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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "k7xBVPzoXxOg"
},
"source": [
"# Unit 3: Deep Q-Learning with Atari Games 👾 using RL Baselines3 Zoo\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/thumbnail.jpg\" alt=\"Unit 3 Thumbnail\">\n",
"\n",
"In this notebook, **you'll train a Deep Q-Learning agent** playing Space Invaders using [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), a training framework based on [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) that provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.\n",
"\n",
"We're using the [RL-Baselines-3 Zoo integration, a vanilla version of Deep Q-Learning](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) with no extensions such as Double-DQN, Dueling-DQN, and Prioritized Experience Replay.\n",
"\n",
"⬇️ Here is an example of what **you will achieve** ⬇️"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J9S713biXntc"
},
"outputs": [],
"source": [
"%%html\n",
"<video controls autoplay><source src=\"https://huggingface.co/ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4/resolve/main/replay.mp4\" type=\"video/mp4\"></video>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ykJiGevCMVc5"
},
"source": [
"### 🎮 Environments:\n",
"\n",
"- [SpacesInvadersNoFrameskip-v4](https://gymnasium.farama.org/environments/atari/space_invaders/)\n",
"\n",
"You can see the difference between Space Invaders versions here 👉 https://gymnasium.farama.org/environments/atari/space_invaders/#variants\n",
"\n",
"### 📚 RL-Library:\n",
"\n",
"- [RL-Baselines3-Zoo](https://github.com/DLR-RM/rl-baselines3-zoo)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wciHGjrFYz9m"
},
"source": [
"## Objectives of this notebook 🏆\n",
"At the end of the notebook, you will:\n",
"- Be able to understand deeper **how RL Baselines3 Zoo works**.\n",
"- Be able to **push your trained agent and the code to the Hub** with a nice video replay and an evaluation score 🔥.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TsnP0rjxMn1e"
},
"source": [
"## This notebook is from Deep Reinforcement Learning Course\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg\" alt=\"Deep RL Course illustration\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nw6fJHIAZd-J"
},
"source": [
"In this free course, you will:\n",
"\n",
"- 📖 Study Deep Reinforcement Learning in **theory and practice**.\n",
"- 🧑💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.\n",
"- 🤖 Train **agents in unique environments**\n",
"\n",
"And more check 📚 the syllabus 👉 https://simoninithomas.github.io/deep-rl-course\n",
"\n",
"Don’t forget to **<a href=\"http://eepurl.com/ic5ZUD\">sign up to the course</a>** (we are collecting your email to be able to **send you the links when each Unit is published and give you information about the challenges and updates).**\n",
"\n",
"\n",
"The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/ydHrjt3WP5"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0vgANIBBZg1p"
},
"source": [
"## Prerequisites 🏗️\n",
"Before diving into the notebook, you need to:\n",
"\n",
"🔲 📚 **[Study Deep Q-Learning by reading Unit 3](https://huggingface.co/deep-rl-course/unit3/introduction)** 🤗"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7kszpGFaRVhq"
},
"source": [
"We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QR0jZtYreSI5"
},
"source": [
"# Let's train a Deep Q-Learning agent playing Atari' Space Invaders 👾 and upload it to the Hub.\n",
"\n",
"We strongly recommend students **to use Google Colab for the hands-on exercises instead of running them on their personal computers**.\n",
"\n",
"By using Google Colab, **you can focus on learning and experimenting without worrying about the technical aspects of setting up your environments**.\n",
"\n",
"To validate this hands-on for the certification process, you need to push your trained model to the Hub and **get a result of >= 200**.\n",
"\n",
"To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**\n",
"\n",
"For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nc8BnyVEc3Ys"
},
"source": [
"## An advice 💡\n",
"It's better to run this colab in a copy on your Google Drive, so that **if it timeouts** you still have the saved notebook on your Google Drive and do not need to fill everything from scratch.\n",
"\n",
"To do that you can either do `Ctrl + S` or `File > Save a copy in Google Drive.`\n",
"\n",
"Also, we're going to **train it for 90 minutes with 1M timesteps**. By typing `!nvidia-smi` will tell you what GPU you're using.\n",
"\n",
