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{
"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
}
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