# Split Moves for Monte-Carlo Tree Search

arXiv: https://arxiv.org/abs/2112.07761 · HTML: https://mlsanity.com/paper/2112.07761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [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)
- [Combinatorial Multi-armed Bandits for Real-Time Strategy Games](https://mlsanity.com/paper/1710.04805/) (2017)
- [Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning](https://mlsanity.com/paper/2006.07970/) (2020)
- [Efficient Reasoning in Regular Boardgames](https://mlsanity.com/paper/2006.08295/) (2020)
- [Fast Random Integer Generation in an Interval](https://mlsanity.com/paper/1805.10941/) (2018)

## Built on by (most cited first, 2 of 2)

- [Proof Number Based Monte-Carlo Tree Search](https://mlsanity.com/paper/2303.09449/) (2023)
- [Dynamic Resource Allocation for Ensemble Determinization MCTS](https://mlsanity.com/paper/2607.13007/) (2026)

## Abstract (arXiv)

In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents' playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents' strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single- as well as multi-action games.