mirror of
https://github.com/saymrwulf/stable-baselines3.git
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4fdb65ecf3
* Fix custom policy example * Add RL Zoo doc link * Add changelog to pypi * Add SBX doc page * Fix small mistake in docstring --------- Co-authored-by: Peter Elmers <peter.elmers@yahoo.com>
66 lines
1.7 KiB
ReStructuredText
66 lines
1.7 KiB
ReStructuredText
.. _sbx:
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==========================
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Stable Baselines Jax (SBX)
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==========================
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`Stable Baselines Jax (SBX) <https://github.com/araffin/sbx>`_ is a proof of concept version of Stable-Baselines3 in Jax.
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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
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Implemented algorithms:
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- Soft Actor-Critic (SAC) and SAC-N
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- Truncated Quantile Critics (TQC)
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- Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)
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- Proximal Policy Optimization (PPO)
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- Deep Q Network (DQN)
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As SBX follows SB3 API, it is also compatible with the `RL Zoo <https://github.com/DLR-RM/rl-baselines3-zoo>`_.
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For that you will need to create two files:
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``train_sbx.py``:
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.. code-block:: python
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import rl_zoo3
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import rl_zoo3.train
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from rl_zoo3.train import train
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from sbx import DQN, PPO, SAC, TQC, DroQ
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rl_zoo3.ALGOS["tqc"] = TQC
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rl_zoo3.ALGOS["droq"] = DroQ
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rl_zoo3.ALGOS["sac"] = SAC
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rl_zoo3.ALGOS["ppo"] = PPO
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rl_zoo3.ALGOS["dqn"] = DQN
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rl_zoo3.train.ALGOS = rl_zoo3.ALGOS
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rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS
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if __name__ == "__main__":
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train()
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Then you can call ``python train_sbx.py --algo sac --env Pendulum-v1`` and use the RL Zoo CLI.
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``enjoy_sbx.py``:
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.. code-block:: python
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import rl_zoo3
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import rl_zoo3.enjoy
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from rl_zoo3.enjoy import enjoy
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from sbx import DQN, PPO, SAC, TQC, DroQ
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rl_zoo3.ALGOS["tqc"] = TQC
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rl_zoo3.ALGOS["droq"] = DroQ
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rl_zoo3.ALGOS["sac"] = SAC
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rl_zoo3.ALGOS["ppo"] = PPO
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rl_zoo3.ALGOS["dqn"] = DQN
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rl_zoo3.enjoy.ALGOS = rl_zoo3.ALGOS
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rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS
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if __name__ == "__main__":
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enjoy()
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