mirror of
https://github.com/huggingface/deep-rl-class.git
synced 2026-04-14 02:11:17 +08:00
Merge branch 'main' into ThomasSimonini/A2C
This commit is contained in:
Thomas Simonini
@@ -509,7 +509,7 @@
|
||||
"\n",
|
||||
"This step is the simplest:\n",
|
||||
"\n",
|
||||
"- Open the game Huggy in your browser: https://huggingface.co/spaces/ThomasSimonini/Huggy\n",
|
||||
"- Open the game Huggy in your browser: https://singularite.itch.io/huggy\n",
|
||||
"\n",
|
||||
"- Click on Play with my Huggy model\n",
|
||||
"\n",
|
||||
@@ -569,4 +569,4 @@
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
}
|
||||
|
||||
5
notebooks/unit1/requirements-unit1.txt
Normal file
5
notebooks/unit1/requirements-unit1.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
stable-baselines3[extra]
|
||||
box2d
|
||||
box2d-kengz
|
||||
huggingface_sb3
|
||||
pyglet==1.5.1
|
||||
@@ -230,15 +230,6 @@
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"TODO CHANGE LINK OF THE REQUIREMENTS"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "32e3NPYgH5ET"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -247,7 +238,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install -r https://huggingface.co/spaces/ThomasSimonini/temp-space-requirements/raw/main/requirements/requirements-unit1.txt"
|
||||
"!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit1/requirements-unit1.txt"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1155,4 +1146,4 @@
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
}
|
||||
|
||||
@@ -127,7 +127,7 @@
|
||||
"source": [
|
||||
"# Let's train a Deep Q-Learning agent playing Atari' Space Invaders 👾 and upload it to the Hub.\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 >= 500**.\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",
|
||||
@@ -799,4 +799,4 @@
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
}
|
||||
|
||||
6
notebooks/unit4/requirements-unit4.txt
Normal file
6
notebooks/unit4/requirements-unit4.txt
Normal file
@@ -0,0 +1,6 @@
|
||||
gym
|
||||
git+https://github.com/ntasfi/PyGame-Learning-Environment.git
|
||||
git+https://github.com/qlan3/gym-games.git
|
||||
huggingface_hub
|
||||
imageio-ffmpeg
|
||||
pyyaml==6.0
|
||||
1614
notebooks/unit4/unit4.ipynb
Normal file
1614
notebooks/unit4/unit4.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
844
notebooks/unit5/unit5.ipynb
Normal file
844
notebooks/unit5/unit5.ipynb
Normal file
@@ -0,0 +1,844 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "view-in-github",
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"<a href=\"https://colab.research.google.com/github/huggingface/deep-rl-class/blob/ThomasSimonini%2FMLAgents/notebooks/unit5/unit5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "2D3NL_e4crQv"
|
||||
},
|
||||
"source": [
|
||||
"# Unit 5: An Introduction to ML-Agents\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/thumbnail.png\" alt=\"Thumbnail\"/>\n",
|
||||
"\n",
|
||||
"In this notebook, you'll learn about ML-Agents and train two agents.\n",
|
||||
"\n",
|
||||
"- The first one will learn to **shoot snowballs onto spawning targets**.\n",
|
||||
"- The second need to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, **and move to the gold brick at the top**. To do that, it will need to explore its environment, and we will use a technique called curiosity.\n",
|
||||
"\n",
|
||||
"After that, you'll be able **to watch your agents playing directly on your browser**.\n",
|
||||
"\n",
|
||||
"For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "97ZiytXEgqIz"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"⬇️ Here is an example of what **you will achieve at the end of this unit.** ⬇️\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "FMYrDriDujzX"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/pyramids.gif\" alt=\"Pyramids\"/>\n",
|
||||
"\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif\" alt=\"SnowballTarget\"/>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "cBmFlh8suma-"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### 🎮 Environments: \n",
|
||||
"\n",
|
||||
"- [Pyramids](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md#pyramids)\n",
|
||||
"- SnowballTarget\n",
|
||||
"\n",
|
||||
"### 📚 RL-Library: \n",
|
||||
"\n",
|
||||
"- [ML-Agents (HuggingFace Experimental Version)](https://github.com/huggingface/ml-agents)\n",
|
||||
"\n",
|
||||
"⚠ We're going to use an experimental version of ML-Agents were you can push to hub and load from hub Unity ML-Agents Models **you need to install the same version**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "A-cYE0K5iL-w"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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)."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "qEhtaFh9i31S"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Objectives of this notebook 🏆\n",
|
||||
"\n",
|
||||
"At the end of the notebook, you will:\n",
|
||||
"\n",
|
||||
"- Understand how works **ML-Agents**, the environment library.\n",
|
||||
