The prevalence of multi-agent applications pervades various interconnected systems in our everyday lives. Despite their ubiquity, the integration and development of intelligent decision-making agents in a shared environment pose challenges to their effective implementation. This survey delves into the domain of multi-agent systems (MAS), placing a specific emphasis on unraveling the intricacies of learning optimal control within the MAS framework, commonly known as multi-agent reinforcement learning (MARL). The objective of this survey is to provide comprehensive insights into various dimensions of MAS, shedding light on myriad opportunities while highlighting the inherent challenges that accompany multi-agent applications. We hope not only to contribute to a deeper understanding of the MAS landscape but also to provide valuable perspectives for both researchers and practitioners. By doing so, we aim to facilitate informed exploration and foster development within the dynamic realm of MAS, recognizing the need for adaptive strategies and continuous evolution in addressing emerging complexities in MARL.

翻译：多智能体应用在日常生活中的各类互联系统中普遍存在。尽管其无所不在，但在共享环境中集成并开发智能决策智能体仍对其有效实施构成挑战。本综述深入探讨多智能体系统（MAS）领域，特别侧重于揭示在MAS框架内学习最优控制（即多智能体强化学习，MARL）的复杂性。本综述旨在提供对MAS各个维度的全面见解，揭示众多机遇，同时强调多智能体应用所固有的挑战。我们期望不仅有助于更深入地理解MAS格局，还能为研究者与实践者提供宝贵视角。通过此举，我们旨在促进知情探索，并推动动态MAS领域的发展，认识到应对MARL中新出现复杂性时需要适应性策略与持续演进。