Multi-Agent Reinforcement Learning: A Survey
Reference
L. Buşoniu,
R. Babuška, 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.
Downloads
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Bibtex entry
@inproceedings{BusBab:06-025,
author={L. Bu{\c{s}}oniu and R. Babu{\v{s}}ka and B. {D}e 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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Last update: February 21, 2026.