"- Be able to **train agents in Unity Environments**.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "j7f63r3Yi5vE"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## This notebook is from the 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\"/>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "viNzVbVaYvY3"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "6p5HnEefISCB"
|
||||
},
|
||||
"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://huggingface.co/deep-rl-course/communication/publishing-schedule\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": "Y-mo_6rXIjRi"
|
||||
},
|
||||
"source": [
|
||||
"## Prerequisites 🏗️\n",
|
||||
"Before diving into the notebook, you need to:\n",
|
||||
"\n",
|
||||
"🔲 📚 **Study [what is ML-Agents and how it works by reading Unit 5](https://huggingface.co/deep-rl-course/unit5/introduction)** 🤗 "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Let's train our agents 🚀\n",
|
||||
"\n",
|
||||
"The ML-Agents integration on the Hub is **still experimental**, some features will be added in the future. \n",
|
||||
"\n",
|
||||
"But for now, **to validate this hands-on for the certification process, you just need to push your trained models to the Hub**. There’s no results to attain to validate this one. But if you want to get nice results you can try to attain:\n",
|
||||
"\n",
|
||||
"- For `Pyramids` : Mean Reward = 1.75\n",
|
||||
"- For `SnowballTarget` : Mean Reward = 15 or 30 targets hit in an episode.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "xYO1uD5Ujgdh"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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\">"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "DssdIjk_8vZE"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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\">"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "sTfCXHy68xBv"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "an3ByrXYQ4iK"
|
||||
},
|
||||
"source": [
|
||||
"## Clone the repository and install the dependencies 🔽\n",
|
||||
"- We need to clone the repository, that **contains the experimental version of the library that allows you to push your trained agent to the Hub.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "6WNoL04M7rTa"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%capture\n",
|
||||
"# Clone the repository\n",
|
||||
"!git clone --depth 1 https://github.com/huggingface/ml-agents/ "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "d8wmVcMk7xKo"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%capture\n",
|
||||
"# Go inside the repository and install the package\n",
|
||||
"%cd ml-agents\n",
|
||||
"!pip3 install -e ./ml-agents-envs\n",
|
||||
"!pip3 install -e ./ml-agents"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## SnowballTarget ⛄\n",
|
||||
"\n",
|
||||
"If you need a refresher on how this environments work check this section 👉\n",
|
||||
"https://huggingface.co/deep-rl-course/unit5/snowball-target"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "R5_7Ptd_kEcG"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "HRY5ufKUKfhI"
|
||||
},
|
||||
"source": [
|
||||
"### Download and move the environment zip file in `./training-envs-executables/linux/`\n",
|
||||
"- Our environment executable is in a zip file.\n",
|
||||
"- We need to download it and place it to `./training-envs-executables/linux/`\n",
|
||||
"- We use a linux executable because we use colab, and colab machines OS is Ubuntu (linux)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "C9Ls6_6eOKiA"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Here, we create training-envs-executables and linux\n",
|
||||
"!mkdir ./training-envs-executables\n",
|
||||
"!mkdir ./training-envs-executables/linux"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "jsoZGxr1MIXY"
|
||||
},
|
||||
"source": [
|
||||
"Download the file SnowballTarget.zip from https://drive.google.com/file/d/1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5 using `wget`. \n",
|
||||
"\n",
|
||||
"Check out the full solution to download large files from GDrive [here](https://bcrf.biochem.wisc.edu/2021/02/05/download-google-drive-files-using-wget/)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "QU6gi8CmWhnA"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5\" -O ./training-envs-executables/linux/SnowballTarget.zip && rm -rf /tmp/cookies.txt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"We unzip the executable.zip file"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "_LLVaEEK3ayi"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "8FPx0an9IAwO"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%capture\n",
|
||||
"!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/SnowballTarget.zip"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "nyumV5XfPKzu"
|
||||
},
|
||||
"source": [
|
||||
"Make sure your file is accessible "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "EdFsLJ11JvQf"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!chmod -R 755 ./training-envs-executables/linux/SnowballTarget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Define the SnowballTarget config file\n",
