Jax version of Stable Baselines, implementations of reinforcement learning algorithms.
Project description
Stable Baselines Jax (SB3 + JAX = SBX)
See https://github.com/araffin/sbx
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)
- Twin Delayed DDPG (TD3)
- Deep Deterministic Policy Gradient (DDPG)
Example
from sbx import TQC, DroQ, SAC, DQN, PPO, TD3, DDPG
model = TQC("MlpPolicy", "Pendulum-v1", verbose=1)
model.learn(total_timesteps=10_000, progress_bar=True)
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