Many of the strongest game playing programs use a combination of Monte Carlo
tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as
policy or value evaluators. Given a limited budget, such as online playing or
during the self-play phase of AlphaZero (AZ) training, a balance needs to be
reached between accurate state estimation and more MCTS simulations, both of
which are critical for a strong game playing agent. Typically, larger DNNs are
better at generalization and accurate evaluation, while smaller DNNs are less
costly, and therefore can lead to more MCTS simulations and bigger search trees
with the same budget. This paper introduces a new method called the multiple
policy value MCTS (MPV-MCTS), which combines multiple policy value neural
networks (PV-NNs) of various sizes to retain advantages of each network, where
two PV-NNs f_S and f_L are used in this paper. We show through experiments on
the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN
with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms
PV-MCTS for AZ training.

介绍了一种名为多策略值 MCTS (MPV-MCTS) 的新方法，它结合了多个不同大小的策略值神经网络 (PV-NN)，以平衡精确状态估计和更多的 MCTS 模拟，以提升游戏运行代理的性能。在 NoGo 游戏和 AZ 训练中，与单个 PV-NN 结合策略值 MCTS 相比，MPV-MCTS 表现更好。