|
||||
"- In ML-Agents, you define the **training hyperparameters into config.yaml files.**\n",
|
||||
"\n",
|
||||
"There are multiple hyperparameters. To know them better, you should check for each explanation with [the documentation](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Training-Configuration-File.md)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"So you need to create a `SnowballTarget.yaml` config file in ./content/ml-agents/config/ppo/\n",
|
||||
"\n",
|
||||
"We'll give you here a first version of this config (to copy and paste into your `SnowballTarget.yaml file`), **but you should modify it**.\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"behaviors:\n",
|
||||
" SnowballTarget:\n",
|
||||
" trainer_type: ppo\n",
|
||||
" summary_freq: 10000\n",
|
||||
" keep_checkpoints: 10\n",
|
||||
" checkpoint_interval: 50000\n",
|
||||
" max_steps: 200000\n",
|
||||
" time_horizon: 64\n",
|
||||
" threaded: true\n",
|
||||
" hyperparameters:\n",
|
||||
" learning_rate: 0.0003\n",
|
||||
" learning_rate_schedule: linear\n",
|
||||
" batch_size: 128\n",
|
||||
" buffer_size: 2048\n",
|
||||
" beta: 0.005\n",
|
||||
" epsilon: 0.2\n",
|
||||
" lambd: 0.95\n",
|
||||
" num_epoch: 3\n",
|
||||
" network_settings:\n",
|
||||
" normalize: false\n",
|
||||
" hidden_units: 256\n",
|
||||
" num_layers: 2\n",
|
||||
" vis_encode_type: simple\n",
|
||||
" reward_signals:\n",
|
||||
" extrinsic:\n",
|
||||
" gamma: 0.99\n",
|
||||
" strength: 1.0\n",
|
||||
"```"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "NAuEq32Mwvtz"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config1.png\" alt=\"Config SnowballTarget\"/>\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config2.png\" alt=\"Config SnowballTarget\"/>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "4U3sRH4N4h_l"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"As an experimentation, you should also try to modify some other hyperparameters. Unity provides very [good documentation explaining each of them here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md).\n",
|
||||
"\n",
|
||||
"Now that you've created the config file and understand what most hyperparameters do, we're ready to train our agent 🔥."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "JJJdo_5AyoGo"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "f9fI555bO12v"
|
||||
},
|
||||
"source": [
|
||||
"### Train the agent\n",
|
||||
"\n",
|
||||
"To train our agent, we just need to **launch mlagents-learn and select the executable containing the environment.**\n",
|
||||
"\n",
|
||||
"We define four parameters:\n",
|
||||
"\n",
|
||||
"1. `mlagents-learn <config>`: the path where the hyperparameter config file is.\n",
|
||||
"2. `--env`: where the environment executable is.\n",
|
||||
"3. `--run_id`: the name you want to give to your training run id.\n",
|
||||
"4. `--no-graphics`: to not launch the visualization during the training.\n",
|
||||
"\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/mlagentslearn.png\" alt=\"MlAgents learn\"/>\n",
|
||||
"\n",
|
||||
"Train the model and use the `--resume` flag to continue training in case of interruption. \n",
|
||||
"\n",
|
||||
"> It will fail first time if and when you use `--resume`, try running the block again to bypass the error. \n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"The training will take 10 to 35min depending on your config, go take a ☕️you deserve it 🤗."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "lN32oWF8zPjs"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "bS-Yh1UdHfzy"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!mlagents-learn ./config/ppo/SnowballTarget.yaml --env=./training-envs-executables/linux/SnowballTarget/SnowballTarget --run-id=\"SnowballTarget1\" --no-graphics"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "5Vue94AzPy1t"
|
||||
},
|
||||
"source": [
|
||||
"### Push the agent to the 🤗 Hub\n",
|
||||
"\n",
|
||||
"- Now that we trained our agent, we’re **ready to push it to the Hub to be able to visualize it playing on your browser🔥.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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\">\n",
|
||||
"\n",
|
||||
"- Copy the token \n",
|
||||
"- Run the cell below and paste the token"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "izT6FpgNzZ6R"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "rKt2vsYoK56o"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from huggingface_hub import notebook_login\n",
|
||||
"notebook_login()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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`"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "aSU9qD9_6dem"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"Then, we simply need to run `mlagents-push-to-hf`.\n",
|
||||
"\n",
|
||||
"And we define 4 parameters:\n",
|
||||
"\n",
|
||||
"1. `--run-id`: the name of the training run id.\n",
|
||||
"2. `--local-dir`: where the agent was saved, it’s results/<run_id name>, so in my case results/First Training.\n",
|
||||
"3. `--repo-id`: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name>\n",
