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Type of publication:Inproceedings
Entered by:LB
TitleMulti-Agent Reinforcement Learning: A Survey
Bibtex cite IDBusoniu-icarcv06-marl
Booktitle Proceedings 9th International Conference of Control, Automation, Robotics, and Vision (ICARCV 2006)
Year published 2006
Month December
Pages 527-532
Location 5-8 December 2006, Singapore
Keywords multi-agent systems,reinforcement learning,game theory,distributed control
Abstract
Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined.
Authors
Busoniu, Lucian
Babuška, Robert
De Schutter, Bart
Topics
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