"And if you want to train more such 10 million steps, this will take about 9 hours, potentially resulting in Colab timing out. In that case, I recommend running this on your local computer (or somewhere else). Just click on: `File>Download`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PU4FVzaoM6fC"
},
"source": [
"## Set the GPU 💪\n",
"- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step1.jpg\" alt=\"GPU Step 1\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KV0NyFdQM9ZG"
},
"source": [
"- `Hardware Accelerator > GPU`\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step2.jpg\" alt=\"GPU Step 2\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wS_cVefO-aYg"
},
"source": [
"# Install RL-Baselines3 Zoo and its dependencies 📚\n",
"\n",
"If you see `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.` **this is normal and it's not a critical error** there's a conflict of version. But the packages we need are installed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "hLTwHqIWdnPb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Collecting git+https://github.com/DLR-RM/rl-baselines3-zoo@update/hf\n",
" Cloning https://github.com/DLR-RM/rl-baselines3-zoo (to revision update/hf) to /tmp/pip-req-build-pa0fi3k1\n",
" Running command git clone --filter=blob:none --quiet https://github.com/DLR-RM/rl-baselines3-zoo /tmp/pip-req-build-pa0fi3k1\n",
" Running command git checkout -b update/hf --track origin/update/hf\n",
" Switched to a new branch 'update/hf'\n",
" branch 'update/hf' set up to track 'origin/update/hf'.\n",
" Resolved https://github.com/DLR-RM/rl-baselines3-zoo to commit 7dcbff7e74e7a12c052452181ff353a4dbed313a\n",
" Running command git submodule update --init --recursive -q\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
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" Obtaining dependency information for sb3-contrib>=2.0.0a9 from https://files.pythonhosted.org/packages/01/d1/5dcefd81d358d74798ba2cde7718f2f9955f7ff80854ed4392ab7f569067/sb3_contrib-2.1.0-py3-none-any.whl.metadata\n",
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"\u001b[?25hBuilding wheels for collected packages: rl-zoo3, gym, lit\n",
" Building wheel for rl-zoo3 (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for rl-zoo3: filename=rl_zoo3-2.0.0a9-py3-none-any.whl size=76401 sha256=353bea9860f77205fe25632e85dae5e55adf610df4c5bfda0669069404ebc74c\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-l6mgejm0/wheels/2e/72/ca/842315ce52754f44dbc51302f9a394003c573ce992b71bce0e\n",
" Building wheel for gym (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for gym: filename=gym-0.26.2-py3-none-any.whl size=827620 sha256=a2aa9bf3431831fab18c0fa3b10d66ea971df439509be25a2e55c49d07fed39d\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-l6mgejm0/wheels/af/2b/30/5e78b8b9599f2a2286a582b8da80594f654bf0e18d825a4405\n",
" Building wheel for lit (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for lit: filename=lit-16.0.6-py3-none-any.whl size=93584 sha256=2f714142002d212723435f09c3ed2f28fadaf9c94f2df889dadf3771af353229\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-l6mgejm0/wheels/a5/36/d6/cac2e6fb891889b33a548f2fddb8b4b7726399aaa2ed32b188\n",
"Successfully built rl-zoo3 gym lit\n",
"Installing collected packages: pytz, mpmath, lit, gym-notices, farama-notifications, cmake, zipp, wasabi, urllib3, tzdata, tqdm, tcolorpy, sympy, pyyaml, pyparsing, pillow, pathvalidate, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, networkx, mdurl, MarkupSafe, kiwisolver, idna, greenlet, fsspec, fonttools, filelock, cycler, colorlog, cloudpickle, charset-normalizer, chardet, certifi, sqlalchemy, requests, pandas, nvidia-cusolver-cu11, nvidia-cudnn-cu11, mbstrdecoder, markdown-it-py, Mako, jinja2, importlib-resources, importlib-metadata, contourpy, cmaes, typepy, rich, matplotlib, huggingface-hub, gymnasium, gym, alembic, optuna, huggingface-sb3, DataProperty, tabledata, pytablewriter, triton, torch, stable-baselines3, sb3-contrib, rl-zoo3\n",
"Successfully installed DataProperty-1.0.1 Mako-1.2.4 MarkupSafe-2.1.3 alembic-1.11.3 certifi-2023.7.22 chardet-5.2.0 charset-normalizer-3.2.0 cloudpickle-2.2.1 cmaes-0.10.0 cmake-3.27.2 colorlog-6.7.0 contourpy-1.1.0 cycler-0.11.0 farama-notifications-0.0.4 filelock-3.12.3 fonttools-4.42.1 fsspec-2023.6.0 greenlet-2.0.2 gym-0.26.2 gym-notices-0.0.8 gymnasium-0.29.1 huggingface-hub-0.16.4 huggingface-sb3-2.3 idna-3.4 importlib-metadata-6.8.0 importlib-resources-6.0.1 jinja2-3.1.2 kiwisolver-1.4.5 lit-16.0.6 markdown-it-py-3.0.0 matplotlib-3.7.2 mbstrdecoder-1.1.3 mdurl-0.1.2 mpmath-1.3.0 networkx-3.1 numpy-1.25.2 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 optuna-3.3.0 pandas-2.1.0 pathvalidate-2.5.2 pillow-10.0.0 pyparsing-3.0.9 pytablewriter-0.64.2 pytz-2023.3 pyyaml-6.0.1 requests-2.31.0 rich-13.5.2 rl-zoo3-2.0.0a9 sb3-contrib-2.1.0 sqlalchemy-2.0.20 stable-baselines3-2.1.0 sympy-1.12 tabledata-1.3.1 tcolorpy-0.1.3 torch-2.0.1 tqdm-4.66.1 triton-2.0.0 typepy-1.3.1 tzdata-2023.3 urllib3-2.0.4 wasabi-1.1.2 zipp-3.16.2\n"
