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 language | English |
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Pages (from-to) | 297-330 |
Journal | Synthese |
Volume | 139 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2004 |