# 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: https://forms.gle/4bbgzs3oVZMjgDed7

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

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

## The Syllabus 🏗️
| 📆 Publishing date  | 📘 Unit           | 👩‍💻 Hands-on |
|---------------|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| May, the 4th  | An Introduction to Deep Reinforcement Learning           | Train a Deep Reinforcement Learning lander agent to land correctly on the Moon 🌕 using Stable-Baselines3 |
| May, the 11th | Bonus                                                    | 🎁 it's a surprise 🎁                                                               |
| May, the 18th | Q-Learning                                               | Train an agent to cross a Frozen lake in this new version of the environment.                                                                                                        |
| June, the 1st | Deep Q-Learning and improvements                         | Train a Deep Q-Learning agent to play Space Invaders                                                     |
|               | Policy-based methods                                     | 🏗️                                                                                                        |
|               | Actor-Critic Methods                                     | 🏗️                                                                                                        |
|               | Proximal Policy Optimization (PPO)                       | 🏗️                                                                                                        |
|               | Decision Transformers and offline Reinforcement Learning | 🏗️                                                                                                        |
|               | Towards better explorations methods                      | 🏗️                                                                                                        |


## The library you'll learn during this course
- [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3)
- [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo)
- [RLlib](https://docs.ray.io/en/latest/rllib/index.html)
- [CleanRL](https://github.com/vwxyzjn/cleanrl)
- More to come 🏗️

## 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](https://blog.unity.com/technology/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit))
![huggy.jpg](./assets/img/huggy.jpg)


- SnowballFight ☃️
![snowballfight.gif](./assets/img/snowballfight.gif)
👉 Play it here: https://huggingface.co/spaces/ThomasSimonini/SnowballFight


- More to come 🚧


### Gym classic controls environments 🕹️
- Lunar-Lander v2 🚀🌙

![lunarlander.gif](./assets/img/lunarlander.gif)


### PyBullet 🤖
- More to come 🚧

### Gym Atari environments 👾
- Space Invaders 👾

![spaceinvaders.gif](./assets/img/spaceinvaders.gif)

### 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