vail=False, use_actions=use_actions
+    )
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+
+    init_reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    init_reward_policy = gail_rp.evaluate(buffer_policy)[0]
+
+    for _ in range(20):
+        gail_rp.update(buffer_policy)
+        reward_expert = gail_rp.evaluate(buffer_expert)[0]
+        reward_policy = gail_rp.evaluate(buffer_policy)[0]
+        assert reward_expert >= 0  # GAIL / VAIL reward always positive
+        assert reward_policy >= 0
+    reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    reward_policy = gail_rp.evaluate(buffer_policy)[0]
+    assert reward_expert > reward_policy  # Expert reward greater than non-expert reward
+    assert (
+        reward_expert > init_reward_expert
+    )  # Expert reward getting better as network trains
+    assert (
+        reward_policy < init_reward_policy
+    )  # Non-expert reward getting worse as network trains
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(8,), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_actions", [False, True])
+@patch(
+    "mlagents.trainers.torch_entities.components.reward_providers.gail_reward_provider.demo_to_buffer"
+)
+def test_reward_decreases_vail(
+    demo_to_buffer: Any, use_actions: bool, behavior_spec: BehaviorSpec, seed: int
+) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    buffer_expert = create_agent_buffer(behavior_spec, 1000)
+    buffer_policy = create_agent_buffer(behavior_spec, 1000)
+    demo_to_buffer.return_value = None, buffer_expert
+    gail_settings = GAILSettings(
+        demo_path="", learning_rate=0.005, use_vail=True, use_actions=use_actions
+    )
+    DiscriminatorNetwork.initial_beta = 0.0
+    # we must set the initial value of beta to 0 for testing
+    # If we do not, the kl-loss will dominate early and will block the estimator
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+
+    for _ in range(20):
+        gail_rp.update(buffer_policy)
+        reward_expert = gail_rp.evaluate(buffer_expert)[0]
+        reward_policy = gail_rp.evaluate(buffer_policy)[0]
+        assert reward_expert >= 0  # GAIL / VAIL reward always positive
+        assert reward_policy >= 0
+    reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    reward_policy = gail_rp.evaluate(buffer_policy)[0]
+    assert reward_expert > reward_policy  # Expert reward greater than non-expert reward
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_rnd.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_rnd.py
new file mode 100644
index 0000000000000000000000000000000000000000..555f567af5f83043962239cce3152ecd74af84a1
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/test_rnd.py
@@ -0,0 +1,96 @@
+import numpy as np
+import pytest
+from mlagents.torch_utils import torch
+from mlagents.trainers.torch_entities.components.reward_providers import (
+    RNDRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionSpec
+from mlagents.trainers.settings import RNDSettings, RewardSignalType
+from mlagents.trainers.tests.torch_entities.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+SEED = [42]
+
+ACTIONSPEC_CONTINUOUS = ActionSpec.create_continuous(5)
+ACTIONSPEC_TWODISCRETE = ActionSpec.create_discrete((2, 3))
+ACTIONSPEC_DISCRETE = ActionSpec.create_discrete((2,))
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = RNDSettings(32, 0.01)
+    curiosity_settings.strength = 0.1
+    curiosity_rp = RNDRewardProvider(behavior_spec, curiosity_settings)
+    assert curiosity_rp.strength == 0.1
+    assert curiosity_rp.name == "RND"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 3), (84, 86, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 1)]),
+            ACTIONSPEC_TWODISCRETE,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = RNDSettings(32, 0.01)
+    curiosity_rp = create_reward_provider(
+        RewardSignalType.RND, behavior_spec, curiosity_settings
+    )
+    assert curiosity_rp.name == "RND"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
+    ],
+)
+def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    rnd_settings = RNDSettings(32, 0.01)
+    rnd_rp = RNDRewardProvider(behavior_spec, rnd_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    rnd_rp.update(buffer)
+    reward_old = rnd_rp.evaluate(buffer)[0]
+    for _ in range(100):
+        rnd_rp.update(buffer)
+        reward_new = rnd_rp.evaluate(buffer)[0]
+    assert reward_new < reward_old
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/utils.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e0454b05984311f23825a41c95bf69d72908186
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_reward_providers/utils.py
@@ -0,0 +1,41 @@
+import numpy as np
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.trajectory import ObsUtil
+
+
+def create_agent_buffer(
+    behavior_spec: BehaviorSpec, number: int, reward: float = 0.0
+) -> AgentBuffer:
+    buffer = AgentBuffer()
+    curr_obs = [
+        np.random.normal(size=obs_spec.shape).astype(np.float32)
+        for obs_spec in behavior_spec.observation_specs
+    ]
+    next_obs = [
+        np.random.normal(size=obs_spec.shape).astype(np.float32)
+        for obs_spec in behavior_spec.observation_specs
+    ]
+    action_buffer = behavior_spec.action_spec.random_action(1)
+    action = {}
+    if behavior_spec.action_spec.continuous_size > 0:
+        action[BufferKey.CONTINUOUS_ACTION] = action_buffer.continuous
+    if behavior_spec.action_spec.discrete_size > 0:
+        action[BufferKey.DISCRETE_ACTION] = action_buffer.discrete
+
+    for _ in range(number):
+        for i, obs in enumerate(curr_obs):
+            buffer[ObsUtil.get_name_at(i)].append(obs)
+        for i, obs in enumerate(next_obs):
+            buffer[ObsUtil.get_name_at_next(i)].append(obs)
+        # TODO
+        # buffer[AgentBufferKey.ACTIONS].append(action)
+        for _act_type, _act in action.items():
+            buffer[_act_type].append(_act[0, :])
+        # TODO was "rewards"
+        buffer[BufferKey.ENVIRONMENT_REWARDS].append(
+            np.ones(1, dtype=np.float32) * reward
+        )
+        buffer[BufferKey.MASKS].append(np.ones(1, dtype=np.float32))
+    buffer[BufferKey.DONE] = np.zeros(number, dtype=np.float32)
+    return buffer
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_sac.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_sac.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff39caeb7119174291d8bfd6c42c36fa96658319
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_sac.py
@@ -0,0 +1,120 @@
+import pytest
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.torch_entities.networks import SimpleActor
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.tests.dummy_config import (  # noqa: F401
+    sac_dummy_config,
+    curiosity_dummy_config,
+)
+
+
+@pytest.fixture
+def dummy_config():
+    return sac_dummy_config()
+
+
+VECTOR_ACTION_SPACE = 2
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 64
+NUM_AGENTS = 12
+
+
+def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
+    mock_brain = mb.setup_test_behavior_specs(
+        use_discrete,
+        use_visual,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE if not use_visual else 0,
+    )
+    trainer_settings = dummy_config
+    trainer_settings.network_settings.memory = (
+        NetworkSettings.MemorySettings(sequence_length=16, memory_size=12)
+        if use_rnn
+        else None
+    )
+    actor_kwargs = {
+        "conditional_sigma": False,
+        "tanh_squash": False,
+    }
+    policy = TorchPolicy(
+        0, mock_brain, trainer_settings.network_settings, SimpleActor, actor_kwargs
+    )
+    optimizer = TorchSACOptimizer(policy, trainer_settings)
+    return optimizer
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_sac_optimizer_update(dummy_config, rnn, visual, discrete):
+    torch.manual_seed(0)
+    # Test evaluate
+    optimizer = create_sac_optimizer_mock(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec, memory_size=12
+    )
+    # Mock out reward signal eval
+    update_buffer[RewardSignalUtil.rewards_key("extrinsic")] = update_buffer[
+        BufferKey.ENVIRONMENT_REWARDS
+    ]
+    # Mock out value memories
+    update_buffer[BufferKey.CRITIC_MEMORY] = update_buffer[BufferKey.MEMORY]
+    return_stats = optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+    # Make sure we have the right stats
+    required_stats = [
+        "Losses/Policy Loss",
+        "Losses/Value Loss",
+        "Losses/Q1 Loss",
+        "Losses/Q2 Loss",
+        "Policy/Continuous Entropy Coeff",
+        "Policy/Discrete Entropy Coeff",
+        "Policy/Learning Rate",
+    ]
+    for stat in required_stats:
+        assert stat in return_stats.keys()
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+def test_sac_update_reward_signals(
+    dummy_config, curiosity_dummy_config, discrete  # noqa: F811
+):
+    # Add a Curiosity module
+    dummy_config.reward_signals = curiosity_dummy_config
+    optimizer = create_sac_optimizer_mock(
+        dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
+    )
+
+    # Test update, while removing PPO-specific buffer elements.
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
+    )
+
+    # Mock out reward signal eval
+    update_buffer[RewardSignalUtil.rewards_key("extrinsic")] = update_buffer[
+        BufferKey.ENVIRONMENT_REWARDS
+    ]
+    update_buffer[RewardSignalUtil.rewards_key("curiosity")] = update_buffer[
+        BufferKey.ENVIRONMENT_REWARDS
+    ]
+    return_stats = optimizer.update_reward_signals(update_buffer)
+    required_stats = ["Losses/Curiosity Forward Loss", "Losses/Curiosity Inverse Loss"]
+    for stat in required_stats:
+        assert stat in return_stats.keys()
+
+
+if __name__ == "__main__":
+    pytest.main()
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_simple_rl.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_simple_rl.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c5b50968271b0e1ee84627954f8e932e9fe184e
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_simple_rl.py
@@ -0,0 +1,541 @@
+import pytest
+import attr
+
+
+from mlagents.trainers.tests.simple_test_envs import (
+    SimpleEnvironment,
+    MultiAgentEnvironment,
+    MemoryEnvironment,
+    RecordEnvironment,
+)
+
+from mlagents.trainers.demo_loader import write_demo
+
+from mlagents.trainers.settings import (
+    NetworkSettings,
+    SelfPlaySettings,
+    BehavioralCloningSettings,
+    GAILSettings,
+    RewardSignalType,
+    EncoderType,
+    ConditioningType,
+)
+
+from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
+    DemonstrationMetaProto,
+)
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
+    BrainParametersProto,
+    ActionSpecProto,
+)
+
+from mlagents.trainers.tests.dummy_config import (
+    ppo_dummy_config,
+    sac_dummy_config,
+    poca_dummy_config,
+)
+from mlagents.trainers.tests.check_env_trains import (
+    check_environment_trains,
+    default_reward_processor,
+)
+
+BRAIN_NAME = "1D"
+
+PPO_TORCH_CONFIG = ppo_dummy_config()
+SAC_TORCH_CONFIG = sac_dummy_config()
+POCA_TORCH_CONFIG = poca_dummy_config()
+
+# tests in this file won't be tested on GPU machine
+pytestmark = pytest.mark.slow
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_poca(action_sizes):
+    env = MultiAgentEnvironment([BRAIN_NAME], action_sizes=action_sizes, num_agents=2)
+    config = attr.evolve(POCA_TORCH_CONFIG)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("num_visual", [1, 2])
+def test_visual_poca(num_visual):
+    env = MultiAgentEnvironment(
+        [BRAIN_NAME], action_sizes=(0, 1), num_agents=2, num_visual=num_visual
+    )
+    new_hyperparams = attr.evolve(
+        POCA_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
+    )
+    config = attr.evolve(POCA_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("conditioning_type", [ConditioningType.HYPER])
+@pytest.mark.parametrize("num_var_len", [1, 2])
+@pytest.mark.parametrize("num_vector", [0, 1])
+@pytest.mark.parametrize("num_vis", [0, 1])
+def test_var_len_obs_and_goal_poca(num_vis, num_vector, num_var_len, conditioning_type):
+    env = MultiAgentEnvironment(
+        [BRAIN_NAME],
+        action_sizes=(0, 1),
+        num_visual=num_vis,
+        num_vector=num_vector,
+        num_var_len=num_var_len,
+        step_size=0.2,
+        num_agents=2,
+        goal_indices=[0],
+    )
+    new_network = attr.evolve(
+        POCA_TORCH_CONFIG.network_settings, goal_conditioning_type=conditioning_type
+    )
+    new_hyperparams = attr.evolve(
+        POCA_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
+    )
+    config = attr.evolve(
+        POCA_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_network,
+        max_steps=5000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("is_multiagent", [True, False])
+def test_recurrent_poca(action_sizes, is_multiagent):
+    if is_multiagent:
+        # This is not a recurrent environment, just check if LSTM doesn't crash
+        env = MultiAgentEnvironment(
+            [BRAIN_NAME], action_sizes=action_sizes, num_agents=2
+        )
+    else:
+        # Actually test LSTM here
+        env = MemoryEnvironment([BRAIN_NAME], action_sizes=action_sizes)
+    new_network_settings = attr.evolve(
+        POCA_TORCH_CONFIG.network_settings,
+        memory=NetworkSettings.MemorySettings(memory_size=16),
+    )
+    new_hyperparams = attr.evolve(
+        POCA_TORCH_CONFIG.hyperparameters,
+        learning_rate=1.0e-3,
+        batch_size=64,
+        buffer_size=128,
+    )
+    config = attr.evolve(
+        POCA_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_network_settings,
+        max_steps=500 if is_multiagent else 6000,
+    )
+    check_environment_trains(
+        env, {BRAIN_NAME: config}, success_threshold=None if is_multiagent else 0.9
+    )
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_ppo(action_sizes):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes)
+    config = attr.evolve(PPO_TORCH_CONFIG)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 2), (2, 0)])
+def test_2d_ppo(action_sizes):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.8)
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters, batch_size=64, buffer_size=640
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=10000
+    )
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_visual", [1, 2])
+@pytest.mark.parametrize("shared_critic", [True, False])
+def test_visual_ppo(shared_critic, num_visual, action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=num_visual,
+        num_vector=0,
+        step_size=0.2,
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters,
+        learning_rate=3.0e-4,
+        shared_critic=shared_critic,
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("conditioning_type", [ConditioningType.HYPER])
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_var_len", [1, 2])
+@pytest.mark.parametrize("num_vector", [0, 1])
+@pytest.mark.parametrize("num_vis", [0, 1])
+def test_var_len_obs_and_goal_ppo(
+    num_vis, num_vector, num_var_len, action_sizes, conditioning_type
+):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=num_vis,
+        num_vector=num_vector,
+        num_var_len=num_var_len,
+        step_size=0.2,
+        goal_indices=[0],
+    )
+    new_network = attr.evolve(
+        POCA_TORCH_CONFIG.network_settings, goal_conditioning_type=conditioning_type
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, network_settings=new_network
+    )
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("num_visual", [1, 2])
+@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
+def test_visual_advanced_ppo(vis_encode_type, num_visual):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=(0, 1),
+        num_visual=num_visual,
+        num_vector=0,
+        step_size=0.5,
+        vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
+    )
+    new_networksettings = attr.evolve(
+        SAC_TORCH_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_networksettings,
+        max_steps=900,
+        summary_freq=100,
+    )
+    # The number of steps is pretty small for these encoders
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_recurrent_ppo(action_sizes):
+    env = MemoryEnvironment([BRAIN_NAME], action_sizes=action_sizes)
+    new_network_settings = attr.evolve(
+        PPO_TORCH_CONFIG.network_settings,
+        memory=NetworkSettings.MemorySettings(memory_size=16),
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters,
+        learning_rate=1.0e-3,
+        batch_size=64,
+        buffer_size=128,
+    )
+    config = attr.evolve(
+        PPO_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_network_settings,
+        max_steps=6000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_sac(action_sizes):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes)
+    config = attr.evolve(SAC_TORCH_CONFIG)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 2), (2, 0)])
+def test_2d_sac(action_sizes):
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.8)
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters, buffer_init_steps=2000
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=3000
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.8)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_visual", [1, 2])
+def test_visual_sac(num_visual, action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=num_visual,
+        num_vector=0,
+        step_size=0.2,
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
+    )
+    config = attr.evolve(SAC_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_var_len", [1, 2])
+def test_var_len_obs_sac(num_var_len, action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=0,
+        num_var_len=num_var_len,
+        num_vector=0,
+        step_size=0.2,
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
+    )
+    config = attr.evolve(SAC_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("num_visual", [1, 2])
+@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
+def test_visual_advanced_sac(vis_encode_type, num_visual):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=(0, 1),
+        num_visual=num_visual,
+        num_vector=0,
+        step_size=0.5,
+        vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
+    )
+    new_networksettings = attr.evolve(
+        SAC_TORCH_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters,
+        batch_size=16,
+        learning_rate=3e-4,
+        buffer_init_steps=0,
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_networksettings,
+        max_steps=100,
+    )
+    # The number of steps is pretty small for these encoders
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_recurrent_sac(action_sizes):
+    step_size = 0.2 if action_sizes == (0, 1) else 0.5
+    env = MemoryEnvironment(
+        [BRAIN_NAME], action_sizes=action_sizes, step_size=step_size
+    )
+    new_networksettings = attr.evolve(
+        SAC_TORCH_CONFIG.network_settings,
+        memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters,
+        batch_size=256,
+        learning_rate=3e-4,
+        buffer_init_steps=1000,
+        steps_per_update=2,
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG,
+        hyperparameters=new_hyperparams,
+        network_settings=new_networksettings,
+        max_steps=4000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1337)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_ghost(action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], action_sizes=action_sizes
+    )
+    self_play_settings = SelfPlaySettings(
+        play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=2000
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, self_play=self_play_settings, max_steps=2500)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_ghost_fails(action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], action_sizes=action_sizes
+    )
+    # This config should fail because the ghosted policy is never swapped with a competent policy.
+    # Swap occurs after max step is reached.
+    self_play_settings = SelfPlaySettings(
+        play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=4000
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, self_play=self_play_settings, max_steps=2500)
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=None)
+    processed_rewards = [
+        default_reward_processor(rewards) for rewards in env.final_rewards.values()
+    ]
+    success_threshold = 0.9
+    assert any(reward > success_threshold for reward in processed_rewards) and any(
+        reward < success_threshold for reward in processed_rewards
+    )
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_asymm_ghost(action_sizes):
+    # Make opponent for asymmetric case
+    brain_name_opp = BRAIN_NAME + "Opp"
+    env = SimpleEnvironment(
+        [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], action_sizes=action_sizes
+    )
+    self_play_settings = SelfPlaySettings(
+        play_against_latest_model_ratio=1.0,
+        save_steps=10000,
+        swap_steps=10000,
+        team_change=400,
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, self_play=self_play_settings, max_steps=4000)
+    check_environment_trains(env, {BRAIN_NAME: config, brain_name_opp: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_simple_asymm_ghost_fails(action_sizes):
+    # Make opponent for asymmetric case
+    brain_name_opp = BRAIN_NAME + "Opp"
+    env = SimpleEnvironment(
+        [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], action_sizes=action_sizes
+    )
+    # This config should fail because the team that us not learning when both have reached
+    # max step should be executing the initial, untrained poliy.
+    self_play_settings = SelfPlaySettings(
+        play_against_latest_model_ratio=0.0,
+        save_steps=5000,
+        swap_steps=5000,
+        team_change=2000,
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, self_play=self_play_settings, max_steps=3000)
+    check_environment_trains(
+        env, {BRAIN_NAME: config, brain_name_opp: config}, success_threshold=None
+    )
+    processed_rewards = [
+        default_reward_processor(rewards) for rewards in env.final_rewards.values()
+    ]
+    success_threshold = 0.9
+    assert any(reward > success_threshold for reward in processed_rewards) and any(
+        reward < success_threshold for reward in processed_rewards
+    )
+
+
+@pytest.fixture(scope="session")
+def simple_record(tmpdir_factory):
+    def record_demo(action_sizes, num_visual=0, num_vector=1):
+        env = RecordEnvironment(
+            [BRAIN_NAME],
+            action_sizes=action_sizes,
+            num_visual=num_visual,
+            num_vector=num_vector,
+            n_demos=100,
+        )
+        # If we want to use true demos, we can solve the env in the usual way
+        # Otherwise, we can just call solve to execute the optimal policy
+        env.solve()
+        agent_info_protos = env.demonstration_protos[BRAIN_NAME]
+        meta_data_proto = DemonstrationMetaProto()
+        continuous_action_size, discrete_action_size = action_sizes
+        action_spec_proto = ActionSpecProto(
+            num_continuous_actions=continuous_action_size,
+            num_discrete_actions=discrete_action_size,
+            discrete_branch_sizes=[2] if discrete_action_size > 0 else None,
+        )
+        brain_param_proto = BrainParametersProto(
+            brain_name=BRAIN_NAME, is_training=True, action_spec=action_spec_proto
+        )
+        action_type = "Discrete" if action_sizes else "Continuous"
+        demo_path_name = "1DTest" + action_type + ".demo"
+        demo_path = str(tmpdir_factory.mktemp("tmp_demo").join(demo_path_name))
+        write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
+        return demo_path
+
+    return record_demo
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("trainer_config", [PPO_TORCH_CONFIG, SAC_TORCH_CONFIG])
+def test_gail(simple_record, action_sizes, trainer_config):
+    demo_path = simple_record(action_sizes)
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.2)
+    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
+    reward_signals = {
+        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
+    }
+    config = attr.evolve(
+        trainer_config,
+        reward_signals=reward_signals,
+        behavioral_cloning=bc_settings,
+        max_steps=500,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_gail_visual_ppo(simple_record, action_sizes):
+    demo_path = simple_record(action_sizes, num_visual=1, num_vector=0)
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        num_visual=1,
+        num_vector=0,
+        action_sizes=action_sizes,
+        step_size=0.3,
+    )
+    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1500)
+    reward_signals = {
+        RewardSignalType.GAIL: GAILSettings(
+            gamma=0.8, encoding_size=32, demo_path=demo_path
+        )
+    }
+    hyperparams = attr.evolve(PPO_TORCH_CONFIG.hyperparameters, learning_rate=1e-3)
+    config = attr.evolve(
+        PPO_TORCH_CONFIG,
+        reward_signals=reward_signals,
+        hyperparameters=hyperparams,
+        behavioral_cloning=bc_settings,
+        max_steps=1000,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+def test_gail_visual_sac(simple_record, action_sizes):
+    demo_path = simple_record(action_sizes, num_visual=1, num_vector=0)
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        num_visual=1,
+        num_vector=0,
+        action_sizes=action_sizes,
+        step_size=0.2,
+    )
+    bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
+    reward_signals = {
+        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
+    }
+    hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters, learning_rate=3e-4, batch_size=16
+    )
+    config = attr.evolve(
+        SAC_TORCH_CONFIG,
+        reward_signals=reward_signals,
+        hyperparameters=hyperparams,
+        behavioral_cloning=bc_settings,
+        max_steps=500,
+    )
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
diff --git a/ml-agents/mlagents/trainers/tests/torch_entities/test_utils.py b/ml-agents/mlagents/trainers/tests/torch_entities/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..943e3a6668100e69600e6d560e3c1fe2d0ee0be5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/tests/torch_entities/test_utils.py
@@ -0,0 +1,214 @@
+import pytest
+from mlagents.torch_utils import torch
+import numpy as np
+
+from mlagents.trainers.settings import EncoderType, ScheduleType
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.torch_entities.encoders import VectorInput
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
+
+
+def test_min_visual_size():
+    # Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER
+    assert set(ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
+
+    for encoder_type in EncoderType:
+        good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
+        vis_input = torch.ones((1, good_size, good_size, 3))
+        ModelUtils._check_resolution_for_encoder(good_size, good_size, encoder_type)
+        enc_func = ModelUtils.get_encoder_for_type(encoder_type)
+        enc = enc_func(good_size, good_size, 3, 1)
+        enc.forward(vis_input)
+
+        # Anything under the min size should raise an exception. If not, decrease the min size!
+        with pytest.raises(Exception):
+            bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1
+            vis_input = torch.ones((1, bad_size, bad_size, 3))
+
+            with pytest.raises(UnityTrainerException):
+                # Make sure we'd hit a friendly error during model setup time.
+                ModelUtils._check_resolution_for_encoder(
+                    bad_size, bad_size, encoder_type
+                )
+
+            enc = enc_func(bad_size, bad_size, 3, 1)
+            enc.forward(vis_input)
+
+
+@pytest.mark.parametrize(
+    "encoder_type",
+    [
+        EncoderType.SIMPLE,
+        EncoderType.NATURE_CNN,
+        EncoderType.SIMPLE,
+        EncoderType.MATCH3,
+    ],
+)
+def test_invalid_visual_input_size(encoder_type):
+    with pytest.raises(UnityTrainerException):
+        obs_spec = create_observation_specs_with_shapes(
+            [
+                (
+                    ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1,
+                    ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type],
+                    1,
+                )
+            ]
+        )
+        ModelUtils.create_input_processors(obs_spec, 20, encoder_type, 20, False)
+
+
+@pytest.mark.parametrize("num_visual", [0, 1, 2])
+@pytest.mark.parametrize("num_vector", [0, 1, 2])
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("encoder_type", [EncoderType.SIMPLE, EncoderType.NATURE_CNN])
+def test_create_inputs(encoder_type, normalize, num_vector, num_visual):
+    vec_obs_shape = (5,)
+    vis_obs_shape = (84, 84, 3)
+    obs_shapes = []
+    for _ in range(num_vector):
+        obs_shapes.append(vec_obs_shape)
+    for _ in range(num_visual):
+        obs_shapes.append(vis_obs_shape)
+    h_size = 128
+    obs_spec = create_observation_specs_with_shapes(obs_shapes)
+    encoders, embedding_sizes = ModelUtils.create_input_processors(
+        obs_spec, h_size, encoder_type, h_size, normalize
+    )
+    total_output = sum(embedding_sizes)
+    vec_enc = []
+    vis_enc = []
+    for i, enc in enumerate(encoders):
+        if len(obs_shapes[i]) == 1:
+            vec_enc.append(enc)
+        else:
+            vis_enc.append(enc)
+    assert len(vec_enc) == num_vector
+    assert len(vis_enc) == num_visual
+    assert total_output == int(num_visual * h_size + vec_obs_shape[0] * num_vector)
+    if num_vector > 0:
+        assert isinstance(vec_enc[0], VectorInput)
+
+    for enc in vis_enc:
+        assert isinstance(enc, ModelUtils.get_encoder_for_type(encoder_type))
+
+
+def test_decayed_value():
+    test_steps = [0, 4, 9]
+    # Test constant decay
+    param = ModelUtils.DecayedValue(ScheduleType.CONSTANT, 1.0, 0.2, test_steps[-1])
+    for _step in test_steps:
+        _param = param.get_value(_step)
+        assert _param == 1.0
+
+    test_results = [1.0, 0.6444, 0.2]
+    # Test linear decay
+    param = ModelUtils.DecayedValue(ScheduleType.LINEAR, 1.0, 0.2, test_steps[-1])
+    for _step, _result in zip(test_steps, test_results):
+        _param = param.get_value(_step)
+        assert _param == pytest.approx(_result, abs=0.01)
+
+    # Test invalid
+    with pytest.raises(UnityTrainerException):
+        ModelUtils.DecayedValue(
+            "SomeOtherSchedule", 1.0, 0.2, test_steps[-1]
+        ).get_value(0)
+
+
+def test_polynomial_decay():
+    test_steps = [0, 4, 9]
+    test_results = [1.0, 0.7, 0.2]
+    for _step, _result in zip(test_steps, test_results):
+        decayed = ModelUtils.polynomial_decay(
+            1.0, 0.2, test_steps[-1], _step, power=0.8
+        )
+        assert decayed == pytest.approx(_result, abs=0.01)
+
+
+def test_list_to_tensor():
+    # Test converting pure list
+    unconverted_list = [[1.0, 2], [1, 3], [1, 4]]
+    tensor = ModelUtils.list_to_tensor(unconverted_list)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list))
+
+    # Test converting pure numpy array
+    np_list = np.asarray(unconverted_list)
+    tensor = ModelUtils.list_to_tensor(np_list)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list))
+
+    # Test converting list of numpy arrays
+    list_of_np = [np.asarray(_el) for _el in unconverted_list]
+    tensor = ModelUtils.list_to_tensor(list_of_np)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list, dtype=torch.float32))
+
+
+def test_break_into_branches():
+    # Test normal multi-branch case
+    all_actions = torch.tensor([[1, 2, 3, 4, 5, 6]])
+    action_size = [2, 1, 3]
+    broken_actions = ModelUtils.break_into_branches(all_actions, action_size)
+    assert len(action_size) == len(broken_actions)
+    for i, _action in enumerate(broken_actions):
+        assert _action.shape == (1, action_size[i])
+
+    # Test 1-branch case
+    action_size = [6]
+    broken_actions = ModelUtils.break_into_branches(all_actions, action_size)
+    assert len(broken_actions) == 1
+    assert broken_actions[0].shape == (1, 6)
+
+
+def test_actions_to_onehot():
+    all_actions = torch.tensor([[1, 0, 2], [1, 0, 2]])
+    action_size = [2, 1, 3]
+    oh_actions = ModelUtils.actions_to_onehot(all_actions, action_size)
+    expected_result = [
+        torch.tensor([[0, 1], [0, 1]], dtype=torch.float),
+        torch.tensor([[1], [1]], dtype=torch.float),
+        torch.tensor([[0, 0, 1], [0, 0, 1]], dtype=torch.float),
+    ]
+    for res, exp in zip(oh_actions, expected_result):
+        assert torch.equal(res, exp)
+
+
+def test_masked_mean():
+    test_input = torch.tensor([1, 2, 3, 4, 5])
+    masks = torch.ones_like(test_input).bool()
+    mean = ModelUtils.masked_mean(test_input, masks=masks)
+    assert mean == 3.0
+
+    masks = torch.tensor([False, False, True, True, True])
+    mean = ModelUtils.masked_mean(test_input, masks=masks)
+    assert mean == 4.0
+
+    # Make sure it works if all masks are off
+    masks = torch.tensor([False, False, False, False, False])
+    mean = ModelUtils.masked_mean(test_input, masks=masks)
+    assert mean == 0.0
+
+    # Make sure it works with 2d arrays of shape (mask_length, N)
+    test_input = torch.tensor([1, 2, 3, 4, 5]).repeat(2, 1).T
+    masks = torch.tensor([False, False, True, True, True])
+    mean = ModelUtils.masked_mean(test_input, masks=masks)
+    assert mean == 4.0
+
+
+def test_soft_update():
+    class TestModule(torch.nn.Module):
+        def __init__(self, vals):
+            super().__init__()
+            self.parameter = torch.nn.Parameter(torch.ones(5, 5, 5) * vals)
+
+    tm1 = TestModule(0)
+    tm2 = TestModule(1)
+    tm3 = TestModule(2)
+
+    ModelUtils.soft_update(tm1, tm3, tau=0.5)
+    assert torch.equal(tm3.parameter, torch.ones(5, 5, 5))
+
+    ModelUtils.soft_update(tm1, tm2, tau=1.0)
+    assert torch.equal(tm2.parameter, tm1.parameter)
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diff --git a/ml-agents/mlagents/trainers/torch_entities/action_flattener.py b/ml-agents/mlagents/trainers/torch_entities/action_flattener.py
new file mode 100644
index 0000000000000000000000000000000000000000..beb529c96361ffc8cc0d679aad9771a3bd1cfed2
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/action_flattener.py
@@ -0,0 +1,44 @@
+from typing import List
+from mlagents.torch_utils import torch
+
+from mlagents_envs.base_env import ActionSpec
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.utils import ModelUtils
+
+
+class ActionFlattener:
+    def __init__(self, action_spec: ActionSpec):
+        """
+        A torch module that creates the flattened form of an AgentAction object.
+        The flattened form is the continuous action concatenated with the
+        concatenated one hot encodings of the discrete actions.
+        :param action_spec: An ActionSpec that describes the action space dimensions
+        """
+        self._specs = action_spec
+
+    @property
+    def flattened_size(self) -> int:
+        """
+        The flattened size is the continuous size plus the sum of the branch sizes
+        since discrete actions are encoded as one hots.
+        """
+        return self._specs.continuous_size + sum(self._specs.discrete_branches)
+
+    def forward(self, action: AgentAction) -> torch.Tensor:
+        """
+        Returns a tensor corresponding the flattened action
+        :param action: An AgentAction object
+        """
+        action_list: List[torch.Tensor] = []
+        if self._specs.continuous_size > 0:
+            action_list.append(action.continuous_tensor)
+        if self._specs.discrete_size > 0:
+            flat_discrete = torch.cat(
+                ModelUtils.actions_to_onehot(
+                    torch.as_tensor(action.discrete_tensor, dtype=torch.long),
+                    self._specs.discrete_branches,
+                ),
+                dim=1,
+            )
+            action_list.append(flat_discrete)
+        return torch.cat(action_list, dim=1)
diff --git a/ml-agents/mlagents/trainers/torch_entities/action_log_probs.py b/ml-agents/mlagents/trainers/torch_entities/action_log_probs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b72e7bb22358404f18a5a6c41ac0d93adacfc303
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/action_log_probs.py
@@ -0,0 +1,118 @@
+from typing import List, Optional, NamedTuple
+from mlagents.torch_utils import torch
+import numpy as np
+
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents_envs.base_env import _ActionTupleBase
+
+
+class LogProbsTuple(_ActionTupleBase):
+    """
+    An object whose fields correspond to the log probs of actions of different types.
+    Continuous and discrete are numpy arrays
+    Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size),
+    respectively. Note, this also holds when continuous or discrete size is
+    zero.
+    """
+
+    @property
+    def discrete_dtype(self) -> np.dtype:
+        """
+        The dtype of a discrete log probability.
+        """
+        return np.float32
+
+    @staticmethod
+    def empty_log_probs() -> "LogProbsTuple":
+        """
+        Generates a dummy LogProbsTuple
+        """
+        return LogProbsTuple()
+
+
+class ActionLogProbs(NamedTuple):
+    """
+    A NamedTuple containing the tensor for continuous log probs and list of tensors for
+    discrete log probs of individual actions as well as all the log probs for an entire branch.
+    Utility functions provide numpy <=> tensor conversions to be used by the optimizers.
+    :param continuous_tensor: Torch tensor corresponding to log probs of continuous actions
+    :param discrete_list: List of Torch tensors each corresponding to log probs of the discrete actions that were
+    sampled.
+    :param all_discrete_list: List of Torch tensors each corresponding to all log probs of
+    a discrete action branch, even the discrete actions that were not sampled. all_discrete_list is a list of Tensors,
+    each Tensor corresponds to one discrete branch log probabilities.
+    """
+
+    continuous_tensor: torch.Tensor
+    discrete_list: Optional[List[torch.Tensor]]
+    all_discrete_list: Optional[List[torch.Tensor]]
+
+    @property
+    def discrete_tensor(self):
+        """
+        Returns the discrete log probs list as a stacked tensor
+        """
+        return torch.stack(self.discrete_list, dim=-1)
+
+    @property
+    def all_discrete_tensor(self):
+        """
+        Returns the discrete log probs of each branch as a tensor
+        """
+        return torch.cat(self.all_discrete_list, dim=1)
+
+    def to_log_probs_tuple(self) -> LogProbsTuple:
+        """
+        Returns a LogProbsTuple. Only adds if tensor is not None. Otherwise,
+        LogProbsTuple uses a default.
+        """
+        log_probs_tuple = LogProbsTuple()
+        if self.continuous_tensor is not None:
+            continuous = ModelUtils.to_numpy(self.continuous_tensor)
+            log_probs_tuple.add_continuous(continuous)
+        if self.discrete_list is not None:
+            discrete = ModelUtils.to_numpy(self.discrete_tensor)
+            log_probs_tuple.add_discrete(discrete)
+        return log_probs_tuple
+
+    def _to_tensor_list(self) -> List[torch.Tensor]:
+        """
+        Returns the tensors in the ActionLogProbs as a flat List of torch Tensors. This
+        is private and serves as a utility for self.flatten()
+        """
+        tensor_list: List[torch.Tensor] = []
+        if self.continuous_tensor is not None:
+            tensor_list.append(self.continuous_tensor)
+        if self.discrete_list is not None:
+            tensor_list.append(self.discrete_tensor)
+        return tensor_list
+
+    def flatten(self) -> torch.Tensor:
+        """
+        A utility method that returns all log probs in ActionLogProbs as a flattened tensor.
+        This is useful for algorithms like PPO which can treat all log probs in the same way.
+        """
+        return torch.cat(self._to_tensor_list(), dim=1)
+
+    @staticmethod
+    def from_buffer(buff: AgentBuffer) -> "ActionLogProbs":
+        """
+        A static method that accesses continuous and discrete log probs fields in an AgentBuffer
+        and constructs the corresponding ActionLogProbs from the retrieved np arrays.
+        """
+        continuous: torch.Tensor = None
+        discrete: List[torch.Tensor] = None  # type: ignore
+
+        if BufferKey.CONTINUOUS_LOG_PROBS in buff:
+            continuous = ModelUtils.list_to_tensor(buff[BufferKey.CONTINUOUS_LOG_PROBS])
+        if BufferKey.DISCRETE_LOG_PROBS in buff:
+            discrete_tensor = ModelUtils.list_to_tensor(
+                buff[BufferKey.DISCRETE_LOG_PROBS]
+            )
+            # This will keep discrete_list = None which enables flatten()
+            if discrete_tensor.shape[1] > 0:
+                discrete = [
+                    discrete_tensor[..., i] for i in range(discrete_tensor.shape[-1])
+                ]
+        return ActionLogProbs(continuous, discrete, None)
diff --git a/ml-agents/mlagents/trainers/torch_entities/action_model.py b/ml-agents/mlagents/trainers/torch_entities/action_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b88c0262d35c02286b3bae894569ce5330640f9
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/action_model.py
@@ -0,0 +1,231 @@
+from typing import List, Tuple, NamedTuple, Optional
+from mlagents.torch_utils import torch, nn
+from mlagents.trainers.torch_entities.distributions import (
+    DistInstance,
+    DiscreteDistInstance,
+    GaussianDistribution,
+    MultiCategoricalDistribution,
+)
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_log_probs import ActionLogProbs
+from mlagents_envs.base_env import ActionSpec
+
+
+EPSILON = 1e-7  # Small value to avoid divide by zero
+
+
+class DistInstances(NamedTuple):
+    """
+    A NamedTuple with fields corresponding the the DistInstance objects
+    output by continuous and discrete distributions, respectively. Discrete distributions
+    output a list of DistInstance objects whereas continuous distributions output a single
+    DistInstance object.
+    """
+
+    continuous: Optional[DistInstance]
+    discrete: Optional[List[DiscreteDistInstance]]
+
+
+class ActionModel(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        action_spec: ActionSpec,
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+        deterministic: bool = False,
+    ):
+        """
+        A torch module that represents the action space of a policy. The ActionModel may contain
+        a continuous distribution, a discrete distribution or both where construction depends on
+        the action_spec.  The ActionModel uses the encoded input of the network body to parameterize
+        these distributions. The forward method of this module outputs the action, log probs,
+        and entropies given the encoding from the network body.
+        :params hidden_size: Size of the input to the ActionModel.
+        :params action_spec: The ActionSpec defining the action space dimensions and distributions.
+        :params conditional_sigma: Whether or not the std of a Gaussian is conditioned on state.
+        :params tanh_squash: Whether to squash the output of a Gaussian with the tanh function.
+        :params deterministic: Whether to select actions deterministically in policy.
+        """
+        super().__init__()
+        self.encoding_size = hidden_size
+        self.action_spec = action_spec
+        self._continuous_distribution = None
+        self._discrete_distribution = None
+
+        if self.action_spec.continuous_size > 0:
+            self._continuous_distribution = GaussianDistribution(
+                self.encoding_size,
+                self.action_spec.continuous_size,
+                conditional_sigma=conditional_sigma,
+                tanh_squash=tanh_squash,
+            )
+
+        if self.action_spec.discrete_size > 0:
+            self._discrete_distribution = MultiCategoricalDistribution(
+                self.encoding_size, self.action_spec.discrete_branches
+            )
+
+        # During training, clipping is done in TorchPolicy, but we need to clip before ONNX
+        # export as well.
+        self.clip_action = not tanh_squash
+        self._deterministic = deterministic
+
+    def _sample_action(self, dists: DistInstances) -> AgentAction:
+        """
+        Samples actions from a DistInstances tuple
+        :params dists: The DistInstances tuple
+        :return: An AgentAction corresponding to the actions sampled from the DistInstances
+        """
+
+        continuous_action: Optional[torch.Tensor] = None
+        discrete_action: Optional[List[torch.Tensor]] = None
+        # This checks None because mypy complains otherwise
+        if dists.continuous is not None:
+            if self._deterministic:
+                continuous_action = dists.continuous.deterministic_sample()
+            else:
+                continuous_action = dists.continuous.sample()
+        if dists.discrete is not None:
+            discrete_action = []
+            if self._deterministic:
+                for discrete_dist in dists.discrete:
+                    discrete_action.append(discrete_dist.deterministic_sample())
+            else:
+                for discrete_dist in dists.discrete:
+                    discrete_action.append(discrete_dist.sample())
+        return AgentAction(continuous_action, discrete_action)
+
+    def _get_dists(self, inputs: torch.Tensor, masks: torch.Tensor) -> DistInstances:
+        """
+        Creates a DistInstances tuple using the continuous and discrete distributions
+        :params inputs: The encoding from the network body
+        :params masks: Action masks for discrete actions
+        :return: A DistInstances tuple
+        """
+        continuous_dist: Optional[DistInstance] = None
+        discrete_dist: Optional[List[DiscreteDistInstance]] = None
+        # This checks None because mypy complains otherwise
+        if self._continuous_distribution is not None:
+            continuous_dist = self._continuous_distribution(inputs)
+        if self._discrete_distribution is not None:
+            discrete_dist = self._discrete_distribution(inputs, masks)
+        return DistInstances(continuous_dist, discrete_dist)
+
+    def _get_probs_and_entropy(
+        self, actions: AgentAction, dists: DistInstances
+    ) -> Tuple[ActionLogProbs, torch.Tensor]:
+        """
+        Computes the log probabilites of the actions given distributions and entropies of
+        the given distributions.
+        :params actions: The AgentAction
+        :params dists: The DistInstances tuple
+        :return: An ActionLogProbs tuple and a torch tensor of the distribution entropies.
+        """
+        entropies_list: List[torch.Tensor] = []
+        continuous_log_prob: Optional[torch.Tensor] = None
+        discrete_log_probs: Optional[List[torch.Tensor]] = None
+        all_discrete_log_probs: Optional[List[torch.Tensor]] = None
+        # This checks None because mypy complains otherwise
+        if dists.continuous is not None:
+            continuous_log_prob = dists.continuous.log_prob(actions.continuous_tensor)
+            entropies_list.append(dists.continuous.entropy())
+        if dists.discrete is not None:
+            discrete_log_probs = []
+            all_discrete_log_probs = []
+            for discrete_action, discrete_dist in zip(
+                actions.discrete_list, dists.discrete  # type: ignore
+            ):
+                discrete_log_prob = discrete_dist.log_prob(discrete_action)
+                entropies_list.append(discrete_dist.entropy())
+                discrete_log_probs.append(discrete_log_prob)
+                all_discrete_log_probs.append(discrete_dist.all_log_prob())
+        action_log_probs = ActionLogProbs(
+            continuous_log_prob, discrete_log_probs, all_discrete_log_probs
+        )
+        entropies = torch.cat(entropies_list, dim=1)
+        return action_log_probs, entropies
+
+    def evaluate(
+        self, inputs: torch.Tensor, masks: torch.Tensor, actions: AgentAction
+    ) -> Tuple[ActionLogProbs, torch.Tensor]:
+        """
+        Given actions and encoding from the network body, gets the distributions and
+        computes the log probabilites and entropies.
+        :params inputs: The encoding from the network body
+        :params masks: Action masks for discrete actions
+        :params actions: The AgentAction
+        :return: An ActionLogProbs tuple and a torch tensor of the distribution entropies.
+        """
+        dists = self._get_dists(inputs, masks)
+        log_probs, entropies = self._get_probs_and_entropy(actions, dists)
+        # Use the sum of entropy across actions, not the mean
+        entropy_sum = torch.sum(entropies, dim=1)
+        return log_probs, entropy_sum
+
+    def get_action_out(self, inputs: torch.Tensor, masks: torch.Tensor) -> torch.Tensor:
+        """
+        Gets the tensors corresponding to the output of the policy network to be used for
+        inference. Called by the Actor's forward call.
+        :params inputs: The encoding from the network body
+        :params masks: Action masks for discrete actions
+        :return: A tuple of torch tensors corresponding to the inference output
+        """
+        dists = self._get_dists(inputs, masks)
+        continuous_out, discrete_out, action_out_deprecated = None, None, None
+        deterministic_continuous_out, deterministic_discrete_out = (
+            None,
+            None,
+        )  # deterministic actions
+        if self.action_spec.continuous_size > 0 and dists.continuous is not None:
+            continuous_out = dists.continuous.exported_model_output()
+            action_out_deprecated = continuous_out
+            deterministic_continuous_out = dists.continuous.deterministic_sample()
+            if self.clip_action:
+                continuous_out = torch.clamp(continuous_out, -3, 3) / 3
+                action_out_deprecated = continuous_out
+                deterministic_continuous_out = (
+                    torch.clamp(deterministic_continuous_out, -3, 3) / 3
+                )
+        if self.action_spec.discrete_size > 0 and dists.discrete is not None:
+            discrete_out_list = [
+                discrete_dist.exported_model_output()
+                for discrete_dist in dists.discrete
+            ]
+            discrete_out = torch.cat(discrete_out_list, dim=1)
+            action_out_deprecated = torch.cat(discrete_out_list, dim=1)
+            deterministic_discrete_out_list = [
+                discrete_dist.deterministic_sample() for discrete_dist in dists.discrete
+            ]
+            deterministic_discrete_out = torch.cat(
+                deterministic_discrete_out_list, dim=1
+            )
+
+        # deprecated action field does not support hybrid action
+        if self.action_spec.continuous_size > 0 and self.action_spec.discrete_size > 0:
+            action_out_deprecated = None
+        return (
+            continuous_out,
+            discrete_out,
+            action_out_deprecated,
+            deterministic_continuous_out,
+            deterministic_discrete_out,
+        )
+
+    def forward(
+        self, inputs: torch.Tensor, masks: torch.Tensor
+    ) -> Tuple[AgentAction, ActionLogProbs, torch.Tensor]:
+        """
+        The forward method of this module. Outputs the action, log probs,
+        and entropies given the encoding from the network body.
+        :params inputs: The encoding from the network body
+        :params masks: Action masks for discrete actions
+        :return: Given the input, an AgentAction of the actions generated by the policy and the corresponding
+        ActionLogProbs and entropies.
+        """
+        dists = self._get_dists(inputs, masks)
+        actions = self._sample_action(dists)
+        log_probs, entropies = self._get_probs_and_entropy(actions, dists)
+        # Use the sum of entropy across actions, not the mean
+        entropy_sum = torch.sum(entropies, dim=1)
+        return (actions, log_probs, entropy_sum)
diff --git a/ml-agents/mlagents/trainers/torch_entities/agent_action.py b/ml-agents/mlagents/trainers/torch_entities/agent_action.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ecc995a55eec6d5237ee358ba264773605fbf55
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/agent_action.py
@@ -0,0 +1,157 @@
+from typing import List, Optional, NamedTuple
+import itertools
+import numpy as np
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents_envs.base_env import ActionTuple
+
+
+class AgentAction(NamedTuple):
+    """
+    A NamedTuple containing the tensor for continuous actions and list of tensors for
+    discrete actions. Utility functions provide numpy <=> tensor conversions to be
+    sent as actions to the environment manager as well as used by the optimizers.
+    :param continuous_tensor: Torch tensor corresponding to continuous actions
+    :param discrete_list: List of Torch tensors each corresponding to discrete actions
+    """
+
+    continuous_tensor: torch.Tensor
+    discrete_list: Optional[List[torch.Tensor]]
+
+    @property
+    def discrete_tensor(self) -> torch.Tensor:
+        """
+        Returns the discrete action list as a stacked tensor
+        """
+        if self.discrete_list is not None and len(self.discrete_list) > 0:
+            return torch.stack(self.discrete_list, dim=-1)
+        else:
+            return torch.empty(0)
+
+    def slice(self, start: int, end: int) -> "AgentAction":
+        """
+        Returns an AgentAction with the continuous and discrete tensors slices
+        from index start to index end.
+        """
+        _cont = None
+        _disc_list = []
+        if self.continuous_tensor is not None:
+            _cont = self.continuous_tensor[start:end]
+        if self.discrete_list is not None and len(self.discrete_list) > 0:
+            for _disc in self.discrete_list:
+                _disc_list.append(_disc[start:end])
+        return AgentAction(_cont, _disc_list)
+
+    def to_action_tuple(self, clip: bool = False) -> ActionTuple:
+        """
+        Returns an ActionTuple
+        """
+        action_tuple = ActionTuple()
+        if self.continuous_tensor is not None:
+            _continuous_tensor = self.continuous_tensor
+            if clip:
+                _continuous_tensor = torch.clamp(_continuous_tensor, -3, 3) / 3
+            continuous = ModelUtils.to_numpy(_continuous_tensor)
+            action_tuple.add_continuous(continuous)
+        if self.discrete_list is not None:
+            discrete = ModelUtils.to_numpy(self.discrete_tensor[:, 0, :])
+            action_tuple.add_discrete(discrete)
+        return action_tuple
+
+    @staticmethod
+    def from_buffer(buff: AgentBuffer) -> "AgentAction":
+        """
+        A static method that accesses continuous and discrete action fields in an AgentBuffer
+        and constructs the corresponding AgentAction from the retrieved np arrays.
+        """
+        continuous: torch.Tensor = None
+        discrete: List[torch.Tensor] = None  # type: ignore
+        if BufferKey.CONTINUOUS_ACTION in buff:
+            continuous = ModelUtils.list_to_tensor(buff[BufferKey.CONTINUOUS_ACTION])
+        if BufferKey.DISCRETE_ACTION in buff:
+            discrete_tensor = ModelUtils.list_to_tensor(
+                buff[BufferKey.DISCRETE_ACTION], dtype=torch.long
+            )
+            discrete = [
+                discrete_tensor[..., i] for i in range(discrete_tensor.shape[-1])
+            ]
+        return AgentAction(continuous, discrete)
+
+    @staticmethod
+    def _group_agent_action_from_buffer(
+        buff: AgentBuffer, cont_action_key: BufferKey, disc_action_key: BufferKey
+    ) -> List["AgentAction"]:
+        """
+        Extracts continuous and discrete groupmate actions, as specified by BufferKey, and
+        returns a List of AgentActions that correspond to the groupmate's actions. List will
+        be of length equal to the maximum number of groupmates in the buffer. Any spots where
+        there are less agents than maximum, the actions will be padded with 0's.
+        """
+        continuous_tensors: List[torch.Tensor] = []
+        discrete_tensors: List[torch.Tensor] = []
+        if cont_action_key in buff:
+            padded_batch = buff[cont_action_key].padded_to_batch()
+            continuous_tensors = [
+                ModelUtils.list_to_tensor(arr) for arr in padded_batch
+            ]
+        if disc_action_key in buff:
+            padded_batch = buff[disc_action_key].padded_to_batch(dtype=np.long)
+            discrete_tensors = [
+                ModelUtils.list_to_tensor(arr, dtype=torch.long) for arr in padded_batch
+            ]
+
+        actions_list = []
+        for _cont, _disc in itertools.zip_longest(
+            continuous_tensors, discrete_tensors, fillvalue=None
+        ):
+            if _disc is not None:
+                _disc = [_disc[..., i] for i in range(_disc.shape[-1])]
+            actions_list.append(AgentAction(_cont, _disc))
+        return actions_list
+
+    @staticmethod
+    def group_from_buffer(buff: AgentBuffer) -> List["AgentAction"]:
+        """
+        A static method that accesses next group continuous and discrete action fields in an AgentBuffer
+        and constructs a padded List of AgentActions that represent the group agent actions.
+        The List is of length equal to max number of groupmate agents in the buffer, and the AgentBuffer iss
+        of the same length as the buffer. Empty spots (e.g. when agents die) are padded with 0.
+        :param buff: AgentBuffer of a batch or trajectory
+        :return: List of groupmate's AgentActions
+        """
+        return AgentAction._group_agent_action_from_buffer(
+            buff, BufferKey.GROUP_CONTINUOUS_ACTION, BufferKey.GROUP_DISCRETE_ACTION
+        )
+
+    @staticmethod
+    def group_from_buffer_next(buff: AgentBuffer) -> List["AgentAction"]:
+        """
+        A static method that accesses next group continuous and discrete action fields in an AgentBuffer
+        and constructs a padded List of AgentActions that represent the next group agent actions.
+        The List is of length equal to max number of groupmate agents in the buffer, and the AgentBuffer iss
+        of the same length as the buffer. Empty spots (e.g. when agents die) are padded with 0.
+        :param buff: AgentBuffer of a batch or trajectory
+        :return: List of groupmate's AgentActions
+        """
+        return AgentAction._group_agent_action_from_buffer(
+            buff, BufferKey.GROUP_NEXT_CONT_ACTION, BufferKey.GROUP_NEXT_DISC_ACTION
+        )
+
+    def to_flat(self, discrete_branches: List[int]) -> torch.Tensor:
+        """
+        Flatten this AgentAction into a single torch Tensor of dimension (batch, num_continuous + num_one_hot_discrete).
+        Discrete actions are converted into one-hot and concatenated with continuous actions.
+        :param discrete_branches: List of sizes for discrete actions.
+        :return: Tensor of flattened actions.
+        """
+        # if there are any discrete actions, create one-hot
+        if self.discrete_list is not None and len(self.discrete_list) > 0:
+            discrete_oh = ModelUtils.actions_to_onehot(
+                self.discrete_tensor, discrete_branches
+            )
+            discrete_oh = torch.cat(discrete_oh, dim=1)
+        else:
+            discrete_oh = torch.empty(0)
+        return torch.cat([self.continuous_tensor, discrete_oh], dim=-1)
diff --git a/ml-agents/mlagents/trainers/torch_entities/attention.py b/ml-agents/mlagents/trainers/torch_entities/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba34e01995d5586da8942620b5d7ec4fd8ce5770
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/attention.py
@@ -0,0 +1,293 @@
+from mlagents.torch_utils import torch
+import warnings
+from typing import Tuple, Optional, List
+from mlagents.trainers.torch_entities.layers import (
+    LinearEncoder,
+    Initialization,
+    linear_layer,
+    LayerNorm,
+)
+from mlagents.trainers.torch_entities.model_serialization import exporting_to_onnx
+from mlagents.trainers.exception import UnityTrainerException
+
+
+def get_zero_entities_mask(entities: List[torch.Tensor]) -> List[torch.Tensor]:
+    """
+    Takes a List of Tensors and returns a List of mask Tensor with 1 if the input was
+    all zeros (on dimension 2) and 0 otherwise. This is used in the Attention
+    layer to mask the padding observations.
+    """
+    with torch.no_grad():
+
+        if exporting_to_onnx.is_exporting():
+            with warnings.catch_warnings():
+                # We ignore a TracerWarning from PyTorch that warns that doing
+                # shape[n].item() will cause the trace to be incorrect (the trace might
+                # not generalize to other inputs)
+                # We ignore this warning because we know the model will always be
+                # run with inputs of the same shape
+                warnings.simplefilter("ignore")
+                # When exporting to ONNX, we want to transpose the entities. This is
+                # because ONNX only support input in NCHW (channel first) format.
+                # Barracuda also expect to get data in NCHW.
+                entities = [
+                    torch.transpose(obs, 2, 1).reshape(
+                        -1, obs.shape[1].item(), obs.shape[2].item()
+                    )
+                    for obs in entities
+                ]
+
+        # Generate the masking tensors for each entities tensor (mask only if all zeros)
+        key_masks: List[torch.Tensor] = [
+            (torch.sum(ent**2, axis=2) < 0.01).float() for ent in entities
+        ]
+    return key_masks
+
+
+class MultiHeadAttention(torch.nn.Module):
+
+    NEG_INF = -1e6
+
+    def __init__(self, embedding_size: int, num_heads: int):
+        """
+        Multi Head Attention module. We do not use the regular Torch implementation since
+        Barracuda does not support some operators it uses.
+        Takes as input to the forward method 3 tensors:
+        - query: of dimensions (batch_size, number_of_queries, embedding_size)
+        - key: of dimensions (batch_size, number_of_keys, embedding_size)
+        - value: of dimensions (batch_size, number_of_keys, embedding_size)
+        The forward method will return 2 tensors:
+        - The output: (batch_size, number_of_queries, embedding_size)
+        - The attention matrix: (batch_size, num_heads, number_of_queries, number_of_keys)
+        :param embedding_size: The size of the embeddings that will be generated (should be
+        dividable by the num_heads)
+        :param total_max_elements: The maximum total number of entities that can be passed to
+        the module
+        :param num_heads: The number of heads of the attention module
+        """
+        super().__init__()
+        self.n_heads = num_heads
+        self.head_size: int = embedding_size // self.n_heads
+        self.embedding_size: int = self.head_size * self.n_heads
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        n_q: int,
+        n_k: int,
+        key_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        b = -1  # the batch size
+
+        query = query.reshape(
+            b, n_q, self.n_heads, self.head_size
+        )  # (b, n_q, h, emb / h)
+        key = key.reshape(b, n_k, self.n_heads, self.head_size)  # (b, n_k, h, emb / h)
+        value = value.reshape(
+            b, n_k, self.n_heads, self.head_size
+        )  # (b, n_k, h, emb / h)
+
+        query = query.permute([0, 2, 1, 3])  # (b, h, n_q, emb / h)
+        # The next few lines are equivalent to : key.permute([0, 2, 3, 1])
+        # This is a hack, ONNX will compress two permute operations and
+        # Barracuda will not like seeing `permute([0,2,3,1])`
+        key = key.permute([0, 2, 1, 3])  # (b, h, emb / h, n_k)
+        key -= 1
+        key += 1
+        key = key.permute([0, 1, 3, 2])  # (b, h, emb / h, n_k)
+
+        qk = torch.matmul(query, key)  # (b, h, n_q, n_k)
+
+        if key_mask is None:
+            qk = qk / (self.embedding_size**0.5)
+        else:
+            key_mask = key_mask.reshape(b, 1, 1, n_k)
+            qk = (1 - key_mask) * qk / (
+                self.embedding_size**0.5
+            ) + key_mask * self.NEG_INF
+
+        att = torch.softmax(qk, dim=3)  # (b, h, n_q, n_k)
+
+        value = value.permute([0, 2, 1, 3])  # (b, h, n_k, emb / h)
+        value_attention = torch.matmul(att, value)  # (b, h, n_q, emb / h)
+
+        value_attention = value_attention.permute([0, 2, 1, 3])  # (b, n_q, h, emb / h)
+        value_attention = value_attention.reshape(
+            b, n_q, self.embedding_size
+        )  # (b, n_q, emb)
+
+        return value_attention, att
+
+
+class EntityEmbedding(torch.nn.Module):
+    """
+    A module used to embed entities before passing them to a self-attention block.
+    Used in conjunction with ResidualSelfAttention to encode information about a self
+    and additional entities. Can also concatenate self to entities for ego-centric self-
+    attention. Inspired by architecture used in https://arxiv.org/pdf/1909.07528.pdf.
+    """
+
+    def __init__(
+        self,
+        entity_size: int,
+        entity_num_max_elements: Optional[int],
+        embedding_size: int,
+    ):
+        """
+        Constructs an EntityEmbedding module.
+        :param x_self_size: Size of "self" entity.
+        :param entity_size: Size of other entities.
+        :param entity_num_max_elements: Maximum elements for a given entity, None for unrestricted.
+            Needs to be assigned in order for model to be exportable to ONNX and Barracuda.
+        :param embedding_size: Embedding size for the entity encoder.
+        :param concat_self: Whether to concatenate x_self to entities. Set True for ego-centric
+            self-attention.
+        """
+        super().__init__()
+        self.self_size: int = 0
+        self.entity_size: int = entity_size
+        self.entity_num_max_elements: int = -1
+        if entity_num_max_elements is not None:
+            self.entity_num_max_elements = entity_num_max_elements
+        self.embedding_size = embedding_size
+        # Initialization scheme from http://www.cs.toronto.edu/~mvolkovs/ICML2020_tfixup.pdf
+        self.self_ent_encoder = LinearEncoder(
+            self.entity_size,
+            1,
+            self.embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / self.embedding_size) ** 0.5,
+        )
+
+    def add_self_embedding(self, size: int) -> None:
+        self.self_size = size
+        self.self_ent_encoder = LinearEncoder(
+            self.self_size + self.entity_size,
+            1,
+            self.embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / self.embedding_size) ** 0.5,
+        )
+
+    def forward(self, x_self: torch.Tensor, entities: torch.Tensor) -> torch.Tensor:
+        num_entities = self.entity_num_max_elements
+        if num_entities < 0:
+            if exporting_to_onnx.is_exporting():
+                raise UnityTrainerException(
+                    "Trying to export an attention mechanism that doesn't have a set max \
+                    number of elements."
+                )
+            num_entities = entities.shape[1]
+
+        if exporting_to_onnx.is_exporting():
+            # When exporting to ONNX, we want to transpose the entities. This is
+            # because ONNX only support input in NCHW (channel first) format.
+            # Barracuda also expect to get data in NCHW.
+            entities = torch.transpose(entities, 2, 1).reshape(
+                -1, num_entities, self.entity_size
+            )
+
+        if self.self_size > 0:
+            expanded_self = x_self.reshape(-1, 1, self.self_size)
+            expanded_self = torch.cat([expanded_self] * num_entities, dim=1)
+            # Concatenate all observations with self
+            entities = torch.cat([expanded_self, entities], dim=2)
+        # Encode entities
+        encoded_entities = self.self_ent_encoder(entities)
+        return encoded_entities
+
+
+class ResidualSelfAttention(torch.nn.Module):
+    """
+    Residual self attentioninspired from https://arxiv.org/pdf/1909.07528.pdf. Can be used
+    with an EntityEmbedding module, to apply multi head self attention to encode information
+    about a "Self" and a list of relevant "Entities".
+    """
+
+    EPSILON = 1e-7
+
+    def __init__(
+        self,
+        embedding_size: int,
+        entity_num_max_elements: Optional[int] = None,
+        num_heads: int = 4,
+    ):
+        """
+        Constructs a ResidualSelfAttention module.
+        :param embedding_size: Embedding sizee for attention mechanism and
+            Q, K, V encoders.
+        :param entity_num_max_elements: A List of ints representing the maximum number
+            of elements in an entity sequence. Should be of length num_entities. Pass None to
+            not restrict the number of elements; however, this will make the module
+            unexportable to ONNX/Barracuda.
+        :param num_heads: Number of heads for Multi Head Self-Attention
+        """
+        super().__init__()
+        self.max_num_ent: Optional[int] = None
+        if entity_num_max_elements is not None:
+            self.max_num_ent = entity_num_max_elements
+
+        self.attention = MultiHeadAttention(
+            num_heads=num_heads, embedding_size=embedding_size
+        )
+
+        # Initialization scheme from http://www.cs.toronto.edu/~mvolkovs/ICML2020_tfixup.pdf
+        self.fc_q = linear_layer(
+            embedding_size,
+            embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / embedding_size) ** 0.5,
+        )
+        self.fc_k = linear_layer(
+            embedding_size,
+            embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / embedding_size) ** 0.5,
+        )
+        self.fc_v = linear_layer(
+            embedding_size,
+            embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / embedding_size) ** 0.5,
+        )
+        self.fc_out = linear_layer(
+            embedding_size,
+            embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / embedding_size) ** 0.5,
+        )
+        self.embedding_norm = LayerNorm()
+        self.residual_norm = LayerNorm()
+
+    def forward(self, inp: torch.Tensor, key_masks: List[torch.Tensor]) -> torch.Tensor:
+        # Gather the maximum number of entities information
+        mask = torch.cat(key_masks, dim=1)
+
+        inp = self.embedding_norm(inp)
+        # Feed to self attention
+        query = self.fc_q(inp)  # (b, n_q, emb)
+        key = self.fc_k(inp)  # (b, n_k, emb)
+        value = self.fc_v(inp)  # (b, n_k, emb)
+
+        # Only use max num if provided
+        if self.max_num_ent is not None:
+            num_ent = self.max_num_ent
+        else:
+            num_ent = inp.shape[1]
+            if exporting_to_onnx.is_exporting():
+                raise UnityTrainerException(
+                    "Trying to export an attention mechanism that doesn't have a set max \
+                    number of elements."
+                )
+
+        output, _ = self.attention(query, key, value, num_ent, num_ent, mask)
+        # Residual
+        output = self.fc_out(output) + inp
+        output = self.residual_norm(output)
+        # Average Pooling
+        numerator = torch.sum(output * (1 - mask).reshape(-1, num_ent, 1), dim=1)
+        denominator = torch.sum(1 - mask, dim=1, keepdim=True) + self.EPSILON
+        output = numerator / denominator
+        return output
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--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/bc/module.py
@@ -0,0 +1,186 @@
+from typing import Dict
+import numpy as np
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.demo_loader import demo_to_buffer
+from mlagents.trainers.settings import BehavioralCloningSettings, ScheduleType
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_log_probs import ActionLogProbs
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.trajectory import ObsUtil
+from mlagents.trainers.buffer import AgentBuffer
+
+
+class BCModule:
+    def __init__(
+        self,
+        policy: TorchPolicy,
+        settings: BehavioralCloningSettings,
+        policy_learning_rate: float,
+        default_batch_size: int,
+        default_num_epoch: int,
+    ):
+        """
+        A BC trainer that can be used inline with RL.
+        :param policy: The policy of the learning model
+        :param settings: The settings for BehavioralCloning including LR strength, batch_size,
+        num_epochs, samples_per_update and LR annealing steps.
+        :param policy_learning_rate: The initial Learning Rate of the policy. Used to set an appropriate learning rate
+            for the pretrainer.
+        """
+        self.policy = policy
+        self._anneal_steps = settings.steps
+        self.current_lr = policy_learning_rate * settings.strength
+
+        learning_rate_schedule: ScheduleType = (
+            ScheduleType.LINEAR if self._anneal_steps > 0 else ScheduleType.CONSTANT
+        )
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            learning_rate_schedule, self.current_lr, 1e-10, self._anneal_steps
+        )
+        params = self.policy.actor.parameters()
+        self.optimizer = torch.optim.Adam(params, lr=self.current_lr)
+        _, self.demonstration_buffer = demo_to_buffer(
+            settings.demo_path, policy.sequence_length, policy.behavior_spec
+        )
+        self.batch_size = (
+            settings.batch_size if settings.batch_size else default_batch_size
+        )
+        self.num_epoch = settings.num_epoch if settings.num_epoch else default_num_epoch
+        self.n_sequences = max(
+            min(self.batch_size, self.demonstration_buffer.num_experiences)
+            // policy.sequence_length,
+            1,
+        )
+
+        self.has_updated = False
+        self.use_recurrent = self.policy.use_recurrent
+        self.samples_per_update = settings.samples_per_update
+
+    def update(self) -> Dict[str, np.ndarray]:
+        """
+        Updates model using buffer.
+        :param max_batches: The maximum number of batches to use per update.
+        :return: The loss of the update.
+        """
+        # Don't continue training if the learning rate has reached 0, to reduce training time.
+
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        if self.current_lr <= 1e-10:  # Unlike in TF, this never actually reaches 0.
+            return {"Losses/Pretraining Loss": 0}
+
+        batch_losses = []
+        possible_demo_batches = (
+            self.demonstration_buffer.num_experiences // self.n_sequences
+        )
+        possible_batches = possible_demo_batches
+
+        max_batches = self.samples_per_update // self.n_sequences
+
+        n_epoch = self.num_epoch
+        for _ in range(n_epoch):
+            self.demonstration_buffer.shuffle(
+                sequence_length=self.policy.sequence_length
+            )
+            if max_batches == 0:
+                num_batches = possible_batches
+            else:
+                num_batches = min(possible_batches, max_batches)
+            for i in range(num_batches // self.policy.sequence_length):
+                demo_update_buffer = self.demonstration_buffer
+                start = i * self.n_sequences * self.policy.sequence_length
+                end = (i + 1) * self.n_sequences * self.policy.sequence_length
+                mini_batch_demo = demo_update_buffer.make_mini_batch(start, end)
+                run_out = self._update_batch(mini_batch_demo, self.n_sequences)
+                loss = run_out["loss"]
+                batch_losses.append(loss)
+
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.current_lr = decay_lr
+
+        self.has_updated = True
+        update_stats = {"Losses/Pretraining Loss": np.mean(batch_losses)}
+        return update_stats
+
+    def _behavioral_cloning_loss(
+        self,
+        selected_actions: AgentAction,
+        log_probs: ActionLogProbs,
+        expert_actions: torch.Tensor,
+    ) -> torch.Tensor:
+        bc_loss = 0
+        if self.policy.behavior_spec.action_spec.continuous_size > 0:
+            bc_loss += torch.nn.functional.mse_loss(
+                selected_actions.continuous_tensor, expert_actions.continuous_tensor
+            )
+        if self.policy.behavior_spec.action_spec.discrete_size > 0:
+            one_hot_expert_actions = ModelUtils.actions_to_onehot(
+                expert_actions.discrete_tensor,
+                self.policy.behavior_spec.action_spec.discrete_branches,
+            )
+            log_prob_branches = ModelUtils.break_into_branches(
+                log_probs.all_discrete_tensor,
+                self.policy.behavior_spec.action_spec.discrete_branches,
+            )
+            bc_loss += torch.mean(
+                torch.stack(
+                    [
+                        torch.sum(
+                            -torch.nn.functional.log_softmax(log_prob_branch, dim=1)
+                            * expert_actions_branch,
+                            dim=1,
+                        )
+                        for log_prob_branch, expert_actions_branch in zip(
+                            log_prob_branches, one_hot_expert_actions
+                        )
+                    ]
+                )
+            )
+        return bc_loss
+
+    def _update_batch(
+        self, mini_batch_demo: AgentBuffer, n_sequences: int
+    ) -> Dict[str, float]:
+        """
+        Helper function for update_batch.
+        """
+        np_obs = ObsUtil.from_buffer(
+            mini_batch_demo, len(self.policy.behavior_spec.observation_specs)
+        )
+        # Convert to tensors
+        tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+        act_masks = None
+        expert_actions = AgentAction.from_buffer(mini_batch_demo)
+        if self.policy.behavior_spec.action_spec.discrete_size > 0:
+
+            act_masks = ModelUtils.list_to_tensor(
+                np.ones(
+                    (
+                        self.n_sequences * self.policy.sequence_length,
+                        sum(self.policy.behavior_spec.action_spec.discrete_branches),
+                    ),
+                    dtype=np.float32,
+                )
+            )
+
+        memories = []
+        if self.policy.use_recurrent:
+            memories = torch.zeros(1, self.n_sequences, self.policy.m_size)
+
+        selected_actions, run_out, _ = self.policy.actor.get_action_and_stats(
+            tensor_obs,
+            masks=act_masks,
+            memories=memories,
+            sequence_length=self.policy.sequence_length,
+        )
+        log_probs = run_out["log_probs"]
+        bc_loss = self._behavioral_cloning_loss(
+            selected_actions, log_probs, expert_actions
+        )
+        self.optimizer.zero_grad()
+        bc_loss.backward()
+
+        self.optimizer.step()
+        run_out = {"loss": bc_loss.item()}
+        return run_out
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/__init__.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..696d978b8ab3e1f34c26e6fecc57b8670ab7659a
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/__init__.py
@@ -0,0 +1,18 @@
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (  # noqa F401
+    BaseRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.extrinsic_reward_provider import (  # noqa F401
+    ExtrinsicRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.curiosity_reward_provider import (  # noqa F401
+    CuriosityRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.gail_reward_provider import (  # noqa F401
+    GAILRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.rnd_reward_provider import (  # noqa F401
+    RNDRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.reward_provider_factory import (  # noqa F401
+    create_reward_provider,
+)
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/__pycache__/__init__.cpython-310.pyc b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/__pycache__/__init__.cpython-310.pyc
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diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/base_reward_provider.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/base_reward_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e258657b37a9e2c0b9ad0340b91c98bdb7c7f59
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/base_reward_provider.py
@@ -0,0 +1,81 @@
+import numpy as np
+from mlagents.torch_utils import torch
+from abc import ABC, abstractmethod
+from typing import Dict
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.settings import RewardSignalSettings
+from mlagents_envs.base_env import BehaviorSpec
+
+
+class BaseRewardProvider(ABC):
+    def __init__(self, specs: BehaviorSpec, settings: RewardSignalSettings) -> None:
+        self._policy_specs = specs
+        self._gamma = settings.gamma
+        self._strength = settings.strength
+        self._ignore_done = False
+
+    @property
+    def gamma(self) -> float:
+        """
+        The discount factor for the reward signal
+        """
+        return self._gamma
+
+    @property
+    def strength(self) -> float:
+        """
+        The strength multiplier of the reward provider
+        """
+        return self._strength
+
+    @property
+    def name(self) -> str:
+        """
+        The name of the reward provider. Is used for reporting and identification
+        """
+        class_name = self.__class__.__name__
+        return class_name.replace("RewardProvider", "")
+
+    @property
+    def ignore_done(self) -> bool:
+        """
+        If true, when the agent is done, the rewards of the next episode must be
+        used to calculate the return of the current episode.
+        Is used to mitigate the positive bias in rewards with no natural end.
+        """
+        return self._ignore_done
+
+    @abstractmethod
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        """
+        Evaluates the reward for the data present in the Dict mini_batch. Use this when evaluating a reward
+        function drawn straight from a Buffer.
+        :param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
+            when drawing from the update buffer.
+        :return: a np.ndarray of rewards generated by the reward provider
+        """
+        raise NotImplementedError(
+            "The reward provider's evaluate method has not been implemented "
+        )
+
+    @abstractmethod
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        """
+        Update the reward for the data present in the Dict mini_batch. Use this when updating a reward
+        function drawn straight from a Buffer.
+        :param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
+            when drawing from the update buffer.
+        :return: A dictionary from string to stats values
+        """
+        raise NotImplementedError(
+            "The reward provider's update method has not been implemented "
+        )
+
+    def get_modules(self) -> Dict[str, torch.nn.Module]:
+        """
+        Returns a dictionary of string identifiers to the torch.nn.Modules used by
+        the reward providers. This method is used for loading and saving the weights
+        of the reward providers.
+        """
+        return {}
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/curiosity_reward_provider.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/curiosity_reward_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4cbf34dd92a847e1051f8e190fab81044a4e660
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/curiosity_reward_provider.py
@@ -0,0 +1,239 @@
+import numpy as np
+from typing import Dict, NamedTuple
+from mlagents.torch_utils import torch, default_device
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import CuriositySettings
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs import logging_util
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_flattener import ActionFlattener
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.torch_entities.networks import NetworkBody
+from mlagents.trainers.torch_entities.layers import LinearEncoder, linear_layer
+from mlagents.trainers.trajectory import ObsUtil
+
+logger = logging_util.get_logger(__name__)
+
+
+class ActionPredictionTuple(NamedTuple):
+    continuous: torch.Tensor
+    discrete: torch.Tensor
+
+
+class CuriosityRewardProvider(BaseRewardProvider):
+    beta = 0.2  # Forward vs Inverse loss weight
+    loss_multiplier = 10.0  # Loss multiplier
+
+    def __init__(self, specs: BehaviorSpec, settings: CuriositySettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = True
+        self._network = CuriosityNetwork(specs, settings)
+        self._network.to(default_device())
+
+        self.optimizer = torch.optim.Adam(
+            self._network.parameters(), lr=settings.learning_rate
+        )
+        self._has_updated_once = False
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            rewards = ModelUtils.to_numpy(self._network.compute_reward(mini_batch))
+        rewards = np.minimum(rewards, 1.0 / self.strength)
+        return rewards * self._has_updated_once
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        self._has_updated_once = True
+        forward_loss = self._network.compute_forward_loss(mini_batch)
+        inverse_loss = self._network.compute_inverse_loss(mini_batch)
+
+        loss = self.loss_multiplier * (
+            self.beta * forward_loss + (1.0 - self.beta) * inverse_loss
+        )
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        return {
+            "Losses/Curiosity Forward Loss": forward_loss.item(),
+            "Losses/Curiosity Inverse Loss": inverse_loss.item(),
+        }
+
+    def get_modules(self):
+        return {f"Module:{self.name}": self._network}
+
+
+class CuriosityNetwork(torch.nn.Module):
+    EPSILON = 1e-10
+
+    def __init__(self, specs: BehaviorSpec, settings: CuriositySettings) -> None:
+        super().__init__()
+        self._action_spec = specs.action_spec
+
+        state_encoder_settings = settings.network_settings
+        if state_encoder_settings.memory is not None:
+            state_encoder_settings.memory = None
+            logger.warning(
+                "memory was specified in network_settings but is not supported by Curiosity. It is being ignored."
+            )
+
+        self._state_encoder = NetworkBody(
+            specs.observation_specs, state_encoder_settings
+        )
+
+        self._action_flattener = ActionFlattener(self._action_spec)
+
+        self.inverse_model_action_encoding = torch.nn.Sequential(
+            LinearEncoder(2 * state_encoder_settings.hidden_units, 1, 256)
+        )
+
+        if self._action_spec.continuous_size > 0:
+            self.continuous_action_prediction = linear_layer(
+                256, self._action_spec.continuous_size
+            )
+        if self._action_spec.discrete_size > 0:
+            self.discrete_action_prediction = linear_layer(
+                256, sum(self._action_spec.discrete_branches)
+            )
+
+        self.forward_model_next_state_prediction = torch.nn.Sequential(
+            LinearEncoder(
+                state_encoder_settings.hidden_units
+                + self._action_flattener.flattened_size,
+                1,
+                256,
+            ),
+            linear_layer(256, state_encoder_settings.hidden_units),
+        )
+
+    def get_current_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Extracts the current state embedding from a mini_batch.
+        """
+        n_obs = len(self._state_encoder.processors)
+        np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
+        # Convert to tensors
+        tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+
+        hidden, _ = self._state_encoder.forward(tensor_obs)
+        return hidden
+
+    def get_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Extracts the next state embedding from a mini_batch.
+        """
+        n_obs = len(self._state_encoder.processors)
+        np_obs = ObsUtil.from_buffer_next(mini_batch, n_obs)
+        # Convert to tensors
+        tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+
+        hidden, _ = self._state_encoder.forward(tensor_obs)
+        return hidden
+
+    def predict_action(self, mini_batch: AgentBuffer) -> ActionPredictionTuple:
+        """
+        In the continuous case, returns the predicted action.
+        In the discrete case, returns the logits.
+        """
+        inverse_model_input = torch.cat(
+            (self.get_current_state(mini_batch), self.get_next_state(mini_batch)), dim=1
+        )
+
+        continuous_pred = None
+        discrete_pred = None
+        hidden = self.inverse_model_action_encoding(inverse_model_input)
+        if self._action_spec.continuous_size > 0:
+            continuous_pred = self.continuous_action_prediction(hidden)
+        if self._action_spec.discrete_size > 0:
+            raw_discrete_pred = self.discrete_action_prediction(hidden)
+            branches = ModelUtils.break_into_branches(
+                raw_discrete_pred, self._action_spec.discrete_branches
+            )
+            branches = [torch.softmax(b, dim=1) for b in branches]
+            discrete_pred = torch.cat(branches, dim=1)
+        return ActionPredictionTuple(continuous_pred, discrete_pred)
+
+    def predict_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Uses the current state embedding and the action of the mini_batch to predict
+        the next state embedding.
+        """
+        actions = AgentAction.from_buffer(mini_batch)
+        flattened_action = self._action_flattener.forward(actions)
+        forward_model_input = torch.cat(
+            (self.get_current_state(mini_batch), flattened_action), dim=1
+        )
+
+        return self.forward_model_next_state_prediction(forward_model_input)
+
+    def compute_inverse_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Computes the inverse loss for a mini_batch. Corresponds to the error on the
+        action prediction (given the current and next state).
+        """
+        predicted_action = self.predict_action(mini_batch)
+        actions = AgentAction.from_buffer(mini_batch)
+        _inverse_loss = 0
+        if self._action_spec.continuous_size > 0:
+            sq_difference = (
+                actions.continuous_tensor - predicted_action.continuous
+            ) ** 2
+            sq_difference = torch.sum(sq_difference, dim=1)
+            _inverse_loss += torch.mean(
+                ModelUtils.dynamic_partition(
+                    sq_difference,
+                    ModelUtils.list_to_tensor(
+                        mini_batch[BufferKey.MASKS], dtype=torch.float
+                    ),
+                    2,
+                )[1]
+            )
+        if self._action_spec.discrete_size > 0:
+            true_action = torch.cat(
+                ModelUtils.actions_to_onehot(
+                    actions.discrete_tensor, self._action_spec.discrete_branches
+                ),
+                dim=1,
+            )
+            cross_entropy = torch.sum(
+                -torch.log(predicted_action.discrete + self.EPSILON) * true_action,
+                dim=1,
+            )
+            _inverse_loss += torch.mean(
+                ModelUtils.dynamic_partition(
+                    cross_entropy,
+                    ModelUtils.list_to_tensor(
+                        mini_batch[BufferKey.MASKS], dtype=torch.float
+                    ),  # use masks not action_masks
+                    2,
+                )[1]
+            )
+        return _inverse_loss
+
+    def compute_reward(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Calculates the curiosity reward for the mini_batch. Corresponds to the error
+        between the predicted and actual next state.
+        """
+        predicted_next_state = self.predict_next_state(mini_batch)
+        target = self.get_next_state(mini_batch)
+        sq_difference = 0.5 * (target - predicted_next_state) ** 2
+        sq_difference = torch.sum(sq_difference, dim=1)
+        return sq_difference
+
+    def compute_forward_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Computes the loss for the next state prediction
+        """
+        return torch.mean(
+            ModelUtils.dynamic_partition(
+                self.compute_reward(mini_batch),
+                ModelUtils.list_to_tensor(
+                    mini_batch[BufferKey.MASKS], dtype=torch.float
+                ),
+                2,
+            )[1]
+        )
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/extrinsic_reward_provider.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/extrinsic_reward_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b847463c78ef052e6dbb6a93d9af2321a5ae02
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/extrinsic_reward_provider.py
@@ -0,0 +1,43 @@
+import numpy as np
+from typing import Dict
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.settings import RewardSignalSettings
+
+
+class ExtrinsicRewardProvider(BaseRewardProvider):
+    """
+    Evaluates extrinsic reward. For single-agent, this equals the individual reward
+    given to the agent. For the POCA algorithm, we want not only the individual reward
+    but also the team and the individual rewards of the other agents.
+    """
+
+    def __init__(self, specs: BehaviorSpec, settings: RewardSignalSettings) -> None:
+        super().__init__(specs, settings)
+        self.add_groupmate_rewards = False
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        indiv_rewards = np.array(
+            mini_batch[BufferKey.ENVIRONMENT_REWARDS], dtype=np.float32
+        )
+        total_rewards = indiv_rewards
+        if BufferKey.GROUPMATE_REWARDS in mini_batch and self.add_groupmate_rewards:
+            groupmate_rewards_list = mini_batch[BufferKey.GROUPMATE_REWARDS]
+            groupmate_rewards_sum = np.array(
+                [sum(_rew) for _rew in groupmate_rewards_list], dtype=np.float32
+            )
+            total_rewards += groupmate_rewards_sum
+        if BufferKey.GROUP_REWARD in mini_batch:
+            group_rewards = np.array(
+                mini_batch[BufferKey.GROUP_REWARD], dtype=np.float32
+            )
+            # Add all the group rewards to the individual rewards
+            total_rewards += group_rewards
+        return total_rewards
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        return {}
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/gail_reward_provider.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/gail_reward_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ae77ba1434d0666420cde871775297fd62d72e4
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/gail_reward_provider.py
@@ -0,0 +1,260 @@
+from typing import Optional, Dict, List
+import numpy as np
+from mlagents.torch_utils import torch, default_device
+
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import GAILSettings
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs import logging_util
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.torch_entities.action_flattener import ActionFlattener
+from mlagents.trainers.torch_entities.networks import NetworkBody
+from mlagents.trainers.torch_entities.layers import linear_layer, Initialization
+from mlagents.trainers.demo_loader import demo_to_buffer
+from mlagents.trainers.trajectory import ObsUtil
+
+logger = logging_util.get_logger(__name__)
+
+
+class GAILRewardProvider(BaseRewardProvider):
+    def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = False
+        self._discriminator_network = DiscriminatorNetwork(specs, settings)
+        self._discriminator_network.to(default_device())
+        _, self._demo_buffer = demo_to_buffer(
+            settings.demo_path, 1, specs
+        )  # This is supposed to be the sequence length but we do not have access here
+        params = list(self._discriminator_network.parameters())
+        self.optimizer = torch.optim.Adam(params, lr=settings.learning_rate)
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            estimates, _ = self._discriminator_network.compute_estimate(
+                mini_batch, use_vail_noise=False
+            )
+            return ModelUtils.to_numpy(
+                -torch.log(
+                    1.0
+                    - estimates.squeeze(dim=1)
+                    * (1.0 - self._discriminator_network.EPSILON)
+                )
+            )
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+
+        expert_batch = self._demo_buffer.sample_mini_batch(
+            mini_batch.num_experiences, 1
+        )
+        self._discriminator_network.encoder.update_normalization(expert_batch)
+
+        loss, stats_dict = self._discriminator_network.compute_loss(
+            mini_batch, expert_batch
+        )
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        return stats_dict
+
+    def get_modules(self):
+        return {f"Module:{self.name}": self._discriminator_network}
+
+
+class DiscriminatorNetwork(torch.nn.Module):
+    gradient_penalty_weight = 10.0
+    z_size = 128
+    alpha = 0.0005
+    mutual_information = 0.5
+    EPSILON = 1e-7
+    initial_beta = 0.0
+
+    def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
+        super().__init__()
+        self._use_vail = settings.use_vail
+        self._settings = settings
+
+        encoder_settings = settings.network_settings
+        if encoder_settings.memory is not None:
+            encoder_settings.memory = None
+            logger.warning(
+                "memory was specified in network_settings but is not supported by GAIL. It is being ignored."
+            )
+
+        self._action_flattener = ActionFlattener(specs.action_spec)
+        unencoded_size = (
+            self._action_flattener.flattened_size + 1 if settings.use_actions else 0
+        )  # +1 is for dones
+        self.encoder = NetworkBody(
+            specs.observation_specs, encoder_settings, unencoded_size
+        )
+
+        estimator_input_size = encoder_settings.hidden_units
+        if settings.use_vail:
+            estimator_input_size = self.z_size
+            self._z_sigma = torch.nn.Parameter(
+                torch.ones((self.z_size), dtype=torch.float), requires_grad=True
+            )
+            self._z_mu_layer = linear_layer(
+                encoder_settings.hidden_units,
+                self.z_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.1,
+            )
+            self._beta = torch.nn.Parameter(
+                torch.tensor(self.initial_beta, dtype=torch.float), requires_grad=False
+            )
+
+        self._estimator = torch.nn.Sequential(
+            linear_layer(estimator_input_size, 1, kernel_gain=0.2), torch.nn.Sigmoid()
+        )
+
+    def get_action_input(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Creates the action Tensor. In continuous case, corresponds to the action. In
+        the discrete case, corresponds to the concatenation of one hot action Tensors.
+        """
+        return self._action_flattener.forward(AgentAction.from_buffer(mini_batch))
+
+    def get_state_inputs(self, mini_batch: AgentBuffer) -> List[torch.Tensor]:
+        """
+        Creates the observation input.
+        """
+        n_obs = len(self.encoder.processors)
+        np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
+        # Convert to tensors
+        tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+        return tensor_obs
+
+    def compute_estimate(
+        self, mini_batch: AgentBuffer, use_vail_noise: bool = False
+    ) -> torch.Tensor:
+        """
+        Given a mini_batch, computes the estimate (How much the discriminator believes
+        the data was sampled from the demonstration data).
+        :param mini_batch: The AgentBuffer of data
+        :param use_vail_noise: Only when using VAIL : If true, will sample the code, if
+        false, will return the mean of the code.
+        """
+        inputs = self.get_state_inputs(mini_batch)
+        if self._settings.use_actions:
+            actions = self.get_action_input(mini_batch)
+            dones = torch.as_tensor(
+                mini_batch[BufferKey.DONE], dtype=torch.float
+            ).unsqueeze(1)
+            action_inputs = torch.cat([actions, dones], dim=1)
+            hidden, _ = self.encoder(inputs, action_inputs)
+        else:
+            hidden, _ = self.encoder(inputs)
+        z_mu: Optional[torch.Tensor] = None
+        if self._settings.use_vail:
+            z_mu = self._z_mu_layer(hidden)
+            hidden = z_mu + torch.randn_like(z_mu) * self._z_sigma * use_vail_noise
+        estimate = self._estimator(hidden)
+        return estimate, z_mu
+
+    def compute_loss(
+        self, policy_batch: AgentBuffer, expert_batch: AgentBuffer
+    ) -> torch.Tensor:
+        """
+        Given a policy mini_batch and an expert mini_batch, computes the loss of the discriminator.
+        """
+        total_loss = torch.zeros(1)
+        stats_dict: Dict[str, np.ndarray] = {}
+        policy_estimate, policy_mu = self.compute_estimate(
+            policy_batch, use_vail_noise=True
+        )
+        expert_estimate, expert_mu = self.compute_estimate(
+            expert_batch, use_vail_noise=True
+        )
+        stats_dict["Policy/GAIL Policy Estimate"] = policy_estimate.mean().item()
+        stats_dict["Policy/GAIL Expert Estimate"] = expert_estimate.mean().item()
+        discriminator_loss = -(
+            torch.log(expert_estimate + self.EPSILON)
+            + torch.log(1.0 - policy_estimate + self.EPSILON)
+        ).mean()
+        stats_dict["Losses/GAIL Loss"] = discriminator_loss.item()
+        total_loss += discriminator_loss
+        if self._settings.use_vail:
+            # KL divergence loss (encourage latent representation to be normal)
+            kl_loss = torch.mean(
+                -torch.sum(
+                    1
+                    + (self._z_sigma**2).log()
+                    - 0.5 * expert_mu**2
+                    - 0.5 * policy_mu**2
+                    - (self._z_sigma**2),
+                    dim=1,
+                )
+            )
+            vail_loss = self._beta * (kl_loss - self.mutual_information)
+            with torch.no_grad():
+                self._beta.data = torch.max(
+                    self._beta + self.alpha * (kl_loss - self.mutual_information),
+                    torch.tensor(0.0),
+                )
+            total_loss += vail_loss
+            stats_dict["Policy/GAIL Beta"] = self._beta.item()
+            stats_dict["Losses/GAIL KL Loss"] = kl_loss.item()
+        if self.gradient_penalty_weight > 0.0:
+            gradient_magnitude_loss = (
+                self.gradient_penalty_weight
+                * self.compute_gradient_magnitude(policy_batch, expert_batch)
+            )
+            stats_dict["Policy/GAIL Grad Mag Loss"] = gradient_magnitude_loss.item()
+            total_loss += gradient_magnitude_loss
+        return total_loss, stats_dict
+
+    def compute_gradient_magnitude(
+        self, policy_batch: AgentBuffer, expert_batch: AgentBuffer
+    ) -> torch.Tensor:
+        """
+        Gradient penalty from https://arxiv.org/pdf/1704.00028. Adds stability esp.
+        for off-policy. Compute gradients w.r.t randomly interpolated input.
+        """
+        policy_inputs = self.get_state_inputs(policy_batch)
+        expert_inputs = self.get_state_inputs(expert_batch)
+        interp_inputs = []
+        for policy_input, expert_input in zip(policy_inputs, expert_inputs):
+            obs_epsilon = torch.rand(policy_input.shape)
+            interp_input = obs_epsilon * policy_input + (1 - obs_epsilon) * expert_input
+            interp_input.requires_grad = True  # For gradient calculation
+            interp_inputs.append(interp_input)
+        if self._settings.use_actions:
+            policy_action = self.get_action_input(policy_batch)
+            expert_action = self.get_action_input(expert_batch)
+            action_epsilon = torch.rand(policy_action.shape)
+            policy_dones = torch.as_tensor(
+                policy_batch[BufferKey.DONE], dtype=torch.float
+            ).unsqueeze(1)
+            expert_dones = torch.as_tensor(
+                expert_batch[BufferKey.DONE], dtype=torch.float
+            ).unsqueeze(1)
+            dones_epsilon = torch.rand(policy_dones.shape)
+            action_inputs = torch.cat(
+                [
+                    action_epsilon * policy_action
+                    + (1 - action_epsilon) * expert_action,
+                    dones_epsilon * policy_dones + (1 - dones_epsilon) * expert_dones,
+                ],
+                dim=1,
+            )
+            action_inputs.requires_grad = True
+            hidden, _ = self.encoder(interp_inputs, action_inputs)
+            encoder_input = tuple(interp_inputs + [action_inputs])
+        else:
+            hidden, _ = self.encoder(interp_inputs)
+            encoder_input = tuple(interp_inputs)
+        if self._settings.use_vail:
+            use_vail_noise = True
+            z_mu = self._z_mu_layer(hidden)
+            hidden = z_mu + torch.randn_like(z_mu) * self._z_sigma * use_vail_noise
+        estimate = self._estimator(hidden).squeeze(1).sum()
+        gradient = torch.autograd.grad(estimate, encoder_input, create_graph=True)[0]
+        # Norm's gradient could be NaN at 0. Use our own safe_norm
+        safe_norm = (torch.sum(gradient**2, dim=1) + self.EPSILON).sqrt()
+        gradient_mag = torch.mean((safe_norm - 1) ** 2)
+        return gradient_mag
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/reward_provider_factory.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/reward_provider_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..825fc49006e4577cfab9ecaf4886b6b1b8c89cef
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/reward_provider_factory.py
@@ -0,0 +1,47 @@
+from typing import Dict, Type
+from mlagents.trainers.exception import UnityTrainerException
+
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.extrinsic_reward_provider import (
+    ExtrinsicRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.curiosity_reward_provider import (
+    CuriosityRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.gail_reward_provider import (
+    GAILRewardProvider,
+)
+from mlagents.trainers.torch_entities.components.reward_providers.rnd_reward_provider import (
+    RNDRewardProvider,
+)
+
+from mlagents_envs.base_env import BehaviorSpec
+
+NAME_TO_CLASS: Dict[RewardSignalType, Type[BaseRewardProvider]] = {
+    RewardSignalType.EXTRINSIC: ExtrinsicRewardProvider,
+    RewardSignalType.CURIOSITY: CuriosityRewardProvider,
+    RewardSignalType.GAIL: GAILRewardProvider,
+    RewardSignalType.RND: RNDRewardProvider,
+}
+
+
+def create_reward_provider(
+    name: RewardSignalType, specs: BehaviorSpec, settings: RewardSignalSettings
+) -> BaseRewardProvider:
+    """
+    Creates a reward provider class based on the name and config entry provided as a dict.
+    :param name: The name of the reward signal
+    :param specs: The BehaviorSpecs of the policy
+    :param settings: The RewardSignalSettings for that reward signal
+    :return: The reward signal class instantiated
+    """
+    rcls = NAME_TO_CLASS.get(name)
+    if not rcls:
+        raise UnityTrainerException(f"Unknown reward signal type {name}")
+
+    class_inst = rcls(specs, settings)
+    return class_inst
diff --git a/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/rnd_reward_provider.py b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/rnd_reward_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..bda1424ab5c07776d10aafd5a6fb33a6c3043553
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/components/reward_providers/rnd_reward_provider.py
@@ -0,0 +1,80 @@
+import numpy as np
+from typing import Dict
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import RNDSettings
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents_envs import logging_util
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.torch_entities.networks import NetworkBody
+from mlagents.trainers.trajectory import ObsUtil
+
+logger = logging_util.get_logger(__name__)
+
+
+class RNDRewardProvider(BaseRewardProvider):
+    """
+    Implementation of Random Network Distillation : https://arxiv.org/pdf/1810.12894.pdf
+    """
+
+    def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = True
+        self._random_network = RNDNetwork(specs, settings)
+        self._training_network = RNDNetwork(specs, settings)
+        self.optimizer = torch.optim.Adam(
+            self._training_network.parameters(), lr=settings.learning_rate
+        )
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            target = self._random_network(mini_batch)
+            prediction = self._training_network(mini_batch)
+            rewards = torch.sum((prediction - target) ** 2, dim=1)
+        return rewards.detach().cpu().numpy()
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        with torch.no_grad():
+            target = self._random_network(mini_batch)
+        prediction = self._training_network(mini_batch)
+        loss = torch.mean(torch.sum((prediction - target) ** 2, dim=1))
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        return {"Losses/RND Loss": loss.detach().cpu().numpy()}
+
+    def get_modules(self):
+        return {
+            f"Module:{self.name}-pred": self._training_network,
+            f"Module:{self.name}-target": self._random_network,
+        }
+
+
+class RNDNetwork(torch.nn.Module):
+    EPSILON = 1e-10
+
+    def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
+        super().__init__()
+        state_encoder_settings = settings.network_settings
+        if state_encoder_settings.memory is not None:
+            state_encoder_settings.memory = None
+            logger.warning(
+                "memory was specified in network_settings but is not supported by RND. It is being ignored."
