A Survey on Multi-Agent Reinforcement Learning: Coordination Problems
Learning in multiagent system needs to solve the complexity of the task, so multiagent reinforcement learning has been focused on theoretical research and various applications. In multiagent reinforcement learning, agents can be compete or cooperate to accomplish the goal. For cooperative multiagent reinforcement learning(CMRL), agents have to coordinate with other agents. Therefore, coordination problems in CMRL are getting more and more important because of increasing the number of agents and actions. There are several algorithms dealt with cooperative multiagent reinforcement learning using stochastic games, coordinated graph, and so on. These algorithms have some assumptions to coordinate each other, however assumptions are not consistent with characteristics of the multiagent system. In this paper, we provide a survey on coordination problems in cooperative multiagent reinforcement learning, and propose new approach to solve coordination problems.
Young-Cheol Choi Hyo-Sung Ahn
Department of Mechatronics, Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea
国际会议
青岛
英文
81-86
2010-07-15（万方平台首次上网日期，不代表论文的发表时间）