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ml-agents
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Run CI on Colab notebooks. (#5409)

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共有 5 个文件被更改，包括 1278 次插入 和 1366 次删除
  1. 992
      colab/Colab_UnityEnvironment_1_Run.ipynb
  2. 1000
      colab/Colab_UnityEnvironment_2_Train.ipynb
  3. 590
      colab/Colab_UnityEnvironment_3_SideChannel.ipynb
  4. 60
      .github/workflows/colab.yml
  5. 2
      colab_requirements.txt

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colab/Colab_UnityEnvironment_1_Run.ipynb
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{
"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",
"display_name": "Python 3"
}
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Colab-UnityEnvironment-1-Run.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
"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_1/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",
"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",
" IN_COLAB = True\n",
"except ImportError:\n",
" IN_COLAB = False\n",
"\n",
"if IN_COLAB:\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",
" import os\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",
" !pip install -q mlagents==0.25.1\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": []
"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_1/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",
" 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))"
],
"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",
" !pip install -q mlagents==0.25.1\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": []
}
]
}

1000
colab/Colab_UnityEnvironment_2_Train.ipynb
文件差异内容过多而无法显示
查看文件

590
colab/Colab_UnityEnvironment_3_SideChannel.ipynb
查看文件


{
"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",
"display_name": "Python 3"
}
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Colab-UnityEnvironment-3-SideChannel.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "pbVXrmEsLXDt"
},
"source": [
"# ML-Agents Use SideChannels\n",
"<img src=\"https://raw.githubusercontent.com/Unity-Technologies/ml-agents/release_1/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",
"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",
" IN_COLAB = True\n",
"except ImportError:\n",
" IN_COLAB = False\n",
"\n",
"if IN_COLAB:\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",
" import os\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",
" !pip install -q mlagents==0.25.1\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_1/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_1/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_1/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_1/docs/Custom-SideChannels.md#custom-side-channels) for advanced use cases.\n"
]
"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 Use SideChannels\n",
"<img src=\"https://raw.githubusercontent.com/Unity-Technologies/ml-agents/release_1/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",
" 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))"
],
"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",
" !pip install -q mlagents==0.25.1\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_1/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_1/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_1/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_1/docs/Custom-SideChannels.md#custom-side-channels) for advanced use cases.\n"
]
}
]
}

60
.github/workflows/colab.yml
查看文件


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]
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:
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: 3.8.x
- uses: actions/setup-dotnet@v1
with:
dotnet-version: '3.1.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-
${{ runner.os }}-
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade setuptools
# Install the local checkouts of ml-agents. This will prevent the colab notebooks from installing a released version.
python -m pip install --progress-bar=off -e ./ml-agents-envs
python -m pip install --progress-bar=off -e ./ml-agents
python -m pip install --progress-bar=off -r colab_requirements.txt
- name: Execute notebook
run: jupyter nbconvert --to notebook --execute --log-level=INFO --ExecutePreprocessor.kernel_name=python3 --output output-${{ 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() }}

2
colab_requirements.txt
查看文件


matplotlib
jupyter
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