 Learning to play games in extensive form by valuationPhilippe Jehiel () and Dov Samet Post-Print from HAL Abstract: Game theoretic models of learning which are based on the strategic form of the game cannot explain learning in games with large extensive form. We study learning in such games by using valuation of moves. A valuation for a player is a numeric assessment of her moves that purports to reflect their desirability. We consider a myopic player, who chooses moves with the highest valuation. Each time the game is played, the player revises her valuation by assigning the payoff obtained in the play to each of the moves she has made. We show for a repeated win-lose game that if the player has a winning strategy in the stage game, there is almost surely a time after which she always wins. When a player has more than two payoffs, a more elaborate learning procedure is required. We consider one that associates with each move the average payoff in the rounds in which this move was made. When all players adopt this learning procedure, with some perturbations, then, with probability 1 there is a time after which strategies that are close to subgame perfect equilibrium are played. A single player who adopts this procedure can guarantee only her individually rational payoff. Keywords: Reinforcement learning; Valuation; Perfect equilibrium (search for similar items in EconPapers) Published in Journal of Economic Theory, 2005, 124 (2), pp.129-148. ⟨10.1016/j.jet.2004.09.004⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00754057 DOI: 10.1016/j.jet.2004.09.004 Access Statistics for this paper More papers in Post-Print from HAL |
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