Multi-agent Reinforcement Learning: A Comprehensive Survey
Dom Huh Prasant Mohapatra
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.