JAX Implementations of Actor-Critic Algorithms

This page summarizes the projects mentioned and recommended in the original post on /r/reinforcementlearning

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  • stable-baselines3

    PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

  • I even have the PyTorch implementation faster in some cases (I created a branch with pytorch optimization that gives a 5% speed improvement https://github.com/DLR-RM/stable-baselines3/tree/exp/torch-optim ).

  • Ray

    Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

  • Folks like me using RLLib have observed this behavior: https://github.com/ray-project/ray/issues/12494

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  • stable-baselines

    A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

  • - tf2 speed: https://github.com/hill-a/stable-baselines/issues/576#issuecomment-573331715

  • rl-baselines3-zoo

    A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

  • for pytorch, use the rl zoo (https://github.com/DLR-RM/rl-baselines3-zoo) and sb3 ;) https://github.com/DLR-RM/stable-baselines3

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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