Three Perspectives on Multi-Agent Reinforcement Learning

Three Perspectives on Multi-Agent Reinforcement Learning

Yang Gao (Nanjing University, China) and Hao Wang (Massey University, New Zealand)
DOI: 10.4018/978-1-59904-108-7.ch012
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Abstract

This chapter concludes three perspectives on multi-agent reinforcement learning (MARL): (1) cooperative MARL, which performs mutual interaction between cooperative agents; (2) equilibrium-based MARL, which focuses on equilibrium solutions among gaming agents; and (3) best-response MARL, which suggests a no-regret policy against other competitive agents. Then the authors present a general framework of MARL, which combines all the three perspectives in order to assist readers in understanding the intricate relationships between different perspectives. Furthermore, a negotiation-based MARL algorithm based on meta-equilibrium is presented, which can interact with cooperative agents, games with gaming agents, and provides the best response to other competitive agents.

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