|
||||
"If the repo does not exist **it will be created automatically**\n",
|
||||
"4. `--commit-message`: since HF repos are git repository you need to define a commit message.\n",
|
||||
"\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/mlagentspushtohub.png\" alt=\"Push to Hub\"/>\n",
|
||||
"\n",
|
||||
"For instance:\n",
|
||||
"\n",
|
||||
"`!mlagents-push-to-hf --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"ThomasSimonini/ppo-SnowballTarget\" --commit-message=\"First Push\"`"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "KK4fPfnczunT"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "dGEFAIboLVc6"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!mlagents-push-to-hf --run-id= # Add your run id --local-dir= # Your local dir --repo-id= # Your repo id --commit-message= # Your commit message"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"Else, if everything worked you should have this at the end of the process(but with a different url 😆) :\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"Your model is pushed to the hub. You can view your model here: https://huggingface.co/ThomasSimonini/ppo-SnowballTarget\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"It’s the link to your model, it contains a model card that explains how to use it, your Tensorboard and your config file. **What’s awesome is that it’s a git repository, that means you can have different commits, update your repository with a new push etc.**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yborB0850FTM"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"But now comes the best: **being able to visualize your agent online 👀.**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "5Uaon2cg0NrL"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Watch your agent playing 👀\n",
|
||||
"\n",
|
||||
"For this step it’s simple:\n",
|
||||
"\n",
|
||||
"1. Remember your repo-id\n",
|
||||
"\n",
|
||||
"2. Go here: https://singularite.itch.io/snowballtarget\n",
|
||||
"\n",
|
||||
"3. Launch the game and put it in full screen by clicking on the bottom right button\n",
|
||||
"\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget_load.png\" alt=\"Snowballtarget load\"/>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "VMc4oOsE0QiZ"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"1. In step 1, choose your model repository which is the model id (in my case ThomasSimonini/ppo-SnowballTarget).\n",
|
||||
"\n",
|
||||
"2. In step 2, **choose what model you want to replay**:\n",
|
||||
" - I have multiple one, since we saved a model every 500000 timesteps. \n",
|
||||
" - But if I want the more recent I choose `SnowballTarget.onnx`\n",
|
||||
"\n",
|
||||
"👉 What’s nice **is to try with different models step to see the improvement of the agent.**\n",
|
||||
"\n",
|
||||
"And don't hesitate to share the best score your agent gets on discord in #rl-i-made-this channel 🔥\n",
|
||||
"\n",
|
||||
"Let's now try a harder environment called Pyramids..."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Djs8c5rR0Z8a"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Pyramids 🏆\n",
|
||||
"\n",
|
||||
"### Download and move the environment zip file in `./training-envs-executables/linux/`\n",
|
||||
"- Our environment executable is in a zip file.\n",
|
||||
"- We need to download it and place it to `./training-envs-executables/linux/`\n",
|
||||
"- We use a linux executable because we use colab, and colab machines OS is Ubuntu (linux)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "rVMwRi4y_tmx"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "NyqYYkLyAVMK"
|
||||
},
|
||||
"source": [
|
||||
"Download the file Pyramids.zip from https://drive.google.com/uc?export=download&id=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H using `wget`. Check out the full solution to download large files from GDrive [here](https://bcrf.biochem.wisc.edu/2021/02/05/download-google-drive-files-using-wget/)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "AxojCsSVAVMP"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H\" -O ./training-envs-executables/linux/Pyramids.zip && rm -rf /tmp/cookies.txt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "bfs6CTJ1AVMP"
|
||||
},
|
||||
"source": [
|
||||
"**OR** Download directly to local machine and then drag and drop the file from local machine to `./training-envs-executables/linux`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "H7JmgOwcSSmF"
|
||||
},
|
||||
"source": [
|
||||
"Wait for the upload to finish and then run the command below. \n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"Unzip it"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "iWUUcs0_794U"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "i2E3K4V2AVMP"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%capture\n",
|
||||
"!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/Pyramids.zip"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "KmKYBgHTAVMP"
|
||||
},
|
||||
"source": [
|
||||
