通过聚焦未知来增强蒙特卡洛树搜索中的探索
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown
AI总结：
提出 AmEx-MCTS 方法，通过解耦价值更新、访问计数与选择路径来排除已探索子树，在同等计算资源下实现更广搜索，显著超越经典 MCTS。
摘要：
蒙特卡洛树搜索（MCTS）是一种有效的 anytime 算法，具有大量应用。它策略性地分配计算资源以专注于搜索树中有前景的部分，使其成为大搜索空间中极具吸引力的搜索算法。然而，当先前探索的区域仍是最有前景的路径时，它经常将有限的资源耗费在重新评估这些区域上。我们提出的方法称为 AmEx-MCTS，通过引入一种新颖的 MCTS 公式来解决此问题。AmEx-MCTS 的核心是在树搜索过程中解耦价值更新、访问计数更新和所选路径，从而能够排除已探索的子树或叶节点。这种分离保留了访问计数在 MCTS 中用于探索-利用平衡和质量度量的效用。由此产生的增强有助于在相同计算资源下进行更广泛的搜索，同时保留 MCTS 的本质特征。扩展的覆盖范围不仅产生更精确的估计，而且在更大更复杂的问题中证明是有效的。我们的实证评估表明 AmEx-MCTS 的卓越性能，以显著幅度超越经典 MCTS 及相关方法。
英文摘要：
Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast amount of applications. It strategically allocates computational resources to focus on promising segments of the search tree, making it a very attractive search algorithm in large search spaces. However, it often expends its limited resources on reevaluating previously explored regions when they remain the most promising path. Our proposed methodology, denoted as AmEx-MCTS, solves this problem by introducing a novel MCTS formulation. Central to AmEx-MCTS is the decoupling of value updates, visit count updates, and the selected path during the tree search, thereby enabling the exclusion of already explored subtrees or leaves. This segregation preserves the utility of visit counts for both exploration-exploitation balancing and quality metrics within MCTS. The resultant augmentation facilitates in a considerably broader search using identical computational resources, preserving the essential characteristics of MCTS. The expanded coverage not only yields more precise estimations but also proves instrumental in larger and more complex problems. Our empirical evaluation demonstrates the superior performance of AmEx-MCTS, surpassing classical MCTS and related approaches by a substantial margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
