Acquiring Strategies for the Board Game Geister by Regret Minimization

Counterfactual Regret Minimization (CFR) is proposed to be an effective method to find approximate Nash Equilibrium for large zero-sum imperfect information games. Deep neural networks make it possible for CFR to be applied in large games by extending tabular CFR to Deep CFR. While there is little research on applying CFR to board games, in this paper, we propose a variant of Deep CFR for board games and apply it to the game Geister. We show that our proposed method can train agents with an appropriate strategy.