# Stable Baselines Jax (SB3 + JAX = SBX)
See https://github.com/araffin/sbx
Proof of concept version of [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) in Jax.
Implemented algorithms:
- [Soft Actor-Critic (SAC)](https://arxiv.org/abs/1801.01290) and [SAC-N](https://arxiv.org/abs/2110.01548)
- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)](https://openreview.net/forum?id=xCVJMsPv3RT)
- [Proximal Policy Optimization (PPO)](https://arxiv.org/abs/1707.06347)
- [Deep Q Network (DQN)](https://arxiv.org/abs/1312.5602)
- [Twin Delayed DDPG (TD3)](https://arxiv.org/abs/1802.09477)
- [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971)
- [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)
## Example
```python
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)
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"description": "\n\n# Stable Baselines Jax (SB3 + JAX = SBX)\n\nSee https://github.com/araffin/sbx\n\nProof of concept version of [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) in Jax.\n\nImplemented algorithms:\n- [Soft Actor-Critic (SAC)](https://arxiv.org/abs/1801.01290) and [SAC-N](https://arxiv.org/abs/2110.01548)\n- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)\n- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)](https://openreview.net/forum?id=xCVJMsPv3RT)\n- [Proximal Policy Optimization (PPO)](https://arxiv.org/abs/1707.06347)\n- [Deep Q Network (DQN)](https://arxiv.org/abs/1312.5602)\n- [Twin Delayed DDPG (TD3)](https://arxiv.org/abs/1802.09477)\n- [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971)\n- [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)\n\n## Example\n\n```python\nfrom sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ\n\nmodel = TQC(\"MlpPolicy\", \"Pendulum-v1\", verbose=1)\nmodel.learn(total_timesteps=10_000, progress_bar=True)\n\n",
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