Welcome to Stable Baselines docs! - RL Baselines Made Easy¶
Stable Baselines is a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines.
Github repository: https://github.com/hill-a/stable-baselines
You can read a detailed presentation of Stable Baselines in the Medium article: link
Main differences with OpenAI Baselines¶
This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups:
- Unified structure for all algorithms
- PEP8 compliant (unified code style)
- Documented functions and classes
- More tests & more code coverage
User Guide
- Installation
- Getting Started
- RL Algorithms
- Examples
- Try it online with Colab Notebooks!
- Basic Usage: Training, Saving, Loading
- Multiprocessing: Unleashing the Power of Vectorized Environments
- Using Callback: Monitoring Training
- Atari Games
- Mujoco: Normalizing input features
- Custom Policy Network
- Continual Learning
- Bonus: Make a GIF of a Trained Agent
- Vectorized Environments
- Using Custom Environments
- Custom Policy Network
- Tensorboard Integration
Citing Stable Baselines¶
To cite this project in publications:
@misc{stable-baselines,
author = {Hill, Ashley and Raffin, Antonin and Traore, Rene and Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai},
title = {Stable Baselines},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/hill-a/stable-baselines}},
}
Contributing¶
To any interested in making the rl baselines better, there is still some improvements that needs to be done: good-to-have features like support for continuous actions (ACER) and more documentation on the rl algorithms.
If you want to contribute, please open an issue first and then propose your pull request on Github at https://github.com/hill-a/stable-baselines.