Multi-agent reinforcement learning is a promising research area that extends established reinforcement learning approaches to problems formulated as multi-agent systems. Recently, a multitude of communication methods have been introduced to this field to address problems such as partially observable environments, non-stationarity, and exponentially growing action spaces. Communication further enables efficient cooperation among all agents interacting in an environment. This work aims at providing an overview of communication techniques in multi-agent reinforcement learning. By an in-depth analysis of 29 publications on this topic, the strengths and weaknesses of explicit, implicit, attention-based, graph-based, and hierarchical/role-based communication are evaluated. The results of this comparison show that there is no general, optimal communication framework for every problem. On the contrary, the choice of communication depends heavily on the problem at hand. The comparison also highlights the importance of communication methods with low computational overhead to enable scalability to environments where many agents interact. Finally, the paper discusses current research gaps, emphasizing the need for standardized benchmarking of system-level metrics and improved robustness under realistic communication conditions to enhance the real-world applicability of these approaches.

翻译：多智能体强化学习是一个前景广阔的研究领域，它将成熟的强化学习方法扩展到以多智能体系统形式表述的问题中。近年来，该领域引入了大量通信方法，以解决部分可观测环境、非平稳性以及指数级增长的动作空间等问题。通信进一步促进了在环境中交互的所有智能体之间的高效协作。本文旨在概述多智能体强化学习中的通信技术。通过对该主题的29篇文献进行深入分析，评估了显式通信、隐式通信、基于注意力的通信、基于图的通信以及分层/基于角色的通信等方法的优缺点。比较结果表明，不存在适用于所有问题的通用最优通信框架。相反，通信方式的选择在很大程度上取决于具体问题。该比较还突显了低计算开销通信方法的重要性，以实现与大量智能体交互环境的可扩展性。最后，本文讨论了当前的研究空白，强调了对系统级指标进行标准化基准测试的必要性，以及在现实通信条件下提高鲁棒性的需求，以增强这些方法在现实世界中的适用性。