Abstract: Deep neural network has made amazing achievements in various foreign games. In recent years, convolutional neural network has gained great attention because of its unique unit structure, and has been frequently used in game AI agents, such as AlphaGo and Cold Flutter Masters. “Fighting the Landlord” is a typical cooperative game based on incomplete information. In this paper, a 7-layer convolutional neural network DDZ-CNN is designed to train the network with nearly 300,000 pieces of data based on the self-gaming of “Fighting the Landlord” based on Monte Carlo tree to learn the “Fighting the Landlord” strategy. In the training process, the training data are down sampled by a weight-based method to overcome the problem of uneven distribution, and the network can converge quickly. Finally, the trained model is combated with intelligent MCTS model and real person, and a good winning rate is obtained, which verifies the effectiveness and feasibility of the algorithm in this paper.

Key words: imperfect information game, convolutional neural network, “Fighting the Landlord” strategy, nonuniform distribution