A fast discard method of public mahjong computer game
Mahjong is a typical game of imperfect information.Currently, most solutions to mahjong problems are studied in the direction of deep reinforcement learning, and fairly good results have been achieved.However, such mahjong AI is built on the basis of high-quality data sets, and the mass mahjong lacks a large number of critical and effectively labeled data sets.How to quickly play cards in the game has become the main problem.To address it, the paper studies the action of playing cards and puts forward the Monte Carlo evaluation method against the opponent ' s cards guided by the heuristic quick card playing.By integrating the heuristic quick card playing method with Monte Carlo evaluation method, the paper evaluates each hand card and determines each round of playing cards through the valuation score.The empirical knowledge is initially employed to build a demarcation point with a certain number of historical card playing times, and the game process is divided into two decision periods.The heuristic fast card playing method is used in the first period, and the Monte Carlo evaluation method in the second period.The optimal playing method is given through the hierarchical and progressive decision-making process of the front and back time method, effectively reducing the decision time of playing cards and the point shot rate.The program using this method wins the first prize in the Chinese Computer Game Tournament, demonstrating its effectiveness.
computer gameimperfect information gamemahjong gameheuristic fast discardMonte Carlo method of evaluation
国家科技期刊平台
NSTL
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