English, Article, Journal or magazine article edition: Global Nash Convergence of Foster and Young's Regret Testing Fabrizio Germano; Gábor Lugosi

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Bookmark: http://trove.nla.gov.au/version/152850
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Global Nash Convergence of Foster and Young's Regret Testing
Author
  • Fabrizio Germano
  • Gábor Lugosi
Physical Description
  • preprint
Notes
  • 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.
  • Regret testing, regret based learning, random search, stochastic dynamics, uncoupled dynamics, global convergence to Nash equilibria
  • RePEc:upf:upfgen:788
Language
  • English
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