Stable Baselines Jax (SBX)

Stable Baselines Jax (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!): https://twitter.com/araffin2/status/1590714558628253698

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)

As SBX follows SB3 API, it is also compatible with the RL Zoo. For that you will need to create two files:

train_sbx.py:

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:

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()