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

TL;DR

This paper extends Counterfactual Regret Minimization (CFR) with hierarchical policy abstraction to two-player Mahjong, demonstrating its effectiveness in a more complex, imperfect information game and suggesting broader applicability.

Contribution

It introduces a hierarchical abstraction framework for CFR tailored to Mahjong, enhancing its scalability and applicability to complex imperfect information games.

Findings

Effective application of CFR to two-player Mahjong

Hierarchical abstraction improves computational efficiency

Framework generalizes to other imperfect information games

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.