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Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version ofStable Baselines.

You can read a detailed presentation of Stable Baselines3 in thev1.0 blog post or ourJMLR paper.

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 thedocumentation to get started with RL.

The performance of each algorithm was tested (seeResults section in their respective page),you can take a look at the issues#48 and#49 for more details.

We also provide detailed logs and reports on theOpenRL Benchmark platform.

Since most of the features from theoriginal roadmap have been implemented, there are no major changes planned for SB3, it is nowstable.If you want to contribute, you can search in the issues for the ones wherehelp is welcomed and the otherproposed enhancements.

While SB3 development is now focused on bug fixes and maintenance (doc update, user experience, ...), there is more active development going on in the associated repositories:

• newer algorithms are regularly added to theSB3 Contrib repository
• faster variants are developed in theSBX (SB3 + Jax) repository
• the training framework for SB3, the RL Zoo, has an activeroadmap

A migration guide from SB2 to SB3 can be found in thedocumentation.

Documentation is available online:https://stable-baselines3.readthedocs.io/

Stable-Baselines3 has some integration with other libraries/services like Weights & Biases for experiment tracking or Hugging Face for storing/sharing trained models. You can find out more in thededicated section of the documentation.

RL Baselines3 Zoo is a training framework for Reinforcement Learning (RL).

It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.

In addition, it includes a collection of tuned hyperparameters for common environments and RL algorithms, and agents trained with those settings.

Goals of this repository:

1. Provide a simple interface to train and enjoy RL agents
2. Benchmark the different Reinforcement Learning algorithms
3. Provide tuned hyperparameters for each environment and RL algorithm
4. Have fun with the trained agents!

Github repo:https://github.com/DLR-RM/rl-baselines3-zoo

Documentation:https://rl-baselines3-zoo.readthedocs.io/en/master/

We implement experimental features in a separate contrib repository:SB3-Contrib

This allows SB3 to maintain a stable and compact core, while still providing the latest features, like Recurrent PPO (PPO LSTM), CrossQ, Truncated Quantile Critics (TQC), Quantile Regression DQN (QR-DQN) or PPO with invalid action masking (Maskable PPO).

Documentation is available online:https://sb3-contrib.readthedocs.io/

Stable Baselines Jax (SBX) is a proof of concept version of Stable-Baselines3 in Jax, with recent algorithms like DroQ or CrossQ.

It provides a minimal number of features compared to SB3 but can be much faster (up to 20x times!):https://twitter.com/araffin2/status/1590714558628253698

Note: Stable-Baselines3 supports PyTorch >= 2.3

Stable Baselines3 requires Python 3.10+.

To install stable-baselines on Windows, please look at thedocumentation.

Install the Stable Baselines3 package:

pip install'stable-baselines3[extra]'

This includes optional dependencies like Tensorboard, OpenCV orale-py to train on atari games. If you do not need those, you can use:

pip install stable-baselines3

Please read thedocumentation for more details and alternatives (from source, using docker).

Most of the code in the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms.

Here is a quick example of how to train and run PPO on a cartpole environment:

importgymnasiumasgymfromstable_baselines3importPPOenv=gym.make("CartPole-v1",render_mode="human")model=PPO("MlpPolicy",env,verbose=1)model.learn(total_timesteps=10_000)vec_env=model.get_env()obs=vec_env.reset()foriinrange(1000):action,_states=model.predict(obs,deterministic=True)obs,reward,done,info=vec_env.step(action)vec_env.render()# VecEnv resets automatically# if done:#   obs = env.reset()env.close()

Or just train a model with a one liner ifthe environment is registered in Gymnasium and ifthe policy is registered:

fromstable_baselines3importPPOmodel=PPO("MlpPolicy","CartPole-v1").learn(10_000)

Please read thedocumentation for more examples.

All the following examples can be executed online using Google Colab notebooks:

• Full Tutorial
• All Notebooks
• Getting Started
• Training, Saving, Loading
• Multiprocessing
• Monitor Training and Plotting
• Atari Games
• RL Baselines Zoo
• PyBullet

1: Implemented inSB3 Contrib GitHub repository.

Actionsgymnasium.spaces:

• Box: A N-dimensional box that contains every point in the action space.
• Discrete: A list of possible actions, where each timestep only one of the actions can be used.
• MultiDiscrete: A list of possible actions, where each timestep only one action of each discrete set can be used.
• MultiBinary: A list of possible actions, where each timestep any of the actions can be used in any combination.

pip install -e'.[docs,tests,extra]'

All unit tests in stable baselines3 can be run usingpytest runner:

make pytest

To run a single test file:

python3 -m pytest -v tests/test_env_checker.py

To run a single test:

python3 -m pytest -v -k'test_check_env_dict_action'

You can also do a static type check usingmypy:

pip install mypymaketype

Codestyle check withruff:

pip install ruffmake lint

We try to maintain a list of projects using stable-baselines3 in thedocumentation,please tell us if you want your project to appear on this page ;)

To cite this repository in publications:

@article{stable-baselines3,author  ={Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},title   ={Stable-Baselines3: Reliable Reinforcement Learning Implementations},journal ={Journal of Machine Learning Research},year    ={2021},volume  ={22},number  ={268},pages   ={1-8},url     ={http://jmlr.org/papers/v22/20-1364.html}}

Note: If you need to refer to a specific version of SB3, you can also use theZenodo DOI.

Stable-Baselines3 is currently maintained byAshley Hill (aka @hill-a),Antonin Raffin (aka@araffin),Maximilian Ernestus (aka @ernestum),Adam Gleave (@AdamGleave),Anssi Kanervisto (@Miffyli) andQuentin Gallouédec (@qgallouedec).

Important Note: We do not provide technical support, or consulting and do not answer personal questions via email.Please post your question on theRL Discord,Reddit, orStack Overflow in that case.

To any interested in making the baselines better, there is still some documentation that needs to be done.If you want to contribute, please readCONTRIBUTING.md guide first.

The initial work to develop Stable Baselines3 was partially funded by the projectReduced Complexity Models from theHelmholtz-Gemeinschaft Deutscher Forschungszentren, and by the EU's Horizon 2020 Research and Innovation Programme under grant number 951992 (VeriDream).

The original version, Stable Baselines, was created in therobotics lab U2IS (INRIA Flowers team) atENSTA ParisTech.

Logo credits:L.M. Tenkes