# Monte Carlo Tree Search with Boltzmann Exploration Source: [Monte Carlo Tree Search with Boltzmann Exploration]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:48:43.283Z Authors: Michael Painter, Mohamed Baioumy, Nick Hawes, Bruno Lacerda Published: 2024-04-11T13:25:35.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Game Playing on Go (round-robin)](): Game Playing; Go (round-robin); Avg Trials per Move: 5375. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()