Coco-Q: Learning in Stochastic Games with Side Payments

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Eric Sodomka, Elizabeth Hilliard, Michael Littman, Amy Greenwald ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1471-1479, 2013.

Abstract

Coco (""cooperative/competitive"") values are a solution concept for two-player normal-form games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that coco values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing how the strategies learned by the Coco-Q algorithm relate to those learned by existing multiagent Q-learning algorithms.

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