Evolutionary Game Theory and Multi-Agent Reinforcement Learning

K.P. Tuyls*, A. Nowé

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.
Original languageEnglish
Pages (from-to)63-90
JournalKnowledge Engineering Review
Publication statusPublished - 1 Jan 2005

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