PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Global Nash convergence of Foster and Young's regret testing
Fabrizio Germano and Gábor Lugosi
(2004) working paper.


We construct an uncoupled randomized strategy of repeated play such that, if every player follows such a strategy, then the joint mixed strategy profiles converge, almost surely, to a Nash equilibrium of the one-shot game. The procedure requires very little in terms of players' information about the game. In fact, players' actions are based only on their own past payoffs and, in a variant of the strategy, players need not even know that their payoffs are determined through other players' actions. The procedure works for general finite games and is based on appropriate modifications of a simple stochastic learning rule introduced by Foster and Young.

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Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:342
Deposited By:Gábor Lugosi
Deposited On:29 December 2004