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Type of publication:Inproceedings
Entered by:LB
TitleReinforcement Learning for Multi-Agent Systems
Bibtex cite IDBusoniu-etfa06
Booktitle Proceedings of the 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2006)
Year published 2006
Month September
Location 20-22 September, Prague, Czech Republic
Note Paper for the keynote presentation
Keywords reinforcement learning,multiagent systems
Abstract
Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, etc. Although the individual agents can be programmed in advance, many tasks require that they learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This paper gives a survey of multi-agent reinforcement learning, starting with a review of the different viewpoints on the learning goal, which is a central issue in the field. Two generic goals are distinguished: stability of the learning dynamics, and adaptation to the other agents' dynamic behavior . The focus on one of these goals, or a combination of both, leads to a categorization of the methods and approaches in the field. The challenges and benefits of multi-agent reinforcement learning are outlined along with open issues and future research directions.
Authors
Babuška, Robert
Busoniu, Lucian
De Schutter, Bart
Topics
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