Proof of concept version of Stable-Baselines3 in Jax.

Implemented algorithms:

• Soft Actor-Critic (SAC) and SAC-N
• Truncated Quantile Critics (TQC)
• Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)
• Proximal Policy Optimization (PPO)
• Deep Q Network (DQN)

For the latest master version:

pip install git+https://github.com/araffin/sbx

or:

pip install sbx-rl

import gymnasium as gym

from sbx import TQC, DroQ, SAC, PPO, DQN

env = gym.make("Pendulum-v1")

model = TQC("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10_000, progress_bar=True)

vec_env = model.get_env()
obs = vec_env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = vec_env.step(action)
    vec_env.render()

vec_env.close()

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}
}

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

Important Note: We do not do technical support, nor consulting and don't answer personal questions per email. Please post your question on the RL Discord, Reddit or Stack 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 read CONTRIBUTING.md guide first.