CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-13 17:09:45 +08:00
Code Issues Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Thomas Simonini
2022-08-05 08:26:47 +02:00
committed by GitHub
parent 81558a6a0e
commit b3e11ff4d2
1 changed files with 7 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
README.md
View File

@@ -31,16 +31,20 @@ This course is **self-paced** you can start when you want 🥳.
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit5#unit-5-policy-gradient-with-pytorch) | [Policy Gradient with PyTorch](https://huggingface.co/blog/deep-rl-pg) | [Code a Reinforce agent from scratch using PyTorch and train it to play Pong 🎾, CartPole and Pixelcopter 🚁](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit5/unit5.ipynb) |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity) | [Towards better explorations methods with Curiosity](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity)| |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Advantage Actor Critic (A2C)](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Train a bipedal walker and a spider to learn to walk using A2C](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) |
| August the 5th | Proximal Policy Optimization (PPO) | 🏗️ |
| August | Decision Transformers and offline Reinforcement Learning | 🏗️ |
| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch) | [Proximal Policy Optimization (PPO)](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch) | [Code a PPO agent from scratch using PyTorch and bulletproof it with Classical Control Environments](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit8/unit8.ipynb) |
| TBA | Decision Transformers and offline Reinforcement Learning | 🏗️ |
## The library you'll learn during this course
Version 1.0 (current):
- [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)
Version 2.0:
- + [RLlib](https://docs.ray.io/en/latest/rllib/index.html)
- + [Sample Factory](https://github.com/alex-petrenko/sample-factory)
- + [Hugging Face Decision Transformers](https://huggingface.co/blog/decision-transformers)
- More to come 🏗️
## The Environments you'll use