Monte Carlo Sampling for Regret Minimization in Extensive Games

Abstract

Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the domain of poker, CFR has proven effective, particularly when using a domain-specific… (More)

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@inproceedings{Lanctot2009MonteCS, title={Monte Carlo Sampling for Regret Minimization in Extensive Games}, author={Marc Lanctot and Kevin Waugh and Martin Zinkevich and Michael H. Bowling}, booktitle={NIPS}, year={2009} }