Design and implementation of a deep learning-based decision system for Xuezhan Mahjong

Aiming at the problems of large state space, many game actions and complex game information of Xuezhan Mahjong, a method of semantic segmentation and feature extraction of Xuezhan Mahjong situation is proposed, and a Xuezhan Mahjong decision system based on DenseNet network and human empirical knowledge is designed and implemented according to the extracted game situation. The experimental results show that the model produces good training results in the original data and has high accuracy on the test set. The proposed Discard model outperforms CNN, ResNet, and XGBoost Discard models, and the decisions made by the system are human-like in playing with real players. It provides a research basis for the subsequent Xuezhan Mahjong Discard model that can surpass human.