+            )
+
+        self._encoder = NetworkBody(specs.observation_specs, state_encoder_settings)
+
+    def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        n_obs = len(self._encoder.processors)
+        np_obs = ObsUtil.from_buffer(mini_batch, n_obs)
+        # Convert to tensors
+        tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
+
+        hidden, _ = self._encoder.forward(tensor_obs)
+        self._encoder.update_normalization(mini_batch)
+        return hidden
diff --git a/ml-agents/mlagents/trainers/torch_entities/conditioning.py b/ml-agents/mlagents/trainers/torch_entities/conditioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f622eba38846658f4bc44423883a28463777ff
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/conditioning.py
@@ -0,0 +1,133 @@
+from mlagents.torch_utils import torch
+from typing import List
+import math
+
+from mlagents.trainers.torch_entities.layers import (
+    linear_layer,
+    Swish,
+    Initialization,
+    LayerNorm,
+)
+
+
+class ConditionalEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        goal_size: int,
+        hidden_size: int,
+        num_layers: int,
+        num_conditional_layers: int,
+        kernel_init: Initialization = Initialization.KaimingHeNormal,
+        kernel_gain: float = 1.0,
+    ):
+        """
+        ConditionalEncoder module. A fully connected network of which some of the
+        weights are generated by a goal conditioning. Uses the HyperNetwork module to
+        generate the weights of the network. Only the weights of the last
+        "num_conditional_layers" layers will be generated by HyperNetworks, the others
+        will use regular parameters.
+        :param input_size: The size of the input of the encoder
+        :param goal_size: The size of the goal tensor that will condition the encoder
+        :param hidden_size: The number of hidden units in the encoder
+        :param num_layers: The total number of layers of the encoder (both regular and
+        generated by HyperNetwork)
+        :param num_conditional_layers: The number of layers generated with hypernetworks
+        :param kernel_init: The Initialization to use for the weights of the layer
+        :param kernel_gain: The multiplier for the weights of the kernel.
+        """
+        super().__init__()
+        layers: List[torch.nn.Module] = []
+        prev_size = input_size
+        for i in range(num_layers):
+            if num_layers - i <= num_conditional_layers:
+                # This means layer i is a conditional layer since the conditional
+                # leyers are the last num_conditional_layers
+                layers.append(
+                    HyperNetwork(prev_size, hidden_size, goal_size, hidden_size, 2)
+                )
+            else:
+                layers.append(
+                    linear_layer(
+                        prev_size,
+                        hidden_size,
+                        kernel_init=kernel_init,
+                        kernel_gain=kernel_gain,
+                    )
+                )
+            layers.append(Swish())
+            prev_size = hidden_size
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(
+        self, input_tensor: torch.Tensor, goal_tensor: torch.Tensor
+    ) -> torch.Tensor:  # type: ignore
+        activation = input_tensor
+        for layer in self.layers:
+            if isinstance(layer, HyperNetwork):
+                activation = layer(activation, goal_tensor)
+            else:
+                activation = layer(activation)
+        return activation
+
+
+class HyperNetwork(torch.nn.Module):
+    def __init__(
+        self, input_size, output_size, hyper_input_size, layer_size, num_layers
+    ):
+        """
+        Hyper Network module. This module will use the hyper_input tensor to generate
+        the weights of the main network. The main network is a single fully connected
+        layer.
+        :param input_size: The size of the input of the main network
+        :param output_size: The size of the output of the main network
+        :param hyper_input_size: The size of the input of the hypernetwork that will
+        generate the main network.
+        :param layer_size: The number of hidden units in the layers of the hypernetwork
+        :param num_layers: The number of layers of the hypernetwork
+        """
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+
+        layer_in_size = hyper_input_size
+        layers = []
+        for _ in range(num_layers):
+            layers.append(
+                linear_layer(
+                    layer_in_size,
+                    layer_size,
+                    kernel_init=Initialization.KaimingHeNormal,
+                    kernel_gain=1.0,
+                    bias_init=Initialization.Zero,
+                )
+            )
+            layers.append(Swish())
+            layer_in_size = layer_size
+        flat_output = linear_layer(
+            layer_size,
+            input_size * output_size,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=0.1,
+            bias_init=Initialization.Zero,
+        )
+
+        # Re-initializing the weights of the last layer of the hypernetwork
+        bound = math.sqrt(1 / (layer_size * self.input_size))
+        flat_output.weight.data.uniform_(-bound, bound)
+
+        self.hypernet = torch.nn.Sequential(*layers, LayerNorm(), flat_output)
+
+        # The hypernetwork will not generate the bias of the main network layer
+        self.bias = torch.nn.Parameter(torch.zeros(output_size))
+
+    def forward(self, input_activation, hyper_input):
+        output_weights = self.hypernet(hyper_input)
+
+        output_weights = output_weights.view(-1, self.input_size, self.output_size)
+
+        result = (
+            torch.bmm(input_activation.unsqueeze(1), output_weights).squeeze(1)
+            + self.bias
+        )
+        return result
diff --git a/ml-agents/mlagents/trainers/torch_entities/decoders.py b/ml-agents/mlagents/trainers/torch_entities/decoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..30f196a455614754b627888ba7e9f380d53aff0d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/decoders.py
@@ -0,0 +1,22 @@
+from typing import List, Dict
+
+from mlagents.torch_utils import torch, nn
+from mlagents.trainers.torch_entities.layers import linear_layer
+
+
+class ValueHeads(nn.Module):
+    def __init__(self, stream_names: List[str], input_size: int, output_size: int = 1):
+        super().__init__()
+        self.stream_names = stream_names
+        _value_heads = {}
+
+        for name in stream_names:
+            value = linear_layer(input_size, output_size)
+            _value_heads[name] = value
+        self.value_heads = nn.ModuleDict(_value_heads)
+
+    def forward(self, hidden: torch.Tensor) -> Dict[str, torch.Tensor]:
+        value_outputs = {}
+        for stream_name, head in self.value_heads.items():
+            value_outputs[stream_name] = head(hidden).squeeze(-1)
+        return value_outputs
diff --git a/ml-agents/mlagents/trainers/torch_entities/distributions.py b/ml-agents/mlagents/trainers/torch_entities/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..47fd0d0847e2ca0b2a1b45030e2c07dab5861dc2
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/distributions.py
@@ -0,0 +1,248 @@
+import abc
+from typing import List
+from mlagents.torch_utils import torch, nn
+import numpy as np
+import math
+from mlagents.trainers.torch_entities.layers import linear_layer, Initialization
+
+EPSILON = 1e-7  # Small value to avoid divide by zero
+
+
+class DistInstance(nn.Module, abc.ABC):
+    @abc.abstractmethod
+    def sample(self) -> torch.Tensor:
+        """
+        Return a sample from this distribution.
+        """
+        pass
+
+    @abc.abstractmethod
+    def deterministic_sample(self) -> torch.Tensor:
+        """
+        Return the most probable sample from this distribution.
+        """
+        pass
+
+    @abc.abstractmethod
+    def log_prob(self, value: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the log probabilities of a particular value.
+        :param value: A value sampled from the distribution.
+        :returns: Log probabilities of the given value.
+        """
+        pass
+
+    @abc.abstractmethod
+    def entropy(self) -> torch.Tensor:
+        """
+        Returns the entropy of this distribution.
+        """
+        pass
+
+    @abc.abstractmethod
+    def exported_model_output(self) -> torch.Tensor:
+        """
+        Returns the tensor to be exported to ONNX for the distribution
+        """
+        pass
+
+
+class DiscreteDistInstance(DistInstance):
+    @abc.abstractmethod
+    def all_log_prob(self) -> torch.Tensor:
+        """
+        Returns the log probabilities of all actions represented by this distribution.
+        """
+        pass
+
+
+class GaussianDistInstance(DistInstance):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+
+    def sample(self):
+        sample = self.mean + torch.randn_like(self.mean) * self.std
+        return sample
+
+    def deterministic_sample(self):
+        return self.mean
+
+    def log_prob(self, value):
+        var = self.std**2
+        log_scale = torch.log(self.std + EPSILON)
+        return (
+            -((value - self.mean) ** 2) / (2 * var + EPSILON)
+            - log_scale
+            - math.log(math.sqrt(2 * math.pi))
+        )
+
+    def pdf(self, value):
+        log_prob = self.log_prob(value)
+        return torch.exp(log_prob)
+
+    def entropy(self):
+        return torch.mean(
+            0.5 * torch.log(2 * math.pi * math.e * self.std**2 + EPSILON),
+            dim=1,
+            keepdim=True,
+        )  # Use equivalent behavior to TF
+
+    def exported_model_output(self):
+        return self.sample()
+
+
+class TanhGaussianDistInstance(GaussianDistInstance):
+    def __init__(self, mean, std):
+        super().__init__(mean, std)
+        self.transform = torch.distributions.transforms.TanhTransform(cache_size=1)
+
+    def sample(self):
+        unsquashed_sample = super().sample()
+        squashed = self.transform(unsquashed_sample)
+        return squashed
+
+    def _inverse_tanh(self, value):
+        capped_value = torch.clamp(value, -1 + EPSILON, 1 - EPSILON)
+        return 0.5 * torch.log((1 + capped_value) / (1 - capped_value) + EPSILON)
+
+    def log_prob(self, value):
+        unsquashed = self.transform.inv(value)
+        return super().log_prob(unsquashed) - self.transform.log_abs_det_jacobian(
+            unsquashed, value
+        )
+
+
+class CategoricalDistInstance(DiscreteDistInstance):
+    def __init__(self, logits):
+        super().__init__()
+        self.logits = logits
+        self.probs = torch.softmax(self.logits, dim=-1)
+
+    def sample(self):
+        return torch.multinomial(self.probs, 1)
+
+    def deterministic_sample(self):
+        return torch.argmax(self.probs, dim=1, keepdim=True)
+
+    def pdf(self, value):
+        # This function is equivalent to torch.diag(self.probs.T[value.flatten().long()]),
+        # but torch.diag is not supported by ONNX export.
+        idx = torch.arange(start=0, end=len(value)).unsqueeze(-1)
+        return torch.gather(
+            self.probs.permute(1, 0)[value.flatten().long()], -1, idx
+        ).squeeze(-1)
+
+    def log_prob(self, value):
+        return torch.log(self.pdf(value) + EPSILON)
+
+    def all_log_prob(self):
+        return torch.log(self.probs + EPSILON)
+
+    def entropy(self):
+        return -torch.sum(
+            self.probs * torch.log(self.probs + EPSILON), dim=-1
+        ).unsqueeze(-1)
+
+    def exported_model_output(self):
+        return self.sample()
+
+
+class GaussianDistribution(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_outputs: int,
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        super().__init__()
+        self.conditional_sigma = conditional_sigma
+        self.mu = linear_layer(
+            hidden_size,
+            num_outputs,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=0.2,
+            bias_init=Initialization.Zero,
+        )
+        self.tanh_squash = tanh_squash
+        if conditional_sigma:
+            self.log_sigma = linear_layer(
+                hidden_size,
+                num_outputs,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.2,
+                bias_init=Initialization.Zero,
+            )
+        else:
+            self.log_sigma = nn.Parameter(
+                torch.zeros(1, num_outputs, requires_grad=True)
+            )
+
+    def forward(self, inputs: torch.Tensor) -> List[DistInstance]:
+        mu = self.mu(inputs)
+        if self.conditional_sigma:
+            log_sigma = torch.clamp(self.log_sigma(inputs), min=-20, max=2)
+        else:
+            # Expand so that entropy matches batch size. Note that we're using
+            # mu*0 here to get the batch size implicitly since Barracuda 1.2.1
+            # throws error on runtime broadcasting due to unknown reason. We
+            # use this to replace torch.expand() becuase it is not supported in
+            # the verified version of Barracuda (1.0.X).
+            log_sigma = mu * 0 + self.log_sigma
+        if self.tanh_squash:
+            return TanhGaussianDistInstance(mu, torch.exp(log_sigma))
+        else:
+            return GaussianDistInstance(mu, torch.exp(log_sigma))
+
+
+class MultiCategoricalDistribution(nn.Module):
+    def __init__(self, hidden_size: int, act_sizes: List[int]):
+        super().__init__()
+        self.act_sizes = act_sizes
+        self.branches = self._create_policy_branches(hidden_size)
+
+    def _create_policy_branches(self, hidden_size: int) -> nn.ModuleList:
+        branches = []
+        for size in self.act_sizes:
+            branch_output_layer = linear_layer(
+                hidden_size,
+                size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.1,
+                bias_init=Initialization.Zero,
+            )
+            branches.append(branch_output_layer)
+        return nn.ModuleList(branches)
+
+    def _mask_branch(
+        self, logits: torch.Tensor, allow_mask: torch.Tensor
+    ) -> torch.Tensor:
+        # Zero out masked logits, then subtract a large value. Technique mentionend here:
+        # https://arxiv.org/abs/2006.14171. Our implementation is ONNX and Barracuda-friendly.
+        block_mask = -1.0 * allow_mask + 1.0
+        # We do -1 * tensor + constant instead of constant - tensor because it seems
+        # Barracuda might swap the inputs of a "Sub" operation
+        logits = logits * allow_mask - 1e8 * block_mask
+
+        return logits
+
+    def _split_masks(self, masks: torch.Tensor) -> List[torch.Tensor]:
+        split_masks = []
+        for idx, _ in enumerate(self.act_sizes):
+            start = int(np.sum(self.act_sizes[:idx]))
+            end = int(np.sum(self.act_sizes[: idx + 1]))
+            split_masks.append(masks[:, start:end])
+        return split_masks
+
+    def forward(self, inputs: torch.Tensor, masks: torch.Tensor) -> List[DistInstance]:
+        # Todo - Support multiple branches in mask code
+        branch_distributions = []
+        masks = self._split_masks(masks)
+        for idx, branch in enumerate(self.branches):
+            logits = branch(inputs)
+            norm_logits = self._mask_branch(logits, masks[idx])
+            distribution = CategoricalDistInstance(norm_logits)
+            branch_distributions.append(distribution)
+        return branch_distributions
diff --git a/ml-agents/mlagents/trainers/torch_entities/encoders.py b/ml-agents/mlagents/trainers/torch_entities/encoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..32b944ddfc7e47c9203eaa70f3fd607855f1ec00
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/encoders.py
@@ -0,0 +1,298 @@
+from typing import Tuple, Optional, Union
+
+from mlagents.trainers.torch_entities.layers import linear_layer, Initialization, Swish
+
+from mlagents.torch_utils import torch, nn
+from mlagents.trainers.torch_entities.model_serialization import exporting_to_onnx
+
+
+class Normalizer(nn.Module):
+    def __init__(self, vec_obs_size: int):
+        super().__init__()
+        self.register_buffer("normalization_steps", torch.tensor(1))
+        self.register_buffer("running_mean", torch.zeros(vec_obs_size))
+        self.register_buffer("running_variance", torch.ones(vec_obs_size))
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        normalized_state = torch.clamp(
+            (inputs - self.running_mean)
+            / torch.sqrt(self.running_variance / self.normalization_steps),
+            -5,
+            5,
+        )
+        return normalized_state
+
+    def update(self, vector_input: torch.Tensor) -> None:
+        with torch.no_grad():
+            steps_increment = vector_input.size()[0]
+            total_new_steps = self.normalization_steps + steps_increment
+
+            input_to_old_mean = vector_input - self.running_mean
+            new_mean: torch.Tensor = self.running_mean + (
+                input_to_old_mean / total_new_steps
+            ).sum(0)
+
+            input_to_new_mean = vector_input - new_mean
+            new_variance = self.running_variance + (
+                input_to_new_mean * input_to_old_mean
+            ).sum(0)
+            # Update references. This is much faster than in-place data update.
+            self.running_mean: torch.Tensor = new_mean
+            self.running_variance: torch.Tensor = new_variance
+            self.normalization_steps: torch.Tensor = total_new_steps
+
+    def copy_from(self, other_normalizer: "Normalizer") -> None:
+        self.normalization_steps.data.copy_(other_normalizer.normalization_steps.data)
+        self.running_mean.data.copy_(other_normalizer.running_mean.data)
+        self.running_variance.copy_(other_normalizer.running_variance.data)
+
+
+def conv_output_shape(
+    h_w: Tuple[int, int],
+    kernel_size: Union[int, Tuple[int, int]] = 1,
+    stride: int = 1,
+    padding: int = 0,
+    dilation: int = 1,
+) -> Tuple[int, int]:
+    """
+    Calculates the output shape (height and width) of the output of a convolution layer.
+    kernel_size, stride, padding and dilation correspond to the inputs of the
+    torch.nn.Conv2d layer (https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)
+    :param h_w: The height and width of the input.
+    :param kernel_size: The size of the kernel of the convolution (can be an int or a
+    tuple [width, height])
+    :param stride: The stride of the convolution
+    :param padding: The padding of the convolution
+    :param dilation: The dilation of the convolution
+    """
+    from math import floor
+
+    if not isinstance(kernel_size, tuple):
+        kernel_size = (int(kernel_size), int(kernel_size))
+    h = floor(
+        ((h_w[0] + (2 * padding) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1
+    )
+    w = floor(
+        ((h_w[1] + (2 * padding) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1
+    )
+    return h, w
+
+
+def pool_out_shape(h_w: Tuple[int, int], kernel_size: int) -> Tuple[int, int]:
+    """
+    Calculates the output shape (height and width) of the output of a max pooling layer.
+    kernel_size corresponds to the inputs of the
+    torch.nn.MaxPool2d layer (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html)
+    :param kernel_size: The size of the kernel of the convolution
+    """
+    height = (h_w[0] - kernel_size) // 2 + 1
+    width = (h_w[1] - kernel_size) // 2 + 1
+    return height, width
+
+
+class VectorInput(nn.Module):
+    def __init__(self, input_size: int, normalize: bool = False):
+        super().__init__()
+        self.normalizer: Optional[Normalizer] = None
+        if normalize:
+            self.normalizer = Normalizer(input_size)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        if self.normalizer is not None:
+            inputs = self.normalizer(inputs)
+        return inputs
+
+    def copy_normalization(self, other_input: "VectorInput") -> None:
+        if self.normalizer is not None and other_input.normalizer is not None:
+            self.normalizer.copy_from(other_input.normalizer)
+
+    def update_normalization(self, inputs: torch.Tensor) -> None:
+        if self.normalizer is not None:
+            self.normalizer.update(inputs)
+
+
+class FullyConnectedVisualEncoder(nn.Module):
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        self.output_size = output_size
+        self.input_size = height * width * initial_channels
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.input_size,
+                self.output_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.41,  # Use ReLU gain
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
+        if not exporting_to_onnx.is_exporting():
+            visual_obs = visual_obs.permute([0, 3, 1, 2])
+        hidden = visual_obs.reshape(-1, self.input_size)
+        return self.dense(hidden)
+
+
+class SmallVisualEncoder(nn.Module):
+    """
+    CNN architecture used by King in their Candy Crush predictor
+    https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning
+    """
+
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        self.h_size = output_size
+        conv_1_hw = conv_output_shape((height, width), 3, 1)
+        conv_2_hw = conv_output_shape(conv_1_hw, 3, 1)
+        self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 144
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(initial_channels, 35, [3, 3], [1, 1]),
+            nn.LeakyReLU(),
+            nn.Conv2d(35, 144, [3, 3], [1, 1]),
+            nn.LeakyReLU(),
+        )
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.final_flat,
+                self.h_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.41,  # Use ReLU gain
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
+        if not exporting_to_onnx.is_exporting():
+            visual_obs = visual_obs.permute([0, 3, 1, 2])
+        hidden = self.conv_layers(visual_obs)
+        hidden = hidden.reshape(-1, self.final_flat)
+        return self.dense(hidden)
+
+
+class SimpleVisualEncoder(nn.Module):
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        self.h_size = output_size
+        conv_1_hw = conv_output_shape((height, width), 8, 4)
+        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
+        self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 32
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(initial_channels, 16, [8, 8], [4, 4]),
+            nn.LeakyReLU(),
+            nn.Conv2d(16, 32, [4, 4], [2, 2]),
+            nn.LeakyReLU(),
+        )
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.final_flat,
+                self.h_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.41,  # Use ReLU gain
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
+        if not exporting_to_onnx.is_exporting():
+            visual_obs = visual_obs.permute([0, 3, 1, 2])
+        hidden = self.conv_layers(visual_obs)
+        hidden = hidden.reshape(-1, self.final_flat)
+        return self.dense(hidden)
+
+
+class NatureVisualEncoder(nn.Module):
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        self.h_size = output_size
+        conv_1_hw = conv_output_shape((height, width), 8, 4)
+        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
+        conv_3_hw = conv_output_shape(conv_2_hw, 3, 1)
+        self.final_flat = conv_3_hw[0] * conv_3_hw[1] * 64
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(initial_channels, 32, [8, 8], [4, 4]),
+            nn.LeakyReLU(),
+            nn.Conv2d(32, 64, [4, 4], [2, 2]),
+            nn.LeakyReLU(),
+            nn.Conv2d(64, 64, [3, 3], [1, 1]),
+            nn.LeakyReLU(),
+        )
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.final_flat,
+                self.h_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.41,  # Use ReLU gain
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
+        if not exporting_to_onnx.is_exporting():
+            visual_obs = visual_obs.permute([0, 3, 1, 2])
+        hidden = self.conv_layers(visual_obs)
+        hidden = hidden.reshape([-1, self.final_flat])
+        return self.dense(hidden)
+
+
+class ResNetBlock(nn.Module):
+    def __init__(self, channel: int):
+        """
+        Creates a ResNet Block.
+        :param channel: The number of channels in the input (and output) tensors of the
+        convolutions
+        """
+        super().__init__()
+        self.layers = nn.Sequential(
+            Swish(),
+            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
+            Swish(),
+            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
+        )
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        return input_tensor + self.layers(input_tensor)
+
+
+class ResNetVisualEncoder(nn.Module):
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        n_channels = [16, 32, 32]  # channel for each stack
+        n_blocks = 2  # number of residual blocks
+        layers = []
+        last_channel = initial_channels
+        for _, channel in enumerate(n_channels):
+            layers.append(nn.Conv2d(last_channel, channel, [3, 3], [1, 1], padding=1))
+            layers.append(nn.MaxPool2d([3, 3], [2, 2]))
+            height, width = pool_out_shape((height, width), 3)
+            for _ in range(n_blocks):
+                layers.append(ResNetBlock(channel))
+            last_channel = channel
+        layers.append(Swish())
+        self.final_flat_size = n_channels[-1] * height * width
+        self.dense = linear_layer(
+            self.final_flat_size,
+            output_size,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=1.41,  # Use ReLU gain
+        )
+        self.sequential = nn.Sequential(*layers)
+
+    def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
+        if not exporting_to_onnx.is_exporting():
+            visual_obs = visual_obs.permute([0, 3, 1, 2])
+        hidden = self.sequential(visual_obs)
+        before_out = hidden.reshape(-1, self.final_flat_size)
+        return torch.relu(self.dense(before_out))
diff --git a/ml-agents/mlagents/trainers/torch_entities/layers.py b/ml-agents/mlagents/trainers/torch_entities/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5a598edf64baa072941acb6a917d9dccef179a5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/layers.py
@@ -0,0 +1,225 @@
+from mlagents.torch_utils import torch
+import abc
+from typing import Tuple
+from enum import Enum
+from mlagents.trainers.torch_entities.model_serialization import exporting_to_onnx
+
+
+class Swish(torch.nn.Module):
+    def forward(self, data: torch.Tensor) -> torch.Tensor:
+        return torch.mul(data, torch.sigmoid(data))
+
+
+class Initialization(Enum):
+    Zero = 0
+    XavierGlorotNormal = 1
+    XavierGlorotUniform = 2
+    KaimingHeNormal = 3  # also known as Variance scaling
+    KaimingHeUniform = 4
+    Normal = 5
+
+
+_init_methods = {
+    Initialization.Zero: torch.zero_,
+    Initialization.XavierGlorotNormal: torch.nn.init.xavier_normal_,
+    Initialization.XavierGlorotUniform: torch.nn.init.xavier_uniform_,
+    Initialization.KaimingHeNormal: torch.nn.init.kaiming_normal_,
+    Initialization.KaimingHeUniform: torch.nn.init.kaiming_uniform_,
+    Initialization.Normal: torch.nn.init.normal_,
+}
+
+
+def linear_layer(
+    input_size: int,
+    output_size: int,
+    kernel_init: Initialization = Initialization.XavierGlorotUniform,
+    kernel_gain: float = 1.0,
+    bias_init: Initialization = Initialization.Zero,
+) -> torch.nn.Module:
+    """
+    Creates a torch.nn.Linear module and initializes its weights.
+    :param input_size: The size of the input tensor
+    :param output_size: The size of the output tensor
+    :param kernel_init: The Initialization to use for the weights of the layer
+    :param kernel_gain: The multiplier for the weights of the kernel. Note that in
+    TensorFlow, the gain is square-rooted. Therefore calling  with scale 0.01 is equivalent to calling
+        KaimingHeNormal with kernel_gain of 0.1
+    :param bias_init: The Initialization to use for the weights of the bias layer
+    """
+    layer = torch.nn.Linear(input_size, output_size)
+    if (
+        kernel_init == Initialization.KaimingHeNormal
+        or kernel_init == Initialization.KaimingHeUniform
+    ):
+        _init_methods[kernel_init](layer.weight.data, nonlinearity="linear")
+    else:
+        _init_methods[kernel_init](layer.weight.data)
+    layer.weight.data *= kernel_gain
+    _init_methods[bias_init](layer.bias.data)
+    return layer
+
+
+def lstm_layer(
+    input_size: int,
+    hidden_size: int,
+    num_layers: int = 1,
+    batch_first: bool = True,
+    forget_bias: float = 1.0,
+    kernel_init: Initialization = Initialization.XavierGlorotUniform,
+    bias_init: Initialization = Initialization.Zero,
+) -> torch.nn.Module:
+    """
+    Creates a torch.nn.LSTM and initializes its weights and biases. Provides a
+    forget_bias offset like is done in TensorFlow.
+    """
+    lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=batch_first)
+    # Add forget_bias to forget gate bias
+    for name, param in lstm.named_parameters():
+        # Each weight and bias is a concatenation of 4 matrices
+        if "weight" in name:
+            for idx in range(4):
+                block_size = param.shape[0] // 4
+                _init_methods[kernel_init](
+                    param.data[idx * block_size : (idx + 1) * block_size]
+                )
+        if "bias" in name:
+            for idx in range(4):
+                block_size = param.shape[0] // 4
+                _init_methods[bias_init](
+                    param.data[idx * block_size : (idx + 1) * block_size]
+                )
+                if idx == 1:
+                    param.data[idx * block_size : (idx + 1) * block_size].add_(
+                        forget_bias
+                    )
+    return lstm
+
+
+class MemoryModule(torch.nn.Module):
+    @abc.abstractproperty
+    def memory_size(self) -> int:
+        """
+        Size of memory that is required at the start of a sequence.
+        """
+        pass
+
+    @abc.abstractmethod
+    def forward(
+        self, input_tensor: torch.Tensor, memories: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Pass a sequence to the memory module.
+        :input_tensor: Tensor of shape (batch_size, seq_length, size) that represents the input.
+        :memories: Tensor of initial memories.
+        :return: Tuple of output, final memories.
+        """
+        pass
+
+
+class LayerNorm(torch.nn.Module):
+    """
+    A vanilla implementation of layer normalization  https://arxiv.org/pdf/1607.06450.pdf
+    norm_x = (x - mean) / sqrt((x - mean) ^ 2)
+    This does not include the trainable parameters gamma and beta for performance speed.
+    Typically, this is norm_x * gamma + beta
+    """
+
+    def forward(self, layer_activations: torch.Tensor) -> torch.Tensor:
+        mean = torch.mean(layer_activations, dim=-1, keepdim=True)
+        var = torch.mean((layer_activations - mean) ** 2, dim=-1, keepdim=True)
+        return (layer_activations - mean) / (torch.sqrt(var + 1e-5))
+
+
+class LinearEncoder(torch.nn.Module):
+    """
+    Linear layers.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        num_layers: int,
+        hidden_size: int,
+        kernel_init: Initialization = Initialization.KaimingHeNormal,
+        kernel_gain: float = 1.0,
+    ):
+        super().__init__()
+        self.layers = [
+            linear_layer(
+                input_size,
+                hidden_size,
+                kernel_init=kernel_init,
+                kernel_gain=kernel_gain,
+            )
+        ]
+        self.layers.append(Swish())
+        for _ in range(num_layers - 1):
+            self.layers.append(
+                linear_layer(
+                    hidden_size,
+                    hidden_size,
+                    kernel_init=kernel_init,
+                    kernel_gain=kernel_gain,
+                )
+            )
+            self.layers.append(Swish())
+        self.seq_layers = torch.nn.Sequential(*self.layers)
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        return self.seq_layers(input_tensor)
+
+
+class LSTM(MemoryModule):
+    """
+    Memory module that implements LSTM.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        memory_size: int,
+        num_layers: int = 1,
+        forget_bias: float = 1.0,
+        kernel_init: Initialization = Initialization.XavierGlorotUniform,
+        bias_init: Initialization = Initialization.Zero,
+    ):
+        super().__init__()
+        # We set hidden size to half of memory_size since the initial memory
+        # will be divided between the hidden state and initial cell state.
+        self.hidden_size = memory_size // 2
+        self.lstm = lstm_layer(
+            input_size,
+            self.hidden_size,
+            num_layers,
+            True,
+            forget_bias,
+            kernel_init,
+            bias_init,
+        )
+
+    @property
+    def memory_size(self) -> int:
+        return 2 * self.hidden_size
+
+    def forward(
+        self, input_tensor: torch.Tensor, memories: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        if exporting_to_onnx.is_exporting():
+            # This transpose is needed both at input and output of the LSTM when
+            # exporting because ONNX will expect (sequence_len, batch, memory_size)
+            # instead of (batch, sequence_len, memory_size)
+            memories = torch.transpose(memories, 0, 1)
+
+        # We don't use torch.split here since it is not supported by Barracuda
+        h0 = memories[:, :, : self.hidden_size].contiguous()
+        c0 = memories[:, :, self.hidden_size :].contiguous()
+
+        hidden = (h0, c0)
+        lstm_out, hidden_out = self.lstm(input_tensor, hidden)
+        output_mem = torch.cat(hidden_out, dim=-1)
+
+        if exporting_to_onnx.is_exporting():
+            output_mem = torch.transpose(output_mem, 0, 1)
+
+        return lstm_out, output_mem
diff --git a/ml-agents/mlagents/trainers/torch_entities/model_serialization.py b/ml-agents/mlagents/trainers/torch_entities/model_serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..f204b52445e5247bc8b921a4eb4207218a9d2747
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/model_serialization.py
@@ -0,0 +1,173 @@
+from typing import Tuple
+import threading
+from mlagents.torch_utils import torch
+
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.settings import SerializationSettings
+
+
+logger = get_logger(__name__)
+
+
+class exporting_to_onnx:
+    """
+    Set this context by calling
+    ```
+    with exporting_to_onnx():
+    ```
+    Within this context, the variable exporting_to_onnx.is_exporting() will be true.
+    This implementation is thread safe.
+    """
+
+    # local is_exporting flag for each thread
+    _local_data = threading.local()
+    _local_data._is_exporting = False
+
+    # global lock shared among all threads, to make sure only one thread is exporting at a time
+    _lock = threading.Lock()
+
+    def __enter__(self):
+        self._lock.acquire()
+        self._local_data._is_exporting = True
+
+    def __exit__(self, *args):
+        self._local_data._is_exporting = False
+        self._lock.release()
+
+    @staticmethod
+    def is_exporting():
+        if not hasattr(exporting_to_onnx._local_data, "_is_exporting"):
+            return False
+        return exporting_to_onnx._local_data._is_exporting
+
+
+class TensorNames:
+    batch_size_placeholder = "batch_size"
+    sequence_length_placeholder = "sequence_length"
+    vector_observation_placeholder = "vector_observation"
+    recurrent_in_placeholder = "recurrent_in"
+    visual_observation_placeholder_prefix = "visual_observation_"
+    observation_placeholder_prefix = "obs_"
+    previous_action_placeholder = "prev_action"
+    action_mask_placeholder = "action_masks"
+    random_normal_epsilon_placeholder = "epsilon"
+
+    value_estimate_output = "value_estimate"
+    recurrent_output = "recurrent_out"
+    memory_size = "memory_size"
+    version_number = "version_number"
+
+    continuous_action_output_shape = "continuous_action_output_shape"
+    discrete_action_output_shape = "discrete_action_output_shape"
+    continuous_action_output = "continuous_actions"
+    discrete_action_output = "discrete_actions"
+    deterministic_continuous_action_output = "deterministic_continuous_actions"
+    deterministic_discrete_action_output = "deterministic_discrete_actions"
+
+    # Deprecated TensorNames entries for backward compatibility
+    is_continuous_control_deprecated = "is_continuous_control"
+    action_output_deprecated = "action"
+    action_output_shape_deprecated = "action_output_shape"
+
+    @staticmethod
+    def get_visual_observation_name(index: int) -> str:
+        """
+        Returns the name of the visual observation with a given index
+        """
+        return TensorNames.visual_observation_placeholder_prefix + str(index)
+
+    @staticmethod
+    def get_observation_name(index: int) -> str:
+        """
+        Returns the name of the observation with a given index
+        """
+        return TensorNames.observation_placeholder_prefix + str(index)
+
+
+class ModelSerializer:
+    def __init__(self, policy):
+        # ONNX only support input in NCHW (channel first) format.
+        # Barracuda also expect to get data in NCHW.
+        # Any multi-dimentional input should follow that otherwise will
+        # cause problem to barracuda import.
+        self.policy = policy
+        observation_specs = self.policy.behavior_spec.observation_specs
+        batch_dim = [1]
+        seq_len_dim = [1]
+        num_obs = len(observation_specs)
+
+        dummy_obs = [
+            torch.zeros(
+                batch_dim + list(ModelSerializer._get_onnx_shape(obs_spec.shape))
+            )
+            for obs_spec in observation_specs
+        ]
+
+        dummy_masks = torch.ones(
+            batch_dim + [sum(self.policy.behavior_spec.action_spec.discrete_branches)]
+        )
+        dummy_memories = torch.zeros(
+            batch_dim + seq_len_dim + [self.policy.export_memory_size]
+        )
+
+        self.dummy_input = (dummy_obs, dummy_masks, dummy_memories)
+
+        self.input_names = [TensorNames.get_observation_name(i) for i in range(num_obs)]
+        self.input_names += [
+            TensorNames.action_mask_placeholder,
+            TensorNames.recurrent_in_placeholder,
+        ]
+
+        self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
+
+        self.output_names = [TensorNames.version_number, TensorNames.memory_size]
+        if self.policy.behavior_spec.action_spec.continuous_size > 0:
+            self.output_names += [
+                TensorNames.continuous_action_output,
+                TensorNames.continuous_action_output_shape,
+                TensorNames.deterministic_continuous_action_output,
+            ]
+            self.dynamic_axes.update(
+                {TensorNames.continuous_action_output: {0: "batch"}}
+            )
+        if self.policy.behavior_spec.action_spec.discrete_size > 0:
+            self.output_names += [
+                TensorNames.discrete_action_output,
+                TensorNames.discrete_action_output_shape,
+                TensorNames.deterministic_discrete_action_output,
+            ]
+            self.dynamic_axes.update({TensorNames.discrete_action_output: {0: "batch"}})
+
+        if self.policy.export_memory_size > 0:
+            self.output_names += [TensorNames.recurrent_output]
+
+    @staticmethod
+    def _get_onnx_shape(shape: Tuple[int, ...]) -> Tuple[int, ...]:
+        """
+        Converts the shape of an observation to be compatible with the NCHW format
+        of ONNX
+        """
+        if len(shape) == 3:
+            return shape[2], shape[0], shape[1]
+        return shape
+
+    def export_policy_model(self, output_filepath: str) -> None:
+        """
+        Exports a Torch model for a Policy to .onnx format for Unity embedding.
+
+        :param output_filepath: file path to output the model (without file suffix)
+        """
+        onnx_output_path = f"{output_filepath}.onnx"
+        logger.debug(f"Converting to {onnx_output_path}")
+
+        with exporting_to_onnx():
+            torch.onnx.export(
+                self.policy.actor,
+                self.dummy_input,
+                onnx_output_path,
+                opset_version=SerializationSettings.onnx_opset,
+                input_names=self.input_names,
+                output_names=self.output_names,
+                dynamic_axes=self.dynamic_axes,
+            )
+        logger.info(f"Exported {onnx_output_path}")
diff --git a/ml-agents/mlagents/trainers/torch_entities/networks.py b/ml-agents/mlagents/trainers/torch_entities/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..555268075c90d6e111f5511f16290fd632be88b3
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/networks.py
@@ -0,0 +1,767 @@
+from typing import Callable, List, Dict, Tuple, Optional, Union, Any
+import abc
+
+from mlagents.torch_utils import torch, nn
+
+from mlagents_envs.base_env import ActionSpec, ObservationSpec, ObservationType
+from mlagents.trainers.torch_entities.action_model import ActionModel
+from mlagents.trainers.torch_entities.agent_action import AgentAction
+from mlagents.trainers.settings import NetworkSettings, EncoderType, ConditioningType
+from mlagents.trainers.torch_entities.utils import ModelUtils
+from mlagents.trainers.torch_entities.decoders import ValueHeads
+from mlagents.trainers.torch_entities.layers import LSTM, LinearEncoder
+from mlagents.trainers.torch_entities.encoders import VectorInput
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.trajectory import ObsUtil
+from mlagents.trainers.torch_entities.conditioning import ConditionalEncoder
+from mlagents.trainers.torch_entities.attention import (
+    EntityEmbedding,
+    ResidualSelfAttention,
+    get_zero_entities_mask,
+)
+from mlagents.trainers.exception import UnityTrainerException
+
+
+ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
+EncoderFunction = Callable[
+    [torch.Tensor, int, ActivationFunction, int, str, bool], torch.Tensor
+]
+
+EPSILON = 1e-7
+
+
+class ObservationEncoder(nn.Module):
+    ATTENTION_EMBEDDING_SIZE = 128  # The embedding size of attention is fixed
+
+    def __init__(
+        self,
+        observation_specs: List[ObservationSpec],
+        h_size: int,
+        vis_encode_type: EncoderType,
+        normalize: bool = False,
+    ):
+        """
+        Returns an ObservationEncoder that can process and encode a set of observations.
+        Will use an RSA if needed for variable length observations.
+        """
+        super().__init__()
+        self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
+            observation_specs,
+            h_size,
+            vis_encode_type,
+            self.ATTENTION_EMBEDDING_SIZE,
+            normalize=normalize,
+        )
+        self.rsa, self.x_self_encoder = ModelUtils.create_residual_self_attention(
+            self.processors, self.embedding_sizes, self.ATTENTION_EMBEDDING_SIZE
+        )
+        if self.rsa is not None:
+            total_enc_size = sum(self.embedding_sizes) + self.ATTENTION_EMBEDDING_SIZE
+        else:
+            total_enc_size = sum(self.embedding_sizes)
+        self.normalize = normalize
+        self._total_enc_size = total_enc_size
+
+        self._total_goal_enc_size = 0
+        self._goal_processor_indices: List[int] = []
+        for i in range(len(observation_specs)):
+            if observation_specs[i].observation_type == ObservationType.GOAL_SIGNAL:
+                self._total_goal_enc_size += self.embedding_sizes[i]
+                self._goal_processor_indices.append(i)
+
+    @property
+    def total_enc_size(self) -> int:
+        """
+        Returns the total encoding size for this ObservationEncoder.
+        """
+        return self._total_enc_size
+
+    @property
+    def total_goal_enc_size(self) -> int:
+        """
+        Returns the total goal encoding size for this ObservationEncoder.
+        """
+        return self._total_goal_enc_size
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        obs = ObsUtil.from_buffer(buffer, len(self.processors))
+        for vec_input, enc in zip(obs, self.processors):
+            if isinstance(enc, VectorInput):
+                enc.update_normalization(torch.as_tensor(vec_input.to_ndarray()))
+
+    def copy_normalization(self, other_encoder: "ObservationEncoder") -> None:
+        if self.normalize:
+            for n1, n2 in zip(self.processors, other_encoder.processors):
+                if isinstance(n1, VectorInput) and isinstance(n2, VectorInput):
+                    n1.copy_normalization(n2)
+
+    def forward(self, inputs: List[torch.Tensor]) -> torch.Tensor:
+        """
+        Encode observations using a list of processors and an RSA.
+        :param inputs: List of Tensors corresponding to a set of obs.
+        """
+        encodes = []
+        var_len_processor_inputs: List[Tuple[nn.Module, torch.Tensor]] = []
+
+        for idx, processor in enumerate(self.processors):
+            if not isinstance(processor, EntityEmbedding):
+                # The input can be encoded without having to process other inputs
+                obs_input = inputs[idx]
+                processed_obs = processor(obs_input)
+                encodes.append(processed_obs)
+            else:
+                var_len_processor_inputs.append((processor, inputs[idx]))
+        if len(encodes) != 0:
+            encoded_self = torch.cat(encodes, dim=1)
+            input_exist = True
+        else:
+            input_exist = False
+        if len(var_len_processor_inputs) > 0 and self.rsa is not None:
+            # Some inputs need to be processed with a variable length encoder
+            masks = get_zero_entities_mask([p_i[1] for p_i in var_len_processor_inputs])
+            embeddings: List[torch.Tensor] = []
+            processed_self = (
+                self.x_self_encoder(encoded_self)
+                if input_exist and self.x_self_encoder is not None
+                else None
+            )
+            for processor, var_len_input in var_len_processor_inputs:
+                embeddings.append(processor(processed_self, var_len_input))
+            qkv = torch.cat(embeddings, dim=1)
+            attention_embedding = self.rsa(qkv, masks)
+            if not input_exist:
+                encoded_self = torch.cat([attention_embedding], dim=1)
+                input_exist = True
+            else:
+                encoded_self = torch.cat([encoded_self, attention_embedding], dim=1)
+
+        if not input_exist:
+            raise UnityTrainerException(
+                "The trainer was unable to process any of the provided inputs. "
+                "Make sure the trained agents has at least one sensor attached to them."
+            )
+
+        return encoded_self
+
+    def get_goal_encoding(self, inputs: List[torch.Tensor]) -> torch.Tensor:
+        """
+        Encode observations corresponding to goals using a list of processors.
+        :param inputs: List of Tensors corresponding to a set of obs.
+        """
+        encodes = []
+        for idx in self._goal_processor_indices:
+            processor = self.processors[idx]
+            if not isinstance(processor, EntityEmbedding):
+                # The input can be encoded without having to process other inputs
+                obs_input = inputs[idx]
+                processed_obs = processor(obs_input)
+                encodes.append(processed_obs)
+            else:
+                raise UnityTrainerException(
+                    "The one of the goals uses variable length observations. This use "
+                    "case is not supported."
+                )
+        if len(encodes) != 0:
+            encoded = torch.cat(encodes, dim=1)
+        else:
+            raise UnityTrainerException(
+                "Trainer was unable to process any of the goals provided as input."
+            )
+        return encoded
+
+
+class NetworkBody(nn.Module):
+    def __init__(
+        self,
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        encoded_act_size: int = 0,
+    ):
+        super().__init__()
+        self.normalize = network_settings.normalize
+        self.use_lstm = network_settings.memory is not None
+        self.h_size = network_settings.hidden_units
+        self.m_size = (
+            network_settings.memory.memory_size
+            if network_settings.memory is not None
+            else 0
+        )
+        self.observation_encoder = ObservationEncoder(
+            observation_specs,
+            self.h_size,
+            network_settings.vis_encode_type,
+            self.normalize,
+        )
+        self.processors = self.observation_encoder.processors
+        total_enc_size = self.observation_encoder.total_enc_size
+        total_enc_size += encoded_act_size
+
+        if (
+            self.observation_encoder.total_goal_enc_size > 0
+            and network_settings.goal_conditioning_type == ConditioningType.HYPER
+        ):
+            self._body_endoder = ConditionalEncoder(
+                total_enc_size,
+                self.observation_encoder.total_goal_enc_size,
+                self.h_size,
+                network_settings.num_layers,
+                1,
+            )
+        else:
+            self._body_endoder = LinearEncoder(
+                total_enc_size, network_settings.num_layers, self.h_size
+            )
+
+        if self.use_lstm:
+            self.lstm = LSTM(self.h_size, self.m_size)
+        else:
+            self.lstm = None  # type: ignore
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        self.observation_encoder.update_normalization(buffer)
+
+    def copy_normalization(self, other_network: "NetworkBody") -> None:
+        self.observation_encoder.copy_normalization(other_network.observation_encoder)
+
+    @property
+    def memory_size(self) -> int:
+        return self.lstm.memory_size if self.use_lstm else 0
+
+    def forward(
+        self,
+        inputs: List[torch.Tensor],
+        actions: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        encoded_self = self.observation_encoder(inputs)
+        if actions is not None:
+            encoded_self = torch.cat([encoded_self, actions], dim=1)
+        if isinstance(self._body_endoder, ConditionalEncoder):
+            goal = self.observation_encoder.get_goal_encoding(inputs)
+            encoding = self._body_endoder(encoded_self, goal)
+        else:
+            encoding = self._body_endoder(encoded_self)
+
+        if self.use_lstm:
+            # Resize to (batch, sequence length, encoding size)
+            encoding = encoding.reshape([-1, sequence_length, self.h_size])
+            encoding, memories = self.lstm(encoding, memories)
+            encoding = encoding.reshape([-1, self.m_size // 2])
+        return encoding, memories
+
+
+class MultiAgentNetworkBody(torch.nn.Module):
+    """
+    A network body that uses a self attention layer to handle state
+    and action input from a potentially variable number of agents that
+    share the same observation and action space.
+    """
+
+    def __init__(
+        self,
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        action_spec: ActionSpec,
+    ):
+        super().__init__()
+        self.normalize = network_settings.normalize
+        self.use_lstm = network_settings.memory is not None
+        self.h_size = network_settings.hidden_units
+        self.m_size = (
+            network_settings.memory.memory_size
+            if network_settings.memory is not None
+            else 0
+        )
+        self.action_spec = action_spec
+        self.observation_encoder = ObservationEncoder(
+            observation_specs,
+            self.h_size,
+            network_settings.vis_encode_type,
+            self.normalize,
+        )
+        self.processors = self.observation_encoder.processors
+
+        # Modules for multi-agent self-attention
+        obs_only_ent_size = self.observation_encoder.total_enc_size
+        q_ent_size = (
+            obs_only_ent_size
+            + sum(self.action_spec.discrete_branches)
+            + self.action_spec.continuous_size
+        )
+
+        attention_embeding_size = self.h_size
+        self.obs_encoder = EntityEmbedding(
+            obs_only_ent_size, None, attention_embeding_size
+        )
+        self.obs_action_encoder = EntityEmbedding(
+            q_ent_size, None, attention_embeding_size
+        )
+
+        self.self_attn = ResidualSelfAttention(attention_embeding_size)
+
+        self.linear_encoder = LinearEncoder(
+            attention_embeding_size,
+            network_settings.num_layers,
+            self.h_size,
+            kernel_gain=(0.125 / self.h_size) ** 0.5,
+        )
+
+        if self.use_lstm:
+            self.lstm = LSTM(self.h_size, self.m_size)
+        else:
+            self.lstm = None  # type: ignore
+        self._current_max_agents = torch.nn.Parameter(
+            torch.as_tensor(1), requires_grad=False
+        )
+
+    @property
+    def memory_size(self) -> int:
+        return self.lstm.memory_size if self.use_lstm else 0
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        self.observation_encoder.update_normalization(buffer)
+
+    def copy_normalization(self, other_network: "MultiAgentNetworkBody") -> None:
+        self.observation_encoder.copy_normalization(other_network.observation_encoder)
+
+    def _get_masks_from_nans(self, obs_tensors: List[torch.Tensor]) -> torch.Tensor:
+        """
+        Get attention masks by grabbing an arbitrary obs across all the agents
+        Since these are raw obs, the padded values are still NaN
+        """
+        only_first_obs = [_all_obs[0] for _all_obs in obs_tensors]
+        # Just get the first element in each obs regardless of its dimension. This will speed up
+        # searching for NaNs.
+        only_first_obs_flat = torch.stack(
+            [_obs.flatten(start_dim=1)[:, 0] for _obs in only_first_obs], dim=1
+        )
+        # Get the mask from NaNs
+        attn_mask = only_first_obs_flat.isnan().float()
+        return attn_mask
+
+    def _copy_and_remove_nans_from_obs(
+        self, all_obs: List[List[torch.Tensor]], attention_mask: torch.Tensor
+    ) -> List[List[torch.Tensor]]:
+        """
+        Helper function to remove NaNs from observations using an attention mask.
+        """
+        obs_with_no_nans = []
+        for i_agent, single_agent_obs in enumerate(all_obs):
+            no_nan_obs = []
+            for obs in single_agent_obs:
+                new_obs = obs.clone()
+                new_obs[attention_mask.bool()[:, i_agent], ::] = 0.0  # Remove NaNs fast
+                no_nan_obs.append(new_obs)
+            obs_with_no_nans.append(no_nan_obs)
+        return obs_with_no_nans
+
+    def forward(
+        self,
+        obs_only: List[List[torch.Tensor]],
+        obs: List[List[torch.Tensor]],
+        actions: List[AgentAction],
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Returns sampled actions.
+        If memory is enabled, return the memories as well.
+        :param obs_only: Observations to be processed that do not have corresponding actions.
+            These are encoded with the obs_encoder.
+        :param obs: Observations to be processed that do have corresponding actions.
+            After concatenation with actions, these are processed with obs_action_encoder.
+        :param actions: After concatenation with obs, these are processed with obs_action_encoder.
+        :param memories: If using memory, a Tensor of initial memories.
+        :param sequence_length: If using memory, the sequence length.
+        """
+        self_attn_masks = []
+        self_attn_inputs = []
+        concat_f_inp = []
+        if obs:
+            obs_attn_mask = self._get_masks_from_nans(obs)
+            obs = self._copy_and_remove_nans_from_obs(obs, obs_attn_mask)
+            for inputs, action in zip(obs, actions):
+                encoded = self.observation_encoder(inputs)
+                cat_encodes = [
+                    encoded,
+                    action.to_flat(self.action_spec.discrete_branches),
+                ]
+                concat_f_inp.append(torch.cat(cat_encodes, dim=1))
+            f_inp = torch.stack(concat_f_inp, dim=1)
+            self_attn_masks.append(obs_attn_mask)
+            self_attn_inputs.append(self.obs_action_encoder(None, f_inp))
+
+        concat_encoded_obs = []
+        if obs_only:
+            obs_only_attn_mask = self._get_masks_from_nans(obs_only)
+            obs_only = self._copy_and_remove_nans_from_obs(obs_only, obs_only_attn_mask)
+            for inputs in obs_only:
+                encoded = self.observation_encoder(inputs)
+                concat_encoded_obs.append(encoded)
+            g_inp = torch.stack(concat_encoded_obs, dim=1)
+            self_attn_masks.append(obs_only_attn_mask)
+            self_attn_inputs.append(self.obs_encoder(None, g_inp))
+
+        encoded_entity = torch.cat(self_attn_inputs, dim=1)
+        encoded_state = self.self_attn(encoded_entity, self_attn_masks)
+
+        flipped_masks = 1 - torch.cat(self_attn_masks, dim=1)
+        num_agents = torch.sum(flipped_masks, dim=1, keepdim=True)
+        if torch.max(num_agents).item() > self._current_max_agents:
+            self._current_max_agents = torch.nn.Parameter(
+                torch.as_tensor(torch.max(num_agents).item()), requires_grad=False
+            )
+
+        # num_agents will be -1 for a single agent and +1 when the current maximum is reached
+        num_agents = num_agents * 2.0 / self._current_max_agents - 1
+
+        encoding = self.linear_encoder(encoded_state)
+        if self.use_lstm:
+            # Resize to (batch, sequence length, encoding size)
+            encoding = encoding.reshape([-1, sequence_length, self.h_size])
+            encoding, memories = self.lstm(encoding, memories)
+            encoding = encoding.reshape([-1, self.m_size // 2])
+        encoding = torch.cat([encoding, num_agents], dim=1)
+        return encoding, memories
+
+
+class Critic(abc.ABC):
+    @abc.abstractmethod
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        """
+        Updates normalization of Actor based on the provided List of vector obs.
+        :param vector_obs: A List of vector obs as tensors.
+        """
+        pass
+
+    def critic_pass(
+        self,
+        inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        """
+        Get value outputs for the given obs.
+        :param inputs: List of inputs as tensors.
+        :param memories: Tensor of memories, if using memory. Otherwise, None.
+        :returns: Dict of reward stream to output tensor for values.
+        """
+        pass
+
+
+class ValueNetwork(nn.Module, Critic):
+    def __init__(
+        self,
+        stream_names: List[str],
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        encoded_act_size: int = 0,
+        outputs_per_stream: int = 1,
+    ):
+
+        # This is not a typo, we want to call __init__ of nn.Module
+        nn.Module.__init__(self)
+        self.network_body = NetworkBody(
+            observation_specs, network_settings, encoded_act_size=encoded_act_size
+        )
+        if network_settings.memory is not None:
+            encoding_size = network_settings.memory.memory_size // 2
+        else:
+            encoding_size = network_settings.hidden_units
+        self.value_heads = ValueHeads(stream_names, encoding_size, outputs_per_stream)
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        self.network_body.update_normalization(buffer)
+
+    @property
+    def memory_size(self) -> int:
+        return self.network_body.memory_size
+
+    def critic_pass(
+        self,
+        inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        value_outputs, critic_mem_out = self.forward(
+            inputs, memories=memories, sequence_length=sequence_length
+        )
+        return value_outputs, critic_mem_out
+
+    def forward(
+        self,
+        inputs: List[torch.Tensor],
+        actions: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        encoding, memories = self.network_body(
+            inputs, actions, memories, sequence_length
+        )
+        output = self.value_heads(encoding)
+        return output, memories
+
+
+class Actor(abc.ABC):
+    @abc.abstractmethod
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        """
+        Updates normalization of Actor based on the provided List of vector obs.
+        :param vector_obs: A List of vector obs as tensors.
+        """
+        pass
+
+    def get_action_and_stats(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[AgentAction, Dict[str, Any], torch.Tensor]:
+        """
+        Returns sampled actions.
+        If memory is enabled, return the memories as well.
+        :param inputs: A List of inputs as tensors.
+        :param masks: If using discrete actions, a Tensor of action masks.
+        :param memories: If using memory, a Tensor of initial memories.
+        :param sequence_length: If using memory, the sequence length.
+        :return: A Tuple of AgentAction, ActionLogProbs, entropies, and memories.
+            Memories will be None if not using memory.
+        """
+        pass
+
+    def get_stats(
+        self,
+        inputs: List[torch.Tensor],
+        actions: AgentAction,
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Dict[str, Any]:
+        """
+        Returns log_probs for actions and entropies.
+        If memory is enabled, return the memories as well.
+        :param inputs: A List of inputs as tensors.
+        :param actions: AgentAction of actions.
+        :param masks: If using discrete actions, a Tensor of action masks.
+        :param memories: If using memory, a Tensor of initial memories.
+        :param sequence_length: If using memory, the sequence length.
+        :return: A Tuple of AgentAction, ActionLogProbs, entropies, and memories.
+            Memories will be None if not using memory.
+        """
+
+        pass
+
+    @abc.abstractmethod
+    def forward(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+    ) -> Tuple[Union[int, torch.Tensor], ...]:
+        """
+        Forward pass of the Actor for inference. This is required for export to ONNX, and
+        the inputs and outputs of this method should not be changed without a respective change
+        in the ONNX export code.
+        """
+        pass
+
+
+class SimpleActor(nn.Module, Actor):
+    MODEL_EXPORT_VERSION = 3  # Corresponds to ModelApiVersion.MLAgents2_0
+
+    def __init__(
+        self,
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        action_spec: ActionSpec,
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        super().__init__()
+        self.action_spec = action_spec
+        self.version_number = torch.nn.Parameter(
+            torch.Tensor([self.MODEL_EXPORT_VERSION]), requires_grad=False
+        )
+        self.is_continuous_int_deprecated = torch.nn.Parameter(
+            torch.Tensor([int(self.action_spec.is_continuous())]), requires_grad=False
+        )
+        self.continuous_act_size_vector = torch.nn.Parameter(
+            torch.Tensor([int(self.action_spec.continuous_size)]), requires_grad=False
+        )
+        self.discrete_act_size_vector = torch.nn.Parameter(
+            torch.Tensor([self.action_spec.discrete_branches]), requires_grad=False
+        )
+        self.act_size_vector_deprecated = torch.nn.Parameter(
+            torch.Tensor(
+                [
+                    self.action_spec.continuous_size
+                    + sum(self.action_spec.discrete_branches)
+                ]
+            ),
+            requires_grad=False,
+        )
+        self.network_body = NetworkBody(observation_specs, network_settings)
+        if network_settings.memory is not None:
+            self.encoding_size = network_settings.memory.memory_size // 2
+        else:
+            self.encoding_size = network_settings.hidden_units
+        self.memory_size_vector = torch.nn.Parameter(
+            torch.Tensor([int(self.network_body.memory_size)]), requires_grad=False
+        )
+
+        self.action_model = ActionModel(
+            self.encoding_size,
+            action_spec,
+            conditional_sigma=conditional_sigma,
+            tanh_squash=tanh_squash,
+            deterministic=network_settings.deterministic,
+        )
+
+    @property
+    def memory_size(self) -> int:
+        return self.network_body.memory_size
+
+    def update_normalization(self, buffer: AgentBuffer) -> None:
+        self.network_body.update_normalization(buffer)
+
+    def get_action_and_stats(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[AgentAction, Dict[str, Any], torch.Tensor]:
+
+        encoding, memories = self.network_body(
+            inputs, memories=memories, sequence_length=sequence_length
+        )
+        action, log_probs, entropies = self.action_model(encoding, masks)
+        run_out = {}
+        # This is the clipped action which is not saved to the buffer
+        # but is exclusively sent to the environment.
+        run_out["env_action"] = action.to_action_tuple(
+            clip=self.action_model.clip_action
+        )
+        run_out["log_probs"] = log_probs
+        run_out["entropy"] = entropies
+
+        return action, run_out, memories
+
+    def get_stats(
+        self,
+        inputs: List[torch.Tensor],
+        actions: AgentAction,
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Dict[str, Any]:
+        encoding, actor_mem_outs = self.network_body(
+            inputs, memories=memories, sequence_length=sequence_length
+        )
+
+        log_probs, entropies = self.action_model.evaluate(encoding, masks, actions)
+        run_out = {}
+        run_out["log_probs"] = log_probs
+        run_out["entropy"] = entropies
+        return run_out
+
+    def forward(
+        self,
+        inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+    ) -> Tuple[Union[int, torch.Tensor], ...]:
+        """
+        Note: This forward() method is required for exporting to ONNX. Don't modify the inputs and outputs.
+
+        At this moment, torch.onnx.export() doesn't accept None as tensor to be exported,
+        so the size of return tuple varies with action spec.
+        """
+        encoding, memories_out = self.network_body(
+            inputs, memories=memories, sequence_length=1
+        )
+
+        (
+            cont_action_out,
+            disc_action_out,
+            action_out_deprecated,
+            deterministic_cont_action_out,
+            deterministic_disc_action_out,
+        ) = self.action_model.get_action_out(encoding, masks)
+        export_out = [self.version_number, self.memory_size_vector]
+        if self.action_spec.continuous_size > 0:
+            export_out += [
+                cont_action_out,
+                self.continuous_act_size_vector,
+                deterministic_cont_action_out,
+            ]
+        if self.action_spec.discrete_size > 0:
+            export_out += [
+                disc_action_out,
+                self.discrete_act_size_vector,
+                deterministic_disc_action_out,
+            ]
+        if self.network_body.memory_size > 0:
+            export_out += [memories_out]
+        return tuple(export_out)
+
+
+class SharedActorCritic(SimpleActor, Critic):
+    def __init__(
+        self,
+        observation_specs: List[ObservationSpec],
+        network_settings: NetworkSettings,
+        action_spec: ActionSpec,
+        stream_names: List[str],
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        self.use_lstm = network_settings.memory is not None
+        super().__init__(
+            observation_specs,
+            network_settings,
+            action_spec,
+            conditional_sigma,
+            tanh_squash,
+        )
+        self.stream_names = stream_names
+        self.value_heads = ValueHeads(stream_names, self.encoding_size)
+
+    def critic_pass(
+        self,
+        inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        encoding, memories_out = self.network_body(
+            inputs, memories=memories, sequence_length=sequence_length
+        )
+        return self.value_heads(encoding), memories_out
+
+
+class GlobalSteps(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.__global_step = nn.Parameter(
+            torch.Tensor([0]).to(torch.int64), requires_grad=False
+        )
+
+    @property
+    def current_step(self):
+        return int(self.__global_step.item())
+
+    @current_step.setter
+    def current_step(self, value):
+        self.__global_step[:] = value
+
+    def increment(self, value):
+        self.__global_step += value
+
+
+class LearningRate(nn.Module):
+    def __init__(self, lr):
+        # Todo: add learning rate decay
+        super().__init__()
+        self.learning_rate = torch.Tensor([lr])
diff --git a/ml-agents/mlagents/trainers/torch_entities/utils.py b/ml-agents/mlagents/trainers/torch_entities/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..048ce8b59174c1b9ac5a2520b4b36f858754f9ac
--- /dev/null
+++ b/ml-agents/mlagents/trainers/torch_entities/utils.py
@@ -0,0 +1,452 @@
+from typing import List, Optional, Tuple, Dict
+from mlagents.torch_utils import torch, nn
+from mlagents.trainers.torch_entities.layers import LinearEncoder, Initialization
+import numpy as np
+
+from mlagents.trainers.torch_entities.encoders import (
+    SimpleVisualEncoder,
+    ResNetVisualEncoder,
+    NatureVisualEncoder,
+    SmallVisualEncoder,
+    FullyConnectedVisualEncoder,
+    VectorInput,
+)
+from mlagents.trainers.settings import EncoderType, ScheduleType
+from mlagents.trainers.torch_entities.attention import (
+    EntityEmbedding,
+    ResidualSelfAttention,
+)
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents_envs.base_env import ObservationSpec, DimensionProperty
+
+
+class ModelUtils:
+    # Minimum supported side for each encoder type. If refactoring an encoder, please
+    # adjust these also.
+    MIN_RESOLUTION_FOR_ENCODER = {
+        EncoderType.FULLY_CONNECTED: 1,
+        EncoderType.MATCH3: 5,
+        EncoderType.SIMPLE: 20,
+        EncoderType.NATURE_CNN: 36,
+        EncoderType.RESNET: 15,
+    }
+
+    VALID_VISUAL_PROP = frozenset(
+        [
+            (
+                DimensionProperty.TRANSLATIONAL_EQUIVARIANCE,
+                DimensionProperty.TRANSLATIONAL_EQUIVARIANCE,
+                DimensionProperty.NONE,
+            ),
+            (DimensionProperty.UNSPECIFIED,) * 3,
+        ]
+    )
+
+    VALID_VECTOR_PROP = frozenset(
+        [(DimensionProperty.NONE,), (DimensionProperty.UNSPECIFIED,)]
+    )
+
+    VALID_VAR_LEN_PROP = frozenset(
+        [(DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)]
+    )
+
+    @staticmethod
+    def update_learning_rate(optim: torch.optim.Optimizer, lr: float) -> None:
+        """
+        Apply a learning rate to a torch optimizer.
+        :param optim: Optimizer
+        :param lr: Learning rate
+        """
+        for param_group in optim.param_groups:
+            param_group["lr"] = lr
+
+    class DecayedValue:
+        def __init__(
+            self,
+            schedule: ScheduleType,
+            initial_value: float,
+            min_value: float,
+            max_step: int,
+        ):
+            """
+            Object that represnets value of a parameter that should be decayed, assuming it is a function of
+            global_step.
+            :param schedule: Type of learning rate schedule.
+            :param initial_value: Initial value before decay.
+            :param min_value: Decay value to this value by max_step.
+            :param max_step: The final step count where the return value should equal min_value.
+            :param global_step: The current step count.
+            :return: The value.
+            """
+            self.schedule = schedule
+            self.initial_value = initial_value
+            self.min_value = min_value
+            self.max_step = max_step
+
+        def get_value(self, global_step: int) -> float:
+            """
+            Get the value at a given global step.
+            :param global_step: Step count.
+            :returns: Decayed value at this global step.
+            """
+            if self.schedule == ScheduleType.CONSTANT:
+                return self.initial_value
+            elif self.schedule == ScheduleType.LINEAR:
+                return ModelUtils.polynomial_decay(
+                    self.initial_value, self.min_value, self.max_step, global_step
+                )
+            else:
+                raise UnityTrainerException(f"The schedule {self.schedule} is invalid.")
+
+    @staticmethod
+    def polynomial_decay(
+        initial_value: float,
+        min_value: float,
+        max_step: int,
+        global_step: int,
+        power: float = 1.0,
+    ) -> float:
+        """
+        Get a decayed value based on a polynomial schedule, with respect to the current global step.
+        :param initial_value: Initial value before decay.
+        :param min_value: Decay value to this value by max_step.
+        :param max_step: The final step count where the return value should equal min_value.
+        :param global_step: The current step count.
+        :param power: Power of polynomial decay. 1.0 (default) is a linear decay.
+        :return: The current decayed value.
+        """
+        global_step = min(global_step, max_step)
+        decayed_value = (initial_value - min_value) * (
+            1 - float(global_step) / max_step
+        ) ** (power) + min_value
+        return decayed_value
+
+    @staticmethod
+    def get_encoder_for_type(encoder_type: EncoderType) -> nn.Module:
+        ENCODER_FUNCTION_BY_TYPE = {
+            EncoderType.SIMPLE: SimpleVisualEncoder,
+            EncoderType.NATURE_CNN: NatureVisualEncoder,
+            EncoderType.RESNET: ResNetVisualEncoder,
+            EncoderType.MATCH3: SmallVisualEncoder,
+            EncoderType.FULLY_CONNECTED: FullyConnectedVisualEncoder,
+        }
+        return ENCODER_FUNCTION_BY_TYPE.get(encoder_type)
+
+    @staticmethod
+    def _check_resolution_for_encoder(
+        height: int, width: int, vis_encoder_type: EncoderType
+    ) -> None:
+        min_res = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[vis_encoder_type]
+        if height < min_res or width < min_res:
+            raise UnityTrainerException(
+                f"Visual observation resolution ({width}x{height}) is too small for"
+                f"the provided EncoderType ({vis_encoder_type.value}). The min dimension is {min_res}"
+            )
+
+    @staticmethod
+    def get_encoder_for_obs(
+        obs_spec: ObservationSpec,
+        normalize: bool,
+        h_size: int,
+        attention_embedding_size: int,
+        vis_encode_type: EncoderType,
+    ) -> Tuple[nn.Module, int]:
+        """
+        Returns the encoder and the size of the appropriate encoder.
+        :param shape: Tuples that represent the observation dimension.
+        :param normalize: Normalize all vector inputs.
+        :param h_size: Number of hidden units per layer excluding attention layers.
+        :param attention_embedding_size: Number of hidden units per attention layer.
+        :param vis_encode_type: Type of visual encoder to use.
+        """
+        shape = obs_spec.shape
+        dim_prop = obs_spec.dimension_property
+
+        # VISUAL
+        if dim_prop in ModelUtils.VALID_VISUAL_PROP:
+            visual_encoder_class = ModelUtils.get_encoder_for_type(vis_encode_type)
+            ModelUtils._check_resolution_for_encoder(
+                shape[0], shape[1], vis_encode_type
+            )
+            return (visual_encoder_class(shape[0], shape[1], shape[2], h_size), h_size)
+        # VECTOR
+        if dim_prop in ModelUtils.VALID_VECTOR_PROP:
+            return (VectorInput(shape[0], normalize), shape[0])
+        # VARIABLE LENGTH
+        if dim_prop in ModelUtils.VALID_VAR_LEN_PROP:
+            return (
+                EntityEmbedding(
+                    entity_size=shape[1],
+                    entity_num_max_elements=shape[0],
+                    embedding_size=attention_embedding_size,
+                ),
+                0,
+            )
+        # OTHER
+        raise UnityTrainerException(f"Unsupported Sensor with specs {obs_spec}")
+
+    @staticmethod
+    def create_input_processors(
+        observation_specs: List[ObservationSpec],
+        h_size: int,
+        vis_encode_type: EncoderType,
+        attention_embedding_size: int,
+        normalize: bool = False,
+    ) -> Tuple[nn.ModuleList, List[int]]:
+        """
+        Creates visual and vector encoders, along with their normalizers.
+        :param observation_specs: List of ObservationSpec that represent the observation dimensions.
+        :param action_size: Number of additional un-normalized inputs to each vector encoder. Used for
+            conditioning network on other values (e.g. actions for a Q function)
+        :param h_size: Number of hidden units per layer excluding attention layers.
+        :param attention_embedding_size: Number of hidden units per attention layer.
+        :param vis_encode_type: Type of visual encoder to use.
+        :param unnormalized_inputs: Vector inputs that should not be normalized, and added to the vector
+            obs.
+        :param normalize: Normalize all vector inputs.
+        :return: Tuple of :
+         - ModuleList of the encoders
+         - A list of embedding sizes (0 if the input requires to be processed with a variable length
+         observation encoder)
+        """
+        encoders: List[nn.Module] = []
+        embedding_sizes: List[int] = []
+        for obs_spec in observation_specs:
+            encoder, embedding_size = ModelUtils.get_encoder_for_obs(
+                obs_spec, normalize, h_size, attention_embedding_size, vis_encode_type
+            )
+            encoders.append(encoder)
+            embedding_sizes.append(embedding_size)
+
+        x_self_size = sum(embedding_sizes)  # The size of the "self" embedding
+        if x_self_size > 0:
+            for enc in encoders:
+                if isinstance(enc, EntityEmbedding):
+                    enc.add_self_embedding(attention_embedding_size)
+        return (nn.ModuleList(encoders), embedding_sizes)
+
+    @staticmethod
+    def list_to_tensor(
+        ndarray_list: List[np.ndarray], dtype: Optional[torch.dtype] = torch.float32
+    ) -> torch.Tensor:
+        """
+        Converts a list of numpy arrays into a tensor. MUCH faster than
+        calling as_tensor on the list directly.
+        """
+        return torch.as_tensor(np.asanyarray(ndarray_list), dtype=dtype)
+
+    @staticmethod
+    def list_to_tensor_list(
+        ndarray_list: List[np.ndarray], dtype: Optional[torch.dtype] = torch.float32
+    ) -> torch.Tensor:
+        """
+        Converts a list of numpy arrays into a list of tensors. MUCH faster than
+        calling as_tensor on the list directly.
+        """
+        return [
+            torch.as_tensor(np.asanyarray(_arr), dtype=dtype) for _arr in ndarray_list
+        ]
+
+    @staticmethod
+    def to_numpy(tensor: torch.Tensor) -> np.ndarray:
+        """
+        Converts a Torch Tensor to a numpy array. If the Tensor is on the GPU, it will
+        be brought to the CPU.
+        """
+        return tensor.detach().cpu().numpy()
+
+    @staticmethod
+    def break_into_branches(
+        concatenated_logits: torch.Tensor, action_size: List[int]
+    ) -> List[torch.Tensor]:
+        """
+        Takes a concatenated set of logits that represent multiple discrete action branches
+        and breaks it up into one Tensor per branch.
+        :param concatenated_logits: Tensor that represents the concatenated action branches
+        :param action_size: List of ints containing the number of possible actions for each branch.
+        :return: A List of Tensors containing one tensor per branch.
+        """
+        action_idx = [0] + list(np.cumsum(action_size))
+        branched_logits = [
+            concatenated_logits[:, action_idx[i] : action_idx[i + 1]]
+            for i in range(len(action_size))
+        ]
+        return branched_logits
+
+    @staticmethod
+    def actions_to_onehot(
+        discrete_actions: torch.Tensor, action_size: List[int]
+    ) -> List[torch.Tensor]:
+        """
+        Takes a tensor of discrete actions and turns it into a List of onehot encoding for each
+        action.
+        :param discrete_actions: Actions in integer form.
+        :param action_size: List of branch sizes. Should be of same size as discrete_actions'
+        last dimension.
+        :return: List of one-hot tensors, one representing each branch.
+        """
+        onehot_branches = [
+            torch.nn.functional.one_hot(_act.T, action_size[i]).float()
+            for i, _act in enumerate(discrete_actions.long().T)
+        ]
+        return onehot_branches
+
+    @staticmethod
+    def dynamic_partition(
+        data: torch.Tensor, partitions: torch.Tensor, num_partitions: int
+    ) -> List[torch.Tensor]:
+        """
+        Torch implementation of dynamic_partition :
+        https://www.tensorflow.org/api_docs/python/tf/dynamic_partition
+        Splits the data Tensor input into num_partitions Tensors according to the indices in
+        partitions.
+        :param data: The Tensor data that will be split into partitions.
+        :param partitions: An indices tensor that determines in which partition each element
+        of data will be in.
+        :param num_partitions: The number of partitions to output. Corresponds to the
+        maximum possible index in the partitions argument.
+        :return: A list of Tensor partitions (Their indices correspond to their partition index).
+        """
+        res: List[torch.Tensor] = []
+        for i in range(num_partitions):
+            res += [data[(partitions == i).nonzero().squeeze(1)]]
+        return res
+
+    @staticmethod
+    def masked_mean(tensor: torch.Tensor, masks: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the mean of the tensor but ignoring the values specified by masks.
+        Used for masking out loss functions.
+        :param tensor: Tensor which needs mean computation.
+        :param masks: Boolean tensor of masks with same dimension as tensor.
+        """
+        if tensor.ndim == 0:
+            return (tensor * masks).sum() / torch.clamp(
+                (torch.ones_like(tensor) * masks).float().sum(), min=1.0
+            )
+        else:
+            return (
+                tensor.permute(*torch.arange(tensor.ndim - 1, -1, -1)) * masks
+            ).sum() / torch.clamp(
+                (
+                    torch.ones_like(
+                        tensor.permute(*torch.arange(tensor.ndim - 1, -1, -1))
+                    )
+                    * masks
+                )
+                .float()
+                .sum(),
+                min=1.0,
+            )
+
+    @staticmethod
+    def soft_update(source: nn.Module, target: nn.Module, tau: float) -> None:
+        """
+        Performs an in-place polyak update of the target module based on the source,
+        by a ratio of tau. Note that source and target modules must have the same
+        parameters, where:
+            target = tau * source + (1-tau) * target
+        :param source: Source module whose parameters will be used.
+        :param target: Target module whose parameters will be updated.
+        :param tau: Percentage of source parameters to use in average. Setting tau to
+            1 will copy the source parameters to the target.
+        """
+        with torch.no_grad():
+            for source_param, target_param in zip(
+                source.parameters(), target.parameters()
+            ):
+                target_param.data.mul_(1.0 - tau)
+                torch.add(
+                    target_param.data,
+                    source_param.data,
+                    alpha=tau,
+                    out=target_param.data,
+                )
+
+    @staticmethod
+    def create_residual_self_attention(
+        input_processors: nn.ModuleList, embedding_sizes: List[int], hidden_size: int
+    ) -> Tuple[Optional[ResidualSelfAttention], Optional[LinearEncoder]]:
+        """
+        Creates an RSA if there are variable length observations found in the input processors.
+        :param input_processors: A ModuleList of input processors as returned by the function
+            create_input_processors().
+        :param embedding sizes: A List of embedding sizes as returned by create_input_processors().
+        :param hidden_size: The hidden size to use for the RSA.
+        :returns: A Tuple of the RSA itself, a self encoder, and the embedding size after the RSA.
+            Returns None for the RSA and encoder if no var len inputs are detected.
+        """
+        rsa, x_self_encoder = None, None
+        entity_num_max: int = 0
+        var_processors = [p for p in input_processors if isinstance(p, EntityEmbedding)]
+        for processor in var_processors:
+            entity_max: int = processor.entity_num_max_elements
+            # Only adds entity max if it was known at construction
+            if entity_max > 0:
+                entity_num_max += entity_max
+        if len(var_processors) > 0:
+            if sum(embedding_sizes):
+                x_self_encoder = LinearEncoder(
+                    sum(embedding_sizes),
+                    1,
+                    hidden_size,
+                    kernel_init=Initialization.Normal,
+                    kernel_gain=(0.125 / hidden_size) ** 0.5,
+                )
+            rsa = ResidualSelfAttention(hidden_size, entity_num_max)
+        return rsa, x_self_encoder
+
+    @staticmethod
+    def trust_region_value_loss(
+        values: Dict[str, torch.Tensor],
+        old_values: Dict[str, torch.Tensor],
+        returns: Dict[str, torch.Tensor],
+        epsilon: float,
+        loss_masks: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Evaluates value loss, clipping to stay within a trust region of old value estimates.
+        Used for PPO and POCA.
+        :param values: Value output of the current network.
+        :param old_values: Value stored with experiences in buffer.
+        :param returns: Computed returns.
+        :param epsilon: Clipping value for value estimate.
+        :param loss_mask: Mask for losses. Used with LSTM to ignore 0'ed out experiences.
+        """
+        value_losses = []
+        for name, head in values.items():
+            old_val_tensor = old_values[name]
+            returns_tensor = returns[name]
+            clipped_value_estimate = old_val_tensor + torch.clamp(
+                head - old_val_tensor, -1 * epsilon, epsilon
+            )
+            v_opt_a = (returns_tensor - head) ** 2
+            v_opt_b = (returns_tensor - clipped_value_estimate) ** 2
+            value_loss = ModelUtils.masked_mean(torch.max(v_opt_a, v_opt_b), loss_masks)
+            value_losses.append(value_loss)
+        value_loss = torch.mean(torch.stack(value_losses))
+        return value_loss
+
+    @staticmethod
+    def trust_region_policy_loss(
+        advantages: torch.Tensor,
+        log_probs: torch.Tensor,
+        old_log_probs: torch.Tensor,
+        loss_masks: torch.Tensor,
+        epsilon: float,
+    ) -> torch.Tensor:
+        """
+        Evaluate policy loss clipped to stay within a trust region. Used for PPO and POCA.
+        :param advantages: Computed advantages.
+        :param log_probs: Current policy probabilities
+        :param old_log_probs: Past policy probabilities
+        :param loss_masks: Mask for losses. Used with LSTM to ignore 0'ed out experiences.
+        """
+        advantage = advantages.unsqueeze(-1)
+        r_theta = torch.exp(log_probs - old_log_probs)
+        p_opt_a = r_theta * advantage
+        p_opt_b = torch.clamp(r_theta, 1.0 - epsilon, 1.0 + epsilon) * advantage
+        policy_loss = -1 * ModelUtils.masked_mean(
+            torch.min(p_opt_a, p_opt_b), loss_masks
+        )
+        return policy_loss
diff --git a/ml-agents/mlagents/trainers/trainer/__init__.py b/ml-agents/mlagents/trainers/trainer/__init__.py
new file mode 100644
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--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/__init__.py
@@ -0,0 +1,2 @@
+from mlagents.trainers.trainer.trainer import Trainer  # noqa
+from mlagents.trainers.trainer.trainer_factory import TrainerFactory  # noqa
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diff --git a/ml-agents/mlagents/trainers/trainer/off_policy_trainer.py b/ml-agents/mlagents/trainers/trainer/off_policy_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..92ae4966928ab45b780815e94e64668fca7e1eb9
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/off_policy_trainer.py
@@ -0,0 +1,263 @@
+# ## ML-Agent Learning (SAC)
+# Contains an implementation of SAC as described in https://arxiv.org/abs/1801.01290
+# and implemented in https://github.com/hill-a/stable-baselines
+
+from collections import defaultdict
+from typing import Dict, cast
+import os
+
+import numpy as np
+from mlagents.trainers.policy.checkpoint_manager import ModelCheckpoint
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.timers import timed
+from mlagents.trainers.buffer import RewardSignalUtil
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.trainer.rl_trainer import RLTrainer
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings, OffPolicyHyperparamSettings
+
+logger = get_logger(__name__)
+
+BUFFER_TRUNCATE_PERCENT = 0.8
+
+
+class OffPolicyTrainer(RLTrainer):
+    """
+    The SACTrainer is an implementation of the SAC algorithm, with support
+    for discrete actions and recurrent networks.
+    """
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training an off-policy model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            trainer_settings,
+            training,
+            load,
+            artifact_path,
+            reward_buff_cap,
+        )
+
+        self.seed = seed
+        self.policy: Policy = None  # type: ignore
+        self.optimizer: TorchOptimizer = None  # type: ignore
+        self.hyperparameters: OffPolicyHyperparamSettings = cast(
+            OffPolicyHyperparamSettings, trainer_settings.hyperparameters
+        )
+
+        self._step = 0
+
+        # Don't divide by zero
+        self.update_steps = 1
+        self.reward_signal_update_steps = 1
+
+        self.steps_per_update = self.hyperparameters.steps_per_update
+        self.reward_signal_steps_per_update = (
+            self.hyperparameters.reward_signal_steps_per_update
+        )
+
+        self.checkpoint_replay_buffer = self.hyperparameters.save_replay_buffer
+
+    def _checkpoint(self) -> ModelCheckpoint:
+        """
+        Writes a checkpoint model to memory
+        Overrides the default to save the replay buffer.
+        """
+        ckpt = super()._checkpoint()
+        if self.checkpoint_replay_buffer:
+            self.save_replay_buffer()
+        return ckpt
+
+    def save_model(self) -> None:
+        """
+        Saves the final training model to memory
+        Overrides the default to save the replay buffer.
+        """
+        super().save_model()
+        if self.checkpoint_replay_buffer:
+            self.save_replay_buffer()
+
+    def save_replay_buffer(self) -> None:
+        """
+        Save the training buffer's update buffer to a pickle file.
+        """
+        filename = os.path.join(self.artifact_path, "last_replay_buffer.hdf5")
+        logger.info(f"Saving Experience Replay Buffer to {filename}...")
+        with open(filename, "wb") as file_object:
+            self.update_buffer.save_to_file(file_object)
+            logger.info(
+                f"Saved Experience Replay Buffer ({os.path.getsize(filename)} bytes)."
+            )
+
+    def load_replay_buffer(self) -> None:
+        """
+        Loads the last saved replay buffer from a file.
+        """
+        filename = os.path.join(self.artifact_path, "last_replay_buffer.hdf5")
+        logger.info(f"Loading Experience Replay Buffer from {filename}...")
+        with open(filename, "rb+") as file_object:
+            self.update_buffer.load_from_file(file_object)
+        logger.debug(
+            "Experience replay buffer has {} experiences.".format(
+                self.update_buffer.num_experiences
+            )
+        )
+
+    def _is_ready_update(self) -> bool:
+        """
+        Returns whether or not the trainer has enough elements to run update model
+        :return: A boolean corresponding to whether or not _update_policy() can be run
+        """
+        return (
+            self.update_buffer.num_experiences >= self.hyperparameters.batch_size
+            and self._step >= self.hyperparameters.buffer_init_steps
+        )
+
+    def maybe_load_replay_buffer(self):
+        # Load the replay buffer if load
+        if self.load and self.checkpoint_replay_buffer:
+            try:
+                self.load_replay_buffer()
+            except (AttributeError, FileNotFoundError):
+                logger.warning(
+                    "Replay buffer was unable to load, starting from scratch."
+                )
+            logger.debug(
+                "Loaded update buffer with {} sequences".format(
+                    self.update_buffer.num_experiences
+                )
+            )
+
+    def add_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
+    ) -> None:
+        """
+        Adds policy to trainer.
+        """
+        if self.policy:
+            logger.warning(
+                "Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
+                    train adversarial games.".format(
+                    self.__class__.__name__
+                )
+            )
+        self.policy = policy
+        self.policies[parsed_behavior_id.behavior_id] = policy
+        self.optimizer = self.create_optimizer()
+        for _reward_signal in self.optimizer.reward_signals.keys():
+            self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
+
+        self.model_saver.register(self.policy)
+        self.model_saver.register(self.optimizer)
+        self.model_saver.initialize_or_load()
+
+        # Needed to resume loads properly
+        self._step = policy.get_current_step()
+        # Assume steps were updated at the correct ratio before
+        self.update_steps = int(max(1, self._step / self.steps_per_update))
+        self.reward_signal_update_steps = int(
+            max(1, self._step / self.reward_signal_steps_per_update)
+        )
+
+    @timed
+    def _update_policy(self) -> bool:
+        """
+        Uses update_buffer to update the policy. We sample the update_buffer and update
+        until the steps_per_update ratio is met.
+        """
+        has_updated = False
+        self.cumulative_returns_since_policy_update.clear()
+        n_sequences = max(
+            int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
+        )
+
+        batch_update_stats: Dict[str, list] = defaultdict(list)
+        while (
+            self._step - self.hyperparameters.buffer_init_steps
+        ) / self.update_steps > self.steps_per_update:
+            logger.debug(f"Updating SAC policy at step {self._step}")
+            buffer = self.update_buffer
+            if self.update_buffer.num_experiences >= self.hyperparameters.batch_size:
+                sampled_minibatch = buffer.sample_mini_batch(
+                    self.hyperparameters.batch_size,
+                    sequence_length=self.policy.sequence_length,
+                )
+                # Get rewards for each reward
+                for name, signal in self.optimizer.reward_signals.items():
+                    sampled_minibatch[RewardSignalUtil.rewards_key(name)] = (
+                        signal.evaluate(sampled_minibatch) * signal.strength
+                    )
+
+                update_stats = self.optimizer.update(sampled_minibatch, n_sequences)
+                for stat_name, value in update_stats.items():
+                    batch_update_stats[stat_name].append(value)
+
+                self.update_steps += 1
+
+                for stat, stat_list in batch_update_stats.items():
+                    self._stats_reporter.add_stat(stat, np.mean(stat_list))
+                has_updated = True
+
+            if self.optimizer.bc_module:
+                update_stats = self.optimizer.bc_module.update()
+                for stat, val in update_stats.items():
+                    self._stats_reporter.add_stat(stat, val)
+
+        # Truncate update buffer if neccessary. Truncate more than we need to to avoid truncating
+        # a large buffer at each update.
+        if self.update_buffer.num_experiences > self.hyperparameters.buffer_size:
+            self.update_buffer.truncate(
+                int(self.hyperparameters.buffer_size * BUFFER_TRUNCATE_PERCENT)
+            )
+        # TODO: revisit this update
+        self._update_reward_signals()
+        return has_updated
+
+    def _update_reward_signals(self) -> None:
+        """
+        Iterate through the reward signals and update them. Unlike in PPO,
+        do it separate from the policy so that it can be done at a different
+        interval.
+        This function should only be used to simulate
+        http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated
+        N times, then the reward signals are updated N times. Normally, the reward signal
+        and policy are updated in parallel.
+        """
+        buffer = self.update_buffer
+        batch_update_stats: Dict[str, list] = defaultdict(list)
+        while (
+            self._step - self.hyperparameters.buffer_init_steps
+        ) / self.reward_signal_update_steps > self.reward_signal_steps_per_update:
+            # Get minibatches for reward signal update if needed
+            minibatch = buffer.sample_mini_batch(
+                self.hyperparameters.batch_size,
+                sequence_length=self.policy.sequence_length,
+            )
+            update_stats = self.optimizer.update_reward_signals(minibatch)
+
+            for stat_name, value in update_stats.items():
+                batch_update_stats[stat_name].append(value)
+            self.reward_signal_update_steps += 1
+
+            for stat, stat_list in batch_update_stats.items():
+                self._stats_reporter.add_stat(stat, np.mean(stat_list))
diff --git a/ml-agents/mlagents/trainers/trainer/on_policy_trainer.py b/ml-agents/mlagents/trainers/trainer/on_policy_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..879640a0e5f95f2594d9f30ab8aed2cec9eb722d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/on_policy_trainer.py
@@ -0,0 +1,144 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agent Learning (PPO)
+# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347
+
+from collections import defaultdict
+from typing import cast
+
+import numpy as np
+
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.buffer import BufferKey
+from mlagents.trainers.trainer.rl_trainer import RLTrainer
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings, OnPolicyHyperparamSettings
+
+logger = get_logger(__name__)
+
+
+class OnPolicyTrainer(RLTrainer):
+    """The PPOTrainer is an implementation of the PPO algorithm."""
+
+    def __init__(
+        self,
+        behavior_name: str,
+        reward_buff_cap: int,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        seed: int,
+        artifact_path: str,
+    ):
+        """
+        Responsible for collecting experiences and training an on-policy model.
+        :param behavior_name: The name of the behavior associated with trainer config
+        :param reward_buff_cap: Max reward history to track in the reward buffer
+        :param trainer_settings: The parameters for the trainer.
+        :param training: Whether the trainer is set for training.
+        :param load: Whether the model should be loaded.
+        :param seed: The seed the model will be initialized with
+        :param artifact_path: The directory within which to store artifacts from this trainer.
+        """
+        super().__init__(
+            behavior_name,
+            trainer_settings,
+            training,
+            load,
+            artifact_path,
+            reward_buff_cap,
+        )
+        self.hyperparameters = cast(
+            OnPolicyHyperparamSettings, self.trainer_settings.hyperparameters
+        )
+        self.seed = seed
+        self.policy: Policy = None  # type: ignore
+        self.optimizer: TorchOptimizer = None  # type: ignore
+
+    def _is_ready_update(self):
+        """
+        Returns whether or not the trainer has enough elements to run update model
+        :return: A boolean corresponding to whether or not update_model() can be run
+        """
+        size_of_buffer = self.update_buffer.num_experiences
+        return size_of_buffer > self.hyperparameters.buffer_size
+
+    def _update_policy(self):
+        """
+        Uses demonstration_buffer to update the policy.
+        The reward signal generators must be updated in this method at their own pace.
+        """
+        buffer_length = self.update_buffer.num_experiences
+        self.cumulative_returns_since_policy_update.clear()
+
+        # Make sure batch_size is a multiple of sequence length. During training, we
+        # will need to reshape the data into a batch_size x sequence_length tensor.
+        batch_size = (
+            self.hyperparameters.batch_size
+            - self.hyperparameters.batch_size % self.policy.sequence_length
+        )
+        # Make sure there is at least one sequence
+        batch_size = max(batch_size, self.policy.sequence_length)
+
+        n_sequences = max(
+            int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
+        )
+
+        advantages = np.array(
+            self.update_buffer[BufferKey.ADVANTAGES].get_batch(), dtype=np.float32
+        )
+        self.update_buffer[BufferKey.ADVANTAGES].set(
+            (advantages - advantages.mean()) / (advantages.std() + 1e-10)
+        )
+        num_epoch = self.hyperparameters.num_epoch
+        batch_update_stats = defaultdict(list)
+        for _ in range(num_epoch):
+            self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
+            buffer = self.update_buffer
+            max_num_batch = buffer_length // batch_size
+            for i in range(0, max_num_batch * batch_size, batch_size):
+                minibatch = buffer.make_mini_batch(i, i + batch_size)
+                update_stats = self.optimizer.update(minibatch, n_sequences)
+                update_stats.update(self.optimizer.update_reward_signals(minibatch))
+                for stat_name, value in update_stats.items():
+                    batch_update_stats[stat_name].append(value)
+
+        for stat, stat_list in batch_update_stats.items():
+            self._stats_reporter.add_stat(stat, np.mean(stat_list))
+
+        if self.optimizer.bc_module:
+            update_stats = self.optimizer.bc_module.update()
+            for stat, val in update_stats.items():
+                self._stats_reporter.add_stat(stat, val)
+        self._clear_update_buffer()
+        return True
+
+    def add_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
+    ) -> None:
+        """
+        Adds policy to trainer.
+        :param parsed_behavior_id: Behavior identifiers that the policy should belong to.
+        :param policy: Policy to associate with name_behavior_id.
+        """
+        if self.policy:
+            logger.warning(
+                "Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
+                    train adversarial games.".format(
+                    self.__class__.__name__
+                )
+            )
+        self.policy = policy
+        self.policies[parsed_behavior_id.behavior_id] = policy
+
+        self.optimizer = self.create_optimizer()
+        for _reward_signal in self.optimizer.reward_signals.keys():
+            self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
+
+        self.model_saver.register(self.policy)
+        self.model_saver.register(self.optimizer)
+        self.model_saver.initialize_or_load()
+
+        # Needed to resume loads properly
+        self._step = policy.get_current_step()
diff --git a/ml-agents/mlagents/trainers/trainer/rl_trainer.py b/ml-agents/mlagents/trainers/trainer/rl_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..57454900a0b61a4af4c728d01373eb63147f485b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/rl_trainer.py
@@ -0,0 +1,305 @@
+# # Unity ML-Agents Toolkit
+from typing import Dict, List, Optional
+from collections import defaultdict
+import abc
+import time
+import attr
+import numpy as np
+from mlagents_envs.side_channel.stats_side_channel import StatsAggregationMethod
+
+from mlagents.trainers.policy.checkpoint_manager import (
+    ModelCheckpoint,
+    ModelCheckpointManager,
+)
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.timers import timed
+from mlagents.trainers.optimizer import Optimizer
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.buffer import AgentBuffer, BufferKey
+from mlagents.trainers.trainer import Trainer
+from mlagents.trainers.torch_entities.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents_envs.timers import hierarchical_timer
+from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.settings import TrainerSettings
+from mlagents.trainers.stats import StatsPropertyType
+from mlagents.trainers.model_saver.model_saver import BaseModelSaver
+
+
+logger = get_logger(__name__)
+
+
+class RLTrainer(Trainer):
+    """
+    This class is the base class for trainers that use Reward Signals.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # collected_rewards is a dictionary from name of reward signal to a dictionary of agent_id to cumulative reward
+        # used for reporting only. We always want to report the environment reward to Tensorboard, regardless
+        # of what reward signals are actually present.
+        self.cumulative_returns_since_policy_update: List[float] = []
+        self.collected_rewards: Dict[str, Dict[str, int]] = {
+            "environment": defaultdict(lambda: 0)
+        }
+        self.update_buffer: AgentBuffer = AgentBuffer()
+        self._stats_reporter.add_property(
+            StatsPropertyType.HYPERPARAMETERS, self.trainer_settings.as_dict()
+        )
+
+        self._next_save_step = 0
+        self._next_summary_step = 0
+        self.model_saver = self.create_model_saver(
+            self.trainer_settings, self.artifact_path, self.load
+        )
+        self._has_warned_group_rewards = False
+
+    def end_episode(self) -> None:
+        """
+        A signal that the Episode has ended. The buffer must be reset.
+        Get only called when the academy resets.
+        """
+        for rewards in self.collected_rewards.values():
+            for agent_id in rewards:
+                rewards[agent_id] = 0
+
+    def _update_end_episode_stats(self, agent_id: str, optimizer: Optimizer) -> None:
+        for name, rewards in self.collected_rewards.items():
+            if name == "environment":
+                self.stats_reporter.add_stat(
+                    "Environment/Cumulative Reward",
+                    rewards.get(agent_id, 0),
+                    aggregation=StatsAggregationMethod.HISTOGRAM,
+                )
+                self.cumulative_returns_since_policy_update.append(
+                    rewards.get(agent_id, 0)
+                )
+                self.reward_buffer.appendleft(rewards.get(agent_id, 0))
+                rewards[agent_id] = 0
+            else:
+                if isinstance(optimizer.reward_signals[name], BaseRewardProvider):
+                    self.stats_reporter.add_stat(
+                        f"Policy/{optimizer.reward_signals[name].name.capitalize()} Reward",
+                        rewards.get(agent_id, 0),
+                    )
+                else:
+                    self.stats_reporter.add_stat(
+                        optimizer.reward_signals[name].stat_name,
+                        rewards.get(agent_id, 0),
+                    )
+                rewards[agent_id] = 0
+
+    def _clear_update_buffer(self) -> None:
+        """
+        Clear the buffers that have been built up during inference.
+        """
+        self.update_buffer.reset_agent()
+
+    @abc.abstractmethod
+    def _is_ready_update(self):
+        """
+        Returns whether or not the trainer has enough elements to run update model
+        :return: A boolean corresponding to wether or not update_model() can be run
+        """
+        return False
+
+    @abc.abstractmethod
+    def create_optimizer(self) -> TorchOptimizer:
+        """
+        Creates an Optimizer object
+        """
+        pass
+
+    @staticmethod
+    def create_model_saver(
+        trainer_settings: TrainerSettings, model_path: str, load: bool
+    ) -> BaseModelSaver:
+        model_saver = TorchModelSaver(  # type: ignore
+            trainer_settings, model_path, load
+        )
+        return model_saver
+
+    def _policy_mean_reward(self) -> Optional[float]:
+        """Returns the mean episode reward for the current policy."""
