In this free course, you will:
- ๐ Study Deep Reinforcement Learning in theory and practice.
- ๐งโ๐ป Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, and RLlib.
- ๐ค Train agents in unique environments such as SnowballFight, Huggy the Doggo ๐ถ, and classical ones such as Space Invaders and PyBullet.
- ๐พ Publish your trained agents in one line of code to the Hugging Face Hub. But also download powerful agents from the community.
- ๐ Participate in challenges where you will evaluate your agents against other teams.
- ๐๏ธ๐จ Learn to share your own environments made with Unity and Godot.
โก๏ธโก๏ธโก๏ธ 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 ๐๐ป https://discord.gg/aYka4Yhff9
And don't forget to share with your friends who want to learn ๐ค!
๐ Publishing date | ๐ Unit | ๐ฉโ๐ป Hands-on |
---|---|---|
May, the 4th | An Introduction to Deep Reinforcement Learning | Train a Deep Reinforcement Learning lander agent to land correctly on the Moon ๐ using Stable-Baselines3 |
May, the 11th | Bonus | ๐ it's a suprise ๐ |
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 | ๐๏ธ | |
Actor-Critic Methods | ๐๏ธ | |
Proximal Policy Optimization (PPO) | ๐๏ธ | |
Decision Transformers and offline Reinforcement Learning | ๐๏ธ | |
Towards better explorations methods | ๐๏ธ |
- Stable-Baselines3
- RL Baselines3 Zoo
- RLlib
- CleanRL
- More to come ๐๏ธ
-
Huggy the Doggo ๐ถ (Based on Unity's Puppo the Corgi work)
-
SnowballFight โ๏ธ ๐ Play it here: https://huggingface.co/spaces/ThomasSimonini/SnowballFight
-
More to come ๐ง
- Lunar-Lander v2 ๐๐
- More to come ๐ง
- Space Invaders ๐พ
- More to come ๐ง