Reinforcement Learning for Multi-Agent Systems
Reference
R. Babuška,
L. Buşoniu, and
B. De Schutter,
"Reinforcement Learning for Multi-Agent Systems," Tech. report 06-041,
Delft Center for Systems and Control, Delft University of Technology,
7 pp., July 2006. Paper for a keynote presentation at the 11th IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2006), Prague, Czech Republic,
Sept. 2006.
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.
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Bibtex entry
@techreport{BabBus:06-041,
author={R. Babu{\v{s}}ka and L. Bu{\c{s}}oniu and B. {D}e Schutter},
title={Reinforcement Learning for Multi-Agent Systems},
number={06-041},
institution={Delft Center for Systems and Control, Delft University of
Technology},
month=jul,
year={2006},
note={Paper for a keynote presentation at the \emph{11th IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA~2006)}, Prague,
Czech Republic, Sept.\ 2006}
}
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