+        rewards = self.cumulative_returns_since_policy_update
+        if len(rewards) == 0:
+            return None
+        else:
+            return sum(rewards) / len(rewards)
+
+    @timed
+    def _checkpoint(self) -> ModelCheckpoint:
+        """
+        Checkpoints the policy associated with this trainer.
+        """
+        n_policies = len(self.policies.keys())
+        if n_policies > 1:
+            logger.warning(
+                "Trainer has multiple policies, but default behavior only saves the first."
+            )
+        export_path, auxillary_paths = self.model_saver.save_checkpoint(
+            self.brain_name, self._step
+        )
+        new_checkpoint = ModelCheckpoint(
+            int(self._step),
+            export_path,
+            self._policy_mean_reward(),
+            time.time(),
+            auxillary_file_paths=auxillary_paths,
+        )
+        ModelCheckpointManager.add_checkpoint(
+            self.brain_name, new_checkpoint, self.trainer_settings.keep_checkpoints
+        )
+        return new_checkpoint
+
+    def save_model(self) -> None:
+        """
+        Saves the policy associated with this trainer.
+        """
+        n_policies = len(self.policies.keys())
+        if n_policies > 1:
+            logger.warning(
+                "Trainer has multiple policies, but default behavior only saves the first."
+            )
+        elif n_policies == 0:
+            logger.warning("Trainer has no policies, not saving anything.")
+            return
+
+        model_checkpoint = self._checkpoint()
+        self.model_saver.copy_final_model(model_checkpoint.file_path)
+        export_ext = "onnx"
+        final_checkpoint = attr.evolve(
+            model_checkpoint, file_path=f"{self.model_saver.model_path}.{export_ext}"
+        )
+        ModelCheckpointManager.track_final_checkpoint(self.brain_name, final_checkpoint)
+
+    @abc.abstractmethod
+    def _update_policy(self) -> bool:
+        """
+        Uses demonstration_buffer to update model.
+        :return: Whether or not the policy was updated.
+        """
+        pass
+
+    def _increment_step(self, n_steps: int, name_behavior_id: str) -> None:
+        """
+        Increment the step count of the trainer
+        :param n_steps: number of steps to increment the step count by
+        """
+        self._step += n_steps
+        self._next_summary_step = self._get_next_interval_step(self.summary_freq)
+        self._next_save_step = self._get_next_interval_step(
+            self.trainer_settings.checkpoint_interval
+        )
+        p = self.get_policy(name_behavior_id)
+        if p:
+            p.increment_step(n_steps)
+        self.stats_reporter.set_stat("Step", float(self.get_step))
+
+    def _get_next_interval_step(self, interval: int) -> int:
+        """
+        Get the next step count that should result in an action.
+        :param interval: The interval between actions.
+        """
+        return self._step + (interval - self._step % interval)
+
+    def _write_summary(self, step: int) -> None:
+        """
+        Saves training statistics to Tensorboard.
+        """
+        self.stats_reporter.add_stat("Is Training", float(self.should_still_train))
+        self.stats_reporter.write_stats(int(step))
+
+    @abc.abstractmethod
+    def _process_trajectory(self, trajectory: Trajectory) -> None:
+        """
+        Takes a trajectory and processes it, putting it into the update buffer.
+        :param trajectory: The Trajectory tuple containing the steps to be processed.
+        """
+        self._maybe_write_summary(self.get_step + len(trajectory.steps))
+        self._maybe_save_model(self.get_step + len(trajectory.steps))
+        self._increment_step(len(trajectory.steps), trajectory.behavior_id)
+
+    def _maybe_write_summary(self, step_after_process: int) -> None:
+        """
+        If processing the trajectory will make the step exceed the next summary write,
+        write the summary. This logic ensures summaries are written on the update step and not in between.
+        :param step_after_process: the step count after processing the next trajectory.
+        """
+        if self._next_summary_step == 0:  # Don't write out the first one
+            self._next_summary_step = self._get_next_interval_step(self.summary_freq)
+        if step_after_process >= self._next_summary_step and self.get_step != 0:
+            self._write_summary(self._next_summary_step)
+
+    def _append_to_update_buffer(self, agentbuffer_trajectory: AgentBuffer) -> None:
+        """
+        Append an AgentBuffer to the update buffer. If the trainer isn't training,
+        don't update to avoid a memory leak.
+        """
+        if self.should_still_train:
+            seq_len = (
+                self.trainer_settings.network_settings.memory.sequence_length
+                if self.trainer_settings.network_settings.memory is not None
+                else 1
+            )
+            agentbuffer_trajectory.resequence_and_append(
+                self.update_buffer, training_length=seq_len
+            )
+
+    def _maybe_save_model(self, step_after_process: int) -> None:
+        """
+        If processing the trajectory will make the step exceed the next model write,
+        save the model. This logic ensures models are written on the update step and not in between.
+        :param step_after_process: the step count after processing the next trajectory.
+        """
+        if self._next_save_step == 0:  # Don't save the first one
+            self._next_save_step = self._get_next_interval_step(
+                self.trainer_settings.checkpoint_interval
+            )
+        if step_after_process >= self._next_save_step and self.get_step != 0:
+            self._checkpoint()
+
+    def _warn_if_group_reward(self, buffer: AgentBuffer) -> None:
+        """
+        Warn if the trainer receives a Group Reward but isn't a multiagent trainer (e.g. POCA).
+        """
+        if not self._has_warned_group_rewards:
+            if np.any(buffer[BufferKey.GROUP_REWARD]):
+                logger.warning(
+                    "An agent recieved a Group Reward, but you are not using a multi-agent trainer. "
+                    "Please use the POCA trainer for best results."
+                )
+                self._has_warned_group_rewards = True
+
+    def advance(self) -> None:
+        """
+        Steps the trainer, taking in trajectories and updates if ready.
+        Will block and wait briefly if there are no trajectories.
+        """
+        with hierarchical_timer("process_trajectory"):
+            for traj_queue in self.trajectory_queues:
+                # We grab at most the maximum length of the queue.
+                # This ensures that even if the queue is being filled faster than it is
+                # being emptied, the trajectories in the queue are on-policy.
+                _queried = False
+                for _ in range(traj_queue.qsize()):
+                    _queried = True
+                    try:
+                        t = traj_queue.get_nowait()
+                        self._process_trajectory(t)
+                    except AgentManagerQueue.Empty:
+                        break
+                if self.threaded and not _queried:
+                    # Yield thread to avoid busy-waiting
+                    time.sleep(0.0001)
+        if self.should_still_train:
+            if self._is_ready_update():
+                with hierarchical_timer("_update_policy"):
+                    if self._update_policy():
+                        for q in self.policy_queues:
+                            # Get policies that correspond to the policy queue in question
+                            q.put(self.get_policy(q.behavior_id))
diff --git a/ml-agents/mlagents/trainers/trainer/trainer.py b/ml-agents/mlagents/trainers/trainer/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..58a339efd2b4e5089d8564bbe51d55d005b2b27b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/trainer.py
@@ -0,0 +1,183 @@
+# # Unity ML-Agents Toolkit
+from typing import List, Deque, Dict
+import abc
+from collections import deque
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.stats import StatsReporter
+from mlagents.trainers.trajectory import Trajectory
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.policy import Policy
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TrainerSettings
+
+
+logger = get_logger(__name__)
+
+
+class Trainer(abc.ABC):
+    """This class is the base class for the mlagents_envs.trainers"""
+
+    def __init__(
+        self,
+        brain_name: str,
+        trainer_settings: TrainerSettings,
+        training: bool,
+        load: bool,
+        artifact_path: str,
+        reward_buff_cap: int = 1,
+    ):
+        """
+        Responsible for collecting experiences and training a neural network model.
+        :param brain_name: Brain name of brain to be trained.
+        :param trainer_settings: The parameters for the trainer (dictionary).
+        :param training: Whether the trainer is set for training.
+        :param artifact_path: The directory within which to store artifacts from this trainer
+        :param reward_buff_cap:
+        """
+        self.brain_name = brain_name
+        self.trainer_settings = trainer_settings
+        self._threaded = trainer_settings.threaded
+        self._stats_reporter = StatsReporter(brain_name)
+        self.is_training = training
+        self.load = load
+        self._reward_buffer: Deque[float] = deque(maxlen=reward_buff_cap)
+        self.policy_queues: List[AgentManagerQueue[Policy]] = []
+        self.trajectory_queues: List[AgentManagerQueue[Trajectory]] = []
+        self._step: int = 0
+        self.artifact_path = artifact_path
+        self.summary_freq = self.trainer_settings.summary_freq
+        self.policies: Dict[str, Policy] = {}
+
+    @property
+    def stats_reporter(self):
+        """
+        Returns the stats reporter associated with this Trainer.
+        """
+        return self._stats_reporter
+
+    @property
+    def parameters(self) -> TrainerSettings:
+        """
+        Returns the trainer parameters of the trainer.
+        """
+        return self.trainer_settings
+
+    @property
+    def get_max_steps(self) -> int:
+        """
+        Returns the maximum number of steps. Is used to know when the trainer should be stopped.
+        :return: The maximum number of steps of the trainer
+        """
+        return self.trainer_settings.max_steps
+
+    @property
+    def get_step(self) -> int:
+        """
+        Returns the number of steps the trainer has performed
+        :return: the step count of the trainer
+        """
+        return self._step
+
+    @property
+    def threaded(self) -> bool:
+        """
+        Whether or not to run the trainer in a thread. True allows the trainer to
+        update the policy while the environment is taking steps. Set to False to
+        enforce strict on-policy updates (i.e. don't update the policy when taking steps.)
+        """
+        return self._threaded
+
+    @property
+    def should_still_train(self) -> bool:
+        """
+        Returns whether or not the trainer should train. A Trainer could
+        stop training if it wasn't training to begin with, or if max_steps
+        is reached.
+        """
+        return self.is_training and self.get_step <= self.get_max_steps
+
+    @property
+    def reward_buffer(self) -> Deque[float]:
+        """
+        Returns the reward buffer. The reward buffer contains the cumulative
+        rewards of the most recent episodes completed by agents using this
+        trainer.
+        :return: the reward buffer.
+        """
+        return self._reward_buffer
+
+    @abc.abstractmethod
+    def save_model(self) -> None:
+        """
+        Saves model file(s) for the policy or policies associated with this trainer.
+        """
+        pass
+
+    @abc.abstractmethod
+    def end_episode(self):
+        """
+        A signal that the Episode has ended. The buffer must be reset.
+        Get only called when the academy resets.
+        """
+        pass
+
+    @abc.abstractmethod
+    def create_policy(
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+    ) -> Policy:
+        """
+        Creates a Policy object
+        """
+        pass
+
+    @abc.abstractmethod
+    def add_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
+    ) -> None:
+        """
+        Adds policy to trainer.
+        """
+        pass
+
+    def get_policy(self, name_behavior_id: str) -> Policy:
+        """
+        Gets policy associated with name_behavior_id
+        :param name_behavior_id: Fully qualified behavior name
+        :return: Policy associated with name_behavior_id
+        """
+        return self.policies[name_behavior_id]
+
+    @abc.abstractmethod
+    def advance(self) -> None:
+        """
+        Advances the trainer. Typically, this means grabbing trajectories
+        from all subscribed trajectory queues (self.trajectory_queues), and updating
+        a policy using the steps in them, and if needed pushing a new policy onto the right
+        policy queues (self.policy_queues).
+        """
+        pass
+
+    def publish_policy_queue(self, policy_queue: AgentManagerQueue[Policy]) -> None:
+        """
+        Adds a policy queue to the list of queues to publish to when this Trainer
+        makes a policy update
+        :param policy_queue: Policy queue to publish to.
+        """
+        self.policy_queues.append(policy_queue)
+
+    def subscribe_trajectory_queue(
+        self, trajectory_queue: AgentManagerQueue[Trajectory]
+    ) -> None:
+        """
+        Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from.
+        :param trajectory_queue: Trajectory queue to read from.
+        """
+        self.trajectory_queues.append(trajectory_queue)
+
+    @staticmethod
+    def get_trainer_name() -> str:
+        raise NotImplementedError
diff --git a/ml-agents/mlagents/trainers/trainer/trainer_factory.py b/ml-agents/mlagents/trainers/trainer/trainer_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffb774151336a5a3e790e25843c62895562ad57d
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/trainer_factory.py
@@ -0,0 +1,131 @@
+import os
+from typing import Dict
+
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.exception import TrainerConfigError
+from mlagents.trainers.trainer import Trainer
+from mlagents.trainers.ghost.trainer import GhostTrainer
+from mlagents.trainers.ghost.controller import GhostController
+from mlagents.trainers.settings import TrainerSettings
+from mlagents.plugins import all_trainer_types
+
+
+logger = get_logger(__name__)
+
+
+class TrainerFactory:
+    def __init__(
+        self,
+        trainer_config: Dict[str, TrainerSettings],
+        output_path: str,
+        train_model: bool,
+        load_model: bool,
+        seed: int,
+        param_manager: EnvironmentParameterManager,
+        init_path: str = None,
+        multi_gpu: bool = False,
+    ):
+        """
+        The TrainerFactory generates the Trainers based on the configuration passed as
+        input.
+        :param trainer_config: A dictionary from behavior name to TrainerSettings
+        :param output_path: The path to the directory where the artifacts generated by
+        the trainer will be saved.
+        :param train_model: If True, the Trainers will train the model and if False,
+        only perform inference.
+        :param load_model: If True, the Trainer will load neural networks weights from
+        the previous run.
+        :param seed: The seed of the Trainers. Dictates how the neural networks will be
+        initialized.
+        :param param_manager: The EnvironmentParameterManager that will dictate when/if
+        the EnvironmentParameters must change.
+        :param init_path: Path from which to load model.
+        :param multi_gpu: If True, multi-gpu will be used. (currently not available)
+        """
+        self.trainer_config = trainer_config
+        self.output_path = output_path
+        self.init_path = init_path
+        self.train_model = train_model
+        self.load_model = load_model
+        self.seed = seed
+        self.param_manager = param_manager
+        self.multi_gpu = multi_gpu
+        self.ghost_controller = GhostController()
+
+    def generate(self, behavior_name: str) -> Trainer:
+        trainer_settings = self.trainer_config[behavior_name]
+        return TrainerFactory._initialize_trainer(
+            trainer_settings,
+            behavior_name,
+            self.output_path,
+            self.train_model,
+            self.load_model,
+            self.ghost_controller,
+            self.seed,
+            self.param_manager,
+            self.multi_gpu,
+        )
+
+    @staticmethod
+    def _initialize_trainer(
+        trainer_settings: TrainerSettings,
+        brain_name: str,
+        output_path: str,
+        train_model: bool,
+        load_model: bool,
+        ghost_controller: GhostController,
+        seed: int,
+        param_manager: EnvironmentParameterManager,
+        multi_gpu: bool = False,
+    ) -> Trainer:
+        """
+        Initializes a trainer given a provided trainer configuration and brain parameters, as well as
+        some general training session options.
+
+        :param trainer_settings: Original trainer configuration loaded from YAML
+        :param brain_name: Name of the brain to be associated with trainer
+        :param output_path: Path to save the model and summary statistics
+        :param keep_checkpoints: How many model checkpoints to keep
+        :param train_model: Whether to train the model (vs. run inference)
+        :param load_model: Whether to load the model or randomly initialize
+        :param ghost_controller: The object that coordinates ghost trainers
+        :param seed: The random seed to use
+        :param param_manager: EnvironmentParameterManager, used to determine a reward buffer length for PPOTrainer
+        :return:
+        """
+        trainer_artifact_path = os.path.join(output_path, brain_name)
+
+        min_lesson_length = param_manager.get_minimum_reward_buffer_size(brain_name)
+
+        trainer: Trainer = None  # type: ignore  # will be set to one of these, or raise
+
+        try:
+            trainer_type = all_trainer_types[trainer_settings.trainer_type]
+            trainer = trainer_type(
+                brain_name,
+                min_lesson_length,
+                trainer_settings,
+                train_model,
+                load_model,
+                seed,
+                trainer_artifact_path,
+            )
+
+        except KeyError:
+            raise TrainerConfigError(
+                f"The trainer config contains an unknown trainer type "
+                f"{trainer_settings.trainer_type} for brain {brain_name}"
+            )
+
+        if trainer_settings.self_play is not None:
+            trainer = GhostTrainer(
+                trainer,
+                brain_name,
+                ghost_controller,
+                min_lesson_length,
+                trainer_settings,
+                train_model,
+                trainer_artifact_path,
+            )
+        return trainer
diff --git a/ml-agents/mlagents/trainers/trainer/trainer_utils.py b/ml-agents/mlagents/trainers/trainer/trainer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad94bd35f066fe37780555d2b3402662f62d4ca5
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer/trainer_utils.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+
+def discount_rewards(r, gamma=0.99, value_next=0.0):
+    """
+    Computes discounted sum of future rewards for use in updating value estimate.
+    :param r: List of rewards.
+    :param gamma: Discount factor.
+    :param value_next: T+1 value estimate for returns calculation.
+    :return: discounted sum of future rewards as list.
+    """
+    discounted_r = np.zeros_like(r)
+    running_add = value_next
+    for t in reversed(range(0, r.size)):
+        running_add = running_add * gamma + r[t]
+        discounted_r[t] = running_add
+    return discounted_r
+
+
+def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
+    """
+    Computes generalized advantage estimate for use in updating policy.
+    :param rewards: list of rewards for time-steps t to T.
+    :param value_next: Value estimate for time-step T+1.
+    :param value_estimates: list of value estimates for time-steps t to T.
+    :param gamma: Discount factor.
+    :param lambd: GAE weighing factor.
+    :return: list of advantage estimates for time-steps t to T.
+    """
+    value_estimates = np.append(value_estimates, value_next)
+    delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
+    advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
+    return advantage
+
+
+def lambda_return(r, value_estimates, gamma=0.99, lambd=0.8, value_next=0.0):
+    returns = np.zeros_like(r)
+    returns[-1] = r[-1] + gamma * value_next
+    for t in reversed(range(0, r.size - 1)):
+        returns[t] = (
+            gamma * lambd * returns[t + 1]
+            + r[t]
+            + (1 - lambd) * gamma * value_estimates[t + 1]
+        )
+    return returns
diff --git a/ml-agents/mlagents/trainers/trainer_controller.py b/ml-agents/mlagents/trainers/trainer_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..69da1e569496d043e1d4c167a2cce963e9fd69d9
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trainer_controller.py
@@ -0,0 +1,297 @@
+# # Unity ML-Agents Toolkit
+# ## ML-Agent Learning
+"""Launches trainers for each External Brains in a Unity Environment."""
+
+import os
+import threading
+from typing import Dict, Set, List
+from collections import defaultdict
+
+import numpy as np
+
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers.env_manager import EnvManager, EnvironmentStep
+from mlagents_envs.exception import (
+    UnityEnvironmentException,
+    UnityCommunicationException,
+    UnityCommunicatorStoppedException,
+)
+from mlagents_envs.timers import (
+    hierarchical_timer,
+    timed,
+    get_timer_stack_for_thread,
+    merge_gauges,
+)
+from mlagents.trainers.trainer import Trainer
+from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
+from mlagents.trainers.trainer import TrainerFactory
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.agent_processor import AgentManager
+from mlagents import torch_utils
+from mlagents.torch_utils.globals import get_rank
+
+
+class TrainerController:
+    def __init__(
+        self,
+        trainer_factory: TrainerFactory,
+        output_path: str,
+        run_id: str,
+        param_manager: EnvironmentParameterManager,
+        train: bool,
+        training_seed: int,
+    ):
+        """
+        :param output_path: Path to save the model.
+        :param summaries_dir: Folder to save training summaries.
+        :param run_id: The sub-directory name for model and summary statistics
+        :param param_manager: EnvironmentParameterManager object which stores information about all
+        environment parameters.
+        :param train: Whether to train model, or only run inference.
+        :param training_seed: Seed to use for Numpy and Torch random number generation.
+        :param threaded: Whether or not to run trainers in a separate thread. Disable for testing/debugging.
+        """
+        self.trainers: Dict[str, Trainer] = {}
+        self.brain_name_to_identifier: Dict[str, Set] = defaultdict(set)
+        self.trainer_factory = trainer_factory
+        self.output_path = output_path
+        self.logger = get_logger(__name__)
+        self.run_id = run_id
+        self.train_model = train
+        self.param_manager = param_manager
+        self.ghost_controller = self.trainer_factory.ghost_controller
+        self.registered_behavior_ids: Set[str] = set()
+
+        self.trainer_threads: List[threading.Thread] = []
+        self.kill_trainers = False
+        np.random.seed(training_seed)
+        torch_utils.torch.manual_seed(training_seed)
+        self.rank = get_rank()
+
+    @timed
+    def _save_models(self):
+        """
+        Saves current model to checkpoint folder.
+        """
+        if self.rank is not None and self.rank != 0:
+            return
+
+        for brain_name in self.trainers.keys():
+            self.trainers[brain_name].save_model()
+        self.logger.debug("Saved Model")
+
+    @staticmethod
+    def _create_output_path(output_path):
+        try:
+            if not os.path.exists(output_path):
+                os.makedirs(output_path)
+        except Exception:
+            raise UnityEnvironmentException(
+                f"The folder {output_path} containing the "
+                "generated model could not be "
+                "accessed. Please make sure the "
+                "permissions are set correctly."
+            )
+
+    @timed
+    def _reset_env(self, env_manager: EnvManager) -> None:
+        """Resets the environment.
+
+        Returns:
+            A Data structure corresponding to the initial reset state of the
+            environment.
+        """
+        new_config = self.param_manager.get_current_samplers()
+        env_manager.reset(config=new_config)
+        # Register any new behavior ids that were generated on the reset.
+        self._register_new_behaviors(env_manager, env_manager.first_step_infos)
+
+    def _not_done_training(self) -> bool:
+        return (
+            any(t.should_still_train for t in self.trainers.values())
+            or not self.train_model
+        ) or len(self.trainers) == 0
+
+    def _create_trainer_and_manager(
+        self, env_manager: EnvManager, name_behavior_id: str
+    ) -> None:
+
+        parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id)
+        brain_name = parsed_behavior_id.brain_name
+        trainerthread = None
+        if brain_name in self.trainers:
+            trainer = self.trainers[brain_name]
+        else:
+            trainer = self.trainer_factory.generate(brain_name)
+            self.trainers[brain_name] = trainer
+            if trainer.threaded:
+                # Only create trainer thread for new trainers
+                trainerthread = threading.Thread(
+                    target=self.trainer_update_func, args=(trainer,), daemon=True
+                )
+                self.trainer_threads.append(trainerthread)
+            env_manager.on_training_started(
+                brain_name, self.trainer_factory.trainer_config[brain_name]
+            )
+
+        policy = trainer.create_policy(
+            parsed_behavior_id,
+            env_manager.training_behaviors[name_behavior_id],
+        )
+        trainer.add_policy(parsed_behavior_id, policy)
+
+        agent_manager = AgentManager(
+            policy,
+            name_behavior_id,
+            trainer.stats_reporter,
+            trainer.parameters.time_horizon,
+            threaded=trainer.threaded,
+        )
+        env_manager.set_agent_manager(name_behavior_id, agent_manager)
+        env_manager.set_policy(name_behavior_id, policy)
+        self.brain_name_to_identifier[brain_name].add(name_behavior_id)
+
+        trainer.publish_policy_queue(agent_manager.policy_queue)
+        trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
+
+        # Only start new trainers
+        if trainerthread is not None:
+            trainerthread.start()
+
+    def _create_trainers_and_managers(
+        self, env_manager: EnvManager, behavior_ids: Set[str]
+    ) -> None:
+        for behavior_id in behavior_ids:
+            self._create_trainer_and_manager(env_manager, behavior_id)
+
+    @timed
+    def start_learning(self, env_manager: EnvManager) -> None:
+        self._create_output_path(self.output_path)
+        try:
+            # Initial reset
+            self._reset_env(env_manager)
+            self.param_manager.log_current_lesson()
+            while self._not_done_training():
+                n_steps = self.advance(env_manager)
+                for _ in range(n_steps):
+                    self.reset_env_if_ready(env_manager)
+            # Stop advancing trainers
+            self.join_threads()
+        except (
+            KeyboardInterrupt,
+            UnityCommunicationException,
+            UnityEnvironmentException,
+            UnityCommunicatorStoppedException,
+        ) as ex:
+            self.join_threads()
+            self.logger.info(
+                "Learning was interrupted. Please wait while the graph is generated."
+            )
+            if isinstance(ex, KeyboardInterrupt) or isinstance(
+                ex, UnityCommunicatorStoppedException
+            ):
+                pass
+            else:
+                # If the environment failed, we want to make sure to raise
+                # the exception so we exit the process with an return code of 1.
+                raise ex
+        finally:
+            if self.train_model:
+                self._save_models()
+
+    def end_trainer_episodes(self) -> None:
+        # Reward buffers reset takes place only for curriculum learning
+        # else no reset.
+        for trainer in self.trainers.values():
+            trainer.end_episode()
+
+    def reset_env_if_ready(self, env: EnvManager) -> None:
+        # Get the sizes of the reward buffers.
+        reward_buff = {k: list(t.reward_buffer) for (k, t) in self.trainers.items()}
+        curr_step = {k: int(t.get_step) for (k, t) in self.trainers.items()}
+        max_step = {k: int(t.get_max_steps) for (k, t) in self.trainers.items()}
+        # Attempt to increment the lessons of the brains who
+        # were ready.
+        updated, param_must_reset = self.param_manager.update_lessons(
+            curr_step, max_step, reward_buff
+        )
+        if updated:
+            for trainer in self.trainers.values():
+                trainer.reward_buffer.clear()
+        # If ghost trainer swapped teams
+        ghost_controller_reset = self.ghost_controller.should_reset()
+        if param_must_reset or ghost_controller_reset:
+            self._reset_env(env)  # This reset also sends the new config to env
+            self.end_trainer_episodes()
+        elif updated:
+            env.set_env_parameters(self.param_manager.get_current_samplers())
+
+    @timed
+    def advance(self, env_manager: EnvManager) -> int:
+        # Get steps
+        with hierarchical_timer("env_step"):
+            new_step_infos = env_manager.get_steps()
+            self._register_new_behaviors(env_manager, new_step_infos)
+            num_steps = env_manager.process_steps(new_step_infos)
+
+        # Report current lesson for each environment parameter
+        for (
+            param_name,
+            lesson_number,
+        ) in self.param_manager.get_current_lesson_number().items():
+            for trainer in self.trainers.values():
+                trainer.stats_reporter.set_stat(
+                    f"Environment/Lesson Number/{param_name}", lesson_number
+                )
+
+        for trainer in self.trainers.values():
+            if not trainer.threaded:
+                with hierarchical_timer("trainer_advance"):
+                    trainer.advance()
+
+        return num_steps
+
+    def _register_new_behaviors(
+        self, env_manager: EnvManager, step_infos: List[EnvironmentStep]
+    ) -> None:
+        """
+        Handle registration (adding trainers and managers) of new behaviors ids.
+        :param env_manager:
+        :param step_infos:
+        :return:
+        """
+        step_behavior_ids: Set[str] = set()
+        for s in step_infos:
+            step_behavior_ids |= set(s.name_behavior_ids)
+        new_behavior_ids = step_behavior_ids - self.registered_behavior_ids
+        self._create_trainers_and_managers(env_manager, new_behavior_ids)
+        self.registered_behavior_ids |= step_behavior_ids
+
+    def join_threads(self, timeout_seconds: float = 1.0) -> None:
+        """
+        Wait for threads to finish, and merge their timer information into the main thread.
+        :param timeout_seconds:
+        :return:
+        """
+        self.kill_trainers = True
+        for t in self.trainer_threads:
+            try:
+                t.join(timeout_seconds)
+            except Exception:
+                pass
+
+        with hierarchical_timer("trainer_threads") as main_timer_node:
+            for trainer_thread in self.trainer_threads:
+                thread_timer_stack = get_timer_stack_for_thread(trainer_thread)
+                if thread_timer_stack:
+                    main_timer_node.merge(
+                        thread_timer_stack.root,
+                        root_name="thread_root",
+                        is_parallel=True,
+                    )
+                    merge_gauges(thread_timer_stack.gauges)
+
+    def trainer_update_func(self, trainer: Trainer) -> None:
+        while not self.kill_trainers:
+            with hierarchical_timer("trainer_advance"):
+                trainer.advance()
diff --git a/ml-agents/mlagents/trainers/training_analytics_side_channel.py b/ml-agents/mlagents/trainers/training_analytics_side_channel.py
new file mode 100644
index 0000000000000000000000000000000000000000..84edad717534342d943feffe6a3c44de270a1f61
--- /dev/null
+++ b/ml-agents/mlagents/trainers/training_analytics_side_channel.py
@@ -0,0 +1,188 @@
+import copy
+import json
+import hmac
+import hashlib
+import sys
+from typing import Optional, Dict
+import mlagents_envs
+import mlagents.trainers
+from mlagents import torch_utils
+from mlagents.trainers.settings import RewardSignalType
+from mlagents_envs.exception import UnityCommunicationException
+from mlagents_envs.side_channel import (
+    IncomingMessage,
+    OutgoingMessage,
+    DefaultTrainingAnalyticsSideChannel,
+)
+from mlagents_envs.communicator_objects.training_analytics_pb2 import (
+    TrainingEnvironmentInitialized,
+    TrainingBehaviorInitialized,
+)
+from google.protobuf.any_pb2 import Any
+
+from mlagents.trainers.settings import TrainerSettings, RunOptions
+
+
+class TrainingAnalyticsSideChannel(DefaultTrainingAnalyticsSideChannel):
+    """
+    Side channel that sends information about the training to the Unity environment so it can be logged.
+    """
+
+    __vendorKey: str = "unity.ml-agents"
+
+    def __init__(self) -> None:
+        # >>> uuid.uuid5(uuid.NAMESPACE_URL, "com.unity.ml-agents/TrainingAnalyticsSideChannel")
+        # UUID('b664a4a9-d86f-5a5f-95cb-e8353a7e8356')
+        # Use the same uuid as the parent side channel
+        super().__init__()
+        self.run_options: Optional[RunOptions] = None
+
+    @classmethod
+    def _hash(cls, data: str) -> str:
+        res = hmac.new(
+            cls.__vendorKey.encode("utf-8"), data.encode("utf-8"), hashlib.sha256
+        ).hexdigest()
+        return res
+
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        raise UnityCommunicationException(
+            "The TrainingAnalyticsSideChannel received a message from Unity, "
+            "this should not have happened."
+        )
+
+    @classmethod
+    def _sanitize_run_options(cls, config: RunOptions) -> Dict[str, Any]:
+        res = copy.deepcopy(config.as_dict())
+
+        # Filter potentially PII behavior names
+        if "behaviors" in res and res["behaviors"]:
+            res["behaviors"] = {cls._hash(k): v for (k, v) in res["behaviors"].items()}
+            for (k, v) in res["behaviors"].items():
+                if "init_path" in v and v["init_path"] is not None:
+                    hashed_path = cls._hash(v["init_path"])
+                    res["behaviors"][k]["init_path"] = hashed_path
+                if "demo_path" in v and v["demo_path"] is not None:
+                    hashed_path = cls._hash(v["demo_path"])
+                    res["behaviors"][k]["demo_path"] = hashed_path
+
+        # Filter potentially PII curriculum and behavior names from Checkpoint Settings
+        if "environment_parameters" in res and res["environment_parameters"]:
+            res["environment_parameters"] = {
+                cls._hash(k): v for (k, v) in res["environment_parameters"].items()
+            }
+            for (curriculumName, curriculum) in res["environment_parameters"].items():
+                updated_lessons = []
+                for lesson in curriculum["curriculum"]:
+                    new_lesson = copy.deepcopy(lesson)
+                    if "name" in lesson:
+                        new_lesson["name"] = cls._hash(lesson["name"])
+                    if (
+                        "completion_criteria" in lesson
+                        and lesson["completion_criteria"] is not None
+                    ):
+                        new_lesson["completion_criteria"]["behavior"] = cls._hash(
+                            new_lesson["completion_criteria"]["behavior"]
+                        )
+                    updated_lessons.append(new_lesson)
+                res["environment_parameters"][curriculumName][
+                    "curriculum"
+                ] = updated_lessons
+
+        # Filter potentially PII filenames from Checkpoint Settings
+        if "checkpoint_settings" in res and res["checkpoint_settings"] is not None:
+            if (
+                "initialize_from" in res["checkpoint_settings"]
+                and res["checkpoint_settings"]["initialize_from"] is not None
+            ):
+                res["checkpoint_settings"]["initialize_from"] = cls._hash(
+                    res["checkpoint_settings"]["initialize_from"]
+                )
+            if (
+                "results_dir" in res["checkpoint_settings"]
+                and res["checkpoint_settings"]["results_dir"] is not None
+            ):
+                res["checkpoint_settings"]["results_dir"] = hash(
+                    res["checkpoint_settings"]["results_dir"]
+                )
+
+        return res
+
+    def environment_initialized(self, run_options: RunOptions) -> None:
+        self.run_options = run_options
+        # Tuple of (major, minor, patch)
+        vi = sys.version_info
+        env_params = run_options.environment_parameters
+        sanitized_run_options = self._sanitize_run_options(run_options)
+
+        msg = TrainingEnvironmentInitialized(
+            python_version=f"{vi[0]}.{vi[1]}.{vi[2]}",
+            mlagents_version=mlagents.trainers.__version__,
+            mlagents_envs_version=mlagents_envs.__version__,
+            torch_version=torch_utils.torch.__version__,
+            torch_device_type=torch_utils.default_device().type,
+            num_envs=run_options.env_settings.num_envs,
+            num_environment_parameters=len(env_params) if env_params else 0,
+            run_options=json.dumps(sanitized_run_options),
+        )
+
+        any_message = Any()
+        any_message.Pack(msg)
+
+        env_init_msg = OutgoingMessage()
+        env_init_msg.set_raw_bytes(any_message.SerializeToString())
+        super().queue_message_to_send(env_init_msg)
+
+    @classmethod
+    def _sanitize_trainer_settings(cls, config: TrainerSettings) -> Dict[str, Any]:
+        config_dict = copy.deepcopy(config.as_dict())
+        if "init_path" in config_dict and config_dict["init_path"] is not None:
+            hashed_path = cls._hash(config_dict["init_path"])
+            config_dict["init_path"] = hashed_path
+        if "demo_path" in config_dict and config_dict["demo_path"] is not None:
+            hashed_path = cls._hash(config_dict["demo_path"])
+            config_dict["demo_path"] = hashed_path
+        return config_dict
+
+    def training_started(self, behavior_name: str, config: TrainerSettings) -> None:
+        raw_config = self._sanitize_trainer_settings(config)
+        msg = TrainingBehaviorInitialized(
+            behavior_name=self._hash(behavior_name),
+            trainer_type=config.trainer_type,
+            extrinsic_reward_enabled=(
+                RewardSignalType.EXTRINSIC in config.reward_signals
+            ),
+            gail_reward_enabled=(RewardSignalType.GAIL in config.reward_signals),
+            curiosity_reward_enabled=(
+                RewardSignalType.CURIOSITY in config.reward_signals
+            ),
+            rnd_reward_enabled=(RewardSignalType.RND in config.reward_signals),
+            behavioral_cloning_enabled=config.behavioral_cloning is not None,
+            recurrent_enabled=config.network_settings.memory is not None,
+            visual_encoder=config.network_settings.vis_encode_type.value,
+            num_network_layers=config.network_settings.num_layers,
+            num_network_hidden_units=config.network_settings.hidden_units,
+            trainer_threaded=config.threaded,
+            self_play_enabled=config.self_play is not None,
+            curriculum_enabled=self._behavior_uses_curriculum(behavior_name),
+            config=json.dumps(raw_config),
+        )
+
+        any_message = Any()
+        any_message.Pack(msg)
+
+        training_start_msg = OutgoingMessage()
+        training_start_msg.set_raw_bytes(any_message.SerializeToString())
+
+        super().queue_message_to_send(training_start_msg)
+
+    def _behavior_uses_curriculum(self, behavior_name: str) -> bool:
+        if not self.run_options or not self.run_options.environment_parameters:
+            return False
+
+        for param_settings in self.run_options.environment_parameters.values():
+            for lesson in param_settings.curriculum:
+                cc = lesson.completion_criteria
+                if cc and cc.behavior == behavior_name:
+                    return True
+
+        return False
diff --git a/ml-agents/mlagents/trainers/training_status.py b/ml-agents/mlagents/trainers/training_status.py
new file mode 100644
index 0000000000000000000000000000000000000000..06bd73cd23af6e838675f815d859cc6e184a8f2b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/training_status.py
@@ -0,0 +1,118 @@
+from typing import Dict, Any
+from enum import Enum
+from collections import defaultdict
+import json
+import attr
+import cattr
+
+from mlagents.torch_utils import torch
+from mlagents_envs.logging_util import get_logger
+from mlagents.trainers import __version__
+from mlagents.trainers.exception import TrainerError
+
+logger = get_logger(__name__)
+
+STATUS_FORMAT_VERSION = "0.3.0"
+
+
+class StatusType(Enum):
+    LESSON_NUM = "lesson_num"
+    STATS_METADATA = "metadata"
+    CHECKPOINTS = "checkpoints"
+    FINAL_CHECKPOINT = "final_checkpoint"
+    ELO = "elo"
+
+
+@attr.s(auto_attribs=True)
+class StatusMetaData:
+    stats_format_version: str = STATUS_FORMAT_VERSION
+    mlagents_version: str = __version__
+    torch_version: str = torch.__version__
+
+    def to_dict(self) -> Dict[str, str]:
+        return cattr.unstructure(self)
+
+    @staticmethod
+    def from_dict(import_dict: Dict[str, str]) -> "StatusMetaData":
+        return cattr.structure(import_dict, StatusMetaData)
+
+    def check_compatibility(self, other: "StatusMetaData") -> None:
+        """
+        Check compatibility with a loaded StatsMetaData and warn the user
+        if versions mismatch. This is used for resuming from old checkpoints.
+        """
+        # This should cover all stats version mismatches as well.
+        if self.mlagents_version != other.mlagents_version:
+            logger.warning(
+                "Checkpoint was loaded from a different version of ML-Agents. Some things may not resume properly."
+            )
+        if self.torch_version != other.torch_version:
+            logger.warning(
+                "PyTorch checkpoint was saved with a different version of PyTorch. Model may not resume properly."
+            )
+
+
+class GlobalTrainingStatus:
+    """
+    GlobalTrainingStatus class that contains static methods to save global training status and
+    load it on a resume. These are values that might be needed for the training resume that
+    cannot/should not be captured in a model checkpoint, such as curriclum lesson.
+    """
+
+    saved_state: Dict[str, Dict[str, Any]] = defaultdict(lambda: {})
+
+    @staticmethod
+    def load_state(path: str) -> None:
+        """
+        Load a JSON file that contains saved state.
+        :param path: Path to the JSON file containing the state.
+        """
+        try:
+            with open(path) as f:
+                loaded_dict = json.load(f)
+            # Compare the metadata
+            _metadata = loaded_dict[StatusType.STATS_METADATA.value]
+            StatusMetaData.from_dict(_metadata).check_compatibility(StatusMetaData())
+            # Update saved state.
+            GlobalTrainingStatus.saved_state.update(loaded_dict)
+        except FileNotFoundError:
+            logger.warning(
+                "Training status file not found. Not all functions will resume properly."
+            )
+        except KeyError:
+            raise TrainerError(
+                "Metadata not found, resuming from an incompatible version of ML-Agents."
+            )
+
+    @staticmethod
+    def save_state(path: str) -> None:
+        """
+        Save a JSON file that contains saved state.
+        :param path: Path to the JSON file containing the state.
+        """
+        GlobalTrainingStatus.saved_state[
+            StatusType.STATS_METADATA.value
+        ] = StatusMetaData().to_dict()
+        with open(path, "w") as f:
+            json.dump(GlobalTrainingStatus.saved_state, f, indent=4)
+
+    @staticmethod
+    def set_parameter_state(category: str, key: StatusType, value: Any) -> None:
+        """
+        Stores an arbitrary-named parameter in the global saved state.
+        :param category: The category (usually behavior name) of the parameter.
+        :param key: The parameter, e.g. lesson number.
+        :param value: The value.
+        """
+        GlobalTrainingStatus.saved_state[category][key.value] = value
+
+    @staticmethod
+    def get_parameter_state(category: str, key: StatusType) -> Any:
+        """
+        Loads an arbitrary-named parameter from training_status.json.
+        If not found, returns None.
+        :param category: The category (usually behavior name) of the parameter.
+        :param key: The statistic, e.g. lesson number.
+        :param value: The value.
+        """
+        return GlobalTrainingStatus.saved_state[category].get(key.value, None)
diff --git a/ml-agents/mlagents/trainers/trajectory.py b/ml-agents/mlagents/trainers/trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a08bc24b497585357b4273b7d048a4218161ac0
--- /dev/null
+++ b/ml-agents/mlagents/trainers/trajectory.py
@@ -0,0 +1,313 @@
+from typing import List, NamedTuple
+import numpy as np
+
+from mlagents.trainers.buffer import (
+    AgentBuffer,
+    ObservationKeyPrefix,
+    AgentBufferKey,
+    BufferKey,
+)
+from mlagents_envs.base_env import ActionTuple
+from mlagents.trainers.torch_entities.action_log_probs import LogProbsTuple
+
+
+class AgentStatus(NamedTuple):
+    """
+    Stores observation, action, and reward for an agent. Does not have additional
+    fields that are present in AgentExperience.
+    """
+
+    obs: List[np.ndarray]
+    reward: float
+    action: ActionTuple
+    done: bool
+
+
+class AgentExperience(NamedTuple):
+    """
+    Stores the full amount of data for an agent in one timestep. Includes
+    the status' of group mates and the group reward, as well as the probabilities
+    outputted by the policy.
+    """
+
+    obs: List[np.ndarray]
+    reward: float
+    done: bool
+    action: ActionTuple
+    action_probs: LogProbsTuple
+    action_mask: np.ndarray
+    prev_action: np.ndarray
+    interrupted: bool
+    memory: np.ndarray
+    group_status: List[AgentStatus]
+    group_reward: float
+
+
+class ObsUtil:
+    @staticmethod
+    def get_name_at(index: int) -> AgentBufferKey:
+        """
+        returns the name of the observation given the index of the observation
+        """
+        return ObservationKeyPrefix.OBSERVATION, index
+
+    @staticmethod
+    def get_name_at_next(index: int) -> AgentBufferKey:
+        """
+        returns the name of the next observation given the index of the observation
+        """
+        return ObservationKeyPrefix.NEXT_OBSERVATION, index
+
+    @staticmethod
+    def from_buffer(batch: AgentBuffer, num_obs: int) -> List[np.array]:
+        """
+        Creates the list of observations from an AgentBuffer
+        """
+        result: List[np.array] = []
+        for i in range(num_obs):
+            result.append(batch[ObsUtil.get_name_at(i)])
+        return result
+
+    @staticmethod
+    def from_buffer_next(batch: AgentBuffer, num_obs: int) -> List[np.array]:
+        """
+        Creates the list of next observations from an AgentBuffer
+        """
+        result = []
+        for i in range(num_obs):
+            result.append(batch[ObsUtil.get_name_at_next(i)])
+        return result
+
+
+class GroupObsUtil:
+    @staticmethod
+    def get_name_at(index: int) -> AgentBufferKey:
+        """
+        returns the name of the observation given the index of the observation
+        """
+        return ObservationKeyPrefix.GROUP_OBSERVATION, index
+
+    @staticmethod
+    def get_name_at_next(index: int) -> AgentBufferKey:
+        """
+        returns the name of the next team observation given the index of the observation
+        """
+        return ObservationKeyPrefix.NEXT_GROUP_OBSERVATION, index
+
+    @staticmethod
+    def _transpose_list_of_lists(
+        list_list: List[List[np.ndarray]],
+    ) -> List[List[np.ndarray]]:
+        return list(map(list, zip(*list_list)))
+
+    @staticmethod
+    def from_buffer(batch: AgentBuffer, num_obs: int) -> List[np.array]:
+        """
+        Creates the list of observations from an AgentBuffer
+        """
+        separated_obs: List[np.array] = []
+        for i in range(num_obs):
+            separated_obs.append(
+                batch[GroupObsUtil.get_name_at(i)].padded_to_batch(pad_value=np.nan)
+            )
+        # separated_obs contains a List(num_obs) of Lists(num_agents), we want to flip
+        # that and get a List(num_agents) of Lists(num_obs)
+        result = GroupObsUtil._transpose_list_of_lists(separated_obs)
+        return result
+
+    @staticmethod
+    def from_buffer_next(batch: AgentBuffer, num_obs: int) -> List[np.array]:
+        """
+        Creates the list of observations from an AgentBuffer
+        """
+        separated_obs: List[np.array] = []
+        for i in range(num_obs):
+            separated_obs.append(
+                batch[GroupObsUtil.get_name_at_next(i)].padded_to_batch(
+                    pad_value=np.nan
+                )
+            )
+        # separated_obs contains a List(num_obs) of Lists(num_agents), we want to flip
+        # that and get a List(num_agents) of Lists(num_obs)
+        result = GroupObsUtil._transpose_list_of_lists(separated_obs)
+        return result
+
+
+class Trajectory(NamedTuple):
+    steps: List[AgentExperience]
+    next_obs: List[
+        np.ndarray
+    ]  # Observation following the trajectory, for bootstrapping
+    next_group_obs: List[List[np.ndarray]]
+    agent_id: str
+    behavior_id: str
+
+    def to_agentbuffer(self) -> AgentBuffer:
+        """
+        Converts a Trajectory to an AgentBuffer
+        :param trajectory: A Trajectory
+        :returns: AgentBuffer. Note that the length of the AgentBuffer will be one
+        less than the trajectory, as the next observation need to be populated from the last
+        step of the trajectory.
+        """
+        agent_buffer_trajectory = AgentBuffer()
+        obs = self.steps[0].obs
+        for step, exp in enumerate(self.steps):
+            is_last_step = step == len(self.steps) - 1
+            if not is_last_step:
+                next_obs = self.steps[step + 1].obs
+            else:
+                next_obs = self.next_obs
+
+            num_obs = len(obs)
+            for i in range(num_obs):
+                agent_buffer_trajectory[ObsUtil.get_name_at(i)].append(obs[i])
+                agent_buffer_trajectory[ObsUtil.get_name_at_next(i)].append(next_obs[i])
+
+            # Take care of teammate obs and actions
+            teammate_continuous_actions, teammate_discrete_actions, teammate_rewards = (
+                [],
+                [],
+                [],
+            )
+            for group_status in exp.group_status:
+                teammate_rewards.append(group_status.reward)
+                teammate_continuous_actions.append(group_status.action.continuous)
+                teammate_discrete_actions.append(group_status.action.discrete)
+
+            # Team actions
+            agent_buffer_trajectory[BufferKey.GROUP_CONTINUOUS_ACTION].append(
+                teammate_continuous_actions
+            )
+            agent_buffer_trajectory[BufferKey.GROUP_DISCRETE_ACTION].append(
+                teammate_discrete_actions
+            )
+            agent_buffer_trajectory[BufferKey.GROUPMATE_REWARDS].append(
+                teammate_rewards
+            )
+            agent_buffer_trajectory[BufferKey.GROUP_REWARD].append(exp.group_reward)
+
+            # Next actions
+            teammate_cont_next_actions = []
+            teammate_disc_next_actions = []
+            if not is_last_step:
+                next_exp = self.steps[step + 1]
+                for group_status in next_exp.group_status:
+                    teammate_cont_next_actions.append(group_status.action.continuous)
+                    teammate_disc_next_actions.append(group_status.action.discrete)
+            else:
+                for group_status in exp.group_status:
+                    teammate_cont_next_actions.append(group_status.action.continuous)
+                    teammate_disc_next_actions.append(group_status.action.discrete)
+
+            agent_buffer_trajectory[BufferKey.GROUP_NEXT_CONT_ACTION].append(
+                teammate_cont_next_actions
+            )
+            agent_buffer_trajectory[BufferKey.GROUP_NEXT_DISC_ACTION].append(
+                teammate_disc_next_actions
+            )
+
+            for i in range(num_obs):
+                ith_group_obs = []
+                for _group_status in exp.group_status:
+                    # Assume teammates have same obs space
+                    ith_group_obs.append(_group_status.obs[i])
+                agent_buffer_trajectory[GroupObsUtil.get_name_at(i)].append(
+                    ith_group_obs
+                )
+
+                ith_group_obs_next = []
+                if is_last_step:
+                    for _obs in self.next_group_obs:
+                        ith_group_obs_next.append(_obs[i])
+                else:
+                    next_group_status = self.steps[step + 1].group_status
+                    for _group_status in next_group_status:
+                        # Assume teammates have same obs space
+                        ith_group_obs_next.append(_group_status.obs[i])
+                agent_buffer_trajectory[GroupObsUtil.get_name_at_next(i)].append(
+                    ith_group_obs_next
+                )
+
+            if exp.memory is not None:
+                agent_buffer_trajectory[BufferKey.MEMORY].append(exp.memory)
+
+            agent_buffer_trajectory[BufferKey.MASKS].append(1.0)
+            agent_buffer_trajectory[BufferKey.DONE].append(exp.done)
+            agent_buffer_trajectory[BufferKey.GROUP_DONES].append(
+                [_status.done for _status in exp.group_status]
+            )
+
+            # Adds the log prob and action of continuous/discrete separately
+            agent_buffer_trajectory[BufferKey.CONTINUOUS_ACTION].append(
+                exp.action.continuous
+            )
+            agent_buffer_trajectory[BufferKey.DISCRETE_ACTION].append(
+                exp.action.discrete
+            )
+
+            if not is_last_step:
+                next_action = self.steps[step + 1].action
+                cont_next_actions = next_action.continuous
+                disc_next_actions = next_action.discrete
+            else:
+                cont_next_actions = np.zeros_like(exp.action.continuous)
+                disc_next_actions = np.zeros_like(exp.action.discrete)
+
+            agent_buffer_trajectory[BufferKey.NEXT_CONT_ACTION].append(
+                cont_next_actions
+            )
+            agent_buffer_trajectory[BufferKey.NEXT_DISC_ACTION].append(
+                disc_next_actions
+            )
+
+            agent_buffer_trajectory[BufferKey.CONTINUOUS_LOG_PROBS].append(
+                exp.action_probs.continuous
+            )
+            agent_buffer_trajectory[BufferKey.DISCRETE_LOG_PROBS].append(
+                exp.action_probs.discrete
+            )
+
+            # Store action masks if necessary. Note that 1 means active, while
+            # in AgentExperience False means active.
+            if exp.action_mask is not None:
+                mask = 1 - np.concatenate(exp.action_mask)
+                agent_buffer_trajectory[BufferKey.ACTION_MASK].append(
+                    mask, padding_value=1
+                )
+            else:
+                # This should never be needed unless the environment somehow doesn't supply the
+                # action mask in a discrete space.
+
+                action_shape = exp.action.discrete.shape
+                agent_buffer_trajectory[BufferKey.ACTION_MASK].append(
+                    np.ones(action_shape, dtype=np.float32), padding_value=1
+                )
+            agent_buffer_trajectory[BufferKey.PREV_ACTION].append(exp.prev_action)
+            agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS].append(exp.reward)
+
+            # Store the next visual obs as the current
+            obs = next_obs
+        return agent_buffer_trajectory
+
+    @property
+    def done_reached(self) -> bool:
+        """
+        Returns true if trajectory is terminated with a Done.
+        """
+        return self.steps[-1].done
+
+    @property
+    def all_group_dones_reached(self) -> bool:
+        """
+        Returns true if all other agents in this trajectory are done at the end of the trajectory.
+        Combine with done_reached to check if the whole team is done.
+        """
+        return all(_status.done for _status in self.steps[-1].group_status)
+
+    @property
+    def interrupted(self) -> bool:
+        """
+        Returns true if trajectory was terminated because max steps was reached.
+        """
+        return self.steps[-1].interrupted
diff --git a/ml-agents/mlagents/trainers/upgrade_config.py b/ml-agents/mlagents/trainers/upgrade_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..d07ce0016d11076a8dad6d8ea206248ff1bacc4b
--- /dev/null
+++ b/ml-agents/mlagents/trainers/upgrade_config.py
@@ -0,0 +1,251 @@
+# NOTE: This upgrade script is a temporary measure for the transition between the old-format
+# configuration file and the new format. It will be marked for deprecation once the
+# Python CLI and configuration files are finalized, and removed the following release.
+
+import attr
+import cattr
+import yaml
+from typing import Dict, Any, Optional
+import argparse
+from mlagents.trainers.settings import TrainerSettings, NetworkSettings
+from mlagents.trainers.cli_utils import load_config
+from mlagents.trainers.exception import TrainerConfigError
+from mlagents.plugins import all_trainer_settings
+
+
+# Take an existing trainer config (e.g. trainer_config.yaml) and turn it into the new format.
+def convert_behaviors(old_trainer_config: Dict[str, Any]) -> Dict[str, Any]:
+    all_behavior_config_dict = {}
+    default_config = old_trainer_config.get("default", {})
+    for behavior_name, config in old_trainer_config.items():
+        if behavior_name != "default":
+            config = default_config.copy()
+            config.update(old_trainer_config[behavior_name])
+
+            # Convert to split TrainerSettings, Hyperparameters, NetworkSettings
+            # Set trainer_type and get appropriate hyperparameter settings
+            try:
+                trainer_type = config["trainer"]
+            except KeyError:
+                raise TrainerConfigError(
+                    "Config doesn't specify a trainer type. "
+                    "Please specify trainer: in your config."
+                )
+            new_config = {}
+            new_config["trainer_type"] = trainer_type
+            hyperparam_cls = all_trainer_settings[trainer_type]
+            # Try to absorb as much as possible into the hyperparam_cls
+            new_config["hyperparameters"] = cattr.structure(config, hyperparam_cls)
+
+            # Try to absorb as much as possible into the network settings
+            new_config["network_settings"] = cattr.structure(config, NetworkSettings)
+            # Deal with recurrent
+            try:
+                if config["use_recurrent"]:
+                    new_config[
+                        "network_settings"
+                    ].memory = NetworkSettings.MemorySettings(
+                        sequence_length=config["sequence_length"],
+                        memory_size=config["memory_size"],
+                    )
+            except KeyError:
+                raise TrainerConfigError(
+                    "Config doesn't specify use_recurrent. "
+                    "Please specify true or false for use_recurrent in your config."
+                )
+            # Absorb the rest into the base TrainerSettings
+            for key, val in config.items():
+                if key in attr.fields_dict(TrainerSettings):
+                    new_config[key] = val
+
+            # Structure the whole thing
+            all_behavior_config_dict[behavior_name] = cattr.structure(
+                new_config, TrainerSettings
+            )
+    return all_behavior_config_dict
+
+
+def write_to_yaml_file(unstructed_config: Dict[str, Any], output_config: str) -> None:
+    with open(output_config, "w") as f:
+        try:
+            yaml.dump(unstructed_config, f, sort_keys=False)
+        except TypeError:  # Older versions of pyyaml don't support sort_keys
+            yaml.dump(unstructed_config, f)
+
+
+def remove_nones(config: Dict[Any, Any]) -> Dict[str, Any]:
+    new_config = {}
+    for key, val in config.items():
+        if isinstance(val, dict):
+            new_config[key] = remove_nones(val)
+        elif val is not None:
+            new_config[key] = val
+    return new_config
+
+
+# Take a sampler from the old format and convert to new sampler structure
+def convert_samplers(old_sampler_config: Dict[str, Any]) -> Dict[str, Any]:
+    new_sampler_config: Dict[str, Any] = {}
+    for parameter, parameter_config in old_sampler_config.items():
+        if parameter == "resampling-interval":
+            print(
+                "resampling-interval is no longer necessary for parameter randomization and is being ignored."
+            )
+            continue
+        new_sampler_config[parameter] = {}
+        new_sampler_config[parameter]["sampler_type"] = parameter_config["sampler-type"]
+        new_samp_parameters = dict(parameter_config)  # Copy dict
+        new_samp_parameters.pop("sampler-type")
+        new_sampler_config[parameter]["sampler_parameters"] = new_samp_parameters
+    return new_sampler_config
+
+
+def convert_samplers_and_curriculum(
+    parameter_dict: Dict[str, Any], curriculum: Dict[str, Any]
+) -> Dict[str, Any]:
+    for key, sampler in parameter_dict.items():
+        if "sampler_parameters" not in sampler:
+            parameter_dict[key]["sampler_parameters"] = {}
+        for argument in [
+            "seed",
+            "min_value",
+            "max_value",
+            "mean",
+            "st_dev",
+            "intervals",
+        ]:
+            if argument in sampler:
+                parameter_dict[key]["sampler_parameters"][argument] = sampler[argument]
+                parameter_dict[key].pop(argument)
+    param_set = set(parameter_dict.keys())
+    for behavior_name, behavior_dict in curriculum.items():
+        measure = behavior_dict["measure"]
+        min_lesson_length = behavior_dict.get("min_lesson_length", 1)
+        signal_smoothing = behavior_dict.get("signal_smoothing", False)
+        thresholds = behavior_dict["thresholds"]
+        num_lessons = len(thresholds) + 1
+        parameters = behavior_dict["parameters"]
+        for param_name in parameters.keys():
+            if param_name in param_set:
+                print(
+                    f"The parameter {param_name} has both a sampler and a curriculum. Will ignore curriculum"
+                )
+            else:
+                param_set.add(param_name)
+                parameter_dict[param_name] = {"curriculum": []}
+                for lesson_index in range(num_lessons - 1):
+                    parameter_dict[param_name]["curriculum"].append(
+                        {
+                            f"Lesson{lesson_index}": {
+                                "completion_criteria": {
+                                    "measure": measure,
+                                    "behavior": behavior_name,
+                                    "signal_smoothing": signal_smoothing,
+                                    "min_lesson_length": min_lesson_length,
+                                    "threshold": thresholds[lesson_index],
+                                },
+                                "value": parameters[param_name][lesson_index],
+                            }
+                        }
+                    )
+                lesson_index += 1  # This is the last lesson
+                parameter_dict[param_name]["curriculum"].append(
+                    {
+                        f"Lesson{lesson_index}": {
+                            "value": parameters[param_name][lesson_index]
+                        }
+                    }
+                )
+    return parameter_dict
+
+
+def parse_args():
+    argparser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    argparser.add_argument(
+        "trainer_config_path",
+        help="Path to old format (<=0.18.X) trainer configuration YAML.",
+    )
+    argparser.add_argument(
+        "--curriculum",
+        help="Path to old format (<=0.16.X) curriculum configuration YAML.",
+        default=None,
+    )
+    argparser.add_argument(
+        "--sampler",
+        help="Path to old format (<=0.16.X) parameter randomization configuration YAML.",
+        default=None,
+    )
+    argparser.add_argument(
+        "output_config_path", help="Path to write converted YAML file."
+    )
+    args = argparser.parse_args()
+    return args
+
+
+def convert(
+    config: Dict[str, Any],
+    old_curriculum: Optional[Dict[str, Any]],
+    old_param_random: Optional[Dict[str, Any]],
+) -> Dict[str, Any]:
+    if "behaviors" not in config:
+        print("Config file format version :  version <= 0.16.X")
+        behavior_config_dict = convert_behaviors(config)
+        full_config = {"behaviors": behavior_config_dict}
+
+        # Convert curriculum and sampler. note that we don't validate these; if it was correct
+        # before it should be correct now.
+        if old_curriculum is not None:
+            full_config["curriculum"] = old_curriculum
+
+        if old_param_random is not None:
+            sampler_config_dict = convert_samplers(old_param_random)
+            full_config["parameter_randomization"] = sampler_config_dict
+
+        # Convert config to dict
+        config = cattr.unstructure(full_config)
+    if "curriculum" in config or "parameter_randomization" in config:
+        print("Config file format version :  0.16.X < version <= 0.18.X")
+        full_config = {"behaviors": config["behaviors"]}
+
+        param_randomization = config.get("parameter_randomization", {})
+        if "resampling-interval" in param_randomization:
+            param_randomization.pop("resampling-interval")
+        if len(param_randomization) > 0:
+            # check if we use the old format sampler-type vs sampler_type
+            if (
+                "sampler-type"
+                in param_randomization[list(param_randomization.keys())[0]]
+            ):
+                param_randomization = convert_samplers(param_randomization)
+
+        full_config["environment_parameters"] = convert_samplers_and_curriculum(
+            param_randomization, config.get("curriculum", {})
+        )
+
+        # Convert config to dict
+        config = cattr.unstructure(full_config)
+    return config
+
+
+def main() -> None:
+    args = parse_args()
+    print(
+        f"Converting {args.trainer_config_path} and saving to {args.output_config_path}."
+    )
+
+    old_config = load_config(args.trainer_config_path)
+    curriculum_config_dict = None
+    old_sampler_config_dict = None
+    if args.curriculum is not None:
+        curriculum_config_dict = load_config(args.curriculum)
+    if args.sampler is not None:
+        old_sampler_config_dict = load_config(args.sampler)
+    new_config = convert(old_config, curriculum_config_dict, old_sampler_config_dict)
+    unstructed_config = remove_nones(new_config)
+    write_to_yaml_file(unstructed_config, args.output_config_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/mlagents/utils/__init__.py b/ml-agents/mlagents/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/ml-agents/mlagents/utils/__pycache__/push_to_hf.cpython-310.pyc b/ml-agents/mlagents/utils/__pycache__/push_to_hf.cpython-310.pyc
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diff --git a/ml-agents/mlagents/utils/load_from_hf.py b/ml-agents/mlagents/utils/load_from_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..593889237e589a3ad10f51b363e37cbf10787627
--- /dev/null
+++ b/ml-agents/mlagents/utils/load_from_hf.py
@@ -0,0 +1,49 @@
+import os
+import argparse
+
+from huggingface_hub import snapshot_download
+
+from mlagents_envs import logging_util
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+logging_util.set_log_level(logging_util.INFO)
+
+
+def load_from_hf(repo_id: str, local_dir: str) -> None:
+    """
+    Download a model from Hugging Face Hub.
+    :param repo_id: id of the model repository from the Hugging Face Hub
+    :param local_dir: local destination of the repository
+    """
+    _, repo_name = repo_id.split("/")
+
+    local_dir = os.path.join(local_dir, repo_name)
+
+    snapshot_download(repo_id=repo_id, local_dir=local_dir)
+
+    logger.info(f"The repository {repo_id} has been downloaded to {local_dir}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--repo-id",
+        help="Repo id of the model repository from the Hugging Face Hub",
+        type=str,
+    )
+    parser.add_argument(
+        "--local-dir",
+        help="Local destination of the repository",
+        type=str,
+        default="./",
+    )
+    args = parser.parse_args()
+
+    # Load model from the Hub
+    load_from_hf(args.repo_id, args.local_dir)
+
+
+# For python debugger to directly run this script
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/mlagents/utils/push_to_hf.py b/ml-agents/mlagents/utils/push_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..934a912bd09ff12f8a434b3173ec583df72bc935
--- /dev/null
+++ b/ml-agents/mlagents/utils/push_to_hf.py
@@ -0,0 +1,216 @@
+import os
+import argparse
+
+import yaml
+import json
+
+from pathlib import Path
+
+from huggingface_hub import HfApi
+from huggingface_hub.repocard import metadata_save
+
+from typing import Tuple
+
+from mlagents_envs import logging_util
+from mlagents_envs.logging_util import get_logger
+
+logger = get_logger(__name__)
+logging_util.set_log_level(logging_util.INFO)
+
+
+def _generate_config(local_dir: Path, configfile_name: str) -> None:
+    """
+    Generate a config.json file from configuration.yaml
+    To do that we convert yaml to json
+    :param local_dir: path of the local directory
+    :param configfile_name: name of the yaml config file (by default configuration.yaml)
+    """
+    # Read the YAML file and generate a config.json
+    with open(os.path.join(local_dir, configfile_name)) as yaml_in:
+        yaml_object = yaml.safe_load(yaml_in)
+        with open(os.path.join(local_dir, "config.json"), "w") as json_out:
+            json.dump(yaml_object, json_out)
+
+
+def _generate_metadata(model_name: str, env_id: str) -> dict:
+    """
+    Define the tags for the model card
+    :param model_name: name of the model
+    :param env_id: name of the environment
+    """
+    env_tag = "ML-Agents-" + env_id
+
+    metadata = {}
+    metadata["library_name"] = "ml-agents"
+    metadata["tags"] = [  # type: ignore
+        env_id,
+        "deep-reinforcement-learning",
+        "reinforcement-learning",
+        env_tag,
+    ]
+
+    return metadata
+
+
+def _generate_model_card(
+    local_dir: Path, configfile_name: str, repo_id: str
+) -> Tuple[str, dict]:
+    """
+    Generate the model card
+    :param local_dir: local path of the directory
+    :param configfile_name: name of the yaml config file (by default configuration.yaml)
+    :param repo_id: id of the model repository from the Hugging Face Hub
+    """
+    # Step 1: Read the config.json
+    with open(os.path.join(local_dir, "config.json")) as f:
+        data = json.load(f)
+        # Get env_id
+        env_id = list(data["behaviors"].keys())[0]
+        # Get trainer_type
+        model_name = data["behaviors"][env_id]["trainer_type"]
+
+    # Step 2: Create the metadata
+    metadata = _generate_metadata(model_name, env_id)
+
+    # Step 3: Generate the model card
+    model_card = f"""
+  # **{model_name}** Agent playing **{env_id}**
+  This is a trained model of a **{model_name}** agent playing **{env_id}**
+  using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
+
+  ## Usage (with ML-Agents)
+  The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
+
+  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
+  - A *short tutorial* where you teach Huggy the Dog � to fetch the stick and then play with him directly in your
+  browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
+  - A *longer tutorial* to understand how works ML-Agents:
+  https://huggingface.co/learn/deep-rl-course/unit5/introduction
+
+  ### Resume the training
+  ```bash
+  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
+  ```
+
+  ### Watch your Agent play
+  You can watch your agent **playing directly in your browser**
+
+  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
+  2. Step 1: Find your model_id: {repo_id}
+  3. Step 2: Select your *.nn /*.onnx file
+  4. Click on Watch the agent play 👀
+  """
+
+    return model_card, metadata
+
+
+def _save_model_card(
+    local_dir: Path, generated_model_card: str, metadata: dict
+) -> None:
+    """Save a model card to the directory.
+    :param local_dir: local directory path
+    :param generated_model_card: model card generated by _generate_model_card() method
+    :param metadata: metadata
+    """
+    readme_path = local_dir / "README.md"
+
+    with readme_path.open("w", encoding="utf-8") as f:
+        f.write(generated_model_card)
+
+    # Save our metrics to Readme metadata
+    metadata_save(readme_path, metadata)
+
+
+def package_to_hub(
+    run_id: str,
+    path_of_run_id: Path,
+    repo_id: str,
+    commit_message: str,
+    configfile_name: str,
+) -> None:
+    """
+    This method generates the model card and upload the run_id folder
+    with all his files into the Hub
+    :param run_id : name of the run
+    :param path_of_run_id: path of the run_id folder that contains the onnx model.
+    :param repo_id: id of the model repository from the Hugging Face Hub
+    :param commit_message: commit message
+    :param configfile_name: name of the yaml config file (by default configuration.yaml)
+    """
+    logger.info(
+        f"This function will create a model card and upload your {run_id} "
+        f"into HuggingFace Hub. This is a work in progress: If you encounter a bug, "
+        f"please send open an issue"
+    )
+
+    _, repo_name = repo_id.split("/")
+
+    # Step 1: Create the repo
+    api = HfApi()
+
+    repo_url = api.create_repo(
+        repo_id=repo_id,
+        exist_ok=True,
+    )
+
+    local_path = Path(path_of_run_id)
+
+    # Step 2: Create a config file
+    _generate_config(local_path, configfile_name)
+
+    # Step 3: Generate and save the model card
+    generated_model_card, metadata = _generate_model_card(
+        local_path, configfile_name, repo_id
+    )
+    _save_model_card(local_path, generated_model_card, metadata)
+
+    logger.info(f"Pushing repo {run_id} to the Hugging Face Hub")
+
+    # Step 4. Push everything to the Hub
+    api.upload_folder(
+        repo_id=repo_id, folder_path=local_path, commit_message=commit_message
+    )
+
+    logger.info(
+        f"Your model is pushed to the hub. You can view your model here: {repo_url}"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--run-id", help="Name of the run-id folder", type=str)
+    parser.add_argument(
+        "--local-dir",
+        help="Path of the run_id folder that contains the trained model",
+        type=str,
+        default="./",
+    )
+    parser.add_argument(
+        "--repo-id",
+        help="Repo id of the model repository from the Hugging Face Hub",
+        type=str,
+    )
+    parser.add_argument(
+        "--commit-message", help="Commit message", type=str, default="Push to Hub"
+    )
+    parser.add_argument(
+        "--configfile-name",
+        help="Name of the configuration yaml file",
+        type=str,
+        default="configuration.yaml",
+    )
+    args = parser.parse_args()
+
+    # Push model to hub
+    package_to_hub(
+        args.run_id,
+        args.local_dir,
+        args.repo_id,
+        args.commit_message,
+        args.configfile_name,
+    )
+
+
+# For python debugger to directly run this script
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/pydoc-config.yaml b/ml-agents/pydoc-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bf2c500c8107e2b350790d3063da0b3cded6fecc
--- /dev/null
+++ b/ml-agents/pydoc-config.yaml
@@ -0,0 +1,16 @@
+# config to specify which modules will be used to render api docs from ml-agents package
+folder: docs
+modules:
+    - name: mlagents
+      file_name: Python-On-Off-Policy-Trainer-Documentation.md
+      submodules:
+          - trainers.trainer.on_policy_trainer
+          - trainers.trainer.off_policy_trainer
+          - trainers.trainer.rl_trainer
+          - trainers.trainer.trainer
+          - trainers.settings
+    - name: mlagents
+      file_name: Python-Optimizer-Documentation.md
+      submodules:
+        - trainers.optimizer.torch_optimizer
+        - trainers.optimizer.optimizer
diff --git a/ml-agents/setup.py b/ml-agents/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce0bc65443b29e99c24a00b3b382f27c32f7c3dd
--- /dev/null
+++ b/ml-agents/setup.py
@@ -0,0 +1,101 @@
+import os
+import sys
+
+from setuptools import setup, find_packages
+from setuptools.command.install import install
+from mlagents.plugins import ML_AGENTS_STATS_WRITER, ML_AGENTS_TRAINER_TYPE
+import mlagents.trainers
+
+VERSION = mlagents.trainers.__version__
+EXPECTED_TAG = mlagents.trainers.__release_tag__
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+
+class VerifyVersionCommand(install):
+    """
+    Custom command to verify that the git tag is the expected one for the release.
+    Originally based on https://circleci.com/blog/continuously-deploying-python-packages-to-pypi-with-circleci/
+    This differs slightly because our tags and versions are different.
+    """
+
+    description = "verify that the git tag matches our version"
+
+    def run(self):
+        tag = os.getenv("GITHUB_REF", "NO GITHUB TAG!").replace("refs/tags/", "")
+
+        if tag != EXPECTED_TAG:
+            info = "Git tag: {} does not match the expected tag of this app: {}".format(
+                tag, EXPECTED_TAG
+            )
+            sys.exit(info)
+
+
+# Get the long description from the README file
+with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
+    long_description = f.read()
+
+setup(
+    name="mlagents",
+    version=VERSION,
+    description="Unity Machine Learning Agents",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/Unity-Technologies/ml-agents",
+    author="Unity Technologies",
+    author_email="ML-Agents@unity3d.com",
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+    ],
+    # find_namespace_packages will recurse through the directories and find all the packages
+    packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
+    zip_safe=False,
+    install_requires=[
+        # Test-only dependencies should go in test_requirements.txt, not here.
+        "grpcio>=1.11.0",
+        "h5py>=2.9.0",
+        f"mlagents_envs=={VERSION}",
+        "numpy>=1.13.3,<2.0",
+        "Pillow>=4.2.1",
+        "protobuf>=3.6,<3.20",
+        "pyyaml>=3.1.0",
+        # Windows ver. of PyTorch doesn't work from PyPi. Installation:
+        # https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Installation.md#windows-installing-pytorch
+        # Torch only working on python 3.9 for 1.8.0 and above. Details see:
+        # https://github.com/pytorch/pytorch/issues/50014
+        "torch>=1.8.0,<=1.11.0;(platform_system!='Windows' and python_version>='3.9')",
+        "torch>=1.6.0,<1.9.0;(platform_system!='Windows' and python_version<'3.9')",
+        "tensorboard>=1.15",
+        # cattrs 1.1.0 dropped support for python 3.6, but 1.0.0 doesn't work for python 3.9
+        # Since there's no version that supports both, we have to draw the line somwehere.
+        "cattrs<1.1.0; python_version<'3.8'",
+        "cattrs>=1.1.0,<1.7; python_version>='3.8'",
+        "attrs>=19.3.0",
+        "huggingface_hub>=0.14",
+        'pypiwin32==223;platform_system=="Windows"',
+        "importlib_metadata==4.4; python_version<'3.8'",
+    ],
+    python_requires=">=3.8.13,<=3.10.12",
+    entry_points={
+        "console_scripts": [
+            "mlagents-learn=mlagents.trainers.learn:main",
+            "mlagents-run-experiment=mlagents.trainers.run_experiment:main",
+            "mlagents-push-to-hf=mlagents.utils.push_to_hf:main",
+            "mlagents-load-from-hf=mlagents.utils.load_from_hf:main",
+        ],
+        # Plugins - each plugin type should have an entry here for the default behavior
+        ML_AGENTS_STATS_WRITER: [
+            "default=mlagents.plugins.stats_writer:get_default_stats_writers"
+        ],
+        ML_AGENTS_TRAINER_TYPE: [
+            "default=mlagents.plugins.trainer_type:get_default_trainer_types"
+        ],
+    },
+    # TODO: Remove this once mypy stops having spurious setuptools issues.
+    cmdclass={"verify": VerifyVersionCommand},  # type: ignore
+)
diff --git a/ml-agents/tests/__init__.py b/ml-agents/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/tests/yamato/__init__.py b/ml-agents/tests/yamato/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/tests/yamato/check_coverage_percent.py b/ml-agents/tests/yamato/check_coverage_percent.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb0856a48271eff16581461707e5f5ed3a2884f6
--- /dev/null
+++ b/ml-agents/tests/yamato/check_coverage_percent.py
@@ -0,0 +1,54 @@
+import sys
+import os
+
+SUMMARY_XML_FILENAME = "Summary.xml"
+
+
+def check_coverage(root_dir, min_percentage):
+    # Walk the root directory looking for the summary file that
+    # is output by ther code coverage checks. It's possible that
+    # we'll need to refine this later in case there are multiple
+    # such files.
+    summary_xml = None
+    for dirpath, _, filenames in os.walk(root_dir):
+        if SUMMARY_XML_FILENAME in filenames:
+            summary_xml = os.path.join(dirpath, SUMMARY_XML_FILENAME)
+            break
+    if not summary_xml:
+        print(f"Couldn't find {SUMMARY_XML_FILENAME} in root directory")
+        sys.exit(1)
+
+    with open(summary_xml) as f:
+        # Rather than try to parse the XML, just look for a line of the form
+        # <Linecoverage>73.9</Linecoverage>
+        lines = f.readlines()
+        for line in lines:
+            if "Linecoverage" in line:
+                pct = line.replace("<Linecoverage>", "").replace("</Linecoverage>", "")
+                pct = float(pct)
+                if pct < min_percentage:
+                    print(
+                        f"Coverage {pct} is below the min percentage of {min_percentage}."
+                    )
+                    sys.exit(1)
+                else:
+                    print(
+                        f"Coverage {pct} is above the min percentage of {min_percentage}."
+                    )
+                    sys.exit(0)
+
+    # Couldn't find the results in the file.
+    print("Couldn't find Linecoverage in summary file")
+    sys.exit(1)
+
+
+def main():
+    root_dir = sys.argv[1]
+    min_percent = float(sys.argv[2])
+    if min_percent > 0:
+        # This allows us to set 0% coverage on 2018.4
+        check_coverage(root_dir, min_percent)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/tests/yamato/scripts/__init__.py b/ml-agents/tests/yamato/scripts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ml-agents/tests/yamato/scripts/run_compressed_sensor.py b/ml-agents/tests/yamato/scripts/run_compressed_sensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..022d4de515850090ade1cc09bf2c8c545098f4b3
--- /dev/null
+++ b/ml-agents/tests/yamato/scripts/run_compressed_sensor.py
@@ -0,0 +1,53 @@
+import argparse
+import numpy as np
+
+from mlagents_envs.environment import UnityEnvironment
+
+EPSILON = 0.001
+
+
+def test_run_environment(env_name):
+    """
+    Run the low-level API test of compressed sensors using the specified environment
+    :param env_name: Name of the Unity environment binary to launch
+    """
+    env = UnityEnvironment(
+        file_name=env_name, no_graphics=True, additional_args=["-logFile", "-"]
+    )
+
+    try:
+        # Reset the environment
+        env.reset()
+
+        env.step()
+
+        # Set the default brain to work with
+        group_name = list(env.behavior_specs.keys())[0]
+
+        # Get the state of the agents
+        decision_steps, _ = env.get_steps(group_name)
+
+        # One observation comes from compressed sensor while the other comes
+        # from an uncompressed sensor
+        obs_1 = decision_steps.obs[0][0, :, :, :]
+        obs_2 = decision_steps.obs[0][1, :, :, :]
+
+        diff = np.abs(obs_1 - obs_2)
+
+        # make sure both are identical
+        assert np.max(diff) < EPSILON
+
+        # make sure an actual observation was collected
+        assert np.max(obs_1) > EPSILON
+
+        print("Observations were identical")
+
+    finally:
+        env.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env", default="artifacts/testPlayer")
+    args = parser.parse_args()
+    test_run_environment(args.env)
diff --git a/ml-agents/tests/yamato/scripts/run_gym.py b/ml-agents/tests/yamato/scripts/run_gym.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d698a662b5574286e162fb056bce5df64d5b695
--- /dev/null
+++ b/ml-agents/tests/yamato/scripts/run_gym.py
@@ -0,0 +1,67 @@
+import argparse
+
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
+
+
+def test_run_environment(env_name):
+    """
+    Run the gym test using the specified environment
+    :param env_name: Name of the Unity environment binary to launch
+    """
+    u_env = UnityEnvironment(env_name, worker_id=1, no_graphics=True)
+    env = UnityToGymWrapper(u_env)
+
+    try:
+        # Examine environment parameters
+        print(str(env))
+
+        # Reset the environment
+        initial_observations = env.reset()
+
+        if len(env.observation_space.shape) == 1:
+            # Examine the initial vector observation
+            print(f"Agent observations look like: \n{initial_observations}")
+
+        for _episode in range(10):
+            env.reset()
+            done = False
+            episode_rewards = 0
+            while not done:
+                actions = env.action_space.sample()
+                obs, reward, done, _ = env.step(actions)
+                episode_rewards += reward
+            print(f"Total reward this episode: {episode_rewards}")
+    finally:
+        env.close()
+
+
+def test_closing(env_name):
+    """
+    Run the gym test and closes the environment multiple times
+    :param env_name: Name of the Unity environment binary to launch
+    """
+
+    try:
+        env1 = UnityToGymWrapper(
+            UnityEnvironment(env_name, worker_id=1, no_graphics=True)
+        )
+        env1.close()
+        env1 = UnityToGymWrapper(
+            UnityEnvironment(env_name, worker_id=1, no_graphics=True)
+        )
+        env2 = UnityToGymWrapper(
+            UnityEnvironment(env_name, worker_id=2, no_graphics=True)
+        )
+        env2.reset()
+    finally:
+        env1.close()
+        env2.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env", default="Project/testPlayer")
+    args = parser.parse_args()
+    test_run_environment(args.env)
+    test_closing(args.env)
diff --git a/ml-agents/tests/yamato/scripts/run_llapi.py b/ml-agents/tests/yamato/scripts/run_llapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e0a6e785e26b72e0afa7242d6775dd139205b48
--- /dev/null
+++ b/ml-agents/tests/yamato/scripts/run_llapi.py
@@ -0,0 +1,112 @@
+import argparse
+
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.engine_configuration_channel import (
+    EngineConfigurationChannel,
+)
+
+
+def test_run_environment(env_name):
+    """
+    Run the low-level API test using the specified environment
+    :param env_name: Name of the Unity environment binary to launch
+    """
+    engine_configuration_channel = EngineConfigurationChannel()
+    env = UnityEnvironment(
+        file_name=env_name,
+        side_channels=[engine_configuration_channel],
+        no_graphics=True,
+        additional_args=["-logFile", "-"],
+    )
+
+    try:
+        # Reset the environment
+        env.reset()
+
+        # Set the default brain to work with
+        group_name = list(env.behavior_specs.keys())[0]
+        group_spec = env.behavior_specs[group_name]
+
+        # Set the time scale of the engine
+        engine_configuration_channel.set_configuration_parameters(time_scale=3.0)
+
+        # Get the state of the agents
+        decision_steps, terminal_steps = env.get_steps(group_name)
+
+        # Examine the number of observations per Agent
+        print("Number of observations : ", len(group_spec.observation_specs))
+
+        for obs_spec in group_spec.observation_specs:
+            # Make sure the name was set in the ObservationSpec
+            assert bool(obs_spec.name) is True, f'obs_spec.name="{obs_spec.name}"'
+
+        # Is there a visual observation ?
+        vis_obs = any(
+            len(obs_spec.shape) == 3 for obs_spec in group_spec.observation_specs
+        )
+        print("Is there a visual observation ?", vis_obs)
+
+        # Examine the state space for the first observation for the first agent
+        print(f"First Agent observation looks like: \n{decision_steps.obs[0][0]}")
+
+        for _episode in range(10):
+            env.reset()
+            decision_steps, terminal_steps = env.get_steps(group_name)
+            done = False
+            episode_rewards = 0
+            tracked_agent = -1
+            while not done:
+                action_tuple = group_spec.action_spec.random_action(len(decision_steps))
+                if tracked_agent == -1 and len(decision_steps) >= 1:
+                    tracked_agent = decision_steps.agent_id[0]
+                env.set_actions(group_name, action_tuple)
+                env.step()
+                decision_steps, terminal_steps = env.get_steps(group_name)
+                done = False
+                if tracked_agent in decision_steps:
+                    episode_rewards += decision_steps[tracked_agent].reward
+                if tracked_agent in terminal_steps:
+                    episode_rewards += terminal_steps[tracked_agent].reward
+                    done = True
+            print(f"Total reward this episode: {episode_rewards}")
+    finally:
+        env.close()
+
+
+def test_closing(env_name):
+    """
+    Run the low-level API and close the environment
+    :param env_name: Name of the Unity environment binary to launch
+    """
+    try:
+        env1 = UnityEnvironment(
+            file_name=env_name,
+            base_port=5006,
+            no_graphics=True,
+            additional_args=["-logFile", "-"],
+        )
+        env1.close()
+        env1 = UnityEnvironment(
+            file_name=env_name,
+            base_port=5006,
+            no_graphics=True,
+            additional_args=["-logFile", "-"],
+        )
+        env2 = UnityEnvironment(
+            file_name=env_name,
+            base_port=5007,
+            no_graphics=True,
+            additional_args=["-logFile", "-"],
+        )
+        env2.reset()
+    finally:
+        env1.close()
+        env2.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env", default="artifacts/testPlayer")
+    args = parser.parse_args()
+    test_run_environment(args.env)
+    test_closing(args.env)
diff --git a/ml-agents/tests/yamato/setup_venv.py b/ml-agents/tests/yamato/setup_venv.py
new file mode 100644
index 0000000000000000000000000000000000000000..9311fbcfbd9a9157228762e4ba999519f0fabe4b
--- /dev/null
+++ b/ml-agents/tests/yamato/setup_venv.py
@@ -0,0 +1,21 @@
+import argparse
+
+from .yamato_utils import init_venv
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mlagents-version", default=None)
+    parser.add_argument("--extra-packages", default=None)
+    args = parser.parse_args()
+    extra_packages = []
+    if args.extra_packages is not None:
+        extra_packages = args.extra_packages.split(",")
+
+    init_venv(
+        mlagents_python_version=args.mlagents_version, extra_packages=extra_packages
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/tests/yamato/standalone_build_tests.py b/ml-agents/tests/yamato/standalone_build_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..436e1e5aa184d2678a80f05ba3eb53ad2f794368
--- /dev/null
+++ b/ml-agents/tests/yamato/standalone_build_tests.py
@@ -0,0 +1,42 @@
+import os
+import sys
+import argparse
+
+from .yamato_utils import get_base_path, run_standalone_build
+
+
+def main(scene_path, build_target):
+    base_path = get_base_path()
+    print(f"Running in base path {base_path}")
+
+    executable_name = "testPlayer"
+    if scene_path is not None:
+        executable_name = os.path.splitext(scene_path)[0]  # Remove extension
+        executable_name = executable_name.split("/")[-1]
+        executable_name = "testPlayer-" + executable_name
+    print(f"Executable name {executable_name}")
+
+    returncode = run_standalone_build(
+        base_path,
+        output_path=executable_name,
+        scene_path=scene_path,
+        build_target=build_target,
+        log_output_path=None,  # Log to stdout so we get timestamps on the logs
+    )
+
+    if returncode == 0:
+        print("Test run SUCCEEDED!")
+    else:
+        print("Test run FAILED!")
+
+    sys.exit(returncode)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--scene", default=None)
+    parser.add_argument(
+        "--build-target", default="mac", choices=["mac", "linux", "ios", "webgl"]
+    )
+    args = parser.parse_args()
+    main(args.scene, args.build_target)
diff --git a/ml-agents/tests/yamato/training_int_tests.py b/ml-agents/tests/yamato/training_int_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..8678c166d74dde400a3035e46a71930ee30eac5e
--- /dev/null
+++ b/ml-agents/tests/yamato/training_int_tests.py
@@ -0,0 +1,197 @@
+import argparse
+import json
+import os
+import shutil
+import sys
+import subprocess
+import time
+
+from .yamato_utils import (
+    find_executables,
+    get_base_path,
+    get_base_output_path,
+    run_standalone_build,
+    init_venv,
+    override_config_file,
+    checkout_csharp_version,
+    undo_git_checkout,
+)
+
+
+def run_training(python_version: str, csharp_version: str) -> bool:
+    latest = "latest"
+    run_id = int(time.time() * 1000.0)
+    print(
+        f"Running training with python={python_version or latest} and c#={csharp_version or latest}"
+    )
+    output_dir = "results"
+    onnx_file_expected = f"./{output_dir}/{run_id}/3DBall.onnx"
+
+    if os.path.exists(onnx_file_expected):
+        # Should never happen - make sure nothing leftover from an old test.
+        print("Artifacts from previous build found!")
+        return False
+
+    base_path = get_base_path()
+    print(f"Running in base path {base_path}")
+
+    # Only build the standalone player if we're overriding the C# version
+    # Otherwise we'll use the one built earlier in the pipeline.
+    if csharp_version is not None:
+        # We can't rely on the old C# code recognizing the commandline argument to set the output
+        # So rename testPlayer (containing the most recent build) to something else temporarily
+        artifact_path = get_base_output_path()
+        full_player_path = os.path.join(artifact_path, "testPlayer.app")
+        temp_player_path = os.path.join(artifact_path, "temp_testPlayer.app")
+        final_player_path = os.path.join(
+            artifact_path, f"testPlayer_{csharp_version}.app"
+        )
+
+        os.rename(full_player_path, temp_player_path)
+
+        checkout_csharp_version(csharp_version)
+        build_returncode = run_standalone_build(base_path)
+
+        if build_returncode != 0:
+            print(f"Standalone build FAILED! with return code {build_returncode}")
+            return False
+
+        # Now rename the newly-built executable, and restore the old one
+        os.rename(full_player_path, final_player_path)
+        os.rename(temp_player_path, full_player_path)
+        standalone_player_path = f"testPlayer_{csharp_version}"
+    else:
+        standalone_player_path = "testPlayer"
+
+    init_venv(python_version)
+
+    # Copy the default training config but override the max_steps parameter,
+    # and reduce the batch_size and buffer_size enough to ensure an update step happens.
+    yaml_out = "override.yaml"
+    overrides = {
+        "hyperparameters": {"batch_size": 10, "buffer_size": 10},
+        "max_steps": 100,
+    }
+    override_config_file("config/ppo/3DBall.yaml", yaml_out, overrides)
+
+    log_output_path = f"{get_base_output_path()}/training.log"
+    env_path = os.path.join(get_base_output_path(), standalone_player_path)
+    mla_learn_cmd = [
+        "mlagents-learn",
+        yaml_out,
+        "--force",
+        "--env",
+        env_path,
+        "--run-id",
+        str(run_id),
+        "--no-graphics",
+        "--env-args",
+        "-logFile",
+        log_output_path,
+    ]
+
+    res = subprocess.run(mla_learn_cmd)
+
+    # Save models as artifacts (only if we're using latest python and C#)
+    if csharp_version is None and python_version is None:
+        model_artifacts_dir = os.path.join(get_base_output_path(), "models")
+        os.makedirs(model_artifacts_dir, exist_ok=True)
+        if os.path.exists(onnx_file_expected):
+            shutil.copy(onnx_file_expected, model_artifacts_dir)
+
+    if res.returncode != 0 or not os.path.exists(onnx_file_expected):
+        print("mlagents-learn run FAILED!")
+        print("Command line: " + " ".join(mla_learn_cmd))
+        subprocess.run(["cat", log_output_path])
+        return False
+
+    if csharp_version is None and python_version is None:
+        # Use abs path so that loading doesn't get confused
+        model_path = os.path.abspath(os.path.dirname(onnx_file_expected))
+        inference_ok = run_inference(env_path, model_path, "onnx")
+        if not inference_ok:
+            return False
+
+    print("mlagents-learn run SUCCEEDED!")
+    return True
+
+
+def run_inference(env_path: str, output_path: str, model_extension: str) -> bool:
+    start_time = time.time()
+    exes = find_executables(env_path)
+    if len(exes) != 1:
+        print(f"Can't determine the player executable in {env_path}. Found {exes}.")
+        return False
+
+    log_output_path = f"{get_base_output_path()}/inference.{model_extension}.txt"
+
+    # 10 minutes for inference is more than enough
+    process_timeout = 10 * 60
+    # Try to gracefully exit a few seconds before that.
+    model_override_timeout = process_timeout - 15
+
+    exe_path = exes[0]
+    args = [
+        exe_path,
+        "-nographics",
+        "-batchmode",
+        "-logfile",
+        log_output_path,
+        "--mlagents-override-model-directory",
+        output_path,
+        "--mlagents-quit-on-load-failure",
+        "--mlagents-quit-after-episodes",
+        "1",
+        "--mlagents-override-model-extension",
+        model_extension,
+        "--mlagents-quit-after-seconds",
+        str(model_override_timeout),
+    ]
+    print(f"Starting inference with args {' '.join(args)}")
+    res = subprocess.run(args, timeout=process_timeout)
+    end_time = time.time()
+    if res.returncode != 0:
+        print("Error running inference!")
+        print("Command line: " + " ".join(args))
+        subprocess.run(["cat", log_output_path])
+        return False
+    else:
+        print(f"Inference finished! Took {end_time - start_time} seconds")
+
+    # Check the artifacts directory for the timers, so we can get the gauges
+    timer_file = f"{exe_path}_Data/ML-Agents/Timers/3DBall_timers.json"
+    with open(timer_file) as f:
+        timer_data = json.load(f)
+
+    gauges = timer_data.get("gauges", {})
+    rewards = gauges.get("Override_3DBall.CumulativeReward", {})
+    max_reward = rewards.get("max")
+    if max_reward is None:
+        print(
+            "Unable to find rewards in timer file. This usually indicates a problem with Barracuda or inference."
+        )
+        return False
+    # We could check that the rewards are over a threshold, but since we train for so short a time,
+    # the values could be highly variable. So don't do it for now.
+
+    return True
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--python", default=None)
+    parser.add_argument("--csharp", default=None)
+    args = parser.parse_args()
+
+    try:
+        ok = run_training(args.python, args.csharp)
+        if not ok:
+            sys.exit(1)
+
+    finally:
+        # Cleanup - this gets executed even if we hit sys.exit()
+        undo_git_checkout()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml-agents/tests/yamato/yamato_utils.py b/ml-agents/tests/yamato/yamato_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5474e56285260303c81e44a03a69a405abe56536
--- /dev/null
+++ b/ml-agents/tests/yamato/yamato_utils.py
@@ -0,0 +1,227 @@
+import os
+import shutil
+import subprocess
+import yaml
+from sys import platform
+from typing import List, Optional, Mapping
+
+
+def get_unity_executable_path():
+    if platform == "darwin":
+        downloader_install_path = "./.Editor/Unity.app/Contents/MacOS/Unity"
+    else:  # if platform == "linux":
+        downloader_install_path = "./.Editor/Unity"
+    if os.path.exists(downloader_install_path):
+        return downloader_install_path
+    raise FileNotFoundError("Can't find executable from unity-downloader-cli")
+
+
+def get_base_path():
+    # We might need to do some more work here if the working directory ever changes
+    # E.g. take the full path and back out the main module main.
+    # But for now, this should work
+    return os.getcwd()
+
+
+def get_base_output_path():
+    """ "
+    Returns the artifact folder to use for yamato jobs.
+    """
+    return os.path.join(get_base_path(), "artifacts")
+
+
+def run_standalone_build(
+    base_path: str,
+    verbose: bool = False,
+    output_path: str = None,
+    scene_path: str = None,
+    build_target: str = None,
+    log_output_path: Optional[str] = f"{get_base_output_path()}/standalone_build.txt",
+) -> int:
+    """
+    Run BuildStandalonePlayerOSX test to produce a player. The location defaults to
+    artifacts/standalonebuild/testPlayer.
+    """
+    unity_exe = get_unity_executable_path()
+    print(f"Running BuildStandalonePlayer via {unity_exe}")
+
+    # enum values from https://docs.unity3d.com/2022.3/Documentation/ScriptReference/BuildTarget.html
+    build_target_to_enum: Mapping[Optional[str], str] = {
+        "mac": "StandaloneOSX",
+        "osx": "StandaloneOSX",
+        "linux": "StandaloneLinux64",
+    }
+    # Convert the short name to the official enum
+    # Just pass through if it's not on the list.
+    build_target_enum = build_target_to_enum.get(build_target, build_target)
+
+    test_args = [
+        unity_exe,
+        "-projectPath",
+        f"{base_path}/Project",
+        "-batchmode",
+        "-executeMethod",
+        "Unity.MLAgents.StandaloneBuildTest.BuildStandalonePlayerOSX",
+    ]
+
+    if log_output_path:
+        os.makedirs(os.path.dirname(log_output_path), exist_ok=True)
+        subprocess.run(["touch", log_output_path])
+        test_args += ["-logfile", log_output_path]
+    else:
+        # Log to stdout
+        test_args += ["-logfile", "-"]
+
+    if output_path is not None:
+        output_path = os.path.join(get_base_output_path(), output_path)
+        test_args += ["--mlagents-build-output-path", output_path]
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    if scene_path is not None:
+        test_args += ["--mlagents-build-scene-path", scene_path]
+    if build_target_enum is not None:
+        test_args += ["--mlagents-build-target", build_target_enum]
+    print(f"{' '.join(test_args)} ...")
+
+    timeout = 30 * 60  # 30 minutes, just in case
+    res: subprocess.CompletedProcess = subprocess.run(test_args, timeout=timeout)
+
+    # Copy the default build name into the artifacts folder.
+    if output_path is None and res.returncode == 0:
+        exe_name = "testPlayer.app" if platform == "darwin" else "testPlayer"
+        shutil.move(
+            os.path.join(base_path, "Project", exe_name),
+            os.path.join(get_base_output_path(), exe_name),
+        )
+
+    # Print if we fail or want verbosity.
+    if verbose or res.returncode != 0:
+        if log_output_path:
+            subprocess.run(["cat", log_output_path])
+
+    return res.returncode
+
+
+def find_executables(root_dir: str) -> List[str]:
+    """
+    Try to find the player executable. This seems to vary between Unity versions.
+    """
+    ignored_extension = frozenset([".dll", ".dylib", ".bundle"])
+    ignored_files = frozenset(["macblas"])
+    exes = []
+    for root, _, files in os.walk(root_dir):
+        for filename in files:
+            file_root, ext = os.path.splitext(filename)
+            if ext in ignored_extension or filename in ignored_files:
+                continue
+            file_path = os.path.join(root, filename)
+            if os.access(file_path, os.X_OK):
+                exes.append(file_path)
+    # Also check the input path
+    if os.access(root_dir, os.X_OK):
+        exes.append(root_dir)
+    return exes
+
+
+def init_venv(
+    mlagents_python_version: str = None, extra_packages: Optional[List[str]] = None
+) -> None:
+    """
+    Install the necessary packages for the venv
+    :param mlagents_python_version: The version of mlagents python packcage to install.