"Make sure your file is accessible "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "Im-nwvLPAVMP"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!chmod -R 755 ./training-envs-executables/linux/Pyramids/Pyramids"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Modify the PyramidsRND config file\n",
|
||||
"- Contrary to the first environment which was a custom one, **Pyramids was made by the Unity team**.\n",
|
||||
"- So the PyramidsRND config file already exists and is in ./content/ml-agents/config/ppo/PyramidsRND.yaml\n",
|
||||
"- You might asked why \"RND\" in PyramidsRND. RND stands for *random network distillation* it's a way to generate curiosity rewards. If you want to know more on that we wrote an article explaning this technique: https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938\n",
|
||||
"\n",
|
||||
"For this training, we’ll modify one thing:\n",
|
||||
"- The total training steps hyperparameter is too high since we can hit the benchmark (mean reward = 1.75) in only 1M training steps.\n",
|
||||
"👉 To do that, we go to config/ppo/PyramidsRND.yaml,**and modify these to max_steps to 1000000.**\n",
|
||||
"\n",
|
||||
"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/pyramids-config.png\" alt=\"Pyramids config\"/>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "fqceIATXAgih"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"As an experimentation, you should also try to modify some other hyperparameters, Unity provides a very [good documentation explaining each of them here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md).\n",
|
||||
"\n",
|
||||
"We’re now ready to train our agent 🔥."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "RI-5aPL7BWVk"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Train the agent\n",
|
||||
"\n",
|
||||
"The training will take 30 to 45min depending on your machine, go take a ☕️you deserve it 🤗."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "s5hr1rvIBdZH"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "fXi4-IaHBhqD"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!mlagents-learn ./config/ppo/PyramidsRND.yaml --env=./training-envs-executables/linux/Pyramids/Pyramids --run-id=\"Pyramids Training\" --no-graphics"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "txonKxuSByut"
|
||||
},
|
||||
"source": [
|
||||
"### Push the agent to the 🤗 Hub\n",
|
||||
"\n",
|
||||
"- Now that we trained our agent, we’re **ready to push it to the Hub to be able to visualize it playing on your browser🔥.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"id": "JZ53caJ99sX_"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"!mlagents-push-to-hf --run-id= # Add your run id --local-dir= # Your local dir --repo-id= # Your repo id --commit-message= # Your commit message"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yiEQbv7rB4mU"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Watch your agent playing 👀\n",
|
||||
"\n",
|
||||
"The temporary link for Pyramids demo is: https://singularite.itch.io/pyramids"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "7aZfgxo-CDeQ"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### 🎁 Bonus: Why not train on another environment?\n",
|
||||
"Now that you know how to train an agent using MLAgents, **why not try another environment?** \n",
|
||||
"\n",
|
||||
"MLAgents provides 18 different and we’re building some custom ones. The best way to learn is to try things of your own, have fun.\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "hGG_oq2n0wjB"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
""
|
||||
],
|
||||
"metadata": {
|
||||
"id": "KSAkJxSr0z6-"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"You have the full list of the one currently available on Hugging Face here 👉 https://github.com/huggingface/ml-agents#the-environments\n",
|
||||
"\n",
|
||||
"For the demos to visualize your agent, the temporary link is: https://singularite.itch.io (temporary because we'll also put the demos on Hugging Face Space)\n",
|
||||
"\n",
|
||||
"For now we have integrated: \n",
|
||||
"- [Worm](https://singularite.itch.io/worm) demo where you teach a **worm to crawl**.\n",
|
||||
"- [Walker](https://singularite.itch.io/walker) demo where you teach an agent **to walk towards a goal**.\n",
|
||||
"\n",
|
||||
"If you want new demos to be added, please open an issue: https://github.com/huggingface/deep-rl-class 🤗"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "YiyF4FX-04JB"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"That’s all for today. Congrats on finishing this tutorial!\n",
|
||||
"\n",
|
||||
"The best way to learn is to practice and try stuff. Why not try another environment? ML-Agents has 18 different environments, but you can also create your own? Check the documentation and have fun!\n",
|
||||
"\n",
|
||||
"See you on Unit 6 🔥,\n",
|
||||
"\n",
|
||||
"## Keep Learning, Stay awesome 🤗"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "PI6dPWmh064H"
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"private_outputs": true,
|
||||
"include_colab_link": true
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in New Issue
Block a user