One approach to enhance Monte Carlo Tree Search (MCTS) is to improve its sample efficiency by grouping/abstracting states or state-action pairs and sharing statistics within a group. Though state-action pair abstractions are mostly easy to find in algorithms such as On the Go Abstractions in Upper Confidence bounds applied to Trees (OGA-UCT), nearly no state abstractions are found in either noisy or large action space settings due to constraining conditions. We provide theoretical and empirical evidence for this claim, and we slightly alleviate this state abstraction problem by proposing a weaker state abstraction condition that trades a minor loss in accuracy for finding many more abstractions. We name this technique Ideal Pruning Abstractions in UCT (IPA-UCT), which outperforms OGA-UCT (and any of its derivatives) across a large range of test domains and iteration budgets as experimentally validated. IPA-UCT uses a different abstraction framework from Abstraction of State-Action Pairs (ASAP) which is the one used by OGA-UCT, which we name IPA. Furthermore, we show that both IPA and ASAP are special cases of a more general framework that we call p-ASAP which itself is a special case of the ASASAP framework.

翻译：提升蒙特卡洛树搜索（MCTS）的一种方法是，通过分组/抽象状态或状态-动作对并在组内共享统计信息来提高其样本效率。尽管在诸如应用于树的置信上界即时抽象（OGA-UCT）等算法中，状态-动作对抽象大多易于发现，但由于约束条件，在噪声或大动作空间设置中几乎找不到状态抽象。我们为此主张提供了理论和实证证据，并通过提出一种较弱的状态抽象条件，以轻微的精度损失为代价来发现更多抽象，从而略微缓解了这一状态抽象问题。我们将此技术命名为UCT中的理想剪枝抽象（IPA-UCT），实验验证表明，其在广泛的测试领域和迭代预算下均优于OGA-UCT（及其任何衍生算法）。IPA-UCT使用与状态-动作对抽象（ASAP）不同的抽象框架，后者为OGA-UCT所采用，我们将其命名为IPA。此外，我们证明IPA和ASAP均是我们称为p-ASAP的更通用框架的特例，而p-ASAP本身又是ASASAP框架的特例。