Solving Large Imperfect Information Games Using CFR+

2014/07/18 by Oskari Tammelin, Tammelin, Oskari · 15 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

paper · pdf · doi:10.48550/arxiv.1407.5042

openalex publication_date 2014/07/18 · 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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• Look-ahead Reasoning with a Learned Model in Imperfect Information Games
• Scale-Invariant Regret Matching and Online Learning with Optimal Convergence: Bridging Theory and Practice in Zero-Sum Games
• On the O(1/T) Convergence of Alternating Gradient Descent-Ascent in Bilinear Games
• Beyond Game Theory Optimal: Profit-Maximizing Poker Agents for No-Limit Holdem
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• Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopes
• Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation
• Last-Iterate Convergence in Adaptive Regret Minimization for Approximate Extensive-Form Perfect Equilibrium
• Solving Pasur Using GPU-Accelerated Counterfactual Regret Minimization

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• Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play
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