]
}
],
"source": [
"# For now we install this update of RL-Baselines3 Zoo\n",
"!pip install git+https://github.com/DLR-RM/rl-baselines3-zoo@update/hf"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p0xe2sJHdtHy"
},
"source": [
"IF AND ONLY IF THE VERSION ABOVE DOES NOT EXIST ANYMORE. UNCOMMENT AND INSTALL THE ONE BELOW"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "N0d6wy-F-f39"
},
"outputs": [],
"source": [
"#!pip install rl_zoo3==2.0.0a9"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "8_MllY6Om1eI"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[sudo] password for zhu: \n"
]
}
],
"source": [
"!apt-get install swig cmake ffmpeg"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4S9mJiKg6SqC"
},
"source": [
"To be able to use Atari games in Gymnasium we need to install atari package. And accept-rom-license to download the rom files (games files)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "NsRP-lX1_2fC"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: gymnasium[atari] in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (0.29.1)\n",
"Requirement already satisfied: numpy>=1.21.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[atari]) (1.25.2)\n",
"Requirement already satisfied: cloudpickle>=1.2.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[atari]) (2.2.1)\n",
"Requirement already satisfied: typing-extensions>=4.3.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[atari]) (4.7.1)\n",
"Requirement already satisfied: farama-notifications>=0.0.1 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[atari]) (0.0.4)\n",
"Requirement already satisfied: importlib-metadata>=4.8.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[atari]) (6.8.0)\n",
"Collecting shimmy[atari]<1.0,>=0.1.0 (from gymnasium[atari])\n",
" Downloading Shimmy-0.2.1-py3-none-any.whl (25 kB)\n",
"Requirement already satisfied: zipp>=0.5 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium[atari]) (3.16.2)\n",
"Collecting ale-py~=0.8.1 (from shimmy[atari]<1.0,>=0.1.0->gymnasium[atari])\n",
" Downloading ale_py-0.8.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: importlib-resources in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from ale-py~=0.8.1->shimmy[atari]<1.0,>=0.1.0->gymnasium[atari]) (6.0.1)\n",
"Installing collected packages: ale-py, shimmy\n",
"Successfully installed ale-py-0.8.1 shimmy-0.2.1\n",
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: gymnasium[accept-rom-license] in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (0.29.1)\n",
"Requirement already satisfied: numpy>=1.21.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[accept-rom-license]) (1.25.2)\n",
"Requirement already satisfied: cloudpickle>=1.2.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[accept-rom-license]) (2.2.1)\n",
"Requirement already satisfied: typing-extensions>=4.3.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[accept-rom-license]) (4.7.1)\n",
"Requirement already satisfied: farama-notifications>=0.0.1 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[accept-rom-license]) (0.0.4)\n",
"Requirement already satisfied: importlib-metadata>=4.8.0 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from gymnasium[accept-rom-license]) (6.8.0)\n",
"Collecting autorom[accept-rom-license]~=0.4.2 (from gymnasium[accept-rom-license])\n",
" Downloading AutoROM-0.4.2-py3-none-any.whl (16 kB)\n",
"Collecting click (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license])\n",
" Obtaining dependency information for click from https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl.metadata\n",
" Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
"Requirement already satisfied: requests in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (2.31.0)\n",
"Requirement already satisfied: tqdm in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (4.66.1)\n",
"Collecting AutoROM.accept-rom-license (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license])\n",
" Downloading AutoROM.accept-rom-license-0.6.1.tar.gz (434 kB)\n",
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"\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: zipp>=0.5 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium[accept-rom-license]) (3.16.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (3.2.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (2.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license]) (2023.7.22)\n",
