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

Shiheng Wang
7/22/2023
cs.AIecon.GN

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Abstract

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.

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Cite this paper

@article{wang2023cfrp,
  title  = {CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong},
  author = {Shiheng Wang},
  year   = {2023},
  eprint = {2307.12087},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.12087v1}
}

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