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Global Nash convergence of Foster and Young's regret testing
Germano, Fabrizio; Lugosi, Gábor
Universitat Pompeu Fabra. Departament d'Economia i Empresa
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 learningrule introduced by Foster and Young.
regret testing
regret based learning
random search
stochastic dynamics
uncoupled dynamics
global convergence to nash equilibria
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