"Downloading click-8.1.7-py3-none-any.whl (97 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.9/97.9 kB\u001b[0m \u001b[31m305.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hBuilding wheels for collected packages: AutoROM.accept-rom-license\n",
" Building wheel for AutoROM.accept-rom-license (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for AutoROM.accept-rom-license: filename=AutoROM.accept_rom_license-0.6.1-py3-none-any.whl size=446660 sha256=a3d153bcb7e9a8b04468055c46336a6ca39b5ad80b31d57f3dd49f540c3bd889\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-dqje2tuh/wheels/b1/1f/f7/2da07cf4f81ea264bdaf043028749d88fe0c2227134a22cf80\n",
"Successfully built AutoROM.accept-rom-license\n",
"Installing collected packages: click, AutoROM.accept-rom-license, autorom\n",
"Successfully installed AutoROM.accept-rom-license-0.6.1 autorom-0.4.2 click-8.1.7\n"
]
}
],
"source": [
"!pip install gymnasium[atari]\n",
"!pip install gymnasium[accept-rom-license]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bTpYcVZVMzUI"
},
"source": [
"## Create a virtual display 🔽\n",
"\n",
"During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames).\n",
"\n",
"Hence the following cell will install the librairies and create and run a virtual screen 🖥"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jV6wjQ7Be7p5"
},
"outputs": [],
"source": [
"%%capture\n",
"!apt install python-opengl\n",
"!apt install ffmpeg\n",
"!apt install xvfb"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Collecting pyvirtualdisplay\n",
" Downloading PyVirtualDisplay-3.0-py3-none-any.whl (15 kB)\n",
"Installing collected packages: pyvirtualdisplay\n",
"Successfully installed pyvirtualdisplay-3.0\n"
]
}
],
"source": [
"!pip3 install pyvirtualdisplay"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "BE5JWP5rQIKf"
},
"outputs": [],
"source": [
"# Virtual display\n",
"from pyvirtualdisplay import Display\n",
"\n",
"virtual_display = Display(visible=0, size=(1400, 900))\n",
"virtual_display.start()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XHGrMu07oOW0"
},
"source": [
"## Train our Deep Q-Learning Agent to Play Space Invaders 👾\n",
"\n",
"To train an agent with RL-Baselines3-Zoo, we just need to do two things:\n",
"\n",
"1. Create a hyperparameter config file that will contain our training hyperparameters called `dqn.yml`.\n",
"\n",
"This is a template example:\n",
"\n",
"```\n",
"SpaceInvadersNoFrameskip-v4:\n",
" env_wrapper:\n",
" - stable_baselines3.common.atari_wrappers.AtariWrapper\n",
" frame_stack: 4\n",
" policy: 'CnnPolicy'\n",
" n_timesteps: !!float 1e7\n",
" buffer_size: 100000\n",
" learning_rate: !!float 1e-4\n",
" batch_size: 32\n",
" learning_starts: 100000\n",
" target_update_interval: 1000\n",
" train_freq: 4\n",
" gradient_steps: 1\n",
" exploration_fraction: 0.1\n",
" exploration_final_eps: 0.01\n",
" # If True, you need to deactivate handle_timeout_termination\n",
" # in the replay_buffer_kwargs\n",
" optimize_memory_usage: False\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_VjblFSVDQOj"
},
"source": [
"Here we see that:\n",
"- We use the `Atari Wrapper` that preprocess the input (Frame reduction ,grayscale, stack 4 frames)\n",
"- We use `CnnPolicy`, since we use Convolutional layers to process the frames\n",
"- We train it for 10 million `n_timesteps`\n",
"- Memory (Experience Replay) size is 100000, aka the amount of experience steps you saved to train again your agent with.\n",
"\n",
"💡 My advice is to **reduce the training timesteps to 1M,** which will take about 90 minutes on a P100. `!nvidia-smi` will tell you what GPU you're using. At 10 million steps, this will take about 9 hours, which could likely result in Colab timing out. I recommend running this on your local computer (or somewhere else). Just click on: `File>Download`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5qTkbWrkECOJ"
},
"source": [
"In terms of hyperparameters optimization, my advice is to focus on these 3 hyperparameters:\n",
"- `learning_rate`\n",
"- `buffer_size (Experience Memory size)`\n",
"- `batch_size`\n",
"\n",
"As a good practice, you need to **check the documentation to understand what each hyperparameters does**: https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html#parameters\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hn8bRTHvERRL"
},
"source": [
"2. We start the training and save the models on `logs` folder 📁\n",
"\n",
