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|