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

K.P. Tuyls, A. Nowé, T. Lenaerts, B. Manderick

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)297-330
JournalSynthese
Volume139
Issue number2
DOIs
Publication statusPublished - 1 Jan 2004

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