The Hugging Face Deep Reinforcement Learning Class 🤗

In this free course, you will:

📖 Study Deep Reinforcement Learning in theory and practice.
🧑‍💻 Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, and RLlib.
🤖 Train agents in unique environments such as SnowballFight, Huggy the Doggo 🐶, and classical ones such as Space Invaders and PyBullet.
💾 Publish your trained agents in one line of code to the Hugging Face Hub. But also download powerful agents from the community.
🏆 Participate in challenges where you will evaluate your agents against other teams.
🖌️🎨 Learn to share your own environments made with Unity and Godot.

➡️➡️➡️ Don't forget to sign up here: http://eepurl.com/h1pElX

The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/aYka4Yhff9

Are you new to Discord? Check our discord 101 to get the best practices 👉 https://github.com/huggingface/deep-rl-class/blob/main/DISCORD.Md

And don't forget to share with your friends who want to learn 🤗!

The Syllabus 🏗️

This course is self-paced you can start when you want 🥳.

📆 Publishing date	📘 Unit	👩‍💻 Hands-on
Published 🥳	An Introduction to Deep Reinforcement Learning	Train a Deep Reinforcement Learning lander agent to land correctly on the Moon 🌕 using Stable-Baselines3
Published 🥳	Bonus
Published 🥳	Q-Learning	Train an agent to cross a Frozen lake ⛄ and train an autonomous taxi 🚖.
Published 🥳	Deep Q-Learning	Train a Deep Q-Learning agent to play Space Invaders using RL-Baselines3-Zoo
Published 🥳	Bonus: Automatic Hyperparameter Tuning using Optuna
Published 🥳	🎁 Learn to train your first Unity MLAgent
Published 🥳	Policy Gradient with PyTorch	Code a Reinforce agent from scratch using PyTorch and train it to play Pong 🎾, CartPole and Pixelcopter 🚁
July the 7th	🎁 A new library integration	🏗️
July the 14th	Actor-Critic Methods	🏗️
July the 21th	Proximal Policy Optimization (PPO)	🏗️
July the 28th	Decision Transformers and offline Reinforcement Learning	🏗️
August the 5th	Towards better explorations methods	🏗️

The library you'll learn during this course

The Environments you'll use

Custom environments made by the Hugging Face Team using Unity and Godot

Huggy the Doggo 🐶 (Based on Unity's Puppo the Corgi work)
SnowballFight ☃️ 👉 Play it here: https://huggingface.co/spaces/ThomasSimonini/SnowballFight
More to come 🚧

Gym classic controls environments 🕹️

Lunar-Lander v2 🚀🌙

PyBullet 🤖

More to come 🚧

Gym Atari environments 👾

Space Invaders 👾

MLAgents environments 🖌️

More to come 🚧

Prerequisites

Good skills in Python 🐍
Basics in Deep Learning and Pytorch

If it's not the case yet, you can check these free resources:

Python: https://www.udacity.com/course/introduction-to-python--ud1110
Intro to Deep Learning with PyTorch: https://www.udacity.com/course/deep-learning-pytorch--ud188
PyTorch in 60min: https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

FAQ

Is this class free?

Yes, totally free 🥳.

Do I need to have a Hugging Face account to follow the course?

Yes, to push your trained agents during the hands-on, you need an account (it's free) 🤗.

You can create one here 👉 https://huggingface.co/join

What’s the format of the class?

The course consists of 8 Units. In each of the Units, we'll have:

A theory explained part: an article and a video (based on Deep Reinforcement Learning Course)
A hands-on Google Colab where you'll learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, and RLlib to train your agents in unique environments such as SnowballFight, Huggy the Doggo 🐶, and classical ones such as Space Invaders and PyBullet.
Some optional challenges: train an agent in another environment, and try to beat the results.

It's not a live course video, so you can watch and read each unit when you want 🤗 You can check the syllabus here 👉 https://github.com/huggingface/deep-rl-class

What I will do during this course?

In this free course, you will:

📖 Study Deep Reinforcement Learning in theory and practice.
🧑‍💻 Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, and RLlib.
🤖 Train agents in unique environments such as SnowballFight, Huggy the Doggo 🐶, and classical ones such as Space Invaders and PyBullet.
💾 Publish your trained agents in one line of code to the Hub. But also download powerful agents from the community.
🏆 Participate in challenges where you will evaluate your agents against other teams.
🖌️🎨 Learn to share your own environments made with Unity and Godot.

Where do I sign up?

Here 👉 http://eepurl.com/h1pElX

Where can I find the course?

On this repository, we'll publish every week the links (chapters, hands-ons, videos).

Where can I exchange with my classmates and with you?

We have a discord server where you can exchange with the community and with us 👉🏻 https://discord.gg/aYka4Yhff9

Don’t forget to introduce yourself when you sign up 🤗

I have some feedback

We want to improve and update the course iteratively with your feedback. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9

How much background knowledge is needed?

Some prerequisites:

Good skills in Python 🐍 Basics in Deep Learning and Pytorch

If it's not the case yet, you can check these free resources:

Python: https://www.udacity.com/course/introduction-to-python--ud1110
Intro to Deep Learning with PyTorch: https://www.udacity.com/course/deep-learning-pytorch--ud188
PyTorch in 60min: https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

Is there a certificate?

Yes 🎉. You'll need to upload the eight models with the eight hands-on.

Citing the project

To cite this repository in publications:

@misc{deep-rl-class,
  author = {Simonini, Thomas and Sanseviero, Omar},
  title = {The Hugging Face Deep Reinforcement Learning Class},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/deep-rl-class}},
}

wbj0110 / deep-rl-class