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.
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BibTeX
@inproceedings{BusBab:06-025,
author = {Bu{\c{s}}oniu, Lucian and Babu{\v{s}}ka, Robert and De
Schutter, Bart},
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}
}