An evolutionary game theoretic perspective on learning in multi-agent systems

Karl Tuyls

Ann Nowe

Tom Lenaerts

Bernard Manderick

pp. 297-330

in: Synthese 139 (2), 2004.

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.