Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of [Stable Baselines](https://github.com/hill-a/stable-baselines).
You can read a detailed presentation of Stable Baselines in the [Medium article](https://medium.com/@araffin/stable-baselines-a-fork-of-openai-baselines-reinforcement-learning-made-easy-df87c4b2fc82).
These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
**Note: despite its simplicity of use, Stable Baselines3 (SB3) assumes you have some knowledge about Reinforcement Learning (RL).** You should not utilize this library without some practice. To that extent, we provide good resources in the [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/rl.html) to get started with RL.
**The performance of each algorithm was tested** (see *Results* section in their respective page),
you can take a look at the issues [#48](https://github.com/DLR-RM/stable-baselines3/issues/48) and [#49](https://github.com/DLR-RM/stable-baselines3/issues/49) for more details.
Please take a look at the [Roadmap](https://github.com/DLR-RM/stable-baselines3/issues/1) and [Milestones](https://github.com/DLR-RM/stable-baselines3/milestones).
## RL Baselines3 Zoo: A Collection of Trained RL Agents
[RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). is a collection of pre-trained Reinforcement Learning agents using Stable-Baselines3.
It also provides basic scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.
We implement experimental features in a separate contrib repository: [SB3-Contrib](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib)
This allows SB3 to maintain a stable and compact core, while still providing the latest features, like Truncated Quantile Critics (TQC) or Quantile Regression DQN (QR-DQN).
Documentation is available online: [https://sb3-contrib.readthedocs.io/](https://sb3-contrib.readthedocs.io/)
To install stable-baselines on Windows, please look at the [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/install.html#prerequisites).
Or just train a model with a one liner if [the environment is registered in Gym](https://github.com/openai/gym/wiki/Environments) and if [the policy is registered](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html):
We try to maintain a list of project using stable-baselines3 in the [documentation](https://stable-baselines3.readthedocs.io/en/master/misc/projects.html),
Please post your question on the [RL Discord](https://discord.com/invite/xhfNqQv), [Reddit](https://www.reddit.com/r/reinforcementlearning/) or [Stack Overflow](https://stackoverflow.com/) in that case.
The initial work to develop Stable Baselines3 was partially funded by the project *Reduced Complexity Models* from the *Helmholtz-Gemeinschaft Deutscher Forschungszentren*.
The original version, Stable Baselines, was created in the [robotics lab U2IS](http://u2is.ensta-paristech.fr/index.php?lang=en) ([INRIA Flowers](https://flowers.inria.fr/) team) at [ENSTA ParisTech](http://www.ensta-paristech.fr/en).
