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(2004) Synthese 139 (2).
An evolutionary game theoretic perspective on learning in multi-agent systems
Karl Tuyls, Ann Nowe, Tom Lenaerts, Bernard Manderick
pp. 297-330
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.
Publication details
DOI: 10.1023/B:SYNT.0000024908.89191.f1
Full citation:
Tuyls, K. , Nowe, A. , Lenaerts, T. , Manderick, B. (2004). An evolutionary game theoretic perspective on learning in multi-agent systems. Synthese 139 (2), pp. 297-330.
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