"- Define the algorithm after `--algo`, where we save the model after `-f` and where the hyperparameter config is after `-c`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "Xr1TVW4xfbz3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"========== SpaceInvadersNoFrameskip-v4 ==========\n",
"Seed: 364634905\n",
"Loading hyperparameters from: dqn.yml\n",
"Default hyperparameters for environment (ones being tuned will be overridden):\n",
"OrderedDict([('batch_size', 32),\n",
" ('buffer_size', 100000),\n",
" ('env_wrapper',\n",
" ['stable_baselines3.common.atari_wrappers.AtariWrapper']),\n",
" ('exploration_final_eps', 0.01),\n",
" ('exploration_fraction', 0.1),\n",
" ('frame_stack', 4),\n",
" ('gradient_steps', 1),\n",
" ('learning_rate', 0.0001),\n",
" ('learning_starts', 100000),\n",
" ('n_timesteps', 12000000.0),\n",
" ('optimize_memory_usage', False),\n",
" ('policy', 'CnnPolicy'),\n",
" ('target_update_interval', 1000),\n",
" ('train_freq', 4)])\n",
"Using 1 environments\n",
"Creating test environment\n",
"A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
"[Powered by Stella]\n",
"Stacking 4 frames\n",
"Wrapping the env in a VecTransposeImage.\n",
"Stacking 4 frames\n",
"Wrapping the env in a VecTransposeImage.\n",
"Using cuda device\n",
"Log path: logs//dqn/SpaceInvadersNoFrameskip-v4_1\n",
"Traceback (most recent call last):\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n",
" return _run_code(code, main_globals, None,\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/runpy.py\", line 87, in _run_code\n",
" exec(code, run_globals)\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/rl_zoo3/train.py\", line 274, in <module>\n",
" train()\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/rl_zoo3/train.py\", line 267, in train\n",
" exp_manager.learn(model)\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/rl_zoo3/exp_manager.py\", line 236, in learn\n",
" model.learn(self.n_timesteps, **kwargs)\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/dqn/dqn.py\", line 267, in learn\n",
" return super().learn(\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/off_policy_algorithm.py\", line 301, in learn\n",
" total_timesteps, callback = self._setup_learn(\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/off_policy_algorithm.py\", line 284, in _setup_learn\n",
" return super()._setup_learn(\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/base_class.py\", line 424, in _setup_learn\n",
" self._last_obs = self.env.reset() # type: ignore[assignment]\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/vec_env/vec_transpose.py\", line 110, in reset\n",
" return self.transpose_observations(self.venv.reset())\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/vec_env/vec_frame_stack.py\", line 41, in reset\n",
" observation = self.venv.reset() # pytype:disable=annotation-type-mismatch\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\", line 76, in reset\n",
" obs, self.reset_infos[env_idx] = self.envs[env_idx].reset(seed=self._seeds[env_idx])\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/gymnasium/core.py\", line 467, in reset\n",
" return self.env.reset(seed=seed, options=options)\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/gymnasium/core.py\", line 467, in reset\n",
" return self.env.reset(seed=seed, options=options)\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/gymnasium/core.py\", line 516, in reset\n",
" return self.observation(obs), info\n",
" File \"/home/zhu/miniconda3/envs/hf39/lib/python3.9/site-packages/stable_baselines3/common/atari_wrappers.py\", line 244, in observation\n",
" assert cv2 is not None, \"OpenCV is not installed, you can do `pip install opencv-python`\"\n",
"AssertionError: OpenCV is not installed, you can do `pip install opencv-python`\n"
]
}
],
"source": [
"!python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -c dqn.yml"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SeChoX-3SZfP"
},
"source": [
"#### Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PuocgdokSab9"
},
"outputs": [],
"source": [
"!python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -c dqn.yml"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_dLomIiMKQaf"
},
"source": [
"## Let's evaluate our agent 👀\n",
"- RL-Baselines3-Zoo provides `enjoy.py`, a python script to evaluate our agent. In most RL libraries, we call the evaluation script `enjoy.py`.\n",
"- Let's evaluate it for 5000 timesteps 🔥"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "co5um_KeKbBJ"
},
"outputs": [],
"source": [
"!python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 --no-render --n-timesteps 5000 --folder logs/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q24K1tyWSj7t"
},
"source": [