+        If None, will do a local install, otherwise will install from pypi
+    :return:
+    """
+    pip_commands = ["--upgrade pip", "--upgrade setuptools"]
+    if mlagents_python_version:
+        # install from pypi
+        pip_commands += [
+            f"mlagents=={mlagents_python_version}",
+            # TODO build these and publish to internal pypi
+            "tf2onnx==1.6.1",
+        ]
+    else:
+        # Local install
+        pip_commands += ["-e ./ml-agents-envs", "-e ./ml-agents"]
+    if extra_packages:
+        pip_commands += extra_packages
+
+    for cmd in pip_commands:
+        pip_index_url = "--index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple"
+        print(f'Running "python3 -m pip install -q {cmd} {pip_index_url}"')
+        subprocess.check_call(
+            f"python3 -m pip install -q {cmd} {pip_index_url}", shell=True
+        )
+
+
+def checkout_csharp_version(csharp_version):
+    """
+    Checks out the specific git revision (usually a tag) for the C# package and Project.
+    If csharp_version is None, no changes are made.
+    :param csharp_version:
+    :return:
+    """
+    if csharp_version is None:
+        return
+
+    csharp_tag = f"com.unity.ml-agents_{csharp_version}"
+    csharp_dirs = ["com.unity.ml-agents", "com.unity.ml-agents.extensions", "Project"]
+    for csharp_dir in csharp_dirs:
+        subprocess.check_call(f"rm -rf {csharp_dir}", shell=True)
+        # Allow the checkout to fail, since the extensions folder isn't availabe in 1.0.0
+        subprocess.call(f"git checkout {csharp_tag} -- {csharp_dir}", shell=True)
+
+
+def undo_git_checkout():
+    """
+    Clean up the git working directory.
+    """
+    subprocess.check_call("git reset HEAD .", shell=True)
+    subprocess.check_call("git checkout -- .", shell=True)
+    # Ensure the cache isn't polluted with old compiled assemblies.
+    subprocess.check_call("rm -rf Project/Library", shell=True)
+
+
+def override_config_file(src_path, dest_path, overrides):
+    """
+    Override settings in a trainer config file. For example,
+        override_config_file(src_path, dest_path, max_steps=42)
+    will copy the config file at src_path to dest_path, but override the max_steps field to 42 for all brains.
+    """
+    with open(src_path) as f:
+        configs = yaml.safe_load(f)
+        behavior_configs = configs["behaviors"]
+
+    for config in behavior_configs.values():
+        _override_config_dict(config, overrides)
+
+    with open(dest_path, "w") as f:
+        yaml.dump(configs, f)
+
+
+def _override_config_dict(config, overrides):
+    for key, val in overrides.items():
+        if isinstance(val, dict):
+            _override_config_dict(config[key], val)
+        else:
+            config[key] = val
+
+
+def override_legacy_config_file(python_version, src_path, dest_path, **kwargs):
+    """
+    Override settings in a trainer config file, using an old version of the src_path. For example,
+        override_config_file("0.16.0", src_path, dest_path, max_steps=42)
+    will sync the file at src_path from version 0.16.0, copy it to dest_path, and override the
+    max_steps field to 42 for all brains.
+    """
+    # Sync the old version of the file
+    python_tag = f"python-packages_{python_version}"
+    subprocess.check_call(f"git checkout {python_tag} -- {src_path}", shell=True)
+
+    with open(src_path) as f:
+        configs = yaml.safe_load(f)
+
+    for config in configs.values():
+        config.update(**kwargs)
+
+    with open(dest_path, "w") as f:
+        yaml.dump(configs, f)
diff --git a/protobuf-definitions/README.md b/protobuf-definitions/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed5a322d4feaf7d0a109c278020479d33b06fd8f
--- /dev/null
+++ b/protobuf-definitions/README.md
@@ -0,0 +1,74 @@
+# Unity ML-Agents Protobuf Definitions
+
+Contains relevant definitions needed to generate probobuf files used in [ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
+
+## Requirements
+
+* protobuf 3.6.0
+* grpcio-tools 1.11.1
+* Grpc.Tools 1.14.1
+
+## Set-up & Installation
+
+First we will follow these steps once install protobuf and grpcio-tools via your terminal.
+Assume the ml-agents repository is checked out to a folder named $MLAGENTS_ROOT.
+**Note:** If you're using Anaconda, don't forget to activate the ml-agents environment first.
+
+`pip install protobuf==3.6.0 --force`
+
+`pip install grpcio-tools==1.11.1`
+
+`pip install mypy-protobuf==1.16.0`
+
+
+#### On Windows
+
+Download and install the latest version of [nuget](https://www.nuget.org/downloads).
+
+#### On Mac
+
+`brew install nuget`
+
+#### On Linux
+
+`sudo apt-get install nuget`
+
+
+Navigate to your installation of nuget and run the following:
+
+`nuget install Grpc.Tools -Version 1.14.1 -OutputDirectory $MLAGENTS_ROOT\protobuf-definitions`
+
+## Running
+
+Whenever you change the fields of a message, you must follow the steps below to create C# and Python files corresponding to the new message.
+
+1. Open a terminal. **Note:** If you're using Anaconda, don't forget to activate the ml-agents environment first.
+2. Un-comment line 7 in `make.sh` (for Windows, use `make_for_win.bat`), and set to correct Grpc.Tools sub-directory.
+3. Run the protobuf generation script from the terminal by navigating to `$MLAGENTS_ROOT\protobuf-definitions` and entering `make.sh` (for Windows, use `make_for_win.bat`)
+4. Note any errors generated that may result from setting the wrong directory in step 2.
+5. In the generated `UnityToExternalGrpc.cs` file in the `$MLAGENTS_ROOT/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects` folder, check to see if you need to add the following to the beginning of the file:
+
+```csharp
+# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
+```
+ and the following line to the end
+
+ ```csharp
+ #endif
+ ```
+This is to make sure the generated code does not try to access the Grpc library
+on platforms that are not supported by Grpc.
+
+Finally, re-install the mlagents packages by running the following commands from the same `$MLAGENTS_ROOT\protobuf-definitions` directory.
+
+```
+cd ..
+cd ml-agents-envs
+pip install -e .
+cd ..
+cd ml-agents
+pip install -e .
+mlagents-learn
+```
+
+The final line will test if everything was generated and installed correctly. If it worked, you should see the Unity logo.
diff --git a/protobuf-definitions/make.sh b/protobuf-definitions/make.sh
new file mode 100644
index 0000000000000000000000000000000000000000..000d371fbb51aa113c1891987e5508bcec7f46c1
--- /dev/null
+++ b/protobuf-definitions/make.sh
@@ -0,0 +1,53 @@
+# variables
+
+# GRPC-TOOLS required. Install with `nuget install Grpc.Tools`.
+# Then un-comment and replace [DIRECTORY] with location of files.
+# For example, on macOS, you might have something like:
+# COMPILER=Grpc.Tools.1.14.1/tools/macosx_x64
+# COMPILER=[DIRECTORY]
+
+SRC_DIR=proto/mlagents_envs/communicator_objects
+DST_DIR_C=../com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects
+DST_DIR_P=../ml-agents-envs
+PROTO_PATH=proto
+PYTHON_PACKAGE=mlagents_envs/communicator_objects
+
+# clean
+rm -rf $DST_DIR_C
+rm -rf $DST_DIR_P/$PYTHON_PACKAGE
+mkdir -p $DST_DIR_C
+mkdir -p $DST_DIR_P/$PYTHON_PACKAGE
+
+# generate proto objects in python and C#
+
+$COMPILER/protoc --proto_path=proto --csharp_opt=internal_access --csharp_out $DST_DIR_C $SRC_DIR/*.proto
+$COMPILER/protoc --proto_path=proto --python_out=$DST_DIR_P --mypy_out=$DST_DIR_P $SRC_DIR/*.proto
+
+# grpc
+
+GRPC=unity_to_external.proto
+
+$COMPILER/protoc --proto_path=proto --csharp_out=$DST_DIR_C --grpc_out=internal_access:$DST_DIR_C $SRC_DIR/$GRPC --plugin=protoc-gen-grpc=$COMPILER/grpc_csharp_plugin
+python3 -m grpc_tools.protoc --proto_path=proto --python_out=$DST_DIR_P --grpc_python_out=$DST_DIR_P $SRC_DIR/$GRPC
+
+
+# Generate the init file for the python module
+# rm -f $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+for FILE in $DST_DIR_P/$PYTHON_PACKAGE/*.py
+do
+FILE=${FILE##*/}
+# echo from .$(basename $FILE) import \* >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+echo from .${FILE%.py} import \* >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+done
+
+# Surround UnityToExternal.cs file with macro
+echo "#if UNITY_EDITOR || UNITY_STANDALONE
+#define MLA_SUPPORTED_TRAINING_PLATFORM
+#endif
+#if MLA_SUPPORTED_TRAINING_PLATFORM
+`cat $DST_DIR_C/UnityToExternalGrpc.cs`
+#endif" > $DST_DIR_C/UnityToExternalGrpc.cs
+
+# Remove the __init__.py file since it is not needed
+rm $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+touch $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
diff --git a/protobuf-definitions/make_for_win.bat b/protobuf-definitions/make_for_win.bat
new file mode 100644
index 0000000000000000000000000000000000000000..303a1ab37b8ee92f53bd7d64d1e0014780a6e247
--- /dev/null
+++ b/protobuf-definitions/make_for_win.bat
@@ -0,0 +1,44 @@
+rem variables
+
+rem GRPC-TOOLS required. Install with `nuget install Grpc.Tools`.
+rem Then un-comment and replace [DIRECTORY] with location of files.
+rem For example, on Windows, you might have something like:
+rem set COMPILER=Grpc.Tools.1.14.1\tools\windows_x64
+rem set COMPILER=[DIRECTORY]
+
+set SRC_DIR=proto\mlagents_envs\communicator_objects
+set DST_DIR_C=..\com.unity.ml-agents\Runtime\Grpc\CommunicatorObjects
+set DST_DIR_P=..\ml-agents-envs
+set PROTO_PATH=proto
+
+set PYTHON_PACKAGE=mlagents_envs\communicator_objects
+
+rem clean
+rd /s /q %DST_DIR_C%
+rd /s /q %DST_DIR_P%\%PYTHON_PACKAGE%
+mkdir %DST_DIR_C%
+mkdir %DST_DIR_P%\%PYTHON_PACKAGE%
+
+rem generate proto objects in python and C#
+
+for %%i in (%SRC_DIR%\*.proto) do (
+    %COMPILER%\protoc --proto_path=proto --csharp_opt=internal_access --csharp_out=%DST_DIR_C% %%i
+    %COMPILER%\protoc --proto_path=proto --python_out=%DST_DIR_P% %%i
+)
+
+rem grpc
+
+set GRPC=unity_to_external.proto
+
+%COMPILER%\protoc --proto_path=proto --csharp_out %DST_DIR_C% --grpc_out=internal_access:%DST_DIR_C% %SRC_DIR%\%GRPC% --plugin=protoc-gen-grpc=%COMPILER%\grpc_csharp_plugin.exe --csharp_opt=internal_access
+python -m grpc_tools.protoc --proto_path=proto --python_out=%DST_DIR_P% --grpc_python_out=%DST_DIR_P% %SRC_DIR%\%GRPC%
+
+rem Generate the init file for the python module
+rem rm -f $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+setlocal enabledelayedexpansion
+for %%i in (%DST_DIR_P%\%PYTHON_PACKAGE%\*.py) do (
+set FILE=%%~ni
+rem echo from .$(basename $FILE) import * >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
+echo from .!FILE! import * >> %DST_DIR_P%\%PYTHON_PACKAGE%\__init__.py
+)
+
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_action.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_action.proto
new file mode 100644
index 0000000000000000000000000000000000000000..45f30b2597ebe56038df146bcb9b9c6521c239bc
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_action.proto
@@ -0,0 +1,14 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message AgentActionProto {
+    repeated float vector_actions_deprecated = 1; // mark as deprecated in communicator v1.3.0
+    reserved 2; // deprecated string text_actions = 2;
+    reserved 3; //deprecated repeated float memories = 3;
+    float value = 4;
+    reserved 5; // deprecated CustomActionProto custom_action = 5;
+    repeated float continuous_actions = 6;
+    repeated int32 discrete_actions = 7;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info.proto
new file mode 100644
index 0000000000000000000000000000000000000000..eacc9567eaa9a37b0000eb98b3d7117d77c8a50f
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info.proto
@@ -0,0 +1,24 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/observation.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message AgentInfoProto {
+    reserved 1; // deprecated repeated float stacked_vector_observation = 1;
+    reserved 2; // deprecated repeated bytes visual_observations = 2;
+    reserved 3; // deprecated string text_observation = 3;
+    reserved 4; // repeated float stored_vector_actions = 4;
+    reserved 5; // deprecated string stored_text_actions = 5;
+    reserved 6; //repeated float memories = 6;
+    float reward = 7;
+    bool done = 8;
+    bool max_step_reached = 9;
+    int32 id = 10;
+    repeated bool action_mask = 11;
+    reserved 12; // deprecated CustomObservationProto custom_observation = 12;
+    repeated ObservationProto observations = 13;
+    int32 group_id = 14;
+    float group_reward = 15;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info_action_pair.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info_action_pair.proto
new file mode 100644
index 0000000000000000000000000000000000000000..e51ed2a3effa78e5ff2c0fefc79842db011b1b87
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info_action_pair.proto
@@ -0,0 +1,12 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/agent_info.proto";
+import "mlagents_envs/communicator_objects/agent_action.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message AgentInfoActionPairProto {
+    AgentInfoProto agent_info = 1;
+    AgentActionProto action_info = 2;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/brain_parameters.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/brain_parameters.proto
new file mode 100644
index 0000000000000000000000000000000000000000..6e9bbf636bb2e44f579a6338ac232c99d2935835
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/brain_parameters.proto
@@ -0,0 +1,25 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/space_type.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message ActionSpecProto {
+    int32 num_continuous_actions = 1;
+    int32 num_discrete_actions = 2;
+    repeated int32 discrete_branch_sizes = 3;
+    repeated string action_descriptions = 4;
+}
+
+message BrainParametersProto {
+    reserved 1; // deprecated int32 vector_observation_size = 1;
+    reserved 2; // deprecated int32 num_stacked_vector_observations = 2;
+    repeated int32 vector_action_size_deprecated = 3; // mark as deprecated in communicator v1.3.0
+    reserved 4; // deprecated repeated ResolutionProto camera_resolutions
+    repeated string vector_action_descriptions_deprecated = 5; // mark as deprecated in communicator v1.3.0
+    SpaceTypeProto vector_action_space_type_deprecated = 6; // mark as deprecated in communicator v1.3.0
+    string brain_name = 7;
+    bool is_training = 8;
+    ActionSpecProto action_spec = 9;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/capabilities.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/capabilities.proto
new file mode 100644
index 0000000000000000000000000000000000000000..4a7690cd7f41b22a895e22e7e10ec544bead541d
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/capabilities.proto
@@ -0,0 +1,31 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+/*
+ * A Capabilities message that will communicate both C# and Python
+ * what features are available to both.
+ */
+message UnityRLCapabilitiesProto {
+    // These are the 1.0 capabilities.
+    bool baseRLCapabilities = 1;
+
+    // concatenated PNG files for compressed visual observations with >3 channels.
+    bool concatenatedPngObservations = 2;
+
+    // compression mapping for stacking compressed observations.
+    bool compressedChannelMapping = 3;
+
+    // support for hybrid action spaces (discrete + continuous)
+    bool hybridActions = 4;
+
+    // support for training analytics
+    bool trainingAnalytics = 5;
+
+    // Support for variable length observations of rank 2
+    bool variableLengthObservation = 6;
+
+    // Support for multi agent groups and group rewards
+    bool multiAgentGroups = 7;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/command.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/command.proto
new file mode 100644
index 0000000000000000000000000000000000000000..b0645d2f8a10410b71479f5b9dc32e72558bc719
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/command.proto
@@ -0,0 +1,10 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+enum CommandProto {
+        STEP = 0;
+        RESET = 1;
+        QUIT = 2;
+    }
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/custom_reset_parameters.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/custom_reset_parameters.proto
new file mode 100644
index 0000000000000000000000000000000000000000..d371166a89b5bb3cfd1387cdb5908794e89c982c
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/custom_reset_parameters.proto
@@ -0,0 +1,7 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message CustomResetParametersProto {
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/demonstration_meta.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/demonstration_meta.proto
new file mode 100644
index 0000000000000000000000000000000000000000..520508e7789dcbb942a54178be66d5d990301070
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/demonstration_meta.proto
@@ -0,0 +1,12 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message DemonstrationMetaProto {
+    int32 api_version = 1;
+    string demonstration_name = 2;
+    int32 number_steps = 3;
+    int32 number_episodes = 4;
+    float mean_reward = 5;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/engine_configuration.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/engine_configuration.proto
new file mode 100644
index 0000000000000000000000000000000000000000..2f014d206606daae11279cd649a080dc1a3f938f
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/engine_configuration.proto
@@ -0,0 +1,13 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message EngineConfigurationProto {
+    int32 width = 1;
+    int32 height = 2;
+    int32 quality_level = 3;
+    float time_scale = 4;
+    int32 target_frame_rate = 5;
+    bool show_monitor = 6;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/header.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/header.proto
new file mode 100644
index 0000000000000000000000000000000000000000..10a9f96342a7c2bd4ab3979f51d878ee3b12d90a
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/header.proto
@@ -0,0 +1,9 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message HeaderProto {
+	int32 status = 1;
+	string message = 2;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/observation.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/observation.proto
new file mode 100644
index 0000000000000000000000000000000000000000..94b65eaa8efe4f837f5462f1c4cc5ad5443aeaaf
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/observation.proto
@@ -0,0 +1,36 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+enum CompressionTypeProto {
+    NONE = 0;
+    PNG = 1;
+}
+
+enum ObservationTypeProto {
+    DEFAULT = 0;
+    GOAL_SIGNAL = 1;
+    reserved 2; // Reserved for potential "reward" type
+    reserved 3; // Reserved for potential "message" type
+}
+
+message ObservationProto {
+    message FloatData {
+        repeated float data = 1;
+    }
+
+    repeated int32 shape = 1;
+    CompressionTypeProto compression_type = 2;
+    oneof observation_data {
+        bytes compressed_data = 3;
+        FloatData float_data = 4;
+    }
+    repeated int32 compressed_channel_mapping = 5;
+    repeated int32 dimension_properties = 6;
+    ObservationTypeProto observation_type = 7;
+    // Optional name of the observation.
+    // This will be set to the ISensor name when writing,
+    // and read into the ObservationSpec in the low-level API
+    string name = 8;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/space_type.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/space_type.proto
new file mode 100644
index 0000000000000000000000000000000000000000..412fdd5109cb5c54cb120ce52dcd8a2422944dc8
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/space_type.proto
@@ -0,0 +1,10 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+enum SpaceTypeProto {
+    discrete = 0;
+    continuous = 1;
+}
+
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/training_analytics.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/training_analytics.proto
new file mode 100644
index 0000000000000000000000000000000000000000..08905c516bb1aecc4a590173302dafe53caab024
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/training_analytics.proto
@@ -0,0 +1,33 @@
+syntax = "proto3";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message TrainingEnvironmentInitialized {
+    string mlagents_version = 1;
+    string mlagents_envs_version = 2;
+    string python_version = 3;
+    string torch_version = 4;
+    string torch_device_type = 5;
+    int32 num_envs = 6;
+    int32 num_environment_parameters = 7;
+    string run_options = 8;
+}
+
+message TrainingBehaviorInitialized {
+    string behavior_name = 1;
+    string trainer_type = 2;
+    bool extrinsic_reward_enabled = 3;
+    bool gail_reward_enabled = 4;
+    bool curiosity_reward_enabled = 5;
+    bool rnd_reward_enabled = 6;
+    bool behavioral_cloning_enabled = 7;
+    bool recurrent_enabled = 8;
+    string visual_encoder = 9;
+    int32 num_network_layers = 10;
+    int32 num_network_hidden_units = 11;
+    bool trainer_threaded = 12;
+    bool self_play_enabled = 13;
+    bool curriculum_enabled = 14;
+    string config = 15;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_input.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_input.proto
new file mode 100644
index 0000000000000000000000000000000000000000..82432fc3a9f2dc5126e968e4977718a272d7141e
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_input.proto
@@ -0,0 +1,14 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/unity_rl_input.proto";
+import "mlagents_envs/communicator_objects/unity_rl_initialization_input.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message UnityInputProto {
+    UnityRLInputProto rl_input = 1;
+    UnityRLInitializationInputProto rl_initialization_input = 2;
+
+    //More messages can be added here
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_message.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_message.proto
new file mode 100644
index 0000000000000000000000000000000000000000..0bff435fb479fa6596ddbc305566dd7adc7bb948
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_message.proto
@@ -0,0 +1,15 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/unity_output.proto";
+import "mlagents_envs/communicator_objects/unity_input.proto";
+import "mlagents_envs/communicator_objects/header.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message UnityMessageProto {
+    HeaderProto header = 1;
+    UnityOutputProto unity_output = 2;
+    UnityInputProto unity_input = 3;
+}
+
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_output.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_output.proto
new file mode 100644
index 0000000000000000000000000000000000000000..2714f256698e3a1584f644dd7afd3b8e9321282d
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_output.proto
@@ -0,0 +1,15 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/unity_rl_output.proto";
+import "mlagents_envs/communicator_objects/unity_rl_initialization_output.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message UnityOutputProto {
+    UnityRLOutputProto rl_output = 1;
+    UnityRLInitializationOutputProto rl_initialization_output = 2;
+
+    //More messages can be added here
+}
+
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_input.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
new file mode 100644
index 0000000000000000000000000000000000000000..458e1d75d4b8bff822f12d3d871b36ef9125877f
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
@@ -0,0 +1,22 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/capabilities.proto";
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+// The initializaiton message - this is typically sent from the Python trainer to the C# environment.
+message UnityRLInitializationInputProto {
+    int32 seed = 1;
+
+    // Communication protocol version that the initiating side (typically the Python trainer) is using.
+    string communication_version = 2;
+
+    // Package/library version that the initiating side (typically the Python trainer) is using.
+    string package_version = 3;
+
+    // The RL Capabilities of the Python trainer.
+    UnityRLCapabilitiesProto capabilities = 4;
+
+    // The number of training areas to instantiate
+    int32 num_areas = 5;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_output.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
new file mode 100644
index 0000000000000000000000000000000000000000..775dd6577cf1322dd5cca86c3033c211adc27d98
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
@@ -0,0 +1,27 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/capabilities.proto";
+import "mlagents_envs/communicator_objects/brain_parameters.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+// The request message containing the academy's parameters.
+message UnityRLInitializationOutputProto {
+    string name = 1;
+
+    // Communication protocol version that the responding side (typically the C# environment) is using.
+    string communication_version = 2;
+
+    string log_path = 3;
+
+    repeated BrainParametersProto brain_parameters = 5;
+
+    reserved 6; //environment parameters
+
+    // Package/library version that the responding side (typically the C# environment) is using.
+    string package_version = 7;
+
+    // The RL Capabilities of the C# package.
+    UnityRLCapabilitiesProto capabilities = 8;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_input.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_input.proto
new file mode 100644
index 0000000000000000000000000000000000000000..1795203409706090f2fffe0910c47dfb3ca7bd5f
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_input.proto
@@ -0,0 +1,18 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/agent_action.proto";
+import "mlagents_envs/communicator_objects/command.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message UnityRLInputProto {
+    message ListAgentActionProto {
+            repeated AgentActionProto value = 1;
+        }
+    map<string, ListAgentActionProto> agent_actions = 1;
+    reserved 2; //deprecated environment proto
+    reserved 3; //deprecated is_trainig
+    CommandProto command = 4;
+    bytes side_channel = 5;
+}
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_output.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_output.proto
new file mode 100644
index 0000000000000000000000000000000000000000..223480cede108c16a230a2cea663b51405bd2a11
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_rl_output.proto
@@ -0,0 +1,16 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/agent_info.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+message UnityRLOutputProto {
+    message ListAgentInfoProto {
+        repeated AgentInfoProto value = 1;
+    }
+    reserved 1; // deprecated bool global_done field
+    map<string, ListAgentInfoProto> agentInfos = 2;
+    bytes side_channel = 3;
+}
+
diff --git a/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_to_external.proto b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_to_external.proto
new file mode 100644
index 0000000000000000000000000000000000000000..798dac86eda8bf5642bd74494ca44d3c6e9790dc
--- /dev/null
+++ b/protobuf-definitions/proto/mlagents_envs/communicator_objects/unity_to_external.proto
@@ -0,0 +1,12 @@
+syntax = "proto3";
+
+import "mlagents_envs/communicator_objects/unity_message.proto";
+
+option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
+package communicator_objects;
+
+service UnityToExternalProto {
+    // Sends the academy parameters
+    rpc Exchange(UnityMessageProto) returns (UnityMessageProto) {}
+}
+
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..146e90862181967f48cc4f66f253585562cb002f
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+addopts = --strict-markers
+markers =
+    slow: Slow tests (likely from training)
diff --git a/results/Pyramids Training/Pyramids.onnx b/results/Pyramids Training/Pyramids.onnx
new file mode 100644
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diff --git a/results/Pyramids Training/configuration.yaml b/results/Pyramids Training/configuration.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d747af353b305feb208147f3185d0cc5223f2008
--- /dev/null
+++ b/results/Pyramids Training/configuration.yaml	
@@ -0,0 +1,90 @@
+default_settings: null
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      shared_critic: false
+      learning_rate_schedule: linear
+      beta_schedule: linear
+      epsilon_schedule: linear
+    checkpoint_interval: 500000
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+      memory: null
+      goal_conditioning_type: hyper
+      deterministic: false
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+        network_settings:
+          normalize: false
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+          memory: null
+          goal_conditioning_type: hyper
+          deterministic: false
+      rnd:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          normalize: false
+          hidden_units: 64
+          num_layers: 3
+          vis_encode_type: simple
+          memory: null
+          goal_conditioning_type: hyper
+          deterministic: false
+        learning_rate: 0.0001
+        encoding_size: null
+    init_path: null
+    keep_checkpoints: 5
+    even_checkpoints: false
+    max_steps: 100000
+    time_horizon: 128
+    summary_freq: 30000
+    threaded: false
+    self_play: null
+    behavioral_cloning: null
+env_settings:
+  env_path: ./training-envs-executables/linux/Pyramids/Pyramids
+  env_args: null
+  base_port: 5005
+  num_envs: 1
+  num_areas: 1
+  seed: -1
+  max_lifetime_restarts: 10
+  restarts_rate_limit_n: 1
+  restarts_rate_limit_period_s: 60
+engine_settings:
+  width: 84
+  height: 84
+  quality_level: 5
+  time_scale: 20
+  target_frame_rate: -1
+  capture_frame_rate: 60
+  no_graphics: true
+environment_parameters: null
+checkpoint_settings:
+  run_id: Pyramids Training
+  initialize_from: null
+  load_model: false
+  resume: false
+  force: false
+  train_model: false
+  inference: false
+  results_dir: results
+torch_settings:
+  device: null
+debug: false
diff --git a/results/Pyramids Training/run_logs/Player-0.log b/results/Pyramids Training/run_logs/Player-0.log
new file mode 100644
index 0000000000000000000000000000000000000000..5145a430cd75f77e5efeb85c664a610de8274219
--- /dev/null
+++ b/results/Pyramids Training/run_logs/Player-0.log	
@@ -0,0 +1,317 @@
+Mono path[0] = '/content/ml-agents/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed'
+Mono config path = '/content/ml-agents/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc'
+Found 1 interfaces on host : 0) 172.28.0.12
+Multi-casting "[IP] 172.28.0.12 [Port] 55029 [Flags] 2 [Guid] 2863475657 [EditorId] 764847374 [Version] 1048832 [Id] LinuxPlayer(13,172.28.0.12) [Debug] 0 [PackageName] LinuxPlayer [ProjectName] UnityEnvironment" to [225.0.0.222:54997]...
+Preloaded 'lib_burst_generated.so'
+Preloaded 'libgrpc_csharp_ext.x64.so'
+PlayerPrefs - Creating folder: /root/.config/unity3d/Unity Technologies
+PlayerPrefs - Creating folder: /root/.config/unity3d/Unity Technologies/UnityEnvironment
+Unable to load player prefs
+Initialize engine version: 2021.3.5f1 (40eb3a945986)
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+NullGfxDevice:
+    Version:  NULL 1.0 [1.0]
+    Renderer: Null Device
+    Vendor:   Unity Technologies
+ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
+ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
+ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
+ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory
+ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default
+FMOD failed to initialize the output device.: "Error initializing output device. " (60)
+Forced to initialize FMOD to to the device driver's system output rate 48000, this may impact performance and/or give inconsistent experiences compared to selected sample rate 48000
+ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
+ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
+ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
+ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
+ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory
+ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default
+FMOD failed to initialize the output device.: "Error initializing output device. " (60)
+FMOD initialized on nosound output
+Begin MonoManager ReloadAssembly
+- Completed reload, in  0.146 seconds
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+ERROR: Shader Sprites/Mask shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+ERROR: Shader Legacy Shaders/VertexLit shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Autodesk Interactive' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader Autodesk Interactive shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Autodesk Interactive' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
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+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader Legacy Shaders/Diffuse shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Legacy Shaders/Diffuse' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+WARNING: Shader Unsupported: 'ML-Agents/GridPattern' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader ML-Agents/GridPattern shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'ML-Agents/GridPattern' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+WARNING: Shader Unsupported: 'Standard' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader Standard shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Standard' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+UnloadTime: 1.131512 ms
+ERROR: Shader UI/Default shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+requesting resize 84 x 84
+Setting up 1 worker threads for Enlighten.
+PlayerConnection::CleanupMemory Statistics:
+[ALLOC_TEMP_TLS] TLS Allocator
+  StackAllocators : 
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diff --git a/results/Pyramids Training/run_logs/timers.json b/results/Pyramids Training/run_logs/timers.json
new file mode 100644
index 0000000000000000000000000000000000000000..414ea86f29620576a959bc06000a38e3e31de9d3
--- /dev/null
+++ b/results/Pyramids Training/run_logs/timers.json	
@@ -0,0 +1,391 @@
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+                                        "TorchPPOOptimizer.update": {
+                                            "total": 19.42383548600054,
+                                            "count": 2286,
+                                            "self": 19.42383548600054
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                },
+                "trainer_threads": {
+                    "total": 1.4109999710854026e-06,
+                    "count": 1,
+                    "self": 1.4109999710854026e-06
+                },
+                "TrainerController._save_models": {
+                    "total": 0.173227155999939,
+                    "count": 1,
+                    "self": 0.002028516999871499,
+                    "children": {
+                        "RLTrainer._checkpoint": {
+                            "total": 0.1711986390000675,
+                            "count": 1,
+                            "self": 0.1711986390000675
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/results/Pyramids Training/run_logs/training_status.json b/results/Pyramids Training/run_logs/training_status.json
new file mode 100644
index 0000000000000000000000000000000000000000..381327128fed52188fc009e6e6603f2b70aa4cd3
--- /dev/null
+++ b/results/Pyramids Training/run_logs/training_status.json	
@@ -0,0 +1,29 @@
+{
+    "Pyramids": {
+        "checkpoints": [
+            {
+                "steps": 100064,
+                "file_path": "results/Pyramids Training/Pyramids/Pyramids-100064.onnx",
+                "reward": null,
+                "creation_time": 1692804747.3104196,
+                "auxillary_file_paths": [
+                    "results/Pyramids Training/Pyramids/Pyramids-100064.pt"
+                ]
+            }
+        ],
+        "final_checkpoint": {
+            "steps": 100064,
+            "file_path": "results/Pyramids Training/Pyramids.onnx",
+            "reward": null,
+            "creation_time": 1692804747.3104196,
+            "auxillary_file_paths": [
+                "results/Pyramids Training/Pyramids/Pyramids-100064.pt"
+            ]
+        }
+    },
+    "metadata": {
+        "stats_format_version": "0.3.0",
+        "mlagents_version": "0.31.0.dev0",
+        "torch_version": "1.11.0+cu102"
+    }
+}
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..939dd953efeb37f79dac576ea2012938b42382e8
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,24 @@
+[coverage:run]
+omit = */tests/*
+
+[coverage:report]
+# Run "pytest --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity" to see the current coverage percentage.
+# Run the above plus "--cov-report html" to get a nice visualization of what is/isn't covered in html format.
+fail_under = 60
+
+
+[flake8]
+# black will apply a line length of 88 to code but not docstrings/comments
+# This seems like a decent compromise between readability and redoing all the docstrings.
+max-line-length=120
+
+ignore =
+    # Black tends to introduce things flake8 doesn't like, such as "line break before binary operator"
+    # or whitespace before ':'. Rather than fight with black, just ignore these for now.
+    W503, E203,
+    # flake-tidy-import adds this warning, which we don't really care about for now
+    I200,
+
+banned-modules = tensorflow = use mlagents.tf_utils instead (it handles tf2 compat).
+                 logging = use mlagents_envs.logging_util instead
+                 torch = use mlagents.torch_utils instead (handles GPU detection).
diff --git a/test_constraints_max_version.txt b/test_constraints_max_version.txt
new file mode 100644
index 0000000000000000000000000000000000000000..18d655ee7ee460b4d6ab9b128ffee39eb0678ecb
--- /dev/null
+++ b/test_constraints_max_version.txt
@@ -0,0 +1,3 @@
+# pip constraints to use the *highest* versions allowed in ml-agents/setup.py
+# For projects with upper bounds, we should periodically update this list to the latest
+torch==1.11.0
diff --git a/test_constraints_mid_version.txt b/test_constraints_mid_version.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea9fa2e27fa49fe93cbb13a276303a96b5aee207
--- /dev/null
+++ b/test_constraints_mid_version.txt
@@ -0,0 +1,2 @@
+# pip constraints to use a version in the middle of allowed ranges in ml-agents/setup.py
+torch==1.8.0
diff --git a/test_constraints_min_version.txt b/test_constraints_min_version.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a6a3046846a2945536b988158f85f0805f1e6c78
--- /dev/null
+++ b/test_constraints_min_version.txt
@@ -0,0 +1,2 @@
+# pip constraints to use the *lowest* versions allowed in ml-agents/setup.py
+torch==1.7.0
diff --git a/test_requirements.txt b/test_requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cceaab1b947ef2403be263ada1e22345400abeb0
--- /dev/null
+++ b/test_requirements.txt
@@ -0,0 +1,5 @@
+# Test-only dependencies should go here, not in setup.py
+pytest>=6.2.5
+pytest-cov>=2.12.1
+pytest-xdist>=2.5.0
+filelock>=3.4.2
diff --git a/training-envs-executables/linux/Pyramids.zip b/training-envs-executables/linux/Pyramids.zip
new file mode 100644
index 0000000000000000000000000000000000000000..3075c626c48ffcd1175cd2269cacfe4d9d9b68d0
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e0f1cfa88a380b42644d974525a7dbfe144089883a401526b8341c5441f7cae
+size 42907187
diff --git a/training-envs-executables/linux/Pyramids/Pyramids b/training-envs-executables/linux/Pyramids/Pyramids
new file mode 100644
index 0000000000000000000000000000000000000000..5cf238f1e77b7b5acdeab0c49cd37ef84a7bb37e
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_BurstDebugInformation_DoNotShip/Data/Plugins/lib_burst_generated.txt b/training-envs-executables/linux/Pyramids/Pyramids_BurstDebugInformation_DoNotShip/Data/Plugins/lib_burst_generated.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4108878b04e6d6145df72e33655f0217d5ac0fe8
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_BurstDebugInformation_DoNotShip/Data/Plugins/lib_burst_generated.txt
@@ -0,0 +1,920 @@
+Library: lib_burst_generated
+--platform=Linux
+--backend=burst-llvm-11
+--target=X64_SSE2
+--global-safety-checks-setting=Off
+--dump=Function
+--float-precision=Standard
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+--compilation-defines=PLATFORM_ARCH_64
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+--compilation-defines=USE_SEARCH_ENGINE_API
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+--compilation-defines=USE_SEARCH_MODULE
+--compilation-defines=USE_PROPERTY_DATABASE
+--compilation-defines=USE_SEARCH_EXTENSION_API
+--compilation-defines=SCENE_TEMPLATE_MODULE
+--compilation-defines=ENABLE_AUDIO
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+--compilation-defines=ENABLE_MULTIPLE_DISPLAYS
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+--compilation-defines=ENABLE_TEXTURE_STREAMING
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+--compilation-defines=ENABLE_MANAGED_UNITYTLS
+--compilation-defines=INCLUDE_DYNAMIC_GI
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+--compilation-defines=PLATFORM_STANDALONE
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+--debug=Full
+--output=/home/singularity/Downloads/ml-agents-main/Project/Temp/StagingArea/Data/Plugins/lib_burst_generated
+--root-assembly=/home/singularity/Downloads/ml-agents-main/Project/Temp/StagingArea/Data/Managed/Unity.ML-Agents.dll
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+LogSoftmaxEndJob_Full_Half, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+LogSoftmaxEndJob_Full_Half&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--eb4a7c0e52363096b192b85e95293dff
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+--platform=Linux
+--backend=burst-llvm-11
+--target=AVX2
+--global-safety-checks-setting=Off
+--dump=Function
+--float-precision=Standard
+--disable-warnings=BC1370
+--compilation-defines=UNITY_2021_3_5
+--compilation-defines=UNITY_2021_3
+--compilation-defines=UNITY_2021
+--compilation-defines=UNITY_5_3_OR_NEWER
+--compilation-defines=UNITY_5_4_OR_NEWER
+--compilation-defines=UNITY_5_5_OR_NEWER
+--compilation-defines=UNITY_5_6_OR_NEWER
+--compilation-defines=UNITY_2017_1_OR_NEWER
+--compilation-defines=UNITY_2017_2_OR_NEWER
+--compilation-defines=UNITY_2017_3_OR_NEWER
+--compilation-defines=UNITY_2017_4_OR_NEWER
+--compilation-defines=UNITY_2018_1_OR_NEWER
+--compilation-defines=UNITY_2018_2_OR_NEWER
+--compilation-defines=UNITY_2018_3_OR_NEWER
+--compilation-defines=UNITY_2018_4_OR_NEWER
+--compilation-defines=UNITY_2019_1_OR_NEWER
+--compilation-defines=UNITY_2019_2_OR_NEWER
+--compilation-defines=UNITY_2019_3_OR_NEWER
+--compilation-defines=UNITY_2019_4_OR_NEWER
+--compilation-defines=UNITY_2020_1_OR_NEWER
+--compilation-defines=UNITY_2020_2_OR_NEWER
+--compilation-defines=UNITY_2020_3_OR_NEWER
+--compilation-defines=UNITY_2021_1_OR_NEWER
+--compilation-defines=UNITY_2021_2_OR_NEWER
+--compilation-defines=UNITY_2021_3_OR_NEWER
+--compilation-defines=PLATFORM_ARCH_64
+--compilation-defines=UNITY_64
+--compilation-defines=USE_SEARCH_ENGINE_API
+--compilation-defines=USE_SEARCH_TABLE
+--compilation-defines=USE_SEARCH_MODULE
+--compilation-defines=USE_PROPERTY_DATABASE
+--compilation-defines=USE_SEARCH_EXTENSION_API
+--compilation-defines=SCENE_TEMPLATE_MODULE
+--compilation-defines=ENABLE_AUDIO
+--compilation-defines=ENABLE_CACHING
+--compilation-defines=ENABLE_CLOTH
+--compilation-defines=ENABLE_MICROPHONE
+--compilation-defines=ENABLE_MULTIPLE_DISPLAYS
+--compilation-defines=ENABLE_PHYSICS
+--compilation-defines=ENABLE_TEXTURE_STREAMING
+--compilation-defines=ENABLE_VIRTUALTEXTURING
+--compilation-defines=ENABLE_UNET
+--compilation-defines=ENABLE_LZMA
+--compilation-defines=ENABLE_UNITYEVENTS
+--compilation-defines=ENABLE_VR
+--compilation-defines=ENABLE_WEBCAM
+--compilation-defines=ENABLE_UNITYWEBREQUEST
+--compilation-defines=ENABLE_WWW
+--compilation-defines=ENABLE_CLOUD_SERVICES
+--compilation-defines=ENABLE_CLOUD_SERVICES_COLLAB
+--compilation-defines=ENABLE_CLOUD_SERVICES_COLLAB_SOFTLOCKS
+--compilation-defines=ENABLE_CLOUD_SERVICES_ADS
+--compilation-defines=ENABLE_CLOUD_SERVICES_USE_WEBREQUEST
+--compilation-defines=ENABLE_CLOUD_SERVICES_CRASH_REPORTING
+--compilation-defines=ENABLE_CLOUD_SERVICES_PURCHASING
+--compilation-defines=ENABLE_CLOUD_SERVICES_ANALYTICS
+--compilation-defines=ENABLE_CLOUD_SERVICES_UNET
+--compilation-defines=ENABLE_CLOUD_SERVICES_BUILD
+--compilation-defines=ENABLE_CLOUD_LICENSE
+--compilation-defines=ENABLE_EDITOR_HUB_LICENSE
+--compilation-defines=ENABLE_WEBSOCKET_CLIENT
+--compilation-defines=ENABLE_DIRECTOR_AUDIO
+--compilation-defines=ENABLE_DIRECTOR_TEXTURE
+--compilation-defines=ENABLE_MANAGED_JOBS
+--compilation-defines=ENABLE_MANAGED_TRANSFORM_JOBS
+--compilation-defines=ENABLE_MANAGED_ANIMATION_JOBS
+--compilation-defines=ENABLE_MANAGED_AUDIO_JOBS
+--compilation-defines=ENABLE_MANAGED_UNITYTLS
+--compilation-defines=INCLUDE_DYNAMIC_GI
+--compilation-defines=ENABLE_SCRIPTING_GC_WBARRIERS
+--compilation-defines=PLATFORM_SUPPORTS_MONO
+--compilation-defines=RENDER_SOFTWARE_CURSOR
+--compilation-defines=ENABLE_VIDEO
+--compilation-defines=ENABLE_ACCELERATOR_CLIENT_DEBUGGING
+--compilation-defines=PLATFORM_STANDALONE
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+--compilation-defines=PLATFORM_STANDALONE_LINUX
+--compilation-defines=UNITY_STANDALONE_LINUX
+--compilation-defines=UNITY_STANDALONE
+--compilation-defines=UNITY_STANDALONE_LINUX_API
+--compilation-defines=ENABLE_RUNTIME_GI
+--compilation-defines=ENABLE_MOVIES
+--compilation-defines=ENABLE_NETWORK
+--compilation-defines=ENABLE_CRUNCH_TEXTURE_COMPRESSION
+--compilation-defines=ENABLE_CLUSTER_SYNC
+--compilation-defines=ENABLE_CLUSTERINPUT
+--compilation-defines=ENABLE_SPATIALTRACKING
+--compilation-defines=ENABLE_MODULAR_UNITYENGINE_ASSEMBLIES
+--compilation-defines=ENABLE_WEBSOCKET_HOST
+--compilation-defines=ENABLE_MONO
+--compilation-defines=NET_4_6
+--compilation-defines=NET_UNITY_4_8
+--compilation-defines=DEVELOPMENT_BUILD
+--compilation-defines=ENABLE_PROFILER
+--compilation-defines=DEBUG
+--compilation-defines=TRACE
+--compilation-defines=UNITY_ASSERTIONS
+--compilation-defines=ENABLE_CUSTOM_RENDER_TEXTURE
+--compilation-defines=ENABLE_DIRECTOR
+--compilation-defines=ENABLE_LOCALIZATION
+--compilation-defines=ENABLE_SPRITES
+--compilation-defines=ENABLE_TERRAIN
+--compilation-defines=ENABLE_TILEMAP
+--compilation-defines=ENABLE_TIMELINE
+--compilation-defines=ENABLE_INPUT_SYSTEM
+--compilation-defines=ENABLE_LEGACY_INPUT_MANAGER
+--compilation-defines=MLA_UNITY_ANALYTICS_MODULE
+--compilation-defines=MLA_UNITY_PHYSICS_MODULE
+--compilation-defines=MLA_UNITY_PHYSICS2D_MODULE
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+--compilation-defines=UNITY_INPUT_SYSTEM_ENABLE_PHYSICS2D
+--compilation-defines=UNITY_INPUT_SYSTEM_ENABLE_UI
+--compilation-defines=MLA_INPUT_SYSTEM
+--generate-link-xml=Temp/burst.link.xml
+--debug=Full
+--output=/home/singularity/Downloads/ml-agents-main/Project/Temp/StagingArea/Data/Plugins/lib_burst_generated
+--root-assembly=/home/singularity/Downloads/ml-agents-main/Project/Temp/StagingArea/Data/Managed/Unity.ML-Agents.dll
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ReduceMeanJob_Full_Half, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ReduceMeanJob_Full_Half&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--c0644485a5766eae9971826e2259addf
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ExpBiasReduceJob_Full_Half, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ExpBiasReduceJob_Full_Half&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--867c852a588f0d3dcef11630a2250457
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MaxPool2DJob_Full_Half, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MaxPool2DJob_Full_Half&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--b092734994d4cd383e6380e5366018b5
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MatrixMultiplyJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MatrixMultiplyJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--f1cddd222bf1120d6d14e6c2919112ee
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MatrixMultiplyLegacyJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MatrixMultiplyLegacyJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--28eccbc6a35f172bf78db634390d9732
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MatrixMultiply3x2Job, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MatrixMultiply3x2Job&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--033a9e36a2b1b45dba309dca1837e57f
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MatrixMultiply4x4Job, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MatrixMultiply4x4Job&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--324a2fd4b39b3e0494a1d98831e85653
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+Im2ColSliceJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+Im2ColSliceJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--a1077d97544f4ae642b822616b46ece6
+--method=Unity.Jobs.IJobExtensions+JobStruct`1[[Unity.Barracuda.BurstCPUOps+ZeroBroadcastJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ZeroBroadcastJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--61bebe1d5e10e40420594e01d06cc35e
+--method=Unity.Jobs.IJobExtensions+JobStruct`1[[Unity.Barracuda.BurstCPUOps+VectorBroadcastJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+VectorBroadcastJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--52fb0c6e384d02df9bcb0d48022b3800
+--method=Unity.Jobs.IJobExtensions+JobStruct`1[[Unity.Barracuda.BurstCPUOps+MemFreeJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MemFreeJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--e5e0cc96e9c5920507d312b13330586a
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+LSTMDense3Job, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+LSTMDense3Job&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--65f072566edc0681170405b4cf5cfd74
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+LSTMDenseJob, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+LSTMDenseJob&, Unity.Barracuda, Version=2.3.1.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--c65df4d214a989caf02bfd86195d5bcc
+--platform=Linux
+--backend=burst-llvm-11
+--target=AVX2
+--global-safety-checks-setting=Off
+--dump=Function
+--float-precision=Low
+--float-mode=Fast
+--disable-warnings=BC1370
+--compilation-defines=UNITY_2021_3_5
+--compilation-defines=UNITY_2021_3
+--compilation-defines=UNITY_2021
+--compilation-defines=UNITY_5_3_OR_NEWER
+--compilation-defines=UNITY_5_4_OR_NEWER
+--compilation-defines=UNITY_5_5_OR_NEWER
+--compilation-defines=UNITY_5_6_OR_NEWER
+--compilation-defines=UNITY_2017_1_OR_NEWER
+--compilation-defines=UNITY_2017_2_OR_NEWER
+--compilation-defines=UNITY_2017_3_OR_NEWER
+--compilation-defines=UNITY_2017_4_OR_NEWER
+--compilation-defines=UNITY_2018_1_OR_NEWER
+--compilation-defines=UNITY_2018_2_OR_NEWER
+--compilation-defines=UNITY_2018_3_OR_NEWER
+--compilation-defines=UNITY_2018_4_OR_NEWER
+--compilation-defines=UNITY_2019_1_OR_NEWER
+--compilation-defines=UNITY_2019_2_OR_NEWER
+--compilation-defines=UNITY_2019_3_OR_NEWER
+--compilation-defines=UNITY_2019_4_OR_NEWER
+--compilation-defines=UNITY_2020_1_OR_NEWER
+--compilation-defines=UNITY_2020_2_OR_NEWER
+--compilation-defines=UNITY_2020_3_OR_NEWER
+--compilation-defines=UNITY_2021_1_OR_NEWER
+--compilation-defines=UNITY_2021_2_OR_NEWER
+--compilation-defines=UNITY_2021_3_OR_NEWER
+--compilation-defines=PLATFORM_ARCH_64
+--compilation-defines=UNITY_64
+--compilation-defines=USE_SEARCH_ENGINE_API
+--compilation-defines=USE_SEARCH_TABLE
+--compilation-defines=USE_SEARCH_MODULE
+--compilation-defines=USE_PROPERTY_DATABASE
+--compilation-defines=USE_SEARCH_EXTENSION_API
+--compilation-defines=SCENE_TEMPLATE_MODULE
+--compilation-defines=ENABLE_AUDIO
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index 0000000000000000000000000000000000000000..67d40fcb4c83377a108fa8c3e18aaf2a8fad2c62
Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestTextureModule.dll differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestTextureModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestTextureModule.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..8394884a90ed599c615b2da4648d7bbc8de394bd
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestWWWModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestWWWModule.dll
new file mode 100644
index 0000000000000000000000000000000000000000..3601cfcb795b3f1e5bcabf658981c2002152ffda
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestWWWModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestWWWModule.pdb
new file mode 100644
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Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.UnityWebRequestWWWModule.pdb differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VFXModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VFXModule.dll
new file mode 100644
index 0000000000000000000000000000000000000000..4c2d1058257921f114b6d30b416c2e68e6df3ea8
Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VFXModule.dll differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VFXModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VFXModule.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..61aea05d2951ced06e7b9adce995ca68a782e221
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VRModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VRModule.dll
new file mode 100644
index 0000000000000000000000000000000000000000..625d65aa38eb4f38dbaa6c056494baec7bcb944e
Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VRModule.dll differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VRModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VRModule.pdb
new file mode 100644
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VehiclesModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VehiclesModule.dll
new file mode 100644
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VehiclesModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VehiclesModule.pdb
new file mode 100644
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VideoModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VideoModule.dll
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index 0000000000000000000000000000000000000000..91555bb1aae1f1e755f97edb52b9ac903de0b894
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VideoModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VideoModule.pdb
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VirtualTexturingModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VirtualTexturingModule.dll
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VirtualTexturingModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.VirtualTexturingModule.pdb
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.WindModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.WindModule.dll
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.WindModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.WindModule.pdb
new file mode 100644
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.XRModule.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.XRModule.dll
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.XRModule.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.XRModule.pdb
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.dll
new file mode 100644
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.pdb b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/UnityEngine.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..3e3a93a25c4febffe9d219928993b2297994e2e8
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diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/mscorlib.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/mscorlib.dll
new file mode 100644
index 0000000000000000000000000000000000000000..4c1568c34f9a799608c9fd75ba47f35af292cac9
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/mscorlib.dll
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4801036d68c6305fc407a29fe49b3eb83b83dd444e36496dc7679fb8d91bbb49
+size 4622848
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/netstandard.dll b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/netstandard.dll
new file mode 100644
index 0000000000000000000000000000000000000000..37729a036b97ad2b25b9631319f12a43408dbaf6
Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/Managed/netstandard.dll differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/config
new file mode 100644
index 0000000000000000000000000000000000000000..c1d2863007699198d3745c8745e4eaaacde70547
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/config
@@ -0,0 +1,46 @@
+<configuration>
+	<dllmap dll="i:cygwin1.dll" target="libc.so.6" os="!windows" />
+	<dllmap dll="libc" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:libxslt.dll" target="libxslt.so" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libodbc.so.2" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libiodbc.dylib" os="osx"/>
+	<dllmap dll="oci" target="libclntsh.so" os="!windows"/>
+	<dllmap dll="db2cli" target="libdb2_36.so" os="!windows"/>
+	<dllmap dll="MonoPosixHelper" target="libMonoPosixHelper.so" os="!windows" />
+	<dllmap dll="System.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Net.Security.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Security.Cryptography.Native.Apple" target="$mono_libdir/libmono-native.so" os="osx" />
+	<dllmap dll="libmono-btls-shared" target="libmono-btls-shared.so" os="!windows" />
+	<dllmap dll="i:msvcrt" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:msvcrt.dll" target="libc.so.6" os="!windows"/>
+	<dllmap dll="sqlite" target="libsqlite.so.0" os="!windows"/>
+	<dllmap dll="sqlite3" target="libsqlite3.so.0" os="!windows"/>
+	<dllmap dll="libX11" target="libX11.so.6" os="!windows" />
+	<dllmap dll="libgdk-x11-2.0" target="libgdk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgdk_pixbuf-2.0" target="libgdk_pixbuf-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgtk-x11-2.0" target="libgtk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libglib-2.0" target="libglib-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgobject-2.0" target="libgobject-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgnomeui-2" target="libgnomeui-2.so.0" os="!windows"/>
+	<dllmap dll="librsvg-2" target="librsvg-2.so.2" os="!windows"/>
+	<dllmap dll="libXinerama" target="libXinerama.so.1" os="!windows" />
+	<dllmap dll="libasound" target="libasound.so.2" os="!windows" />
+	<dllmap dll="libcairo-2.dll" target="libcairo.so.2" os="!windows"/>
+	<dllmap dll="libcairo-2.dll" target="libcairo.2.dylib" os="osx"/>
+	<dllmap dll="libcups" target="libcups.so.2" os="!windows"/>
+	<dllmap dll="libcups" target="libcups.dylib" os="osx"/>
+	<dllmap dll="i:kernel32.dll">
+		<dllentry dll="__Internal" name="CopyMemory" target="mono_win32_compat_CopyMemory"/>
+		<dllentry dll="__Internal" name="FillMemory" target="mono_win32_compat_FillMemory"/>
+		<dllentry dll="__Internal" name="MoveMemory" target="mono_win32_compat_MoveMemory"/>
+		<dllentry dll="__Internal" name="ZeroMemory" target="mono_win32_compat_ZeroMemory"/>
+	</dllmap>
+	<dllmap dll="gdiplus" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdiplus.dll" target="libgdiplus.so.0"  os="!windows"/>
+	<dllmap dll="gdi32" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdi32.dll" target="libgdiplus.so.0" os="!windows"/>
+</configuration>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/machine.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..2577c81dd92aa49271253b58a0be8d18d25a24ec
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/machine.config
@@ -0,0 +1,280 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.DiagnosticsConfigurationHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/settings.map b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..9a52ccc0295c867dc6fb0cf190b94b70c11037d0
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/web.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..a5190c338bc6e4f9b78790721db81dd26ed69ae7
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/2.0/web.config
@@ -0,0 +1,153 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+			<add verb="*" path="Trace.axd" type="System.Web.Handlers.TraceHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="UrlRouting.axd" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.aspx" type="System.Web.UI.PageHandlerFactory, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.asmx" validate="false" type="System.Web.Services.Protocols.WebServiceHandlerFactory, System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.ashx" type="System.Web.UI.SimpleHandlerFactory, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="GET" path="WebResource.axd" type="System.Web.Handlers.AssemblyResourceLoader, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.master" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.resources" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.skin" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.browser" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.sitemap" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.webinfo" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.resx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.asax" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.ascx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.config" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.Config" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.cs" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.vb" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.csproj" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.vbproj" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.licx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.dll" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.rem" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="false" />
+			<add verb="*" path="*.soap" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="false" />
+			<add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add verb="GET,HEAD" path="*" type="System.Web.StaticFileHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*" type="System.Web.HttpMethodNotAllowedHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</httpHandlers>
+		<httpModules>
+			<add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="OutputCache" type="System.Web.Caching.OutputCacheModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="RoleManager" type="System.Web.Security.RoleManagerModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="Session" type="System.Web.SessionState.SessionStateModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<!--<add assembly="mscorlib" /> -->
+				<add assembly="System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+            			<add assembly="System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+            			<add assembly="System.Drawing, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Runtime.Serialization, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, processorArchitecture=MSIL"/>
+				<add assembly="System.IdentityModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, processorArchitecture=MSIL"/>
+				<add assembly="System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+				<add assembly="System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+            			<add assembly="System.Xml, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/machine.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..f3b71c43c53b54af5383704069d39ba3d72c0a7b
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/machine.config
@@ -0,0 +1,307 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.DiagnosticsConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+			<sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+				<section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				<sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+					<section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere"/>
+					<section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				</sectionGroup>
+			</sectionGroup>
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="standardEndpoints" type="System.ServiceModel.Configuration.StandardEndpointsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+			<section name="routing" type="System.ServiceModel.Routing.Configuration.RoutingSection, System.ServiceModel.Routing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			<section name="protocolMapping" type="System.ServiceModel.Configuration.ProtocolMappingSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.runtime.caching" type="System.Runtime.Caching.Configuration.CachingSectionGroup, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+		  <section name="memoryCache" type="System.Runtime.Caching.Configuration.MemoryCacheSection, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+                </sectionGroup>
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+			<endpointExtensions>
+				<add name="dynamicEndpoint" type="System.ServiceModel.Discovery.Configuration.DynamicEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="discoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.DiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpDiscoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpDiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="announcementEndpoint" type="System.ServiceModel.Discovery.Configuration.AnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpAnnouncementEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpAnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttpEndpoint" type="System.ServiceModel.Configuration.WebHttpEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webScriptEndpoint" type="System.ServiceModel.Configuration.WebScriptEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</endpointExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/settings.map b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..4c53aca6776656859d35fb7b7e6a64a5d54bfc6a
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/web.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..44cbe180dfd479bfdb79481475bae8ebaf95bebd
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.0/web.config
@@ -0,0 +1,252 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+  <system.codedom>
+        <compilers>
+            <compiler language="c#;cs;csharp" extension=".cs" warningLevel="4" type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+            <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4" type="Microsoft.VisualBasic.VBCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="OptionInfer" value="true"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+        </compilers>
+  </system.codedom>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+		  <add path="trace.axd" verb="*" type="System.Web.Handlers.TraceHandler" validate="True" />
+		  <add path="WebResource.axd" verb="GET" type="System.Web.Handlers.AssemblyResourceLoader" validate="True" />
+		  <add verb="*" path="*_AppService.axd" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.axd" verb="*" type="System.Web.HttpNotFoundHandler" validate="True" />
+		  <add path="*.aspx" verb="*" type="System.Web.UI.PageHandlerFactory" validate="True" />
+		  <add path="*.ashx" verb="*" type="System.Web.UI.SimpleHandlerFactory" validate="True" />
+		  <add path="*.asmx" verb="*" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add path="*.rem" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.soap" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.asax" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ascx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.master" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.skin" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.browser" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sitemap" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dll.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.exe.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.config" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cs" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.csproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vbproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.webinfo" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.licx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resources" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vjsproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.java" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.jsl" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldb" verb="*" type="System.Web.HttpForbiddenHandler"  validate="True" />
+		  <add path="*.ad" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.adprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.lddprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdm" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdmDocument" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.exclude" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.refresh" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <!--
+		  <add path="*.svc" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+                  <add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		  <add path="*.rules" verb="*" type="System.Web.HttpForbiddenHandler" validate="True"/>
+		  <!--
+		  <add path="*.xoml" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.xamlx" verb="*" type="System.Xaml.Hosting.XamlHttpHandlerFactory, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+		  <add path="*" verb="GET,HEAD,POST" type="System.Web.DefaultHttpHandler" validate="True" />
+		  <add path="*" verb="*" type="System.Web.HttpMethodNotAllowedHandler" validate="True" />
+		</httpHandlers>
+		<httpModules>
+		  <add name="OutputCache" type="System.Web.Caching.OutputCacheModule" />
+		  <add name="Session" type="System.Web.SessionState.SessionStateModule" />
+		  <!--
+		  <add name="WindowsAuthentication" type="System.Web.Security.WindowsAuthenticationModule" />
+		  -->
+		  <add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule" />
+		  <!--
+		  <add name="PassportAuthentication" type="System.Web.Security.PassportAuthenticationModule" />
+		  -->
+		  <add name="RoleManager" type="System.Web.Security.RoleManagerModule" />
+		  <add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule" />
+		  <!--
+		  <add name="FileAuthorization" type="System.Web.Security.FileAuthorizationModule" />
+		  -->
+		  <add name="AnonymousIdentification" type="System.Web.Security.AnonymousIdentificationModule" />
+		  <add name="Profile" type="System.Web.Profile.ProfileModule" />
+		  <!--
+		  <add name="ErrorHandlerModule" type="System.Web.Mobile.ErrorHandlerModule, System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		  <add name="ServiceModel" type="System.ServiceModel.Activation.HttpModule, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+		  -->
+		  <add name="UrlRoutingModule-4.0" type="System.Web.Routing.UrlRoutingModule" />
+		  <add name="ScriptModule-4.0" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">					
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+        		
+        		<controls>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.WebParts" assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.Expressions" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.DynamicData" assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</controls>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<add assembly="mscorlib" />
+				<add assembly="Microsoft.CSharp, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Xml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.EnterpriseServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<!-- <add assembly="System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" /> -->
+				<add assembly="System.IdentityModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xaml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Channels, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.Core, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="System.Data.DataSetExtensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xml.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ComponentModel.DataAnnotations, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Data.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<!-- <add assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> -->
+				<add assembly="System.Data.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.Data.Entity.Design, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<add assembly="System.Web.ApplicationServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="RouteUrl" type="System.Web.Compilation.RouteUrlExpressionBuilder"/>
+				<!--
+				<add expressionPrefix="RouteValue" type="System.Web.Compilation.RouteValueExpressionBuilder"/>
+				-->
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<!--
+				<add extension=".edmx" type="System.Data.Entity.Design.AspNet.EntityDesignerBuildProvider" />
+				<add extension=".xoml" type="System.ServiceModel.Activation.WorkflowServiceBuildProvider, System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add extension=".svc" type="System.ServiceModel.Activation.ServiceBuildProvider, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add extension=".xamlx" type="System.Xaml.Hosting.XamlBuildProvider, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				-->
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/machine.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..455709536f85b93df0239cb1664317d118990f34
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/machine.config
@@ -0,0 +1,310 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.SystemDiagnosticsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.runtime.serialization" type="System.Runtime.Serialization.Configuration.SerializationSectionGroup, System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="dataContractSerializer" type="System.Runtime.Serialization.Configuration.DataContractSerializerSection, System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+			<sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+				<section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				<sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+					<section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere"/>
+					<section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				</sectionGroup>
+			</sectionGroup>
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="standardEndpoints" type="System.ServiceModel.Configuration.StandardEndpointsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+			<section name="routing" type="System.ServiceModel.Routing.Configuration.RoutingSection, System.ServiceModel.Routing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			<section name="protocolMapping" type="System.ServiceModel.Configuration.ProtocolMappingSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.runtime.caching" type="System.Runtime.Caching.Configuration.CachingSectionGroup, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+		  <section name="memoryCache" type="System.Runtime.Caching.Configuration.MemoryCacheSection, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+                </sectionGroup>
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+			<endpointExtensions>
+				<add name="dynamicEndpoint" type="System.ServiceModel.Discovery.Configuration.DynamicEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="discoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.DiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpDiscoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpDiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="announcementEndpoint" type="System.ServiceModel.Discovery.Configuration.AnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpAnnouncementEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpAnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttpEndpoint" type="System.ServiceModel.Configuration.WebHttpEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webScriptEndpoint" type="System.ServiceModel.Configuration.WebScriptEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</endpointExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/settings.map b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..4c53aca6776656859d35fb7b7e6a64a5d54bfc6a
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/web.config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..30524c17c3d929bb9bec1c6a564b07c63cb01251
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/4.5/web.config
@@ -0,0 +1,252 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+  <system.codedom>
+        <compilers>
+            <compiler language="c#;cs;csharp" extension=".cs" warningLevel="4" type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+            <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4" type="Microsoft.VisualBasic.VBCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="OptionInfer" value="true"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+        </compilers>
+  </system.codedom>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+		  <add path="trace.axd" verb="*" type="System.Web.Handlers.TraceHandler" validate="True" />
+		  <add path="WebResource.axd" verb="GET" type="System.Web.Handlers.AssemblyResourceLoader" validate="True" />
+		  <add verb="*" path="*_AppService.axd" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.axd" verb="*" type="System.Web.HttpNotFoundHandler" validate="True" />
+		  <add path="*.aspx" verb="*" type="System.Web.UI.PageHandlerFactory" validate="True" />
+		  <add path="*.ashx" verb="*" type="System.Web.UI.SimpleHandlerFactory" validate="True" />
+		  <add path="*.asmx" verb="*" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add path="*.rem" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.soap" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.asax" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ascx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.master" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.skin" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.browser" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sitemap" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dll.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.exe.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.config" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cs" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.csproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vbproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.webinfo" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.licx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resources" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vjsproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.java" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.jsl" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldb" verb="*" type="System.Web.HttpForbiddenHandler"  validate="True" />
+		  <add path="*.ad" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.adprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.lddprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdm" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdmDocument" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.exclude" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.refresh" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <!--
+		  <add path="*.svc" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+                  <add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		  <add path="*.rules" verb="*" type="System.Web.HttpForbiddenHandler" validate="True"/>
+		  <!--
+		  <add path="*.xoml" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.xamlx" verb="*" type="System.Xaml.Hosting.XamlHttpHandlerFactory, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+		  <add path="*" verb="GET,HEAD,POST" type="System.Web.DefaultHttpHandler" validate="True" />
+		  <add path="*" verb="*" type="System.Web.HttpMethodNotAllowedHandler" validate="True" />
+		</httpHandlers>
+		<httpModules>
+		  <add name="OutputCache" type="System.Web.Caching.OutputCacheModule" />
+		  <add name="Session" type="System.Web.SessionState.SessionStateModule" />
+		  <!--
+		  <add name="WindowsAuthentication" type="System.Web.Security.WindowsAuthenticationModule" />
+		  -->
+		  <add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule" />
+		  <!--
+		  <add name="PassportAuthentication" type="System.Web.Security.PassportAuthenticationModule" />
+		  -->
+		  <add name="RoleManager" type="System.Web.Security.RoleManagerModule" />
+		  <add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule" />
+		  <!--
+		  <add name="FileAuthorization" type="System.Web.Security.FileAuthorizationModule" />
+		  -->
+		  <add name="AnonymousIdentification" type="System.Web.Security.AnonymousIdentificationModule" />
+		  <add name="Profile" type="System.Web.Profile.ProfileModule" />
+		  <!--
+		  <add name="ErrorHandlerModule" type="System.Web.Mobile.ErrorHandlerModule, System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		  <add name="ServiceModel" type="System.ServiceModel.Activation.HttpModule, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+		  -->
+		  <add name="UrlRoutingModule-4.0" type="System.Web.Routing.UrlRoutingModule" />
+		  <add name="ScriptModule-4.0" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">					
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+        		
+        		<controls>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.WebParts" assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.Expressions" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.DynamicData" assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</controls>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<!-- <add assembly="mscorlib" /> -->
+				<add assembly="Microsoft.CSharp, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Xml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.EnterpriseServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<!-- <add assembly="System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" /> -->
+				<add assembly="System.IdentityModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xaml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Channels, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.Core, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="System.Data.DataSetExtensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xml.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ComponentModel.DataAnnotations, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Data.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<!-- <add assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> -->
+				<add assembly="System.Data.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.Data.Entity.Design, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<add assembly="System.Web.ApplicationServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="RouteUrl" type="System.Web.Compilation.RouteUrlExpressionBuilder"/>
+				<!--
+				<add expressionPrefix="RouteValue" type="System.Web.Compilation.RouteValueExpressionBuilder"/>
+				-->
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<!--
+				<add extension=".edmx" type="System.Data.Entity.Design.AspNet.EntityDesignerBuildProvider" />
+				<add extension=".xoml" type="System.ServiceModel.Activation.WorkflowServiceBuildProvider, System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add extension=".svc" type="System.ServiceModel.Activation.ServiceBuildProvider, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add extension=".xamlx" type="System.Xaml.Hosting.XamlBuildProvider, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				-->
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/browscap.ini b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/browscap.ini
new file mode 100644
index 0000000000000000000000000000000000000000..1267e1deb4061a8250ad25125fc7d20fd2ae04ac
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/browscap.ini
@@ -0,0 +1,16979 @@
+;;; Provided courtesy of http://browsers.garykeith.com
+;;; Created on Wednesday, June 17, 2009 at 6:30 AM GMT
+
+[GJK_Browscap_Version]
+Version=4476
+Released=Wed, 17 Jun 2009 06:30:21 -0000
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DefaultProperties
+
+[DefaultProperties]
+Browser=DefaultProperties
+Version=0
+MajorVer=0
+MinorVer=0
+Platform=unknown
+Alpha=false
+Beta=false
+Win16=false
+Win32=false
+Win64=false
+Frames=false
+IFrames=false
+Tables=false
+Cookies=false
+BackgroundSounds=false
+CDF=false
+VBScript=false
+JavaApplets=false
+JavaScript=false
+ActiveXControls=false
+isBanned=false
+isMobileDevice=false
+isSyndicationReader=false
+Crawler=false
+CssVersion=0
+supportsCSS=false
+AOL=false
+aolVersion=0
+ECMAScriptVersion=0.0
+W3CDOMVersion=0.0
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Ask
+
+[Ask]
+Parent=DefaultProperties
+Browser=Ask
+Frames=true
+Tables=true
+Crawler=true
+
+[Mozilla/?.0 (compatible; Ask Jeeves/Teoma*)]
+Parent=Ask
+Browser=Teoma
+
+[Mozilla/2.0 (compatible; Ask Jeeves)]
+Parent=Ask
+Browser=AskJeeves
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Baidu
+
+[Baidu]
+Parent=DefaultProperties
+Browser=Baidu
+Frames=true
+Tables=true
+Crawler=true
+
+[BaiduImageSpider*]
+Parent=Baidu
+Browser=BaiduImageSpider
+
+[Baiduspider*]
+Parent=Baidu
+Browser=BaiDu
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Google
+
+[Google]
+Parent=DefaultProperties
+Browser=Google
+Frames=true
+IFrames=true
+Tables=true
+JavaScript=true
+Crawler=true
+
+[* (compatible; Googlebot-Mobile/2.1; *http://www.google.com/bot.html)]
+Parent=Google
+Browser=Googlebot-Mobile
+Frames=false
+IFrames=false
+Tables=false
+
+[*Google Wireless Transcoder*]
+Parent=Google
+Browser=Google Wireless Transcoder
+
+[AdsBot-Google (?http://www.google.com/adsbot.html)]
+Parent=Google
+Browser=AdsBot-Google
+
+[Feedfetcher-Google-iGoogleGadgets;*]
+Parent=Google
+Browser=iGoogleGadgets
+isBanned=true
+isSyndicationReader=true
+
+[Feedfetcher-Google;*]
+Parent=Google
+Browser=Feedfetcher-Google
+isBanned=true
+isSyndicationReader=true
+
+[Google OpenSocial agent (http://www.google.com/feedfetcher.html)]
+Parent=Google
+Browser=Google OpenSocial
+
+[Google-Site-Verification/1.0]
+Parent=Google
+Browser=Google-Site-Verification
+
+[Google-Sitemaps/*]
+Parent=Google
+Browser=Google-Sitemaps
+
+[Googlebot-Image/*]
+Parent=Google
+Browser=Googlebot-Image
+CDF=true
+
+[googlebot-urlconsole]
+Parent=Google
+Browser=googlebot-urlconsole
+
+[Googlebot-Video/1.0]
+Parent=Google
+Browser=Google-Video
+
+[Googlebot/2.1 (?http://www.google.com/bot.html)]
+Parent=Google
+Browser=Googlebot
+
+[Googlebot/2.1 (?http://www.googlebot.com/bot.html)]
+Parent=Google
+Browser=Googlebot
+
+[Googlebot/Test*]
+Parent=Google
+Browser=Googlebot/Test
+
+[gsa-crawler*]
+Parent=Google
+Browser=Google Search Appliance
+isBanned=true
+
+[Mediapartners-Google*]
+Parent=Google
+Browser=Mediapartners-Google
+
+[Mozilla/4.0 (compatible; Google Desktop)]
+Parent=Google
+Browser=Google Desktop
+
+[Mozilla/4.0 (compatible; GoogleToolbar*)]
+Parent=Google
+Browser=Google Toolbar
+isBanned=true
+
+[Mozilla/5.0 (compatible; Google Keyword Tool;*)]
+Parent=Google
+Browser=Google Keyword Tool
+
+[Mozilla/5.0 (compatible; Googlebot/2.1; ?http://www.google.com/bot.html)]
+Parent=Google
+Browser=Google Webmaster Tools
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Inktomi
+
+[Inktomi]
+Parent=DefaultProperties
+Browser=Inktomi
+Frames=true
+Tables=true
+Crawler=true
+
+[* (compatible;YahooSeeker/M1A1-R2D2; *)]
+Parent=Inktomi
+Browser=YahooSeeker-Mobile
+Frames=false
+Tables=false
+
+[Mozilla/4.0]
+Parent=Inktomi
+
+[Mozilla/4.0 (compatible; MSIE 5.0; Windows NT)]
+Parent=Inktomi
+Win32=true
+
+[Mozilla/4.0 (compatible; Yahoo Japan; for robot study; kasugiya)]
+Parent=Inktomi
+Browser=Yahoo! RobotStudy
+isBanned=true
+
+[Mozilla/5.0 (compatible; BMC/1.0 (Y!J-AGENT))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMC
+
+[Mozilla/5.0 (compatible; BMF/1.0 (Y!J-AGENT))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMF
+
+[Mozilla/5.0 (compatible; BMI/1.0 (Y!J-AGENT; 1.0))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMI
+
+[Mozilla/5.0 (compatible; Yahoo! DE Slurp; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Directory Engine
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp China; http://misc.yahoo.com.cn/help.html)]
+Parent=Inktomi
+Browser=Yahoo! Slurp China
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp/3.0; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Slurp
+Version=3.0
+MajorVer=3
+MinorVer=0
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Slurp
+
+[Mozilla/5.0 (compatible; Yahoo! Verifier/1.1)]
+Parent=Inktomi
+Browser=Yahoo! Verifier
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[Mozilla/5.0 (Slurp/cat; slurp@inktomi.com; http://www.inktomi.com/slurp.html)]
+Parent=Inktomi
+Browser=Slurp/cat
+
+[Mozilla/5.0 (Slurp/si; slurp@inktomi.com; http://www.inktomi.com/slurp.html)]
+Parent=Inktomi
+
+[Mozilla/5.0 (Yahoo-MMCrawler/4.0; mailto:vertical-crawl-support@yahoo-inc.com)]
+Parent=Inktomi
+Browser=Yahoo-MMCrawler
+Version=4.0
+MajorVer=4
+MinorVer=0
+
+[Scooter/*]
+Parent=Inktomi
+Browser=Scooter
+
+[Scooter/3.3Y!CrawlX]
+Parent=Inktomi
+Browser=Scooter/3.3Y!CrawlX
+Version=3.3
+MajorVer=3
+MinorVer=3
+
+[slurp]
+Parent=Inktomi
+Browser=slurp
+
+[Y!J-BSC/1.0*]
+Parent=Inktomi
+Browser=Y!J-BSC
+Version=1.0
+MajorVer=1
+MinorVer=0
+isBanned=true
+
+[Y!J-SRD/1.0]
+Parent=Inktomi
+Browser=Y!J-SRD
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Yahoo Mindset]
+Parent=Inktomi
+Browser=Yahoo Mindset
+
+[Yahoo Pipes*]
+Parent=Inktomi
+Browser=Yahoo Pipes
+
+[Yahoo! Mindset]
+Parent=Inktomi
+Browser=Yahoo! Mindset
+
+[Yahoo! Slurp/Site Explorer]
+Parent=Inktomi
+Browser=Yahoo! Site Explorer
+
+[Yahoo-Blogs/*]
+Parent=Inktomi
+Browser=Yahoo-Blogs
+
+[Yahoo-MMAudVid*]
+Parent=Inktomi
+Browser=Yahoo-MMAudVid
+
+[Yahoo-MMCrawler*]
+Parent=Inktomi
+Browser=Yahoo-MMCrawler
+isBanned=true
+
+[YahooFeedSeeker*]
+Parent=Inktomi
+Browser=YahooFeedSeeker
+isSyndicationReader=true
+Crawler=false
+
+[YahooSeeker/*]
+Parent=Inktomi
+Browser=YahooSeeker
+isMobileDevice=true
+
+[YahooSeeker/CafeKelsa (compatible; Konqueror/3.2; FreeBSD*) (KHTML, like Gecko)]
+Parent=Inktomi
+Browser=YahooSeeker/CafeKelsa
+
+[YahooSeeker/CafeKelsa-dev (compatible; Konqueror/3.2; FreeBSD*) (KHTML, like Gecko)]
+Parent=Inktomi
+
+[YahooVideoSearch*]
+Parent=Inktomi
+Browser=YahooVideoSearch
+
+[YahooYSMcm*]
+Parent=Inktomi
+Browser=YahooYSMcm
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; MSN
+
+[MSN]
+Parent=DefaultProperties
+Browser=MSN
+Frames=true
+Tables=true
+Crawler=true
+
+[adidxbot/1.1 (?http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=adidxbot
+
+[librabot/1.0 (*)]
+Parent=MSN
+Browser=librabot
+
+[llssbot/1.0]
+Parent=MSN
+Browser=llssbot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[MSMOBOT/1.1*]
+Parent=MSN
+Browser=msnbot-mobile
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[MSNBot-Academic/1.0*]
+Parent=MSN
+Browser=MSNBot-Academic
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot-media/1.0*]
+Parent=MSN
+Browser=msnbot-media
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot-media/1.1*]
+Parent=MSN
+Browser=msnbot-media
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[MSNBot-News/1.0*]
+Parent=MSN
+Browser=MSNBot-News
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[MSNBot-NewsBlogs/1.0*]
+Parent=MSN
+Browser=MSNBot-NewsBlogs
+Version=1
+MajorVer=1
+MinorVer=0
+
+[msnbot-products]
+Parent=MSN
+Browser=msnbot-products
+
+[msnbot-webmaster/1.0 (*http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=msnbot-webmaster tools
+
+[msnbot/1.0*]
+Parent=MSN
+Browser=msnbot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot/1.1*]
+Parent=MSN
+Browser=msnbot
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[msnbot/2.0b*]
+Parent=MSN
+Version=2.0
+MajorVer=2
+MinorVer=0
+Beta=true
+
+[MSR-ISRCCrawler]
+Parent=MSN
+Browser=MSR-ISRCCrawler
+
+[renlifangbot/1.0 (?http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=renlifangbot
+
+[T-Mobile Dash Mozilla/4.0 (*) MSNBOT-MOBILE/1.1 (*)]
+Parent=MSN
+Browser=msnbot-mobile
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Yahoo
+
+[Yahoo]
+Parent=DefaultProperties
+Browser=Yahoo
+Frames=true
+Tables=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; Y!J; for robot study*)]
+Parent=Yahoo
+Browser=Y!J
+
+[Mozilla/5.0 (Yahoo-Test/4.0*)]
+Parent=Yahoo
+Browser=Yahoo-Test
+Version=4.0
+MajorVer=4
+MinorVer=0
+
+[mp3Spider cn-search-devel at yahoo-inc dot com]
+Parent=Yahoo
+Browser=Yahoo! Media
+isBanned=true
+
+[My Browser]
+Parent=Yahoo
+Browser=Yahoo! My Browser
+
+[Y!OASIS/*]
+Parent=Yahoo
+Browser=Y!OASIS
+isBanned=true
+
+[YahooYSMcm/2.0.0]
+Parent=Yahoo
+Browser=YahooYSMcm
+Version=2.0
+MajorVer=2
+MinorVer=0
+isBanned=true
+
+[YRL_ODP_CRAWLER]
+Parent=Yahoo
+Browser=YRL_ODP_CRAWLER
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Yandex
+
+[Yandex]
+Parent=DefaultProperties
+Browser=Yandex
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; MSIE 5.0; YANDEX)]
+Parent=Yandex
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.9) Gecko VisualParser/3.0]
+Parent=Yandex
+Browser=VisualParser
+isBanned=true
+
+[YaDirectBot/*]
+Parent=Yandex
+Browser=YaDirectBot
+
+[Yandex/*]
+Parent=Yandex
+
+[YandexBlog/*]
+Parent=Yandex
+Browser=YandexBlog
+isSyndicationReader=true
+
+[YandexSomething/*]
+Parent=Yandex
+Browser=YandexSomething
+isSyndicationReader=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Best of the Web
+
+[Best of the Web]
+Parent=DefaultProperties
+Browser=Best of the Web
+Frames=true
+Tables=true
+
+[Mozilla/4.0 (compatible; BOTW Feed Grabber; *http://botw.org)]
+Parent=Best of the Web
+Browser=BOTW Feed Grabber
+isSyndicationReader=true
+Crawler=false
+
+[Mozilla/4.0 (compatible; BOTW Spider; *http://botw.org)]
+Parent=Best of the Web
+Browser=BOTW Spider
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Boitho
+
+[Boitho]
+Parent=DefaultProperties
+Browser=Boitho
+Frames=true
+Tables=true
+Crawler=true
+
+[boitho.com-dc/*]
+Parent=Boitho
+Browser=boitho.com-dc
+
+[boitho.com-robot/*]
+Parent=Boitho
+Browser=boitho.com-robot
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Convera
+
+[Convera]
+Parent=DefaultProperties
+Browser=Convera
+Frames=true
+Tables=true
+Crawler=true
+
+[ConveraCrawler/*]
+Parent=Convera
+Browser=ConveraCrawler
+
+[ConveraMultiMediaCrawler/0.1*]
+Parent=Convera
+Browser=ConveraMultiMediaCrawler
+Version=0.1
+MajorVer=0
+MinorVer=1
+
+[CrawlConvera*]
+Parent=Convera
+Browser=CrawlConvera
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DotBot
+
+[DotBot]
+Parent=DefaultProperties
+Browser=DotBot
+Frames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[DotBot/* (http://www.dotnetdotcom.org/*)]
+Parent=DotBot
+
+[Mozilla/5.0 (compatible; DotBot/*; http://www.dotnetdotcom.org/*)]
+Parent=DotBot
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Entireweb
+
+[Entireweb]
+Parent=DefaultProperties
+Browser=Entireweb
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; SpeedySpider; www.entireweb.com)]
+Parent=Entireweb
+
+[Speedy Spider (*Beta/*)]
+Parent=Entireweb
+
+[Speedy?Spider?(http://www.entireweb.com*)]
+Parent=Entireweb
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Envolk
+
+[Envolk]
+Parent=DefaultProperties
+Browser=Envolk
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[envolk/* (?http://www.envolk.com/envolk*)]
+Parent=Envolk
+
+[envolk?ITS?spider/* (?http://www.envolk.com/envolk*)]
+Parent=Envolk
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Exalead
+
+[Exalead]
+Parent=DefaultProperties
+Browser=Exalead
+Frames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Exabot-Images/1.0]
+Parent=Exalead
+Browser=Exabot-Images
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Exabot-Test/*]
+Parent=Exalead
+Browser=Exabot-Test
+
+[Exabot/2.0]
+Parent=Exalead
+Browser=Exabot
+
+[Exabot/3.0]
+Parent=Exalead
+Browser=Exabot
+Version=3.0
+MajorVer=3
+MinorVer=0
+Platform=Liberate
+
+[Exalead NG/*]
+Parent=Exalead
+Browser=Exalead NG
+isBanned=true
+
+[Mozilla/5.0 (compatible; Exabot-Images/3.0;*)]
+Parent=Exalead
+Browser=Exabot-Images
+
+[Mozilla/5.0 (compatible; Exabot/3.0 (BiggerBetter/tests);*)]
+Parent=Exalead
+Browser=Exabot/BiggerBetter/tests
+
+[Mozilla/5.0 (compatible; Exabot/3.0;*)]
+Parent=Exalead
+Browser=Exabot
+isBanned=false
+
+[Mozilla/5.0 (compatible; NGBot/*)]
+Parent=Exalead
+
+[ng/*]
+Parent=Exalead
+Browser=Exalead Previewer
+Version=1.0
+MajorVer=1
+MinorVer=0
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Fast/AllTheWeb
+
+[Fast/AllTheWeb]
+Parent=DefaultProperties
+Browser=Fast/AllTheWeb
+Alpha=true
+Beta=true
+Win16=true
+Win32=true
+Win64=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+CDF=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+isBanned=true
+isMobileDevice=true
+isSyndicationReader=true
+Crawler=true
+
+[*FAST Enterprise Crawler*]
+Parent=Fast/AllTheWeb
+Browser=FAST Enterprise Crawler
+
+[FAST Data Search Document Retriever/4.0*]
+Parent=Fast/AllTheWeb
+Browser=FAST Data Search Document Retriever
+
+[FAST MetaWeb Crawler (helpdesk at fastsearch dot com)]
+Parent=Fast/AllTheWeb
+Browser=FAST MetaWeb Crawler
+
+[Fast PartnerSite Crawler*]
+Parent=Fast/AllTheWeb
+Browser=FAST PartnerSite
+
+[FAST-WebCrawler/*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler
+
+[FAST-WebCrawler/*/FirstPage*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/FirstPage
+
+[FAST-WebCrawler/*/Fresh*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/Fresh
+
+[FAST-WebCrawler/*/PartnerSite*]
+Parent=Fast/AllTheWeb
+Browser=FAST PartnerSite
+
+[FAST-WebCrawler/*?Multimedia*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/Multimedia
+
+[FastSearch Web Crawler for*]
+Parent=Fast/AllTheWeb
+Browser=FastSearch Web Crawler
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Gigabot
+
+[Gigabot]
+Parent=DefaultProperties
+Browser=Gigabot
+Frames=true
+IFrames=true
+Tables=true
+Crawler=true
+
+[Gigabot*]
+Parent=Gigabot
+
+[GigabotSiteSearch/*]
+Parent=Gigabot
+Browser=GigabotSiteSearch
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Ilse
+
+[Ilse]
+Parent=DefaultProperties
+Browser=Ilse
+Frames=true
+Tables=true
+Crawler=true
+
+[IlseBot/*]
+Parent=Ilse
+
+[INGRID/?.0*]
+Parent=Ilse
+Browser=Ilse
+
+[Mozilla/3.0 (INGRID/*]
+Parent=Ilse
+Browser=Ilse
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iVia Project
+
+[iVia Project]
+Parent=DefaultProperties
+Browser=iVia Project
+Frames=true
+IFrames=true
+Tables=true
+Crawler=true
+
+[DataFountains/DMOZ Downloader*]
+Parent=iVia Project
+Browser=DataFountains/DMOZ Downloader
+isBanned=true
+
+[DataFountains/DMOZ Feature Vector Corpus Creator*]
+Parent=iVia Project
+Browser=DataFountains/DMOZ Feature Vector Corpus
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Jayde Online
+
+[Jayde Online]
+Parent=DefaultProperties
+Browser=Jayde Online
+Frames=true
+Tables=true
+Crawler=true
+
+[ExactSeek Crawler/*]
+Parent=Jayde Online
+Browser=ExactSeek Crawler
+
+[exactseek-pagereaper-* (crawler@exactseek.com)]
+Parent=Jayde Online
+Browser=exactseek-pagereaper
+isBanned=true
+
+[exactseek.com]
+Parent=Jayde Online
+Browser=exactseek.com
+
+[Jayde Crawler*]
+Parent=Jayde Online
+Browser=Jayde Crawler
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Lycos
+
+[Lycos]
+Parent=DefaultProperties
+Browser=Lycos
+Frames=true
+Tables=true
+Crawler=true
+
+[Lycos*]
+Parent=Lycos
+Browser=Lycos
+
+[Lycos-Proxy]
+Parent=Lycos
+Browser=Lycos-Proxy
+
+[Lycos-Spider_(modspider)]
+Parent=Lycos
+Browser=Lycos-Spider_(modspider)
+
+[Lycos-Spider_(T-Rex)]
+Parent=Lycos
+Browser=Lycos-Spider_(T-Rex)
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Naver
+
+[Naver]
+Parent=DefaultProperties
+Browser=Naver
+isBanned=true
+Crawler=true
+
+[Cowbot-* (NHN Corp*naver.com)]
+Parent=Naver
+Browser=Naver Cowbot
+
+[Mozilla/4.0 (compatible; NaverBot/*; *)]
+Parent=Naver
+
+[Mozilla/4.0 (compatible; NaverBot/*; nhnbot@naver.com)]
+Parent=Naver
+Browser=Naver NaverBot
+
+[NaverBot-* (NHN Corp*naver.com)]
+Parent=Naver
+Browser=Naver NHN Corp
+
+[Yeti/*]
+Parent=Naver
+Browser=Yeti
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Snap
+
+[Snap]
+Parent=DefaultProperties
+Browser=Snap
+isBanned=true
+Crawler=true
+
+[Mozilla/5.0 (SnapPreviewBot) Gecko/* Firefox/*]
+Parent=Snap
+
+[Snapbot/*]
+Parent=Snap
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Sogou
+
+[Sogou]
+Parent=DefaultProperties
+Browser=Sogou
+Frames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[shaboyi spider]
+Parent=Sogou
+Browser=Sogou/Shaboyi Spider
+
+[Sogou develop spider/*]
+Parent=Sogou
+Browser=Sogou Develop Spider
+
+[Sogou head spider*]
+Parent=Sogou
+Browser=Sogou/HEAD Spider
+
+[sogou js robot(*)]
+Parent=Sogou
+
+[Sogou Orion spider/*]
+Parent=Sogou
+Browser=Sogou Orion spider
+
+[Sogou Pic Agent]
+Parent=Sogou
+Browser=Sogou/Image Crawler
+
+[Sogou Pic Spider]
+Parent=Sogou
+Browser=Sogou Pic Spider
+
+[Sogou Push Spider/*]
+Parent=Sogou
+Browser=Sogou Push Spider
+
+[sogou spider]
+Parent=Sogou
+Browser=Sogou/Spider
+
+[sogou web spider*]
+Parent=Sogou
+Browser=sogou web spider
+
+[Sogou-Test-Spider/*]
+Parent=Sogou
+Browser=Sogou-Test-Spider
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; YodaoBot
+
+[YodaoBot]
+Parent=DefaultProperties
+Browser=YodaoBot
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Mozilla/5.0 (compatible; YodaoBot/1.*)]
+Parent=YodaoBot
+
+[Mozilla/5.0 (compatible;YodaoBot-Image/1.*)]
+Parent=YodaoBot
+Browser=YodaoBot-Image
+
+[WAP_Browser/5.0 (compatible; YodaoBot/1.*)]
+Parent=YodaoBot
+
+[YodaoBot/1.* (*)]
+Parent=YodaoBot
+
+[Best Whois (http://www.bestwhois.net/)]
+Parent=DNS Tools
+Browser=Best Whois
+
+[DNSGroup/*]
+Parent=DNS Tools
+Browser=DNS Group Crawler
+
+[NG-Search/*]
+Parent=Exalead
+Browser=NG-SearchBot
+
+[TouchStone]
+Parent=Feeds Syndicators
+Browser=TouchStone
+isSyndicationReader=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; General Crawlers
+
+[General Crawlers]
+Parent=DefaultProperties
+Browser=General Crawlers
+Crawler=true
+
+[A .NET Web Crawler]
+Parent=General Crawlers
+isBanned=true
+
+[BabalooSpider/1.*]
+Parent=General Crawlers
+Browser=BabalooSpider
+
+[BilgiBot/*]
+Parent=General Crawlers
+Browser=BilgiBot
+isBanned=true
+
+[bot/* (bot; *bot@bot.bot)]
+Parent=General Crawlers
+Browser=bot
+isBanned=true
+
+[CyberPatrol*]
+Parent=General Crawlers
+Browser=CyberPatrol
+isBanned=true
+
+[Cynthia 1.0]
+Parent=General Crawlers
+Browser=Cynthia
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[ddetailsbot (http://www.displaydetails.com)]
+Parent=General Crawlers
+Browser=ddetailsbot
+
+[DomainCrawler/1.0 (info@domaincrawler.com; http://www.domaincrawler.com/domains/view/*)]
+Parent=General Crawlers
+Browser=DomainCrawler
+
+[DomainsBotBot/1.*]
+Parent=General Crawlers
+Browser=DomainsBotBot
+isBanned=true
+
+[DomainsDB.net MetaCrawler*]
+Parent=General Crawlers
+Browser=DomainsDB
+
+[Drupal (*)]
+Parent=General Crawlers
+Browser=Drupal
+
+[Dumbot (version *)*]
+Parent=General Crawlers
+Browser=Dumbfind
+
+[EuripBot/*]
+Parent=General Crawlers
+Browser=Europe Internet Portal
+
+[eventax/*]
+Parent=General Crawlers
+Browser=eventax
+
+[FANGCrawl/*]
+Parent=General Crawlers
+Browser=Safe-t.net Web Filtering Service
+isBanned=true
+
+[favorstarbot/*]
+Parent=General Crawlers
+Browser=favorstarbot
+isBanned=true
+
+[FollowSite.com (*)]
+Parent=General Crawlers
+Browser=FollowSite
+isBanned=true
+
+[Gaisbot*]
+Parent=General Crawlers
+Browser=Gaisbot
+
+[Healthbot/Health_and_Longevity_Project_(HealthHaven.com) ]
+Parent=General Crawlers
+Browser=Healthbot
+isBanned=true
+
+[hitcrawler_0.*]
+Parent=General Crawlers
+Browser=hitcrawler
+isBanned=true
+
+[htdig/*]
+Parent=General Crawlers
+Browser=ht://Dig
+
+[http://hilfe.acont.de/bot.html ACONTBOT]
+Parent=General Crawlers
+Browser=ACONTBOT
+isBanned=true
+
+[JetBrains*]
+Parent=General Crawlers
+Browser=Omea Pro
+
+[KakleBot - www.kakle.com/0.1]
+Parent=General Crawlers
+Browser=KakleBot
+
+[KBeeBot/0.*]
+Parent=General Crawlers
+Browser=KBeeBot
+isBanned=true
+
+[Keyword Density/*]
+Parent=General Crawlers
+Browser=Keyword Density
+
+[LetsCrawl.com/1.0*]
+Parent=General Crawlers
+Browser=LetsCrawl.com
+isBanned=true
+
+[Lincoln State Web Browser]
+Parent=General Crawlers
+Browser=Lincoln State Web Browser
+isBanned=true
+
+[Links4US-Crawler,*]
+Parent=General Crawlers
+Browser=Links4US-Crawler
+isBanned=true
+
+[Lorkyll *.* -- lorkyll@444.net]
+Parent=General Crawlers
+Browser=Lorkyll
+isBanned=true
+
+[Lsearch/sondeur]
+Parent=General Crawlers
+Browser=Lsearch/sondeur
+isBanned=true
+
+[LucidMedia ClickSense/4.?]
