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)
- Batch Normalization in Deep Reinforcement Learning (CrossQ)
- Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning (SimBa)
Example
from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ
model = TQC("MlpPolicy", "Pendulum-v1", verbose=1)
model.learn(total_timesteps=10_000, progress_bar=True)
Release files for sbx-rl 0.28.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| sbx_rl-0.28.0.tar.gz | 52.4 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sbx_rl-0.28.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 115.2 kB
Release files / sbx_rl-0.28.0.tar.gz
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