Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead of using either a search tree or a transposition table we propose to use both in the same algorithm. The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search. We also propose to analyze multiple heuristics that improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the Second Move heuristics. They are evaluated for the game of Go using a MobileNet neural network.

翻译：与一组州深神经网络的推论相比,与GPU相比,在一组州进行深神经网络推论的速度要快得多。我们在此属性的基础上利用分批推论提出蒙特卡洛树搜索算法。我们建议在同一算法中使用搜索树或转置表,而不是同时使用搜索树或转置表。转置表包含推论结果,而搜索树则包含蒙特卡洛树搜索的统计。我们还提议分析有助于改进搜索的多种超常学:$\mu$ FPU、虚拟平均值、最后一次迭代和第二次移动图变图。它们被评估为使用移动网络神经网络的Go游戏。