Reference
L. Buşoniu, R. Babuška, and B. De Schutter, "A comprehensive
survey of multi-agent reinforcement learning,"
IEEE
Transactions on Systems, Man, and Cybernetics, Part C: Applications and
Reviews, vol. 38, no. 2, pp. 156-172, Mar. 2008.
Abstract
Multi-agent systems are rapidly finding applications 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 paper provides a
comprehensive survey of multi-agent reinforcement learning (MARL). A central
issue in the field is the formal statement of the multi-agent learning goal.
Different viewpoints on this issue have led to the proposal of many different
goals, among which two focal points can be distinguished: stability of the
agents' learning dynamics, and adaptation to the changing behavior of the other
agents. The MARL algorithms described in the literature aim-either explicitly
or implicitly-at one of these two goals or at a combination of both, in a fully
cooperative, fully competitive, or more general setting. A representative
selection of these algorithms is discussed in detail in this paper, together
with the specific issues that arise in each category. Additionally, the
benefits and challenges of MARL are described along with some of the problem
domains where MARL techniques have been applied. Finally, an outlook for the
field is provided.
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BibTeX
@article{BusBab:07-019,
author = {Bu{\c{s}}oniu, Lucian and Babu{\v{s}}ka, Robert and De Schutter,
Bart},
title = {A Comprehensive Survey of Multi-Agent Reinforcement Learning},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part C:
Applications and Reviews},
volume = {38},
number = {2},
pages = {156--172},
month = mar,
year = {2008}
}