重庆理工大学学报2024，Vol.38Issue(9)：130-136,7.DOI:10.3969/j.issn.1674-8425(z).2024.05.017
维护全局博弈图的蒙特卡洛图搜索
Monte Carlo tree search for maintaining the global game graph
摘要
Abstract
The AlphaGo series algorithms have significantly advanced artificial intelligence in board games by employing neural networks with learning value and policy networks to guide the Monte Carlo Tree Search method.Recent research results indicate replacing Monte Carlo Tree Search with Monte Carlo Graph Search can further enhance the program' s search efficiency.On this basis, this paper employs a novel method known as the Monte Carlo graph search for maintaining the global game graph.This method, by maintaining a global game graph, utilizes the expired node deletion algorithm to eliminate nodes and edges without value.Additionally, it employs measures such as reasoning calculations during the opponent' s turn, enhancing the program's search efficiency.Our experiment on Hex demonstrates this method, under limited computing resources, exhibits an enhanced winning rate compared to alternative search strategies.关键词
AlphaGo系列算法/计算机博弈/蒙特卡洛图搜索/计算资源Key words
AlphaGo series algorithms/computer-based game/Monte Carlo graph search/computational resources分类
信息技术与安全科学引用本文复制引用
徐长明,周其磊,王一川,王栋年,金张根,王军伟..维护全局博弈图的蒙特卡洛图搜索[J].重庆理工大学学报,2024,38(9):130-136,7.基金项目
河北省自然科学基金面上项目(F2023501006) （F2023501006）