+Parent=General Crawlers
+Browser=LucidMedia-ClickSense
+isBanned=true
+
+[MapoftheInternet.com?(?http://MapoftheInternet.com)]
+Parent=General Crawlers
+Browser=MapoftheInternet
+isBanned=true
+
+[Marvin v0.3]
+Parent=General Crawlers
+Browser=MedHunt
+Version=0.3
+MajorVer=0
+MinorVer=3
+
+[masidani_bot_v0.6*]
+Parent=General Crawlers
+Browser=masidani_bot
+
+[Metaspinner/0.01 (Metaspinner; http://www.meta-spinner.de/; support@meta-spinner.de/)]
+Parent=General Crawlers
+Browser=Metaspinner/0.01
+Version=0.01
+MajorVer=0
+MinorVer=01
+
+[metatagsdir/*]
+Parent=General Crawlers
+Browser=metatagsdir
+isBanned=true
+
+[Microsoft Windows Network Diagnostics]
+Parent=General Crawlers
+Browser=Microsoft Windows Network Diagnostics
+isBanned=true
+
+[Miva (AlgoFeedback@miva.com)]
+Parent=General Crawlers
+Browser=Miva
+
+[moget/*]
+Parent=General Crawlers
+Browser=Goo
+
+[Mozdex/0.7.2*]
+Parent=General Crawlers
+Browser=Mozdex
+
+[Mozilla Compatible (MS IE 3.01 WinNT)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/* (compatible; WebCapture*)]
+Parent=General Crawlers
+Browser=WebCapture
+
+[Mozilla/4.0 (compatible; DepSpid/*)]
+Parent=General Crawlers
+Browser=DepSpid
+
+[Mozilla/4.0 (compatible; MSIE *; Windows NT *; SV1)]
+Parent=General Crawlers
+Browser=AVG
+
+[Mozilla/4.0 (compatible; MSIE 4.01; Vonna.com b o t)]
+Parent=General Crawlers
+Browser=Vonna.com
+isBanned=true
+
+[Mozilla/4.0 (compatible; MSIE 4.01; Windows95)]
+Parent=General Crawlers
+Win32=true
+
+[Mozilla/4.0 (compatible; MSIE 4.5; Windows 98; )]
+Parent=General Crawlers
+Win32=true
+
+[Mozilla/4.0 (compatible; MyFamilyBot/*)]
+Parent=General Crawlers
+Browser=MyFamilyBot
+
+[Mozilla/4.0 (compatible; N-Stealth)]
+Parent=General Crawlers
+Browser=N-Stealth
+
+[Mozilla/4.0 (compatible; Scumbot/*; Linux/*)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.0 (compatible; Spider; Linux)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.0 (compatible; Win32)]
+Parent=General Crawlers
+Browser=Unknown Crawler
+isBanned=true
+
+[Mozilla/4.1]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.5]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/5.0 (*http://gnomit.com/) Gecko/* Gnomit/1.0]
+Parent=General Crawlers
+Browser=Gnomit
+isBanned=true
+
+[Mozilla/5.0 (compatible; AboutUsBot/*)]
+Parent=General Crawlers
+Browser=AboutUsBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; BuzzRankingBot/*)]
+Parent=General Crawlers
+Browser=BuzzRankingBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; Diffbot/0.1;  http://www.diffbot.com)]
+Parent=General Crawlers
+Browser=Diffbot
+
+[Mozilla/5.0 (compatible; FirstSearchBot/1.0; *)]
+Parent=General Crawlers
+Browser=FirstSearchBot
+
+[mozilla/5.0 (compatible; genevabot  http://www.healthdash.com)]
+Parent=General Crawlers
+Browser=Healthdash
+
+[Mozilla/5.0 (compatible; JadynAveBot; *http://www.jadynave.com/robot*]
+Parent=General Crawlers
+Browser=JadynAveBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; Kyluka crawl; http://www.kyluka.com/crawl.html; crawl@kyluka.com)]
+Parent=General Crawlers
+Browser=Kyluka
+
+[Mozilla/5.0 (compatible; MJ12bot/v1.2.*; http://www.majestic12.co.uk/bot.php*)]
+Parent=General Crawlers
+Browser=MJ12bot
+Version=1.2
+MajorVer=1
+MinorVer=2
+
+[Mozilla/5.0 (compatible; MSIE 7.0 ?http://www.europarchive.org)]
+Parent=General Crawlers
+Browser=Europe Web Archive
+
+[Mozilla/5.0 (compatible; Seznam screenshot-generator 2.0;*)]
+Parent=General Crawlers
+Browser=Seznam screenshot-generator
+isBanned=true
+
+[Mozilla/5.0 (compatible; Twingly Recon; http://www.twingly.com/)]
+Parent=General Crawlers
+Browser=Twingly Recon
+
+[Mozilla/5.0 (compatible; unwrapbot/2.*;  http://www.unwrap.jp*)]
+Parent=General Crawlers
+Browser=UnWrap
+
+[Mozilla/5.0 (compatible; Vermut*)]
+Parent=General Crawlers
+Browser=Vermut
+
+[Mozilla/5.0 (compatible; Webbot/*)]
+Parent=General Crawlers
+Browser=Webbot.ru
+isBanned=true
+
+[n4p_bot*]
+Parent=General Crawlers
+Browser=n4p_bot
+
+[nabot*]
+Parent=General Crawlers
+Browser=Nabot
+
+[NetCarta_WebMapper/*]
+Parent=General Crawlers
+Browser=NetCarta_WebMapper
+isBanned=true
+
+[NetID.com Bot*]
+Parent=General Crawlers
+Browser=NetID.com Bot
+isBanned=true
+
+[neTVision AG andreas.heidoetting@thomson-webcast.net]
+Parent=General Crawlers
+Browser=neTVision
+
+[NextopiaBOT*]
+Parent=General Crawlers
+Browser=NextopiaBOT
+
+[nicebot]
+Parent=General Crawlers
+Browser=nicebot
+isBanned=true
+
+[niXXieBot?Foster*]
+Parent=General Crawlers
+Browser=niXXiebot-Foster
+
+[Nozilla/P.N (Just for IDS woring)]
+Parent=General Crawlers
+Browser=Nozilla/P.N
+isBanned=true
+
+[Nudelsalat/*]
+Parent=General Crawlers
+Browser=Nudelsalat
+isBanned=true
+
+[NV32ts]
+Parent=General Crawlers
+Browser=NV32ts
+isBanned=true
+
+[Ocelli/*]
+Parent=General Crawlers
+Browser=Ocelli
+
+[OpenTaggerBot (http://www.opentagger.com/opentaggerbot.htm)]
+Parent=General Crawlers
+Browser=OpenTaggerBot
+
+[Oracle Enterprise Search]
+Parent=General Crawlers
+Browser=Oracle Enterprise Search
+isBanned=true
+
+[Oracle Ultra Search]
+Parent=General Crawlers
+Browser=Oracle Ultra Search
+
+[Pajaczek/*]
+Parent=General Crawlers
+Browser=Pajaczek
+isBanned=true
+
+[panscient.com]
+Parent=General Crawlers
+Browser=panscient.com
+isBanned=true
+
+[Patwebbot (http://www.herz-power.de/technik.html)]
+Parent=General Crawlers
+Browser=Patwebbot
+
+[PDFBot (crawler@pdfind.com)]
+Parent=General Crawlers
+Browser=PDFBot
+
+[Pete-Spider/1.*]
+Parent=General Crawlers
+Browser=Pete-Spider
+isBanned=true
+
+[PhpDig/*]
+Parent=General Crawlers
+Browser=PhpDig
+
+[PlantyNet_WebRobot*]
+Parent=General Crawlers
+Browser=PlantyNet
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; PluckIt
+
+[PluckItCrawler/1.0 (*)]
+Parent=General Crawlers
+isMobileDevice=true
+
+[PMAFind]
+Parent=General Crawlers
+Browser=PMAFind
+isBanned=true
+
+[Poodle_predictor_1.0]
+Parent=General Crawlers
+Browser=Poodle Predictor
+
+[QuickFinder Crawler]
+Parent=General Crawlers
+Browser=QuickFinder
+isBanned=true
+
+[Radiation Retriever*]
+Parent=General Crawlers
+Browser=Radiation Retriever
+isBanned=true
+
+[RedCarpet/*]
+Parent=General Crawlers
+Browser=RedCarpet
+isBanned=true
+
+[RixBot (http://babelserver.org/rix)]
+Parent=General Crawlers
+Browser=RixBot
+
+[Rome Client (http://tinyurl.com/64t5n) Ver: 0.*]
+Parent=General Crawlers
+Browser=TinyURL
+
+[SBIder/*]
+Parent=General Crawlers
+Browser=SiteSell
+
+[ScollSpider/2.*]
+Parent=General Crawlers
+Browser=ScollSpider
+isBanned=true
+
+[Search Fst]
+Parent=General Crawlers
+Browser=Search Fst
+
+[searchbot admin@google.com]
+Parent=General Crawlers
+Browser=searchbot
+isBanned=true
+
+[Seeker.lookseek.com]
+Parent=General Crawlers
+Browser=LookSeek
+isBanned=true
+
+[semanticdiscovery/*]
+Parent=General Crawlers
+Browser=Semantic Discovery
+
+[SeznamBot/*]
+Parent=General Crawlers
+Browser=SeznamBot
+isBanned=true
+
+[Shelob (shelob@gmx.net)]
+Parent=General Crawlers
+Browser=Shelob
+isBanned=true
+
+[shelob v1.*]
+Parent=General Crawlers
+Browser=shelob
+isBanned=true
+
+[ShopWiki/1.0*]
+Parent=General Crawlers
+Browser=ShopWiki
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[ShowXML/1.0 libwww/5.4.0]
+Parent=General Crawlers
+Browser=ShowXML
+isBanned=true
+
+[sitecheck.internetseer.com*]
+Parent=General Crawlers
+Browser=Internetseer
+
+[SMBot/*]
+Parent=General Crawlers
+Browser=SMBot
+
+[sohu*]
+Parent=General Crawlers
+Browser=sohu-search
+isBanned=true
+
+[SpankBot*]
+Parent=General Crawlers
+Browser=SpankBot
+isBanned=true
+
+[spider (tspyyp@tom.com)]
+Parent=General Crawlers
+Browser=spider (tspyyp@tom.com)
+isBanned=true
+
+[Sunrise/0.*]
+Parent=General Crawlers
+Browser=Sunrise
+isBanned=true
+
+[Superpages URL Verification Engine]
+Parent=General Crawlers
+Browser=Superpages
+
+[Surf Knight]
+Parent=General Crawlers
+Browser=Surf Knight
+isBanned=true
+
+[SurveyBot/*]
+Parent=General Crawlers
+Browser=SurveyBot
+isBanned=true
+
+[SynapticSearch/AI Crawler 1.?]
+Parent=General Crawlers
+Browser=SynapticSearch
+isBanned=true
+
+[SyncMgr]
+Parent=General Crawlers
+Browser=SyncMgr
+
+[Tagyu Agent/1.0]
+Parent=General Crawlers
+Browser=Tagyu
+
+[Talkro Web-Shot/*]
+Parent=General Crawlers
+Browser=Talkro Web-Shot
+isBanned=true
+
+[Tecomi Bot (http://www.tecomi.com/bot.htm)]
+Parent=General Crawlers
+Browser=Tecomi
+
+[TheInformant*]
+Parent=General Crawlers
+Browser=TheInformant
+isBanned=true
+
+[Toata dragostea*]
+Parent=General Crawlers
+Browser=Toata dragostea
+isBanned=true
+
+[Tutorial Crawler*]
+Parent=General Crawlers
+isBanned=true
+
+[UbiCrawler/*]
+Parent=General Crawlers
+Browser=UbiCrawler
+
+[UCmore]
+Parent=General Crawlers
+Browser=UCmore
+
+[User*Agent:*]
+Parent=General Crawlers
+isBanned=true
+
+[USER_AGENT]
+Parent=General Crawlers
+Browser=USER_AGENT
+isBanned=true
+
+[VadixBot]
+Parent=General Crawlers
+Browser=VadixBot
+
+[VengaBot/*]
+Parent=General Crawlers
+Browser=VengaBot
+isBanned=true
+
+[Visicom Toolbar]
+Parent=General Crawlers
+Browser=Visicom Toolbar
+
+[W3C-WebCon/*]
+Parent=General Crawlers
+Browser=W3C-WebCon
+
+[Webclipping.com]
+Parent=General Crawlers
+Browser=Webclipping.com
+isBanned=true
+
+[webcollage/*]
+Parent=General Crawlers
+Browser=WebCollage
+isBanned=true
+
+[WebCrawler_1.*]
+Parent=General Crawlers
+Browser=WebCrawler
+
+[WebFilter Robot*]
+Parent=General Crawlers
+Browser=WebFilter Robot
+
+[WeBoX/*]
+Parent=General Crawlers
+Browser=WeBoX
+
+[WebTrends/*]
+Parent=General Crawlers
+Browser=WebTrends
+
+[West Wind Internet Protocols*]
+Parent=General Crawlers
+Browser=Versatel
+isBanned=true
+
+[WhizBang]
+Parent=General Crawlers
+Browser=WhizBang
+
+[Willow Internet Crawler by Twotrees V*]
+Parent=General Crawlers
+Browser=Willow Internet Crawler
+
+[WIRE/* (Linux; i686; Bot,Robot,Spider,Crawler)]
+Parent=General Crawlers
+Browser=WIRE
+isBanned=true
+
+[www.fi crawler, contact crawler@www.fi]
+Parent=General Crawlers
+Browser=www.fi crawler
+
+[Xerka WebBot v1.*]
+Parent=General Crawlers
+Browser=Xerka
+isBanned=true
+
+[XML Sitemaps Generator*]
+Parent=General Crawlers
+Browser=XML Sitemaps Generator
+
+[XSpider*]
+Parent=General Crawlers
+Browser=XSpider
+isBanned=true
+
+[YooW!/* (?http://www.yoow.eu)]
+Parent=General Crawlers
+Browser=YooW!
+isBanned=true
+
+[HiddenMarket-*]
+Parent=General RSS
+Browser=HiddenMarket
+isBanned=true
+
+[FOTOCHECKER]
+Parent=Image Crawlers
+Browser=FOTOCHECKER
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Search Engines
+
+[Search Engines]
+Parent=DefaultProperties
+Browser=Search Engines
+Crawler=true
+
+[*FDSE robot*]
+Parent=Search Engines
+Browser=FDSE Robot
+
+[*Fluffy the spider*]
+Parent=Search Engines
+Browser=SearchHippo
+
+[Abacho*]
+Parent=Search Engines
+Browser=Abacho
+
+[ah-ha.com crawler (crawler@ah-ha.com)]
+Parent=Search Engines
+Browser=Ah-Ha
+
+[AIBOT/*]
+Parent=Search Engines
+Browser=21Seek.Com
+
+[ALeadSoftbot/*]
+Parent=Search Engines
+Browser=ALeadSoftbot
+
+[Amfibibot/*]
+Parent=Search Engines
+Browser=Amfibi
+
+[AnswerBus (http://www.answerbus.com/)]
+Parent=Search Engines
+
+[antibot-V*]
+Parent=Search Engines
+Browser=antibot
+
+[appie*(www.walhello.com)]
+Parent=Search Engines
+Browser=Walhello
+
+[ASPSeek/*]
+Parent=Search Engines
+Browser=ASPSeek
+
+[BigCliqueBOT/*]
+Parent=Search Engines
+Browser=BigClique.com/BigClic.com
+
+[Blaiz-Bee/*]
+Parent=Search Engines
+Browser=RawGrunt
+
+[btbot/*]
+Parent=Search Engines
+Browser=Bit Torrent Search Engine
+
+[Busiversebot/v1.0 (http://www.busiverse.com/bot.php)]
+Parent=Search Engines
+Browser=Busiversebot
+isBanned=true
+
+[CatchBot/*;  http://www.catchbot.com]
+Parent=Search Engines
+Browser=CatchBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[CipinetBot (http://www.cipinet.com/bot.html)]
+Parent=Search Engines
+Browser=CipinetBot
+
+[Cogentbot/1.?*]
+Parent=Search Engines
+Browser=Cogentbot
+
+[compatible; Mozilla 4.0; MSIE 5.5; (SqwidgeBot v1.01 - http://www.sqwidge.com/bot/)]
+Parent=Search Engines
+Browser=SqwidgeBot
+
+[cosmos*]
+Parent=Search Engines
+Browser=Xyleme
+
+[Deepindex]
+Parent=Search Engines
+Browser=Deepindex
+
+[DiamondBot]
+Parent=Search Engines
+Browser=DiamondBot
+
+[Dumbot*]
+Parent=Search Engines
+Browser=Dumbot
+Version=0.2
+MajorVer=0
+MinorVer=2
+Beta=true
+
+[Eule?Robot*]
+Parent=Search Engines
+Browser=Eule-Robot
+
+[Faxobot/*]
+Parent=Search Engines
+Browser=Faxo
+
+[Filangy/*]
+Parent=Search Engines
+Browser=Filangy
+
+[flatlandbot/*]
+Parent=Search Engines
+Browser=Flatland
+
+[Fooky.com/ScorpionBot/ScoutOut;*]
+Parent=Search Engines
+Browser=ScorpionBot
+isBanned=true
+
+[FyberSpider*]
+Parent=Search Engines
+Browser=FyberSpider
+isBanned=true
+
+[Gaisbot/*]
+Parent=Search Engines
+Browser=Gaisbot
+
+[gazz/*(gazz@nttr.co.jp)]
+Parent=Search Engines
+Browser=gazz
+
+[geniebot*]
+Parent=Search Engines
+Browser=GenieKnows
+
+[GOFORITBOT (?http://www.goforit.com/about/?)]
+Parent=Search Engines
+Browser=GoForIt
+
+[GoGuidesBot/*]
+Parent=Search Engines
+Browser=GoGuidesBot
+
+[GroschoBot/*]
+Parent=Search Engines
+Browser=GroschoBot
+
+[GurujiBot/1.*]
+Parent=Search Engines
+Browser=GurujiBot
+isBanned=true
+
+[HenryTheMiragoRobot*]
+Parent=Search Engines
+Browser=Mirago
+
+[HolmesBot (http://holmes.ge)]
+Parent=Search Engines
+Browser=HolmesBot
+
+[Hotzonu/*]
+Parent=Search Engines
+Browser=Hotzonu
+
+[HyperEstraier/*]
+Parent=Search Engines
+Browser=HyperEstraier
+isBanned=true
+
+[i1searchbot/*]
+Parent=Search Engines
+Browser=i1searchbot
+
+[IIITBOT/1.*]
+Parent=Search Engines
+Browser=Indian Language Web Search Engine
+
+[Iltrovatore-?etaccio/*]
+Parent=Search Engines
+Browser=Iltrovatore-Setaccio
+
+[InfociousBot (?http://corp.infocious.com/tech_crawler.php)]
+Parent=Search Engines
+Browser=InfociousBot
+isBanned=true
+
+[Infoseek SideWinder/*]
+Parent=Search Engines
+Browser=Infoseek
+
+[iSEEKbot/*]
+Parent=Search Engines
+Browser=iSEEKbot
+
+[Knight/0.? (Zook Knight; http://knight.zook.in/; knight@zook.in)]
+Parent=Search Engines
+Browser=Knight
+
+[Kolinka Forum Search (www.kolinka.com)]
+Parent=Search Engines
+Browser=Kolinka Forum Search
+isBanned=true
+
+[KRetrieve/]
+Parent=Search Engines
+Browser=KRetrieve
+isBanned=true
+
+[LapozzBot/*]
+Parent=Search Engines
+Browser=LapozzBot
+
+[Linknzbot*]
+Parent=Search Engines
+Browser=Linknzbot
+
+[LocalcomBot/*]
+Parent=Search Engines
+Browser=LocalcomBot
+
+[Mail.Ru/1.0]
+Parent=Search Engines
+Browser=Mail.Ru
+
+[MaSagool/*]
+Parent=Search Engines
+Browser=Sagoo
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[miniRank/*]
+Parent=Search Engines
+Browser=miniRank
+
+[Mnogosearch*]
+Parent=Search Engines
+Browser=Mnogosearch
+
+[Mozilla/0.9* no dos :) (Linux)]
+Parent=Search Engines
+Browser=goliat
+isBanned=true
+
+[Mozilla/4.0 (compatible; Arachmo)]
+Parent=Search Engines
+Browser=Arachmo
+
+[Mozilla/4.0 (compatible; http://search.thunderstone.com/texis/websearch/about.html)]
+Parent=Search Engines
+Browser=ThunderStone
+isBanned=true
+
+[Mozilla/4.0 (compatible; MSIE *; Windows NT; Girafabot; girafabot at girafa dot com; http://www.girafa.com)]
+Parent=Search Engines
+Browser=Girafabot
+Win32=true
+
+[Mozilla/4.0 (compatible; Vagabondo/*; webcrawler at wise-guys dot nl; *)]
+Parent=Search Engines
+Browser=Vagabondo
+
+[Mozilla/4.0(?compatible; MSIE 6.0; Qihoo *)]
+Parent=Search Engines
+Browser=Qihoo
+
+[Mozilla/4.7 (compatible; WhizBang; http://www.whizbang.com/crawler)]
+Parent=Search Engines
+Browser=Inxight Software
+
+[Mozilla/5.0 (*) VoilaBot*]
+Parent=Search Engines
+Browser=VoilaBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; ActiveTouristBot*; http://www.activetourist.com)]
+Parent=Search Engines
+Browser=ActiveTouristBot
+
+[Mozilla/5.0 (compatible; Butterfly/1.0; *)*]
+Parent=Search Engines
+Browser=Butterfly
+
+[Mozilla/5.0 (compatible; Charlotte/*; *)]
+Parent=Search Engines
+Browser=Charlotte
+Beta=true
+isBanned=true
+
+[Mozilla/5.0 (compatible; CXL-FatAssANT*)]
+Parent=Search Engines
+Browser=FatAssANT
+
+[Mozilla/5.0 (compatible; DBLBot/1.0; ?http://www.dontbuylists.com/)]
+Parent=Search Engines
+Browser=DBLBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (compatible; EARTHCOM.info/*)]
+Parent=Search Engines
+Browser=EARTHCOM
+
+[Mozilla/5.0 (compatible; Lipperhey Spider; http://www.lipperhey.com/)]
+Parent=Search Engines
+Browser=Lipperhey Spider
+
+[Mozilla/5.0 (compatible; MojeekBot/*; http://www.mojeek.com/bot.html)]
+Parent=Search Engines
+Browser=MojeekBot
+
+[Mozilla/5.0 (compatible; NLCrawler/*]
+Parent=Search Engines
+Browser=Northern Light Web Search
+
+[Mozilla/5.0 (compatible; OsO;*]
+Parent=Search Engines
+Browser=Octopodus
+isBanned=true
+
+[Mozilla/5.0 (compatible; Pogodak.*)]
+Parent=Search Engines
+Browser=Pogodak
+
+[Mozilla/5.0 (compatible; Quantcastbot/1.*)]
+Parent=Search Engines
+Browser=Quantcastbot
+
+[Mozilla/5.0 (compatible; ScoutJet;  *http://www.scoutjet.com/)]
+Parent=Search Engines
+Browser=ScoutJet
+
+[Mozilla/5.0 (compatible; Scrubby/*;  http://www.scrubtheweb.com/abs/meta-check.html)]
+Parent=Search Engines
+Browser=Scrubby
+isBanned=true
+
+[Mozilla/5.0 (compatible; YoudaoBot/1.*; http://www.youdao.com/help/webmaster/spider/*)]
+Parent=Search Engines
+Browser=YoudaoBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (Twiceler*)]
+Parent=Search Engines
+Browser=Twiceler
+isBanned=true
+
+[Mozilla/5.0 CostaCider Search*]
+Parent=Search Engines
+Browser=CostaCider Search
+
+[Mozilla/5.0 GurujiBot/1.0 (*)]
+Parent=Search Engines
+Browser=GurujiBot
+
+[NavissoBot]
+Parent=Search Engines
+Browser=NavissoBot
+
+[NextGenSearchBot*(for information visit *)]
+Parent=Search Engines
+Browser=ZoomInfo
+isBanned=true
+
+[Norbert the Spider(Burf.com)]
+Parent=Search Engines
+Browser=Norbert the Spider
+
+[NuSearch Spider*]
+Parent=Search Engines
+Browser=nuSearch
+
+[ObjectsSearch/*]
+Parent=Search Engines
+Browser=ObjectsSearch
+
+[OpenISearch/1.*]
+Parent=Search Engines
+Browser=OpenISearch (Amazon)
+
+[Pagebull http://www.pagebull.com/]
+Parent=Search Engines
+Browser=Pagebull
+
+[PEERbot*]
+Parent=Search Engines
+Browser=PEERbot
+
+[Pompos/*]
+Parent=Search Engines
+Browser=Pompos
+
+[Popdexter/*]
+Parent=Search Engines
+Browser=Popdex
+
+[Qweery*]
+Parent=Search Engines
+Browser=QweeryBot
+
+[RedCell/* (*)]
+Parent=Search Engines
+Browser=RedCell
+
+[Scrubby/*]
+Parent=Search Engines
+Browser=Scrub The Web
+
+[Search-10/*]
+Parent=Search Engines
+Browser=Search-10
+
+[search.ch*]
+Parent=Search Engines
+Browser=Swiss Search Engine
+
+[Searchmee! Spider*]
+Parent=Search Engines
+Browser=Searchmee!
+
+[Seekbot/*]
+Parent=Search Engines
+Browser=Seekbot
+
+[SiteSpider  (http://www.SiteSpider.com/)]
+Parent=Search Engines
+Browser=SiteSpider
+
+[Spinne/*]
+Parent=Search Engines
+Browser=Spinne
+
+[sproose/*]
+Parent=Search Engines
+Browser=Sproose
+
+[Sqeobot/0.*]
+Parent=Search Engines
+Browser=Branzel
+isBanned=true
+
+[SquigglebotBot/*]
+Parent=Search Engines
+Browser=SquigglebotBot
+isBanned=true
+
+[StackRambler/*]
+Parent=Search Engines
+Browser=StackRambler
+
+[SygolBot*]
+Parent=Search Engines
+Browser=SygolBot
+
+[SynoBot]
+Parent=Search Engines
+Browser=SynoBot
+
+[Szukacz/*]
+Parent=Search Engines
+Browser=Szukacz
+
+[Tarantula/*]
+Parent=Search Engines
+Browser=Tarantula
+isBanned=true
+
+[TerrawizBot/*]
+Parent=Search Engines
+Browser=TerrawizBot
+isBanned=true
+
+[Tkensaku/*]
+Parent=Search Engines
+Browser=Tkensaku
+
+[TMCrawler]
+Parent=Search Engines
+Browser=TMCrawler
+isBanned=true
+
+[Twingly Recon]
+Parent=Search Engines
+Browser=Twingly Recon
+isBanned=true
+
+[updated/*]
+Parent=Search Engines
+Browser=Updated!
+
+[URL Spider Pro/*]
+Parent=Search Engines
+Browser=URL Spider Pro
+
+[URL Spider SQL*]
+Parent=Search Engines
+Browser=Innerprise Enterprise Search
+
+[VMBot/*]
+Parent=Search Engines
+Browser=VMBot
+
+[voyager/2.0 (http://www.kosmix.com/html/crawler.html)]
+Parent=Search Engines
+Browser=Voyager
+
+[wadaino.jp-crawler*]
+Parent=Search Engines
+Browser=wadaino.jp
+isBanned=true
+
+[WebAlta Crawler/*]
+Parent=Search Engines
+Browser=WebAlta Crawler
+isBanned=true
+
+[WebCorp/*]
+Parent=Search Engines
+Browser=WebCorp
+isBanned=true
+
+[webcrawl.net]
+Parent=Search Engines
+Browser=webcrawl.net
+
+[WISEbot/*]
+Parent=Search Engines
+Browser=WISEbot
+isBanned=true
+
+[Wotbox/*]
+Parent=Search Engines
+Browser=Wotbox
+
+[www.zatka.com]
+Parent=Search Engines
+Browser=Zatka
+
+[WWWeasel Robot v*]
+Parent=Search Engines
+Browser=World Wide Weasel
+
+[YadowsCrawler*]
+Parent=Search Engines
+Browser=YadowsCrawler
+
+[YodaoBot/*]
+Parent=Search Engines
+Browser=YodaoBot
+isBanned=true
+
+[ZeBot_www.ze.bz*]
+Parent=Search Engines
+Browser=ZE.bz
+
+[zibber-v*]
+Parent=Search Engines
+Browser=Zibb
+
+[ZipppBot/*]
+Parent=Search Engines
+Browser=ZipppBot
+
+[ATA-Translation-Service]
+Parent=Translators
+Browser=ATA-Translation-Service
+
+[GJK_Browser_Check]
+Parent=Version Checkers
+Browser=GJK_Browser_Check
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Hatena
+
+[Hatena]
+Parent=DefaultProperties
+Browser=Hatena
+isBanned=true
+Crawler=true
+
+[Feed::Find/*]
+Parent=Hatena
+Browser=Feed Find
+isSyndicationReader=true
+
+[Hatena Antenna/*]
+Parent=Hatena
+Browser=Hatena Antenna
+
+[Hatena Bookmark/*]
+Parent=Hatena
+Browser=Hatena Bookmark
+
+[Hatena RSS/*]
+Parent=Hatena
+Browser=Hatena RSS
+isSyndicationReader=true
+
+[Hatena::Crawler/*]
+Parent=Hatena
+Browser=Hatena Crawler
+
+[HatenaScreenshot*]
+Parent=Hatena
+Browser=HatenaScreenshot
+
+[URI::Fetch/*]
+Parent=Hatena
+Browser=URI::Fetch
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Internet Archive
+
+[Internet Archive]
+Parent=DefaultProperties
+Browser=Internet Archive
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[*heritrix*]
+Parent=Internet Archive
+Browser=Heritrix
+isBanned=true
+
+[ia_archiver*]
+Parent=Internet Archive
+Browser=Internet Archive
+
+[InternetArchive/*]
+Parent=Internet Archive
+Browser=InternetArchive
+
+[Mozilla/5.0 (compatible; archive.org_bot/1.*)]
+Parent=Internet Archive
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Nutch
+
+[Nutch]
+Parent=DefaultProperties
+Browser=Nutch
+isBanned=true
+Crawler=true
+
+[*Nutch*]
+Parent=Nutch
+isBanned=true
+
+[CazoodleBot/*]
+Parent=Nutch
+Browser=CazoodleBot
+
+[LOOQ/0.1*]
+Parent=Nutch
+Browser=LOOQ
+
+[Nutch/0.? (OpenX Spider)]
+Parent=Nutch
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Webaroo
+
+[Webaroo]
+Parent=DefaultProperties
+Browser=Webaroo
+
+[Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Webaroo/*)]
+Parent=Webaroo
+Browser=Webaroo
+
+[Mozilla/5.0 (Windows; U; Windows *; *; rv:*) Gecko/* Firefox/* webaroo/*]
+Parent=Webaroo
+Browser=Webaroo
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Word Press
+
+[Word Press]
+Parent=DefaultProperties
+Browser=Word Press
+Alpha=true
+Beta=true
+Win16=true
+Win32=true
+Win64=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+CDF=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+isBanned=true
+isMobileDevice=true
+isSyndicationReader=true
+Crawler=true
+
+[WordPress-B-/2.*]
+Parent=Word Press
+Browser=WordPress-B
+
+[WordPress-Do-P-/2.*]
+Parent=Word Press
+Browser=WordPress-Do-P
+
+[BlueCoat ProxySG]
+Parent=Blue Coat Systems
+Browser=BlueCoat ProxySG
+
+[CerberianDrtrs/*]
+Parent=Blue Coat Systems
+Browser=Cerberian
+
+[Inne: Mozilla/4.0 (compatible; Cerberian Drtrs*)]
+Parent=Blue Coat Systems
+Browser=Cerberian
+
+[Mozilla/4.0 (compatible; Cerberian Drtrs*)]
+Parent=Blue Coat Systems
+Browser=Cerberian
+
+[Mozilla/4.0 (compatible; MSIE 6.0; Bluecoat DRTR)]
+Parent=Blue Coat Systems
+Browser=Bluecoat
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Copyright/Plagiarism
+
+[Copyright/Plagiarism]
+Parent=DefaultProperties
+Browser=Copyright/Plagiarism
+isBanned=true
+Crawler=true
+
+[BDFetch]
+Parent=Copyright/Plagiarism
+Browser=BDFetch
+
+[copyright sheriff (*)]
+Parent=Copyright/Plagiarism
+Browser=copyright sheriff
+
+[CopyRightCheck*]
+Parent=Copyright/Plagiarism
+Browser=CopyRightCheck
+
+[FairAd Client*]
+Parent=Copyright/Plagiarism
+Browser=FairAd Client
+
+[iCopyright Conductor*]
+Parent=Copyright/Plagiarism
+Browser=iCopyright Conductor
+
+[IPiumBot laurion(dot)com]
+Parent=Copyright/Plagiarism
+Browser=IPiumBot
+
+[IWAgent/*]
+Parent=Copyright/Plagiarism
+Browser=Brand Protect
+
+[Mozilla/5.0 (compatible; DKIMRepBot/*)]
+Parent=Copyright/Plagiarism
+Browser=DKIMRepBot
+
+[oBot]
+Parent=Copyright/Plagiarism
+Browser=oBot
+
+[SlySearch/*]
+Parent=Copyright/Plagiarism
+Browser=SlySearch
+
+[TurnitinBot/*]
+Parent=Copyright/Plagiarism
+Browser=TurnitinBot
+
+[TutorGigBot/*]
+Parent=Copyright/Plagiarism
+Browser=TutorGig
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DNS Tools
+
+[DNS Tools]
+Parent=DefaultProperties
+Browser=DNS Tools
+Crawler=true
+
+[Domain Dossier utility*]
+Parent=DNS Tools
+Browser=Domain Dossier
+
+[Mozilla/5.0 (compatible; DNS-Digger/*)]
+Parent=DNS Tools
+Browser=DNS-Digger
+
+[OpenDNS Domain Crawler noc@opendns.com]
+Parent=DNS Tools
+Browser=OpenDNS Domain Crawler
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Download Managers
+
+[Download Managers]
+Parent=DefaultProperties
+Browser=Download Managers
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[AndroidDownloadManager]
+Parent=Download Managers
+Browser=Android Download Manager
+
+[AutoMate5]
+Parent=Download Managers
+Browser=AutoMate5
+
+[Beamer*]
+Parent=Download Managers
+Browser=Beamer
+
+[BitBeamer/*]
+Parent=Download Managers
+Browser=BitBeamer
+
+[BitTorrent/*]
+Parent=Download Managers
+Browser=BitTorrent
+
+[DA *]
+Parent=Download Managers
+Browser=Download Accelerator
+
+[Download Demon*]
+Parent=Download Managers
+Browser=Download Demon
+
+[Download Express*]
+Parent=Download Managers
+Browser=Download Express
+
+[Download Master*]
+Parent=Download Managers
+Browser=Download Master
+
+[Download Ninja*]
+Parent=Download Managers
+Browser=Download Ninja
+
+[Download Wonder*]
+Parent=Download Managers
+Browser=Download Wonder
+
+[DownloadSession*]
+Parent=Download Managers
+Browser=DownloadSession
+
+[EasyDL/*]
+Parent=Download Managers
+Browser=EasyDL
+
+[FDM 1.x]
+Parent=Download Managers
+Browser=Free Download Manager
+
+[FlashGet]
+Parent=Download Managers
+Browser=FlashGet
+
+[FreshDownload/*]
+Parent=Download Managers
+Browser=FreshDownload
+
+[GetRight/*]
+Parent=Download Managers
+Browser=GetRight
+
+[GetRightPro/*]
+Parent=Download Managers
+Browser=GetRightPro
+
+[GetSmart/*]
+Parent=Download Managers
+Browser=GetSmart
+
+[Go!Zilla*]
+Parent=Download Managers
+Browser=GoZilla
+
+[Gozilla/*]
+Parent=Download Managers
+Browser=Gozilla
+
+[Internet Ninja*]
+Parent=Download Managers
+Browser=Internet Ninja
+
+[Kontiki Client*]
+Parent=Download Managers
+Browser=Kontiki Client
+
+[lftp/3.2.1]
+Parent=Download Managers
+Browser=lftp
+
+[LightningDownload/*]
+Parent=Download Managers
+Browser=LightningDownload
+
+[LMQueueBot/*]
+Parent=Download Managers
+Browser=LMQueueBot
+
+[MetaProducts Download Express/*]
+Parent=Download Managers
+Browser=Download Express
+
+[Mozilla/4.0 (compatible; Getleft*)]
+Parent=Download Managers
+Browser=Getleft
+
+[Myzilla]
+Parent=Download Managers
+Browser=Myzilla
+
+[Net Vampire/*]
+Parent=Download Managers
+Browser=Net Vampire
+
+[Net_Vampire*]
+Parent=Download Managers
+Browser=Net_Vampire
+
+[NetAnts*]
+Parent=Download Managers
+Browser=NetAnts
+
+[NetPumper*]
+Parent=Download Managers
+Browser=NetPumper
+
+[NetSucker*]
+Parent=Download Managers
+Browser=NetSucker
+
+[NetZip Downloader*]
+Parent=Download Managers
+Browser=NetZip Downloader
+
+[NexTools WebAgent*]
+Parent=Download Managers
+Browser=NexTools WebAgent
+
+[Offline Downloader*]
+Parent=Download Managers
+Browser=Offline Downloader
+
+[P3P Client]
+Parent=Download Managers
+Browser=P3P Client
+
+[PageDown*]
+Parent=Download Managers
+Browser=PageDown
+
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+
+[Microsoft Visio MSIE]
+Parent=Microsoft
+Browser=Microsoft Visio
+
+[Microsoft-WebDAV-MiniRedir/*]
+Parent=Microsoft
+Browser=Microsoft-WebDAV
+
+[Mozilla/5.0 (Macintosh; Intel Mac OS X) Excel/12.*]
+Parent=Microsoft
+Browser=Microsoft Excel
+Version=12.0
+MajorVer=12
+MinorVer=0
+Platform=MacOSX
+
+[MSN Feed Manager]
+Parent=Microsoft
+Browser=MSN Feed Manager
+isBanned=false
+isSyndicationReader=true
+
+[MSProxy/*]
+Parent=Microsoft
+Browser=MS Proxy
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Miscellaneous Browsers
+
+[Miscellaneous Browsers]
+Parent=DefaultProperties
+Browser=Miscellaneous Browsers
+Frames=true
+Tables=true
+Cookies=true
+
+[*Amiga*]
+Parent=Miscellaneous Browsers
+Browser=Amiga
+Platform=Amiga
+
+[*avantbrowser*]
+Parent=Miscellaneous Browsers
+Browser=Avant Browser
+
+[12345]
+Parent=Miscellaneous Browsers
+Browser=12345
+isBanned=true
+
+[Ace Explorer]
+Parent=Miscellaneous Browsers
+Browser=Ace Explorer
+
+[Enigma Browser*]
+Parent=Miscellaneous Browsers
+Browser=Enigma Browser
+
+[EVE-minibrowser/*]
+Parent=Miscellaneous Browsers
+Browser=EVE-minibrowser
+IFrames=false
+Tables=false
+BackgroundSounds=false
+VBScript=false
+JavaApplets=false
+JavaScript=false
+ActiveXControls=false
+isBanned=false
+Crawler=false
+
+[Godzilla/* (Basic*; *; Commodore C=64; *; rv:1.*)*]
+Parent=Miscellaneous Browsers
+Browser=Godzilla
+
+[GreenBrowser]
+Parent=Miscellaneous Browsers
+Browser=GreenBrowser
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+CssVersion=2
+supportsCSS=true
+
+[Kopiczek/* (WyderOS*; *)]
+Parent=Miscellaneous Browsers
+Browser=Kopiczek
+Platform=WyderOS
+IFrames=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/* (*) - BrowseX (*)]
+Parent=Miscellaneous Browsers
+Browser=BrowseX
+
+[Mozilla/* (Win32;*Escape?*; ?)]
+Parent=Miscellaneous Browsers
+Browser=Escape
+Platform=Win32
+
+[Mozilla/4.0 (compatible; ibisBrowser)]
+Parent=Miscellaneous Browsers
+Browser=ibisBrowser
+
+[Mozilla/5.0 (Macintosh; ?; PPC Mac OS X;*) AppleWebKit/* (*) HistoryHound/*]
+Parent=Miscellaneous Browsers
+Browser=HistoryHound
+
+[NetRecorder*]
+Parent=Miscellaneous Browsers
+Browser=NetRecorder
+
+[NetSurfer*]
+Parent=Miscellaneous Browsers
+Browser=NetSurfer
+
+[ogeb browser , Version 1.1.0]
+Parent=Miscellaneous Browsers
+Browser=ogeb browser
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[SCEJ PSP BROWSER 0102pspNavigator]
+Parent=Miscellaneous Browsers
+Browser=Wipeout Pure
+
+[SlimBrowser]
+Parent=Miscellaneous Browsers
+Browser=SlimBrowser
+
+[WWW_Browser/*]
+Parent=Miscellaneous Browsers
+Browser=WWW Browser
+Version=1.69
+MajorVer=1
+MinorVer=69
+Platform=Win16
+CssVersion=3
+supportsCSS=true
+
+[*Netcraft Webserver Survey*]
+Parent=Netcraft
+Browser=Netcraft Webserver Survey
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Offline Browsers
+
+[Offline Browsers]
+Parent=DefaultProperties
+Browser=Offline Browsers
+Frames=true
+Tables=true
+Cookies=true
+isBanned=true
+Crawler=true
+
+[*Check&Get*]
+Parent=Offline Browsers
+Browser=Check&Get
+
+[*HTTrack*]
+Parent=Offline Browsers
+Browser=HTTrack
+
+[*MSIECrawler*]
+Parent=Offline Browsers
+Browser=IE Offline Browser
+
+[*TweakMASTER*]
+Parent=Offline Browsers
+Browser=TweakMASTER
+
+[BackStreet Browser *]
+Parent=Offline Browsers
+Browser=BackStreet Browser
+
+[Go-Ahead-Got-It*]
+Parent=Offline Browsers
+Browser=Go Ahead Got-It
+
+[iGetter/*]
+Parent=Offline Browsers
+Browser=iGetter
+
+[Teleport*]
+Parent=Offline Browsers
+Browser=Teleport
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Online Scanners
+
+[Online Scanners]
+Parent=DefaultProperties
+Browser=Online Scanners
+isBanned=true
+
+[JoeDog/* (X11; I; Siege *)]
+Parent=Online Scanners
+Browser=JoeDog
+isBanned=false
+
+[Morfeus Fucking Scanner]
+Parent=Online Scanners
+Browser=Morfeus Fucking Scanner
+
+[Mozilla/4.0 (compatible; Trend Micro tmdr 1.*]
+Parent=Online Scanners
+Browser=Trend Micro
+
+[Titanium 2005 (4.02.01)]
+Parent=Online Scanners
+Browser=Panda Antivirus Titanium
+
+[virus_detector*]
+Parent=Online Scanners
+Browser=Secure Computing Corporation
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Proxy Servers
+
+[Proxy Servers]
+Parent=DefaultProperties
+Browser=Proxy Servers
+isBanned=true
+
+[*squid*]
+Parent=Proxy Servers
+Browser=Squid
+
+[Anonymisiert*]
+Parent=Proxy Servers
+Browser=Anonymizied
+
+[Anonymizer/*]
+Parent=Proxy Servers
+Browser=Anonymizer
+
+[Anonymizied*]
+Parent=Proxy Servers
+Browser=Anonymizied
+
+[Anonymous*]
+Parent=Proxy Servers
+Browser=Anonymous
+
+[Anonymous/*]
+Parent=Proxy Servers
+Browser=Anonymous
+
+[CE-Preload]
+Parent=Proxy Servers
+Browser=CE-Preload
+
+[http://Anonymouse.org/*]
+Parent=Proxy Servers
+Browser=Anonymouse
+
+[IE/6.01 (CP/M; 8-bit*)]
+Parent=Proxy Servers
+Browser=Squid
+
+[Mozilla/* (TuringOS; Turing Machine; 0.0)]
+Parent=Proxy Servers
+Browser=Anonymizer
+
+[Mozilla/4.0 (compatible; MSIE ?.0; SaferSurf*)]
+Parent=Proxy Servers
+Browser=SaferSurf
+
+[Mozilla/5.0 (compatible; del.icio.us-thumbnails/*; *) KHTML/* (like Gecko)]
+Parent=Proxy Servers
+Browser=Yahoo!
+isBanned=true
+Crawler=true
+
+[Nutscrape]
+Parent=Proxy Servers
+Browser=Squid
+
+[Nutscrape/* (CP/M; 8-bit*)]
+Parent=Proxy Servers
+Browser=Squid
+
+[Privoxy/*]
+Parent=Proxy Servers
+Browser=Privoxy
+
+[ProxyTester*]
+Parent=Proxy Servers
+Browser=ProxyTester
+isBanned=true
+Crawler=true
+
+[SilentSurf*]
+Parent=Proxy Servers
+Browser=SilentSurf
+
+[SmallProxy*]
+Parent=Proxy Servers
+Browser=SmallProxy
+
+[Space*Bison/*]
+Parent=Proxy Servers
+Browser=Proxomitron
+
+[Sqworm/*]
+Parent=Proxy Servers
+Browser=Websense
+
+[SurfControl]
+Parent=Proxy Servers
+Browser=SurfControl
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Research Projects
+
+[Research Projects]
+Parent=DefaultProperties
+Browser=Research Projects
+isBanned=true
+Crawler=true
+
+[*research*]
+Parent=Research Projects
+
+[AcadiaUniversityWebCensusClient]
+Parent=Research Projects
+Browser=AcadiaUniversityWebCensusClient
+
+[Amico Alpha * (*) Gecko/* AmicoAlpha/*]
+Parent=Research Projects
+Browser=Amico Alpha
+
+[annotate_google; http://ponderer.org/*]
+Parent=Research Projects
+Browser=Annotate Google
+
+[CMS crawler (?http://buytaert.net/crawler/)]
+Parent=Research Projects
+
+[e-SocietyRobot(http://www.yama.info.waseda.ac.jp/~yamana/es/)]
+Parent=Research Projects
+Browser=e-SocietyRobot
+
+[Forschungsportal/*]
+Parent=Research Projects
+Browser=Forschungsportal
+
+[Gulper Web *]
+Parent=Research Projects
+Browser=Gulper Web Bot
+
+[HooWWWer/*]
+Parent=Research Projects
+Browser=HooWWWer
+
+[http://buytaert.net/crawler]
+Parent=Research Projects
+
+[inetbot/* (?http://www.inetbot.com/bot.html)]
+Parent=Research Projects
+Browser=inetbot
+
+[IRLbot/*]
+Parent=Research Projects
+Browser=IRLbot
+
+[Lachesis]
+Parent=Research Projects
+Browser=Lachesis
+
+[Mozilla/5.0 (compatible; nextthing.org/*)]
+Parent=Research Projects
+Browser=nextthing.org
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (compatible; Theophrastus/*)]
+Parent=Research Projects
+Browser=Theophrastus
+
+[Mozilla/5.0 (compatible; Webscan v0.*;  http://otc.dyndns.org/webscan/)]
+Parent=Research Projects
+Browser=Webscan
+
+[MQbot*]
+Parent=Research Projects
+Browser=MQbot
+
+[OutfoxBot/*]
+Parent=Research Projects
+Browser=OutfoxBot
+
+[polybot?*]
+Parent=Research Projects
+Browser=Polybot
+
+[Shim?Crawler*]
+Parent=Research Projects
+Browser=Shim Crawler
+
+[Steeler/*]
+Parent=Research Projects
+Browser=Steeler
+
+[Taiga web spider]
+Parent=Research Projects
+Browser=Taiga
+
+[Theme Spider*]
+Parent=Research Projects
+Browser=Theme Spider
+
+[UofTDB_experiment* (leehyun@cs.toronto.edu)]
+Parent=Research Projects
+Browser=UofTDB Experiment
+
+[USyd-NLP-Spider*]
+Parent=Research Projects
+Browser=USyd-NLP-Spider
+
+[woriobot*]
+Parent=Research Projects
+Browser=woriobot
+
+[wwwster/* (Beta, mailto:gue@cis.uni-muenchen.de)]
+Parent=Research Projects
+Browser=wwwster
+Beta=true
+
+[Zao-Crawler]
+Parent=Research Projects
+Browser=Zao-Crawler
+
+[Zao/*]
+Parent=Research Projects
+Browser=Zao
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Rippers
+
+[Rippers]
+Parent=DefaultProperties
+Browser=Rippers
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[*grub*]
+Parent=Rippers
+Browser=grub
+
+[*ickHTTP*]
+Parent=Rippers
+Browser=IP*Works
+
+[*java*]
+Parent=Rippers
+
+[*libwww-perl*]
+Parent=Rippers
+Browser=libwww-perl
+
+[*WebGrabber*]
+Parent=Rippers
+
+[*WinHttpRequest*]
+Parent=Rippers
+Browser=WinHttp
+
+[3D-FTP/*]
+Parent=Rippers
+Browser=3D-FTP
+
+[3wGet/*]
+Parent=Rippers
+Browser=3wGet
+
+[ActiveRefresh*]
+Parent=Rippers
+Browser=ActiveRefresh
+
+[Artera (Version *)]
+Parent=Rippers
+Browser=Artera
+
+[AutoHotkey]
+Parent=Rippers
+Browser=AutoHotkey
+
+[b2w/*]
+Parent=Rippers
+Browser=b2w
+
+[BasicHTTP/*]
+Parent=Rippers
+Browser=BasicHTTP
+
+[BlockNote.Net]
+Parent=Rippers
+Browser=BlockNote.Net
+
+[CAST]
+Parent=Rippers
+Browser=CAST
+
+[CFNetwork/*]
+Parent=Rippers
+Browser=CFNetwork
+
+[CFSCHEDULE*]
+Parent=Rippers
+Browser=ColdFusion Task Scheduler
+
+[CobWeb/*]
+Parent=Rippers
+Browser=CobWeb
+
+[ColdFusion*]
+Parent=Rippers
+Browser=ColdFusion
+
+[Crawl_Application]
+Parent=Rippers
+Browser=Crawl_Application
+
+[curl/*]
+Parent=Rippers
+Browser=cURL
+
+[Custo*]
+Parent=Rippers
+Browser=Custo
+
+[DataCha0s/*]
+Parent=Rippers
+Browser=DataCha0s
+
+[DeepIndexer*]
+Parent=Rippers
+Browser=DeepIndexer
+
+[DISCo Pump *]
+Parent=Rippers
+Browser=DISCo Pump
+
+[eStyleSearch * (compatible; MSIE 6.0; Windows NT 5.0)]
+Parent=Rippers
+Browser=eStyleSearch
+Win32=true
+
+[ezic.com http agent *]
+Parent=Rippers
+Browser=Ezic.com
+
+[fetch libfetch/*]
+Parent=Rippers
+
+[FGet*]
+Parent=Rippers
+Browser=FGet
+
+[Flaming AttackBot*]
+Parent=Rippers
+Browser=Flaming AttackBot
+
+[Foobot*]
+Parent=Rippers
+Browser=Foobot
+
+[GameSpyHTTP/*]
+Parent=Rippers
+Browser=GameSpyHTTP
+
+[gnome-vfs/*]
+Parent=Rippers
+Browser=gnome-vfs
+
+[Harvest/*]
+Parent=Rippers
+Browser=Harvest
+
+[hcat/*]
+Parent=Rippers
+Browser=hcat
+
+[HLoader]
+Parent=Rippers
+Browser=HLoader
+
+[Holmes/*]
+Parent=Rippers
+Browser=Holmes
+
+[HTMLParser/*]
+Parent=Rippers
+Browser=HTMLParser
+
+[http generic]
+Parent=Rippers
+Browser=http generic
+
+[httpclient*]
+Parent=Rippers
+
+[httperf/*]
+Parent=Rippers
+Browser=httperf
+
+[HTTPFetch/*]
+Parent=Rippers
+Browser=HTTPFetch
+
+[HTTPGrab]
+Parent=Rippers
+Browser=HTTPGrab
+
+[HttpSession]
+Parent=Rippers
+Browser=HttpSession
+
+[httpunit/*]
+Parent=Rippers
+Browser=HttpUnit
+
+[ICE_GetFile]
+Parent=Rippers
+Browser=ICE_GetFile
+
+[iexplore.exe]
+Parent=Rippers
+
+[Inet - Eureka App]
+Parent=Rippers
+Browser=Inet - Eureka App
+
+[INetURL/*]
+Parent=Rippers
+Browser=INetURL
+
+[InetURL:/*]
+Parent=Rippers
+Browser=InetURL
+
+[Internet Exploiter/*]
+Parent=Rippers
+
+[Internet Explore *]
+Parent=Rippers
+Browser=Fake IE
+
+[Internet Explorer *]
+Parent=Rippers
+Browser=Fake IE
+
+[IP*Works!*/*]
+Parent=Rippers
+Browser=IP*Works!
+
+[IrssiUrlLog/*]
+Parent=Rippers
+Browser=IrssiUrlLog
+
+[JPluck/*]
+Parent=Rippers
+Browser=JPluck
+
+[Kapere (http://www.kapere.com)]
+Parent=Rippers
+Browser=Kapere
+
+[LeechFTP]
+Parent=Rippers
+Browser=LeechFTP
+
+[LeechGet*]
+Parent=Rippers
+Browser=LeechGet
+
+[libcurl-agent/*]
+Parent=Rippers
+Browser=libcurl
+
+[libWeb/clsHTTP*]
+Parent=Rippers
+Browser=libWeb/clsHTTP
+
+[lwp*]
+Parent=Rippers
+
+[MFC_Tear_Sample]
+Parent=Rippers
+Browser=MFC_Tear_Sample
+
+[Moozilla]
+Parent=Rippers
+Browser=Moozilla
+
+[MovableType/*]
+Parent=Rippers
+Browser=MovableType Web Log
+
+[Mozilla/2.0 (compatible; NEWT ActiveX; Win32)]
+Parent=Rippers
+Browser=NEWT ActiveX
+Platform=Win32
+
+[Mozilla/3.0 (compatible)]
+Parent=Rippers
+
+[Mozilla/3.0 (compatible; Indy Library)]
+Parent=Rippers
+Cookies=true
+
+[Mozilla/3.01 (compatible;)]
+Parent=Rippers
+
+[Mozilla/4.0 (compatible; BorderManager*)]
+Parent=Rippers
+Browser=Novell BorderManager
+
+[Mozilla/4.0 (compatible;)]
+Parent=Rippers
+
+[Mozilla/5.0 (compatible; IPCheck Server Monitor*)]
+Parent=Rippers
+Browser=IPCheck Server Monitor
+
+[OCN-SOC/*]
+Parent=Rippers
+Browser=OCN-SOC
+
+[Offline Explorer*]
+Parent=Rippers
+Browser=Offline Explorer
+
+[Open Web Analytics Bot*]
+Parent=Rippers
+Browser=Open Web Analytics Bot
+
+[OSSProxy*]
+Parent=Rippers
+Browser=OSSProxy
+
+[Pageload*]
+Parent=Rippers
+Browser=PageLoad
+
+[PageNest/*]
+Parent=Rippers
+Browser=PageNest
+
+[pavuk/*]
+Parent=Rippers
+Browser=Pavuk
+
+[PEAR HTTP_Request*]
+Parent=Rippers
+Browser=PEAR-PHP
+
+[PHP*]
+Parent=Rippers
+Browser=PHP
+
+[PigBlock (Windows NT 5.1; U)*]
+Parent=Rippers
+Browser=PigBlock
+Win32=true
+
+[Pockey*]
+Parent=Rippers
+Browser=Pockey-GetHTML
+
+[POE-Component-Client-HTTP/*]
+Parent=Rippers
+Browser=POE-Component-Client-HTTP
+
+[PycURL/*]
+Parent=Rippers
+Browser=PycURL
+
+[Python*]
+Parent=Rippers
+Browser=Python
+
+[RepoMonkey*]
+Parent=Rippers
+Browser=RepoMonkey
+
+[SBL-BOT*]
+Parent=Rippers
+Browser=BlackWidow
+
+[ScoutAbout*]
+Parent=Rippers
+Browser=ScoutAbout
+
+[sherlock/*]
+Parent=Rippers
+Browser=Sherlock
+
+[SiteParser/*]
+Parent=Rippers
+Browser=SiteParser
+
+[SiteSnagger*]
+Parent=Rippers
+Browser=SiteSnagger
+
+[SiteSucker/*]
+Parent=Rippers
+Browser=SiteSucker
+
+[SiteWinder*]
+Parent=Rippers
+Browser=SiteWinder
+
+[Snoopy*]
+Parent=Rippers
+Browser=Snoopy
+
+[SOFTWING_TEAR_AGENT*]
+Parent=Rippers
+Browser=AspTear
+
+[SuperHTTP/*]
+Parent=Rippers
+Browser=SuperHTTP
+
+[Tcl http client package*]
+Parent=Rippers
+Browser=Tcl http client package
+
+[Twisted PageGetter]
+Parent=Rippers
+Browser=Twisted PageGetter
+
+[URL2File/*]
+Parent=Rippers
+Browser=URL2File
+
+[UtilMind HTTPGet]
+Parent=Rippers
+Browser=UtilMind HTTPGet
+
+[VCI WebViewer*]
+Parent=Rippers
+Browser=VCI WebViewer
+
+[W3CRobot/*]
+Parent=Rippers
+Browser=W3CRobot
+
+[Web Downloader*]
+Parent=Rippers
+Browser=Web Downloader
+
+[Web Downloader/*]
+Parent=Rippers
+Browser=Web Downloader
+
+[Web Magnet*]
+Parent=Rippers
+Browser=Web Magnet
+
+[WebAuto/*]
+Parent=Rippers
+
+[webbandit/*]
+Parent=Rippers
+Browser=webbandit
+
+[WebCopier*]
+Parent=Rippers
+Browser=WebCopier
+
+[WebDownloader*]
+Parent=Rippers
+Browser=WebDownloader
+
+[WebFetch]
+Parent=Rippers
+Browser=WebFetch
+
+[webfetch/*]
+Parent=Rippers
+Browser=WebFetch
+
+[WebGatherer*]
+Parent=Rippers
+Browser=WebGatherer
+
+[WebGet]
+Parent=Rippers
+Browser=WebGet
+
+[WebReaper*]
+Parent=Rippers
+Browser=WebReaper
+
+[WebRipper]
+Parent=Rippers
+Browser=WebRipper
+
+[WebSauger*]
+Parent=Rippers
+Browser=WebSauger
+
+[Website Downloader*]
+Parent=Rippers
+Browser=Website Downloader
+
+[Website eXtractor*]
+Parent=Rippers
+Browser=Website eXtractor
+
+[Website Quester]
+Parent=Rippers
+Browser=Website Quester
+
+[WebsiteExtractor*]
+Parent=Rippers
+Browser=Website eXtractor
+
+[WebSnatcher*]
+Parent=Rippers
+Browser=WebSnatcher
+
+[Webster Pro*]
+Parent=Rippers
+Browser=Webster Pro
+
+[WebStripper*]
+Parent=Rippers
+Browser=WebStripper
+
+[WebWhacker*]
+Parent=Rippers
+Browser=WebWhacker
+
+[WinScripter iNet Tools]
+Parent=Rippers
+Browser=WinScripter iNet Tools
+
+[WWW-Mechanize/*]
+Parent=Rippers
+Browser=WWW-Mechanize
+
+[Zend_Http_Client]
+Parent=Rippers
+Browser=Zend_Http_Client
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Site Monitors
+
+[Site Monitors]
+Parent=DefaultProperties
+Browser=Site Monitors
+Cookies=true
+isBanned=true
+Crawler=true
+
+[*EasyRider*]
+Parent=Site Monitors
+Browser=EasyRider
+
+[*maxamine.com--robot*]
+Parent=Site Monitors
+Browser=maxamine.com--robot
+isBanned=true
+
+[*WebMon ?.*]
+Parent=Site Monitors
+Browser=WebMon
+
+[Kenjin Spider*]
+Parent=Site Monitors
+Browser=Kenjin Spider
+
+[Kevin http://*]
+Parent=Site Monitors
+Browser=Kevin
+isBanned=true
+
+[Mozilla/4.0 (compatible; ChangeDetection/*]
+Parent=Site Monitors
+Browser=ChangeDetection
+
+[Myst Monitor Service v*]
+Parent=Site Monitors
+Browser=Myst Monitor Service
+
+[Net Probe]
+Parent=Site Monitors
+Browser=Net Probe
+
+[NetMechanic*]
+Parent=Site Monitors
+Browser=NetMechanic
+
+[NetReality*]
+Parent=Site Monitors
+Browser=NetReality
+
+[Pingdom GIGRIB*]
+Parent=Site Monitors
+Browser=Pingdom
+
+[Site Valet Online*]
+Parent=Site Monitors
+Browser=Site Valet
+isBanned=true
+
+[SITECHECKER]
+Parent=Site Monitors
+Browser=SITECHECKER
+
+[sitemonitor@dnsvr.com/*]
+Parent=Site Monitors
+Browser=ZoneEdit Failover Monitor
+isBanned=false
+
+[UpTime Checker*]
+Parent=Site Monitors
+Browser=UpTime Checker
+
+[URL Control*]
+Parent=Site Monitors
+Browser=URL Control
+
+[URL_Access/*]
+Parent=Site Monitors
+
+[URLCHECK]
+Parent=Site Monitors
+Browser=URLCHECK
+
+[URLy Warning*]
+Parent=Site Monitors
+Browser=URLy Warning
+
+[Webcheck *]
+Parent=Site Monitors
+Browser=Webcheck
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[WebPatrol/*]
+Parent=Site Monitors
+Browser=WebPatrol
+
+[websitepulse checker/*]
+Parent=Site Monitors
+Browser=websitepulse checker
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Social Bookmarkers
+
+[Social Bookmarkers]
+Parent=DefaultProperties
+Browser=Social Bookmarkers
+Frames=true
+Tables=true
+Cookies=true
+JavaScript=true
+
+[BookmarkBase(2/;http://bookmarkbase.com)]
+Parent=Social Bookmarkers
+Browser=BookmarkBase
+
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NewsGator
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 0.3
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 1.0
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.1
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.2
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.3
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.4
+
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+[QuickTime/7.4.* (qtver=7.4.*;*;os=Mac 10.*)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Google Android
+
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; BlackBerry
+
+[BlackBerry]
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+[*BlackBerry*]
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+[*BlackBerrySimulator/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Handspring Blazer
+
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DoCoMo
+
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+[DoCoMo/1.0*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IEMobile
+
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iPhone
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+[Mozilla/5.0 (iPhone Simulator; U; CPU iPhone OS 2_0_1 like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone)]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_* like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_0* like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+
+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_0_2 like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko)]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_1 like Mac OS X; *)*]
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+
+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_2_1 like Mac OS X; *)]
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+[Mozilla/5.0 (iPhone; U; CPU like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.0 Mobile/* Safari/*]
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+
+[Mozilla/5.0 (iPod; U; *Mac OS X; *) AppleWebKit/* (*) Version/* Mobile/*]
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+
+[Mozilla/5.0 (iPod; U; CPU iPhone OS 2_2* like Mac OS X; *)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; KDDI
+
+[KDDI]
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+
+[KDDI-* UP.Browser/* (GUI) MMP/*]
+Parent=KDDI
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Miscellaneous Mobile
+
+[Miscellaneous Mobile]
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+
+[Mozilla/5.0 (X11; *; CentOS; *) AppleWebKit/* (KHTML, like Gecko) Bolt/0.* Version/3.0 Safari/*]
+Parent=Miscellaneous Mobile
+Browser=Bolt
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Motorola Internet Browser
+
+[Motorola Internet Browser]
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+Frames=true
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+
+[MOT-*/*]
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+
+[MOT-1*/* UP.Browser/*]
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+
+[MOT-8700_/* UP.Browser/*]
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+
+[MOT-A-0A/* UP.Browser/*]
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+
+[MOT-A-2B/* UP.Browser/*]
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+
+[MOT-A-88/* UP.Browser/*]
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+
+[MOT-C???/* MIB/*]
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+
+[MOT-GATW_/* UP.Browser/*]
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+
+[MOT-L6/* MIB/*]
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+
+[MOT-L7/* MIB/*]
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+
+[MOT-M*/* UP.Browser/*]
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+
+[MOT-MP*/* Mozilla/* (compatible; MSIE *; Windows CE; *)]
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+
+[MOT-MP*/* Mozilla/4.0 (compatible; MSIE *; Windows CE; *)]
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+
+[MOT-SAP4_/* UP.Browser/*]
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+
+[MOT-T*/*]
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+
+[MOT-T7*/* MIB/*]
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+
+[MOT-T721*]
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+
+[MOT-TA02/* MIB/*]
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+
+[MOT-V*/*]
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+
+[MOT-V*/* MIB/*]
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+
+[MOT-V*/* UP.Browser/*]
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+
+[MOT-V3/* MIB/*]
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+
+[MOT-V4*/* MIB/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; MSN Mobile Proxy
+
+[MSN Mobile Proxy]
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+
+[Mozilla/* (compatible; MSIE *; Windows*; MSN Mobile Proxy)]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NetFront
+
+[NetFront]
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+
+[*NetFront/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Nokia
+
+[Nokia]
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+
+[*Nokia*/*]
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+
+[Mozilla/* (SymbianOS/*; ?; *) AppleWebKit/* (KHTML, like Gecko) Safari/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Openwave Mobile Browser
+
+[Openwave Mobile Browser]
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+[*UP.Browser/*]
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+
+[*UP.Link/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Opera Mini
+
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+[Opera/* (J2ME/MIDP; Opera Mini/1.0*)*]
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+[Opera/* (J2ME/MIDP; Opera Mini/4.2*)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Opera Mobile
+
+[Opera Mobile]
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+
+[Opera/9.5 (Microsoft Windows; PPC; *Opera Mobile/*)]
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+
+[Opera/9.5 (Microsoft Windows; PPC; Opera Mobi/*)]
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+
+[Opera/9.51 Beta (Microsoft Windows; PPC; Opera Mobi/*)*]
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+Beta=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Playstation
+
+[Playstation]
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+isMobileDevice=true
+
+[Mozilla/* (PLAYSTATION *; *)]
+Parent=Playstation
+Browser=PlayStation 3
+Frames=false
+
+[Mozilla/* (PSP (PlayStation Portable); *)]
+Parent=Playstation
+
+[Sony PS2 (Linux)]
+Parent=Playstation
+Browser=Sony PS2
+Platform=Linux
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Pocket PC
+
+[Pocket PC]
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+JavaScript=true
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+
+[*(compatible; MSIE *.*; Windows CE; PPC; *)]
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+
+[HTC-*/* Mozilla/* (compatible; MSIE *.*; Windows CE*)*]
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+
+[Mozilla/* (compatible; MSPIE *.*; *Windows CE*)*]
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+
+[T-Mobile* Mozilla/* (compatible; MSIE *.*; Windows CE; *)]
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+
+[Vodafone* Mozilla/* (compatible; MSIE *.*; Windows CE; *)*]
+Parent=Pocket PC
+
+[Windows CE (Pocket PC) - Version *.*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SEMC Browser
+
+[SEMC Browser]
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+Browser=SEMC Browser
+Platform=JAVA
+Tables=true
+isMobileDevice=true
+CssVersion=1
+supportsCSS=true
+
+[*SEMC-Browser/*]
+Parent=SEMC Browser
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SonyEricsson
+
+[SonyEricsson]
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+Browser=SonyEricsson
+Frames=true
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+JavaScript=true
+isMobileDevice=true
+CssVersion=1
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+
+[*Ericsson*]
+Parent=SonyEricsson
+
+[*SonyEricsson*]
+Parent=SonyEricsson
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Netbox
+
+[Netbox]
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+JavaScript=true
+CssVersion=1
+supportsCSS=true
+
+[Mozilla/3.01 (compatible; Netbox/*; Linux*)]
+Parent=Netbox
+Browser=Netbox
+Platform=Linux
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; PowerTV
+
+[PowerTV]
+Parent=DefaultProperties
+Browser=PowerTV
+Platform=PowerTV
+Frames=true
+Tables=true
+Cookies=true
+JavaScript=true
+
+[Mozilla/4.0 PowerTV/1.5 (Compatible; Spyglass DM 3.2.1, EXPLORER)]
+Parent=PowerTV
+Version=1.5
+MajorVer=1
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; WebTV/MSNTV
+
+[WebTV]
+Parent=DefaultProperties
+Browser=WebTV/MSNTV
+Platform=WebTV
+Frames=true
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+JavaScript=true
+
+[Mozilla/3.0 WebTV/1.*(compatible; MSIE 2.0)]
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+
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+
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+
+[Mozilla/4.0 WebTV/2.2*(compatible; MSIE 3.0)]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; AppleWebKit
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Camino
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+[Mozilla/5.0 (Macintosh; *Mac OS X*) Gecko/* Camino/1.6*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chimera
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+[Mozilla/5.0 (Macintosh; U; *Mac OS X*; *; rv:1.*) Gecko/* Chimera/*]
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+[Mozilla/5.0 Gecko/* Chimera/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Dillo
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+[Dillo]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Emacs/W3
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+[Emacs/W3]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; fantomas
+
+[fantomas]
+Parent=DefaultProperties
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+
+[Mozilla/4.0 (cloakBrowser)]
+Parent=fantomas
+Browser=fantomas cloakBrowser
+
+[Mozilla/4.0 (fantomas shadowMaker Browser)]
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+Browser=fantomas shadowMaker Browser
+
+[Mozilla/4.0 (fantomBrowser)]
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+Browser=fantomas fantomBrowser
+
+[Mozilla/4.0 (fantomCrew Browser)]
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+
+[Mozilla/4.0 (stealthBrowser)]
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+Browser=fantomas stealthBrowser
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+[multiBlocker browser*]
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+Browser=fantomas multiBlocker browser
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; FrontPage
+
+[FrontPage]
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+[Mozilla/?* (compatible; MS FrontPage*)]
+Parent=FrontPage
+
+[MSFrontPage/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Galeon
+
+[Galeon]
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+[Mozilla/5.0 (X11; U; Linux*) Gecko/* Galeon/1.*]
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+[Mozilla/5.0 (X11; U; Linux*) Gecko/* Galeon/2.*]
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+[Mozilla/5.0 Galeon/1.* (X11; Linux*)*]
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+[Mozilla/5.0 Galeon/2.* (X11; Linux*)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; HP Secure Web Browser
+
+[HP Secure Web Browser]
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+Platform=OpenVMS
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+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.0*) Gecko/*]
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+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.1*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.2*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.3*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.4*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.5*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.6*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.7*) Gecko/*]
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+
+[Mozilla/5.0 (X11; U; OpenVMS*; *; rv:1.8*) Gecko/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IBrowse
+
+[IBrowse]
+Parent=DefaultProperties
+Browser=IBrowse
+Platform=Amiga
+Frames=true
+Tables=true
+Cookies=true
+JavaScript=true
+
+[Arexx (compatible; MSIE 6.0; AmigaOS5.0) IBrowse 4.0]
+Parent=IBrowse
+Version=4.0
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+
+[IBrowse/1.22 (AmigaOS *)]
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+[IBrowse/2.1 (AmigaOS *)]
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+[Mozilla/* (Win98; I) IBrowse/2.3 (AmigaOS 3.1)]
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+Version=2.3
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iCab
+
+[iCab]
+Parent=DefaultProperties
+Browser=iCab
+Frames=true
+Tables=true
+Cookies=true
+JavaScript=true
+CssVersion=1
+supportsCSS=true
+
+[iCab/2.7* (Macintosh; ?; 68K*)]
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+Version=2.7
+MajorVer=2
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+Platform=Mac68K
+
+[iCab/2.7* (Macintosh; ?; PPC*)]
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+Version=2.7
+MajorVer=2
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+Platform=MacPPC
+
+[iCab/2.8* (Macintosh; ?; *Mac OS X*)]
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+Version=2.8
+MajorVer=2
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+Platform=MacOSX
+
+[iCab/2.8* (Macintosh; ?; 68K*)]
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+Version=2.8
+MajorVer=2
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+Platform=Mac68K
+
+[iCab/2.8* (Macintosh; ?; PPC)]
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+[iCab/2.9* (Macintosh; ?; *Mac OS X*)]
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+
+[iCab/2.9* (Macintosh; ?; 68K*)]
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+
+[iCab/2.9* (Macintosh; ?; PPC*)]
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+
+[iCab/3.0* (Macintosh; ?; *Mac OS X*)]
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+
+[iCab/3.0* (Macintosh; ?; PPC*)]
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+
+[iCab/4.0 (Macintosh; U; *Mac OS X)]
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+[Mozilla/* (compatible; iCab 3.0*; Macintosh; *Mac OS X*)]
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+[Mozilla/* (compatible; iCab 3.0*; Macintosh; ?; PPC*)]
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+
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+
+[Mozilla/4.5 (compatible; iCab 2.7*; Macintosh; ?; PPC*)]
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+
+[Mozilla/4.5 (compatible; iCab 2.8*; Macintosh; ?; *Mac OS X*)]
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+
+[Mozilla/4.5 (compatible; iCab 2.8*; Macintosh; ?; PPC*)]
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+
+[Mozilla/4.5 (compatible; iCab 2.9*; Macintosh; *Mac OS X*)]
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+Platform=MacOSX
+
+[Mozilla/4.5 (compatible; iCab 2.9*; Macintosh; ?; PPC*)]
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+
+[Mozilla/4.5 (compatible; iCab 4.2*; Macintosh; *Mac OS X*)]
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+Version=4.2
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+Platform=MacOSX
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iSiloX
+
+[iSiloX]
+Parent=DefaultProperties
+Browser=iSiloX
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaScript=true
+Crawler=true
+CssVersion=2
+supportsCSS=true
+
+[iSiloX/4.0* MacOS]
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+Version=4.0
+MajorVer=4
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+Platform=MacPPC
+
+[iSiloX/4.0* Windows/32]
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+Version=4.0
+MajorVer=4
+MinorVer=0
+Platform=Win32
+Win32=true
+
+[iSiloX/4.1* MacOS]
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+Platform=MacPPC
+
+[iSiloX/4.1* Windows/32]
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+Version=4.1
+MajorVer=4
+MinorVer=1
+Platform=Win32
+Win32=true
+
+[iSiloX/4.2* MacOS]
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+Version=4.2
+MajorVer=4
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+Platform=MacPPC
+
+[iSiloX/4.2* Windows/32]
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+Version=4.2
+MajorVer=4
+MinorVer=2
+Platform=Win32
+Win32=true
+
+[iSiloX/4.3* MacOS]
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+Platform=MacOSX
+
+[iSiloX/4.3* Windows/32]
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+Version=4.3
+MajorVer=4
+MinorVer=3
+Platform=Win32
+Win32=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Lycoris Desktop/LX
+
+[Lycoris Desktop/LX]
+Parent=DefaultProperties
+Browser=Lycoris Desktop/LX
+Frames=true
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+Tables=true
+Cookies=true
+JavaApplets=true
+JavaScript=true
+Crawler=true
+
+[Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.*: Desktop/LX Amethyst) Gecko/*]
+Parent=Lycoris Desktop/LX
+Version=1.1
+MajorVer=1
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+Platform=Linux
+
+[Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.*; Desktop/LX Amethyst) Gecko/*]
+Parent=Lycoris Desktop/LX
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Linux
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mosaic
+
+[Mosaic]
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+Browser=Mosaic
+Frames=true
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+Tables=true
+Cookies=true
+JavaApplets=true
+JavaScript=true
+
+[Mozilla/4.0 (VMS_Mosaic)]
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+[VMS_Mosaic/3.7*]
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+
+[VMS_Mosaic/3.8*]
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+Version=3.8
+MajorVer=3
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+Platform=OpenVMS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NetPositive
+
+[NetPositive]
+Parent=DefaultProperties
+Browser=NetPositive
+Platform=BeOS
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+JavaApplets=true
+JavaScript=true
+
+[*NetPositive/2.2*]
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+[*NetPositive/2.2*BeOS*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; OmniWeb
+
+[OmniWeb]
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+Browser=OmniWeb
+Platform=MacOSX
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+Cookies=true
+JavaApplets=true
+JavaScript=true
+isMobileDevice=true
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+supportsCSS=true
+
+[Mozilla/* (Macintosh; ?; *Mac OS X; *) AppleWebKit/* (*) OmniWeb/v4*]
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+Version=4.5
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+Platform=MacOSX
+
+[Mozilla/* (Macintosh; ?; *Mac OS X; *) AppleWebKit/* (*) OmniWeb/v5*]
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+
+[Mozilla/* (Macintosh; ?; *Mac OS X; *) AppleWebKit/* (*) OmniWeb/v6*]
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+Version=6.0
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+
+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/4*]
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+
+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/5*]
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+
+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/6*]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/125.4 (KHTML, like Gecko, Safari) OmniWeb/v563.34]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/125.4 (KHTML, like Gecko, Safari) OmniWeb/v563.34]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/420+ (KHTML, like Gecko, Safari/420) OmniWeb/v607]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/420+ (KHTML, like Gecko, Safari/420) OmniWeb/v607]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/522+ (KHTML, like Gecko, Safari/522) OmniWeb/v613]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/522+ (KHTML, like Gecko, Safari/522) OmniWeb/v613]
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/85 (KHTML, like Gecko) OmniWeb/v496]
+Parent=OmniWeb
+Version=4.5
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/85 (KHTML, like Gecko) OmniWeb/v558.36 ]
+Parent=OmniWeb
+Version=5.0
+MajorVer=5
+MinorVer=0
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Shiira
+
+[Shiira]
+Parent=DefaultProperties
+Browser=Shiira
+Platform=MacOSX
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/0.9*]
+Parent=Shiira
+Version=0.9
+MajorVer=0
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/1.0*]
+Parent=Shiira
+Version=1.0
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.0
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.0*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.0*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.1
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+[K-Meleon 1.1]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* K-Meleon/1.1*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.1*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.1*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.5
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+[K-Meleon 1.5]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* K-Meleon?1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* K-Meleon/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* K-Meleon/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.*) Gecko/* K-Meleon/1.5*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.5*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 3.0
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+[Konqueror 3.0]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.0
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+[Konqueror 4.0]
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+[Mozilla/5.0 (compatible; Konqueror/4.0.*; *Linux) KHTML/4.* (like Gecko)]
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+[Mozilla/5.0 (compatible; Konqueror/4.0.*; FreeBSD) KHTML/4.* (like Gecko)]
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+[Mozilla/5.0 (compatible; Konqueror/4.0.*; NetBSD) KHTML/4.* (like Gecko)]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.1
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+[Konqueror 4.1]
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+[Mozilla/5.0 (compatible; Konqueror/4.1*; NetBSD) KHTML/4.* (like Gecko)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.2
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+[Konqueror 4.2]
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+[Mozilla/5.0 (compatible; Konqueror/4.2*; NetBSD) KHTML/4.* (like Gecko)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari 3.0
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+[Safari 3.0]
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+[Mozilla/5.0 (Windows; ?; Windows NT 6.1; *) AppleWebKit/* (*) Version/3.0* Safari/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari 3.1
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+[Safari 3.1]
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+[Mozilla/5.0 (Windows; ?; Windows NT 6.1; *) AppleWebKit/* (*) Version/3.1* Safari/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari 3.2
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+[Safari 3.2]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari 4.0
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+[Safari 4.0]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Opera 10.0
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Flock 2.0
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firebird
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox
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+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=Win2000
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=WinVista
+Win32=true
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=WinNT
+Win32=true
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=Linux
+
+[Mozilla/5.0 (X11; *; *Linux*; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=Linux
+
+[Mozilla/5.0 (X11; *; DragonFly*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=FreeBSD
+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=IRIX64
+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 1.4
+
+[Firefox 1.4]
+Parent=DefaultProperties
+Browser=Firefox
+Version=1.4
+MajorVer=1
+MinorVer=4
+Frames=true
+IFrames=true
+Tables=true
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+ecmascriptversion=1.3
+w3cdomversion=1.0
+
+[Mozilla/5.0 (Linux; *; PPC*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+Platform=Linux
+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=MacOSX
+
+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=OS/2
+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=WinME
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Win95*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=Win95
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+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=Win98
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=WinVista
+Win32=true
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=WinNT
+Win32=true
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=Linux
+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=IRIX64
+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 1.5
+
+[Firefox 1.5]
+Parent=DefaultProperties
+Browser=Firefox
+Version=1.5
+MajorVer=1
+MinorVer=5
+Frames=true
+IFrames=true
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+ecmascriptversion=1.5
+w3cdomversion=1.0
+
+[Mozilla/5.0 (Linux; *; PPC*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=MacOSX
+
+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=OS/2
+
+[Mozilla/5.0 (rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=WinME
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+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
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+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.5*]
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+Platform=Win98
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.5*]
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+Platform=Win2000
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2 x64; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=WinVista
+Win32=true
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=WinNT
+Win32=true
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
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+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=FreeBSD
+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 2.0
+
+[Firefox 2.0]
+Parent=DefaultProperties
+Browser=Firefox
+Version=2.0
+MajorVer=2
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaApplets=true
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+CssVersion=2
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+ecmascriptversion=1.5
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+
+[Mozilla/5.0 (Linux; *; PPC*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+Platform=MacOSX
+
+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=OS/2
+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=WinME
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+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/2.0*]
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+Platform=Win2000
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=WinVista
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=Win7
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=WinNT
+Win32=true
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.0
+
+[Firefox 3.0]
+Parent=DefaultProperties
+Browser=Firefox
+Version=3.0
+MajorVer=3
+Frames=true
+IFrames=true
+Tables=true
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+JavaApplets=true
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+CssVersion=3
+supportsCSS=true
+ecmascriptversion=1.5
+w3cdomversion=1.0
+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=MacOSX
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=Win2000
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
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+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=WinVista
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=Win7
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.9*) Gecko/* Firefox/3.0*]
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+Platform=WinNT
+Win32=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.1 x64; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=WinXP
+Win32=false
+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2 x64; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=Win2003
+Win32=false
+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=WinVista
+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1 x64; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=Win7
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=Linux
+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=FreeBSD
+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=IRIX64
+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.9*) Gecko/* Firefox/3.0*]
+Parent=Firefox 3.0
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.1
+
+[Firefox 3.1]
+Parent=DefaultProperties
+Browser=Firefox
+Version=3.1
+MajorVer=3
+MinorVer=1
+Beta=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaApplets=true
+JavaScript=true
+CssVersion=3
+supportsCSS=true
+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=MacOSX
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
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+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.9*) Gecko/* Firefox/3.1*]
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+Win32=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.1 x64; *; rv:1.9*) Gecko/* Firefox/3.1*]
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+Platform=WinXP
+Win32=false
+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2 x64; *; rv:1.9*) Gecko/* Firefox/3.1*]
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+Win32=false
+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=WinVista
+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1 x64; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=Win7
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=Linux
+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=FreeBSD
+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=HP-UX
+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.9*) Gecko/* Firefox/3.1*]
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+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.9*) Gecko/* Firefox/3.1*]
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+Platform=OpenBSD
+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.9*) Gecko/* Firefox/3.1*]
+Parent=Firefox 3.1
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.5
+
+[Firefox 3.5]
+Parent=DefaultProperties
+Browser=Firefox
+Version=3.5
+MajorVer=3
+MinorVer=5
+Beta=true
+Frames=true
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+Tables=true
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+JavaApplets=true
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+supportsCSS=true
+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
+Parent=Firefox 3.5
+Platform=WinVista
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
+Parent=Firefox 3.5
+Platform=Win7
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.1 x64; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+Win32=false
+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2 x64; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1 x64; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
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+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
+Parent=Firefox 3.5
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+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+
+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
+Parent=Firefox 3.5
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Phoenix
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Iceweasel
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.0
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.1
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.2
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.3
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.4
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.5
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.6
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.7
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.8
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.9
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE Mac
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; AOL 9.0/IE 5.5
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+JavaApplets=true
+JavaScript=true
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+CssVersion=2
+supportsCSS=true
+
+[Mozilla/?.* (?compatible; *MSIE 4.01*)*]
+Parent=IE 4.01
+
+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows 95*)*]
+Parent=IE 4.01
+Platform=Win95
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+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows 98*)*]
+Parent=IE 4.01
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+
+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows 98; Win 9x 4.90;*)*]
+Parent=IE 4.01
+Platform=WinME
+
+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows NT 4.0*)*]
+Parent=IE 4.01
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+
+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows NT 5.0*)*]
+Parent=IE 4.01
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+
+[Mozilla/4.0 (compatible; MSIE 4.01; *Windows NT 5.01*)*]
+Parent=IE 4.01
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 4.01; Windows NT)]
+Parent=IE 4.01
+Platform=WinNT
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 5.0
+
+[IE 5.0]
+Parent=DefaultProperties
+Browser=IE
+Version=5.0
+MajorVer=5
+Win32=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
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+CssVersion=2
+supportsCSS=true
+
+[Mozilla/?.* (?compatible; *MSIE 5.0*)*]
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+[Mozilla/4.0 (compatible; MSIE 5.0; *Windows 95*)*]
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+
+[Mozilla/4.0 (compatible; MSIE 5.0; *Windows 98; Win 9x 4.90;*)*]
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+
+[Mozilla/4.0 (compatible; MSIE 5.0; *Windows NT 4.0*)*]
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+
+[Mozilla/4.0 (compatible; MSIE 5.0; *Windows NT 5.0*)*]
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+
+[Mozilla/4.0 (compatible; MSIE 5.0; *Windows NT 5.01*)*]
+Parent=IE 5.0
+Platform=Win2000
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 5.01
+
+[IE 5.01]
+Parent=DefaultProperties
+Browser=IE
+Version=5.01
+MajorVer=5
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+Win32=true
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+CssVersion=2
+supportsCSS=true
+
+[Mozilla/?.* (?compatible; *MSIE 5.01*)*]
+Parent=IE 5.01
+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows 95*)*]
+Parent=IE 5.01
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+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows 98*)*]
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+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows 98; Win 9x 4.90;*)*]
+Parent=IE 5.01
+Platform=WinME
+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows NT 4.0*)*]
+Parent=IE 5.01
+Platform=WinNT
+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows NT 5.0*)*]
+Parent=IE 5.01
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 5.01; *Windows NT 5.01*)*]
+Parent=IE 5.01
+Platform=Win2000
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 5.5
+
+[IE 5.5]
+Parent=DefaultProperties
+Browser=IE
+Version=5.5
+MajorVer=5
+MinorVer=5
+Win32=true
+Frames=true
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+CssVersion=2
+supportsCSS=true
+ecmascriptversion=1.2
+w3cdomversion=1.0
+
+[Mozilla/?.* (?compatible; *MSIE 5.5*)*]
+Parent=IE 5.5
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows 95*)*]
+Parent=IE 5.5
+Platform=Win95
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows 98*)*]
+Parent=IE 5.5
+Platform=Win98
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows 98; Win 9x 4.90*)*]
+Parent=IE 5.5
+Platform=WinME
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows NT 4.0*)*]
+Parent=IE 5.5
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+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows NT 5.0*)*]
+Parent=IE 5.5
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+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows NT 5.01*)*]
+Parent=IE 5.5
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows NT 5.1*)*]
+Parent=IE 5.5
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 5.5; *Windows NT 5.2*)*]
+Parent=IE 5.5
+Platform=Win2003
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 6.0
+
+[IE 6.0]
+Parent=DefaultProperties
+Browser=IE
+Version=6.0
+MajorVer=6
+Win32=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+CDF=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+CssVersion=2
+supportsCSS=true
+ecmascriptversion=1.2
+w3cdomversion=1.0
+msdomversion=6.0
+
+[Mozilla/?.* (?compatible; *MSIE 6.0*)*]
+Parent=IE 6.0
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows 95*)*]
+Parent=IE 6.0
+Platform=Win95
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows 98*)*]
+Parent=IE 6.0
+Platform=Win98
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows 98; Win 9x 4.90*)*]
+Parent=IE 6.0
+Platform=WinME
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 4.0*)*]
+Parent=IE 6.0
+Platform=WinNT
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.0*)*]
+Parent=IE 6.0
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.01*)*]
+Parent=IE 6.0
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.1*)*]
+Parent=IE 6.0
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.2*)*]
+Parent=IE 6.0
+Platform=Win2003
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.2;*Win64;*)*]
+Parent=IE 6.0
+Platform=WinXP
+Win32=false
+Win64=true
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 5.2;*WOW64;*)*]
+Parent=IE 6.0
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 6.0; *Windows NT 6.0*)*]
+Parent=IE 6.0
+Platform=WinVista
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 7.0
+
+[IE 7.0]
+Parent=DefaultProperties
+Browser=IE
+Version=7.0
+MajorVer=7
+Win32=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
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+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+CssVersion=2
+supportsCSS=true
+ecmascriptversion=1.2
+msdomversion=7.0
+w3cdomversion=1.0
+
+[Mozilla/?.* (?compatible; *MSIE 7.0*)*]
+Parent=IE 7.0
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows 98*)*]
+Parent=IE 7.0
+Platform=Win98
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows 98; Win 9x 4.90;*)*]
+Parent=IE 7.0
+Platform=WinME
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 4.0*)*]
+Parent=IE 7.0
+Platform=WinNT
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.0*)*]
+Parent=IE 7.0
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.01*)*]
+Parent=IE 7.0
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.1*)*]
+Parent=IE 7.0
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.2*)*]
+Parent=IE 7.0
+Platform=Win2003
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.2;*Win64;*)*]
+Parent=IE 7.0
+Platform=WinXP
+Win32=false
+Win64=true
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 5.2;*WOW64;*)*]
+Parent=IE 7.0
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 6.0*)*]
+Parent=IE 7.0
+Platform=WinVista
+
+[Mozilla/4.0 (compatible; MSIE 7.0; *Windows NT 6.1*)*]
+Parent=IE 7.0
+Platform=Win7
+
+[Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; WOW64; Trident/4.0; *)*]
+Parent=IE 7.0
+Platform=Win7
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IE 8.0
+
+[IE 8.0]
+Parent=DefaultProperties
+Browser=IE
+Version=8.0
+MajorVer=8
+Win32=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+CDF=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+CssVersion=3
+supportsCSS=true
+ecmascriptversion=1.2
+msdomversion=8.0
+w3cdomversion=1.0
+
+[Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=WinVista
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Win32*)*]
+Parent=IE 8.0
+Platform=Win32
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.0*)*]
+Parent=IE 8.0
+Platform=Win2000
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.1*)*]
+Parent=IE 8.0
+Platform=WinXP
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.2*)*]
+Parent=IE 8.0
+Platform=Win2003
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0*)*]
+Parent=IE 8.0
+Platform=WinVista
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=WinVista
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Win64; x64; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=WinVista
+Win32=false
+Win64=true
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; WOW64; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=WinVista
+Win64=false
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1*)*]
+Parent=IE 8.0
+Platform=Win7
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=Win7
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Win64; x64; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=Win7
+Win32=false
+Win64=true
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=Win7
+Win64=false
+
+[Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 7.0; Trident/4.0*)*]
+Parent=IE 8.0
+Platform=Win7
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Default Browser
+
+[*]
+Browser=Default Browser
+Version=0
+MajorVer=0
+MinorVer=0
+Platform=unknown
+Alpha=false
+Beta=false
+Win16=false
+Win32=false
+Win64=false
+Frames=true
+IFrames=false
+Tables=true
+Cookies=false
+BackgroundSounds=false
+CDF=false
+VBScript=false
+JavaApplets=false
+JavaScript=false
+ActiveXControls=false
+Stripper=false
+isBanned=false
+isMobileDevice=false
+isSyndicationReader=false
+Crawler=false
+CssVersion=0
+supportsCSS=false
+AOL=false
+aolVersion=0
+AuthenticodeUpdate=0
+CSS=0
+WAP=false
+netCLR=false
+ClrVersion=0
+ECMAScriptVersion=0.0
+W3CDOMVersion=0.0
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/config b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/config
new file mode 100644
index 0000000000000000000000000000000000000000..c1d2863007699198d3745c8745e4eaaacde70547
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/config
@@ -0,0 +1,46 @@
+<configuration>
+	<dllmap dll="i:cygwin1.dll" target="libc.so.6" os="!windows" />
+	<dllmap dll="libc" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:libxslt.dll" target="libxslt.so" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libodbc.so.2" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libiodbc.dylib" os="osx"/>
+	<dllmap dll="oci" target="libclntsh.so" os="!windows"/>
+	<dllmap dll="db2cli" target="libdb2_36.so" os="!windows"/>
+	<dllmap dll="MonoPosixHelper" target="libMonoPosixHelper.so" os="!windows" />
+	<dllmap dll="System.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Net.Security.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Security.Cryptography.Native.Apple" target="$mono_libdir/libmono-native.so" os="osx" />
+	<dllmap dll="libmono-btls-shared" target="libmono-btls-shared.so" os="!windows" />
+	<dllmap dll="i:msvcrt" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:msvcrt.dll" target="libc.so.6" os="!windows"/>
+	<dllmap dll="sqlite" target="libsqlite.so.0" os="!windows"/>
+	<dllmap dll="sqlite3" target="libsqlite3.so.0" os="!windows"/>
+	<dllmap dll="libX11" target="libX11.so.6" os="!windows" />
+	<dllmap dll="libgdk-x11-2.0" target="libgdk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgdk_pixbuf-2.0" target="libgdk_pixbuf-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgtk-x11-2.0" target="libgtk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libglib-2.0" target="libglib-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgobject-2.0" target="libgobject-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgnomeui-2" target="libgnomeui-2.so.0" os="!windows"/>
+	<dllmap dll="librsvg-2" target="librsvg-2.so.2" os="!windows"/>
+	<dllmap dll="libXinerama" target="libXinerama.so.1" os="!windows" />
+	<dllmap dll="libasound" target="libasound.so.2" os="!windows" />
+	<dllmap dll="libcairo-2.dll" target="libcairo.so.2" os="!windows"/>
+	<dllmap dll="libcairo-2.dll" target="libcairo.2.dylib" os="osx"/>
+	<dllmap dll="libcups" target="libcups.so.2" os="!windows"/>
+	<dllmap dll="libcups" target="libcups.dylib" os="osx"/>
+	<dllmap dll="i:kernel32.dll">
+		<dllentry dll="__Internal" name="CopyMemory" target="mono_win32_compat_CopyMemory"/>
+		<dllentry dll="__Internal" name="FillMemory" target="mono_win32_compat_FillMemory"/>
+		<dllentry dll="__Internal" name="MoveMemory" target="mono_win32_compat_MoveMemory"/>
+		<dllentry dll="__Internal" name="ZeroMemory" target="mono_win32_compat_ZeroMemory"/>
+	</dllmap>
+	<dllmap dll="gdiplus" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdiplus.dll" target="libgdiplus.so.0"  os="!windows"/>
+	<dllmap dll="gdi32" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdi32.dll" target="libgdiplus.so.0" os="!windows"/>
+</configuration>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a3df3b5e9c236e124791fc1dfd5beccaca7e3727
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml
@@ -0,0 +1,616 @@
+<?xml version="1.0" encoding="utf-8"?>
+<mconfig>
+  <configuration>
+    <handlers>
+      <handler section="feature" 
+	       type="Mono.MonoConfig.FeatureNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.FeatureNode, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="configBlock"
+	       type="Mono.MonoConfig.ConfigBlockNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.ConfigBlockBlock, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="default"
+	       type="Mono.MonoConfig.DefaultNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.DefaultNode, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="defaultConfigFile"
+	       type="Mono.MonoConfig.DefaultConfigFileNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.DefaultConfigFile, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+    </handlers>
+  </configuration>
+
+  <feature name="AJAX" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any .NET 3.5 AJAX.NET application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX config sections"/>
+      <block name="AJAX controls registration"/>
+      <block name="AJAX compilation"/>
+      <block name="AJAX HTTP handlers"/>
+      <block name="AJAX HTTP modules"/>
+      <block name="AJAX CodeDOM 3.5 settings"/>
+      <block name="AJAX runtime settings"/>
+      <block name="AJAX system.webServer"/>
+      <block name="" />
+    </blocks>
+  </feature>
+
+  <feature name="AJAX1" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any AJAX.NET 1.0 application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX1 config sections"/>
+      <block name="AJAX1 controls registration"/>
+      <block name="AJAX1 compilation"/>
+      <block name="AJAX1 HTTP handlers"/>
+      <block name="AJAX1 HTTP modules"/>
+      <block name="AJAX1 system.web.extensions"/>
+      <block name="AJAX1 system.webServer"/>
+      <block name="" />
+    </blocks>
+  </feature>
+
+  <feature name="DynamicData" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any ASP.NET DynamicData application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX config sections"/>
+      <block name="AJAX controls registration"/>
+      <block name="AJAX compilation"/>
+      <block name="AJAX HTTP handlers"/>
+      <block name="AJAX HTTP modules"/>
+      <block name="AJAX system.web.extensions"/>
+      <block name="AJAX system.webServer"/>
+
+      <block name="DynamicData controls registration" />
+      <block name="DynamicData compilation" />
+      <block name="DynamicData HTTP modules" />
+    </blocks>
+  </feature>
+
+  <feature name="SettingsMapProtection" target="web">
+    <description><![CDATA[
+      Adds an entry to your config file which will prevent download of the 'settings.map' file. The
+file is used by the settings mapping manager to modify configuration settings depending on the operating
+system under which your application runs. Add this feature only if you have your own custom 'settings.map'
+file in the top-level directory of your ASP.NET application.
