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An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems

Karl Tuyls, Ann Nowe, Tom Lenaerts and Bernard Manderick
Synthese
Vol. 139, No. 2, Knowledge, Rationality & Action (Mar., 2004), pp. 297-330
Published by: Springer
Stable URL: http://www.jstor.org/stable/20118420
Page Count: 34
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An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems
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Abstract

In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields of Multi-Agent Systems, Reinforcement Learning and Evolutionary Game Theory. We illustrate how these new insights can contribute to a better understanding of learning in MAS and to new improved learning algorithms. All three fields are introduced in a self-contained manner. Each relation is discussed in detail with the necessary background information to understand it, along with major references to relevant work.

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