1A Comprehensive Survey of Multi-Agent Reinforcement Learning

Abstract Multi-agent systems are rapidly nding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difcult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A signicant part of the research on multi-agent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multi-agent reinforcement learning (MARL). A central issue in the eld is the formal statement of the multi-agent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents ’ learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aimeither explic-itly or implicitlyat one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specic issues that arise in each category. Additionally, the benets and challenges of MARL are described along with some of the problem domains where MARL techniques have been applied. Finally, an outlook for the eld is provided. Index Terms multi-agent systems, reinforcement learning, game theory, distributed control. I