Unverified50% confidenceFactExact time
Counterfactual regret minimization (CFR) is an iterative algorithm that approximates a Nash equilibrium by minimizing counterfactual regret and has become the mainstream solution method for games with incomplete information such as Texas Hold'em.
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10/29/2026
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UnverifiedCounterfactual Regret Minimization (CFR) was proposed by Zinkevich et al. in 2007 and was originally used to solve Nash equilibria in large-scale imperfect information games.79% similarUnverifiedHidden-information games are defined in game theory as extensive-form games with imperfect information, and theoretical equilibrium solution methods such as CFR (Counterfactual Regret Minimization) are practically infeasible to deploy at the scale of Stratego due to computational costs72% similarVerifiedLibratus, developed by Carnegie Mellon University in 2017, defeated top professional players in no-limit Texas Hold'em poker, with its core algorithm being Counterfactual Regret Minimization (CFR).68% similarUnverifiedMCCFR (Monte Carlo CFR) reduces the cost of each iteration by sampling only a subset of game paths per iteration, enabling CFR to scale to larger games.68% similarUnverifiedMCTS does not rely on domain-specific evaluation functions but instead evaluates position value through large numbers of simulations, so in games with incomplete information such as poker it is often used in combination with techniques such as opponent modeling and counterfactual regret minimization (CFR).64% similar
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