A Game Model for Gomoku Based on Deep Learning and Monte Carlo Tree Search

摘要：

　　Alpha Zero has made remarkable achievements in Go,Chess and Japanese Chess without human knowledge.Generally,the hardware resources have much influence on the effect of model training significantly.It is important to study game model that do not rely excessively on high-performance computing capabilities.In view of this,by referring to the methods used in AlphaGo Zero,this paper studies the model applying deep learning(DL)and monte carlo tree search(MCTS)with a simple deep neural network(DNN)structure on the Game of Gomoku Model,without considering human expert knowledge.Additionally,an improved method to accelerate MCTS search is proposed on the base of the characteristics of Gomoku.Experiments show that this model can improve the chess power in a short training time with limited hardware resources.

关键词： Game theory Gomoku Deep learning Monte Carlo tree search

作者: Xiali Li Shuai He Licheng Wu Daiyao Chen Yue Zhao

作者单位: School of Information Engineering,Minzu University of China,Beijing 100081,China

会议类型: 国际会议

会议名称: 2019中国智能自动化大会(CIA,2019)

会议地点: 江苏镇江

会议语种:英文

页码: 88-97

在线出版日期: 2019-09-20（万方平台首次上网日期，不代表论文的发表时间）