Meta-game equilibrium for multi-agent reinforcement learning

Authors:
Yang Gao;Joshua Zhexue Huang;Hongqiang Rong;Zhi-Hua Zhou
Affiliations:
National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;E-Business Technology Institute, The University of Hong Kong, Hong Kong, China;E-Business Technology Institute, The University of Hong Kong, Hong Kong, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.