Reference
L. Buşoniu, R. Babuška, and B. De Schutter, "Multi-agent
reinforcement learning: An overview," Chapter 7 in
Innovations
in Multi-Agent Systems and Applications - 1 (D. Srinivasan and
L. C. Jain, eds.), vol. 310 of
Studies in Computational Intelligence, Berlin, Germany:
Springer, ISBN 978-3-642-14434-9, pp. 183-221, 2010.
Abstract
Multi-agent systems can be used to address problems in a variety of domains,
including robotics, distributed control, telecommunications, and economics. The
complexity of many tasks arising in these domains makes them difficult to solve
with preprogrammed agent behaviors. The agents must instead discover a solution
on their own, using learning. A significant part of the research on multi-agent
learning concerns reinforcement learning techniques. This chapter reviews a
representative selection of Multi-Agent Reinforcement Learning (MARL)
algorithms for fully cooperative, fully competitive, and more general (neither
cooperative nor competitive) tasks. The benefits and challenges of MARL are
described. A central challenge in the field is the formal statement of a
multi-agent learning goal; this chapter reviews the learning goals proposed in
the literature. The problem domains where MARL techniques have been applied are
briefly discussed. Several MARL algorithms are applied to an illustrative
example involving the coordinated transportation of an object by two
cooperative robots. In an outlook for the MARL field, a set of important open
issues are identified, and promising research directions to address these
issues are outlined.
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BibTeX
@incollection{BusBab:10-003,
author = {Bu{\c{s}}oniu, Lucian and Babu{\v{s}}ka, Robert and De
Schutter, Bart},
title = {Multi-Agent Reinforcement Learning: {An} Overview},
chapter = {7},
booktitle = {Innovations in Multi-Agent Systems and Applications -- 1},
series = {Studies in Computational Intelligence},
volume = {310},
editor = {Srinivasan, Dipti and Jain, Lakhmi C.},
publisher = {Springer},
address = {Berlin, Germany},
pages = {183--221},
year = {2010}
}