Gymnasium: A Standard Interface for Reinforcement Learning Environments

TL;DR

Gymnasium is an open-source library that standardizes the interface for reinforcement learning environments, facilitating easier development, comparison, and reproducibility of RL algorithms to accelerate research progress.

Contribution

It introduces a unified API and a collection of environments, tools, and abstractions to improve interoperability and reproducibility in RL research.

Findings

Streamlines RL algorithm development and testing

Enhances reproducibility and robustness of RL experiments

Provides a comprehensive suite of customizable environments

Abstract

Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gymnasium is an open-source library that provides a standard API for RL environments, aiming to tackle this issue. Gymnasium's main feature is a set of abstractions that allow for wide interoperability between environments and training algorithms, making it easier for researchers to develop and test RL algorithms. In addition, Gymnasium provides a collection of easy-to-use environments, tools for easily customizing environments, and tools to ensure the reproducibility and robustness of RL research. Through this unified framework, Gymnasium significantly streamlines the process of developing and testing RL algorithms, enabling researchers to focus more on innovation and less on implementation details. By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential. Gymnasium is available online at https://github.com/Farama-Foundation/Gymnasium