Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound
applied to Trees (UCT), are instrumental to automated planning techniques.
However, UCT can be slow to explore an optimal action when it initially appears
inferior to other actions. Maximum ENtropy Tree-Search (MENTS) incorporates the
maximum entropy principle into an MCTS approach, utilising Boltzmann policies
to sample actions, naturally encouraging more exploration. In this paper, we
highlight a major limitation of MENTS: optimal actions for the maximum entropy
objective do not necessarily correspond to optimal actions for the original
objective. We introduce two algorithms, Boltzmann Tree Search (BTS) and
Decaying ENtropy Tree-Search (DENTS), that address these limitations and
preserve the benefits of Boltzmann policies, such as allowing actions to be
sampled faster by using the Alias method. Our empirical analysis shows that our
algorithms show consistent high performance across several benchmark domains,
including the game of Go.

此研究以蒙特卡洛树搜索方法为基础，介绍了最大熵树搜索 (MENTS) 的局限性，并提出了两种新算法，Boltzmann 树搜索 (BTS) 和 Decaying 熵树搜索 (DENTS)，以解决这些局限性，并保留了 Boltzmann 策略的优势。通过实证分析，发现这些算法在多个基准领域，包括围棋游戏，都能保持高性能。