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Learning to play games in extensive form by valuation

By P Jehiel and D Samet

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 I 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. (c) 2004 Elsevier Inc. All rights reserved

Topics: reinforcement learning, valuation, perfect equilibrium, FICTITIOUS PLAY, REINFORCEMENT, EQUILIBRIUM, INDUCTION, BACKWARD, DYNAMICS
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
Year: 2005
OAI identifier: oai:eprints.ucl.ac.uk.OAI2:16670
Provided by: UCL Discovery
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