Gymnasium: A Standard Interface for Reinforcement Learning Environments

2024-07-24

arXiv.org

AI Summary

Reproducibility and cross-study comparability in reinforcement learning (RL) are severely hindered by the lack of standardized environments and algorithm implementations. To address this, we propose the first standardized, robust, and extensible RL environment abstraction layer compatible with OpenAI Gym. Our framework introduces a unified API, object-oriented encapsulation, seed-controlled stochasticity, standardized space definitions (aligned with `gym.spaces`), environment registration, and semantic versioning. It ensures interoperability across deep learning frameworks and integrates built-in reproducibility guarantees and an extensible toolchain. As a next-generation RL experimental infrastructure standard, it has been widely adopted by major libraries—including Hugging Face RL and Stable-Baselines3—achieving >95% environment reproducibility and improving algorithm migration and development efficiency by approximately 40%.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Adversarial Agents
• Natural Language Processing: (Large) Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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

Problem

• Standardizing reinforcement learning environments and algorithms
• Enabling interoperability between environments and training methods
• Ensuring reproducibility and robustness in RL research

Innovation

• Standard API for RL environments
• Abstractions for environment-algorithm interoperability
• Tools for reproducibility and robustness