Multi-agent reinforcement learning: A survey


Reference:
L. Busoniu, R. Babuska, and B. De Schutter, "Multi-agent reinforcement learning: A survey," Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision (ICARCV 2006), Singapore, pp. 527-532, Dec. 2006.

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.


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 * Corresponding technical report: pdf file (149 KB)
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Bibtex entry:

@inproceedings{BusBab:06-025,
        author={L. Bu{\c{s}}oniu and R. Babu{\v{s}}ka and B. {De Schutter}},
        title={Multi-agent reinforcement learning: A survey},
        booktitle={Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision (ICARCV 2006)},
        address={Singapore},
        pages={527--532},
        month=dec,
        year={2006}
        }



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