"#### Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "P_uSmwGRSk0z"
},
"outputs": [],
"source": [
"!python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 --no-render --n-timesteps 5000 --folder logs/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "liBeTltiHJtr"
},
"source": [
"## Publish our trained model on the Hub 🚀\n",
"Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code.\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit3/space-invaders-model.gif\" alt=\"Space Invaders model\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ezbHS1q3HYVV"
},
"source": [
"By using `rl_zoo3.push_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the hub**.\n",
"\n",
"This way:\n",
"- You can **showcase our work** 🔥\n",
"- You can **visualize your agent playing** 👀\n",
"- You can **share with the community an agent that others can use** 💾\n",
"- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XMSeZRBiHk6X"
},
"source": [
"To be able to share your model with the community there are three more steps to follow:\n",
"\n",
"1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join\n",
"\n",
"2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.\n",
"- Create a new token (https://huggingface.co/settings/tokens) **with write role**\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/create-token.jpg\" alt=\"Create HF Token\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9O6FI0F8HnzE"
},
"source": [
"- Copy the token\n",
"- Run the cell below and past the token"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Ppu9yePwHrZX"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e31577a26311464dab1bca2583b252a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/bin/bash: /home/zhu/miniconda3/envs/hf39/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n"
]
}
],
"source": [
"from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
"notebook_login()\n",
"!git config --global credential.helper store"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2RVEdunPHs8B"
},
"source": [
"If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dSLwdmvhHvjw"
},
"source": [
"3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PW436XnhHw1H"
},
"source": [
"Let's run push_to_hub.py file to upload our trained agent to the Hub.\n",
"\n",
"`--repo-name `: The name of the repo\n",
"\n",
"`-orga`: Your Hugging Face username\n",
"\n",
"`-f`: Where the trained model folder is (in our case `logs`)\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit3/select-id.png\" alt=\"Select Id\">"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "Ygk2sEktTDEw"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/bin/bash: /home/zhu/miniconda3/envs/hf39/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
"Loading latest experiment, id=2\n",
"Loading logs/dqn/SpaceInvadersNoFrameskip-v4_2/SpaceInvadersNoFrameskip-v4.zip\n",
"A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
"[Powered by Stella]\n",
"Stacking 4 frames\n",
"Wrapping the env in a VecTransposeImage.\n",
"Uploading to czl/SpaceInvadersNoFrameskip-v4, make sure to have the rights\n",
"\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
"create a model card and push everything to the hub. It might take up to some\n",
"minutes if video generation is activated. This is a work in progress: if you\n",
"encounter a bug, please open an issue.\u001b[0m\n",
"Cloning https://huggingface.co/czl/SpaceInvadersNoFrameskip-v4 into local empty directory.\n",
"WARNING:huggingface_hub.repository:Cloning https://huggingface.co/czl/SpaceInvadersNoFrameskip-v4 into local empty directory.\n",
"Saving model to: hub/SpaceInvadersNoFrameskip-v4/dqn-SpaceInvadersNoFrameskip-v4\n",
"Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory\n"
]
}
],
"source": [
"!python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 --repo-name SpaceInvadersNoFrameskip-v4 -orga czl -f logs/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "otgpa0rhS9wR"
},
"source": [
"#### Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_HQNlAXuEhci"
},
"outputs": [],
"source": [
"!python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 --repo-name dqn-SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0D4F5zsTTJ-L"
},
"source": [
"###."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ff89kd2HL1_s"
},
"source": [
"Congrats 🥳 you've just trained and uploaded your first Deep Q-Learning agent using RL-Baselines-3 Zoo. The script above should have displayed a link to a model repository such as https://huggingface.co/ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4. When you go to this link, you can:\n",
"\n",
"- See a **video preview of your agent** at the right.\n",