+]]></description>
+    <blocks>
+      <block name="SettingsMap Handler"/>
+    </blocks>
+  </feature>
+
+  <!-- configuration blocks required by the features -->
+  <configBlock name="SettingsMap Handler">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add verb="*" path="settings.map" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+]]>
+    </contents>
+  </configBlock>
+
+  <!-- AJAX.NET 3.5 start -->
+  <configBlock name="AJAX config sections">
+    <requires>
+      <section name="configuration">
+	<section name="configSections" attachPoint="true"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+        <sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+          <section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication"/>
+          <sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+            <section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="Everywhere" />
+            <section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+            <section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+            <section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+          </sectionGroup>
+        </sectionGroup>
+      </sectionGroup>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX controls registration">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="pages" defaultBlockName="system.web.pages">
+	    <section name="controls" defaultBlockName="system.web.pages.controls"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+          <add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+          <add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX compilation">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="compilation" defaultBlockName="system.web.compilation">
+	    <section name="assemblies" defaultBlockName="system.web.compilation.assemblies"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	    <add assembly="System.Core, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+            <add assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+            <add assembly="System.Data.DataSetExtensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+            <add assembly="System.Xml.Linq, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX HTTP handlers">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+    
+    <contents>
+      <![CDATA[
+	<remove verb="*" path="*.asmx"/>
+        <add verb="*" path="*.asmx" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add verb="*" path="*_AppService.axd" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" validate="false"/>   
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX HTTP modules">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpModules" defaultBlockName="system.web.httpModules"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add name="ScriptModule" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX CodeDOM 3.5 settings">
+    <requires>
+      <section name="configuration">
+	<section name="system.codedom">
+	  <section name="compilers" defaultBlockName="system.codedom.compilers"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	<compiler language="c#;cs;csharp" extension=".cs" warningLevel="4"
+                  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+          <providerOption name="CompilerVersion" value="v3.5"/>
+          <providerOption name="WarnAsError" value="false"/>
+        </compiler>
+        <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4"
+                  type="Microsoft.VisualBasic.VBCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+          <providerOption name="CompilerVersion" value="v3.5"/>
+          <providerOption name="OptionInfer" value="true"/>
+          <providerOption name="WarnAsError" value="false"/>
+        </compiler>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX runtime settings">
+    <requires>
+      <section name="configuration">
+	<section name="runtime"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	<assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1">
+        <dependentAssembly>
+          <assemblyIdentity name="System.Web.Extensions" publicKeyToken="31bf3856ad364e35"/>
+          <bindingRedirect oldVersion="1.0.0.0-1.1.0.0" newVersion="3.5.0.0"/>
+        </dependentAssembly>
+        <dependentAssembly>
+          <assemblyIdentity name="System.Web.Extensions.Design" publicKeyToken="31bf3856ad364e35"/>
+          <bindingRedirect oldVersion="1.0.0.0-1.1.0.0" newVersion="3.5.0.0"/>
+        </dependentAssembly>
+      </assemblyBinding>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX system.webServer">
+    <requires>
+      <section name="configuration">
+	<section name="system.webServer"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+      <validation validateIntegratedModeConfiguration="false"/>
+      <modules>
+        <remove name="ScriptModule" />
+        <add name="ScriptModule" preCondition="managedHandler" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+      </modules>
+      <handlers>
+        <remove name="WebServiceHandlerFactory-Integrated"/>
+        <remove name="ScriptHandlerFactory" />
+        <remove name="ScriptHandlerFactoryAppServices" />
+        <remove name="ScriptResource" />
+        <add name="ScriptHandlerFactory" verb="*" path="*.asmx" preCondition="integratedMode"
+             type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add name="ScriptHandlerFactoryAppServices" verb="*" path="*_AppService.axd" preCondition="integratedMode"
+             type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add name="ScriptResource" preCondition="integratedMode" verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" />
+      </handlers>
+]]>
+    </contents>
+  </configBlock>
+  <!-- AJAX.NET 3.5 end -->
+
+  <!-- AJAX.NET 1.0 start -->
+  <configBlock name="AJAX1 config sections">
+    <requires>
+      <section name="configuration">
+	<section name="configSections" attachPoint="true"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+      <sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+          <section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+        <sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+          <section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere" />
+          <section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication" />
+          <section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication" />
+        </sectionGroup>
+      </sectionGroup>
+    </sectionGroup>
+      ]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 controls registration">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="pages" defaultBlockName="system.web.pages">
+	    <section name="controls" defaultBlockName="system.web.pages.controls"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 compilation">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="compilation" defaultBlockName="system.web.compilation">
+	    <section name="assemblies" defaultBlockName="system.web.compilation.assemblies"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add assembly="System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 HTTP handlers">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+    
+    <contents>
+      <![CDATA[
+<remove verb="*" path="*.asmx"/>
+      <add verb="*" path="*.asmx" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add verb="*" path="*_AppService.axd" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="false"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 HTTP modules">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpModules" defaultBlockName="system.web.httpModules"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add name="ScriptModule" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 system.web.extensions">
+    <requires>
+      <section name="configuration">
+	<section name="system.web.extensions"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<scripting>
+      <webServices>
+      <!-- Uncomment this line to customize maxJsonLength and add a custom converter -->
+      <!--
+      <jsonSerialization maxJsonLength="500">
+        <converters>
+          <add name="ConvertMe" type="Acme.SubAcme.ConvertMeTypeConverter"/>
+        </converters>
+      </jsonSerialization>
+      -->
+      <!-- Uncomment this line to enable the authentication service. Include requireSSL="true" if appropriate. -->
+      <!--
+        <authenticationService enabled="true" requireSSL = "true|false"/>
+      -->
+
+      <!-- Uncomment these lines to enable the profile service. To allow profile properties to be retrieved
+           and modified in ASP.NET AJAX applications, you need to add each property name to the readAccessProperties and
+           writeAccessProperties attributes. -->
+      <!--
+      <profileService enabled="true"
+                      readAccessProperties="propertyname1,propertyname2"
+                      writeAccessProperties="propertyname1,propertyname2" />
+      -->
+      </webServices>
+      <!--
+      <scriptResourceHandler enableCompression="true" enableCaching="true" />
+      -->
+    </scripting>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 system.webServer">
+    <requires>
+      <section name="configuration">
+	<section name="system.webServer"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<validation validateIntegratedModeConfiguration="false"/>
+    <modules>
+      <add name="ScriptModule" preCondition="integratedMode" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+    </modules>
+    <handlers>
+      <remove name="WebServiceHandlerFactory-Integrated" />
+      <add name="ScriptHandlerFactory" verb="*" path="*.asmx" preCondition="integratedMode"
+           type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add name="ScriptHandlerFactoryAppServices" verb="*" path="*_AppService.axd" preCondition="integratedMode"
+           type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add name="ScriptResource" preCondition="integratedMode" verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+    </handlers>
+]]>
+    </contents>
+  </configBlock>
+  <!-- AJAX.NET 1.0 end -->
+
+  <configBlock name="DynamicData controls registration">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="pages" defaultBlockName="system.web.pages">
+	    <section name="controls" defaultBlockName="system.web.pages.controls"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add tagPrefix="asp" namespace="System.Web.DynamicData" assembly="System.Web.DynamicData, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="DynamicData compilation">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="compilation" defaultBlockName="system.web.compilation">
+	    <section name="assemblies" defaultBlockName="system.web.compilation.assemblies"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add assembly="System.Core, Version=3.5.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+<add assembly="System.Data.DataSetExtensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+<add assembly="System.Xml.Linq, Version=3.5.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+<add assembly="System.Data.Linq, Version=3.5.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+<add assembly="System.Web.Abstractions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+<add assembly="System.Web.Routing, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+<add assembly="System.ComponentModel.DataAnnotations, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+<add assembly="System.Web.DynamicData, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="DynamicData HTTP modules">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpModules" defaultBlockName="system.web.httpModules"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add name="UrlRoutingModule" type="System.Web.Routing.UrlRoutingModule, System.Web.Routing, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <!-- default contents for missing sections -->
+  <default section="configuration" target="any">
+    <![CDATA[
+    <configuration>
+    </configuration>
+    ]]>
+  </default>
+
+  <default section="configSections" target="any">
+    <![CDATA[
+    <configSections>
+    </configSections>
+    ]]>
+  </default>
+
+  <default section="system.web" target="web">
+    <![CDATA[
+    <system.web>
+    </system.web>
+    ]]>
+  </default>
+
+  <default section="system.web.pages" target="web">
+    <![CDATA[
+<pages>
+</pages>
+]]>
+  </default>
+
+  <default section="system.web.pages.controls" target="web">
+    <![CDATA[
+<controls>
+</controls>
+]]>
+  </default>
+
+  <default section="system.web.compilation" target="web">
+    <![CDATA[
+<compilation debug="true">
+</compilation>
+]]>
+  </default>
+
+  <default section="system.web.compilation.assemblies" target="web">
+    <![CDATA[
+<assemblies>
+</assemblies>
+]]>
+  </default>
+
+  <default section="system.web.httpHandlers" target="web">
+    <![CDATA[
+<httpHandlers>
+</httpHandlers>
+]]>
+  </default>
+
+  <default section="system.web.httpModules" target="web">
+    <![CDATA[
+<httpModules>
+</httpModules>
+]]>
+  </default>
+
+  <default section="system.web.extensions" target="web">
+    <![CDATA[
+<system.web.extensions>
+</system.web.extensions>
+]]>
+  </default>
+
+  <default section="system.webServer" target="web">
+    <![CDATA[
+<system.webServer>
+</system.webServer>
+]]>
+  </default>
+
+  <default section="system.web.customErrors" target="web">
+    <![CDATA[
+<customErrors mode="RemoteOnly"/>
+]]>
+  </default>
+
+  <default section="system.codedom" target="any">
+    <![CDATA[
+    <system.codedom>
+    </system.codedom>
+]]>
+  </default>
+
+  <default section="system.codedom.compilers" target="any">
+    <![CDATA[
+    <compilers>
+    </compilers>
+]]>
+  </default>
+
+  <default section="runtime" target="any">
+    <![CDATA[
+    <runtime>
+    </runtime>
+]]>
+  </default>
+
+  <!-- default config file definitions -->
+  <defaultConfigFile name="web.config" fileName="Web.config" target="web">
+    <section name="configuration">
+      <section name="system.web">
+	<section name="compilation" defaultBlockName="system.web.compilation"/>
+	<section name="customErrors" defaultBlockName="system.web.customErrors"/>
+      </section>
+    </section>
+  </defaultConfigFile>
+</mconfig>
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libMonoPosixHelper.so b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libMonoPosixHelper.so
new file mode 100644
index 0000000000000000000000000000000000000000..b7d5a167e08c7f2265b2b72cdaad278afc844744
Binary files /dev/null and b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libMonoPosixHelper.so differ
diff --git a/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libmono-native.so b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libmono-native.so
new file mode 100644
index 0000000000000000000000000000000000000000..a77f26d119883177302a598f908a44d317ebb0fd
--- /dev/null
+++ b/training-envs-executables/linux/Pyramids/Pyramids_Data/MonoBleedingEdge/x86_64/libmono-native.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MatrixMultiply4x4Job, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MatrixMultiply4x4Job&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--b6eb248d7e4b0db49d55955cf1a9a0da
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+MaxPool2DJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+MaxPool2DJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--bcd6afe8e43b228c7b75d65ce38e19d5
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+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+DepthwiseConv2DJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+DepthwiseConv2DJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--4c3ed5e991f09fde6891b7f39dce2794
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+PReluJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+PReluJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--d78fdf0fa8150f88299cf806cd5b6e5c
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ExpBiasReduceJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ExpBiasReduceJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--63dfd736ec2bf201f1d681254a16eadd
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ElementwiseAddJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ElementwiseAddJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--6a87a3f3d4975629ff332fabeea1328f
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ElementwiseMulJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ElementwiseMulJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--e9510e249dc35fdab7859782c4e45722
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ElementwiseDivJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ElementwiseDivJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--0ef83fb566a286993ed2802f8706d1af
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ElementwisePowJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ElementwisePowJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--e536db71fdbcfa4cb03871aa84df295a
+--method=Unity.Jobs.IJobParallelForExtensions+ParallelForJobStruct`1[[Unity.Barracuda.BurstCPUOps+ElementwiseMaxJob, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null]], UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null::Execute(Unity.Barracuda.BurstCPUOps+ElementwiseMaxJob&, Unity.Barracuda, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|System.IntPtr, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089|Unity.Jobs.LowLevel.Unsafe.JobRanges&, UnityEngine.CoreModule, Version=0.0.0.0, Culture=neutral, PublicKeyToken=null|System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089)--8896efcff99b6ff320537fab462f1785
+--platform=Linux
+--backend=burst-llvm-11
+--target=AVX2
+--global-safety-checks-setting=Off
+--dump=Function
+--float-precision=Low
+--float-mode=Fast
+--disable-warnings=BC1370
+--compilation-defines=UNITY_2021_3_14
+--compilation-defines=UNITY_2021_3
+--compilation-defines=UNITY_2021
+--compilation-defines=UNITY_5_3_OR_NEWER
+--compilation-defines=UNITY_5_4_OR_NEWER
+--compilation-defines=UNITY_5_5_OR_NEWER
+--compilation-defines=UNITY_5_6_OR_NEWER
+--compilation-defines=UNITY_2017_1_OR_NEWER
+--compilation-defines=UNITY_2017_2_OR_NEWER
+--compilation-defines=UNITY_2017_3_OR_NEWER
+--compilation-defines=UNITY_2017_4_OR_NEWER
+--compilation-defines=UNITY_2018_1_OR_NEWER
+--compilation-defines=UNITY_2018_2_OR_NEWER
+--compilation-defines=UNITY_2018_3_OR_NEWER
+--compilation-defines=UNITY_2018_4_OR_NEWER
+--compilation-defines=UNITY_2019_1_OR_NEWER
+--compilation-defines=UNITY_2019_2_OR_NEWER
+--compilation-defines=UNITY_2019_3_OR_NEWER
+--compilation-defines=UNITY_2019_4_OR_NEWER
+--compilation-defines=UNITY_2020_1_OR_NEWER
+--compilation-defines=UNITY_2020_2_OR_NEWER
+--compilation-defines=UNITY_2020_3_OR_NEWER
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+--compilation-defines=UNITY_2018_4_OR_NEWER
+--compilation-defines=UNITY_2019_1_OR_NEWER
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+--generate-link-xml=Temp\burst.link.xml
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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3cca606dc8b7707a048e327182884d38e033c37962181f694eaeebc99e83cc1
+size 1196032
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diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/Managed/mscorlib.dll b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/Managed/mscorlib.dll
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee9b27461ef399e34277b6a21edd65b4f9b199625f39f1c0dd129fe3ba2bab2a
+size 4622848
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diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/config
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index 0000000000000000000000000000000000000000..c1d2863007699198d3745c8745e4eaaacde70547
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/config
@@ -0,0 +1,46 @@
+<configuration>
+	<dllmap dll="i:cygwin1.dll" target="libc.so.6" os="!windows" />
+	<dllmap dll="libc" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:libxslt.dll" target="libxslt.so" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libodbc.so.2" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libiodbc.dylib" os="osx"/>
+	<dllmap dll="oci" target="libclntsh.so" os="!windows"/>
+	<dllmap dll="db2cli" target="libdb2_36.so" os="!windows"/>
+	<dllmap dll="MonoPosixHelper" target="libMonoPosixHelper.so" os="!windows" />
+	<dllmap dll="System.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Net.Security.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Security.Cryptography.Native.Apple" target="$mono_libdir/libmono-native.so" os="osx" />
+	<dllmap dll="libmono-btls-shared" target="libmono-btls-shared.so" os="!windows" />
+	<dllmap dll="i:msvcrt" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:msvcrt.dll" target="libc.so.6" os="!windows"/>
+	<dllmap dll="sqlite" target="libsqlite.so.0" os="!windows"/>
+	<dllmap dll="sqlite3" target="libsqlite3.so.0" os="!windows"/>
+	<dllmap dll="libX11" target="libX11.so.6" os="!windows" />
+	<dllmap dll="libgdk-x11-2.0" target="libgdk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgdk_pixbuf-2.0" target="libgdk_pixbuf-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgtk-x11-2.0" target="libgtk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libglib-2.0" target="libglib-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgobject-2.0" target="libgobject-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgnomeui-2" target="libgnomeui-2.so.0" os="!windows"/>
+	<dllmap dll="librsvg-2" target="librsvg-2.so.2" os="!windows"/>
+	<dllmap dll="libXinerama" target="libXinerama.so.1" os="!windows" />
+	<dllmap dll="libasound" target="libasound.so.2" os="!windows" />
+	<dllmap dll="libcairo-2.dll" target="libcairo.so.2" os="!windows"/>
+	<dllmap dll="libcairo-2.dll" target="libcairo.2.dylib" os="osx"/>
+	<dllmap dll="libcups" target="libcups.so.2" os="!windows"/>
+	<dllmap dll="libcups" target="libcups.dylib" os="osx"/>
+	<dllmap dll="i:kernel32.dll">
+		<dllentry dll="__Internal" name="CopyMemory" target="mono_win32_compat_CopyMemory"/>
+		<dllentry dll="__Internal" name="FillMemory" target="mono_win32_compat_FillMemory"/>
+		<dllentry dll="__Internal" name="MoveMemory" target="mono_win32_compat_MoveMemory"/>
+		<dllentry dll="__Internal" name="ZeroMemory" target="mono_win32_compat_ZeroMemory"/>
+	</dllmap>
+	<dllmap dll="gdiplus" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdiplus.dll" target="libgdiplus.so.0"  os="!windows"/>
+	<dllmap dll="gdi32" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdi32.dll" target="libgdiplus.so.0" os="!windows"/>
+</configuration>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/machine.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..2577c81dd92aa49271253b58a0be8d18d25a24ec
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/machine.config
@@ -0,0 +1,280 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.DiagnosticsConfigurationHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=2.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/settings.map b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..9a52ccc0295c867dc6fb0cf190b94b70c11037d0
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/web.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..a5190c338bc6e4f9b78790721db81dd26ed69ae7
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/2.0/web.config
@@ -0,0 +1,153 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+			<add verb="*" path="Trace.axd" type="System.Web.Handlers.TraceHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="UrlRouting.axd" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.aspx" type="System.Web.UI.PageHandlerFactory, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.asmx" validate="false" type="System.Web.Services.Protocols.WebServiceHandlerFactory, System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.ashx" type="System.Web.UI.SimpleHandlerFactory, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="GET" path="WebResource.axd" type="System.Web.Handlers.AssemblyResourceLoader, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.master" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.resources" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.skin" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.browser" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.sitemap" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.webinfo" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.resx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.asax" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.ascx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.config" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.Config" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.cs" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.vb" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.csproj" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.vbproj" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.licx" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.dll" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*.rem" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="false" />
+			<add verb="*" path="*.soap" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="false" />
+			<add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add verb="GET,HEAD" path="*" type="System.Web.StaticFileHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add verb="*" path="*" type="System.Web.HttpMethodNotAllowedHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</httpHandlers>
+		<httpModules>
+			<add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="OutputCache" type="System.Web.Caching.OutputCacheModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="RoleManager" type="System.Web.Security.RoleManagerModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="Session" type="System.Web.SessionState.SessionStateModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<!--<add assembly="mscorlib" /> -->
+				<add assembly="System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+            			<add assembly="System.Configuration, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Data, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+            			<add assembly="System.Drawing, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+            			<add assembly="System.Web.Services, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Runtime.Serialization, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, processorArchitecture=MSIL"/>
+				<add assembly="System.IdentityModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, processorArchitecture=MSIL"/>
+				<add assembly="System.ServiceModel, Version=3.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+				<add assembly="System.ServiceModel.Web, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+            			<add assembly="System.Xml, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/machine.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..f3b71c43c53b54af5383704069d39ba3d72c0a7b
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/machine.config
@@ -0,0 +1,307 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.DiagnosticsConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+			<sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+				<section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				<sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+					<section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere"/>
+					<section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				</sectionGroup>
+			</sectionGroup>
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="standardEndpoints" type="System.ServiceModel.Configuration.StandardEndpointsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+			<section name="routing" type="System.ServiceModel.Routing.Configuration.RoutingSection, System.ServiceModel.Routing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			<section name="protocolMapping" type="System.ServiceModel.Configuration.ProtocolMappingSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.runtime.caching" type="System.Runtime.Caching.Configuration.CachingSectionGroup, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+		  <section name="memoryCache" type="System.Runtime.Caching.Configuration.MemoryCacheSection, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+                </sectionGroup>
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+			<endpointExtensions>
+				<add name="dynamicEndpoint" type="System.ServiceModel.Discovery.Configuration.DynamicEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="discoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.DiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpDiscoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpDiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="announcementEndpoint" type="System.ServiceModel.Discovery.Configuration.AnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpAnnouncementEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpAnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttpEndpoint" type="System.ServiceModel.Configuration.WebHttpEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webScriptEndpoint" type="System.ServiceModel.Configuration.WebScriptEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</endpointExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/settings.map b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..4c53aca6776656859d35fb7b7e6a64a5d54bfc6a
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/web.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..44cbe180dfd479bfdb79481475bae8ebaf95bebd
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.0/web.config
@@ -0,0 +1,252 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+  <system.codedom>
+        <compilers>
+            <compiler language="c#;cs;csharp" extension=".cs" warningLevel="4" type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+            <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4" type="Microsoft.VisualBasic.VBCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="OptionInfer" value="true"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+        </compilers>
+  </system.codedom>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+		  <add path="trace.axd" verb="*" type="System.Web.Handlers.TraceHandler" validate="True" />
+		  <add path="WebResource.axd" verb="GET" type="System.Web.Handlers.AssemblyResourceLoader" validate="True" />
+		  <add verb="*" path="*_AppService.axd" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.axd" verb="*" type="System.Web.HttpNotFoundHandler" validate="True" />
+		  <add path="*.aspx" verb="*" type="System.Web.UI.PageHandlerFactory" validate="True" />
+		  <add path="*.ashx" verb="*" type="System.Web.UI.SimpleHandlerFactory" validate="True" />
+		  <add path="*.asmx" verb="*" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add path="*.rem" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.soap" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.asax" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ascx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.master" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.skin" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.browser" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sitemap" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dll.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.exe.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.config" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cs" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.csproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vbproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.webinfo" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.licx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resources" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vjsproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.java" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.jsl" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldb" verb="*" type="System.Web.HttpForbiddenHandler"  validate="True" />
+		  <add path="*.ad" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.adprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.lddprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdm" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdmDocument" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.exclude" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.refresh" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <!--
+		  <add path="*.svc" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+                  <add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		  <add path="*.rules" verb="*" type="System.Web.HttpForbiddenHandler" validate="True"/>
+		  <!--
+		  <add path="*.xoml" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.xamlx" verb="*" type="System.Xaml.Hosting.XamlHttpHandlerFactory, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+		  <add path="*" verb="GET,HEAD,POST" type="System.Web.DefaultHttpHandler" validate="True" />
+		  <add path="*" verb="*" type="System.Web.HttpMethodNotAllowedHandler" validate="True" />
+		</httpHandlers>
+		<httpModules>
+		  <add name="OutputCache" type="System.Web.Caching.OutputCacheModule" />
+		  <add name="Session" type="System.Web.SessionState.SessionStateModule" />
+		  <!--
+		  <add name="WindowsAuthentication" type="System.Web.Security.WindowsAuthenticationModule" />
+		  -->
+		  <add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule" />
+		  <!--
+		  <add name="PassportAuthentication" type="System.Web.Security.PassportAuthenticationModule" />
+		  -->
+		  <add name="RoleManager" type="System.Web.Security.RoleManagerModule" />
+		  <add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule" />
+		  <!--
+		  <add name="FileAuthorization" type="System.Web.Security.FileAuthorizationModule" />
+		  -->
+		  <add name="AnonymousIdentification" type="System.Web.Security.AnonymousIdentificationModule" />
+		  <add name="Profile" type="System.Web.Profile.ProfileModule" />
+		  <!--
+		  <add name="ErrorHandlerModule" type="System.Web.Mobile.ErrorHandlerModule, System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		  <add name="ServiceModel" type="System.ServiceModel.Activation.HttpModule, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+		  -->
+		  <add name="UrlRoutingModule-4.0" type="System.Web.Routing.UrlRoutingModule" />
+		  <add name="ScriptModule-4.0" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">					
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+        		
+        		<controls>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.WebParts" assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.Expressions" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.DynamicData" assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</controls>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<add assembly="mscorlib" />
+				<add assembly="Microsoft.CSharp, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Xml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.EnterpriseServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<!-- <add assembly="System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" /> -->
+				<add assembly="System.IdentityModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xaml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Channels, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.Core, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="System.Data.DataSetExtensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xml.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ComponentModel.DataAnnotations, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Data.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<!-- <add assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> -->
+				<add assembly="System.Data.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.Data.Entity.Design, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<add assembly="System.Web.ApplicationServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="RouteUrl" type="System.Web.Compilation.RouteUrlExpressionBuilder"/>
+				<!--
+				<add expressionPrefix="RouteValue" type="System.Web.Compilation.RouteValueExpressionBuilder"/>
+				-->
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<!--
+				<add extension=".edmx" type="System.Data.Entity.Design.AspNet.EntityDesignerBuildProvider" />
+				<add extension=".xoml" type="System.ServiceModel.Activation.WorkflowServiceBuildProvider, System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add extension=".svc" type="System.ServiceModel.Activation.ServiceBuildProvider, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add extension=".xamlx" type="System.Xaml.Hosting.XamlBuildProvider, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				-->
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser
new file mode 100644
index 0000000000000000000000000000000000000000..dcedf7f7333565268eb99488767618235e7d24f1
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/Browsers/Compat.browser
@@ -0,0 +1,42 @@
+<!--
+    This file defines some of the browsers that Microsoft's implementation provides in
+    <windir>\Microsoft.NET\Framework\<ver>\CONFIG\Browsers\*.browser
+	
+	It is not derived from any file distributed with Microsoft's implementation.  Since
+	we can't distribute MS's browser files, we use browscap.ini to determine 
+	browser capabilities.  Then, if and only if the application contains App_Browser/*.browser
+	files and we are using .NET 2.0 or higher, we supplement the capabilities with the 
+	information in those files and the files in this directory.  The primary goal of this file
+	is provide browser definitions that might be referenced in App_Browser/*.browser files.
+-->
+<browsers>
+  <defaultBrowser id="Default">
+  </defaultBrowser>
+  <browser id="Default">
+    <identification>
+    	<userAgent match="." />
+    </identification>
+  </browser>
+  <browser id="IE6to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[6-9]" />
+      <capability name="browser" match="^(IE|AOL)$" />
+    </identification>
+  </browser>
+  <browser id="Opera8to9" parentID="Default">
+    <identification>
+      <capability name="majorver" match="^[8-9]" />
+      <capability name="browser" match="^Opera$" />
+    </identification>
+  </browser>
+  <browser id="Safari" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Safari$" />
+    </identification>
+  </browser>
+  <browser id="Mozilla" parentID="Default">
+    <identification>
+      <capability name="browser" match="^Mozilla" />
+    </identification>
+  </browser>
+</browsers>
\ No newline at end of file
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx
new file mode 100644
index 0000000000000000000000000000000000000000..f4d74bff7f5bca96ad3fc764e518b4d710854177
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/DefaultWsdlHelpGenerator.aspx
@@ -0,0 +1,1901 @@
+<%--
+//
+// DefaultWsdlHelpGenerator.aspx: 
+//
+// Author:
+//   Lluis Sanchez Gual (lluis@ximian.com)
+//
+// (C) 2003 Ximian, Inc.  http://www.ximian.com
+//
+--%>
+
+<%@ Import Namespace="System.Collections" %>
+<%@ Import Namespace="System.Collections.Generic" %>
+<%@ Import Namespace="System.IO" %>
+<%@ Import Namespace="System.Xml.Serialization" %>
+<%@ Import Namespace="System.Xml" %>
+<%@ Import Namespace="System.Xml.Schema" %>
+<%@ Import Namespace="System.Web.Services" %>
+<%@ Import Namespace="System.Web.Services.Description" %>
+<%@ Import Namespace="System.Web.Services.Configuration" %>
+<%@ Import Namespace="System.Web.Configuration" %>
+<%@ Import Namespace="System" %>
+<%@ Import Namespace="System.Net" %>
+<%@ Import Namespace="System.Globalization" %>
+<%@ Import Namespace="System.Resources" %>
+<%@ Import Namespace="System.Diagnostics" %>
+<%@ Import Namespace="System.CodeDom" %>
+<%@ Import Namespace="System.CodeDom.Compiler" %>
+<%@ Import Namespace="Microsoft.CSharp" %>
+<%@ Import Namespace="Microsoft.VisualBasic" %>
+<%@ Import Namespace="System.Text" %>
+<%@ Import Namespace="System.Text.RegularExpressions" %>
+<%@ Import Namespace="System.Security.Cryptography.X509Certificates" %>
+<%@ Assembly name="System.Web.Services" %>
+<%@ Page debug="true" %>
+
+<html>
+<script language="C#" runat="server">
+
+ServiceDescriptionCollection descriptions;
+XmlSchemas schemas;
+
+string WebServiceName;
+string WebServiceDescription;
+string PageName;
+
+string DefaultBinding;
+ArrayList ServiceProtocols;
+
+string CurrentOperationName;
+string CurrentOperationBinding;
+string OperationDocumentation;
+string CurrentOperationFormat;
+bool CurrentOperationSupportsTest;
+ArrayList InParams;
+ArrayList OutParams;
+string CurrentOperationProtocols;
+int CodeTextColumns = 95;
+BasicProfileViolationCollection ProfileViolations;
+
+void Page_Load(object sender, EventArgs e)
+{
+	descriptions = (ServiceDescriptionCollection) Context.Items["wsdls"];
+	schemas = (XmlSchemas) Context.Items["schemas"];
+
+	ServiceDescription desc = descriptions [0];
+	if (schemas.Count == 0) schemas = desc.Types.Schemas;
+	
+	Service service = desc.Services[0];
+	WebServiceName = service.Name;
+	if (desc.Bindings.Count == 0)
+		return;
+	
+	DefaultBinding = desc.Bindings[0].Name;
+	WebServiceDescription = service.Documentation;
+	if (WebServiceDescription == "" || WebServiceDescription == null)
+		WebServiceDescription = "Description has not been provided";
+	ServiceProtocols = FindServiceProtocols (null);
+	
+	CurrentOperationName = Request.QueryString["op"];
+	CurrentOperationBinding = Request.QueryString["bnd"];
+	if (CurrentOperationName != null) BuildOperationInfo ();
+
+	PageName = HttpUtility.UrlEncode (Path.GetFileName(Request.Path), Encoding.UTF8);
+
+	ArrayList list = new ArrayList ();
+	foreach (ServiceDescription sd in descriptions) {
+		foreach (Binding bin in sd.Bindings)
+			if (bin.Extensions.Find (typeof(SoapBinding)) != null) list.Add (bin);
+	}
+
+	BindingsRepeater.DataSource = list;
+	Page.DataBind();
+	
+	ProfileViolations = new BasicProfileViolationCollection ();
+	foreach (WsiProfilesElement claims in ((WebServicesSection) WebConfigurationManager.GetSection("system.web/webServices")).ConformanceWarnings)
+		if (claims.Name != WsiProfiles.None)
+			WebServicesInteroperability.CheckConformance (claims.Name, descriptions, ProfileViolations);
+}
+
+void BuildOperationInfo ()
+{
+	InParams = new ArrayList ();
+	OutParams = new ArrayList ();
+	
+	Port port = FindPort (CurrentOperationBinding, null);
+	Binding binding = descriptions.GetBinding (port.Binding);
+	
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	OperationDocumentation = oper.Documentation;
+	if (OperationDocumentation == null || OperationDocumentation == "")
+		OperationDocumentation = "No additional remarks";
+	
+	foreach (OperationMessage opm in oper.Messages)
+	{
+		if (opm is OperationInput)
+			BuildParameters (InParams, opm);
+		else if (opm is OperationOutput)
+			BuildParameters (OutParams, opm);
+	}
+	
+	// Protocols supported by the operation
+	CurrentOperationProtocols = "";
+	WebServiceProtocols testProtocols = 0;
+	ArrayList prots = FindServiceProtocols (CurrentOperationName);
+	for (int n=0; n<prots.Count; n++) {
+		string prot = (string) prots [n];
+		if (n != 0) CurrentOperationProtocols += ", ";
+		CurrentOperationProtocols += prot;
+		if (prot == "HttpGet")
+			testProtocols |= WebServiceProtocols.HttpGet;
+		else if (prot == "HttpPost") {
+			testProtocols |= WebServiceProtocols.HttpPost;
+			if (Context.Request.IsLocal)
+				testProtocols |= WebServiceProtocols.HttpPostLocalhost;
+		}
+	}
+	CurrentOperationSupportsTest = (WebServicesSection.Current.EnabledProtocols & testProtocols) != 0;
+
+	// Operation format
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	if (obin != null)
+		CurrentOperationFormat = GetOperationFormat (obin);
+
+	InputParamsRepeater.DataSource = InParams;
+	InputFormParamsRepeater.DataSource = InParams;
+	OutputParamsRepeater.DataSource = OutParams;
+}
+
+void BuildParameters (ArrayList list, OperationMessage opm)
+{
+	Message msg = descriptions.GetMessage (opm.Message);
+	if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+	{
+		MessagePart part = msg.Parts[0];
+		XmlSchemaComplexType ctype;
+		if (part.Element == XmlQualifiedName.Empty)
+		{
+			ctype = (XmlSchemaComplexType) schemas.Find (part.Type, typeof(XmlSchemaComplexType));
+		}
+		else
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+			ctype = (XmlSchemaComplexType) elem.SchemaType;
+		}
+		XmlSchemaSequence seq = ctype.Particle as XmlSchemaSequence;
+		if (seq == null) return;
+		
+		foreach (XmlSchemaObject ob in seq.Items)
+		{
+			Parameter p = new Parameter();
+			p.Description = "No additional remarks";
+			
+			if (ob is XmlSchemaElement)
+			{
+				XmlSchemaElement selem = GetRefElement ((XmlSchemaElement)ob);
+				p.Name = selem.Name;
+				p.Type = selem.SchemaTypeName.Name;
+			}
+			else
+			{
+				p.Name = "Unknown";
+				p.Type = "Unknown";
+			}
+			list.Add (p);
+		}
+	}
+	else
+	{
+		foreach (MessagePart part in msg.Parts)
+		{
+			Parameter p = new Parameter ();
+			p.Description = "No additional remarks";
+			p.Name = part.Name;
+			if (part.Element == XmlQualifiedName.Empty)
+				p.Type = part.Type.Name;
+			else
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (part.Element, typeof(XmlSchemaElement));
+				p.Type = elem.SchemaTypeName.Name;
+			}
+			list.Add (p);
+		}
+	}
+}
+
+string GetOperationFormat (OperationBinding obin)
+{
+	string format = "";
+	SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+	if (sob != null) {
+		format = sob.Style.ToString ();
+		SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+		if (sbb != null)
+			format += " / " + sbb.Use;
+	}
+	return format;
+}
+
+XmlSchemaElement GetRefElement (XmlSchemaElement elem)
+{
+	if (!elem.RefName.IsEmpty)
+		return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+	else
+		return elem;
+}
+
+ArrayList FindServiceProtocols(string operName)
+{
+	ArrayList table = new ArrayList ();
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		string prot = null;
+		Binding bin = descriptions.GetBinding (port.Binding);
+		if (bin.Extensions.Find (typeof(SoapBinding)) != null)
+			prot = "Soap";
+		else 
+		{
+			HttpBinding hb = (HttpBinding) bin.Extensions.Find (typeof(HttpBinding));
+			if (hb != null && hb.Verb == "POST") prot = "HttpPost";
+			else if (hb != null && hb.Verb == "GET") prot = "HttpGet";
+		}
+		
+		if (prot != null && operName != null)
+		{
+			if (FindOperation (bin, operName) == null)
+				prot = null;
+		}
+
+		if (prot != null && !table.Contains (prot))
+			table.Add (prot);
+	}
+	return table;
+}
+
+Port FindPort (string portName, string protocol)
+{
+	Service service = descriptions[0].Services[0];
+	foreach (Port port in service.Ports)
+	{
+		if (portName == null)
+		{
+			Binding binding = descriptions.GetBinding (port.Binding);
+			if (GetProtocol (binding) == protocol) return port;
+		}
+		else if (port.Name == portName)
+			return port;
+	}
+	return null;
+}
+
+string GetProtocol (Binding binding)
+{
+	if (binding.Extensions.Find (typeof(SoapBinding)) != null) return "Soap";
+	HttpBinding hb = (HttpBinding) binding.Extensions.Find (typeof(HttpBinding));
+	if (hb == null) return "";
+	if (hb.Verb == "POST") return "HttpPost";
+	if (hb.Verb == "GET") return "HttpGet";
+	return "";
+}
+
+
+Operation FindOperation (PortType portType, string name)
+{
+	foreach (Operation oper in portType.Operations) {
+		if (oper.Messages.Input.Name != null) {
+			if (oper.Messages.Input.Name == name) return oper;
+		}
+		else
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+OperationBinding FindOperation (Binding binding, string name)
+{
+	foreach (OperationBinding oper in binding.Operations) {
+		if (oper.Input.Name != null) {
+			if (oper.Input.Name == name) return oper;
+		}
+		else 
+			if (oper.Name == name) return oper;
+	}
+		
+	return null;
+}
+
+string FormatBindingName (string name)
+{
+	if (name == DefaultBinding) return "Methods";
+	else return "Methods for binding<br>" + name;
+}
+
+string GetOpName (object op)
+{
+	OperationBinding ob = op as OperationBinding;
+	if (ob == null) return "";
+	if (ob.Input.Name != null) return ob.Input.Name;
+	else return ob.Name;
+}
+
+bool HasFormResult
+{
+	get { return Request.QueryString ["ext"] == "testform"; }
+}
+
+class NoCheckCertificatePolicy : ICertificatePolicy {
+	public bool CheckValidationResult (ServicePoint a, X509Certificate b, WebRequest c, int d)
+	{
+		return true;
+	}
+}
+
+string GetOrPost ()
+{
+	return (CurrentOperationProtocols.IndexOf ("HttpGet") >= 0) ? "GET" : "POST";
+}
+
+string GetQS ()
+{
+	bool fill = false;
+	string qs = "";
+	NameValueCollection query_string = Request.QueryString;
+	for (int n = 0; n < query_string.Count; n++) {
+		if (fill) {
+			if (qs != "") qs += "&";
+			qs += query_string.GetKey(n) + "=" + Server.UrlEncode (query_string [n]);
+		}
+		if (query_string.GetKey(n) == "ext") fill = true;
+	}
+
+	return qs;
+}
+
+string GetTestResultUrl ()
+{ 
+	if (!HasFormResult) return "";
+	
+	string location = null;
+	ServiceDescription desc = descriptions [0];
+	Service service = desc.Services[0];
+	foreach (Port port in service.Ports)
+		if (port.Name == CurrentOperationBinding)
+		{
+			SoapAddressBinding sbi = (SoapAddressBinding) port.Extensions.Find (typeof(SoapAddressBinding));
+			if (sbi != null)
+				location = sbi.Location;
+		}
+
+	if (location == null) 
+		return "Could not locate web service";
+	
+	return location + "/" + CurrentOperationName;
+}
+
+string GenerateOperationMessages (string protocol, bool generateInput)
+{
+	if (!IsOperationSupported (protocol)) return "";
+	
+	Port port;
+	if (protocol != "Soap") port = FindPort (null, protocol);
+	else port = FindPort (CurrentOperationBinding, null);
+	
+	Binding binding = descriptions.GetBinding (port.Binding);
+	OperationBinding obin = FindOperation (binding, CurrentOperationName);
+	PortType portType = descriptions.GetPortType (binding.Type);
+	Operation oper = FindOperation (portType, CurrentOperationName);
+	
+	HtmlSampleGenerator sg = new HtmlSampleGenerator (descriptions, schemas);
+	string txt = sg.GenerateMessage (port, obin, oper, protocol, generateInput);
+	if (protocol == "Soap") txt = WrapText (txt,CodeTextColumns);
+	txt = ColorizeXml (txt);
+	txt = txt.Replace ("@placeholder!","<span class='literal-placeholder'>");
+	txt = txt.Replace ("!placeholder@","</span>");
+	return txt;
+}
+
+bool IsOperationSupported (string protocol)
+{
+	if (CurrentPage != "op" || CurrentTab != "msg") return false;
+	if (protocol == "Soap") return true;
+
+	Port port = FindPort (null, protocol);
+	if (port == null) return false;
+	Binding binding = descriptions.GetBinding (port.Binding);
+	if (binding == null) return false;
+	return FindOperation (binding, CurrentOperationName) != null;
+}
+
+//
+// Proxy code generation
+//
+
+string GetProxyCode ()
+{
+	CodeNamespace codeNamespace = new CodeNamespace();
+	CodeCompileUnit codeUnit = new CodeCompileUnit();
+	
+	codeUnit.Namespaces.Add (codeNamespace);
+
+	ServiceDescriptionImporter importer = new ServiceDescriptionImporter();
+	
+	foreach (ServiceDescription sd in descriptions)
+		importer.AddServiceDescription(sd, null, null);
+
+	foreach (XmlSchema sc in schemas)
+		importer.Schemas.Add (sc);
+
+	importer.Import(codeNamespace, codeUnit);
+
+	string langId = Request.QueryString ["lang"];
+	if (langId == null || langId == "") langId = "cs";
+	CodeDomProvider provider = GetProvider (langId);
+	ICodeGenerator generator = provider.CreateGenerator();
+	CodeGeneratorOptions options = new CodeGeneratorOptions();
+	
+	StringWriter sw = new StringWriter ();
+	generator.GenerateCodeFromCompileUnit(codeUnit, sw, options);
+
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), langId);
+}
+
+public string CurrentLanguage
+{
+	get {
+		string langId = Request.QueryString ["lang"];
+		if (langId == null || langId == "") langId = "cs";
+		return langId;
+	}
+}
+
+public string CurrentProxytName
+{
+	get {
+		string lan = CurrentLanguage == "cs" ? "C#" : "Visual Basic";
+		return lan + " Client Proxy";
+	}
+}
+
+private CodeDomProvider GetProvider(string langId)
+{
+	switch (langId.ToUpper())
+	{
+		case "CS": return new CSharpCodeProvider();
+		case "VB": return new VBCodeProvider();
+		default: return null;
+	}
+}
+
+//
+// Document generation
+//
+class UTF8StringWriter : StringWriter {
+	public override Encoding Encoding {
+		get { return Encoding.UTF8; }
+	}
+}
+
+string GenerateDocument ()
+{
+	UTF8StringWriter sw = new UTF8StringWriter ();
+	
+	if (CurrentDocType == "wsdl")
+		descriptions [CurrentDocInd].Write (sw);
+	else if (CurrentDocType == "schema")
+		schemas [CurrentDocInd].Write (sw);
+		
+	return Colorize (WrapText (sw.ToString (), CodeTextColumns), "xml");
+}
+
+public string CurrentDocType
+{
+	get { return Request.QueryString ["doctype"] != null ? Request.QueryString ["doctype"] : "wsdl"; }
+}
+
+public int CurrentDocInd
+{
+	get { return Request.QueryString ["docind"] != null ? int.Parse (Request.QueryString ["docind"]) : 0; }
+}
+
+public string CurrentDocumentName
+{
+	get {
+		if (CurrentDocType == "wsdl")
+			return "WSDL document for namespace \"" + descriptions [CurrentDocInd].TargetNamespace + "\"";
+		else
+			return "Xml Schema for namespace \"" + schemas [CurrentDocInd].TargetNamespace + "\"";
+	}
+}
+
+//
+// Pages and tabs
+//
+
+bool firstTab = true;
+ArrayList disabledTabs = new ArrayList ();
+
+string CurrentTab
+{
+	get { return Request.QueryString["tab"] != null ? Request.QueryString["tab"] : "main" ; }
+}
+
+string CurrentPage
+{
+	get { return Request.QueryString["page"] != null ? Request.QueryString["page"] : "main" ; }
+}
+
+void WriteTabs ()
+{
+	if (CurrentOperationName != null)
+	{
+		WriteTab ("main","Overview");
+		WriteTab ("test","Test Form");
+		WriteTab ("msg","Message Layout");
+	}
+}
+
+void WriteTab (string id, string label)
+{
+	if (!firstTab) Response.Write("&nbsp;|&nbsp;");
+	firstTab = false;
+	
+	string cname = CurrentTab == id ? "tabLabelOn" : "tabLabelOff";
+	Response.Write ("<a href='" + PageName + "?" + GetPageContext(null) + GetDataContext() + "tab=" + id + "' style='text-decoration:none'>");
+	Response.Write ("<span class='" + cname + "'>" + label + "</span>");
+	Response.Write ("</a>");
+}
+
+string GetTabContext (string pag, string tab)
+{
+	if (tab == null) tab = CurrentTab;
+	if (pag == null) pag = CurrentPage;
+	if (pag != CurrentPage) tab = "main";
+	return "page=" + pag + "&tab=" + tab + "&"; 
+}
+
+string GetPageContext (string pag)
+{
+	if (pag == null) pag = CurrentPage;
+	return "page=" + pag + "&"; 
+}
+
+class Tab
+{
+	public string Id;
+	public string Label;
+}
+
+//
+// Syntax coloring
+//
+
+static string keywords_cs =
+	"(\\babstract\\b|\\bevent\\b|\\bnew\\b|\\bstruct\\b|\\bas\\b|\\bexplicit\\b|\\bnull\\b|\\bswitch\\b|\\bbase\\b|\\bextern\\b|" +
+	"\\bobject\\b|\\bthis\\b|\\bbool\\b|\\bfalse\\b|\\boperator\\b|\\bthrow\\b|\\bbreak\\b|\\bfinally\\b|\\bout\\b|\\btrue\\b|" +
+	"\\bbyte\\b|\\bfixed\\b|\\boverride\\b|\\btry\\b|\\bcase\\b|\\bfloat\\b|\\bparams\\b|\\btypeof\\b|\\bcatch\\b|\\bfor\\b|" +
+	"\\bprivate\\b|\\buint\\b|\\bchar\\b|\\bforeach\\b|\\bprotected\\b|\\bulong\\b|\\bchecked\\b|\\bgoto\\b|\\bpublic\\b|" +
+	"\\bunchecked\\b|\\bclass\\b|\\bif\\b|\\breadonly\\b|\\bunsafe\\b|\\bconst\\b|\\bimplicit\\b|\\bref\\b|\\bushort\\b|" +
+	"\\bcontinue\\b|\\bin\\b|\\breturn\\b|\\busing\\b|\\bdecimal\\b|\\bint\\b|\\bsbyte\\b|\\bvirtual\\b|\\bdefault\\b|" +
+	"\\binterface\\b|\\bsealed\\b|\\bvolatile\\b|\\bdelegate\\b|\\binternal\\b|\\bshort\\b|\\bvoid\\b|\\bdo\\b|\\bis\\b|" +
+	"\\bsizeof\\b|\\bwhile\\b|\\bdouble\\b|\\block\\b|\\bstackalloc\\b|\\belse\\b|\\blong\\b|\\bstatic\\b|\\benum\\b|" +
+	"\\bnamespace\\b|\\bstring\\b)";
+
+static string keywords_vb =
+	"(\\bAddHandler\\b|\\bAddressOf\\b|\\bAlias\\b|\\bAnd\\b|\\bAndAlso\\b|\\bAnsi\\b|\\bAs\\b|\\bAssembly\\b|" +
+	"\\bAuto\\b|\\bBoolean\\b|\\bByRef\\b|\\bByte\\b|\\bByVal\\b|\\bCall\\b|\\bCase\\b|\\bCatch\\b|" +
+	"\\bCBool\\b|\\bCByte\\b|\\bCChar\\b|\\bCDate\\b|\\bCDec\\b|\\bCDbl\\b|\\bChar\\b|\\bCInt\\b|" +
+	"\\bClass\\b|\\bCLng\\b|\\bCObj\\b|\\bConst\\b|\\bCShort\\b|\\bCSng\\b|\\bCStr\\b|\\bCType\\b|" +
+	"\\bDate\\b|\\bDecimal\\b|\\bDeclare\\b|\\bDefault\\b|\\bDelegate\\b|\\bDim\\b|\\bDirectCast\\b|\\bDo\\b|" +
+	"\\bDouble\\b|\\bEach\\b|\\bElse\\b|\\bElseIf\\b|\\bEnd\\b|\\bEnum\\b|\\bErase\\b|\\bError\\b|" +
+	"\\bEvent\\b|\\bExit\\b|\\bFalse\\b|\\bFinally\\b|\\bFor\\b|\\bFriend\\b|\\bFunction\\b|\\bGet\\b|" +
+	"\\bGetType\\b|\\bGoSub\\b|\\bGoTo\\b|\\bHandles\\b|\\bIf\\b|\\bImplements\\b|\\bImports\\b|\\bIn\\b|" +
+	"\\bInherits\\b|\\bInteger\\b|\\bInterface\\b|\\bIs\\b|\\bLet\\b|\\bLib\\b|\\bLike\\b|\\bLong\\b|" +
+	"\\bLoop\\b|\\bMe\\b|\\bMod\\b|\\bModule\\b|\\bMustInherit\\b|\\bMustOverride\\b|\\bMyBase\\b|\\bMyClass\\b|" +
+	"\\bNamespace\\b|\\bNew\\b|\\bNext\\b|\\bNot\\b|\\bNothing\\b|\\bNotInheritable\\b|\\bNotOverridable\\b|\\bObject\\b|" +
+	"\\bOn\\b|\\bOption\\b|\\bOptional\\b|\\bOr\\b|\\bOrElse\\b|\\bOverloads\\b|\\bOverridable\\b|\\bOverrides\\b|" +
+	"\\bParamArray\\b|\\bPreserve\\b|\\bPrivate\\b|\\bProperty\\b|\\bProtected\\b|\\bPublic\\b|\\bRaiseEvent\\b|\\bReadOnly\\b|" +
+	"\\bReDim\\b|\\bREM\\b|\\bRemoveHandler\\b|\\bResume\\b|\\bReturn\\b|\\bSelect\\b|\\bSet\\b|\\bShadows\\b|" +
+	"\\bShared\\b|\\bShort\\b|\\bSingle\\b|\\bStatic\\b|\\bStep\\b|\\bStop\\b|\\bString\\b|\\bStructure\\b|" +
+	"\\bSub\\b|\\bSyncLock\\b|\\bThen\\b|\\bThrow\\b|\\bTo\\b|\\bTrue\\b|\\bTry\\b|\\bTypeOf\\b|" +
+	"\\bUnicode\\b|\\bUntil\\b|\\bVariant\\b|\\bWhen\\b|\\bWhile\\b|\\bWith\\b|\\bWithEvents\\b|\\bWriteOnly\\b|\\bXor\\b)";
+
+string Colorize (string text, string lang)
+{
+	if (lang == "xml") return ColorizeXml (text);
+	else if (lang == "cs") return ColorizeCs (text);
+	else if (lang == "vb") return ColorizeVb (text);
+	else return text;
+}
+
+string ColorizeXml (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	Regex re = new Regex ("\r\n|\r|\n");
+	text = re.Replace (text, "_br_");
+	
+	re = new Regex ("<\\s*(\\/?)\\s*([\\s\\S]*?)\\s*(\\/?)\\s*>");
+	text = re.Replace (text,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = new Regex ("\\{(\\w*):([\\s\\S]*?)\\}");
+	text = re.Replace (text,"<span style='color:$1'>$2</span>");
+
+	re = new Regex ("\"(.*?)\"");
+	text = re.Replace (text,"\"<span style='color:purple'>$1</span>\"");
+
+	
+	text = text.Replace ("\t", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("_br_", "<br>");
+	return text;
+}
+
+string ColorizeCs (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+
+	text = text.Replace ("<", "&lt;");
+	text = text.Replace (">", "&gt;");
+
+	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("//(((.(?!\"</span>))|\"(((?!\").)*)\"</span>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_cs);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	
+	return text;
+}
+
+string ColorizeVb (string text)
+{
+	text = text.Replace (" ", "&nbsp;");
+	
+/*	Regex re = new Regex ("\"((((?!\").)|\\\")*?)\"");
+	text = re.Replace (text,"<span style='color:purple'>\"$1\"</span>");
+
+	re = new Regex ("'(((.(?!\"\\<\\/span\\>))|\"(((?!\").)*)\"\\<\\/span\\>)*)(\r|\n|\r\n)");
+	text = re.Replace (text,"<span style='color:green'>//$1</span><br/>");
+	
+	re = new Regex (keywords_vb);
+	text = re.Replace (text,"<span style='color:blue'>$1</span>");
+*/	
+	text = text.Replace ("\t","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	text = text.Replace ("\n","<br/>");
+	return text;
+}
+
+//
+// Helper methods and classes
+//
+
+string GetDataContext ()
+{
+	return "op=" + CurrentOperationName + "&bnd=" + CurrentOperationBinding + "&";
+}
+
+string GetOptionSel (string v1, string v2)
+{
+	string op = "<option ";
+	if (v1 == v2) op += "selected ";
+	return op + "value='" + v1 + "'>";
+}
+
+string WrapText (string text, int maxChars)
+{
+	text =  text.Replace(" />","/>");
+	
+	string linspace = null;
+	int lincount = 0;
+	int breakpos = 0;
+	int linstart = 0;
+	bool inquotes = false;
+	char lastc = ' ';
+	string sublineIndent = "";
+	System.Text.StringBuilder sb = new System.Text.StringBuilder ();
+	for (int n=0; n<text.Length; n++)
+	{
+		char c = text [n];
+		
+		if (c=='\r' || c=='\n' || n==text.Length-1)
+		{
+			sb.Append (linspace + sublineIndent + text.Substring (linstart, n-linstart+1));
+			linspace = null;
+			lincount = 0;
+			linstart = n+1;
+			breakpos = linstart;
+			sublineIndent = "";
+			lastc = c;
+			continue;
+		}
+		
+		if (lastc==',' || lastc=='(')
+		{
+			if (!inquotes) breakpos = n;
+		}
+		
+		if (lincount > maxChars && breakpos >= linstart)
+		{
+			if (linspace != null)
+				sb.Append (linspace + sublineIndent);
+			sb.Append (text.Substring (linstart, breakpos-linstart));
+			sb.Append ("\n");
+			sublineIndent = "     ";
+			lincount = linspace.Length + sublineIndent.Length + (n-breakpos);
+			linstart = breakpos;
+		}
+		
+		if (c==' ' || c=='\t')
+		{
+			if (!inquotes)
+				breakpos = n;
+		}
+		else if (c=='"')
+		{
+			inquotes = !inquotes;
+		}
+		else 
+			if (linspace == null) {
+				linspace = text.Substring (linstart, n-linstart);
+				linstart = n;
+			}
+
+		lincount++;
+		lastc = c;
+	}
+	return sb.ToString ();
+}
+
+class Parameter
+{
+	string name;
+	string type;
+	string description;
+
+	public string Name { get { return name; } set { name = value; } }
+	public string Type { get { return type; } set { type = value; } }
+	public string Description { get { return description; } set { description = value; } }
+}
+
+public class HtmlSampleGenerator: SampleGenerator
+{
+	public HtmlSampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+	: base (services, schemas)
+	{
+	}
+		
+	protected override string GetLiteral (string s)
+	{
+		return "@placeholder!" + s + "!placeholder@";
+	}
+}
+
+
+	public class SampleGenerator
+	{
+		protected ServiceDescriptionCollection descriptions;
+		protected XmlSchemas schemas;
+		XmlSchemaElement anyElement;
+		ArrayList queue;
+		SoapBindingUse currentUse;
+		XmlDocument document = new XmlDocument ();
+		
+		static readonly XmlQualifiedName anyType = new XmlQualifiedName ("anyType",XmlSchema.Namespace);
+		static readonly XmlQualifiedName arrayType = new XmlQualifiedName ("Array","http://schemas.xmlsoap.org/soap/encoding/");
+		static readonly XmlQualifiedName arrayTypeRefName = new XmlQualifiedName ("arrayType","http://schemas.xmlsoap.org/soap/encoding/");
+		const string SoapEnvelopeNamespace = "http://schemas.xmlsoap.org/soap/envelope/";
+		const string WsdlNamespace = "http://schemas.xmlsoap.org/wsdl/";
+		const string SoapEncodingNamespace = "http://schemas.xmlsoap.org/soap/encoding/";
+		
+		class EncodedType
+		{
+			public EncodedType (string ns, XmlSchemaElement elem) { Namespace = ns; Element = elem; }
+			public string Namespace;
+			public XmlSchemaElement Element;
+		}
+
+		public SampleGenerator (ServiceDescriptionCollection services, XmlSchemas schemas)
+		{
+			descriptions = services;
+			this.schemas = schemas;
+			queue = new ArrayList ();
+		}
+		
+		public string GenerateMessage (Port port, OperationBinding obin, Operation oper, string protocol, bool generateInput)
+		{
+			OperationMessage msg = null;
+			foreach (OperationMessage opm in oper.Messages)
+			{
+				if (opm is OperationInput && generateInput) msg = opm;
+				else if (opm is OperationOutput && !generateInput) msg = opm;
+			}
+			if (msg == null) return null;
+			
+			switch (protocol) {
+				case "Soap": return GenerateHttpSoapMessage (port, obin, oper, msg);
+				case "HttpGet": return GenerateHttpGetMessage (port, obin, oper, msg);
+				case "HttpPost": return GenerateHttpPostMessage (port, obin, oper, msg);
+			}
+			return "Unknown protocol";
+		}
+		
+		public string GenerateHttpSoapMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				SoapAddressBinding sab = port.Extensions.Find (typeof(SoapAddressBinding)) as SoapAddressBinding;
+				SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+				req += "POST " + new Uri (sab.Location).AbsolutePath + "\n";
+				req += "SOAPAction: " + sob.SoapAction + "\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			}
+			
+			req += GenerateSoapMessage (obin, oper, msg);
+			return req;
+		}
+		
+		public string GenerateHttpGetMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location + "?" + BuildQueryString (msg);
+				req += "GET " + location + "\n";
+				req += "Host: " + GetLiteral ("string");
+			}
+			else
+			{
+				req += "HTTP/1.0 200 OK\n";
+				req += "Content-Type: text/xml; charset=utf-8\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n\n";
+			
+				MimeXmlBinding mxb = (MimeXmlBinding) obin.Output.Extensions.Find (typeof(MimeXmlBinding)) as MimeXmlBinding;
+				if (mxb == null) return req;
+				
+				Message message = descriptions.GetMessage (msg.Message);
+				XmlQualifiedName ename = null;
+				foreach (MessagePart part in message.Parts)
+					if (part.Name == mxb.Part) ename = part.Element;
+					
+				if (ename == null) return req + GetLiteral("string");
+				
+				StringWriter sw = new StringWriter ();
+				XmlTextWriter xtw = new XmlTextWriter (sw);
+				xtw.Formatting = Formatting.Indented;
+				currentUse = SoapBindingUse.Literal;
+				WriteRootElementSample (xtw, ename);
+				xtw.Close ();
+				req += sw.ToString ();
+			}
+			
+			return req;
+		}
+		
+		public string GenerateHttpPostMessage (Port port, OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			string req = "";
+			
+			if (msg is OperationInput)
+			{
+				HttpAddressBinding sab = port.Extensions.Find (typeof(HttpAddressBinding)) as HttpAddressBinding;
+				HttpOperationBinding sob = obin.Extensions.Find (typeof(HttpOperationBinding)) as HttpOperationBinding;
+				string location = new Uri (sab.Location).AbsolutePath + sob.Location;
+				req += "POST " + location + "\n";
+				req += "Content-Type: application/x-www-form-urlencoded\n";
+				req += "Content-Length: " + GetLiteral ("string") + "\n";
+				req += "Host: " + GetLiteral ("string") + "\n\n";
+				req += BuildQueryString (msg);
+			}
+			else return GenerateHttpGetMessage (port, obin, oper, msg);
+			
+			return req;
+		}
+		
+		string BuildQueryString (OperationMessage opm)
+		{
+			string s = "";
+			Message msg = descriptions.GetMessage (opm.Message);
+			foreach (MessagePart part in msg.Parts)
+			{
+				if (s.Length != 0) s += "&";
+				s += part.Name + "=" + GetLiteral (part.Type.Name);
+			}
+			return s;
+		}
+		
+		public string GenerateSoapMessage (OperationBinding obin, Operation oper, OperationMessage msg)
+		{
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			SoapBindingStyle style = (sob != null) ? sob.Style : SoapBindingStyle.Document;
+			
+			MessageBinding msgbin = (msg is OperationInput) ? (MessageBinding) obin.Input : (MessageBinding)obin.Output;
+			SoapBodyBinding sbb = msgbin.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+			SoapBindingUse bodyUse = (sbb != null) ? sbb.Use : SoapBindingUse.Literal;
+			
+			StringWriter sw = new StringWriter ();
+			XmlTextWriter xtw = new XmlTextWriter (sw);
+			xtw.Formatting = Formatting.Indented;
+			
+			xtw.WriteStartDocument ();
+			xtw.WriteStartElement ("soap", "Envelope", SoapEnvelopeNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsi", null, XmlSchema.InstanceNamespace);
+			xtw.WriteAttributeString ("xmlns", "xsd", null, XmlSchema.Namespace);
+			
+			if (bodyUse == SoapBindingUse.Encoded) 
+			{
+				xtw.WriteAttributeString ("xmlns", "soapenc", null, SoapEncodingNamespace);
+				xtw.WriteAttributeString ("xmlns", "tns", null, msg.Message.Namespace);
+			}
+
+			// Serialize headers
+			
+			bool writtenHeader = false;
+			foreach (object ob in msgbin.Extensions)
+			{
+				SoapHeaderBinding hb = ob as SoapHeaderBinding;
+				if (hb == null) continue;
+				
+				if (!writtenHeader) {
+					xtw.WriteStartElement ("soap", "Header", SoapEnvelopeNamespace);
+					writtenHeader = true;
+				}
+				
+				WriteHeader (xtw, hb);
+			}
+			
+			if (writtenHeader)
+				xtw.WriteEndElement ();
+
+			// Serialize body
+			xtw.WriteStartElement ("soap", "Body", SoapEnvelopeNamespace);
+			
+			currentUse = bodyUse;
+			WriteBody (xtw, oper, msg, sbb, style);
+			
+			xtw.WriteEndElement ();
+			xtw.WriteEndElement ();
+			xtw.Close ();
+			return sw.ToString ();
+		}
+		
+		void WriteHeader (XmlTextWriter xtw, SoapHeaderBinding header)
+		{
+			Message msg = descriptions.GetMessage (header.Message);
+			if (msg == null) throw new InvalidOperationException ("Message " + header.Message + " not found");
+			MessagePart part = msg.Parts [header.Part];
+			if (part == null) throw new InvalidOperationException ("Message part " + header.Part + " not found in message " + header.Message);
+
+			currentUse = header.Use;
+			
+			if (currentUse == SoapBindingUse.Literal)
+				WriteRootElementSample (xtw, part.Element);
+			else
+				WriteTypeSample (xtw, part.Type);
+		}
+		
+		void WriteBody (XmlTextWriter xtw, Operation oper, OperationMessage opm, SoapBodyBinding sbb, SoapBindingStyle style)
+		{
+			Message msg = descriptions.GetMessage (opm.Message);
+			if (msg.Parts.Count > 0 && msg.Parts[0].Name == "parameters")
+			{
+				MessagePart part = msg.Parts[0];
+				if (part.Element == XmlQualifiedName.Empty)
+					WriteTypeSample (xtw, part.Type);
+				else
+					WriteRootElementSample (xtw, part.Element);
+			}
+			else
+			{
+				string elemName = oper.Name;
+				string ns = "";
+				if (opm is OperationOutput) elemName += "Response";
+				
+				if (style == SoapBindingStyle.Rpc) {
+					xtw.WriteStartElement (elemName, sbb.Namespace);
+					ns = sbb.Namespace;
+				}
+					
+				foreach (MessagePart part in msg.Parts)
+				{
+					if (part.Element == XmlQualifiedName.Empty)
+					{
+						XmlSchemaElement elem = new XmlSchemaElement ();
+						elem.SchemaTypeName = part.Type;
+						elem.Name = part.Name;
+						WriteElementSample (xtw, ns, elem);
+					}
+					else
+						WriteRootElementSample (xtw, part.Element);
+				}
+				
+				if (style == SoapBindingStyle.Rpc)
+					xtw.WriteEndElement ();
+			}
+			WriteQueuedTypeSamples (xtw);
+		}
+		
+		void WriteRootElementSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaElement elem = (XmlSchemaElement) schemas.Find (qname, typeof(XmlSchemaElement));
+			if (elem == null) throw new InvalidOperationException ("Element not found: " + qname);
+			WriteElementSample (xtw, qname.Namespace, elem);
+		}
+
+		void WriteElementSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			bool sharedAnnType = false;
+			XmlQualifiedName root;
+			
+			if (!elem.RefName.IsEmpty) {
+				XmlSchemaElement refElem = FindRefElement (elem);
+				if (refElem == null) throw new InvalidOperationException ("Global element not found: " + elem.RefName);
+				root = elem.RefName;
+				elem = refElem;
+				sharedAnnType = true;
+			}
+			else
+				root = new XmlQualifiedName (elem.Name, ns);
+			
+			if (!elem.SchemaTypeName.IsEmpty)
+			{
+				XmlSchemaComplexType st = FindComplexTyype (elem.SchemaTypeName);
+				if (st != null) 
+					WriteComplexTypeSample (xtw, st, root);
+				else
+				{
+					xtw.WriteStartElement (root.Name, root.Namespace);
+					if (currentUse == SoapBindingUse.Encoded) 
+						xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, elem.SchemaTypeName));
+					xtw.WriteString (GetLiteral (FindBuiltInType (elem.SchemaTypeName)));
+					xtw.WriteEndElement ();
+				}
+			}
+			else if (elem.SchemaType == null)
+			{
+				xtw.WriteStartElement ("any");
+				xtw.WriteEndElement ();
+			}
+			else
+				WriteComplexTypeSample (xtw, (XmlSchemaComplexType) elem.SchemaType, root);
+		}
+		
+		void WriteTypeSample (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			if (ctype != null) {
+				WriteComplexTypeSample (xtw, ctype, qname);
+				return;
+			}
+			
+			XmlSchemaSimpleType stype = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (stype != null) {
+				WriteSimpleTypeSample (xtw, stype);
+				return;
+			}
+			
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+			throw new InvalidOperationException ("Type not found: " + qname);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName)
+		{
+			WriteComplexTypeSample (xtw, stype, rootName, -1);
+		}
+		
+		void WriteComplexTypeSample (XmlTextWriter xtw, XmlSchemaComplexType stype, XmlQualifiedName rootName, int id)
+		{
+			string ns = rootName.Namespace;
+			
+			if (rootName.Name.IndexOf ("[]") != -1) rootName = arrayType;
+			
+			if (currentUse == SoapBindingUse.Encoded) {
+				string pref = xtw.LookupPrefix (rootName.Namespace);
+				if (pref == null) pref = "q1";
+				xtw.WriteStartElement (pref, rootName.Name, rootName.Namespace);
+				ns = "";
+			}
+			else
+				xtw.WriteStartElement (rootName.Name, rootName.Namespace);
+			
+			if (id != -1)
+			{
+				xtw.WriteAttributeString ("id", "id" + id);
+				if (rootName != arrayType)
+					xtw.WriteAttributeString ("type", XmlSchema.InstanceNamespace, GetQualifiedNameString (xtw, rootName));
+			}
+			
+			WriteComplexTypeAttributes (xtw, stype);
+			WriteComplexTypeElements (xtw, ns, stype);
+			
+			xtw.WriteEndElement ();
+		}
+		
+		void WriteComplexTypeAttributes (XmlTextWriter xtw, XmlSchemaComplexType stype)
+		{
+			WriteAttributes (xtw, stype.Attributes, stype.AnyAttribute);
+		}
+
+		Dictionary<XmlSchemaComplexType,int> recursed_types = new Dictionary<XmlSchemaComplexType,int> ();
+		void WriteComplexTypeElements (XmlTextWriter xtw, string ns, XmlSchemaComplexType stype)
+		{
+			int prev = 0;
+			if (recursed_types.ContainsKey (stype))
+				prev = recursed_types [stype];
+
+			if (prev > 1)
+				return;
+			recursed_types [stype] = ++prev;
+
+			if (stype.Particle != null)
+				WriteParticleComplexContent (xtw, ns, stype.Particle);
+			else
+			{
+				if (stype.ContentModel is XmlSchemaSimpleContent)
+					WriteSimpleContent (xtw, (XmlSchemaSimpleContent)stype.ContentModel);
+				else if (stype.ContentModel is XmlSchemaComplexContent)
+					WriteComplexContent (xtw, ns, (XmlSchemaComplexContent)stype.ContentModel);
+			}
+			prev = recursed_types [stype];
+			recursed_types [stype] = --prev;
+		}
+
+		void WriteAttributes (XmlTextWriter xtw, XmlSchemaObjectCollection atts, XmlSchemaAnyAttribute anyat)
+		{
+			foreach (XmlSchemaObject at in atts)
+			{
+				if (at is XmlSchemaAttribute)
+				{
+					string ns;
+					XmlSchemaAttribute attr = (XmlSchemaAttribute)at;
+					XmlSchemaAttribute refAttr = attr;
+					
+					// refAttr.Form; TODO
+					
+					if (!attr.RefName.IsEmpty) {
+						refAttr = FindRefAttribute (attr.RefName);
+						if (refAttr == null) throw new InvalidOperationException ("Global attribute not found: " + attr.RefName);
+					}
+					
+					string val;
+					if (!refAttr.SchemaTypeName.IsEmpty) val = FindBuiltInType (refAttr.SchemaTypeName);
+					else val = FindBuiltInType ((XmlSchemaSimpleType) refAttr.SchemaType);
+					
+					xtw.WriteAttributeString (refAttr.Name, val);
+				}
+				else if (at is XmlSchemaAttributeGroupRef)
+				{
+					XmlSchemaAttributeGroupRef gref = (XmlSchemaAttributeGroupRef)at;
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					WriteAttributes (xtw, grp.Attributes, grp.AnyAttribute);
+				}
+			}
+			
+			if (anyat != null)
+				xtw.WriteAttributeString ("custom-attribute","value");
+		}
+		
+		void WriteParticleComplexContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle)
+		{
+			WriteParticleContent (xtw, ns, particle, false);
+		}
+		
+		void WriteParticleContent (XmlTextWriter xtw, string ns, XmlSchemaParticle particle, bool multiValue)
+		{
+			if (particle is XmlSchemaGroupRef)
+				particle = GetRefGroupParticle ((XmlSchemaGroupRef)particle);
+
+			if (particle.MaxOccurs > 1) multiValue = true;
+			
+			if (particle is XmlSchemaSequence) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaSequence)particle).Items, multiValue);
+			}
+			else if (particle is XmlSchemaChoice) {
+				if (((XmlSchemaChoice)particle).Items.Count == 1)
+					WriteSequenceContent (xtw, ns, ((XmlSchemaChoice)particle).Items, multiValue);
+				else
+					WriteChoiceContent (xtw, ns, (XmlSchemaChoice)particle, multiValue);
+			}
+			else if (particle is XmlSchemaAll) {
+				WriteSequenceContent (xtw, ns, ((XmlSchemaAll)particle).Items, multiValue);
+			}
+		}
+
+		void WriteSequenceContent (XmlTextWriter xtw, string ns, XmlSchemaObjectCollection items, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+		
+		void WriteContentItem (XmlTextWriter xtw, string ns, XmlSchemaObject item, bool multiValue)
+		{
+			if (item is XmlSchemaGroupRef)
+				item = GetRefGroupParticle ((XmlSchemaGroupRef)item);
+					
+			if (item is XmlSchemaElement)
+			{
+				XmlSchemaElement elem = (XmlSchemaElement) item;
+				XmlSchemaElement refElem;
+				if (!elem.RefName.IsEmpty) refElem = FindRefElement (elem);
+				else refElem = elem;
+
+				int num = (elem.MaxOccurs == 1 && !multiValue) ? 1 : 2;
+				for (int n=0; n<num; n++)
+				{
+					if (currentUse == SoapBindingUse.Literal)
+						WriteElementSample (xtw, ns, refElem);
+					else
+						WriteRefTypeSample (xtw, ns, refElem);
+				}
+			}
+			else if (item is XmlSchemaAny)
+			{
+				xtw.WriteString (GetLiteral ("xml"));
+			}
+			else if (item is XmlSchemaParticle) {
+				WriteParticleContent (xtw, ns, (XmlSchemaParticle)item, multiValue);
+			}
+		}
+		
+		void WriteChoiceContent (XmlTextWriter xtw, string ns, XmlSchemaChoice choice, bool multiValue)
+		{
+			foreach (XmlSchemaObject item in choice.Items)
+				WriteContentItem (xtw, ns, item, multiValue);
+		}
+
+		void WriteSimpleContent (XmlTextWriter xtw, XmlSchemaSimpleContent content)
+		{
+			XmlSchemaSimpleContentExtension ext = content.Content as XmlSchemaSimpleContentExtension;
+			if (ext != null)
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				
+			XmlQualifiedName qname = GetContentBaseType (content.Content);
+			xtw.WriteString (GetLiteral (FindBuiltInType (qname)));
+		}
+
+		string FindBuiltInType (XmlQualifiedName qname)
+		{
+			if (qname.Namespace == XmlSchema.Namespace)
+				return qname.Name;
+
+			XmlSchemaComplexType ct = FindComplexTyype (qname);
+			if (ct != null)
+			{
+				XmlSchemaSimpleContent sc = ct.ContentModel as XmlSchemaSimpleContent;
+				if (sc == null) throw new InvalidOperationException ("Invalid schema");
+				return FindBuiltInType (GetContentBaseType (sc.Content));
+			}
+			
+			XmlSchemaSimpleType st = (XmlSchemaSimpleType) schemas.Find (qname, typeof(XmlSchemaSimpleType));
+			if (st != null)
+				return FindBuiltInType (st);
+
+			throw new InvalidOperationException ("Definition of type " + qname + " not found");
+		}
+
+		string FindBuiltInType (XmlSchemaSimpleType st)
+		{
+			if (st.Content is XmlSchemaSimpleTypeRestriction) {
+				return FindBuiltInType (GetContentBaseType (st.Content));
+			}
+			else if (st.Content is XmlSchemaSimpleTypeList) {
+				string s = FindBuiltInType (GetContentBaseType (st.Content));
+				return s + " " + s + " ...";
+			}
+			else if (st.Content is XmlSchemaSimpleTypeUnion)
+			{
+				//Check if all types of the union are equal. If not, then will use anyType.
+				XmlSchemaSimpleTypeUnion uni = (XmlSchemaSimpleTypeUnion) st.Content;
+				string utype = null;
+
+				// Anonymous types are unique
+				if (uni.BaseTypes.Count != 0 && uni.MemberTypes.Length != 0)
+					return "string";
+
+				foreach (XmlQualifiedName mt in uni.MemberTypes)
+				{
+					string qn = FindBuiltInType (mt);
+					if (utype != null && qn != utype) return "string";
+					else utype = qn;
+				}
+				return utype;
+			}
+			else
+				return "string";
+		}
+		
+
+		XmlQualifiedName GetContentBaseType (XmlSchemaObject ob)
+		{
+			if (ob is XmlSchemaSimpleContentExtension)
+				return ((XmlSchemaSimpleContentExtension)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleContentRestriction)
+				return ((XmlSchemaSimpleContentRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeRestriction)
+				return ((XmlSchemaSimpleTypeRestriction)ob).BaseTypeName;
+			else if (ob is XmlSchemaSimpleTypeList)
+				return ((XmlSchemaSimpleTypeList)ob).ItemTypeName;
+			else
+				return null;
+		}
+
+		void WriteComplexContent (XmlTextWriter xtw, string ns, XmlSchemaComplexContent content)
+		{
+			XmlQualifiedName qname;
+
+			XmlSchemaComplexContentExtension ext = content.Content as XmlSchemaComplexContentExtension;
+			if (ext != null) qname = ext.BaseTypeName;
+			else {
+				XmlSchemaComplexContentRestriction rest = (XmlSchemaComplexContentRestriction)content.Content;
+				qname = rest.BaseTypeName;
+				if (qname == arrayType) {
+					ParseArrayType (rest, out qname);
+					XmlSchemaElement elem = new XmlSchemaElement ();
+					elem.Name = "Item";
+					elem.SchemaTypeName = qname;
+					
+					xtw.WriteAttributeString ("arrayType", SoapEncodingNamespace, qname.Name + "[2]");
+					WriteContentItem (xtw, ns, elem, true);
+					return;
+				}
+			}
+			
+			// Add base map members to this map
+			XmlSchemaComplexType ctype = FindComplexTyype (qname);
+			WriteComplexTypeAttributes (xtw, ctype);
+			
+			if (ext != null) {
+				// Add the members of this map
+				WriteAttributes (xtw, ext.Attributes, ext.AnyAttribute);
+				if (ext.Particle != null)
+					WriteParticleComplexContent (xtw, ns, ext.Particle);
+			}
+			
+			WriteComplexTypeElements (xtw, ns, ctype);
+		}
+		
+		void ParseArrayType (XmlSchemaComplexContentRestriction rest, out XmlQualifiedName qtype)
+		{
+			XmlSchemaAttribute arrayTypeAt = FindArrayAttribute (rest.Attributes);
+			XmlAttribute[] uatts = arrayTypeAt.UnhandledAttributes;
+			if (uatts == null || uatts.Length == 0) throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			XmlAttribute xat = null;
+			foreach (XmlAttribute at in uatts)
+				if (at.LocalName == "arrayType" && at.NamespaceURI == WsdlNamespace)
+					{ xat = at; break; }
+			
+			if (xat == null) 
+				throw new InvalidOperationException ("arrayType attribute not specified in array declaration");
+			
+			string arrayType = xat.Value;
+			string type, ns;
+			int i = arrayType.LastIndexOf (":");
+			if (i == -1) ns = "";
+			else ns = arrayType.Substring (0,i);
+			
+			int j = arrayType.IndexOf ("[", i+1);
+			if (j == -1) throw new InvalidOperationException ("Cannot parse WSDL array type: " + arrayType);
+			type = arrayType.Substring (i+1);
+			type = type.Substring (0, type.Length-2);
+			
+			qtype = new XmlQualifiedName (type, ns);
+		}
+		
+		XmlSchemaAttribute FindArrayAttribute (XmlSchemaObjectCollection atts)
+		{
+			foreach (object ob in atts)
+			{
+				XmlSchemaAttribute att = ob as XmlSchemaAttribute;
+				if (att != null && att.RefName == arrayTypeRefName) return att;
+				
+				XmlSchemaAttributeGroupRef gref = ob as XmlSchemaAttributeGroupRef;
+				if (gref != null)
+				{
+					XmlSchemaAttributeGroup grp = (XmlSchemaAttributeGroup) schemas.Find (gref.RefName, typeof(XmlSchemaAttributeGroup));
+					att = FindArrayAttribute (grp.Attributes);
+					if (att != null) return att;
+				}
+			}
+			return null;
+		}
+		
+		void WriteSimpleTypeSample (XmlTextWriter xtw, XmlSchemaSimpleType stype)
+		{
+			xtw.WriteString (GetLiteral (FindBuiltInType (stype)));
+		}
+		
+		XmlSchemaParticle GetRefGroupParticle (XmlSchemaGroupRef refGroup)
+		{
+			XmlSchemaGroup grp = (XmlSchemaGroup) schemas.Find (refGroup.RefName, typeof (XmlSchemaGroup));
+			return grp.Particle;
+		}
+
+		XmlSchemaElement FindRefElement (XmlSchemaElement elem)
+		{
+			if (elem.RefName.Namespace == XmlSchema.Namespace)
+			{
+				if (anyElement != null) return anyElement;
+				anyElement = new XmlSchemaElement ();
+				anyElement.Name = "any";
+				anyElement.SchemaTypeName = anyType;
+				return anyElement;
+			}
+			return (XmlSchemaElement) schemas.Find (elem.RefName, typeof(XmlSchemaElement));
+		}
+		
+		XmlSchemaAttribute FindRefAttribute (XmlQualifiedName refName)
+		{
+			if (refName.Namespace == XmlSchema.Namespace)
+			{
+				XmlSchemaAttribute at = new XmlSchemaAttribute ();
+				at.Name = refName.Name;
+				at.SchemaTypeName = new XmlQualifiedName ("string",XmlSchema.Namespace);
+				return at;
+			}
+			return (XmlSchemaAttribute) schemas.Find (refName, typeof(XmlSchemaAttribute));
+		}
+		
+		void WriteRefTypeSample (XmlTextWriter xtw, string ns, XmlSchemaElement elem)
+		{
+			if (elem.SchemaTypeName.Namespace == XmlSchema.Namespace || schemas.Find (elem.SchemaTypeName, typeof(XmlSchemaSimpleType)) != null)
+				WriteElementSample (xtw, ns, elem);
+			else
+			{
+				xtw.WriteStartElement (elem.Name, ns);
+				xtw.WriteAttributeString ("href", "#id" + (queue.Count+1));
+				xtw.WriteEndElement ();
+				queue.Add (new EncodedType (ns, elem));
+			}
+		}
+		
+		void WriteQueuedTypeSamples (XmlTextWriter xtw)
+		{
+			for (int n=0; n<queue.Count; n++)
+			{
+				EncodedType ec = (EncodedType) queue[n];
+				XmlSchemaComplexType st = FindComplexTyype (ec.Element.SchemaTypeName);
+				WriteComplexTypeSample (xtw, st, ec.Element.SchemaTypeName, n+1);
+			}
+		}
+		
+		XmlSchemaComplexType FindComplexTyype (XmlQualifiedName qname)
+		{
+			if (qname.Name.IndexOf ("[]") != -1)
+			{
+				XmlSchemaComplexType stype = new XmlSchemaComplexType ();
+				stype.ContentModel = new XmlSchemaComplexContent ();
+				
+				XmlSchemaComplexContentRestriction res = new XmlSchemaComplexContentRestriction ();
+				stype.ContentModel.Content = res;
+				res.BaseTypeName = arrayType;
+				
+				XmlSchemaAttribute att = new XmlSchemaAttribute ();
+				att.RefName = arrayTypeRefName;
+				res.Attributes.Add (att);
+				
+				XmlAttribute xat = document.CreateAttribute ("arrayType", WsdlNamespace);
+				xat.Value = qname.Namespace + ":" + qname.Name;
+				att.UnhandledAttributes = new XmlAttribute[] {xat};
+				return stype;
+			}
+				
+			return (XmlSchemaComplexType) schemas.Find (qname, typeof(XmlSchemaComplexType));
+		}
+		
+		string GetQualifiedNameString (XmlTextWriter xtw, XmlQualifiedName qname)
+		{
+			string pref = xtw.LookupPrefix (qname.Namespace);
+			if (pref != null) return pref + ":" + qname.Name;
+			
+			xtw.WriteAttributeString ("xmlns", "q1", null, qname.Namespace);
+			return "q1:" + qname.Name;
+		}
+				
+		protected virtual string GetLiteral (string s)
+		{
+			return s;
+		}
+
+		void GetOperationFormat (OperationBinding obin, out SoapBindingStyle style, out SoapBindingUse use)
+		{
+			style = SoapBindingStyle.Document;
+			use = SoapBindingUse.Literal;
+			SoapOperationBinding sob = obin.Extensions.Find (typeof(SoapOperationBinding)) as SoapOperationBinding;
+			if (sob != null) {
+				style = sob.Style;
+				SoapBodyBinding sbb = obin.Input.Extensions.Find (typeof(SoapBodyBinding)) as SoapBodyBinding;
+				if (sbb != null)
+					use = sbb.Use;
+			}
+		}
+	}
+
+
+
+
+
+</script>
+
+<head runat="server">
+	<%
+	Response.Write ("<link rel=\"alternate\" type=\"text/xml\" href=\"" + Request.FilePath + "?disco\"/>");
+	%>
+	<title><%=WebServiceName%> Web Service</title>
+    <style type="text/css">
+		BODY { font-family: Arial; margin-left: 20px; margin-top: 20px; font-size: x-small}
+		TABLE { font-size: x-small }
+		.title { color:dimgray; font-family: Arial; font-size:20pt; font-weight:900}
+		.operationTitle { color:dimgray; font-family: Arial; font-size:15pt; font-weight:900}
+		.method { font-size: x-small }
+		.bindingLabel { font-size: x-small; font-weight:bold; color:darkgray; line-height:8pt; display:block; margin-bottom:3px }
+		.label { font-size: small; font-weight:bold; color:darkgray }
+		.paramTable { font-size: x-small }
+		.paramTable TR { background-color: gainsboro }
+		.paramFormTable { font-size: x-small; padding: 10px; background-color: gainsboro }
+		.paramFormTable TR { background-color: gainsboro }
+		.paramInput { border: solid 1px gray }
+		.button {border: solid 1px gray }
+		.smallSeparator { height:3px; overflow:hidden }
+		.panel { background-color:whitesmoke; border: solid 1px silver; border-top: solid 1px silver  }
+		.codePanel { background-color: white; font-size:x-small; padding:7px; border:solid 1px silver}
+		.code-xml { font-size:10pt; font-family:courier }
+		.code-cs { font-size:10pt; font-family:courier }
+		.code-vb { font-size:10pt; font-family:courier }
+		.tabLabelOn { font-weight:bold }
+		.tabLabelOff {color: darkgray }
+		.literal-placeholder {color: darkblue; font-weight:bold}
+		A:link { color: black; }
+		A:visited { color: black; }
+		A:active { color: black; }
+		A:hover { color: blue }
+    </style>
+	
+<script language="javascript" type="text/javascript">
+var req;
+function getXML (command, url, qs) {
+	if (url == "" || url.substring (0, 4) != "http")
+		return;
+	
+	var post_data = null;
+	req = getReq ();
+	req.onreadystatechange = stateChange;
+	if (command == "GET") {
+		url = url + "?" + qs;
+	} else {
+		post_data = qs;
+	}
+	req.open (command, url,  true); 
+	if (command == "POST")
+		req.setRequestHeader ("Content-Type", "application/x-www-form-urlencoded");
+	req.send (post_data); 
+}
+
+function stateChange () {
+	if (req.readyState == 4) {
+		var node = document.getElementById("testresult_div");
+		var text = "";
+		if (req.status == 200) {
+			node.innerHTML = "<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+		} else {
+			var ht = "<b style='color: red'>" + formatXml (req.status + " - " + req.statusText) + "</b>";
+			if (req.responseText != "")
+				ht = ht + "\n<div class='code-xml'>" + formatXml (req.responseText) + "</div>";
+			node.innerHTML = ht;
+					
+		}
+	}
+}
+
+function formatXml (text)
+{	
+	var re = / /g;
+	text = text.replace (re, "&nbsp;");
+
+	re = /\t/g;
+	text = text.replace (re, "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;");
+	
+	re = /\<\s*(\/?)\s*(.*?)\s*(\/?)\s*\>/g;
+	text = text.replace (re,"{blue:&lt;$1}{maroon:$2}{blue:$3&gt;}");
+	
+	re = /{(\w*):(.*?)}/g;
+	text = text.replace (re,"<span style='color:$1'>$2</span>");
+
+	re = /"(.*?)"/g;
+	text = text.replace (re,"\"<span style='color:purple'>$1</span>\"");
+
+	re = /\r\n|\r|\n/g;
+	text = text.replace (re, "<br/>");
+	
+	return text;
+}
+
+function getReq () {
+	if (window.XMLHttpRequest) {
+		return new XMLHttpRequest();     // Firefox, Safari, ...
+	} else if (window.ActiveXObject) {
+		return new ActiveXObject("Microsoft.XMLHTTP");
+	}
+}
+
+function clearForm ()
+{
+	document.getElementById("testFormResult").style.display="none";
+}
+</script>
+
+</head>
+
+<body>
+<div class="title" style="margin-left:20px">
+<span class="label">Web Service</span><br>
+<%=WebServiceName%>
+</div>
+
+<!--
+	**********************************************************
+	Left panel
+-->
+
+<table border="0" width="100%" cellpadding="15px" cellspacing="15px">
+<tr valign="top"><td width="150px" class="panel">
+<div style="width:150px"></div>
+<a class="method" href='<%=PageName%>'>Overview</a><br>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("wsdl")%>'>Service Description</a>
+<div class="smallSeparator"></div>
+<a class="method" href='<%=PageName + "?" + GetPageContext("proxy")%>'>Client proxy</a>
+<br><br>
+	<asp:repeater id="BindingsRepeater" runat=server>
+		<itemtemplate name="itemtemplate">
+			<span class="bindingLabel"><%#FormatBindingName(DataBinder.Eval(Container.DataItem, "Name").ToString())%></span>
+			<asp:repeater id="OperationsRepeater" runat=server datasource='<%# ((Binding)Container.DataItem).Operations %>'>
+				<itemtemplate>
+					<a class="method" href="<%=PageName%>?<%=GetTabContext("op",null)%>op=<%#GetOpName(Container.DataItem)%>&bnd=<%#DataBinder.Eval(Container.DataItem, "Binding.Name")%>"><%#GetOpName(Container.DataItem)%></a>
+					<div class="smallSeparator"></div>
+				</itemtemplate>
+			</asp:repeater>
+			<br>
+		</itemtemplate>
+	</asp:repeater>
+
+</td><td class="panel">
+
+<% if (CurrentPage == "main") {%>
+
+<!--
+	**********************************************************
+	Web service overview
+-->
+
+	<p class="label">Web Service Overview</p>
+	<%=WebServiceDescription%>
+	<br/><br/>
+	<% if (ProfileViolations != null && ProfileViolations.Count > 0) { %>
+		<p class="label">Basic Profile Conformance</p>
+		This web service does not conform to WS-I Basic Profile v1.1
+	<%
+		Response.Write ("<ul>");
+		foreach (BasicProfileViolation vio in ProfileViolations) {
+			Response.Write ("<li><b>" + vio.NormativeStatement + "</b>: " + vio.Details);
+			Response.Write ("<ul>");
+			foreach (string ele in vio.Elements)
+				Response.Write ("<li>" + ele + "</li>");
+			Response.Write ("</ul>");
+			Response.Write ("</li>");
+		}
+		Response.Write ("</ul>");
+	}%>
+
+<%} if (DefaultBinding == null) {%>
+This service does not contain any public web method.
+<%} else if (CurrentPage == "op") {%>
+
+<!--
+	**********************************************************
+	Operation description
+-->
+
+	<span class="operationTitle"><%=CurrentOperationName%></span>
+	<br><br>
+	<% WriteTabs (); %>
+	<br><br><br>
+	
+	<% if (CurrentTab == "main") { %>
+		<span class="label">Input Parameters</span>
+		<div class="smallSeparator"></div>
+		<% if (InParams.Count == 0) { %>
+			No input parameters<br>
+		<% } else { %>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="InputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<% } %>
+		<br>
+		
+		<% if (OutParams.Count > 0) { %>
+		<span class="label">Output Parameters</span>
+			<div class="smallSeparator"></div>
+			<table class="paramTable" cellspacing="1" cellpadding="5">
+			<asp:repeater id="OutputParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Name")%></td>
+					<td width="150"><%#DataBinder.Eval(Container.DataItem, "Type")%></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			</table>
+		<br>
+		<% } %>
+		
+		<span class="label">Remarks</span>
+		<div class="smallSeparator"></div>
+		<%=OperationDocumentation%>
+		<br><br>
+		<span class="label">Technical information</span>
+		<div class="smallSeparator"></div>
+		Format: <%=CurrentOperationFormat%>
+		<br>Supported protocols: <%=CurrentOperationProtocols%>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Test form
+-->
+
+	<% if (CurrentTab == "test") { 
+		if (CurrentOperationSupportsTest) {%>
+			Enter values for the parameters and click the 'Invoke' button to test this method:<br><br>
+			<form action="<%=PageName%>" method="GET">
+			<input type="hidden" name="page" value="<%=CurrentPage%>">
+			<input type="hidden" name="tab" value="<%=CurrentTab%>">
+			<input type="hidden" name="op" value="<%=CurrentOperationName%>">
+			<input type="hidden" name="bnd" value="<%=CurrentOperationBinding%>">
+			<input type="hidden" name="ext" value="testform">
+			<table class="paramFormTable" cellspacing="0" cellpadding="3">
+			<asp:repeater id="InputFormParamsRepeater" runat=server>
+				<itemtemplate>
+					<tr>
+					<td><%#DataBinder.Eval(Container.DataItem, "Name")%>:&nbsp;</td>
+					<td width="150"><input class="paramInput" type="text" size="20" name="<%#DataBinder.Eval(Container.DataItem, "Name")%>"></td>
+					</tr>
+				</itemtemplate>
+			</asp:repeater>
+			<tr><td></td><td><input class="button" type="submit" value="Invoke">&nbsp;<input class="button" type="button" onclick="clearForm()" value="Clear"></td></tr>
+			</table>
+			</form>
+			<div id="testFormResult" style="display:<%= (HasFormResult?"block":"none") %>">
+			The web service returned the following result:<br/><br/>
+			<div class="codePanel" id="testresult_div">
+			</div>
+			<script language="javascript">
+				getXML ("<%= GetOrPost () %>", "<%= GetTestResultUrl () %>", "<%= GetQS () %>");
+			</script>
+			</div>
+		<% } else {%>
+		The test form is not available for this operation because it has parameters with a complex structure.
