Solving Large Imperfect Information Games Using CFR+

2014/07/18 by Oskari Tammelin, Tammelin, Oskari · 30 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence in Games #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Applications #cs.GT

paper · pdf · doi:10.48550/arxiv.1407.5042

arxiv created 2014/07/18 · openalex publication_date 2014/07/18 · arxiv updated 2014/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Counterfactual Regret Minimization and variants (e.g. Public Chance Sampling CFR and Pure CFR) have been known as the best approaches for creating approximate Nash equilibrium solutions for imperfect information games such as poker. This paper introduces CFR⁺, a new algorithm that typically outperforms the previously known algorithms by an order of magnitude or more in terms of computation time while also potentially requiring less memory.

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• Single Deep Counterfactual Regret Minimization
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