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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,100 +1,100 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "QKWvMXWMBEJA" | ||
| }, | ||
| "source": [ | ||
| "# Behavioural Planning for Autonomous Highway Driving\n", | ||
| "\n", | ||
| "We plan a trajectory using the _Optimistic Planning for Deterministic systems_ ([OPD](https://hal.inria.fr/hal-00830182)) algorithm.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "s-ghXis6A_md" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#@title Imports for env, agent, and visualisation.\n", | ||
| "# Environment\n", | ||
| "!pip install highway-env\n", | ||
| "import gymnasium as gym\n", | ||
| "import highway_env\n", | ||
| "\n", | ||
| "# Agent\n", | ||
| "!pip install git+https://github.com/eleurent/rl-agents#egg=rl-agents\n", | ||
| "from rl_agents.agents.common.factory import agent_factory\n", | ||
| "\n", | ||
| "# Visualisation\n", | ||
| "import sys\n", | ||
| "from tqdm.notebook import trange\n", | ||
| "!pip install moviepy -U\n", | ||
| "!pip install imageio_ffmpeg\n", | ||
| "!pip install pyvirtualdisplay\n", | ||
| "!apt-get install -y xvfb ffmpeg\n", | ||
| "!git clone https://github.com/Farama-Foundation/HighwayEnv.git\n", | ||
| "sys.path.insert(0, './highway-env/scripts/')\n", | ||
| "from utils import record_videos, show_videos\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "bgNDDWwqCj8l" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#@title Run an episode\n", | ||
| "\n", | ||
| "# Make environment\n", | ||
| "env = gym.make(\"highway-fast-v0\", render_mode=\"rgb_array\")\n", | ||
| "env = record_videos(env)\n", | ||
| "(obs, info), done = env.reset(), False\n", | ||
| "\n", | ||
| "# Make agent\n", | ||
| "agent_config = {\n", | ||
| " \"__class__\": \"<class 'rl_agents.agents.tree_search.deterministic.DeterministicPlannerAgent'>\",\n", | ||
| " \"env_preprocessors\": [{\"method\":\"simplify\"}],\n", | ||
| " \"budget\": 50,\n", | ||
| " \"gamma\": 0.7,\n", | ||
| "}\n", | ||
| "agent = agent_factory(env, agent_config)\n", | ||
| "\n", | ||
| "# Run episode\n", | ||
| "for step in trange(env.unwrapped.config[\"duration\"], desc=\"Running...\"):\n", | ||
| " action = agent.act(obs)\n", | ||
| " obs, reward, done, truncated, info = env.step(action)\n", | ||
| " \n", | ||
| "env.close()\n", | ||
| "show_videos()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "colab": { | ||
| "name": "highway-planning.ipynb", | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "pycharm": { | ||
| "stem_cell": { | ||
| "cell_type": "raw", | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "source": [] | ||
| } | ||
| } | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "QKWvMXWMBEJA" | ||
| }, | ||
| "source": [ | ||
| "# Behavioural Planning for Autonomous Highway Driving\n", | ||
| "\n", | ||
| "We plan a trajectory using the _Optimistic Planning for Deterministic systems_ ([OPD](https://hal.inria.fr/hal-00830182)) algorithm.\n" | ||
| ] | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "s-ghXis6A_md" | ||
| }, | ||
| "source": [ | ||
| "#@title Imports for env, agent, and visualisation.\n", | ||
| "# Environment\n", | ||
| "!pip install highway-env\n", | ||
| "import gymnasium as gym\n", | ||
| "import highway_env\n", | ||
| "\n", | ||
| "# Agent\n", | ||
| "!pip install git+https://github.com/eleurent/rl-agents#egg=rl-agents\n", | ||
| "from rl_agents.agents.common.factory import agent_factory\n", | ||
| "\n", | ||
| "# Visualisation\n", | ||
| "import sys\n", | ||
| "from tqdm.notebook import trange\n", | ||
| "!pip install moviepy -U\n", | ||
| "!pip install imageio_ffmpeg\n", | ||
| "!pip install pyvirtualdisplay\n", | ||
| "!apt-get install -y xvfb ffmpeg\n", | ||
| "!git clone https://github.com/Farama-Foundation/HighwayEnv.git\n", | ||
| "sys.path.insert(0, './highway-env/scripts/')\n", | ||
| "from utils import record_videos, show_videos\n" | ||
| ], | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "bgNDDWwqCj8l" | ||
| }, | ||
| "source": [ | ||
| "#@title Run an episode\n", | ||
| "\n", | ||
| "# Make environment\n", | ||
| "env = gym.make(\"highway-fast-v0\", render_mode=\"rgb_array\")\n", | ||
| "env = record_videos(env)\n", | ||
| "(obs, info), done = env.reset(), False\n", | ||
| "\n", | ||
| "# Make agent\n", | ||
| "agent_config = {\n", | ||
| " \"__class__\": \"<class 'rl_agents.agents.tree_search.deterministic.DeterministicPlannerAgent'>\",\n", | ||
| " \"env_preprocessors\": [{\"method\":\"simplify\"}],\n", | ||
| " \"budget\": 50,\n", | ||
| " \"gamma\": 0.7,\n", | ||
| "}\n", | ||
| "agent = agent_factory(env, agent_config)\n", | ||
| "\n", | ||
| "# Run episode\n", | ||
| "for step in trange(env.unwrapped.config[\"duration\"], desc=\"Running...\"):\n", | ||
| " action = agent.act(obs)\n", | ||
| " obs, reward, done, truncated, info = env.step(action)\n", | ||
| " \n", | ||
| "env.close()\n", | ||
| "show_videos()" | ||
| ], | ||
| "outputs": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "colab": { | ||
| "name": "highway-planning.ipynb", | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "pycharm": { | ||
| "stem_cell": { | ||
| "cell_type": "raw", | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "source": [] | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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