(sbx)= # Stable Baselines Jax (SBX) [Stable Baselines Jax (SBX)](https://github.com/araffin/sbx) is a proof of concept version of Stable-Baselines3 in Jax. It provides a minimal number of features compared to SB3 but can be much faster (up to 20x times!): 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) - Twin Delayed DDPG (TD3) - Deep Deterministic Policy Gradient (DDPG) - Batch Normalization in Deep Reinforcement Learning (CrossQ) - Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning (SimBa) As SBX follows SB3 API, it is also compatible with the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). For that you will need to create two files: `train_sbx.py`: ```python import rl_zoo3 import rl_zoo3.train from rl_zoo3.train import train from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ rl_zoo3.ALGOS["ddpg"] = DDPG rl_zoo3.ALGOS["dqn"] = DQN # See SBX readme to use DroQ configuration # rl_zoo3.ALGOS["droq"] = DroQ rl_zoo3.ALGOS["sac"] = SAC rl_zoo3.ALGOS["ppo"] = PPO rl_zoo3.ALGOS["td3"] = TD3 rl_zoo3.ALGOS["tqc"] = TQC rl_zoo3.ALGOS["crossq"] = CrossQ rl_zoo3.train.ALGOS = rl_zoo3.ALGOS rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS if __name__ == "__main__": train() ``` Then you can call `python train_sbx.py --algo sac --env Pendulum-v1` and use the RL Zoo CLI. `enjoy_sbx.py`: ```python import rl_zoo3 import rl_zoo3.enjoy from rl_zoo3.enjoy import enjoy from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ rl_zoo3.ALGOS["ddpg"] = DDPG rl_zoo3.ALGOS["dqn"] = DQN # See SBX readme to use DroQ configuration # rl_zoo3.ALGOS["droq"] = DroQ rl_zoo3.ALGOS["sac"] = SAC rl_zoo3.ALGOS["ppo"] = PPO rl_zoo3.ALGOS["td3"] = TD3 rl_zoo3.ALGOS["tqc"] = TQC rl_zoo3.ALGOS["crossq"] = CrossQ rl_zoo3.enjoy.ALGOS = rl_zoo3.ALGOS rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS if __name__ == "__main__": enjoy() ```