Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments

• dm_control

  7 3,540 7.5 Python

  Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

  

Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.

• PettingZoo

  19 2,370 8.8 Python

  An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

  

To address this issue, we are excited to announce the release of Shimmy as a mature Farama Foundation project. Shimmy is an API compatibility tool for converting external RL environments to the Gymnasium and PettingZoo APIs. This allows users to access a wide range of single and multi-agent environments, all under a single standard API.

• InfluxDB

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• stable-baselines3

  46 7,894 8.2 Python

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

  

Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.

• lab

  4 7,002 0.0 C

  A customisable 3D platform for agent-based AI research

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

• bsuite

  2 1,465 0.0 Python

  bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

• Arcade-Learning-Environment

  6 2,070 5.3 C++

  The Arcade Learning Environment (ALE) -- a platform for AI research.

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

• gym

  96 33,873 0.0 Python

  A toolkit for developing and comparing reinforcement learning algorithms.

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

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• open_spiel

  44 3,999 9.5 C++

  OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

• meltingpot

  4 532 8.9 Python

  A suite of test scenarios for multi-agent reinforcement learning.

  

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

• Shimmy

  1 114 6.2 Python

  An API conversion tool for popular external reinforcement learning environments

  

Check out our blog post or release notes for more details. Our documentation website at https://shimmy.farama.org/, containing an overview of each environment with screenshots, installation instructions, full usage scripts, and API information.

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