The multi-scale backbone neural network model for Chinese standard mahjong
Chinese standard mahjong involves multiple rounds, immense state space, 81 different categories of tiles, and complex winning strategies.Conventional neural networks struggle to express and fit the intricate data of Chinese standard mahjong.For the first time, a multi-scale backbone deep neural network is employed to build a mahjong AI algorithm, to better capture local and global features of national standard mahjong, suitable for processing complex data and making more accurate game strategies.Based on the game data from the IJCAI 2020 Championship, the training dataset undergoes data augmentation.Using the augmented data, the proposed algorithm receives 5 days of supervised learning training on an NVIDIA GeForce RTX 3090 Laptop GPU.The trained model has 52 million parameters, achieving an action accuracy of 93 .47％, discard accuracy of 88 .93％, and declaration accuracy of 97 .56％.The proposed model is deployed on Botzone platform developed by Peking University and it has entered the top 1％ of the leaderboard.
deep learningmahjongconvolutional neural networkRes2 Net50multi-scale backbone ar-chitecture
国家科技期刊平台
NSTL
万方数据
