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 status||Published - 1 Jan 2004|
Tuyls, K. P., Nowé, A., Lenaerts, T., & Manderick, B. (2004). An Evolutionary Game Theoretic Perspective on Learning in Multi-Agent Systems. Synthese, 139(2), 297-330. https://doi.org/10.1023/B:SYNT.0000024908.89191.f1