AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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Solving Games with Functional Regret Estimation
Kevin Waugh, Dustin Morrill, James Andrew Bagnell, Michael Bowling

Last modified: 2015-02-18

Abstract


We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action. A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies. We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm. A corollary being that the method is guaranteed to converge to a Nash equilibrium in self-play so long as the regrets are ultimately realizable by the function approximator. Our technique can be understood as a principled generalization of existing work onabstraction in large games; in our work, both the abstraction as well as the equilibrium are learned during self-play. We demonstrate empirically the method achieves higher quality strategies than state-of-the-art abstraction techniques given the same resources.

Keywords


Extensive-form Games; Abstraction; Nash Equilibrium; Regret; Counterfactual Regret

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