+		<% } %>
+	<% } %>
+	
+<!--
+	**********************************************************
+	Operation description - Message Layout
+-->
+
+	<% if (CurrentTab == "msg") { %>
+		
+		The following are sample SOAP requests and responses for each protocol supported by this method:
+			<br/><br/>
+		
+		<% if (IsOperationSupported ("Soap")) { %>
+			<span class="label">Soap</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("Soap", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpGet")) { %>
+			<span class="label">HTTP Get</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpGet", false)%></div></div>
+			<br/>
+		<% } %>
+		<% if (IsOperationSupported ("HttpPost")) { %>
+			<span class="label">HTTP Post</span>
+			<br/><br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", true)%></div></div>
+			<br/>
+			<div class="codePanel"><div class="code-xml"><%=GenerateOperationMessages ("HttpPost", false)%></div></div>
+			<br/>
+		<% } %>
+		
+	<% } %>
+<%} else if (CurrentPage == "proxy") {%>
+<!--
+	**********************************************************
+	Client Proxy
+-->
+	<form action="<%=PageName%>" name="langForm" method="GET">
+		Select the language for which you want to generate a proxy 
+		<input type="hidden" name="page" value="<%=CurrentPage%>">&nbsp;
+		<SELECT name="lang" onchange="langForm.submit()">
+			<%=GetOptionSel("cs",CurrentLanguage)%>C#</option>
+			<%=GetOptionSel("vb",CurrentLanguage)%>Visual Basic</option>
+		</SELECT>
+		&nbsp;&nbsp;
+	</form>
+	<br>
+	<span class="label"><%=CurrentProxytName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?code=" + CurrentLanguage%>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-<%=CurrentLanguage%>"><%=GetProxyCode ()%></div>
+	</div>
+<%} else if (CurrentPage == "wsdl") {%>
+<!--
+	**********************************************************
+	Service description
+-->
+	<% if (descriptions.Count > 1 || schemas.Count > 1) {%>
+	The description of this web service is composed by several documents. Click on the document you want to see:
+	
+	<ul>
+	<% 
+		for (int n=0; n<descriptions.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=wsdl&docind=" + n + "'>WSDL document " + descriptions[n].TargetNamespace + "</a></li>");
+		for (int n=0; n<schemas.Count; n++)
+			Response.Write ("<li><a href='" + PageName + "?" + GetPageContext(null) + "doctype=schema&docind=" + n + "'>Xml Schema " + schemas[n].TargetNamespace + "</a></li>");
+	%>
+	</ul>
+	
+	<%} else {%>
+	<%}%>
+	<br>
+	<span class="label"><%=CurrentDocumentName%></span>&nbsp;&nbsp;&nbsp;
+	<a href="<%=PageName + "?" + CurrentDocType + "=" + CurrentDocInd %>">Download</a>
+	<br><br>
+	<div class="codePanel">
+	<div class="code-xml"><%=GenerateDocument ()%></div>
+	</div>
+
+<%}%>
+
+<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
+</td>
+<td width="20px"></td>
+</tr>
+
+</table>
+</body>
+</html>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/machine.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/machine.config
new file mode 100644
index 0000000000000000000000000000000000000000..455709536f85b93df0239cb1664317d118990f34
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/machine.config
@@ -0,0 +1,310 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+	<configSections>
+		<section name="configProtectedData" type="System.Configuration.ProtectedConfigurationSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="appSettings" type="System.Configuration.AppSettingsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="connectionStrings" type="System.Configuration.ConnectionStringsSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="mscorlib" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="runtime" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="assemblyBinding"  type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="satelliteassemblies" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="startup" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false"/>
+		<section name="system.data" type="System.Data.Common.DbProviderFactoriesConfigurationHandler, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.diagnostics" type="System.Diagnostics.SystemDiagnosticsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.runtime.remoting" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<section name="system.windows.forms" type="System.Windows.Forms.WindowsFormsSection, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="system.codedom" type="System.CodeDom.Compiler.CodeDomConfigurationHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<section name="windows" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowLocation="false" />
+		<section name="strongNames" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" allowLocation="false"/>
+		<sectionGroup name="system.runtime.serialization" type="System.Runtime.Serialization.Configuration.SerializationSectionGroup, System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="dataContractSerializer" type="System.Runtime.Serialization.Configuration.DataContractSerializerSection, System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.web" type="System.Web.Configuration.SystemWebSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+			<section name="anonymousIdentification" type="System.Web.Configuration.AnonymousIdentificationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authentication" type="System.Web.Configuration.AuthenticationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="authorization" type="System.Web.Configuration.AuthorizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="browserCaps" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="clientTarget" type="System.Web.Configuration.ClientTargetSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="compilation" type="System.Web.Configuration.CompilationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="customErrors" type="System.Web.Configuration.CustomErrorsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="deployment" type="System.Web.Configuration.DeploymentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" />
+			<section name="globalization" type="System.Web.Configuration.GlobalizationSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="healthMonitoring" type="System.Web.Configuration.HealthMonitoringSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="hostingEnvironment" type="System.Web.Configuration.HostingEnvironmentSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="httpCookies" type="System.Web.Configuration.HttpCookiesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpHandlers" type="System.Web.Configuration.HttpHandlersSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpModules" type="System.Web.Configuration.HttpModulesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="httpRuntime" type="System.Web.Configuration.HttpRuntimeSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="identity" type="System.Web.Configuration.IdentitySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="machineKey" type="System.Web.Configuration.MachineKeySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="membership" type="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="mobileControls" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+                        <section name="deviceFilters" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="pages" type="System.Web.Configuration.PagesSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="processModel" type="System.Web.Configuration.ProcessModelSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineOnly" allowLocation="false" />
+			<section name="profile" type="System.Web.Configuration.ProfileSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="roleManager" type="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="securityPolicy" type="System.Web.Configuration.SecurityPolicySection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="sessionPageState" type="System.Web.Configuration.SessionPageStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="sessionState" type="System.Web.Configuration.SessionStateSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="siteMap" type="System.Web.Configuration.SiteMapSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="trace" type="System.Web.Configuration.TraceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="trust" type="System.Web.Configuration.TrustSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication" />
+			<section name="urlMappings" type="System.Web.Configuration.UrlMappingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			<section name="webControls" type="System.Web.Configuration.WebControlsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webParts" type="System.Web.Configuration.WebPartsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="webServices" type="System.Web.Services.Configuration.WebServicesSection, System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<section name="xhtmlConformance" type="System.Web.Configuration.XhtmlConformanceSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+			<sectionGroup name="caching" type="System.Web.Configuration.SystemWebCachingSectionGroup, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+				<section name="cache" type="System.Web.Configuration.CacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCache" type="System.Web.Configuration.OutputCacheSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="outputCacheSettings" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+				<section name="sqlCacheDependency" type="System.Web.Configuration.OutputCacheSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+			</sectionGroup>
+			<section name="monoSettings" type="System.Web.Configuration.MonoSettingsSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		</sectionGroup>
+		<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+			<sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+				<section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				<sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+					<section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere"/>
+					<section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+					<section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+				</sectionGroup>
+			</sectionGroup>
+		</sectionGroup>
+		<sectionGroup name="system.net" type="System.Net.Configuration.NetSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="authenticationModules" type="System.Net.Configuration.AuthenticationModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="connectionManagement" type="System.Net.Configuration.ConnectionManagementSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="defaultProxy" type="System.Net.Configuration.DefaultProxySection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<sectionGroup name="mailSettings" type="System.Net.Configuration.MailSettingsSectionGroup, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+				<section name="smtp" type="System.Net.Configuration.SmtpSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</sectionGroup>
+			<section name="requestCaching" type="System.Net.Configuration.RequestCachingSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="settings" type="System.Net.Configuration.SettingsSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="webRequestModules" type="System.Net.Configuration.WebRequestModulesSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</sectionGroup>
+		<section name="system.drawing" type="System.Configuration.NameValueSectionHandler, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.serviceModel" type="System.ServiceModel.Configuration.ServiceModelSectionGroup, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+			<section name="behaviors" type="System.ServiceModel.Configuration.BehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="bindings" type="System.ServiceModel.Configuration.BindingsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="client" type="System.ServiceModel.Configuration.ClientSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="commonBehaviors" type="System.ServiceModel.Configuration.CommonBehaviorsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="diagnostics" type="System.ServiceModel.Configuration.DiagnosticSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="extensions" type="System.ServiceModel.Configuration.ExtensionsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="serviceHostingEnvironment" type="System.ServiceModel.Configuration.ServiceHostingEnvironmentSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="services" type="System.ServiceModel.Configuration.ServicesSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<section name="standardEndpoints" type="System.ServiceModel.Configuration.StandardEndpointsSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+			<section name="routing" type="System.ServiceModel.Routing.Configuration.RoutingSection, System.ServiceModel.Routing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			<section name="protocolMapping" type="System.ServiceModel.Configuration.ProtocolMappingSection, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+		</sectionGroup>
+		<sectionGroup name="system.transactions" type="System.Transactions.Configuration.TransactionsSectionGroup, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null">
+			<section name="defaultSettings" type="System.Transactions.Configuration.DefaultSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null"/>
+			<section name="machineSettings" type="System.Transactions.Configuration.MachineSettingsSection, System.Transactions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089, Custom=null" allowDefinition="MachineOnly" allowExeDefinition="MachineOnly"/>
+		</sectionGroup>
+		<section name="system.webServer" type="System.Configuration.IgnoreSection, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		<section name="uri" type="System.Configuration.UriSection, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		<sectionGroup name="system.runtime.caching" type="System.Runtime.Caching.Configuration.CachingSectionGroup, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+		  <section name="memoryCache" type="System.Runtime.Caching.Configuration.MemoryCacheSection, System.Runtime.Caching, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" allowDefinition="MachineToApplication"/>
+                </sectionGroup>
+	</configSections>
+
+	<connectionStrings>
+		<add name="LocalSqlServer" connectionString="data source=.\SQLEXPRESS;Integrated Security=SSPI;AttachDBFilename=|DataDirectory|aspnetdb.mdf;User Instance=true" providerName="System.Data.SqlClient"/>
+		<add name="LocalSqliteServer" connectionString="Data Source=|DataDirectory|/aspnetdb.sqlite;version=3" providerName="Mono.Data.Sqlite"/>
+	</connectionStrings>
+	
+	<configProtectedData defaultProvider="RsaProtectedConfigurationProvider">
+		<providers>
+			<add name="RsaProtectedConfigurationProvider" type="System.Configuration.RsaProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" keyContainerName="MonoFrameworkConfigurationKey" cspProviderName="" useMachineContainer="true" useOAEP="false" />
+			<add name="DataProtectionConfigurationProvider" type="System.Configuration.DpapiProtectedConfigurationProvider, System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+                             description="" useMachineProtection="true" keyEntropy="" />
+		</providers>
+	</configProtectedData>
+
+	<system.net>
+		<authenticationModules>
+			<add type="System.Net.BasicClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.DigestClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add type="System.Net.NtlmClient, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</authenticationModules>
+		<webRequestModules>
+			<add prefix="http" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="https" type="System.Net.HttpRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="file" type="System.Net.FileWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<add prefix="ftp" type="System.Net.FtpWebRequestCreator, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</webRequestModules>
+	</system.net>
+	
+	<system.runtime.remoting>
+		<application>
+			<channels>
+				<channel ref="http client" displayName="http client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="tcp client" displayName="tcp client (delay loaded)" delayLoadAsClientChannel="true" />
+				<channel ref="ipc client" displayName="ipc client (delay loaded)" delayLoadAsClientChannel="true" />
+			</channels>
+		</application>
+		<channels>
+			<channel id="http" type="System.Runtime.Remoting.Channels.Http.HttpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http client" type="System.Runtime.Remoting.Channels.Http.HttpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="http server" type="System.Runtime.Remoting.Channels.Http.HttpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp" type="System.Runtime.Remoting.Channels.Tcp.TcpChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp client" type="System.Runtime.Remoting.Channels.Tcp.TcpClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="tcp server" type="System.Runtime.Remoting.Channels.Tcp.TcpServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc" type="System.Runtime.Remoting.Channels.Ipc.IpcChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc client" type="System.Runtime.Remoting.Channels.Ipc.IpcClientChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			<channel id="ipc server" type="System.Runtime.Remoting.Channels.Ipc.IpcServerChannel, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		</channels>
+			<channelSinkProviders>
+				<clientProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryClientFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</clientProviders>
+				<serverProviders>
+					<formatter id="soap" type="System.Runtime.Remoting.Channels.SoapServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<formatter id="binary" type="System.Runtime.Remoting.Channels.BinaryServerFormatterSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+					<provider id="wsdl" type="System.Runtime.Remoting.MetadataServices.SdlChannelSinkProvider, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				</serverProviders>
+			</channelSinkProviders>
+	</system.runtime.remoting>
+	
+	<appSettings>
+	<!--<add key="yourkey" value="your value" /> -->
+	<!--<remove key="a key defined higher in the hierarchy" /> -->
+	<!--<clear/> Removes all defined settings -->
+	</appSettings>
+	<system.diagnostics>
+		<trace autoflush="false" indentsize="4" />
+	</system.diagnostics>
+        <system.drawing>
+        </system.drawing>
+
+	<system.data>
+	  <DbProviderFactories>
+	    <add name="Mono Sqlite Data Provider"  invariant="Mono.Data.SqliteClient" 
+		 description="Mono Framework Data Provider for SQLite (old version)" 
+		 type="Mono.Data.SqliteClient.SqliteFactory, Mono.Data.SqliteClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Mono Sqlite Provider"  invariant="Mono.Data.Sqlite" 
+		 description="Mono Framework Data Provider for SQLite (new version)"
+		 type="Mono.Data.Sqlite.SqliteFactory, Mono.Data.Sqlite, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	    <add name="Odbc Data Provider"         invariant="System.Data.Odbc"         
+		 description=".Net Framework Data Provider for Odbc"      
+		 type="System.Data.Odbc.OdbcFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OleDb Data Provider"        invariant="System.Data.OleDb"        
+		 description=".Net Framework Data Provider for OleDb"     
+		 type="System.Data.OleDb.OleDbFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="OracleClient Data Provider" invariant="System.Data.OracleClient" 
+		 description=".Net Framework Data Provider for Oracle"    
+		 type="System.Data.OracleClient.OracleClientFactory, System.Data.OracleClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="SqlClient Data Provider"    invariant="System.Data.SqlClient"    
+		 description=".Net Framework Data Provider for SqlServer" 
+		 type="System.Data.SqlClient.SqlClientFactory, System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+            <add name="Sybase Data Provider"    invariant="Mono.Data.SybaseClient"    
+		 description=".Net Framework Data Provider for Sybase" 
+		 type="Mono.Data.SybaseClient.SybaseClientFactory, Mono.Data.SybaseClient, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756"/>
+	  </DbProviderFactories>
+	</system.data>
+
+	<mscorlib>
+		<cryptographySettings>
+			<cryptoNameMapping>
+				<cryptoClasses>
+					<cryptoClass monoMD2="Mono.Security.Cryptography.MD2Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+					<cryptoClass monoMD4="Mono.Security.Cryptography.MD4Managed, Mono.Security, Version=4.0.0.0, Culture=neutral, PublicKeyToken=0738eb9f132ed756" />
+				</cryptoClasses>
+				<nameEntry name="MD2" class="monoMD2" />
+				<nameEntry name="MD4" class="monoMD4" />
+			</cryptoNameMapping>
+			<oidMap>
+				<oidEntry OID="1.2.840.113549.2.2" name="MD2" />
+				<oidEntry OID="1.2.840.113549.2.2" name="Mono.Security.Cryptography.MD2Managed" />
+				<oidEntry OID="1.2.840.113549.2.4" name="MD4" />
+				<oidEntry OID="1.2.840.113549.2.4" name="Mono.Security.Cryptography.MD4Managed" />
+			</oidMap>
+		</cryptographySettings>
+	</mscorlib>
+
+	<strongNames>
+		<pubTokenMapping>
+			<!-- ECMA key -->
+			<map Token="b77a5c561934e089" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (final) key -->
+			<map Token="b03f5f7f11d50a3a" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Microsoft (Web Service Enhancement) key -->
+			<map Token="31bf3856ad364e35" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- IBM (DB2 Data Provider) key -->
+			<map Token="7c307b91aa13d208" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- Silverlight 2.0 key -->
+			<map Token="7cec85d7bea7798e" PublicKey="002400000480000094000000060200000024000052534131000400000100010079159977d2d03a8e6bea7a2e74e8d1afcc93e8851974952bb480a12c9134474d04062447c37e0e68c080536fcf3c3fbe2ff9c979ce998475e506e8ce82dd5b0f350dc10e93bf2eeecf874b24770c5081dbea7447fddafa277b22de47d6ffea449674a4f9fccf84d15069089380284dbdd35f46cdff12a1bd78e4ef0065d016df" />
+			<!-- XNA Framework key -->                                                            
+			<map Token="6d5c3888ef60e27d" PublicKey="0024000004800000940000000602000000240000525341310004000001000100f9a2641bac9847900d92a33d652ccc4e8b529360f908e7af53e57008b2a9a1938c32a160d47f795a23590557608d2c8d0c0e8846a052d070f9298281b8185343dbe5b479bd52de256f73c2a943e1a8a42065b5c918622dc14b1c0151dbd94d9a4543e7cd03e536b1b1d2d6d99af535d227ab9bdac76af9312a21d457bdf817e6" />
+		</pubTokenMapping>
+	</strongNames>
+
+	<system.web>
+		<webServices>
+			<protocols>
+				<add name="HttpSoap"/>
+				<add name="HttpSoap12"/>
+				<add name="HttpPost"/>
+				<add name="HttpGet"/>
+				<add name="Documentation"/>
+			</protocols>
+			<conformanceWarnings>
+				<add name="BasicProfile1_1"/>
+			</conformanceWarnings>
+			<wsdlHelpGenerator href="DefaultWsdlHelpGenerator.aspx" />
+		</webServices>
+
+		<membership>
+			<providers>
+				<add name="AspNetSqlMembershipProvider" type="System.Web.Security.SqlMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" connectionStringName="LocalSqlServer" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="true" applicationName="/" requiresUniqueEmail="false" passwordFormat="Hashed" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="7" minRequiredNonalphanumericCharacters="1" passwordAttemptWindow="10" passwordStrengthRegularExpression=""/>
+				<!-- <add name="AspNetSqlMembershipProvider" type="Mainsoft.Web.Security.GenericMembershipProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</membership>
+
+		<roleManager>
+			<providers>
+				<add name="AspNetSqlRoleProvider" type="System.Web.Security.SqlRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" applicationName="/" connectionStringName="LocalSqlServer" />
+				<!-- <add name="AspNetSqlRoleProvider" type="Mainsoft.Web.Security.GenericRoleProvider, Mainsoft.Web.Security" applicationName="/" connectionStringName="LocalSqlServer" /> -->
+			</providers>
+		</roleManager>
+
+		<profile>
+        		<providers>
+            		    <add name="AspNetSqlProfileProvider" connectionStringName="LocalSqlServer" applicationName="/" type="System.Web.Profile.SqlProfileProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		        </providers>
+    		</profile>
+	</system.web>
+
+
+	<system.serviceModel>
+		<extensions>
+			<behaviorExtensions>
+				<add name="enableWebScript" type="System.ServiceModel.Configuration.WebScriptEnablingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttp" type="System.ServiceModel.Configuration.WebHttpElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</behaviorExtensions>
+			<bindingElementExtensions>
+				<add name="webMessageEncoding" type="System.ServiceModel.Configuration.WebMessageEncodingElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingElementExtensions>
+			<bindingExtensions>
+				<add name="webHttpBinding" type="System.ServiceModel.Configuration.WebHttpBindingCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</bindingExtensions>
+			<endpointExtensions>
+				<add name="dynamicEndpoint" type="System.ServiceModel.Discovery.Configuration.DynamicEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="discoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.DiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpDiscoveryEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpDiscoveryEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="announcementEndpoint" type="System.ServiceModel.Discovery.Configuration.AnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="udpAnnouncementEndpoint" type="System.ServiceModel.Discovery.Configuration.UdpAnnouncementEndpointCollectionElement, System.ServiceModel.Discovery, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webHttpEndpoint" type="System.ServiceModel.Configuration.WebHttpEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add name="webScriptEndpoint" type="System.ServiceModel.Configuration.WebScriptEndpointCollectionElement, System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+			</endpointExtensions>
+		</extensions>
+	</system.serviceModel>
+</configuration>
+
+
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/settings.map b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/settings.map
new file mode 100644
index 0000000000000000000000000000000000000000..4c53aca6776656859d35fb7b7e6a64a5d54bfc6a
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/settings.map
@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<settingsMap>
+  <map sectionType="System.Web.Configuration.MembershipSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.MembershipSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlMembershipProvider" 
+	       type="System.Web.Security.SqliteMembershipProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+
+  <map sectionType="System.Web.Configuration.RoleManagerSection, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       mapperType="Mono.Web.Util.RoleManagerSectionMapper, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+       platform="Unix">
+
+    <!-- The 'what' tag specifies which region of the section to modify. The 'value' attribute value is mapper-specific and is not defined here. It can be
+         any expression understood by the mapper to designate the section region to modify.
+    -->
+    <what value="providers">
+      <!-- 'what' can contain any number of occurrences of any three elements:
+              replace - replace the designated region
+	      add - add a new entry to the region
+	      clear - clear the region
+	      remove - remove the designatedregion
+
+              The attributes to any of the above are freeform and are not processed by the mapper manager. They are stored verbatim for the
+	      mapper to peruse.
+      -->
+      <replace name="AspNetSqlRoleProvider" 
+	       type="System.Web.Security.SqliteRoleProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" 
+	       connectionStringName="LocalSqliteServer" />
+    </what>
+  </map>
+</settingsMap>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/web.config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/web.config
new file mode 100644
index 0000000000000000000000000000000000000000..30524c17c3d929bb9bec1c6a564b07c63cb01251
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/4.5/web.config
@@ -0,0 +1,252 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<configuration>
+
+  <system.codedom>
+        <compilers>
+            <compiler language="c#;cs;csharp" extension=".cs" warningLevel="4" type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+            <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4" type="Microsoft.VisualBasic.VBCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+                <providerOption name="CompilerVersion" value="v4.0"/>
+                <providerOption name="OptionInfer" value="true"/>
+                <providerOption name="WarnAsError" value="false"/>
+            </compiler>
+        </compilers>
+  </system.codedom>
+
+	<system.web>
+		<monoSettings>
+			<compilersCompatibility>
+				<compiler language="c#;cs;csharp" extension=".cs" compilerOptions="/nowarn:0169"
+					  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</compilersCompatibility>
+		</monoSettings>
+		
+		<authorization>
+			<allow users="*" />
+		</authorization>
+		<httpHandlers>
+		  <add path="trace.axd" verb="*" type="System.Web.Handlers.TraceHandler" validate="True" />
+		  <add path="WebResource.axd" verb="GET" type="System.Web.Handlers.AssemblyResourceLoader" validate="True" />
+		  <add verb="*" path="*_AppService.axd" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.axd" verb="*" type="System.Web.HttpNotFoundHandler" validate="True" />
+		  <add path="*.aspx" verb="*" type="System.Web.UI.PageHandlerFactory" validate="True" />
+		  <add path="*.ashx" verb="*" type="System.Web.UI.SimpleHandlerFactory" validate="True" />
+		  <add path="*.asmx" verb="*" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False" />
+		  <add path="*.rem" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.soap" verb="*" type="System.Runtime.Remoting.Channels.Http.HttpRemotingHandlerFactory, System.Runtime.Remoting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" validate="False" />
+		  <add path="*.asax" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ascx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.master" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.skin" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.browser" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sitemap" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dll.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.exe.config" verb="GET,HEAD" type="System.Web.StaticFileHandler" validate="True" />
+		  <add path="*.config" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cs" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.csproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vbproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.webinfo" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.licx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resx" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.resources" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdb" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.vjsproj" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.java" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.jsl" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldb" verb="*" type="System.Web.HttpForbiddenHandler"  validate="True" />
+		  <add path="*.ad" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.dd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.cd" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.adprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.lddprototype" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdm" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.sdmDocument" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.mdf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.ldf" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.exclude" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <add path="*.refresh" verb="*" type="System.Web.HttpForbiddenHandler" validate="True" />
+		  <!--
+		  <add path="*.svc" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+                  <add verb="*" path="*.svc" type="System.ServiceModel.Channels.SvcHttpHandlerFactory, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+		  <add path="*.rules" verb="*" type="System.Web.HttpForbiddenHandler" validate="True"/>
+		  <!--
+		  <add path="*.xoml" verb="*" type="System.ServiceModel.Activation.HttpHandler, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  <add path="*.xamlx" verb="*" type="System.Xaml.Hosting.XamlHttpHandlerFactory, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="False"/>
+		  -->
+		  <add path="*" verb="GET,HEAD,POST" type="System.Web.DefaultHttpHandler" validate="True" />
+		  <add path="*" verb="*" type="System.Web.HttpMethodNotAllowedHandler" validate="True" />
+		</httpHandlers>
+		<httpModules>
+		  <add name="OutputCache" type="System.Web.Caching.OutputCacheModule" />
+		  <add name="Session" type="System.Web.SessionState.SessionStateModule" />
+		  <!--
+		  <add name="WindowsAuthentication" type="System.Web.Security.WindowsAuthenticationModule" />
+		  -->
+		  <add name="FormsAuthentication" type="System.Web.Security.FormsAuthenticationModule" />
+		  <!--
+		  <add name="PassportAuthentication" type="System.Web.Security.PassportAuthenticationModule" />
+		  -->
+		  <add name="RoleManager" type="System.Web.Security.RoleManagerModule" />
+		  <add name="UrlAuthorization" type="System.Web.Security.UrlAuthorizationModule" />
+		  <!--
+		  <add name="FileAuthorization" type="System.Web.Security.FileAuthorizationModule" />
+		  -->
+		  <add name="AnonymousIdentification" type="System.Web.Security.AnonymousIdentificationModule" />
+		  <add name="Profile" type="System.Web.Profile.ProfileModule" />
+		  <!--
+		  <add name="ErrorHandlerModule" type="System.Web.Mobile.ErrorHandlerModule, System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+		  <add name="ServiceModel" type="System.ServiceModel.Activation.HttpModule, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+		  -->
+		  <add name="UrlRoutingModule-4.0" type="System.Web.Routing.UrlRoutingModule" />
+		  <add name="ScriptModule-4.0" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+		</httpModules>
+		<authentication mode="Forms">
+			<forms name=".MONOAUTH" loginUrl="login.aspx" protection="All" timeout="30" path="/">
+				<credentials passwordFormat="Clear">					
+				</credentials>
+			</forms>
+		</authentication>
+		<machineKey validationKey="AutoGenerate" decryptionKey="AutoGenerate" validation="SHA1" />
+		<globalization  requestEncoding="utf-8"
+				responseEncoding="utf-8"
+				fileEncoding="utf-8"/>
+		<!--
+				culture="en-US"
+				uiculture="en-US" />
+		-->
+		<sessionState mode="InProc" />
+		<pages>
+        		<namespaces>
+            			<add namespace="System" />
+            			<add namespace="System.Collections" />
+            			<add namespace="System.Collections.Specialized" />
+            			<add namespace="System.Configuration" />
+            			<add namespace="System.Text" />
+            			<add namespace="System.Text.RegularExpressions" />
+            			<add namespace="System.Web" />
+            			<add namespace="System.Web.Caching" />
+            			<add namespace="System.Web.SessionState" />
+            			<add namespace="System.Web.Security" />
+            			<add namespace="System.Web.Profile" />
+            			<add namespace="System.Web.UI" />
+            			<add namespace="System.Web.UI.WebControls" />
+            			<!-- <add namespace="System.Web.UI.WebControls.WebParts" /> -->
+            			<add namespace="System.Web.UI.HtmlControls" />
+        		</namespaces>
+        		
+        		<controls>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.WebParts" assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls.Expressions" assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.DynamicData" assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+			</controls>
+    		</pages>
+		<webControls clientScriptsLocation="/web_scripts" />
+		<compilation debug="false" defaultLanguage="c#" explicit="true" strict="false" >
+			<assemblies>
+				<!-- <add assembly="mscorlib" /> -->
+				<add assembly="Microsoft.CSharp, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Configuration, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Data, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Services, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.Xml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add assembly="System.EnterpriseServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<!-- <add assembly="System.Web.Mobile, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" /> -->
+				<add assembly="System.IdentityModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Runtime.Serialization, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xaml, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Channels, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.ServiceModel.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.ServiceModel.Activities, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<!-- <add assembly="System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> -->
+				<add assembly="System.Core, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Web.Extensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="System.Data.DataSetExtensions, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.Xml.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<add assembly="System.ComponentModel.DataAnnotations, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add assembly="System.Web.DynamicData, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<!-- <add assembly="System.Data.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<!-- <add assembly="System.Web.Entity, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> -->
+				<add assembly="System.Data.Linq, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" />
+				<!-- <add assembly="System.Data.Entity.Design, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" /> -->
+				<add assembly="System.Web.ApplicationServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add assembly="*" /> <!-- Add assemblies in bin directory -->
+			</assemblies>
+			<expressionBuilders>
+				<add expressionPrefix="Resources"
+				     type="System.Web.Compilation.ResourceExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="ConnectionStrings"
+				     type="System.Web.Compilation.ConnectionStringsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="AppSettings"
+				     type="System.Web.Compilation.AppSettingsExpressionBuilder, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+				<add expressionPrefix="RouteUrl" type="System.Web.Compilation.RouteUrlExpressionBuilder"/>
+				<!--
+				<add expressionPrefix="RouteValue" type="System.Web.Compilation.RouteValueExpressionBuilder"/>
+				-->
+			</expressionBuilders>
+			<buildProviders>
+				<add extension=".aspx" type="System.Web.Compilation.PageBuildProvider" />
+				<add extension=".ascx" type="System.Web.Compilation.UserControlBuildProvider" />
+				<add extension=".master" type="System.Web.Compilation.MasterPageBuildProvider" />
+				<add extension=".asmx" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".ashx" type="System.Web.Compilation.WebHandlerBuildProvider" />
+				<add extension=".soap" type="System.Web.Compilation.WebServiceBuildProvider" />
+				<add extension=".resx" type="System.Web.Compilation.ResXBuildProvider" />
+				<add extension=".resources" type="System.Web.Compilation.ResourcesBuildProvider" />
+				<add extension=".wsdl" type="System.Web.Compilation.WsdlBuildProvider" />
+				<add extension=".xsd" type="System.Web.Compilation.XsdBuildProvider" />
+				<add extension=".js" type="System.Web.Compilation.ForceCopyBuildProvider" />
+				<add extension=".lic" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".licx" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".exclude" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<add extension=".refresh" type="System.Web.Compilation.IgnoreFileBuildProvider" />
+				<!--
+				<add extension=".edmx" type="System.Data.Entity.Design.AspNet.EntityDesignerBuildProvider" />
+				<add extension=".xoml" type="System.ServiceModel.Activation.WorkflowServiceBuildProvider, System.WorkflowServices, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+				<add extension=".svc" type="System.ServiceModel.Activation.ServiceBuildProvider, System.ServiceModel.Activation, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				<add extension=".xamlx" type="System.Xaml.Hosting.XamlBuildProvider, System.Xaml.Hosting, Version=4.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" />
+				-->
+			</buildProviders>
+		</compilation>
+		<httpRuntime executionTimeout="110"
+			     maxRequestLength="4096"
+			     useFullyQualifiedRedirectUrl="false"
+			     minFreeThreads="8"
+			     minLocalRequestFreeThreads="4"
+			     appRequestQueueLimit="5000" />
+		<clientTarget>
+			<add alias="ie5" userAgent="Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 4.0)" />
+			<add alias="ie4" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="uplevel" userAgent="Mozilla/4.0 (compatible; MSIE 4.0; Windows NT 4.0)" />
+			<add alias="downlevel" userAgent="Unknown" />
+		</clientTarget>
+
+		<siteMap>
+			<providers>
+				<add name="AspNetXmlSiteMapProvider"
+				 description="Default site map provider that reads in .sitemap xml files."
+				 type="System.Web.XmlSiteMapProvider, System.Web, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a"
+				 siteMapFile="Web.sitemap" />
+			</providers>
+		</siteMap>
+	</system.web>
+
+</configuration>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/browscap.ini b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/browscap.ini
new file mode 100644
index 0000000000000000000000000000000000000000..1267e1deb4061a8250ad25125fc7d20fd2ae04ac
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/browscap.ini
@@ -0,0 +1,16979 @@
+;;; Provided courtesy of http://browsers.garykeith.com
+;;; Created on Wednesday, June 17, 2009 at 6:30 AM GMT
+
+[GJK_Browscap_Version]
+Version=4476
+Released=Wed, 17 Jun 2009 06:30:21 -0000
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DefaultProperties
+
+[DefaultProperties]
+Browser=DefaultProperties
+Version=0
+MajorVer=0
+MinorVer=0
+Platform=unknown
+Alpha=false
+Beta=false
+Win16=false
+Win32=false
+Win64=false
+Frames=false
+IFrames=false
+Tables=false
+Cookies=false
+BackgroundSounds=false
+CDF=false
+VBScript=false
+JavaApplets=false
+JavaScript=false
+ActiveXControls=false
+isBanned=false
+isMobileDevice=false
+isSyndicationReader=false
+Crawler=false
+CssVersion=0
+supportsCSS=false
+AOL=false
+aolVersion=0
+ECMAScriptVersion=0.0
+W3CDOMVersion=0.0
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Ask
+
+[Ask]
+Parent=DefaultProperties
+Browser=Ask
+Frames=true
+Tables=true
+Crawler=true
+
+[Mozilla/?.0 (compatible; Ask Jeeves/Teoma*)]
+Parent=Ask
+Browser=Teoma
+
+[Mozilla/2.0 (compatible; Ask Jeeves)]
+Parent=Ask
+Browser=AskJeeves
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Baidu
+
+[Baidu]
+Parent=DefaultProperties
+Browser=Baidu
+Frames=true
+Tables=true
+Crawler=true
+
+[BaiduImageSpider*]
+Parent=Baidu
+Browser=BaiduImageSpider
+
+[Baiduspider*]
+Parent=Baidu
+Browser=BaiDu
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Google
+
+[Google]
+Parent=DefaultProperties
+Browser=Google
+Frames=true
+IFrames=true
+Tables=true
+JavaScript=true
+Crawler=true
+
+[* (compatible; Googlebot-Mobile/2.1; *http://www.google.com/bot.html)]
+Parent=Google
+Browser=Googlebot-Mobile
+Frames=false
+IFrames=false
+Tables=false
+
+[*Google Wireless Transcoder*]
+Parent=Google
+Browser=Google Wireless Transcoder
+
+[AdsBot-Google (?http://www.google.com/adsbot.html)]
+Parent=Google
+Browser=AdsBot-Google
+
+[Feedfetcher-Google-iGoogleGadgets;*]
+Parent=Google
+Browser=iGoogleGadgets
+isBanned=true
+isSyndicationReader=true
+
+[Feedfetcher-Google;*]
+Parent=Google
+Browser=Feedfetcher-Google
+isBanned=true
+isSyndicationReader=true
+
+[Google OpenSocial agent (http://www.google.com/feedfetcher.html)]
+Parent=Google
+Browser=Google OpenSocial
+
+[Google-Site-Verification/1.0]
+Parent=Google
+Browser=Google-Site-Verification
+
+[Google-Sitemaps/*]
+Parent=Google
+Browser=Google-Sitemaps
+
+[Googlebot-Image/*]
+Parent=Google
+Browser=Googlebot-Image
+CDF=true
+
+[googlebot-urlconsole]
+Parent=Google
+Browser=googlebot-urlconsole
+
+[Googlebot-Video/1.0]
+Parent=Google
+Browser=Google-Video
+
+[Googlebot/2.1 (?http://www.google.com/bot.html)]
+Parent=Google
+Browser=Googlebot
+
+[Googlebot/2.1 (?http://www.googlebot.com/bot.html)]
+Parent=Google
+Browser=Googlebot
+
+[Googlebot/Test*]
+Parent=Google
+Browser=Googlebot/Test
+
+[gsa-crawler*]
+Parent=Google
+Browser=Google Search Appliance
+isBanned=true
+
+[Mediapartners-Google*]
+Parent=Google
+Browser=Mediapartners-Google
+
+[Mozilla/4.0 (compatible; Google Desktop)]
+Parent=Google
+Browser=Google Desktop
+
+[Mozilla/4.0 (compatible; GoogleToolbar*)]
+Parent=Google
+Browser=Google Toolbar
+isBanned=true
+
+[Mozilla/5.0 (compatible; Google Keyword Tool;*)]
+Parent=Google
+Browser=Google Keyword Tool
+
+[Mozilla/5.0 (compatible; Googlebot/2.1; ?http://www.google.com/bot.html)]
+Parent=Google
+Browser=Google Webmaster Tools
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Inktomi
+
+[Inktomi]
+Parent=DefaultProperties
+Browser=Inktomi
+Frames=true
+Tables=true
+Crawler=true
+
+[* (compatible;YahooSeeker/M1A1-R2D2; *)]
+Parent=Inktomi
+Browser=YahooSeeker-Mobile
+Frames=false
+Tables=false
+
+[Mozilla/4.0]
+Parent=Inktomi
+
+[Mozilla/4.0 (compatible; MSIE 5.0; Windows NT)]
+Parent=Inktomi
+Win32=true
+
+[Mozilla/4.0 (compatible; Yahoo Japan; for robot study; kasugiya)]
+Parent=Inktomi
+Browser=Yahoo! RobotStudy
+isBanned=true
+
+[Mozilla/5.0 (compatible; BMC/1.0 (Y!J-AGENT))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMC
+
+[Mozilla/5.0 (compatible; BMF/1.0 (Y!J-AGENT))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMF
+
+[Mozilla/5.0 (compatible; BMI/1.0 (Y!J-AGENT; 1.0))]
+Parent=Inktomi
+Browser=Y!J-AGENT/BMI
+
+[Mozilla/5.0 (compatible; Yahoo! DE Slurp; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Directory Engine
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp China; http://misc.yahoo.com.cn/help.html)]
+Parent=Inktomi
+Browser=Yahoo! Slurp China
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp/3.0; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Slurp
+Version=3.0
+MajorVer=3
+MinorVer=0
+
+[Mozilla/5.0 (compatible; Yahoo! Slurp; http://help.yahoo.com/help/us/ysearch/slurp)]
+Parent=Inktomi
+Browser=Yahoo! Slurp
+
+[Mozilla/5.0 (compatible; Yahoo! Verifier/1.1)]
+Parent=Inktomi
+Browser=Yahoo! Verifier
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[Mozilla/5.0 (Slurp/cat; slurp@inktomi.com; http://www.inktomi.com/slurp.html)]
+Parent=Inktomi
+Browser=Slurp/cat
+
+[Mozilla/5.0 (Slurp/si; slurp@inktomi.com; http://www.inktomi.com/slurp.html)]
+Parent=Inktomi
+
+[Mozilla/5.0 (Yahoo-MMCrawler/4.0; mailto:vertical-crawl-support@yahoo-inc.com)]
+Parent=Inktomi
+Browser=Yahoo-MMCrawler
+Version=4.0
+MajorVer=4
+MinorVer=0
+
+[Scooter/*]
+Parent=Inktomi
+Browser=Scooter
+
+[Scooter/3.3Y!CrawlX]
+Parent=Inktomi
+Browser=Scooter/3.3Y!CrawlX
+Version=3.3
+MajorVer=3
+MinorVer=3
+
+[slurp]
+Parent=Inktomi
+Browser=slurp
+
+[Y!J-BSC/1.0*]
+Parent=Inktomi
+Browser=Y!J-BSC
+Version=1.0
+MajorVer=1
+MinorVer=0
+isBanned=true
+
+[Y!J-SRD/1.0]
+Parent=Inktomi
+Browser=Y!J-SRD
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Yahoo Mindset]
+Parent=Inktomi
+Browser=Yahoo Mindset
+
+[Yahoo Pipes*]
+Parent=Inktomi
+Browser=Yahoo Pipes
+
+[Yahoo! Mindset]
+Parent=Inktomi
+Browser=Yahoo! Mindset
+
+[Yahoo! Slurp/Site Explorer]
+Parent=Inktomi
+Browser=Yahoo! Site Explorer
+
+[Yahoo-Blogs/*]
+Parent=Inktomi
+Browser=Yahoo-Blogs
+
+[Yahoo-MMAudVid*]
+Parent=Inktomi
+Browser=Yahoo-MMAudVid
+
+[Yahoo-MMCrawler*]
+Parent=Inktomi
+Browser=Yahoo-MMCrawler
+isBanned=true
+
+[YahooFeedSeeker*]
+Parent=Inktomi
+Browser=YahooFeedSeeker
+isSyndicationReader=true
+Crawler=false
+
+[YahooSeeker/*]
+Parent=Inktomi
+Browser=YahooSeeker
+isMobileDevice=true
+
+[YahooSeeker/CafeKelsa (compatible; Konqueror/3.2; FreeBSD*) (KHTML, like Gecko)]
+Parent=Inktomi
+Browser=YahooSeeker/CafeKelsa
+
+[YahooSeeker/CafeKelsa-dev (compatible; Konqueror/3.2; FreeBSD*) (KHTML, like Gecko)]
+Parent=Inktomi
+
+[YahooVideoSearch*]
+Parent=Inktomi
+Browser=YahooVideoSearch
+
+[YahooYSMcm*]
+Parent=Inktomi
+Browser=YahooYSMcm
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; MSN
+
+[MSN]
+Parent=DefaultProperties
+Browser=MSN
+Frames=true
+Tables=true
+Crawler=true
+
+[adidxbot/1.1 (?http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=adidxbot
+
+[librabot/1.0 (*)]
+Parent=MSN
+Browser=librabot
+
+[llssbot/1.0]
+Parent=MSN
+Browser=llssbot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[MSMOBOT/1.1*]
+Parent=MSN
+Browser=msnbot-mobile
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[MSNBot-Academic/1.0*]
+Parent=MSN
+Browser=MSNBot-Academic
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot-media/1.0*]
+Parent=MSN
+Browser=msnbot-media
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot-media/1.1*]
+Parent=MSN
+Browser=msnbot-media
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[MSNBot-News/1.0*]
+Parent=MSN
+Browser=MSNBot-News
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[MSNBot-NewsBlogs/1.0*]
+Parent=MSN
+Browser=MSNBot-NewsBlogs
+Version=1
+MajorVer=1
+MinorVer=0
+
+[msnbot-products]
+Parent=MSN
+Browser=msnbot-products
+
+[msnbot-webmaster/1.0 (*http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=msnbot-webmaster tools
+
+[msnbot/1.0*]
+Parent=MSN
+Browser=msnbot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[msnbot/1.1*]
+Parent=MSN
+Browser=msnbot
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[msnbot/2.0b*]
+Parent=MSN
+Version=2.0
+MajorVer=2
+MinorVer=0
+Beta=true
+
+[MSR-ISRCCrawler]
+Parent=MSN
+Browser=MSR-ISRCCrawler
+
+[renlifangbot/1.0 (?http://search.msn.com/msnbot.htm)]
+Parent=MSN
+Browser=renlifangbot
+
+[T-Mobile Dash Mozilla/4.0 (*) MSNBOT-MOBILE/1.1 (*)]
+Parent=MSN
+Browser=msnbot-mobile
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Yahoo
+
+[Yahoo]
+Parent=DefaultProperties
+Browser=Yahoo
+Frames=true
+Tables=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; Y!J; for robot study*)]
+Parent=Yahoo
+Browser=Y!J
+
+[Mozilla/5.0 (Yahoo-Test/4.0*)]
+Parent=Yahoo
+Browser=Yahoo-Test
+Version=4.0
+MajorVer=4
+MinorVer=0
+
+[mp3Spider cn-search-devel at yahoo-inc dot com]
+Parent=Yahoo
+Browser=Yahoo! Media
+isBanned=true
+
+[My Browser]
+Parent=Yahoo
+Browser=Yahoo! My Browser
+
+[Y!OASIS/*]
+Parent=Yahoo
+Browser=Y!OASIS
+isBanned=true
+
+[YahooYSMcm/2.0.0]
+Parent=Yahoo
+Browser=YahooYSMcm
+Version=2.0
+MajorVer=2
+MinorVer=0
+isBanned=true
+
+[YRL_ODP_CRAWLER]
+Parent=Yahoo
+Browser=YRL_ODP_CRAWLER
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Yandex
+
+[Yandex]
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+Browser=Yandex
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; MSIE 5.0; YANDEX)]
+Parent=Yandex
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.9) Gecko VisualParser/3.0]
+Parent=Yandex
+Browser=VisualParser
+isBanned=true
+
+[YaDirectBot/*]
+Parent=Yandex
+Browser=YaDirectBot
+
+[Yandex/*]
+Parent=Yandex
+
+[YandexBlog/*]
+Parent=Yandex
+Browser=YandexBlog
+isSyndicationReader=true
+
+[YandexSomething/*]
+Parent=Yandex
+Browser=YandexSomething
+isSyndicationReader=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Best of the Web
+
+[Best of the Web]
+Parent=DefaultProperties
+Browser=Best of the Web
+Frames=true
+Tables=true
+
+[Mozilla/4.0 (compatible; BOTW Feed Grabber; *http://botw.org)]
+Parent=Best of the Web
+Browser=BOTW Feed Grabber
+isSyndicationReader=true
+Crawler=false
+
+[Mozilla/4.0 (compatible; BOTW Spider; *http://botw.org)]
+Parent=Best of the Web
+Browser=BOTW Spider
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Boitho
+
+[Boitho]
+Parent=DefaultProperties
+Browser=Boitho
+Frames=true
+Tables=true
+Crawler=true
+
+[boitho.com-dc/*]
+Parent=Boitho
+Browser=boitho.com-dc
+
+[boitho.com-robot/*]
+Parent=Boitho
+Browser=boitho.com-robot
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Convera
+
+[Convera]
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+Browser=Convera
+Frames=true
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+Crawler=true
+
+[ConveraCrawler/*]
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+Browser=ConveraCrawler
+
+[ConveraMultiMediaCrawler/0.1*]
+Parent=Convera
+Browser=ConveraMultiMediaCrawler
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+
+[CrawlConvera*]
+Parent=Convera
+Browser=CrawlConvera
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DotBot
+
+[DotBot]
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+Frames=true
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+isBanned=true
+Crawler=true
+
+[DotBot/* (http://www.dotnetdotcom.org/*)]
+Parent=DotBot
+
+[Mozilla/5.0 (compatible; DotBot/*; http://www.dotnetdotcom.org/*)]
+Parent=DotBot
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Entireweb
+
+[Entireweb]
+Parent=DefaultProperties
+Browser=Entireweb
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Mozilla/4.0 (compatible; SpeedySpider; www.entireweb.com)]
+Parent=Entireweb
+
+[Speedy Spider (*Beta/*)]
+Parent=Entireweb
+
+[Speedy?Spider?(http://www.entireweb.com*)]
+Parent=Entireweb
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Envolk
+
+[Envolk]
+Parent=DefaultProperties
+Browser=Envolk
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[envolk/* (?http://www.envolk.com/envolk*)]
+Parent=Envolk
+
+[envolk?ITS?spider/* (?http://www.envolk.com/envolk*)]
+Parent=Envolk
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Exalead
+
+[Exalead]
+Parent=DefaultProperties
+Browser=Exalead
+Frames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Exabot-Images/1.0]
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+Version=1.0
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+
+[Exabot-Test/*]
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+
+[Exabot/2.0]
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+Browser=Exabot
+
+[Exabot/3.0]
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+Browser=Exabot
+Version=3.0
+MajorVer=3
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+Platform=Liberate
+
+[Exalead NG/*]
+Parent=Exalead
+Browser=Exalead NG
+isBanned=true
+
+[Mozilla/5.0 (compatible; Exabot-Images/3.0;*)]
+Parent=Exalead
+Browser=Exabot-Images
+
+[Mozilla/5.0 (compatible; Exabot/3.0 (BiggerBetter/tests);*)]
+Parent=Exalead
+Browser=Exabot/BiggerBetter/tests
+
+[Mozilla/5.0 (compatible; Exabot/3.0;*)]
+Parent=Exalead
+Browser=Exabot
+isBanned=false
+
+[Mozilla/5.0 (compatible; NGBot/*)]
+Parent=Exalead
+
+[ng/*]
+Parent=Exalead
+Browser=Exalead Previewer
+Version=1.0
+MajorVer=1
+MinorVer=0
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Fast/AllTheWeb
+
+[Fast/AllTheWeb]
+Parent=DefaultProperties
+Browser=Fast/AllTheWeb
+Alpha=true
+Beta=true
+Win16=true
+Win32=true
+Win64=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+CDF=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+isBanned=true
+isMobileDevice=true
+isSyndicationReader=true
+Crawler=true
+
+[*FAST Enterprise Crawler*]
+Parent=Fast/AllTheWeb
+Browser=FAST Enterprise Crawler
+
+[FAST Data Search Document Retriever/4.0*]
+Parent=Fast/AllTheWeb
+Browser=FAST Data Search Document Retriever
+
+[FAST MetaWeb Crawler (helpdesk at fastsearch dot com)]
+Parent=Fast/AllTheWeb
+Browser=FAST MetaWeb Crawler
+
+[Fast PartnerSite Crawler*]
+Parent=Fast/AllTheWeb
+Browser=FAST PartnerSite
+
+[FAST-WebCrawler/*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler
+
+[FAST-WebCrawler/*/FirstPage*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/FirstPage
+
+[FAST-WebCrawler/*/Fresh*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/Fresh
+
+[FAST-WebCrawler/*/PartnerSite*]
+Parent=Fast/AllTheWeb
+Browser=FAST PartnerSite
+
+[FAST-WebCrawler/*?Multimedia*]
+Parent=Fast/AllTheWeb
+Browser=FAST-WebCrawler/Multimedia
+
+[FastSearch Web Crawler for*]
+Parent=Fast/AllTheWeb
+Browser=FastSearch Web Crawler
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Gigabot
+
+[Gigabot]
+Parent=DefaultProperties
+Browser=Gigabot
+Frames=true
+IFrames=true
+Tables=true
+Crawler=true
+
+[Gigabot*]
+Parent=Gigabot
+
+[GigabotSiteSearch/*]
+Parent=Gigabot
+Browser=GigabotSiteSearch
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Ilse
+
+[Ilse]
+Parent=DefaultProperties
+Browser=Ilse
+Frames=true
+Tables=true
+Crawler=true
+
+[IlseBot/*]
+Parent=Ilse
+
+[INGRID/?.0*]
+Parent=Ilse
+Browser=Ilse
+
+[Mozilla/3.0 (INGRID/*]
+Parent=Ilse
+Browser=Ilse
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iVia Project
+
+[iVia Project]
+Parent=DefaultProperties
+Browser=iVia Project
+Frames=true
+IFrames=true
+Tables=true
+Crawler=true
+
+[DataFountains/DMOZ Downloader*]
+Parent=iVia Project
+Browser=DataFountains/DMOZ Downloader
+isBanned=true
+
+[DataFountains/DMOZ Feature Vector Corpus Creator*]
+Parent=iVia Project
+Browser=DataFountains/DMOZ Feature Vector Corpus
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Jayde Online
+
+[Jayde Online]
+Parent=DefaultProperties
+Browser=Jayde Online
+Frames=true
+Tables=true
+Crawler=true
+
+[ExactSeek Crawler/*]
+Parent=Jayde Online
+Browser=ExactSeek Crawler
+
+[exactseek-pagereaper-* (crawler@exactseek.com)]
+Parent=Jayde Online
+Browser=exactseek-pagereaper
+isBanned=true
+
+[exactseek.com]
+Parent=Jayde Online
+Browser=exactseek.com
+
+[Jayde Crawler*]
+Parent=Jayde Online
+Browser=Jayde Crawler
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Lycos
+
+[Lycos]
+Parent=DefaultProperties
+Browser=Lycos
+Frames=true
+Tables=true
+Crawler=true
+
+[Lycos*]
+Parent=Lycos
+Browser=Lycos
+
+[Lycos-Proxy]
+Parent=Lycos
+Browser=Lycos-Proxy
+
+[Lycos-Spider_(modspider)]
+Parent=Lycos
+Browser=Lycos-Spider_(modspider)
+
+[Lycos-Spider_(T-Rex)]
+Parent=Lycos
+Browser=Lycos-Spider_(T-Rex)
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Naver
+
+[Naver]
+Parent=DefaultProperties
+Browser=Naver
+isBanned=true
+Crawler=true
+
+[Cowbot-* (NHN Corp*naver.com)]
+Parent=Naver
+Browser=Naver Cowbot
+
+[Mozilla/4.0 (compatible; NaverBot/*; *)]
+Parent=Naver
+
+[Mozilla/4.0 (compatible; NaverBot/*; nhnbot@naver.com)]
+Parent=Naver
+Browser=Naver NaverBot
+
+[NaverBot-* (NHN Corp*naver.com)]
+Parent=Naver
+Browser=Naver NHN Corp
+
+[Yeti/*]
+Parent=Naver
+Browser=Yeti
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Snap
+
+[Snap]
+Parent=DefaultProperties
+Browser=Snap
+isBanned=true
+Crawler=true
+
+[Mozilla/5.0 (SnapPreviewBot) Gecko/* Firefox/*]
+Parent=Snap
+
+[Snapbot/*]
+Parent=Snap
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Sogou
+
+[Sogou]
+Parent=DefaultProperties
+Browser=Sogou
+Frames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[shaboyi spider]
+Parent=Sogou
+Browser=Sogou/Shaboyi Spider
+
+[Sogou develop spider/*]
+Parent=Sogou
+Browser=Sogou Develop Spider
+
+[Sogou head spider*]
+Parent=Sogou
+Browser=Sogou/HEAD Spider
+
+[sogou js robot(*)]
+Parent=Sogou
+
+[Sogou Orion spider/*]
+Parent=Sogou
+Browser=Sogou Orion spider
+
+[Sogou Pic Agent]
+Parent=Sogou
+Browser=Sogou/Image Crawler
+
+[Sogou Pic Spider]
+Parent=Sogou
+Browser=Sogou Pic Spider
+
+[Sogou Push Spider/*]
+Parent=Sogou
+Browser=Sogou Push Spider
+
+[sogou spider]
+Parent=Sogou
+Browser=Sogou/Spider
+
+[sogou web spider*]
+Parent=Sogou
+Browser=sogou web spider
+
+[Sogou-Test-Spider/*]
+Parent=Sogou
+Browser=Sogou-Test-Spider
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; YodaoBot
+
+[YodaoBot]
+Parent=DefaultProperties
+Browser=YodaoBot
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[Mozilla/5.0 (compatible; YodaoBot/1.*)]
+Parent=YodaoBot
+
+[Mozilla/5.0 (compatible;YodaoBot-Image/1.*)]
+Parent=YodaoBot
+Browser=YodaoBot-Image
+
+[WAP_Browser/5.0 (compatible; YodaoBot/1.*)]
+Parent=YodaoBot
+
+[YodaoBot/1.* (*)]
+Parent=YodaoBot
+
+[Best Whois (http://www.bestwhois.net/)]
+Parent=DNS Tools
+Browser=Best Whois
+
+[DNSGroup/*]
+Parent=DNS Tools
+Browser=DNS Group Crawler
+
+[NG-Search/*]
+Parent=Exalead
+Browser=NG-SearchBot
+
+[TouchStone]
+Parent=Feeds Syndicators
+Browser=TouchStone
+isSyndicationReader=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; General Crawlers
+
+[General Crawlers]
+Parent=DefaultProperties
+Browser=General Crawlers
+Crawler=true
+
+[A .NET Web Crawler]
+Parent=General Crawlers
+isBanned=true
+
+[BabalooSpider/1.*]
+Parent=General Crawlers
+Browser=BabalooSpider
+
+[BilgiBot/*]
+Parent=General Crawlers
+Browser=BilgiBot
+isBanned=true
+
+[bot/* (bot; *bot@bot.bot)]
+Parent=General Crawlers
+Browser=bot
+isBanned=true
+
+[CyberPatrol*]
+Parent=General Crawlers
+Browser=CyberPatrol
+isBanned=true
+
+[Cynthia 1.0]
+Parent=General Crawlers
+Browser=Cynthia
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[ddetailsbot (http://www.displaydetails.com)]
+Parent=General Crawlers
+Browser=ddetailsbot
+
+[DomainCrawler/1.0 (info@domaincrawler.com; http://www.domaincrawler.com/domains/view/*)]
+Parent=General Crawlers
+Browser=DomainCrawler
+
+[DomainsBotBot/1.*]
+Parent=General Crawlers
+Browser=DomainsBotBot
+isBanned=true
+
+[DomainsDB.net MetaCrawler*]
+Parent=General Crawlers
+Browser=DomainsDB
+
+[Drupal (*)]
+Parent=General Crawlers
+Browser=Drupal
+
+[Dumbot (version *)*]
+Parent=General Crawlers
+Browser=Dumbfind
+
+[EuripBot/*]
+Parent=General Crawlers
+Browser=Europe Internet Portal
+
+[eventax/*]
+Parent=General Crawlers
+Browser=eventax
+
+[FANGCrawl/*]
+Parent=General Crawlers
+Browser=Safe-t.net Web Filtering Service
+isBanned=true
+
+[favorstarbot/*]
+Parent=General Crawlers
+Browser=favorstarbot
+isBanned=true
+
+[FollowSite.com (*)]
+Parent=General Crawlers
+Browser=FollowSite
+isBanned=true
+
+[Gaisbot*]
+Parent=General Crawlers
+Browser=Gaisbot
+
+[Healthbot/Health_and_Longevity_Project_(HealthHaven.com) ]
+Parent=General Crawlers
+Browser=Healthbot
+isBanned=true
+
+[hitcrawler_0.*]
+Parent=General Crawlers
+Browser=hitcrawler
+isBanned=true
+
+[htdig/*]
+Parent=General Crawlers
+Browser=ht://Dig
+
+[http://hilfe.acont.de/bot.html ACONTBOT]
+Parent=General Crawlers
+Browser=ACONTBOT
+isBanned=true
+
+[JetBrains*]
+Parent=General Crawlers
+Browser=Omea Pro
+
+[KakleBot - www.kakle.com/0.1]
+Parent=General Crawlers
+Browser=KakleBot
+
+[KBeeBot/0.*]
+Parent=General Crawlers
+Browser=KBeeBot
+isBanned=true
+
+[Keyword Density/*]
+Parent=General Crawlers
+Browser=Keyword Density
+
+[LetsCrawl.com/1.0*]
+Parent=General Crawlers
+Browser=LetsCrawl.com
+isBanned=true
+
+[Lincoln State Web Browser]
+Parent=General Crawlers
+Browser=Lincoln State Web Browser
+isBanned=true
+
+[Links4US-Crawler,*]
+Parent=General Crawlers
+Browser=Links4US-Crawler
+isBanned=true
+
+[Lorkyll *.* -- lorkyll@444.net]
+Parent=General Crawlers
+Browser=Lorkyll
+isBanned=true
+
+[Lsearch/sondeur]
+Parent=General Crawlers
+Browser=Lsearch/sondeur
+isBanned=true
+
+[LucidMedia ClickSense/4.?]
+Parent=General Crawlers
+Browser=LucidMedia-ClickSense
+isBanned=true
+
+[MapoftheInternet.com?(?http://MapoftheInternet.com)]
+Parent=General Crawlers
+Browser=MapoftheInternet
+isBanned=true
+
+[Marvin v0.3]
+Parent=General Crawlers
+Browser=MedHunt
+Version=0.3
+MajorVer=0
+MinorVer=3
+
+[masidani_bot_v0.6*]
+Parent=General Crawlers
+Browser=masidani_bot
+
+[Metaspinner/0.01 (Metaspinner; http://www.meta-spinner.de/; support@meta-spinner.de/)]
+Parent=General Crawlers
+Browser=Metaspinner/0.01
+Version=0.01
+MajorVer=0
+MinorVer=01
+
+[metatagsdir/*]
+Parent=General Crawlers
+Browser=metatagsdir
+isBanned=true
+
+[Microsoft Windows Network Diagnostics]
+Parent=General Crawlers
+Browser=Microsoft Windows Network Diagnostics
+isBanned=true
+
+[Miva (AlgoFeedback@miva.com)]
+Parent=General Crawlers
+Browser=Miva
+
+[moget/*]
+Parent=General Crawlers
+Browser=Goo
+
+[Mozdex/0.7.2*]
+Parent=General Crawlers
+Browser=Mozdex
+
+[Mozilla Compatible (MS IE 3.01 WinNT)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/* (compatible; WebCapture*)]
+Parent=General Crawlers
+Browser=WebCapture
+
+[Mozilla/4.0 (compatible; DepSpid/*)]
+Parent=General Crawlers
+Browser=DepSpid
+
+[Mozilla/4.0 (compatible; MSIE *; Windows NT *; SV1)]
+Parent=General Crawlers
+Browser=AVG
+
+[Mozilla/4.0 (compatible; MSIE 4.01; Vonna.com b o t)]
+Parent=General Crawlers
+Browser=Vonna.com
+isBanned=true
+
+[Mozilla/4.0 (compatible; MSIE 4.01; Windows95)]
+Parent=General Crawlers
+Win32=true
+
+[Mozilla/4.0 (compatible; MSIE 4.5; Windows 98; )]
+Parent=General Crawlers
+Win32=true
+
+[Mozilla/4.0 (compatible; MyFamilyBot/*)]
+Parent=General Crawlers
+Browser=MyFamilyBot
+
+[Mozilla/4.0 (compatible; N-Stealth)]
+Parent=General Crawlers
+Browser=N-Stealth
+
+[Mozilla/4.0 (compatible; Scumbot/*; Linux/*)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.0 (compatible; Spider; Linux)]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.0 (compatible; Win32)]
+Parent=General Crawlers
+Browser=Unknown Crawler
+isBanned=true
+
+[Mozilla/4.1]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/4.5]
+Parent=General Crawlers
+isBanned=true
+
+[Mozilla/5.0 (*http://gnomit.com/) Gecko/* Gnomit/1.0]
+Parent=General Crawlers
+Browser=Gnomit
+isBanned=true
+
+[Mozilla/5.0 (compatible; AboutUsBot/*)]
+Parent=General Crawlers
+Browser=AboutUsBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; BuzzRankingBot/*)]
+Parent=General Crawlers
+Browser=BuzzRankingBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; Diffbot/0.1;  http://www.diffbot.com)]
+Parent=General Crawlers
+Browser=Diffbot
+
+[Mozilla/5.0 (compatible; FirstSearchBot/1.0; *)]
+Parent=General Crawlers
+Browser=FirstSearchBot
+
+[mozilla/5.0 (compatible; genevabot  http://www.healthdash.com)]
+Parent=General Crawlers
+Browser=Healthdash
+
+[Mozilla/5.0 (compatible; JadynAveBot; *http://www.jadynave.com/robot*]
+Parent=General Crawlers
+Browser=JadynAveBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; Kyluka crawl; http://www.kyluka.com/crawl.html; crawl@kyluka.com)]
+Parent=General Crawlers
+Browser=Kyluka
+
+[Mozilla/5.0 (compatible; MJ12bot/v1.2.*; http://www.majestic12.co.uk/bot.php*)]
+Parent=General Crawlers
+Browser=MJ12bot
+Version=1.2
+MajorVer=1
+MinorVer=2
+
+[Mozilla/5.0 (compatible; MSIE 7.0 ?http://www.europarchive.org)]
+Parent=General Crawlers
+Browser=Europe Web Archive
+
+[Mozilla/5.0 (compatible; Seznam screenshot-generator 2.0;*)]
+Parent=General Crawlers
+Browser=Seznam screenshot-generator
+isBanned=true
+
+[Mozilla/5.0 (compatible; Twingly Recon; http://www.twingly.com/)]
+Parent=General Crawlers
+Browser=Twingly Recon
+
+[Mozilla/5.0 (compatible; unwrapbot/2.*;  http://www.unwrap.jp*)]
+Parent=General Crawlers
+Browser=UnWrap
+
+[Mozilla/5.0 (compatible; Vermut*)]
+Parent=General Crawlers
+Browser=Vermut
+
+[Mozilla/5.0 (compatible; Webbot/*)]
+Parent=General Crawlers
+Browser=Webbot.ru
+isBanned=true
+
+[n4p_bot*]
+Parent=General Crawlers
+Browser=n4p_bot
+
+[nabot*]
+Parent=General Crawlers
+Browser=Nabot
+
+[NetCarta_WebMapper/*]
+Parent=General Crawlers
+Browser=NetCarta_WebMapper
+isBanned=true
+
+[NetID.com Bot*]
+Parent=General Crawlers
+Browser=NetID.com Bot
+isBanned=true
+
+[neTVision AG andreas.heidoetting@thomson-webcast.net]
+Parent=General Crawlers
+Browser=neTVision
+
+[NextopiaBOT*]
+Parent=General Crawlers
+Browser=NextopiaBOT
+
+[nicebot]
+Parent=General Crawlers
+Browser=nicebot
+isBanned=true
+
+[niXXieBot?Foster*]
+Parent=General Crawlers
+Browser=niXXiebot-Foster
+
+[Nozilla/P.N (Just for IDS woring)]
+Parent=General Crawlers
+Browser=Nozilla/P.N
+isBanned=true
+
+[Nudelsalat/*]
+Parent=General Crawlers
+Browser=Nudelsalat
+isBanned=true
+
+[NV32ts]
+Parent=General Crawlers
+Browser=NV32ts
+isBanned=true
+
+[Ocelli/*]
+Parent=General Crawlers
+Browser=Ocelli
+
+[OpenTaggerBot (http://www.opentagger.com/opentaggerbot.htm)]
+Parent=General Crawlers
+Browser=OpenTaggerBot
+
+[Oracle Enterprise Search]
+Parent=General Crawlers
+Browser=Oracle Enterprise Search
+isBanned=true
+
+[Oracle Ultra Search]
+Parent=General Crawlers
+Browser=Oracle Ultra Search
+
+[Pajaczek/*]
+Parent=General Crawlers
+Browser=Pajaczek
+isBanned=true
+
+[panscient.com]
+Parent=General Crawlers
+Browser=panscient.com
+isBanned=true
+
+[Patwebbot (http://www.herz-power.de/technik.html)]
+Parent=General Crawlers
+Browser=Patwebbot
+
+[PDFBot (crawler@pdfind.com)]
+Parent=General Crawlers
+Browser=PDFBot
+
+[Pete-Spider/1.*]
+Parent=General Crawlers
+Browser=Pete-Spider
+isBanned=true
+
+[PhpDig/*]
+Parent=General Crawlers
+Browser=PhpDig
+
+[PlantyNet_WebRobot*]
+Parent=General Crawlers
+Browser=PlantyNet
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; PluckIt
+
+[PluckItCrawler/1.0 (*)]
+Parent=General Crawlers
+isMobileDevice=true
+
+[PMAFind]
+Parent=General Crawlers
+Browser=PMAFind
+isBanned=true
+
+[Poodle_predictor_1.0]
+Parent=General Crawlers
+Browser=Poodle Predictor
+
+[QuickFinder Crawler]
+Parent=General Crawlers
+Browser=QuickFinder
+isBanned=true
+
+[Radiation Retriever*]
+Parent=General Crawlers
+Browser=Radiation Retriever
+isBanned=true
+
+[RedCarpet/*]
+Parent=General Crawlers
+Browser=RedCarpet
+isBanned=true
+
+[RixBot (http://babelserver.org/rix)]
+Parent=General Crawlers
+Browser=RixBot
+
+[Rome Client (http://tinyurl.com/64t5n) Ver: 0.*]
+Parent=General Crawlers
+Browser=TinyURL
+
+[SBIder/*]
+Parent=General Crawlers
+Browser=SiteSell
+
+[ScollSpider/2.*]
+Parent=General Crawlers
+Browser=ScollSpider
+isBanned=true
+
+[Search Fst]
+Parent=General Crawlers
+Browser=Search Fst
+
+[searchbot admin@google.com]
+Parent=General Crawlers
+Browser=searchbot
+isBanned=true
+
+[Seeker.lookseek.com]
+Parent=General Crawlers
+Browser=LookSeek
+isBanned=true
+
+[semanticdiscovery/*]
+Parent=General Crawlers
+Browser=Semantic Discovery
+
+[SeznamBot/*]
+Parent=General Crawlers
+Browser=SeznamBot
+isBanned=true
+
+[Shelob (shelob@gmx.net)]
+Parent=General Crawlers
+Browser=Shelob
+isBanned=true
+
+[shelob v1.*]
+Parent=General Crawlers
+Browser=shelob
+isBanned=true
+
+[ShopWiki/1.0*]
+Parent=General Crawlers
+Browser=ShopWiki
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[ShowXML/1.0 libwww/5.4.0]
+Parent=General Crawlers
+Browser=ShowXML
+isBanned=true
+
+[sitecheck.internetseer.com*]
+Parent=General Crawlers
+Browser=Internetseer
+
+[SMBot/*]
+Parent=General Crawlers
+Browser=SMBot
+
+[sohu*]
+Parent=General Crawlers
+Browser=sohu-search
+isBanned=true
+
+[SpankBot*]
+Parent=General Crawlers
+Browser=SpankBot
+isBanned=true
+
+[spider (tspyyp@tom.com)]
+Parent=General Crawlers
+Browser=spider (tspyyp@tom.com)
+isBanned=true
+
+[Sunrise/0.*]
+Parent=General Crawlers
+Browser=Sunrise
+isBanned=true
+
+[Superpages URL Verification Engine]
+Parent=General Crawlers
+Browser=Superpages
+
+[Surf Knight]
+Parent=General Crawlers
+Browser=Surf Knight
+isBanned=true
+
+[SurveyBot/*]
+Parent=General Crawlers
+Browser=SurveyBot
+isBanned=true
+
+[SynapticSearch/AI Crawler 1.?]
+Parent=General Crawlers
+Browser=SynapticSearch
+isBanned=true
+
+[SyncMgr]
+Parent=General Crawlers
+Browser=SyncMgr
+
+[Tagyu Agent/1.0]
+Parent=General Crawlers
+Browser=Tagyu
+
+[Talkro Web-Shot/*]
+Parent=General Crawlers
+Browser=Talkro Web-Shot
+isBanned=true
+
+[Tecomi Bot (http://www.tecomi.com/bot.htm)]
+Parent=General Crawlers
+Browser=Tecomi
+
+[TheInformant*]
+Parent=General Crawlers
+Browser=TheInformant
+isBanned=true
+
+[Toata dragostea*]
+Parent=General Crawlers
+Browser=Toata dragostea
+isBanned=true
+
+[Tutorial Crawler*]
+Parent=General Crawlers
+isBanned=true
+
+[UbiCrawler/*]
+Parent=General Crawlers
+Browser=UbiCrawler
+
+[UCmore]
+Parent=General Crawlers
+Browser=UCmore
+
+[User*Agent:*]
+Parent=General Crawlers
+isBanned=true
+
+[USER_AGENT]
+Parent=General Crawlers
+Browser=USER_AGENT
+isBanned=true
+
+[VadixBot]
+Parent=General Crawlers
+Browser=VadixBot
+
+[VengaBot/*]
+Parent=General Crawlers
+Browser=VengaBot
+isBanned=true
+
+[Visicom Toolbar]
+Parent=General Crawlers
+Browser=Visicom Toolbar
+
+[W3C-WebCon/*]
+Parent=General Crawlers
+Browser=W3C-WebCon
+
+[Webclipping.com]
+Parent=General Crawlers
+Browser=Webclipping.com
+isBanned=true
+
+[webcollage/*]
+Parent=General Crawlers
+Browser=WebCollage
+isBanned=true
+
+[WebCrawler_1.*]
+Parent=General Crawlers
+Browser=WebCrawler
+
+[WebFilter Robot*]
+Parent=General Crawlers
+Browser=WebFilter Robot
+
+[WeBoX/*]
+Parent=General Crawlers
+Browser=WeBoX
+
+[WebTrends/*]
+Parent=General Crawlers
+Browser=WebTrends
+
+[West Wind Internet Protocols*]
+Parent=General Crawlers
+Browser=Versatel
+isBanned=true
+
+[WhizBang]
+Parent=General Crawlers
+Browser=WhizBang
+
+[Willow Internet Crawler by Twotrees V*]
+Parent=General Crawlers
+Browser=Willow Internet Crawler
+
+[WIRE/* (Linux; i686; Bot,Robot,Spider,Crawler)]
+Parent=General Crawlers
+Browser=WIRE
+isBanned=true
+
+[www.fi crawler, contact crawler@www.fi]
+Parent=General Crawlers
+Browser=www.fi crawler
+
+[Xerka WebBot v1.*]
+Parent=General Crawlers
+Browser=Xerka
+isBanned=true
+
+[XML Sitemaps Generator*]
+Parent=General Crawlers
+Browser=XML Sitemaps Generator
+
+[XSpider*]
+Parent=General Crawlers
+Browser=XSpider
+isBanned=true
+
+[YooW!/* (?http://www.yoow.eu)]
+Parent=General Crawlers
+Browser=YooW!
+isBanned=true
+
+[HiddenMarket-*]
+Parent=General RSS
+Browser=HiddenMarket
+isBanned=true
+
+[FOTOCHECKER]
+Parent=Image Crawlers
+Browser=FOTOCHECKER
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Search Engines
+
+[Search Engines]
+Parent=DefaultProperties
+Browser=Search Engines
+Crawler=true
+
+[*FDSE robot*]
+Parent=Search Engines
+Browser=FDSE Robot
+
+[*Fluffy the spider*]
+Parent=Search Engines
+Browser=SearchHippo
+
+[Abacho*]
+Parent=Search Engines
+Browser=Abacho
+
+[ah-ha.com crawler (crawler@ah-ha.com)]
+Parent=Search Engines
+Browser=Ah-Ha
+
+[AIBOT/*]
+Parent=Search Engines
+Browser=21Seek.Com
+
+[ALeadSoftbot/*]
+Parent=Search Engines
+Browser=ALeadSoftbot
+
+[Amfibibot/*]
+Parent=Search Engines
+Browser=Amfibi
+
+[AnswerBus (http://www.answerbus.com/)]
+Parent=Search Engines
+
+[antibot-V*]
+Parent=Search Engines
+Browser=antibot
+
+[appie*(www.walhello.com)]
+Parent=Search Engines
+Browser=Walhello
+
+[ASPSeek/*]
+Parent=Search Engines
+Browser=ASPSeek
+
+[BigCliqueBOT/*]
+Parent=Search Engines
+Browser=BigClique.com/BigClic.com
+
+[Blaiz-Bee/*]
+Parent=Search Engines
+Browser=RawGrunt
+
+[btbot/*]
+Parent=Search Engines
+Browser=Bit Torrent Search Engine
+
+[Busiversebot/v1.0 (http://www.busiverse.com/bot.php)]
+Parent=Search Engines
+Browser=Busiversebot
+isBanned=true
+
+[CatchBot/*;  http://www.catchbot.com]
+Parent=Search Engines
+Browser=CatchBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[CipinetBot (http://www.cipinet.com/bot.html)]
+Parent=Search Engines
+Browser=CipinetBot
+
+[Cogentbot/1.?*]
+Parent=Search Engines
+Browser=Cogentbot
+
+[compatible; Mozilla 4.0; MSIE 5.5; (SqwidgeBot v1.01 - http://www.sqwidge.com/bot/)]
+Parent=Search Engines
+Browser=SqwidgeBot
+
+[cosmos*]
+Parent=Search Engines
+Browser=Xyleme
+
+[Deepindex]
+Parent=Search Engines
+Browser=Deepindex
+
+[DiamondBot]
+Parent=Search Engines
+Browser=DiamondBot
+
+[Dumbot*]
+Parent=Search Engines
+Browser=Dumbot
+Version=0.2
+MajorVer=0
+MinorVer=2
+Beta=true
+
+[Eule?Robot*]
+Parent=Search Engines
+Browser=Eule-Robot
+
+[Faxobot/*]
+Parent=Search Engines
+Browser=Faxo
+
+[Filangy/*]
+Parent=Search Engines
+Browser=Filangy
+
+[flatlandbot/*]
+Parent=Search Engines
+Browser=Flatland
+
+[Fooky.com/ScorpionBot/ScoutOut;*]
+Parent=Search Engines
+Browser=ScorpionBot
+isBanned=true
+
+[FyberSpider*]
+Parent=Search Engines
+Browser=FyberSpider
+isBanned=true
+
+[Gaisbot/*]
+Parent=Search Engines
+Browser=Gaisbot
+
+[gazz/*(gazz@nttr.co.jp)]
+Parent=Search Engines
+Browser=gazz
+
+[geniebot*]
+Parent=Search Engines
+Browser=GenieKnows
+
+[GOFORITBOT (?http://www.goforit.com/about/?)]
+Parent=Search Engines
+Browser=GoForIt
+
+[GoGuidesBot/*]
+Parent=Search Engines
+Browser=GoGuidesBot
+
+[GroschoBot/*]
+Parent=Search Engines
+Browser=GroschoBot
+
+[GurujiBot/1.*]
+Parent=Search Engines
+Browser=GurujiBot
+isBanned=true
+
+[HenryTheMiragoRobot*]
+Parent=Search Engines
+Browser=Mirago
+
+[HolmesBot (http://holmes.ge)]
+Parent=Search Engines
+Browser=HolmesBot
+
+[Hotzonu/*]
+Parent=Search Engines
+Browser=Hotzonu
+
+[HyperEstraier/*]
+Parent=Search Engines
+Browser=HyperEstraier
+isBanned=true
+
+[i1searchbot/*]
+Parent=Search Engines
+Browser=i1searchbot
+
+[IIITBOT/1.*]
+Parent=Search Engines
+Browser=Indian Language Web Search Engine
+
+[Iltrovatore-?etaccio/*]
+Parent=Search Engines
+Browser=Iltrovatore-Setaccio
+
+[InfociousBot (?http://corp.infocious.com/tech_crawler.php)]
+Parent=Search Engines
+Browser=InfociousBot
+isBanned=true
+
+[Infoseek SideWinder/*]
+Parent=Search Engines
+Browser=Infoseek
+
+[iSEEKbot/*]
+Parent=Search Engines
+Browser=iSEEKbot
+
+[Knight/0.? (Zook Knight; http://knight.zook.in/; knight@zook.in)]
+Parent=Search Engines
+Browser=Knight
+
+[Kolinka Forum Search (www.kolinka.com)]
+Parent=Search Engines
+Browser=Kolinka Forum Search
+isBanned=true
+
+[KRetrieve/]
+Parent=Search Engines
+Browser=KRetrieve
+isBanned=true
+
+[LapozzBot/*]
+Parent=Search Engines
+Browser=LapozzBot
+
+[Linknzbot*]
+Parent=Search Engines
+Browser=Linknzbot
+
+[LocalcomBot/*]
+Parent=Search Engines
+Browser=LocalcomBot
+
+[Mail.Ru/1.0]
+Parent=Search Engines
+Browser=Mail.Ru
+
+[MaSagool/*]
+Parent=Search Engines
+Browser=Sagoo
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[miniRank/*]
+Parent=Search Engines
+Browser=miniRank
+
+[Mnogosearch*]
+Parent=Search Engines
+Browser=Mnogosearch
+
+[Mozilla/0.9* no dos :) (Linux)]
+Parent=Search Engines
+Browser=goliat
+isBanned=true
+
+[Mozilla/4.0 (compatible; Arachmo)]
+Parent=Search Engines
+Browser=Arachmo
+
+[Mozilla/4.0 (compatible; http://search.thunderstone.com/texis/websearch/about.html)]
+Parent=Search Engines
+Browser=ThunderStone
+isBanned=true
+
+[Mozilla/4.0 (compatible; MSIE *; Windows NT; Girafabot; girafabot at girafa dot com; http://www.girafa.com)]
+Parent=Search Engines
+Browser=Girafabot
+Win32=true
+
+[Mozilla/4.0 (compatible; Vagabondo/*; webcrawler at wise-guys dot nl; *)]
+Parent=Search Engines
+Browser=Vagabondo
+
+[Mozilla/4.0(?compatible; MSIE 6.0; Qihoo *)]
+Parent=Search Engines
+Browser=Qihoo
+
+[Mozilla/4.7 (compatible; WhizBang; http://www.whizbang.com/crawler)]
+Parent=Search Engines
+Browser=Inxight Software
+
+[Mozilla/5.0 (*) VoilaBot*]
+Parent=Search Engines
+Browser=VoilaBot
+isBanned=true
+
+[Mozilla/5.0 (compatible; ActiveTouristBot*; http://www.activetourist.com)]
+Parent=Search Engines
+Browser=ActiveTouristBot
+
+[Mozilla/5.0 (compatible; Butterfly/1.0; *)*]
+Parent=Search Engines
+Browser=Butterfly
+
+[Mozilla/5.0 (compatible; Charlotte/*; *)]
+Parent=Search Engines
+Browser=Charlotte
+Beta=true
+isBanned=true
+
+[Mozilla/5.0 (compatible; CXL-FatAssANT*)]
+Parent=Search Engines
+Browser=FatAssANT
+
+[Mozilla/5.0 (compatible; DBLBot/1.0; ?http://www.dontbuylists.com/)]
+Parent=Search Engines
+Browser=DBLBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (compatible; EARTHCOM.info/*)]
+Parent=Search Engines
+Browser=EARTHCOM
+
+[Mozilla/5.0 (compatible; Lipperhey Spider; http://www.lipperhey.com/)]
+Parent=Search Engines
+Browser=Lipperhey Spider
+
+[Mozilla/5.0 (compatible; MojeekBot/*; http://www.mojeek.com/bot.html)]
+Parent=Search Engines
+Browser=MojeekBot
+
+[Mozilla/5.0 (compatible; NLCrawler/*]
+Parent=Search Engines
+Browser=Northern Light Web Search
+
+[Mozilla/5.0 (compatible; OsO;*]
+Parent=Search Engines
+Browser=Octopodus
+isBanned=true
+
+[Mozilla/5.0 (compatible; Pogodak.*)]
+Parent=Search Engines
+Browser=Pogodak
+
+[Mozilla/5.0 (compatible; Quantcastbot/1.*)]
+Parent=Search Engines
+Browser=Quantcastbot
+
+[Mozilla/5.0 (compatible; ScoutJet;  *http://www.scoutjet.com/)]
+Parent=Search Engines
+Browser=ScoutJet
+
+[Mozilla/5.0 (compatible; Scrubby/*;  http://www.scrubtheweb.com/abs/meta-check.html)]
+Parent=Search Engines
+Browser=Scrubby
+isBanned=true
+
+[Mozilla/5.0 (compatible; YoudaoBot/1.*; http://www.youdao.com/help/webmaster/spider/*)]
+Parent=Search Engines
+Browser=YoudaoBot
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (Twiceler*)]
+Parent=Search Engines
+Browser=Twiceler
+isBanned=true
+
+[Mozilla/5.0 CostaCider Search*]
+Parent=Search Engines
+Browser=CostaCider Search
+
+[Mozilla/5.0 GurujiBot/1.0 (*)]
+Parent=Search Engines
+Browser=GurujiBot
+
+[NavissoBot]
+Parent=Search Engines
+Browser=NavissoBot
+
+[NextGenSearchBot*(for information visit *)]
+Parent=Search Engines
+Browser=ZoomInfo
+isBanned=true
+
+[Norbert the Spider(Burf.com)]
+Parent=Search Engines
+Browser=Norbert the Spider
+
+[NuSearch Spider*]
+Parent=Search Engines
+Browser=nuSearch
+
+[ObjectsSearch/*]
+Parent=Search Engines
+Browser=ObjectsSearch
+
+[OpenISearch/1.*]
+Parent=Search Engines
+Browser=OpenISearch (Amazon)
+
+[Pagebull http://www.pagebull.com/]
+Parent=Search Engines
+Browser=Pagebull
+
+[PEERbot*]
+Parent=Search Engines
+Browser=PEERbot
+
+[Pompos/*]
+Parent=Search Engines
+Browser=Pompos
+
+[Popdexter/*]
+Parent=Search Engines
+Browser=Popdex
+
+[Qweery*]
+Parent=Search Engines
+Browser=QweeryBot
+
+[RedCell/* (*)]
+Parent=Search Engines
+Browser=RedCell
+
+[Scrubby/*]
+Parent=Search Engines
+Browser=Scrub The Web
+
+[Search-10/*]
+Parent=Search Engines
+Browser=Search-10
+
+[search.ch*]
+Parent=Search Engines
+Browser=Swiss Search Engine
+
+[Searchmee! Spider*]
+Parent=Search Engines
+Browser=Searchmee!
+
+[Seekbot/*]
+Parent=Search Engines
+Browser=Seekbot
+
+[SiteSpider  (http://www.SiteSpider.com/)]
+Parent=Search Engines
+Browser=SiteSpider
+
+[Spinne/*]
+Parent=Search Engines
+Browser=Spinne
+
+[sproose/*]
+Parent=Search Engines
+Browser=Sproose
+
+[Sqeobot/0.*]
+Parent=Search Engines
+Browser=Branzel
+isBanned=true
+
+[SquigglebotBot/*]
+Parent=Search Engines
+Browser=SquigglebotBot
+isBanned=true
+
+[StackRambler/*]
+Parent=Search Engines
+Browser=StackRambler
+
+[SygolBot*]
+Parent=Search Engines
+Browser=SygolBot
+
+[SynoBot]
+Parent=Search Engines
+Browser=SynoBot
+
+[Szukacz/*]
+Parent=Search Engines
+Browser=Szukacz
+
+[Tarantula/*]
+Parent=Search Engines
+Browser=Tarantula
+isBanned=true
+
+[TerrawizBot/*]
+Parent=Search Engines
+Browser=TerrawizBot
+isBanned=true
+
+[Tkensaku/*]
+Parent=Search Engines
+Browser=Tkensaku
+
+[TMCrawler]
+Parent=Search Engines
+Browser=TMCrawler
+isBanned=true
+
+[Twingly Recon]
+Parent=Search Engines
+Browser=Twingly Recon
+isBanned=true
+
+[updated/*]
+Parent=Search Engines
+Browser=Updated!
+
+[URL Spider Pro/*]
+Parent=Search Engines
+Browser=URL Spider Pro
+
+[URL Spider SQL*]
+Parent=Search Engines
+Browser=Innerprise Enterprise Search
+
+[VMBot/*]
+Parent=Search Engines
+Browser=VMBot
+
+[voyager/2.0 (http://www.kosmix.com/html/crawler.html)]
+Parent=Search Engines
+Browser=Voyager
+
+[wadaino.jp-crawler*]
+Parent=Search Engines
+Browser=wadaino.jp
+isBanned=true
+
+[WebAlta Crawler/*]
+Parent=Search Engines
+Browser=WebAlta Crawler
+isBanned=true
+
+[WebCorp/*]
+Parent=Search Engines
+Browser=WebCorp
+isBanned=true
+
+[webcrawl.net]
+Parent=Search Engines
+Browser=webcrawl.net
+
+[WISEbot/*]
+Parent=Search Engines
+Browser=WISEbot
+isBanned=true
+
+[Wotbox/*]
+Parent=Search Engines
+Browser=Wotbox
+
+[www.zatka.com]
+Parent=Search Engines
+Browser=Zatka
+
+[WWWeasel Robot v*]
+Parent=Search Engines
+Browser=World Wide Weasel
+
+[YadowsCrawler*]
+Parent=Search Engines
+Browser=YadowsCrawler
+
+[YodaoBot/*]
+Parent=Search Engines
+Browser=YodaoBot
+isBanned=true
+
+[ZeBot_www.ze.bz*]
+Parent=Search Engines
+Browser=ZE.bz
+
+[zibber-v*]
+Parent=Search Engines
+Browser=Zibb
+
+[ZipppBot/*]
+Parent=Search Engines
+Browser=ZipppBot
+
+[ATA-Translation-Service]
+Parent=Translators
+Browser=ATA-Translation-Service
+
+[GJK_Browser_Check]
+Parent=Version Checkers
+Browser=GJK_Browser_Check
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Hatena
+
+[Hatena]
+Parent=DefaultProperties
+Browser=Hatena
+isBanned=true
+Crawler=true
+
+[Feed::Find/*]
+Parent=Hatena
+Browser=Feed Find
+isSyndicationReader=true
+
+[Hatena Antenna/*]
+Parent=Hatena
+Browser=Hatena Antenna
+
+[Hatena Bookmark/*]
+Parent=Hatena
+Browser=Hatena Bookmark
+
+[Hatena RSS/*]
+Parent=Hatena
+Browser=Hatena RSS
+isSyndicationReader=true
+
+[Hatena::Crawler/*]
+Parent=Hatena
+Browser=Hatena Crawler
+
+[HatenaScreenshot*]
+Parent=Hatena
+Browser=HatenaScreenshot
+
+[URI::Fetch/*]
+Parent=Hatena
+Browser=URI::Fetch
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Internet Archive
+
+[Internet Archive]
+Parent=DefaultProperties
+Browser=Internet Archive
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[*heritrix*]
+Parent=Internet Archive
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+Parent=Image Crawlers
+Browser=Custo
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Link Checkers
+
+[Link Checkers]
+Parent=DefaultProperties
+Browser=Link Checkers
+Frames=true
+IFrames=true
+Tables=true
+Crawler=true
+
+[!Susie (http://www.sync2it.com/susie)]
+Parent=Link Checkers
+Browser=!Susie
+
+[*AgentName/*]
+Parent=Link Checkers
+Browser=AgentName
+
+[*Linkman*]
+Parent=Link Checkers
+Browser=Linkman
+
+[*LinksManager.com*]
+Parent=Link Checkers
+Browser=LinksManager
+
+[*Powermarks/*]
+Parent=Link Checkers
+Browser=Powermarks
+
+[*W3C-checklink/*]
+Parent=Link Checkers
+Browser=W3C Link Checker
+
+[*Web Link Validator*]
+Parent=Link Checkers
+Browser=Web Link Validator
+
+[*Zeus*]
+Parent=Link Checkers
+Browser=Zeus
+isBanned=true
+
+[ActiveBookmark *]
+Parent=Link Checkers
+Browser=ActiveBookmark
+
+[Bookdog/*]
+Parent=Link Checkers
+Browser=Bookdog
+
+[Bookmark Buddy*]
+Parent=Link Checkers
+Browser=Bookmark Buddy
+
+[Bookmark Renewal Check Agent*]
+Parent=Link Checkers
+Browser=Bookmark Renewal Check Agent
+
+[Bookmark search tool*]
+Parent=Link Checkers
+Browser=Bookmark search tool
+
+[Bookmark-Manager]
+Parent=Link Checkers
+Browser=Bookmark-Manager
+
+[Checkbot*]
+Parent=Link Checkers
+Browser=Checkbot
+
+[CheckLinks/*]
+Parent=Link Checkers
+Browser=CheckLinks
+
+[CyberSpyder Link Test/*]
+Parent=Link Checkers
+Browser=CyberSpyder Link Test
+
+[DLC/*]
+Parent=Link Checkers
+Browser=DLC
+
+[DocWeb Link Crawler (http://doc.php.net)]
+Parent=Link Checkers
+Browser=DocWeb Link Crawler
+
+[FavOrg]
+Parent=Link Checkers
+Browser=FavOrg
+
+[Favorites Sweeper v.3.*]
+Parent=Link Checkers
+Browser=Favorites Sweeper
+
+[FindLinks/*]
+Parent=Link Checkers
+Browser=FindLinks
+
+[Funnel Web Profiler*]
+Parent=Link Checkers
+Browser=Funnel Web Profiler
+
+[Html Link Validator (www.lithopssoft.com)]
+Parent=Link Checkers
+Browser=HTML Link Validator
+
+[IECheck]
+Parent=Link Checkers
+Browser=IECheck
+
+[JCheckLinks/*]
+Parent=Link Checkers
+Browser=JCheckLinks
+
+[JRTwine Software Check Favorites Utility]
+Parent=Link Checkers
+Browser=JRTwine
+
+[Link Valet Online*]
+Parent=Link Checkers
+Browser=Link Valet
+isBanned=true
+
+[LinkAlarm/*]
+Parent=Link Checkers
+Browser=LinkAlarm
+
+[Linkbot*]
+Parent=Link Checkers
+Browser=Linkbot
+
+[LinkChecker/*]
+Parent=Link Checkers
+Browser=LinkChecker
+
+[LinkextractorPro*]
+Parent=Link Checkers
+Browser=LinkextractorPro
+isBanned=true
+
+[LinkLint-checkonly/*]
+Parent=Link Checkers
+Browser=LinkLint
+
+[LinkScan/*]
+Parent=Link Checkers
+Browser=LinkScan
+
+[LinkSweeper/*]
+Parent=Link Checkers
+Browser=LinkSweeper
+
+[LinkWalker*]
+Parent=Link Checkers
+Browser=LinkWalker
+
+[MetaGer-LinkChecker]
+Parent=Link Checkers
+Browser=MetaGer-LinkChecker
+
+[Mozilla/* (compatible; linktiger/*; *http://www.linktiger.com*)]
+Parent=Link Checkers
+Browser=LinkTiger
+isBanned=true
+
+[Mozilla/4.0 (Compatible); URLBase*]
+Parent=Link Checkers
+Browser=URLBase
+
+[Mozilla/4.0 (compatible; Link Utility; http://net-promoter.com)]
+Parent=Link Checkers
+Browser=NetPromoter Link Utility
+
+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98) Web Link Validator*]
+Parent=Link Checkers
+Browser=Web Link Validator
+Win32=true
+
+[Mozilla/4.0 (compatible; MSIE 7.0; Win32) Link Commander 3.0]
+Parent=Link Checkers
+Browser=Link Commander
+Version=3.0
+MajorVer=3
+MinorVer=0
+Platform=Win32
+
+[Mozilla/4.0 (compatible; smartBot/1.*; checking links; *)]
+Parent=Link Checkers
+Browser=smartBot
+
+[Mozilla/4.0 (compatible; SuperCleaner*;*)]
+Parent=Link Checkers
+Browser=SuperCleaner
+
+[Mozilla/5.0 gURLChecker/*]
+Parent=Link Checkers
+Browser=gURLChecker
+isBanned=true
+
+[Newsgroupreporter LinkCheck]
+Parent=Link Checkers
+Browser=Newsgroupreporter LinkCheck
+
+[onCHECK Linkchecker von www.scientec.de fuer www.onsinn.de]
+Parent=Link Checkers
+Browser=onCHECK Linkchecker
+
+[online link validator (http://www.dead-links.com/)]
+Parent=Link Checkers
+Browser=Dead-Links.com
+isBanned=true
+
+[REL Link Checker*]
+Parent=Link Checkers
+Browser=REL Link Checker
+
+[RLinkCheker*]
+Parent=Link Checkers
+Browser=RLinkCheker
+
+[Robozilla/*]
+Parent=Link Checkers
+Browser=Robozilla
+
+[RPT-HTTPClient/*]
+Parent=Link Checkers
+Browser=RPT-HTTPClient
+isBanned=true
+
+[SafariBookmarkChecker*(?http://www.coriolis.ch/)]
+Parent=Link Checkers
+Browser=SafariBookmarkChecker
+Platform=MacOSX
+CssVersion=2
+supportsCSS=true
+
+[Simpy/* (Simpy; http://www.simpy.com/?ref=bot; feedback at simpy dot com)]
+Parent=Link Checkers
+Browser=Simpy
+
+[SiteBar/*]
+Parent=Link Checkers
+Browser=SiteBar
+
+[Susie (http://www.sync2it.com/bms/susie.php]
+Parent=Link Checkers
+Browser=Susie
+
+[URLBase/6.*]
+Parent=Link Checkers
+
+[VSE/*]
+Parent=Link Checkers
+Browser=VSE Link Tester
+
+[WebTrends Link Analyzer]
+Parent=Link Checkers
+Browser=WebTrends Link Analyzer
+
+[WorQmada/*]
+Parent=Link Checkers
+Browser=WorQmada
+
+[Xenu* Link Sleuth*]
+Parent=Link Checkers
+Browser=Xenu's Link Sleuth
+isBanned=true
+
+[Z-Add Link Checker*]
+Parent=Link Checkers
+Browser=Z-Add Link Checker
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Microsoft
+
+[Microsoft]
+Parent=DefaultProperties
+Browser=Microsoft
+isBanned=true
+
+[Live (http://www.live.com/)]
+Parent=Microsoft
+Browser=Microsoft Live
+isBanned=false
+isSyndicationReader=true
+
+[MFC Foundation Class Library*]
+Parent=Microsoft
+Browser=MFC Foundation Class Library
+
+[MFHttpScan]
+Parent=Microsoft
+Browser=MFHttpScan
+
+[Microsoft BITS/*]
+Parent=Microsoft
+Browser=BITS
+
+[Microsoft Data Access Internet Publishing Provider Cache Manager]
+Parent=Microsoft
+Browser=MS IPP
+
+[Microsoft Data Access Internet Publishing Provider DAV*]
+Parent=Microsoft
+Browser=MS IPP DAV
+
+[Microsoft Data Access Internet Publishing Provider Protocol Discovery]
+Parent=Microsoft
+Browser=MS IPPPD
+
+[Microsoft Internet Explorer]
+Parent=Microsoft
+Browser=Fake IE
+
+[Microsoft Office Existence Discovery]
+Parent=Microsoft
+Browser=Microsoft Office Existence Discovery
+
+[Microsoft Office Protocol Discovery]
+Parent=Microsoft
+Browser=MS OPD
+
+[Microsoft Office/* (*Picture Manager*)]
+Parent=Microsoft
+Browser=Microsoft Office Picture Manager
+
+[Microsoft URL Control*]
+Parent=Microsoft
+Browser=Microsoft URL Control
+
+[Microsoft Visio MSIE]
+Parent=Microsoft
+Browser=Microsoft Visio
+
+[Microsoft-WebDAV-MiniRedir/*]
+Parent=Microsoft
+Browser=Microsoft-WebDAV
+
+[Mozilla/5.0 (Macintosh; Intel Mac OS X) Excel/12.*]
+Parent=Microsoft
+Browser=Microsoft Excel
+Version=12.0
+MajorVer=12
+MinorVer=0
+Platform=MacOSX
+
+[MSN Feed Manager]
+Parent=Microsoft
+Browser=MSN Feed Manager
+isBanned=false
+isSyndicationReader=true
+
+[MSProxy/*]
+Parent=Microsoft
+Browser=MS Proxy
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Miscellaneous Browsers
+
+[Miscellaneous Browsers]
+Parent=DefaultProperties
+Browser=Miscellaneous Browsers
+Frames=true
+Tables=true
+Cookies=true
+
+[*Amiga*]
+Parent=Miscellaneous Browsers
+Browser=Amiga
+Platform=Amiga
+
+[*avantbrowser*]
+Parent=Miscellaneous Browsers
+Browser=Avant Browser
+
+[12345]
+Parent=Miscellaneous Browsers
+Browser=12345
+isBanned=true
+
+[Ace Explorer]
+Parent=Miscellaneous Browsers
+Browser=Ace Explorer
+
+[Enigma Browser*]
+Parent=Miscellaneous Browsers
+Browser=Enigma Browser
+
+[EVE-minibrowser/*]
+Parent=Miscellaneous Browsers
+Browser=EVE-minibrowser
+IFrames=false
+Tables=false
+BackgroundSounds=false
+VBScript=false
+JavaApplets=false
+JavaScript=false
+ActiveXControls=false
+isBanned=false
+Crawler=false
+
+[Godzilla/* (Basic*; *; Commodore C=64; *; rv:1.*)*]
+Parent=Miscellaneous Browsers
+Browser=Godzilla
+
+[GreenBrowser]
+Parent=Miscellaneous Browsers
+Browser=GreenBrowser
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+VBScript=true
+JavaApplets=true
+JavaScript=true
+ActiveXControls=true
+CssVersion=2
+supportsCSS=true
+
+[Kopiczek/* (WyderOS*; *)]
+Parent=Miscellaneous Browsers
+Browser=Kopiczek
+Platform=WyderOS
+IFrames=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/* (*) - BrowseX (*)]
+Parent=Miscellaneous Browsers
+Browser=BrowseX
+
+[Mozilla/* (Win32;*Escape?*; ?)]
