Generalized Rapid Action Value Estimation
Generalized Rapid Action Value Estimation
复制标题
广义快速行动价值估计
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
T. Cazenave
中科院分区：
文献类型：
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作者：
T. Cazenave
蒙特卡洛树搜索（MCTS）是许多游戏的最先进算法，包括围棋和一般游戏（GGP）。MCTS的标准算法是应用于树的置信上限（UCT）。对于像围棋这样的游戏，UCT的一个很大的改进是快速动作值估计（RAVE）启发式。我们建议推广的RAVE启发式，以便有更准确的估计附近的叶子。我们测试所产生的算法名为GRAVE的Atarigo，alphoghthrough，Domestic和Go。
Monte Carlo Tree Search (MCTS) is the state of the art algorithm for many games including the game of Go and General Game Playing (GGP). The standard algorithm for MCTS is Upper Confidence bounds applied to Trees (UCT). For games such as Go a big improvement over UCT is the Rapid Action Value Estimation (RAVE) heuristic. We propose to generalize the RAVE heuristic so as to have more accurate estimates near the leaves. We test the resulting algorithm named GRAVE for Atarigo, Knighthrough, Domineering and Go.