Data to 14 Oct 2026

Deep Counterfactual Regret Minimization

arXiv 1811.00164 ↗First posted 1 Nov 2018cs.AI cs.GT cs.LGcited by 103

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Abstract (arXiv)

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.

Key facts. Cited by 103 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 20; 2022: 16; 2023: 16; 2024: 11; 2025: 16; 2026: 24.

103 papers.

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2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 152
2023Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4cited by 68
2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 66
2022A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Gamescited by 66
2021Independent Natural Policy Gradient Always Converges in Markov Potential Gamescited by 37
2021XDO: A Double Oracle Algorithm for Extensive-Form Gamescited by 34
2023Fast swap regret minimization and applications to approximate correlated equilibriacited by 32
2021Last-iterate Convergence in Extensive-Form Gamescited by 28
2022Near-Optimal Learning of Extensive-Form Games with Imperfect Informationcited by 26
2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
2022ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regretcited by 24
2021Student of Games: A unified learning algorithm for both perfect and imperfect information gamescited by 23

38 more

2022The Power of Regularization in Solving Extensive-Form Gamescited by 21
2024PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Modelcited by 20
2023Evolving Diverse Red-team Language Models in Multi-round Multi-agent Gamescited by 17
2021Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Gamescited by 15
2022PerfectDou: Dominating DouDizhu with Perfect Information Distillationcited by 15
2025Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimizationcited by 15
2022Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Gamescited by 11
2022Adapting to game trees in zero-sum imperfect information gamescited by 11
2023Mastering Strategy Card Game (Hearthstone) with Improved Techniquescited by 10
2024Policy Space Response Oracles: A Surveycited by 10
2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learningcited by 10
2021Search in Imperfect Information Gamescited by 8
2021Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Playcited by 7
2021Time-series Imputation of Temporally-occluded Multiagent Trajectoriescited by 7
2021Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descentcited by 7
2021Learning Diverse Policies in MOBA Games via Macro-Goalscited by 7
2023Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RLcited by 7
2024Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descentcited by 7
2023Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Gamescited by 6
2023DanZero+: Dominating the GuanDan Game through Reinforcement Learningcited by 6
2022Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Gamescited by 5
2022DanZero: Mastering GuanDan Game with Reinforcement Learningcited by 5
2024A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergencecited by 5
2021CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Spacecited by 4
2021Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chesscited by 4
2022Public Information Representation for Adversarial Team Gamescited by 4
2022Feasible Adversarial Robust Reinforcement Learning for Underspecified Environmentscited by 4
2023Hierarchical Deep Counterfactual Regret Minimizationcited by 4
2024The complexity of approximate (coarse) correlated equilibrium for incomplete information gamescited by 4
2026Discovering Multiagent Learning Algorithms with Large Language Modelscited by 4
2026Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitationcited by 4
2021Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategiescited by 3
2021Computational Performance of Deep Reinforcement Learning to find Nash Equilibriacited by 3
2021Learning to Be Cautiouscited by 3
2022A Survey of Decision Making in Adversarial Gamescited by 3
2025Should I Trust You? Detecting Deception in Negotiations using Counterfactual RLcited by 3
2021Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Reportcited by 2
2021Temporal Induced Self-Play for Stochastic Bayesian Gamescited by 2

50 most cited of 103 All 103 (JSON)