Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -3.97 +/- 1.78
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d8d4e90d92979db54159fde21f4439e2c7640fce57cf7dc27dd82c095edace6
+size 107756

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f898791e4c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f8987996a80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678029217377939821,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]]",
+        "desired_goal": "[[ 1.5825973   0.97347254  1.252285  ]\n [ 1.5474476  -1.2804943  -0.43887895]\n [ 1.5885801  -0.5450705  -0.71566004]\n [-1.1283166  -0.33799368  0.19680594]]",
+        "observation": "[[ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.07631451 -0.12140615  0.21177393]\n [-0.08981221 -0.1415263   0.27137974]\n [-0.00117239  0.1354393   0.02993037]\n [ 0.11597343 -0.10042005  0.04244273]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7d650b6966990a838b0837bc62cdeee1d2fc6e5c4a897948e7a35e2d49188e1
+size 44606

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ae0781e3418a8933257529093ce80d0badf2e7a722a9bdba77d5ad2510c868f
+size 45886

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: False
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f898791e4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8987996a80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678029217377939821, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]\n [0.45418802 0.03559573 0.6297915 ]]", "desired_goal": "[[ 1.5825973   0.97347254  1.252285  ]\n [ 1.5474476  -1.2804943  -0.43887895]\n [ 1.5885801  -0.5450705  -0.71566004]\n [-1.1283166  -0.33799368  0.19680594]]", "observation": "[[ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]\n [ 0.45418802  0.03559573  0.6297915  -0.01690198  0.007708   -0.00160922]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.07631451 -0.12140615  0.21177393]\n [-0.08981221 -0.1415263   0.27137974]\n [-0.00117239  0.1354393   0.02993037]\n [ 0.11597343 -0.10042005  0.04244273]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.972485378338024, "std_reward": 1.7802376916932188, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-05T16:10:57.756596"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c42d8ccdaa42ba3b56c11bf8285d36b38b93978a7102039b51dfd1c47815285
+size 3056