  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.
