mlsanityData to 14 Oct 2026

CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong

arXiv 2307.12087 ↗First posted 22 Jul 2023cs.AI econ.GN q-fin.ECcited by 1

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Abstract (arXiv)

Counterfactual Regret Minimization(CFR) has shown its success in Texas Hold'em poker. We apply this algorithm to another popular incomplete information game, Mahjong. Compared to the poker game, Mahjong is much more complex with many variants. We study two-player Mahjong by conducting game theoretical analysis and making a hierarchical abstraction to CFR based on winning policies. This framework can be generalized to other imperfect information games.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 22 Jul 2023.

Builds on

3 of 10 references.

References · 3

  1. 2018Deep Counterfactual Regret Minimizationcited by 103
  2. 2017Safe and Nested Subgame Solving for Imperfect-Information Gamescited by 41
  3. 2013Solving Imperfect Information Games Using Decompositioncited by 29

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learningcited by 10