Pareto-Q learning algorithm for cooperative agents in general-sum games

Authors:
Meiping Song;Guochang Gu;Guoyin Zhang
Affiliations:
College of Computer Science and Technology, Harbin Engineering University, China;College of Computer Science and Technology, Harbin Engineering University, China;College of Computer Science and Technology, Harbin Engineering University, China
Venue:
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Year:
2005

Citing 2
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Abstract

Rationality and convergence are two important criterions for multi-agent learning. A novel method called Pareto-Q learning is prompted for cooperative general-sum games, with the Pareto Optimum allowing rationality and social conventions benefiting the convergence. Experiments with the grid game suggest the efficiency of Pareto-Q. Compared with the single-agent Q-learning and Nash agent Q-learning, Pareto-Q learning performs best.