A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues. In this paper, we present MARLlib, a library designed to address the aforementioned challenge by leveraging three key mechanisms: 1) a standardized multi-agent environment wrapper, 2) an agent-level algorithm implementation, and 3) a flexible policy mapping strategy. By utilizing these mechanisms, MARLlib can effectively disentangle the intertwined nature of the multi-agent task and the learning process of the algorithm, with the ability to automatically alter the training strategy based on the current task's attributes. The MARLlib library's source code is publicly accessible on GitHub: \url{https://github.com/Replicable-MARL/MARLlib}.

翻译：多智能体强化学习（MARL）领域的研究者面临一项重大挑战：如何找到一个能够为多智能体任务与算法组合提供快速且兼容的开发支持，同时避免兼容性问题的库。本文提出MARLlib，该库通过三种关键机制应对上述挑战：1）标准化的多智能体环境封装接口，2）基于智能体级别的算法实现，3）灵活的策略映射策略。利用这些机制，MARLlib能够有效解耦多智能体任务与算法学习过程的相互缠结，并根据当前任务属性自动调整训练策略。MARLlib库的源代码已在GitHub公开发布：\url{https://github.com/Replicable-MARL/MARLlib}。