Multi-Agent Reinforcement Learning: A Survey
作者： Lucian Busoniu , Robert Babuska , Bart De Schutter
DOI: 10.1109/ICARCV.2006.345353
关键词:
摘要: Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising these domains require that the agents learn behaviors online. A significant part research on multi-agent learning concerns reinforcement techniques. However, due to different viewpoints central issues, such as formal statement goal, large number methods and approaches have been introduced. In this paper we aim present an integrated survey field. First, issue goal is discussed, after which representative selection algorithms reviewed. Finally, open issues identified future directions outlined
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