论文标题
蒙特卡洛游戏求解器
Monte Carlo Game Solver
论文作者
论文摘要
我们提出了一种通用算法来订购移动，以加快精确的游戏求解器。它使用竞争政策的在线学习和蒙特卡洛树搜索。学习的政策和蒙特卡洛树中的信息用于在游戏求解器中订购移动。他们大大提高了多个游戏的解决时间。
We present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and the information in the Monte Carlo tree are used to order moves in game solvers. They improve greatly the solving time for multiple games.
