NDLI: Utility Based Q-learning to Maintain Cooperation in Prisoner's Dilemma Games

Content Provider	IEEE Xplore Digital Library
Author	Moriyama, K.
Copyright Year	2007
Description	Author affiliation: Osaka Univ., Osaka (Moriyama, K.)
Abstract	This work deals with Q-learning in a multiagent environment. There are many multiagent Q-learning methods, and most of them aim to converge to a Nash equilibrium, which is not desirable in games like the prisoner's dilemma (PD). However, normal Q-learning agents that use a stochastic method in choosing actions to avoid local optima may bring mutual cooperation in PD. Although such mutual cooperation usually occurs singly, it can be maintained if the Q- function of cooperation becomes larger than that of defection after the cooperation. This work derives a theorem on how many times the cooperation is needed to make the Q- function of cooperation larger than that of defection. In addition, from the perspective of the author's previous works that discriminate utilities from rewards and use utilities for learning in PD, this work also derives a corollary on how much utility is necessary to make the Q-function larger by one-shot mutual cooperation.
Starting Page	146
Ending Page	152
File Size	499110
Page Count	7
File Format	PDF
ISBN	9780769530277
DOI	10.1109/IAT.2007.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nash equilibrium Machine learning Stochastic processes Intelligent agent Toy industry Multiagent systems Learning systems Game theory
Content Type	Text
Resource Type	Article

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