UCT-RAVE算法在多人非完备信息博弈中的应用

摘要

Aimed at the problems that traditional gaming search algorithms do not suit to multi-palyer games with imperfect information, a method of combining UCT-RAVE and Monte-Carlo sampling is proposed, after analyszing the principle and characteristic of UCT-RAVE algorithm. First, the imperfect information is replaced by simulating perfect information with Monte-Carlo sampling, then UCT-RAVE is used based on perfect information for searching, at last most suitable action is selected after considering the best profits of many Monte-Carlo samples. Simulation demonstrated the feasibility and the effectiveness of the method%针对传统博弈搜索算法无法适用于多人非完备信息博弈,通过分析UCT-RAVE算法的原理和特性,提出了运用UCT-RAVE算法与蒙特卡罗抽样技术相结合的方法.通过蒙特卡罗抽样技术将非完备信息提取为有一定可信度的完备信息,运用UCT-RAVE算法基于此完备信息进行搜索,结合多次蒙特卡罗抽样下的最佳收益,选择最适行动.实例结果表明了该方法的可行性和有效性.