"- Click \"Files and versions\" to see all the files in the repository.\n",
"- Click \"Use in stable-baselines3\" to get a code snippet that shows how to load the model.\n",
"- A model card (`README.md` file) which gives a description of the model and the hyperparameters you used.\n",
"\n",
"Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent.\n",
"\n",
"**Compare the results of your agents with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) 🏆"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fyRKcCYY-dIo"
},
"source": [
"## Load a powerful trained model 🔥\n",
"- The Stable-Baselines3 team uploaded **more than 150 trained Deep Reinforcement Learning agents on the Hub**.\n",
"\n",
"You can find them here: 👉 https://huggingface.co/sb3\n",
"\n",
"Some examples:\n",
"- Asteroids: https://huggingface.co/sb3/dqn-AsteroidsNoFrameskip-v4\n",
"- Beam Rider: https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4\n",
"- Breakout: https://huggingface.co/sb3/dqn-BreakoutNoFrameskip-v4\n",
"- Road Runner: https://huggingface.co/sb3/dqn-RoadRunnerNoFrameskip-v4\n",
"\n",
"Let's load an agent playing Beam Rider: https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "B-9QVFIROI5Y"
},
"outputs": [],
"source": [
"%%html\n",
"<video controls autoplay><source src=\"https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4/resolve/main/replay.mp4\" type=\"video/mp4\"></video>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7ZQNY_r6NJtC"
},
"source": [
"1. We download the model using `rl_zoo3.load_from_hub`, and place it in a new folder that we can call `rl_trained`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OdBNZHy0NGTR"
},
"outputs": [],
"source": [
"# Download model and save it into the logs/ folder\n",
"!python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga sb3 -f rl_trained/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LFt6hmWsNdBo"
},
"source": [
"2. Let's evaluate if for 5000 timesteps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aOxs0rNuN0uS"
},
"outputs": [],
"source": [
"!python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -n 5000 -f rl_trained/ --no-render"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kxMDuDfPON57"
},
"source": [
"Why not trying to train your own **Deep Q-Learning Agent playing BeamRiderNoFrameskip-v4? 🏆.**\n",
"\n",
"If you want to try, check https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4#hyperparameters **in the model card, you have the hyperparameters of the trained agent.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xL_ZtUgpOuY6"
},
"source": [
"But finding hyperparameters can be a daunting task. Fortunately, we'll see in the next Unit, how we can **use Optuna for optimizing the Hyperparameters 🔥.**\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-pqaco8W-huW"
},
"source": [
"## Some additional challenges 🏆\n",
"The best way to learn **is to try things by your own**!\n",
"\n",
"In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?\n",
"\n",
"Here's a list of environments you can try to train your agent with:\n",
"- BeamRiderNoFrameskip-v4\n",
"- BreakoutNoFrameskip-v4\n",
"- EnduroNoFrameskip-v4\n",
"- PongNoFrameskip-v4\n",
"\n",
"Also, **if you want to learn to implement Deep Q-Learning by yourself**, you definitely should look at CleanRL implementation: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif\" alt=\"Environments\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "paS-XKo4-kmu"
},
"source": [
"________________________________________________________________________\n",
"Congrats on finishing this chapter!\n",
"\n",
"If you’re still feel confused with all these elements...it's totally normal! **This was the same for me and for all people who studied RL.**\n",
"\n",
"Take time to really **grasp the material before continuing and try the additional challenges**. It’s important to master these elements and having a solid foundations.\n",
"\n",
"In the next unit, **we’re going to learn about [Optuna](https://optuna.org/)**. One of the most critical task in Deep Reinforcement Learning is to find a good set of training hyperparameters. And Optuna is a library that helps you to automate the search.\n",
"\n",
"\n"
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"\n",
"### This is a course built with you 👷🏿♀️\n",
"\n",
"Finally, we want to improve and update the course iteratively with your feedback. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9\n",
"\n",
"We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues)."
]
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"See you on Bonus unit 2! 🔥"
]
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