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.

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