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

翻译：强化学习（Reinforcement Learning, RL）是一个持续发展的领域，具有革新人工智能诸多领域的潜力。然而，尽管前景广阔，RL研究常因环境和算法实现缺乏标准化而受阻。这使得研究人员难以比较和借鉴彼此的工作，从而减缓了该领域的进展。Gymnasium是一个开源库，旨在通过提供RL环境的标准API来解决这一问题。Gymnasium的核心特性是一组抽象接口，实现了环境与训练算法之间的广泛互操作性，使研究人员能够更便捷地开发和测试RL算法。此外，Gymnasium还提供了一系列易于使用的环境、便于自定义环境的工具，以及确保RL研究可复现性和鲁棒性的工具。通过这一统一框架，Gymnasium显著简化了RL算法的开发和测试流程，使研究人员能够更专注于创新，而非实现细节。通过为RL研究提供标准化平台，Gymnasium有助于推动强化学习领域的发展，并释放其全部潜力。Gymnasium可通过 https://github.com/Farama-Foundation/Gymnasium 在线获取。