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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.
➡️➡️➡️ 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 🏗️
| 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 | Train Huggy the Doggo to catch the stick |
| 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
- RL Baselines3 Zoo
- RLlib
- 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)

-
SnowballFight ☃️
👉 Play it here: https://huggingface.co/spaces/ThomasSimonini/SnowballFight -
More to come
Gym classic controls environments 🕹️
PyBullet 🤖
Gym Atari environments 👾
MLAgents environments 🖌️
Description
Languages
MDX
58.7%
Jupyter Notebook
41.3%

