# Anytime Sequential Halving in Monte-Carlo Tree Search

arXiv: https://arxiv.org/abs/2411.07171 · HTML: https://mlsanity.com/paper/2411.07171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Lookahead Pathology in Monte-Carlo Tree Search](https://mlsanity.com/paper/2212.05208/) (2022)
- [Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games](https://mlsanity.com/paper/2406.09242/) (2024)

## Built on by (most cited first, 1 of 1)

- [Best Agent Identification for General Game Playing](https://mlsanity.com/paper/2507.00451/) (2025)

## Abstract (arXiv)

Monte-Carlo Tree Search (MCTS) typically uses multi-armed bandit (MAB) strategies designed to minimize cumulative regret, such as UCB1, as its selection strategy. However, in the root node of the search tree, it is more sensible to minimize simple regret. Previous work has proposed using Sequential Halving as selection strategy in the root node, as, in theory, it performs better with respect to simple regret. However, Sequential Halving requires a budget of iterations to be predetermined, which is often impractical. This paper proposes an anytime version of the algorithm, which can be halted at any arbitrary time and still return a satisfactory result, while being designed such that it approximates the behavior of Sequential Halving. Empirical results in synthetic MAB problems and ten different board games demonstrate that the algorithm's performance is competitive with Sequential Halving and UCB1 (and their analogues in MCTS).