How to: Industry Strength Reinforcement Learning
* [Acme](https://github.com/google-deepmind/acme) ![](https://img.shields.io/github/stars/google-deepmind/acme.svg?cacheSeconds=86400) - Acme is a library of reinforcement learning (RL) building blocks that strives to expose simple, efficient, and readable agents.
* [AReaL](https://github.com/inclusionAI/AReaL) ![](https://img.shields.io/github/stars/inclusionAI/AReaL.svg?cacheSeconds=86400) - AReaL is a reinforcement learning library.
* [CleanRL](https://github.com/vwxyzjn/cleanrl) ![](https://img.shields.io/github/stars/vwxyzjn/cleanrl.svg?cacheSeconds=86400) - CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch.
* [CompilerGym](https://github.com/facebookresearch/CompilerGym) ![](https://img.shields.io/github/stars/facebookresearch/CompilerGym.svg?cacheSeconds=86400) - CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks.
* [d3rlpy](https://github.com/takuseno/d3rlpy) ![](https://img.shields.io/github/stars/takuseno/d3rlpy.svg?cacheSeconds=86400) - d3rlpy is an offline deep reinforcement learning library for practitioners and researchers.
* [D4RL](https://github.com/Farama-Foundation/D4RL) ![](https://img.shields.io/github/stars/Farama-Foundation/D4RL.svg?cacheSeconds=86400) - D4RL is an open-source benchmark for offline reinforcement learning.
* [Dopamine](https://github.com/google/dopamine) ![](https://img.shields.io/github/stars/google/dopamine.svg?cacheSeconds=86400) - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research).
* [EvoTorch](https://github.com/nnaisense/evotorch) ![](https://img.shields.io/github/stars/nnaisense/evotorch.svg?cacheSeconds=86400) - EvoTorch is an open source evolutionary computation library developed at NNAISENSE, built on top of PyTorch.
* [FinRL](https://github.com/AI4Finance-Foundation/FinRL) ![](https://img.shields.io/github/stars/AI4Finance-Foundation/FinRL.svg?cacheSeconds=86400) - FinRL is the first open-source framework to demonstrate the great potential of financial reinforcement learning.
* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) ![](https://img.shields.io/github/stars/Farama-Foundation/Gymnasium.svg?cacheSeconds=86400) - Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.
* [Gymnasium-Robotics](https://github.com/Farama-Foundation/Gymnasium-Robotics) ![](https://img.shields.io/github/stars/Farama-Foundation/Gymnasium-Robotics.svg?cacheSeconds=86400) - Gymnasium-Robotics contains a collection of Reinforcement Learning robotic environments that use the Gymansium API. The environments run with the MuJoCo physics engine and the maintained mujoco python bindings.
* [Jumanji](https://github.com/instadeepai/jumanji) ![](https://img.shields.io/github/stars/instadeepai/jumanji.svg?cacheSeconds=86400) - Jumanji is a suite of Reinforcement Learning (RL) environments written in JAX providing clean, hardware-accelerated environments for industry-driven research.
* [MARLlib](https://github.com/Replicable-MARL/MARLlib) ![](https://img.shields.io/github/stars/Replicable-MARL/MARLlib.svg?cacheSeconds=86400) - MARLlib is a comprehensive Multi-Agent Reinforcement Learning algorithm library based on RLlib. It provides MARL research community with a unified platform for building, training, and evaluating MARL algorithms.
* [Mava](https://github.com/instadeepai/Mava) ![](https://img.shields.io/github/stars/instadeepai/Mava.svg?cacheSeconds=86400) - Mava is a framework for distributed multi-agent reinforcement learning in JAX.
* [Melting Pot](https://github.com/google-deepmind/meltingpot) ![](https://img.shields.io/github/stars/google-deepmind/meltingpot.svg?cacheSeconds=86400) - Melting Pot is a suite of test scenarios for multi-agent reinforcement learning.
* [MetaDrive](https://github.com/metadriverse/metadrive) ![](https://img.shields.io/github/stars/metadriverse/metadrive.svg?cacheSeconds=86400) - MetaDrive is a driving simulator that composes diverse driving scenarios for generalizable RL.
* [Minigrid](https://github.com/Farama-Foundation/Minigrid) ![](https://img.shields.io/github/stars/Farama-Foundation/Minigrid.svg?cacheSeconds=86400) - The Minigrid library contains a collection of discrete grid-world environments to conduct research on Reinforcement Learning. The environments follow the Gymnasium standard API and they are designed to be lightweight, fast, and easily customizable.
* [MiniWorld](https://github.com/Farama-Foundation/Mini