Using CNN for solving two-player zero-sum games

Abstract

We study a two-player zero-sum game (matrix game for short) with the objective of finding the saddle point and its value. We develop a novel convolutional neural network (CNN for short) approach to achieve the goal. We propose a complete training pipeline, including a specific CNN model structure to handle varying game sizes, generating training datasets, and model fitting. The experiment results show that our proposed method outperforms the traditional linear programming (LP for short) method and two regret minimization learning algorithms in terms of computational efforts.