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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. 相似文献
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