+Parent=Miscellaneous Browsers
+Browser=Escape
+Platform=Win32
+
+[Mozilla/4.0 (compatible; ibisBrowser)]
+Parent=Miscellaneous Browsers
+Browser=ibisBrowser
+
+[Mozilla/5.0 (Macintosh; ?; PPC Mac OS X;*) AppleWebKit/* (*) HistoryHound/*]
+Parent=Miscellaneous Browsers
+Browser=HistoryHound
+
+[NetRecorder*]
+Parent=Miscellaneous Browsers
+Browser=NetRecorder
+
+[NetSurfer*]
+Parent=Miscellaneous Browsers
+Browser=NetSurfer
+
+[ogeb browser , Version 1.1.0]
+Parent=Miscellaneous Browsers
+Browser=ogeb browser
+Version=1.1
+MajorVer=1
+MinorVer=1
+
+[SCEJ PSP BROWSER 0102pspNavigator]
+Parent=Miscellaneous Browsers
+Browser=Wipeout Pure
+
+[SlimBrowser]
+Parent=Miscellaneous Browsers
+Browser=SlimBrowser
+
+[WWW_Browser/*]
+Parent=Miscellaneous Browsers
+Browser=WWW Browser
+Version=1.69
+MajorVer=1
+MinorVer=69
+Platform=Win16
+CssVersion=3
+supportsCSS=true
+
+[*Netcraft Webserver Survey*]
+Parent=Netcraft
+Browser=Netcraft Webserver Survey
+isBanned=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Offline Browsers
+
+[Offline Browsers]
+Parent=DefaultProperties
+Browser=Offline Browsers
+Frames=true
+Tables=true
+Cookies=true
+isBanned=true
+Crawler=true
+
+[*Check&Get*]
+Parent=Offline Browsers
+Browser=Check&Get
+
+[*HTTrack*]
+Parent=Offline Browsers
+Browser=HTTrack
+
+[*MSIECrawler*]
+Parent=Offline Browsers
+Browser=IE Offline Browser
+
+[*TweakMASTER*]
+Parent=Offline Browsers
+Browser=TweakMASTER
+
+[BackStreet Browser *]
+Parent=Offline Browsers
+Browser=BackStreet Browser
+
+[Go-Ahead-Got-It*]
+Parent=Offline Browsers
+Browser=Go Ahead Got-It
+
+[iGetter/*]
+Parent=Offline Browsers
+Browser=iGetter
+
+[Teleport*]
+Parent=Offline Browsers
+Browser=Teleport
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Online Scanners
+
+[Online Scanners]
+Parent=DefaultProperties
+Browser=Online Scanners
+isBanned=true
+
+[JoeDog/* (X11; I; Siege *)]
+Parent=Online Scanners
+Browser=JoeDog
+isBanned=false
+
+[Morfeus Fucking Scanner]
+Parent=Online Scanners
+Browser=Morfeus Fucking Scanner
+
+[Mozilla/4.0 (compatible; Trend Micro tmdr 1.*]
+Parent=Online Scanners
+Browser=Trend Micro
+
+[Titanium 2005 (4.02.01)]
+Parent=Online Scanners
+Browser=Panda Antivirus Titanium
+
+[virus_detector*]
+Parent=Online Scanners
+Browser=Secure Computing Corporation
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Proxy Servers
+
+[Proxy Servers]
+Parent=DefaultProperties
+Browser=Proxy Servers
+isBanned=true
+
+[*squid*]
+Parent=Proxy Servers
+Browser=Squid
+
+[Anonymisiert*]
+Parent=Proxy Servers
+Browser=Anonymizied
+
+[Anonymizer/*]
+Parent=Proxy Servers
+Browser=Anonymizer
+
+[Anonymizied*]
+Parent=Proxy Servers
+Browser=Anonymizied
+
+[Anonymous*]
+Parent=Proxy Servers
+Browser=Anonymous
+
+[Anonymous/*]
+Parent=Proxy Servers
+Browser=Anonymous
+
+[CE-Preload]
+Parent=Proxy Servers
+Browser=CE-Preload
+
+[http://Anonymouse.org/*]
+Parent=Proxy Servers
+Browser=Anonymouse
+
+[IE/6.01 (CP/M; 8-bit*)]
+Parent=Proxy Servers
+Browser=Squid
+
+[Mozilla/* (TuringOS; Turing Machine; 0.0)]
+Parent=Proxy Servers
+Browser=Anonymizer
+
+[Mozilla/4.0 (compatible; MSIE ?.0; SaferSurf*)]
+Parent=Proxy Servers
+Browser=SaferSurf
+
+[Mozilla/5.0 (compatible; del.icio.us-thumbnails/*; *) KHTML/* (like Gecko)]
+Parent=Proxy Servers
+Browser=Yahoo!
+isBanned=true
+Crawler=true
+
+[Nutscrape]
+Parent=Proxy Servers
+Browser=Squid
+
+[Nutscrape/* (CP/M; 8-bit*)]
+Parent=Proxy Servers
+Browser=Squid
+
+[Privoxy/*]
+Parent=Proxy Servers
+Browser=Privoxy
+
+[ProxyTester*]
+Parent=Proxy Servers
+Browser=ProxyTester
+isBanned=true
+Crawler=true
+
+[SilentSurf*]
+Parent=Proxy Servers
+Browser=SilentSurf
+
+[SmallProxy*]
+Parent=Proxy Servers
+Browser=SmallProxy
+
+[Space*Bison/*]
+Parent=Proxy Servers
+Browser=Proxomitron
+
+[Sqworm/*]
+Parent=Proxy Servers
+Browser=Websense
+
+[SurfControl]
+Parent=Proxy Servers
+Browser=SurfControl
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Research Projects
+
+[Research Projects]
+Parent=DefaultProperties
+Browser=Research Projects
+isBanned=true
+Crawler=true
+
+[*research*]
+Parent=Research Projects
+
+[AcadiaUniversityWebCensusClient]
+Parent=Research Projects
+Browser=AcadiaUniversityWebCensusClient
+
+[Amico Alpha * (*) Gecko/* AmicoAlpha/*]
+Parent=Research Projects
+Browser=Amico Alpha
+
+[annotate_google; http://ponderer.org/*]
+Parent=Research Projects
+Browser=Annotate Google
+
+[CMS crawler (?http://buytaert.net/crawler/)]
+Parent=Research Projects
+
+[e-SocietyRobot(http://www.yama.info.waseda.ac.jp/~yamana/es/)]
+Parent=Research Projects
+Browser=e-SocietyRobot
+
+[Forschungsportal/*]
+Parent=Research Projects
+Browser=Forschungsportal
+
+[Gulper Web *]
+Parent=Research Projects
+Browser=Gulper Web Bot
+
+[HooWWWer/*]
+Parent=Research Projects
+Browser=HooWWWer
+
+[http://buytaert.net/crawler]
+Parent=Research Projects
+
+[inetbot/* (?http://www.inetbot.com/bot.html)]
+Parent=Research Projects
+Browser=inetbot
+
+[IRLbot/*]
+Parent=Research Projects
+Browser=IRLbot
+
+[Lachesis]
+Parent=Research Projects
+Browser=Lachesis
+
+[Mozilla/5.0 (compatible; nextthing.org/*)]
+Parent=Research Projects
+Browser=nextthing.org
+Version=1.0
+MajorVer=1
+MinorVer=0
+
+[Mozilla/5.0 (compatible; Theophrastus/*)]
+Parent=Research Projects
+Browser=Theophrastus
+
+[Mozilla/5.0 (compatible; Webscan v0.*;  http://otc.dyndns.org/webscan/)]
+Parent=Research Projects
+Browser=Webscan
+
+[MQbot*]
+Parent=Research Projects
+Browser=MQbot
+
+[OutfoxBot/*]
+Parent=Research Projects
+Browser=OutfoxBot
+
+[polybot?*]
+Parent=Research Projects
+Browser=Polybot
+
+[Shim?Crawler*]
+Parent=Research Projects
+Browser=Shim Crawler
+
+[Steeler/*]
+Parent=Research Projects
+Browser=Steeler
+
+[Taiga web spider]
+Parent=Research Projects
+Browser=Taiga
+
+[Theme Spider*]
+Parent=Research Projects
+Browser=Theme Spider
+
+[UofTDB_experiment* (leehyun@cs.toronto.edu)]
+Parent=Research Projects
+Browser=UofTDB Experiment
+
+[USyd-NLP-Spider*]
+Parent=Research Projects
+Browser=USyd-NLP-Spider
+
+[woriobot*]
+Parent=Research Projects
+Browser=woriobot
+
+[wwwster/* (Beta, mailto:gue@cis.uni-muenchen.de)]
+Parent=Research Projects
+Browser=wwwster
+Beta=true
+
+[Zao-Crawler]
+Parent=Research Projects
+Browser=Zao-Crawler
+
+[Zao/*]
+Parent=Research Projects
+Browser=Zao
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Rippers
+
+[Rippers]
+Parent=DefaultProperties
+Browser=Rippers
+Frames=true
+IFrames=true
+Tables=true
+isBanned=true
+Crawler=true
+
+[*grub*]
+Parent=Rippers
+Browser=grub
+
+[*ickHTTP*]
+Parent=Rippers
+Browser=IP*Works
+
+[*java*]
+Parent=Rippers
+
+[*libwww-perl*]
+Parent=Rippers
+Browser=libwww-perl
+
+[*WebGrabber*]
+Parent=Rippers
+
+[*WinHttpRequest*]
+Parent=Rippers
+Browser=WinHttp
+
+[3D-FTP/*]
+Parent=Rippers
+Browser=3D-FTP
+
+[3wGet/*]
+Parent=Rippers
+Browser=3wGet
+
+[ActiveRefresh*]
+Parent=Rippers
+Browser=ActiveRefresh
+
+[Artera (Version *)]
+Parent=Rippers
+Browser=Artera
+
+[AutoHotkey]
+Parent=Rippers
+Browser=AutoHotkey
+
+[b2w/*]
+Parent=Rippers
+Browser=b2w
+
+[BasicHTTP/*]
+Parent=Rippers
+Browser=BasicHTTP
+
+[BlockNote.Net]
+Parent=Rippers
+Browser=BlockNote.Net
+
+[CAST]
+Parent=Rippers
+Browser=CAST
+
+[CFNetwork/*]
+Parent=Rippers
+Browser=CFNetwork
+
+[CFSCHEDULE*]
+Parent=Rippers
+Browser=ColdFusion Task Scheduler
+
+[CobWeb/*]
+Parent=Rippers
+Browser=CobWeb
+
+[ColdFusion*]
+Parent=Rippers
+Browser=ColdFusion
+
+[Crawl_Application]
+Parent=Rippers
+Browser=Crawl_Application
+
+[curl/*]
+Parent=Rippers
+Browser=cURL
+
+[Custo*]
+Parent=Rippers
+Browser=Custo
+
+[DataCha0s/*]
+Parent=Rippers
+Browser=DataCha0s
+
+[DeepIndexer*]
+Parent=Rippers
+Browser=DeepIndexer
+
+[DISCo Pump *]
+Parent=Rippers
+Browser=DISCo Pump
+
+[eStyleSearch * (compatible; MSIE 6.0; Windows NT 5.0)]
+Parent=Rippers
+Browser=eStyleSearch
+Win32=true
+
+[ezic.com http agent *]
+Parent=Rippers
+Browser=Ezic.com
+
+[fetch libfetch/*]
+Parent=Rippers
+
+[FGet*]
+Parent=Rippers
+Browser=FGet
+
+[Flaming AttackBot*]
+Parent=Rippers
+Browser=Flaming AttackBot
+
+[Foobot*]
+Parent=Rippers
+Browser=Foobot
+
+[GameSpyHTTP/*]
+Parent=Rippers
+Browser=GameSpyHTTP
+
+[gnome-vfs/*]
+Parent=Rippers
+Browser=gnome-vfs
+
+[Harvest/*]
+Parent=Rippers
+Browser=Harvest
+
+[hcat/*]
+Parent=Rippers
+Browser=hcat
+
+[HLoader]
+Parent=Rippers
+Browser=HLoader
+
+[Holmes/*]
+Parent=Rippers
+Browser=Holmes
+
+[HTMLParser/*]
+Parent=Rippers
+Browser=HTMLParser
+
+[http generic]
+Parent=Rippers
+Browser=http generic
+
+[httpclient*]
+Parent=Rippers
+
+[httperf/*]
+Parent=Rippers
+Browser=httperf
+
+[HTTPFetch/*]
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+Browser=HTTPFetch
+
+[HTTPGrab]
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+Browser=HTTPGrab
+
+[HttpSession]
+Parent=Rippers
+Browser=HttpSession
+
+[httpunit/*]
+Parent=Rippers
+Browser=HttpUnit
+
+[ICE_GetFile]
+Parent=Rippers
+Browser=ICE_GetFile
+
+[iexplore.exe]
+Parent=Rippers
+
+[Inet - Eureka App]
+Parent=Rippers
+Browser=Inet - Eureka App
+
+[INetURL/*]
+Parent=Rippers
+Browser=INetURL
+
+[InetURL:/*]
+Parent=Rippers
+Browser=InetURL
+
+[Internet Exploiter/*]
+Parent=Rippers
+
+[Internet Explore *]
+Parent=Rippers
+Browser=Fake IE
+
+[Internet Explorer *]
+Parent=Rippers
+Browser=Fake IE
+
+[IP*Works!*/*]
+Parent=Rippers
+Browser=IP*Works!
+
+[IrssiUrlLog/*]
+Parent=Rippers
+Browser=IrssiUrlLog
+
+[JPluck/*]
+Parent=Rippers
+Browser=JPluck
+
+[Kapere (http://www.kapere.com)]
+Parent=Rippers
+Browser=Kapere
+
+[LeechFTP]
+Parent=Rippers
+Browser=LeechFTP
+
+[LeechGet*]
+Parent=Rippers
+Browser=LeechGet
+
+[libcurl-agent/*]
+Parent=Rippers
+Browser=libcurl
+
+[libWeb/clsHTTP*]
+Parent=Rippers
+Browser=libWeb/clsHTTP
+
+[lwp*]
+Parent=Rippers
+
+[MFC_Tear_Sample]
+Parent=Rippers
+Browser=MFC_Tear_Sample
+
+[Moozilla]
+Parent=Rippers
+Browser=Moozilla
+
+[MovableType/*]
+Parent=Rippers
+Browser=MovableType Web Log
+
+[Mozilla/2.0 (compatible; NEWT ActiveX; Win32)]
+Parent=Rippers
+Browser=NEWT ActiveX
+Platform=Win32
+
+[Mozilla/3.0 (compatible)]
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+[Mozilla/3.0 (compatible; Indy Library)]
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+[Mozilla/4.0 (compatible; BorderManager*)]
+Parent=Rippers
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+
+[Mozilla/4.0 (compatible;)]
+Parent=Rippers
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+[Mozilla/5.0 (compatible; IPCheck Server Monitor*)]
+Parent=Rippers
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+
+[OCN-SOC/*]
+Parent=Rippers
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+[Offline Explorer*]
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+
+[Open Web Analytics Bot*]
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+[OSSProxy*]
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+[Pageload*]
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+[pavuk/*]
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+[PEAR HTTP_Request*]
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+Parent=Rippers
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+[PigBlock (Windows NT 5.1; U)*]
+Parent=Rippers
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+
+[Pockey*]
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+[POE-Component-Client-HTTP/*]
+Parent=Rippers
+Browser=POE-Component-Client-HTTP
+
+[PycURL/*]
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+
+[Python*]
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+[SBL-BOT*]
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+[ScoutAbout*]
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+[SOFTWING_TEAR_AGENT*]
+Parent=Rippers
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+
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+[Tcl http client package*]
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+
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+
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+[WinScripter iNet Tools]
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+
+[WWW-Mechanize/*]
+Parent=Rippers
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+[Zend_Http_Client]
+Parent=Rippers
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Site Monitors
+
+[Site Monitors]
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+Cookies=true
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+Crawler=true
+
+[*EasyRider*]
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+[*maxamine.com--robot*]
+Parent=Site Monitors
+Browser=maxamine.com--robot
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+
+[*WebMon ?.*]
+Parent=Site Monitors
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+
+[Kenjin Spider*]
+Parent=Site Monitors
+Browser=Kenjin Spider
+
+[Kevin http://*]
+Parent=Site Monitors
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+isBanned=true
+
+[Mozilla/4.0 (compatible; ChangeDetection/*]
+Parent=Site Monitors
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+
+[Myst Monitor Service v*]
+Parent=Site Monitors
+Browser=Myst Monitor Service
+
+[Net Probe]
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+
+[NetMechanic*]
+Parent=Site Monitors
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+
+[NetReality*]
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+Browser=NetReality
+
+[Pingdom GIGRIB*]
+Parent=Site Monitors
+Browser=Pingdom
+
+[Site Valet Online*]
+Parent=Site Monitors
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+isBanned=true
+
+[SITECHECKER]
+Parent=Site Monitors
+Browser=SITECHECKER
+
+[sitemonitor@dnsvr.com/*]
+Parent=Site Monitors
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+[UpTime Checker*]
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+[URL Control*]
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+[URL_Access/*]
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+[URLCHECK]
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+[URLy Warning*]
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+
+[Webcheck *]
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+Version=1.0
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+[WebPatrol/*]
+Parent=Site Monitors
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+[websitepulse checker/*]
+Parent=Site Monitors
+Browser=websitepulse checker
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Social Bookmarkers
+
+[Social Bookmarkers]
+Parent=DefaultProperties
+Browser=Social Bookmarkers
+Frames=true
+Tables=true
+Cookies=true
+JavaScript=true
+
+[BookmarkBase(2/;http://bookmarkbase.com)]
+Parent=Social Bookmarkers
+Browser=BookmarkBase
+
+[Cocoal.icio.us/1.0 (v43) (Mac OS X; http://www.scifihifi.com/cocoalicious)]
+Parent=Social Bookmarkers
+Browser=Cocoalicious
+
+[Mozilla/5.0 (compatible; FriendFeedBot/0.*;  Http://friendfeed.com/about/bot)]
+Parent=Social Bookmarkers
+Browser=FriendFeedBot
+
+[Twitturly*]
+Parent=Social Bookmarkers
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+
+[WinkBot/*]
+Parent=Social Bookmarkers
+Browser=WinkBot
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Translators
+
+[Translators]
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+Frames=true
+Tables=true
+Cookies=true
+
+[Seram Server]
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+
+[TeragramWebcrawler/*]
+Parent=Translators
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+Version=1.0
+MajorVer=1
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+
+[WebIndexer/* (Web Indexer; *)]
+Parent=Translators
+Browser=WorldLingo
+
+[WebTrans]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Version Checkers
+
+[Version Checkers]
+Parent=DefaultProperties
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+Crawler=true
+
+[Automated Browscap.ini Updater. To report issues contact us at http://www.skycomp.ca]
+Parent=Version Checkers
+Browser=Automated Browscap.ini Updater
+
+[BMC Link Validator (http://www.briansmodelcars.com/links/)]
+Parent=Version Checkers
+Browser=BMC Link Validator
+MajorVer=1
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+Platform=Win2000
+
+[Browscap updater]
+Parent=Version Checkers
+Browser=Browscap updater
+
+[BrowscapUpdater1.0]
+Parent=Version Checkers
+
+[Browser Capabilities Project (http://browsers.garykeith.com; http://browsers.garykeith.com/sitemail/contact-me.asp)]
+Parent=Version Checkers
+Browser=Gary Keith's Version Checker
+
+[Browser Capabilities Project AutoDownloader]
+Parent=Version Checkers
+Browser=TKC AutoDownloader
+
+[browsers.garykeith.com browscap.ini bot BETA]
+Parent=Version Checkers
+
+[Code Sample Web Client]
+Parent=Version Checkers
+Browser=Code Sample Web Client
+
+[Desktop Sidebar*]
+Parent=Version Checkers
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+[Mono Browser Capabilities Updater*]
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+isBanned=true
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+[Rewmi/*]
+Parent=Version Checkers
+isBanned=true
+
+[Subtext Version 1.9* - http://subtextproject.com/ (Microsoft Windows NT 5.2.*)]
+Parent=Version Checkers
+Browser=Subtext
+
+[TherapeuticResearch]
+Parent=Version Checkers
+Browser=TherapeuticResearch
+
+[UpdateBrowscap*]
+Parent=Version Checkers
+Browser=UpdateBrowscap
+
+[www.garykeith.com browscap.ini bot*]
+Parent=Version Checkers
+Browser=clarkson.edu 
+
+[www.substancia.com AutoHTTPAgent (ver *)]
+Parent=Version Checkers
+Browser=Substância
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Become
+
+[Become]
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+Frames=true
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+
+[*BecomeBot/*]
+Parent=Become
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+[Mozilla/5.0 (compatible; BecomeJPBot/2.3; *)]
+Parent=Become
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Blue Coat Systems
+
+[Blue Coat Systems]
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+isBanned=true
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Browscap Abusers
+
+[Browscap Abusers]
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+
+[Apple-PubSub/*]
+Parent=Browscap Abusers
+Browser=Apple-PubSub
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; FeedHub
+
+[FeedHub]
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+isSyndicationReader=true
+
+[FeedHub FeedDiscovery/1.0 (http://www.feedhub.com)]
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+Version=1.0
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+
+[FeedHub FeedFetcher/1.0 (http://www.feedhub.com)]
+Parent=FeedHub
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+
+[FeedHub MetaDataFetcher/1.0 (http://www.feedhub.com)]
+Parent=FeedHub
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+Version=1.0
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+
+[Internet Content Rating Association]
+Parent=DefaultProperties
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+Frames=true
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+
+[ICRA_label_generator/1.?]
+Parent=Internet Content Rating Association
+Browser=ICRA_label_generator
+
+[ICRA_Semantic_spider/0.?]
+Parent=Internet Content Rating Association
+Browser=ICRA_Semantic_spider
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NameProtect
+
+[NameProtect]
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+[abot/*]
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+[NPBot*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Netcraft
+
+[Netcraft]
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+Crawler=true
+
+[*Netcraft Web Server Survey*]
+Parent=Netcraft
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+[Mozilla/5.0 (compatible; NetcraftSurveyAgent/1.0;  info@netcraft.com)]
+Parent=Netcraft
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NewsGator
+
+[NewsGator]
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+[MarsEdit*]
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+[NewsGatorOnline/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 0.2
+
+[Chrome 0.2]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.2.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.2.* Safari/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 0.3
+
+[Chrome 0.3]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.3.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.3.* Safari/*]
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+Platform=WinVista
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 0.4
+
+[Chrome 0.4]
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+[Mozilla/5.0 (Windows; U; Windows NT 5.1; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.4.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.4.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.4.* Safari/*]
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+Platform=WinVista
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 0.5
+
+[Chrome 0.5]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.5.* Safari/*]
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+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/0.5.* Safari/*]
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+Platform=WinVista
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 1.0
+
+[Chrome 1.0]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/1.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/1.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1; *) AppleWebKit/* (KHTML, like Gecko) Chrome/1.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 7.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/1.0.* Safari/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 2.0
+
+[Chrome 2.0]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/2.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/2.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1; *) AppleWebKit/* (KHTML, like Gecko) Chrome/2.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 7.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/2.0.* Safari/*]
+Parent=Chrome 2.0
+Platform=Win7
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Chrome 3.0
+
+[Chrome 3.0]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.1; *) AppleWebKit/* (KHTML, like Gecko) Chrome/3.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Chrome/3.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/3.0.* Safari/*]
+Parent=Chrome 3.0
+Platform=WinVista
+
+[Mozilla/5.0 (Windows; U; Windows NT 6.1; *) AppleWebKit/* (KHTML, like Gecko) Chrome/3.0.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 7.0; *) AppleWebKit/* (KHTML, like Gecko) Chrome/3.0.* Safari/*]
+Parent=Chrome 3.0
+Platform=Win7
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Google Code
+
+[Google Code]
+Parent=DefaultProperties
+Browser=Google Code
+Tables=true
+Cookies=true
+JavaApplets=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Iron 0.2
+
+[Iron 0.2]
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+CssVersion=3
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.1; *) AppleWebKit/* (KHTML, like Gecko) Iron/0.2.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2; *) AppleWebKit/* (KHTML, like Gecko) Iron/0.2.* Safari/*]
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+
+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Iron/0.2.* Safari/*]
+Parent=Iron 0.2
+Platform=Win7
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Iron 0.3
+
+[Iron 0.3]
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+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Iron/0.3.* Safari/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Iron 0.4
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+[Mozilla/5.0 (Windows; U; Windows NT 6.0; *) AppleWebKit/* (KHTML, like Gecko) Iron/0.4.* Safari/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iPod
+
+[iPod]
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+[Mozilla/5.0 (iPod; U; CPU iPhone OS 2_2 like Mac OS X; en-us) AppleWebKit/* (KHTML, like Gecko) Mobile/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iTunes
+
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Media Players
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+[Microsoft NetShow(TM) Player with RealVideo(R)]
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+[MPlayer HEAD CVS]
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+
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+[WinampMPEG/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Nintendo
+
+[Nintendo Wii]
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+
+[Opera/* (Nintendo DSi; Opera/*; *; *)]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Windows Media Player
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Zune
+
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+[Mozilla/4.0 (compatible; MSIE ?.0; *Zune 2.0*)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.0
+
+[QuickTime 7.0]
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+[QuickTime/7.0.* (qtver=7.0.*;*;os=Mac 10.*)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.1
+
+[QuickTime 7.1]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.2
+
+[QuickTime 7.2]
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+[QuickTime/7.2.* (qtver=7.2.*;*;os=Mac 10.*)*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.3
+
+[QuickTime 7.3]
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+[QuickTime (qtver=7.3*;cpu=PPC;os=Mac 10.*)]
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+[QuickTime (qtver=7.3*;os=Windows NT 5.2*)]
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+[QuickTime/7.3.* (qtver=7.3.*;*;os=Mac 10.*)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; QuickTime 7.4
+
+[QuickTime 7.4]
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+
+[QuickTime (qtver=7.4*;cpu=PPC;os=Mac 10.*)]
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+[QuickTime (qtver=7.4*;cpu=PPC;os=Mac 9.*)]
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+[QuickTime (qtver=7.4*;os=Windows 98*)]
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+[QuickTime (qtver=7.4*;os=Windows NT 4.0*)]
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+[QuickTime (qtver=7.4*;os=Windows NT 5.0*)]
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+
+[QuickTime (qtver=7.4*;os=Windows NT 5.1*)]
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+[QuickTime (qtver=7.4*;os=Windows NT 5.2*)]
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+[QuickTime/7.4.* (qtver=7.4.*;*;os=Mac 10.*)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Google Android
+
+[Android]
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+
+[Mozilla/5.0 (Linux; U; Android *; *) AppleWebKit/* (KHTML, like Gecko) Safari/*]
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+
+[Mozilla/5.0 (Linux; U; Android *; *) AppleWebKit/* (KHTML, like Gecko) Version/3.0.* Mobile Safari/*]
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+Platform=Linux
+isMobileDevice=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; BlackBerry
+
+[BlackBerry]
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+Frames=true
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+isMobileDevice=true
+
+[*BlackBerry*]
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+
+[*BlackBerrySimulator/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Handspring Blazer
+
+[Blazer]
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+isMobileDevice=true
+
+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 95; PalmSource; Blazer 3.0) 16;160x160]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; PalmSource/*; Blazer/4.0) 16;320x448]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; PalmSource/*; Blazer/4.1) 16;320x320]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; PalmSource/*; Blazer/4.2) 16;320x320]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; PalmSource/*; Blazer/4.4) 16;320x320]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows 98; PalmSource/*; Blazer/4.5) 16;320x320]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; DoCoMo
+
+[DoCoMo]
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+
+[DoCoMo/1.0*]
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+[DoCoMo/2.0*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; IEMobile
+
+[IEMobile]
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+[Mozilla/4.0 (compatible; MSIE 6.0; Windows CE; IEMobile 7.*)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; iPhone
+
+[iPhone]
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+[Mozilla/4.0 (iPhone; *)]
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+[Mozilla/4.0 (iPhone; U; CPU like Mac OS X; *)]
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+[Mozilla/5.0 (iPhone Simulator; U; CPU iPhone OS 2_* like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone Simulator; U; CPU iPhone OS 2_0_1 like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone Simulator; U; CPU iPhone OS 2_1 like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone)]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_* like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+
+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_0* like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.1* Mobile/* Safari/*]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_0_2 like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko)]
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+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_1 like Mac OS X; *)*]
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+
+[Mozilla/5.0 (iPhone; U; CPU iPhone OS 2_2_1 like Mac OS X; *)]
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+[Mozilla/5.0 (iPhone; U; CPU like Mac OS X; *) AppleWebKit/* (KHTML, like Gecko) Version/3.0 Mobile/* Safari/*]
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+
+[Mozilla/5.0 (iPod; U; *Mac OS X; *) AppleWebKit/* (*) Version/* Mobile/*]
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+
+[Mozilla/5.0 (iPod; U; CPU iPhone OS 2_2* like Mac OS X; *)*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; KDDI
+
+[KDDI]
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+
+[KDDI-* UP.Browser/* (GUI) MMP/*]
+Parent=KDDI
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Miscellaneous Mobile
+
+[Miscellaneous Mobile]
+Parent=DefaultProperties
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+IFrames=true
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+isMobileDevice=true
+CssVersion=2
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+
+[Mozilla/5.0 (X11; *; CentOS; *) AppleWebKit/* (KHTML, like Gecko) Bolt/0.* Version/3.0 Safari/*]
+Parent=Miscellaneous Mobile
+Browser=Bolt
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Motorola Internet Browser
+
+[Motorola Internet Browser]
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+Frames=true
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+isMobileDevice=true
+
+[MOT-*/*]
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+
+[MOT-1*/* UP.Browser/*]
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+
+[MOT-8700_/* UP.Browser/*]
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+
+[MOT-A-0A/* UP.Browser/*]
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+
+[MOT-A-2B/* UP.Browser/*]
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+
+[MOT-A-88/* UP.Browser/*]
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+
+[MOT-C???/* MIB/*]
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+
+[MOT-GATW_/* UP.Browser/*]
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+
+[MOT-L6/* MIB/*]
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+
+[MOT-L7/* MIB/*]
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+
+[MOT-M*/* UP.Browser/*]
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+
+[MOT-MP*/* Mozilla/* (compatible; MSIE *; Windows CE; *)]
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+
+[MOT-MP*/* Mozilla/4.0 (compatible; MSIE *; Windows CE; *)]
+Parent=Motorola Internet Browser
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+
+[MOT-SAP4_/* UP.Browser/*]
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+
+[MOT-T*/*]
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+
+[MOT-T7*/* MIB/*]
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+
+[MOT-T721*]
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+
+[MOT-TA02/* MIB/*]
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+
+[MOT-V*/*]
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+
+[MOT-V*/* MIB/*]
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+
+[MOT-V*/* UP.Browser/*]
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+
+[MOT-V3/* MIB/*]
+Parent=Motorola Internet Browser
+
+[MOT-V4*/* MIB/*]
+Parent=Motorola Internet Browser
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; MSN Mobile Proxy
+
+[MSN Mobile Proxy]
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+Browser=MSN Mobile Proxy
+Win32=true
+Frames=true
+Tables=true
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+JavaScript=true
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SEMC Browser
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SonyEricsson
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Netbox
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; PowerTV
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; WebTV/MSNTV
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; w3m
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+[iSiloX/4.0* Windows/32]
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+[iSiloX/4.1* MacOS]
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+
+[iSiloX/4.1* Windows/32]
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+
+[iSiloX/4.2* MacOS]
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+Platform=MacPPC
+
+[iSiloX/4.2* Windows/32]
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+Platform=Win32
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+
+[iSiloX/4.3* MacOS]
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+Platform=MacOSX
+
+[iSiloX/4.3* Windows/32]
+Parent=iSiloX
+Version=4.3
+MajorVer=4
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+Platform=Win32
+Win32=true
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Lycoris Desktop/LX
+
+[Lycoris Desktop/LX]
+Parent=DefaultProperties
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+Frames=true
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+JavaApplets=true
+JavaScript=true
+Crawler=true
+
+[Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.*: Desktop/LX Amethyst) Gecko/*]
+Parent=Lycoris Desktop/LX
+Version=1.1
+MajorVer=1
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+Platform=Linux
+
+[Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.*; Desktop/LX Amethyst) Gecko/*]
+Parent=Lycoris Desktop/LX
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Linux
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mosaic
+
+[Mosaic]
+Parent=DefaultProperties
+Browser=Mosaic
+Frames=true
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+JavaApplets=true
+JavaScript=true
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+[Mozilla/4.0 (VMS_Mosaic)]
+Parent=Mosaic
+Platform=OpenVMS
+
+[VMS_Mosaic/3.7*]
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+
+[VMS_Mosaic/3.8*]
+Parent=Mosaic
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+Platform=OpenVMS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; NetPositive
+
+[NetPositive]
+Parent=DefaultProperties
+Browser=NetPositive
+Platform=BeOS
+Frames=true
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+JavaApplets=true
+JavaScript=true
+
+[*NetPositive/2.2*]
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+MajorVer=2
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+[*NetPositive/2.2*BeOS*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; OmniWeb
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+[OmniWeb]
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+[Mozilla/* (Macintosh; ?; *Mac OS X; *) AppleWebKit/* (*) OmniWeb/v6*]
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+
+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/4*]
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+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/5*]
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+[Mozilla/* (Macintosh; ?; PPC) OmniWeb/6*]
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+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/125.4 (KHTML, like Gecko, Safari) OmniWeb/v563.34]
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+MajorVer=5
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/125.4 (KHTML, like Gecko, Safari) OmniWeb/v563.34]
+Parent=OmniWeb
+Version=5.1
+MajorVer=5
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+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/420+ (KHTML, like Gecko, Safari/420) OmniWeb/v607]
+Parent=OmniWeb
+Version=5.5
+MajorVer=5
+MinorVer=5
+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/420+ (KHTML, like Gecko, Safari/420) OmniWeb/v607]
+Parent=OmniWeb
+Version=5.5
+MajorVer=5
+MinorVer=5
+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/522+ (KHTML, like Gecko, Safari/522) OmniWeb/v613]
+Parent=OmniWeb
+Version=5.6
+MajorVer=5
+MinorVer=6
+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/522+ (KHTML, like Gecko, Safari/522) OmniWeb/v613]
+Parent=OmniWeb
+Version=5.6
+MajorVer=5
+MinorVer=6
+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/85 (KHTML, like Gecko) OmniWeb/v496]
+Parent=OmniWeb
+Version=4.5
+MajorVer=4
+MinorVer=5
+
+[Mozilla/5.0 (Macintosh; U; PPC Mac OS X; en-US) AppleWebKit/85 (KHTML, like Gecko) OmniWeb/v558.36 ]
+Parent=OmniWeb
+Version=5.0
+MajorVer=5
+MinorVer=0
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Shiira
+
+[Shiira]
+Parent=DefaultProperties
+Browser=Shiira
+Platform=MacOSX
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+BackgroundSounds=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/0.9*]
+Parent=Shiira
+Version=0.9
+MajorVer=0
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/1.0*]
+Parent=Shiira
+Version=1.0
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/1.1*]
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/1.2*]
+Parent=Shiira
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/2.1*]
+Parent=Shiira
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+MajorVer=2
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Shiira/2.2*]
+Parent=Shiira
+Version=2.2
+MajorVer=2
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+
+[Windows Maker]
+Parent=DefaultProperties
+Browser=WMaker
+Platform=Linux
+Frames=true
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+VBScript=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[WMaker*]
+Parent=Windows Maker
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.0
+
+[K-Meleon 1.0]
+Parent=DefaultProperties
+Browser=K-Meleon
+Version=1.0
+MajorVer=1
+Win32=true
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* K-Meleon/1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win95
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* K-Meleon/1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win98
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* K-Meleon?1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
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+Platform=Win2000
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* K-Meleon/1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.0*]
+Parent=K-Meleon 1.0
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=WinNT
+Win32=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.1
+
+[K-Meleon 1.1]
+Parent=DefaultProperties
+Browser=K-Meleon
+Version=1.1
+MajorVer=1
+MinorVer=1
+Win32=true
+Frames=true
+IFrames=true
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+JavaApplets=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* K-Meleon/1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win95
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* K-Meleon/1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win98
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* K-Meleon?1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win2000
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* K-Meleon/1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.1*]
+Parent=K-Meleon 1.1
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=WinNT
+Win32=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; K-Meleon 1.5
+
+[K-Meleon 1.5]
+Parent=DefaultProperties
+Browser=K-Meleon
+Version=1.5
+MajorVer=1
+MinorVer=5
+Win32=true
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+supportsCSS=true
+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
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+Platform=Win95
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Version=1.0
+MajorVer=1
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+Platform=Win98
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* K-Meleon?1.5*]
+Parent=K-Meleon 1.5
+Version=1.0
+MajorVer=1
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+Platform=Win2000
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Version=1.0
+MajorVer=1
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+Platform=WinXP
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=Win2003
+Win32=true
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Platform=WinVista
+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Platform=Win7
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* K-Meleon/1.5*]
+Parent=K-Meleon 1.5
+Version=1.0
+MajorVer=1
+MinorVer=0
+Platform=WinNT
+Win32=true
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 3.0
+
+[Konqueror 3.0]
+Parent=DefaultProperties
+Browser=Konqueror
+Platform=Linux
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[*Konqueror/3.0*]
+Parent=Konqueror 3.0
+Version=3.0
+MajorVer=3
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+IFrames=false
+
+[*Konqueror/3.0*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.0
+MajorVer=3
+MinorVer=0
+Platform=FreeBSD
+IFrames=false
+
+[*Konqueror/3.0*Linux*]
+Parent=Konqueror 3.0
+Version=3.0
+MajorVer=3
+MinorVer=0
+Platform=Linux
+IFrames=false
+
+[*Konqueror/3.1*]
+Parent=Konqueror 3.0
+Version=3.1
+MajorVer=3
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+
+[*Konqueror/3.1*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.1
+MajorVer=3
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+Platform=FreeBSD
+
+[*Konqueror/3.1*Linux*]
+Parent=Konqueror 3.0
+Version=3.1
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+
+[*Konqueror/3.2*]
+Parent=Konqueror 3.0
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+
+[*Konqueror/3.2*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.2
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+Platform=FreeBSD
+
+[*Konqueror/3.2*Linux*]
+Parent=Konqueror 3.0
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+Platform=Linux
+
+[*Konqueror/3.3*]
+Parent=Konqueror 3.0
+Version=3.3
+MajorVer=3
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+
+[*Konqueror/3.3*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.3
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+Platform=FreeBSD
+
+[*Konqueror/3.3*Linux*]
+Parent=Konqueror 3.0
+Version=3.3
+MajorVer=3
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+Platform=Linux
+
+[*Konqueror/3.3*OpenBSD*]
+Parent=Konqueror 3.0
+Version=3.3
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+Platform=OpenBSD
+
+[*Konqueror/3.4*]
+Parent=Konqueror 3.0
+Version=3.4
+MajorVer=3
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+
+[*Konqueror/3.4*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.4
+MajorVer=3
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+Platform=FreeBSD
+
+[*Konqueror/3.4*Linux*]
+Parent=Konqueror 3.0
+Version=3.4
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+Platform=Linux
+
+[*Konqueror/3.4*OpenBSD*]
+Parent=Konqueror 3.0
+Version=3.4
+MajorVer=3
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+Platform=OpenBSD
+
+[*Konqueror/3.5*]
+Parent=Konqueror 3.0
+Version=3.5
+MajorVer=3
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+
+[*Konqueror/3.5*FreeBSD*]
+Parent=Konqueror 3.0
+Version=3.5
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+Platform=FreeBSD
+
+[*Konqueror/3.5*Linux*]
+Parent=Konqueror 3.0
+Version=3.5
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+Platform=Linux
+
+[*Konqueror/3.5*OpenBSD*]
+Parent=Konqueror 3.0
+Version=3.5
+MajorVer=3
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+Platform=OpenBSD
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.0
+
+[Konqueror 4.0]
+Parent=DefaultProperties
+Browser=Konqueror
+Version=4.0
+MajorVer=4
+Frames=true
+IFrames=true
+Tables=true
+Cookies=true
+JavaScript=true
+CssVersion=2
+supportsCSS=true
+
+[Mozilla/5.0 (compatible; Konqueror/4.0*; Debian) KHTML/4.* (like Gecko)]
+Parent=Konqueror 4.0
+Platform=Debian
+
+[Mozilla/5.0 (compatible; Konqueror/4.0.*; *Linux) KHTML/4.* (like Gecko)]
+Parent=Konqueror 4.0
+Platform=Linux
+
+[Mozilla/5.0 (compatible; Konqueror/4.0.*; FreeBSD) KHTML/4.* (like Gecko)]
+Parent=Konqueror 4.0
+Platform=FreeBSD
+
+[Mozilla/5.0 (compatible; Konqueror/4.0.*; NetBSD) KHTML/4.* (like Gecko)]
+Parent=Konqueror 4.0
+Platform=NetBSD
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.1
+
+[Konqueror 4.1]
+Parent=DefaultProperties
+Browser=Konqueror
+Version=4.1
+MajorVer=4
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+Frames=true
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+
+[Mozilla/5.0 (compatible; Konqueror/4.1*; *Linux*) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.1
+Platform=Linux
+
+[Mozilla/5.0 (compatible; Konqueror/4.1*; Debian) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.1
+Platform=Debian
+
+[Mozilla/5.0 (compatible; Konqueror/4.1*; FreeBSD) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.1
+Platform=FreeBSD
+
+[Mozilla/5.0 (compatible; Konqueror/4.1*; NetBSD) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.1
+Platform=NetBSD
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Konqueror 4.2
+
+[Konqueror 4.2]
+Parent=DefaultProperties
+Browser=Konqueror
+Version=4.2
+MajorVer=4
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+Frames=true
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+
+[Mozilla/5.0 (compatible; Konqueror/4.2*; *Linux*) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.2
+Platform=Linux
+
+[Mozilla/5.0 (compatible; Konqueror/4.2*; Debian) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.2
+Platform=Debian
+
+[Mozilla/5.0 (compatible; Konqueror/4.2*; FreeBSD) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.2
+Platform=FreeBSD
+
+[Mozilla/5.0 (compatible; Konqueror/4.2*; NetBSD) KHTML/4.* (like Gecko)*]
+Parent=Konqueror 4.2
+Platform=NetBSD
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Safari
+
+[Safari]
+Parent=DefaultProperties
+Browser=Safari
+Platform=MacOSX
+Frames=true
+IFrames=true
+Tables=true
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+BackgroundSounds=true
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+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Safari/100*]
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Safari/125*]
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+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Safari/312*]
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+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Safari/412*]
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+
+[Mozilla/5.0 (Macintosh; *Mac OS X*) AppleWebKit/* (*) Safari/416*]
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+[Mozilla/5.0 (X11; *; IRIX*; *; rv:1.*) Gecko/* Mozilla Firebird/0.*]
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+[Mozilla/5.0 (X11; *; Linux*; *; rv:1.*) Gecko/* Firebird/0.*]
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+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Mozilla Firebird/0.*]
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+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Mozilla Firebird/0.*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox
+
+[Firefox]
+Parent=DefaultProperties
+Browser=Firefox
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+[Mozilla/5.0 (Windows NT 5.?; ?; rv:1.*) Gecko/* Firefox]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.*; *; rv:1.*) Gecko/* Deer Park/Alpha*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; rv:1.*) Gecko/* Firefox/0.*]
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+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/0.*]
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+
+[Mozilla/5.0 (X11; *; FreeBSD*; rv:1.*) Gecko/* Firefox/0.*]
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+[Mozilla/5.0 (X11; *; HP-UX*; rv:1.*) Gecko/* Firefox/0.*]
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+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/0.*]
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+[Mozilla/5.0 (X11; *; Linux*; *; rv:1.*) Gecko/* Firefox/0.*]
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+[Mozilla/5.0 (X11; *; Linux*; rv:1.*) Gecko/* Firefox/0.*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 1.0
+
+[Firefox 1.0]
+Parent=DefaultProperties
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+[Mozilla/5.0 (Linux; *; PPC*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Win 9x 4.90*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.0*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.0*]
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+Win32=true
+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (X11; *; *Linux*; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (X11; *; DragonFly*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.0*]
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+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.0*]
+Parent=Firefox 1.0
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 1.4
+
+[Firefox 1.4]
+Parent=DefaultProperties
+Browser=Firefox
+Version=1.4
+MajorVer=1
+MinorVer=4
+Frames=true
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+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+
+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.*) Gecko/* Firefox/1.4*]
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+
+[Mozilla/5.0 (Windows; *; Win95*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.4*]
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+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.4*]
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+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.4*]
+Parent=Firefox 1.4
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 1.5
+
+[Firefox 1.5]
+Parent=DefaultProperties
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+Version=1.5
+MajorVer=1
+MinorVer=5
+Frames=true
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+w3cdomversion=1.0
+
+[Mozilla/5.0 (Linux; *; PPC*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+Platform=OS/2
+
+[Mozilla/5.0 (rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.*) Gecko/* Firefox/1.5*]
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+Platform=WinME
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+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2 x64; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
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+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+[Mozilla/5.0 (X11; *; OpenBSD*; *; rv:1.*) Gecko/* Firefox/1.5*]
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+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.*) Gecko/* Firefox/1.5*]
+Parent=Firefox 1.5
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 2.0
+
+[Firefox 2.0]
+Parent=DefaultProperties
+Browser=Firefox
+Version=2.0
+MajorVer=2
+Frames=true
+IFrames=true
+Tables=true
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+CssVersion=2
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+w3cdomversion=1.0
+
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+
+[Mozilla/5.0 (Macintosh; *; *Mac OS X*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (OS/2; *; Warp*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=OS/2
+
+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (Windows; *; Win95; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+[Mozilla/5.0 (Windows; *; Win98; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0; *; rv:1.*) Gecko/* Firefox/2.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.2; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.0; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+Platform=Win7
+
+[Mozilla/5.0 (Windows; *; WinNT4.0; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=WinNT
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+
+[Mozilla/5.0 (X11; *; *Linux*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (X11; *; FreeBSD*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (X11; *; HP-UX*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
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+
+[Mozilla/5.0 (X11; *; IRIX64*; *; rv:1.8*) Gecko/* Firefox/2.0*]
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+
+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.8*) Gecko/* Firefox/2.0*]
+Parent=Firefox 2.0
+Platform=SunOS
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.0
+
+[Firefox 3.0]
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+Browser=Firefox
+Version=3.0
+MajorVer=3
+Frames=true
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+
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+
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+
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+
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+
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+Win64=true
+
+[Mozilla/5.0 (Windows; U; Windows NT 5.2 x64; *; rv:1.9*) Gecko/* Firefox/3.0*]
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+
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+
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+
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.1
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Firefox 3.5
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+[Firefox 3.5]
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+[Mozilla/5.0 (Windows; *; Windows NT 6.1; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+[Mozilla/5.0 (Windows; U; Windows NT 5.2 x64; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+[Mozilla/5.0 (X11; *; SunOS*; *; rv:1.9.*) Gecko/* Firefox/3.5b*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Phoenix
+
+[Phoenix]
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+[Mozilla/5.0 (Windows; *; Win 9x 4.90; *; rv:1.4*) Gecko/* Phoenix/0.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.0*; *; rv:1.4*) Gecko/* Phoenix/0.5*]
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+[Mozilla/5.0 (Windows; *; Windows NT 5.1; *; rv:1.4*) Gecko/* Phoenix/0.5*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Iceweasel
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.0
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+[Mozilla 1.0]
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+[Mozilla/5.0 (*rv:1.0.*) Gecko/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.1
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+[Mozilla 1.1]
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+[Mozilla/5.0 (*rv:1.1.*) Gecko/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.2
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+[Mozilla 1.2]
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+[Mozilla/5.0 (*rv:1.2.*) Gecko/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.3
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+[Mozilla/5.0 (*rv:1.3.*) Gecko/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.4
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+[Mozilla/5.0 (Macintosh; ?; *Mac OS X*; *rv:1.4*) Gecko/*]
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+[Mozilla/5.0 (X11; *Linux*; *rv:1.4*) Gecko/*]
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+[Mozilla/5.0 (X11; *OpenBSD*; *rv:1.4*) Gecko/*]
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+[Mozilla/5.0 (X11; *SunOS*; *rv:1.4*) Gecko/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.5
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+[Mozilla 1.5]
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+[Mozilla/5.0 (*rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Macintosh; ?; *Mac OS X*; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Win 9x 4.90; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Win3.1; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Win3.11; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Windows NT 5.0; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Windows NT 5.1; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; WinNT4.0; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (X11; *FreeBSD*; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (X11; *OpenBSD*; *rv:1.5*) Gecko/*]
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+[Mozilla/5.0 (X11; *SunOS*; *rv:1.5*) Gecko/*]
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.6
+
+[Mozilla 1.6]
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+[Mozilla/5.0 (X11; *SunOS*; *rv:1.6*) Gecko/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.7
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+
+[Mozilla/5.0 (X11; *Linux*; *rv:1.7*) Gecko/*]
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+[Mozilla/5.0 (X11; *OpenBSD*; *rv:1.7*) Gecko/*]
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+[Mozilla/5.0 (X11; *SunOS*; *rv:1.7*) Gecko/*]
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+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Mozilla 1.8
+
+[Mozilla 1.8]
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+MajorVer=1
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+
+[Mozilla/5.0 (Macintosh; ?; *Mac OS X*; *rv:1.8*) Gecko/*]
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+[Mozilla/5.0 (Windows; ?; Win3.1; *rv:1.8*) Gecko/*]
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diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/config b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/config
new file mode 100644
index 0000000000000000000000000000000000000000..c1d2863007699198d3745c8745e4eaaacde70547
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/config
@@ -0,0 +1,46 @@
+<configuration>
+	<dllmap dll="i:cygwin1.dll" target="libc.so.6" os="!windows" />
+	<dllmap dll="libc" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="intl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" name="bind_textdomain_codeset" target="libc.so.6" os="solaris"/>
+	<dllmap dll="libintl" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:libxslt.dll" target="libxslt.so" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libodbc.so.2" os="!windows"/>
+	<dllmap dll="i:odbc32.dll" target="libiodbc.dylib" os="osx"/>
+	<dllmap dll="oci" target="libclntsh.so" os="!windows"/>
+	<dllmap dll="db2cli" target="libdb2_36.so" os="!windows"/>
+	<dllmap dll="MonoPosixHelper" target="libMonoPosixHelper.so" os="!windows" />
+	<dllmap dll="System.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Net.Security.Native" target="$mono_libdir/libmono-native.so" os="!windows" />
+	<dllmap dll="System.Security.Cryptography.Native.Apple" target="$mono_libdir/libmono-native.so" os="osx" />
+	<dllmap dll="libmono-btls-shared" target="libmono-btls-shared.so" os="!windows" />
+	<dllmap dll="i:msvcrt" target="libc.so.6" os="!windows"/>
+	<dllmap dll="i:msvcrt.dll" target="libc.so.6" os="!windows"/>
+	<dllmap dll="sqlite" target="libsqlite.so.0" os="!windows"/>
+	<dllmap dll="sqlite3" target="libsqlite3.so.0" os="!windows"/>
+	<dllmap dll="libX11" target="libX11.so.6" os="!windows" />
+	<dllmap dll="libgdk-x11-2.0" target="libgdk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgdk_pixbuf-2.0" target="libgdk_pixbuf-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgtk-x11-2.0" target="libgtk-x11-2.0.so.0" os="!windows"/>
+	<dllmap dll="libglib-2.0" target="libglib-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgobject-2.0" target="libgobject-2.0.so.0" os="!windows"/>
+	<dllmap dll="libgnomeui-2" target="libgnomeui-2.so.0" os="!windows"/>
+	<dllmap dll="librsvg-2" target="librsvg-2.so.2" os="!windows"/>
+	<dllmap dll="libXinerama" target="libXinerama.so.1" os="!windows" />
+	<dllmap dll="libasound" target="libasound.so.2" os="!windows" />
+	<dllmap dll="libcairo-2.dll" target="libcairo.so.2" os="!windows"/>
+	<dllmap dll="libcairo-2.dll" target="libcairo.2.dylib" os="osx"/>
+	<dllmap dll="libcups" target="libcups.so.2" os="!windows"/>
+	<dllmap dll="libcups" target="libcups.dylib" os="osx"/>
+	<dllmap dll="i:kernel32.dll">
+		<dllentry dll="__Internal" name="CopyMemory" target="mono_win32_compat_CopyMemory"/>
+		<dllentry dll="__Internal" name="FillMemory" target="mono_win32_compat_FillMemory"/>
+		<dllentry dll="__Internal" name="MoveMemory" target="mono_win32_compat_MoveMemory"/>
+		<dllentry dll="__Internal" name="ZeroMemory" target="mono_win32_compat_ZeroMemory"/>
+	</dllmap>
+	<dllmap dll="gdiplus" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdiplus.dll" target="libgdiplus.so.0"  os="!windows"/>
+	<dllmap dll="gdi32" target="libgdiplus.so.0" os="!windows"/>
+	<dllmap dll="gdi32.dll" target="libgdiplus.so.0" os="!windows"/>
+</configuration>
diff --git a/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml
new file mode 100644
index 0000000000000000000000000000000000000000..a3df3b5e9c236e124791fc1dfd5beccaca7e3727
--- /dev/null
+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc/mono/mconfig/config.xml
@@ -0,0 +1,616 @@
+<?xml version="1.0" encoding="utf-8"?>
+<mconfig>
+  <configuration>
+    <handlers>
+      <handler section="feature" 
+	       type="Mono.MonoConfig.FeatureNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.FeatureNode, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="configBlock"
+	       type="Mono.MonoConfig.ConfigBlockNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.ConfigBlockBlock, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="default"
+	       type="Mono.MonoConfig.DefaultNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.DefaultNode, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+      <handler section="defaultConfigFile"
+	       type="Mono.MonoConfig.DefaultConfigFileNodeHandler, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null"
+	       storageType="System.Collections.Generic.Dictionary`2[[System.String, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089],[Mono.MonoConfig.DefaultConfigFile, mconfig, Version=0.1.0.0, Culture=neutral, PublicKeyToken=null]], mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/>
+    </handlers>
+  </configuration>
+
+  <feature name="AJAX" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any .NET 3.5 AJAX.NET application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX config sections"/>
+      <block name="AJAX controls registration"/>
+      <block name="AJAX compilation"/>
+      <block name="AJAX HTTP handlers"/>
+      <block name="AJAX HTTP modules"/>
+      <block name="AJAX CodeDOM 3.5 settings"/>
+      <block name="AJAX runtime settings"/>
+      <block name="AJAX system.webServer"/>
+      <block name="" />
+    </blocks>
+  </feature>
+
+  <feature name="AJAX1" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any AJAX.NET 1.0 application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX1 config sections"/>
+      <block name="AJAX1 controls registration"/>
+      <block name="AJAX1 compilation"/>
+      <block name="AJAX1 HTTP handlers"/>
+      <block name="AJAX1 HTTP modules"/>
+      <block name="AJAX1 system.web.extensions"/>
+      <block name="AJAX1 system.webServer"/>
+      <block name="" />
+    </blocks>
+  </feature>
+
+  <feature name="DynamicData" target="web">
+    <description><![CDATA[
+      Adds entries to your Web.config file which are required by any ASP.NET DynamicData application.
+    ]]></description>
+    <blocks>
+      <block name="AJAX config sections"/>
+      <block name="AJAX controls registration"/>
+      <block name="AJAX compilation"/>
+      <block name="AJAX HTTP handlers"/>
+      <block name="AJAX HTTP modules"/>
+      <block name="AJAX system.web.extensions"/>
+      <block name="AJAX system.webServer"/>
+
+      <block name="DynamicData controls registration" />
+      <block name="DynamicData compilation" />
+      <block name="DynamicData HTTP modules" />
+    </blocks>
+  </feature>
+
+  <feature name="SettingsMapProtection" target="web">
+    <description><![CDATA[
+      Adds an entry to your config file which will prevent download of the 'settings.map' file. The
+file is used by the settings mapping manager to modify configuration settings depending on the operating
+system under which your application runs. Add this feature only if you have your own custom 'settings.map'
+file in the top-level directory of your ASP.NET application.
+]]></description>
+    <blocks>
+      <block name="SettingsMap Handler"/>
+    </blocks>
+  </feature>
+
+  <!-- configuration blocks required by the features -->
+  <configBlock name="SettingsMap Handler">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add verb="*" path="settings.map" type="System.Web.HttpForbiddenHandler, System.Web, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a" />
+]]>
+    </contents>
+  </configBlock>
+
+  <!-- AJAX.NET 3.5 start -->
+  <configBlock name="AJAX config sections">
+    <requires>
+      <section name="configuration">
+	<section name="configSections" attachPoint="true"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+        <sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+          <section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication"/>
+          <sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35">
+            <section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="Everywhere" />
+            <section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+            <section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+            <section name="roleService" type="System.Web.Configuration.ScriptingRoleServiceSection, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" requirePermission="false" allowDefinition="MachineToApplication" />
+          </sectionGroup>
+        </sectionGroup>
+      </sectionGroup>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX controls registration">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="pages" defaultBlockName="system.web.pages">
+	    <section name="controls" defaultBlockName="system.web.pages.controls"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+          <add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+          <add tagPrefix="asp" namespace="System.Web.UI.WebControls" assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX compilation">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="compilation" defaultBlockName="system.web.compilation">
+	    <section name="assemblies" defaultBlockName="system.web.compilation.assemblies"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	    <add assembly="System.Core, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+            <add assembly="System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+            <add assembly="System.Data.DataSetExtensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+            <add assembly="System.Xml.Linq, Version=3.5.0.0, Culture=neutral, PublicKeyToken=B77A5C561934E089"/>
+
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX HTTP handlers">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+    
+    <contents>
+      <![CDATA[
+	<remove verb="*" path="*.asmx"/>
+        <add verb="*" path="*.asmx" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add verb="*" path="*_AppService.axd" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" validate="false"/>   
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX HTTP modules">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpModules" defaultBlockName="system.web.httpModules"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add name="ScriptModule" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX CodeDOM 3.5 settings">
+    <requires>
+      <section name="configuration">
+	<section name="system.codedom">
+	  <section name="compilers" defaultBlockName="system.codedom.compilers"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	<compiler language="c#;cs;csharp" extension=".cs" warningLevel="4"
+                  type="Microsoft.CSharp.CSharpCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+          <providerOption name="CompilerVersion" value="v3.5"/>
+          <providerOption name="WarnAsError" value="false"/>
+        </compiler>
+        <compiler language="vb;vbs;visualbasic;vbscript" extension=".vb" warningLevel="4"
+                  type="Microsoft.VisualBasic.VBCodeProvider, System, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089">
+          <providerOption name="CompilerVersion" value="v3.5"/>
+          <providerOption name="OptionInfer" value="true"/>
+          <providerOption name="WarnAsError" value="false"/>
+        </compiler>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX runtime settings">
+    <requires>
+      <section name="configuration">
+	<section name="runtime"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+	<assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1">
+        <dependentAssembly>
+          <assemblyIdentity name="System.Web.Extensions" publicKeyToken="31bf3856ad364e35"/>
+          <bindingRedirect oldVersion="1.0.0.0-1.1.0.0" newVersion="3.5.0.0"/>
+        </dependentAssembly>
+        <dependentAssembly>
+          <assemblyIdentity name="System.Web.Extensions.Design" publicKeyToken="31bf3856ad364e35"/>
+          <bindingRedirect oldVersion="1.0.0.0-1.1.0.0" newVersion="3.5.0.0"/>
+        </dependentAssembly>
+      </assemblyBinding>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX system.webServer">
+    <requires>
+      <section name="configuration">
+	<section name="system.webServer"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+      <validation validateIntegratedModeConfiguration="false"/>
+      <modules>
+        <remove name="ScriptModule" />
+        <add name="ScriptModule" preCondition="managedHandler" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+      </modules>
+      <handlers>
+        <remove name="WebServiceHandlerFactory-Integrated"/>
+        <remove name="ScriptHandlerFactory" />
+        <remove name="ScriptHandlerFactoryAppServices" />
+        <remove name="ScriptResource" />
+        <add name="ScriptHandlerFactory" verb="*" path="*.asmx" preCondition="integratedMode"
+             type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add name="ScriptHandlerFactoryAppServices" verb="*" path="*_AppService.axd" preCondition="integratedMode"
+             type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35"/>
+        <add name="ScriptResource" preCondition="integratedMode" verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31BF3856AD364E35" />
+      </handlers>
+]]>
+    </contents>
+  </configBlock>
+  <!-- AJAX.NET 3.5 end -->
+
+  <!-- AJAX.NET 1.0 start -->
+  <configBlock name="AJAX1 config sections">
+    <requires>
+      <section name="configuration">
+	<section name="configSections" attachPoint="true"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<sectionGroup name="system.web.extensions" type="System.Web.Configuration.SystemWebExtensionsSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+      <sectionGroup name="scripting" type="System.Web.Configuration.ScriptingSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+          <section name="scriptResourceHandler" type="System.Web.Configuration.ScriptingScriptResourceHandlerSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication"/>
+        <sectionGroup name="webServices" type="System.Web.Configuration.ScriptingWebServicesSectionGroup, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35">
+          <section name="jsonSerialization" type="System.Web.Configuration.ScriptingJsonSerializationSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="Everywhere" />
+          <section name="profileService" type="System.Web.Configuration.ScriptingProfileServiceSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication" />
+          <section name="authenticationService" type="System.Web.Configuration.ScriptingAuthenticationServiceSection, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" requirePermission="false" allowDefinition="MachineToApplication" />
+        </sectionGroup>
+      </sectionGroup>
+    </sectionGroup>
+      ]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 controls registration">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="pages" defaultBlockName="system.web.pages">
+	    <section name="controls" defaultBlockName="system.web.pages.controls"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add tagPrefix="asp" namespace="System.Web.UI" assembly="System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 compilation">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="compilation" defaultBlockName="system.web.compilation">
+	    <section name="assemblies" defaultBlockName="system.web.compilation.assemblies"/>
+	  </section>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add assembly="System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 HTTP handlers">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpHandlers" defaultBlockName="system.web.httpHandlers"/>
+	</section>
+      </section>
+    </requires>
+    
+    <contents>
+      <![CDATA[
+<remove verb="*" path="*.asmx"/>
+      <add verb="*" path="*.asmx" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add verb="*" path="*_AppService.axd" validate="false" type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add verb="GET,HEAD" path="ScriptResource.axd" type="System.Web.Handlers.ScriptResourceHandler, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" validate="false"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 HTTP modules">
+    <requires>
+      <section name="configuration">
+	<section name="system.web">
+	  <section name="httpModules" defaultBlockName="system.web.httpModules"/>
+	</section>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<add name="ScriptModule" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 system.web.extensions">
+    <requires>
+      <section name="configuration">
+	<section name="system.web.extensions"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<scripting>
+      <webServices>
+      <!-- Uncomment this line to customize maxJsonLength and add a custom converter -->
+      <!--
+      <jsonSerialization maxJsonLength="500">
+        <converters>
+          <add name="ConvertMe" type="Acme.SubAcme.ConvertMeTypeConverter"/>
+        </converters>
+      </jsonSerialization>
+      -->
+      <!-- Uncomment this line to enable the authentication service. Include requireSSL="true" if appropriate. -->
+      <!--
+        <authenticationService enabled="true" requireSSL = "true|false"/>
+      -->
+
+      <!-- Uncomment these lines to enable the profile service. To allow profile properties to be retrieved
+           and modified in ASP.NET AJAX applications, you need to add each property name to the readAccessProperties and
+           writeAccessProperties attributes. -->
+      <!--
+      <profileService enabled="true"
+                      readAccessProperties="propertyname1,propertyname2"
+                      writeAccessProperties="propertyname1,propertyname2" />
+      -->
+      </webServices>
+      <!--
+      <scriptResourceHandler enableCompression="true" enableCaching="true" />
+      -->
+    </scripting>
+]]>
+    </contents>
+  </configBlock>
+
+  <configBlock name="AJAX1 system.webServer">
+    <requires>
+      <section name="configuration">
+	<section name="system.webServer"/>
+      </section>
+    </requires>
+
+    <contents>
+      <![CDATA[
+<validation validateIntegratedModeConfiguration="false"/>
+    <modules>
+      <add name="ScriptModule" preCondition="integratedMode" type="System.Web.Handlers.ScriptModule, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+    </modules>
+    <handlers>
+      <remove name="WebServiceHandlerFactory-Integrated" />
+      <add name="ScriptHandlerFactory" verb="*" path="*.asmx" preCondition="integratedMode"
+           type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
+      <add name="ScriptHandlerFactoryAppServices" verb="*" path="*_AppService.axd" preCondition="integratedMode"
+           type="System.Web.Script.Services.ScriptHandlerFactory, System.Web.Extensions, Version=1.0.61025.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
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+	  <section name="pages" defaultBlockName="system.web.pages">
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+<add name="UrlRoutingModule" type="System.Web.Routing.UrlRoutingModule, System.Web.Routing, Version=3.5.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/>
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+    <![CDATA[
+<httpHandlers>
+</httpHandlers>
+]]>
+  </default>
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+  <default section="system.web.httpModules" target="web">
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+]]>
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+<system.web.extensions>
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+]]>
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+  <default section="system.codedom.compilers" target="any">
+    <![CDATA[
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+]]>
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+  <default section="runtime" target="any">
+    <![CDATA[
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+    </runtime>
+]]>
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+
+  <!-- default config file definitions -->
+  <defaultConfigFile name="web.config" fileName="Web.config" target="web">
+    <section name="configuration">
+      <section name="system.web">
+	<section name="compilation" defaultBlockName="system.web.compilation"/>
+	<section name="customErrors" defaultBlockName="system.web.customErrors"/>
+      </section>
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+  </defaultConfigFile>
+</mconfig>
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+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/x86_64/libmono-native.so
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+size 2176912
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+size 3868592
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+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/Resources/unity default resources	
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4832152
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--- /dev/null
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@@ -0,0 +1 @@
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+++ b/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/app.info
@@ -0,0 +1,2 @@
+Hugging Face
+SnowballTarget
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--- /dev/null
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--- /dev/null
+++ b/unity-volume/.gitignore
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+*
+# Ignore everything in this directory except for .gitignore. This directory is for illustrative purposes
+!.gitignore
diff --git a/utils/__init__.py b/utils/__init__.py
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index 0000000000000000000000000000000000000000..7566b1bdc7378519477fd681e7c3939f05a78fec
--- /dev/null
+++ b/utils/generate_markdown_docs.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+import os
+import subprocess
+import sys
+import yaml
+import argparse
+import hashlib
+
+
+# pydoc-markdown -I . -m module_name --render_toc > doc.md
+
+
+def hash_file(filename):
+    """
+    Calculate the md5 hash of a file. Used to check for stale files.
+
+    :param filename: The name of the file to check
+    :type str:
+    :return: A string containing the md5 hash of the file
+    :rtype: str
+    """
+    if os.path.exists(filename):
+        hasher = hashlib.md5()
+        with open(filename, "rb") as file_to_hash:
+            buffer = file_to_hash.read()
+            hasher.update(buffer)
+        return hasher.hexdigest()
+    else:
+        return 0
+
+
+def remove_trailing_whitespace(filename):
+    """
+    Removes trailing whitespace from a file.
+
+    :param filename: The name of the file to process
+    :type str:
+    """
+    num_changed = 0
+    # open the source file
+    with open(filename, "rb") as f:
+        source_file = f.read().decode()
+    # grab all the lines, removing the trailing whitespace
+    lines = [line.rstrip() for line in source_file.splitlines()]
+
+    # process lines to construct destination file
+    while lines and not lines[-1]:
+        lines.pop(-1)
+    lines.append("")
+    destination_file = "\n".join(lines)
+
+    # compare source and destination and write only if changed
+    if source_file != destination_file:
+        num_changed += 1
+        with open(filename, "wb") as f:
+            f.write(destination_file.encode())
+
+
+if __name__ == "__main__":
+    """
+    Pre-commit hook to generate Python API documentation using pydoc-markdown
+    and write as markdown files. Each package should have a config file,
+    pydoc-config.yaml, at the root level that provides configurations for
+    the generation. Fails if documentation was updated, passes otherwise. This
+    allows for pre-commit to fail in CI/CD and makes sure dev commits the doc
+    updates.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--package_dirs", nargs="+")
+    args = parser.parse_args()
+
+    ok = True
+    for package_dir in args.package_dirs:
+        config_path = os.path.join(os.getcwd(), package_dir, "pydoc-config.yaml")
+        print(config_path)
+        with open(config_path) as config_file:
+            config = yaml.full_load(config_file)
+            for module in config["modules"]:
+                module_name = module["name"]
+                submodules = module["submodules"]
+                output_file_name = f"./{config['folder']}/{module['file_name']}"
+                old_hash = hash_file(output_file_name)
+                module_args = []
+                for submodule in submodules:
+                    module_args.append("-m")
+                    module_args.append(f"{module_name}.{submodule}")
+                with open(output_file_name, "w") as output_file:
+                    subprocess_args = [
+                        "pydoc-markdown",
+                        "-I",
+                        f"./{package_dir}",
+                        *module_args,
+                        "--render-toc",
+                    ]
+                    subprocess.check_call(subprocess_args, stdout=output_file)
+                remove_trailing_whitespace(output_file_name)
+                new_hash = hash_file(output_file_name)
+                ok &= old_hash == new_hash
+
+    sys.exit(0 if ok else 1)
diff --git a/utils/make_readme_table.py b/utils/make_readme_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3bcbc4c1e33c6efec053f36ff1897aacc2ca1ed
--- /dev/null
+++ b/utils/make_readme_table.py
@@ -0,0 +1,170 @@
+"""
+Generate the "Releases" table on the main readme. Update the versions lists, run this script, and copy the output
+into the markdown file.
+"""
+from distutils.version import LooseVersion, StrictVersion
+from datetime import datetime
+from typing import NamedTuple
+from collections import Counter
+
+MAX_DAYS = 150  # do not print releases older than this many days
+
+
+def table_line(version_info, bold=False):
+    bold_str = "**" if bold else ""
+
+    cells = [
+        f"**{version_info.display_name}**",
+        f"{bold_str}{version_info.release_date}{bold_str}",
+        f"{bold_str}[source]({version_info.source_link}){bold_str}",
+        f"{bold_str}[docs]({version_info.doc_link}){bold_str}",
+        f"{bold_str}[download]({version_info.download_link}){bold_str}",
+    ]
+    if version_info.is_main:
+        cells.append("--")  # python
+        cells.append("--")  # Unity
+    else:
+        cells.append(
+            f"{bold_str}[{version_info.python_verion}]({version_info.pypi_link}){bold_str}"
+        )
+        cells.append(
+            f"{bold_str}[{version_info.csharp_version}]({version_info.package_link}){bold_str}"
+        )
+    joined_cells = " | ".join(cells)
+    return f"| {joined_cells} |"
+
+
+class ReleaseInfo(NamedTuple):
+    release_tag: str
+    csharp_version: str
+    python_verion: str
+    release_date: str
+    is_verified: bool = False
+
+    @property
+    def loose_version(self) -> LooseVersion:
+        return LooseVersion(self.python_verion)
+
+    @property
+    def is_main(self) -> bool:
+        return self.release_tag == "main"
+
+    @property
+    def release_datetime(self) -> datetime:
+        if self.is_main:
+            return datetime.today()
+        return datetime.strptime(self.release_date, "%B %d, %Y")
+
+    @property
+    def elapsed_days(self) -> int:
+        """
+        Days since this version was released.
+        :return:
+        """
+        return (datetime.today() - self.release_datetime).days
+
+    @property
+    def display_name(self) -> str:
+        """
+        Clean up the tag name for display, e.g. "release_1" -> "Release 1"
+        :return:
+        """
+        if self.is_verified:
+            return f"Verified Package {self.csharp_version}"
+        elif self.is_main:
+            return "main (unstable)"
+        else:
+            return self.release_tag.replace("_", " ").title()
+
+    @property
+    def source_link(self):
+        if self.is_verified:
+            return f"https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_{self.csharp_version}"
+        else:
+            return f"https://github.com/Unity-Technologies/ml-agents/tree/{self.release_tag}"
+
+    @property
+    def download_link(self):
+        if self.is_verified:
+            tag = f"com.unity.ml-agents_{self.csharp_version}"
+        else:
+            tag = self.release_tag
+        return f"https://github.com/Unity-Technologies/ml-agents/archive/{tag}.zip"
+
+    @property
+    def doc_link(self):
+        if self.is_verified:
+            return "https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md"
+
+        # For release_X branches, docs are on a separate tag.
+        if self.release_tag.startswith("release"):
+            docs_name = self.release_tag + "_docs"
+        else:
+            docs_name = self.release_tag
+        return f"https://github.com/Unity-Technologies/ml-agents/tree/{docs_name}/docs/Readme.md"
+
+    @property
+    def package_link(self):
+        try:
+            v = StrictVersion(self.csharp_version).version
+            return f"https://docs.unity3d.com/Packages/com.unity.ml-agents@{v[0]}.{v[1]}/manual/index.html"
+        except ValueError:
+            return "--"
+
+    @property
+    def pypi_link(self):
+        return f"https://pypi.org/project/mlagents/{self.python_verion}/"
+
+
+versions = [
+    ReleaseInfo("main", "main", "main", "--"),
+    ReleaseInfo("release_1", "1.0.0", "0.16.0", "April 30, 2020"),
+    ReleaseInfo("release_2", "1.0.2", "0.16.1", "May 20, 2020"),
+    ReleaseInfo("release_3", "1.1.0", "0.17.0", "June 10, 2020"),
+    ReleaseInfo("release_4", "1.2.0", "0.18.0", "July 15, 2020"),
+    ReleaseInfo("release_5", "1.2.1", "0.18.1", "July 31, 2020"),
+    ReleaseInfo("release_6", "1.3.0", "0.19.0", "August 12, 2020"),
+    ReleaseInfo("release_7", "1.4.0", "0.20.0", "September 16, 2020"),
+    ReleaseInfo("release_8", "1.5.0", "0.21.0", "October 14, 2020"),
+    ReleaseInfo("release_9", "1.5.0", "0.21.1", "November 4, 2020"),
+    ReleaseInfo("release_10", "1.6.0", "0.22.0", "November 18, 2020"),
+    ReleaseInfo("release_11", "1.7.0", "0.23.0", "December 21, 2020"),
+    ReleaseInfo("release_12", "1.7.2", "0.23.0", "December 22, 2020"),
+    ReleaseInfo("release_13", "1.8.0", "0.24.0", "February 17, 2021"),
+    ReleaseInfo("release_14", "1.8.1", "0.24.1", "March 5, 2021"),
+    ReleaseInfo("release_15", "1.9.0", "0.25.0", "March 17, 2021"),
+    ReleaseInfo("release_16", "1.9.1", "0.25.1", "April 13, 2021"),
+    ReleaseInfo("release_17", "2.0.0", "0.26.0", "April 22, 2021"),
+    ReleaseInfo("release_18", "2.1.0", "0.27.0", "June 9, 2021"),
+    ReleaseInfo("release_19", "2.2.1", "0.28.0", "January 14, 2022"),
+    ReleaseInfo("release_20", "2.3.0", "0.30.0", "November 21, 2022"),
+    # Verified releases
+    ReleaseInfo("", "1.0.8", "0.16.1", "May 26, 2021", is_verified=True),
+    ReleaseInfo("", "1.0.7", "0.16.1", "March 8, 2021", is_verified=True),
+    ReleaseInfo("", "1.0.6", "0.16.1", "November 16, 2020", is_verified=True),
+    ReleaseInfo("", "1.0.5", "0.16.1", "September 23, 2020", is_verified=True),
+    ReleaseInfo("", "1.0.4", "0.16.1", "August 20, 2020", is_verified=True),
+]
+
+sorted_versions = sorted(versions, key=lambda x: x.release_datetime, reverse=True)
+
+highlight_versions = set()
+# Highlight the most recent verified version
+highlight_versions.add([v for v in sorted_versions if v.is_verified][0])
+# Highlight the most recent regular version
+highlight_versions.add(
+    [v for v in sorted_versions if (not v.is_verified and not v.is_main)][0]
+)
+
+count_by_verified = Counter()
+
+for version_info in sorted_versions:
+    highlight = version_info in highlight_versions
+    if version_info.elapsed_days > MAX_DAYS:
+        # Make sure we always have at least regular and one verified entry
+        if count_by_verified[version_info.is_verified] > 0:
+            continue
+    print(table_line(version_info, highlight))
+    count_by_verified[version_info.is_verified] += 1
+
+print("\n\n")
diff --git a/utils/run_markdown_link_check.py b/utils/run_markdown_link_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f45491c22ede8103e6d087340f86510cbd836c1
--- /dev/null
+++ b/utils/run_markdown_link_check.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+
+import argparse
+import subprocess
+
+if __name__ == "__main__":
+    # markdown-link-check doesn't support multiple files on the commandline, so this hacks around that.
+    # Note that you must install the package separately via npm. For example:
+    #  brew install npm; npm install -g markdown-link-check
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--check-remote", action="store_true")
+    parser.add_argument("files", nargs="*")
+    args = parser.parse_args()
+
+    config_file = (
+        "markdown-link-check.full.json"
+        if args.check_remote
+        else "markdown-link-check.fast.json"
+    )
+
+    for f in args.files:
+        subprocess_args = ["markdown-link-check", "-q", "-c", config_file, f]
+        subprocess.check_call(subprocess_args)
diff --git a/utils/validate_inits.py b/utils/validate_inits.py
new file mode 100644
index 0000000000000000000000000000000000000000..39bd762c99d0669725f6d2b419f9a7abba00ac68
--- /dev/null
+++ b/utils/validate_inits.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+
+import os
+import glob
+from setuptools import find_packages, PEP420PackageFinder
+
+
+class NonTrivialPEP420PackageFinder(PEP420PackageFinder):
+    """
+    The PEP420PackageFinder (used by find_namespace_packages) thinks everything
+    looks like a package, even if there are no python files in it. This is a
+    little stricter and only considers directories with python files in it.
+    """
+
+    @staticmethod
+    def _looks_like_package(path, package_name=None):
+        glob_path = os.path.join(path, "*.py")
+        return any(glob.iglob(glob_path))
+
+
+def validate_packages(root_dir):
+    """
+    Makes sure that all python files are discoverable by find_packages(), which
+    is what we use in setup.py. We could potentially use
+    find_namespace_packages instead, but depending on PEP420 has been flaky
+    in the past (particularly with regards to mypy).
+    """
+    exclude = ["*.tests", "*.tests.*", "tests.*", "tests"]
+    found_packages = find_packages(root_dir, exclude=exclude)
+    found_ns_packages = NonTrivialPEP420PackageFinder.find(root_dir, exclude=exclude)
+    assert found_packages, f"Couldn't find anything in directory {root_dir}"
+    if set(found_packages) != set(found_ns_packages):
+        raise RuntimeError(
+            "The following packages are not discoverable using found_packages():\n"
+            f"{set(found_ns_packages) - set(found_packages)}\n"
+            "Make sure you have an __init__.py file in the directories."
+        )
+    else:
+        print(f"__init__.py files for {root_dir} are OK.")
+
+
+def main():
+    for root_dir in ["ml-agents", "ml-agents-envs"]:
+        validate_packages(root_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/utils/validate_meta_files.py b/utils/validate_meta_files.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d247fa76f847ad820565cfd08411d2690320a81
--- /dev/null
+++ b/utils/validate_meta_files.py
@@ -0,0 +1,81 @@
+import os
+
+
+def main():
+    asset_paths = [
+        "Project/Assets",
+        "DevProject/Assets",
+        "com.unity.ml-agents",
+        "com.unity.ml-agents.extensions",
+    ]
+    meta_suffix = ".meta"
+    python_suffix = ".py"
+    allow_list = frozenset(
+        [
+            "com.unity.ml-agents/.editorconfig",
+            "com.unity.ml-agents/.gitignore",
+            "com.unity.ml-agents/.npmignore",
+            "com.unity.ml-agents/.git",
+            "com.unity.ml-agents/Tests/.tests.json",
+            "com.unity.ml-agents/.pre-commit-config.yaml",
+            "com.unity.ml-agents/.pre-commit-search-and-replace.yaml",
+            "com.unity.ml-agents.extensions/.gitignore",
+            "com.unity.ml-agents.extensions/.npmignore",
+            "com.unity.ml-agents.extensions/Tests/.tests.json",
+            "com.unity.ml-agents/Samples/3DBall/.sample.json",
+        ]
+    )
+    ignored_dirs = {
+        "Documentation~",
+        ".github",
+        ".yamato",
+    }
+
+    num_matched = 0
+
+    unmatched = set()
+
+    for asset_path in asset_paths:
+        for root, dirs, files in os.walk(asset_path):
+            # Modifying the dirs list with topdown=True (the default) will prevent us from recursing those directories
+            for ignored in ignored_dirs:
+                try:
+                    dirs.remove(ignored)
+                except ValueError:
+                    pass
+
+            dirs = set(dirs)
+            files = set(files)
+
+            combined = dirs | files
+            for f in combined:
+
+                if f.endswith(python_suffix):
+                    # Probably this script; skip it
+                    continue
+
+                full_path = os.path.join(root, f)
+                if full_path in allow_list:
+                    continue
+
+                # We expect each non-.meta file to have a .meta file, and each .meta file to have a non-.meta file
+                if f.endswith(meta_suffix):
+                    expected = f.replace(meta_suffix, "")
+                else:
+                    expected = f + meta_suffix
+
+                if expected not in combined:
+                    unmatched.add(full_path)
+                else:
+                    num_matched += 1
+
+    if unmatched:
+        raise Exception(
+            f"Mismatch between expected files and their .meta files: {sorted(unmatched)}"
+        )
+
+    print(f"Found {num_matched} correctly matched files")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/utils/validate_release_links.py b/utils/validate_release_links.py
new file mode 100644
index 0000000000000000000000000000000000000000..372e65672b5272f65d1282286b35aebf5dfe095f
--- /dev/null
+++ b/utils/validate_release_links.py
@@ -0,0 +1,260 @@
+#!/usr/bin/env python3
+
+import ast
+import sys
+import os
+import re
+import subprocess
+import tempfile
+from typing import List, Optional, Pattern
+
+RELEASE_PATTERN = re.compile(r"release_[0-9]+(_docs)*")
+# This matches the various ways to invoke pip: "pip", "pip3", "python -m pip"
+# It matches "mlagents" and "mlagents_envs", accessible as group "package"
+# and optionally matches the version, e.g. "==1.2.3"
+PIP_INSTALL_PATTERN = re.compile(
+    r"(python -m )?pip3* install (?P<quiet>-q )?(?P<package>mlagents(_envs)?)(==[0-9]+\.[0-9]+\.[0-9]+(\.dev[0-9]+)?)?"
+)
+TRAINER_INIT_FILE = "ml-agents/mlagents/trainers/__init__.py"
+
+MATCH_ANY = re.compile(r"(?s).*")
+# Filename -> regex list to allow specific lines.
+# To allow everything in the file (effectively skipping it), use MATCH_ANY for the value
+ALLOW_LIST = {
+    # Previous release table
+    "docs/Python-PettingZoo-API.md": re.compile(
+        r"\*\*(Verified Package ([0-9]\.?)*|Release [0-9]+)\*\*"
+    ),
+    "docs/Versioning.md": MATCH_ANY,
+    "com.unity.ml-agents/CHANGELOG.md": MATCH_ANY,
+    "utils/make_readme_table.py": MATCH_ANY,
+    "utils/validate_release_links.py": MATCH_ANY,
+}
+
+
+def test_release_pattern():
+    # Just some sanity check that the regex works as expected.
+    for s, expected in [
+        (
+            "https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/Food.md",
+            True,
+        ),
+        ("https://github.com/Unity-Technologies/ml-agents/blob/release_4/Foo.md", True),
+        (
+            "git clone --branch release_4 https://github.com/Unity-Technologies/ml-agents.git",
+            True,
+        ),
+        (
+            "https://github.com/Unity-Technologies/ml-agents/blob/release_123_docs/Foo.md",
+            True,
+        ),
+        (
+            "https://github.com/Unity-Technologies/ml-agents/blob/release_123/Foo.md",
+            True,
+        ),
+        (
+            "https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Foo.md",
+            False,
+        ),
+    ]:
+        assert bool(RELEASE_PATTERN.search(s)) is expected
+
+    print("release tests OK!")
+
+
+def test_pip_pattern():
+    # Just some sanity check that the regex works as expected.
+    for s, expected in [
+        ("pip install mlagents", True),
+        ("pip3 install -q mlagents", True),
+        ("python -m pip install mlagents", True),
+        ("python -m pip install mlagents==1.2.3", True),
+        ("python -m pip install mlagents_envs==1.2.3", True),
+        ("python -m pip install mlagents==11.222.3333", True),
+        ("python -m pip install mlagents_envs==11.222.3333", True),
+    ]:
+        assert bool(PIP_INSTALL_PATTERN.search(s)) is expected
+
+    sub_expected = "Try running rm -rf / to install"
+    assert sub_expected == PIP_INSTALL_PATTERN.sub(
+        "rm -rf /", "Try running python -m pip install mlagents==1.2.3 to install"
+    )
+
+    print("pip tests OK!")
+
+
+def update_pip_install_line(line, package_verion):
+    match = PIP_INSTALL_PATTERN.search(line)
+    if match is not None:  # if there is a pip install line
+        package_name = match.group("package")
+        quiet_option = match.group("quiet") or ""
+        replacement_version = (
+            f"python -m pip install {quiet_option}{package_name}=={package_verion}"
+        )
+        updated = PIP_INSTALL_PATTERN.sub(replacement_version, line)
+        return updated
+    else:  # Don't do anything
+        return line
+
+
+def git_ls_files() -> List[str]:
+    """
+    Run "git ls-files" and return a list with one entry per line.
+    This returns the list of all files tracked by git.
+    """
+    return subprocess.check_output(["git", "ls-files"], universal_newlines=True).split(
+        "\n"
+    )
+
+
+def get_release_tag() -> Optional[str]:
+    """
+    Returns the release tag for the mlagents python package.
+    This will be None on the main branch.
+    :return:
+    """
+    with open(TRAINER_INIT_FILE) as f:
+        for line in f:
+            if "__release_tag__" in line:
+                lhs, equals_string, rhs = line.strip().partition(" = ")
+                # Evaluate the right hand side of the expression
+                return ast.literal_eval(rhs)
+    # If we couldn't find the release tag, raise an exception
+    # (since we can't return None here)
+    raise RuntimeError("Can't determine release tag")
+
+
+def get_python_package_version() -> str:
+    """
+    Returns the mlagents python package.
+    :return:
+    """
+    with open(TRAINER_INIT_FILE) as f:
+        for line in f:
+            if "__version__" in line:
+                lhs, equals_string, rhs = line.strip().partition(" = ")
+                # Evaluate the right hand side of the expression
+                return ast.literal_eval(rhs)
+    # If we couldn't find the release tag, raise an exception
+    # (since we can't return None here)
+    raise RuntimeError("Can't determine python package version")
+
+
+def check_file(
+    filename: str,
+    release_tag_pattern: Pattern,
+    release_tag: str,
+    pip_allow_pattern: Pattern,
+    package_version: str,
+) -> List[str]:
+    """
+    Validate a single file and return any offending lines.
+    """
+    bad_lines = []
+    with tempfile.TemporaryDirectory() as tempdir:
+        if not os.path.exists(tempdir):
+            os.makedirs(tempdir)
+        new_file_name = os.path.join(tempdir, os.path.basename(filename))
+        with open(new_file_name, "w+") as new_file:
+            # default to match everything if there is nothing in the ALLOW_LIST
+            allow_list_pattern = ALLOW_LIST.get(filename, None)
+            with open(filename) as f:
+                for line in f:
+                    # Does it contain anything of the form release_123
+                    has_release_pattern = RELEASE_PATTERN.search(line) is not None
+                    # Does it contain this particular release, e.g. release_42 or release_42_docs
+                    has_release_tag_pattern = (
+                        release_tag_pattern.search(line) is not None
+                    )
+                    # Does it contain the allow list pattern for the file (if there is one)
+                    has_allow_list_pattern = (
+                        allow_list_pattern
+                        and allow_list_pattern.search(line) is not None
+                    )
+
+                    pip_install_ok = (
+                        has_allow_list_pattern
+                        or PIP_INSTALL_PATTERN.search(line) is None
+                        or pip_allow_pattern.search(line) is not None
+                    )
+
+                    release_tag_ok = (
+                        not has_release_pattern
+                        or has_release_tag_pattern
+                        or has_allow_list_pattern
+                    )
+
+                    if release_tag_ok and pip_install_ok:
+                        new_file.write(line)
+                    else:
+                        bad_lines.append(f"{filename}: {line}")
+                        new_line = re.sub(r"release_[0-9]+", rf"{release_tag}", line)
+                        new_line = update_pip_install_line(new_line, package_version)
+                        new_file.write(new_line)
+        if bad_lines:
+            if os.path.exists(filename):
+                os.remove(filename)
+                os.rename(new_file_name, filename)
+
+    return bad_lines
+
+
+def check_all_files(
+    release_allow_pattern: Pattern,
+    release_tag: str,
+    pip_allow_pattern: Pattern,
+    package_version: str,
+) -> List[str]:
+    """
+    Validate all files tracked by git.
+    :param release_allow_pattern:
+    """
+    bad_lines = []
+    file_types = {".py", ".md", ".cs", ".ipynb"}
+    for file_name in git_ls_files():
+        if "localized" in file_name or os.path.splitext(file_name)[1] not in file_types:
+            continue
+        bad_lines += check_file(
+            file_name,
+            release_allow_pattern,
+            release_tag,
+            pip_allow_pattern,
+            package_version,
+        )
+    return bad_lines
+
+
+def main():
+    release_tag = get_release_tag()
+    if not release_tag:
+        print("Release tag is None, exiting")
+        sys.exit(0)
+
+    package_version = get_python_package_version()
+    print(f"Release tag: {release_tag}")
+    print(f"Python package version: {package_version}")
+    release_allow_pattern = re.compile(f"{release_tag}(_docs)?")
+    pip_allow_pattern = re.compile(
+        rf"python -m pip install (-q )?mlagents(_envs)?=={package_version}"
+    )
+    bad_lines = check_all_files(
+        release_allow_pattern, release_tag, pip_allow_pattern, package_version
+    )
+    if bad_lines:
+        for line in bad_lines:
+            print(line)
+
+        print("*************************************************************")
+        print(
+            "This script attempted to fix the above errors. Please double "
+            + "check them to make sure the replacements were done correctly"
+        )
+
+    sys.exit(1 if bad_lines else 0)
+
+
+if __name__ == "__main__":
+    if "--test" in sys.argv:
+        test_release_pattern()
+        test_pip_pattern()
+    main()
diff --git a/utils/validate_versions.py b/utils/validate_versions.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdc6f0a503f760c83d355fe6381ca6d58965ee27
--- /dev/null
+++ b/utils/validate_versions.py
@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+
+import os
+import json
+import sys
+from typing import Dict, Optional
+import argparse
+
+VERSION_LINE_START = "__version__ = "
+
+DIRECTORIES = ["ml-agents/mlagents/trainers", "ml-agents-envs/mlagents_envs"]
+
+MLAGENTS_PACKAGE_JSON_PATH = "com.unity.ml-agents/package.json"
+MLAGENTS_EXTENSIONS_PACKAGE_JSON_PATH = "com.unity.ml-agents.extensions/package.json"
+
+ACADEMY_PATH = "com.unity.ml-agents/Runtime/Academy.cs"
+
+PYTHON_VERSION_FILE_TEMPLATE = """# Version of the library that will be used to upload to pypi
+__version__ = {version}
+
+# Git tag that will be checked to determine whether to trigger upload to pypi
+__release_tag__ = {release_tag}
+"""
+
+
+def _escape_non_none(s: Optional[str]) -> str:
+    """
+    Returns s escaped in quotes if it is non-None, else "None"
+    :param s:
+    :return:
+    """
+    if s is not None:
+        return f'"{s}"'
+    else:
+        return "None"
+
+
+def extract_version_string(filename):
+    with open(filename) as f:
+        for line in f.readlines():
+            if line.startswith(VERSION_LINE_START):
+                return line.replace(VERSION_LINE_START, "").strip()
+    return None
+
+
+def check_versions() -> bool:
+    version_by_dir: Dict[str, str] = {}
+    for directory in DIRECTORIES:
+        path = os.path.join(directory, "__init__.py")
+        version = extract_version_string(path)
+        print(f"Found version {version} for {directory}")
+        version_by_dir[directory] = version
+
+    # Make sure we have exactly one version, and it's not none
+    versions = set(version_by_dir.values())
+    if len(versions) != 1 or None in versions:
+        print("Each setup.py must have the same VERSION string.")
+        return False
+    return True
+
+
+def set_version(
+    python_version: str,
+    csharp_version: str,
+    csharp_extensions_version: str,
+    release_tag: Optional[str],
+) -> None:
+    # Sanity check - make sure test tags have a test or dev version
+    if release_tag and "test" in release_tag:
+        if not ("dev" in python_version or "test" in python_version):
+            raise RuntimeError('Test tags must use a "test" or "dev" version.')
+
+    new_contents = PYTHON_VERSION_FILE_TEMPLATE.format(
+        version=_escape_non_none(python_version),
+        release_tag=_escape_non_none(release_tag),
+    )
+    for directory in DIRECTORIES:
+        path = os.path.join(directory, "__init__.py")
+        print(f"Setting {path} to version {python_version}")
+        with open(path, "w") as f:
+            f.write(new_contents)
+
+    if csharp_version is not None:
+        package_version = f"{csharp_version}-exp.1"
+        if csharp_extensions_version is not None:
+            # since this has never been promoted we need to keep
+            # it in preview forever or CI will fail
+            extension_version = f"{csharp_extensions_version}-preview"
+        print(
+            f"Setting package version to {package_version} in {MLAGENTS_PACKAGE_JSON_PATH}"
+            f" and {MLAGENTS_EXTENSIONS_PACKAGE_JSON_PATH}"
+        )
+        set_package_version(package_version)
+        set_extension_package_version(package_version, extension_version)
+        print(f"Setting package version to {package_version} in {ACADEMY_PATH}")
+        set_academy_version_string(package_version)
+
+
+def set_package_version(new_version: str) -> None:
+    with open(MLAGENTS_PACKAGE_JSON_PATH) as f:
+        package_json = json.load(f)
+    if "version" in package_json:
+        package_json["version"] = new_version
+    with open(MLAGENTS_PACKAGE_JSON_PATH, "w") as f:
+        json.dump(package_json, f, indent=2)
+        f.write("\n")
+
+
+def set_extension_package_version(
+    new_dependency_version: str, new_extension_version
+) -> None:
+    with open(MLAGENTS_EXTENSIONS_PACKAGE_JSON_PATH) as f:
+        package_json = json.load(f)
+    package_json["dependencies"]["com.unity.ml-agents"] = new_dependency_version
+    if new_extension_version is not None:
+        package_json["version"] = new_extension_version
+    with open(MLAGENTS_EXTENSIONS_PACKAGE_JSON_PATH, "w") as f:
+        json.dump(package_json, f, indent=2)
+        f.write("\n")
+
+
+def set_academy_version_string(new_version):
+    needle = "internal const string k_PackageVersion"
+    found = 0
+    with open(ACADEMY_PATH) as f:
+        lines = f.readlines()
+    for i, l in enumerate(lines):
+        if needle in l:
+            left, right = l.split(" = ")
+            right = f' = "{new_version}";\n'
+            lines[i] = left + right
+            found += 1
+    if found != 1:
+        raise RuntimeError(
+            f'Expected to find search string "{needle}" exactly once, but found it {found} times'
+        )
+    with open(ACADEMY_PATH, "w") as f:
+        f.writelines(lines)
+
+
+def print_release_tag_commands(
+    python_version: str, csharp_version: str, release_tag: str
+):
+    python_tag = f"python-packages_{python_version}"
+    csharp_tag = f"com.unity.ml-agents_{csharp_version}"
+    docs_tag = f"{release_tag}_docs"
+    print(
+        f"""
+###
+Use these commands to create the tags after the release:
+###
+git checkout {release_tag}
+git tag -f latest_release
+git push -f origin latest_release
+git tag -f {docs_tag}
+git push -f origin {docs_tag}
+git tag {python_tag}
+git push -f origin {python_tag}
+git tag {csharp_tag}
+git push -f origin {csharp_tag}
+"""
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--python-version", default=None)
+    parser.add_argument("--csharp-version", default=None)
+    parser.add_argument("--csharp-extensions-version", default=None)
+    parser.add_argument("--release-tag", default=None)
+    # unused, but allows precommit to pass filenames
+    parser.add_argument("files", nargs="*")
+    args = parser.parse_args()
+
+    if args.python_version:
+        print(f"Updating python library to version {args.python_version}")
+        if args.csharp_version:
+            print(f"Updating C# package to version {args.csharp_version}")
+        if args.csharp_extensions_version:
+            print(
+                f"Updating C# extensions package to version {args.csharp_extensions_version}"
+            )
+        set_version(
+            args.python_version,
+            args.csharp_version,
+            args.csharp_extensions_version,
+            args.release_tag,
+        )
+        if args.release_tag is not None:
+            print_release_tag_commands(
+                args.python_version, args.csharp_version, args.release_tag
+            )
+    else:
+        ok = check_versions()
+        return_code = 0 if ok else 1
+        sys.exit(return_code)