# Monte Carlo Permutation Search

arXiv: https://arxiv.org/abs/2510.06381 · HTML: https://mlsanity.com/paper/2510.06381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 7 Oct 2025.

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

- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Quantum Circuit Optimization with AlphaTensor](https://mlsanity.com/paper/2402.14396/) (2024)
- [Double-oracle sampling method for Stackelberg Equilibrium approximation in general-sum extensive-form games](https://mlsanity.com/paper/1909.03934/) (2019)
- [Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games](https://mlsanity.com/paper/2406.09242/) (2024)

## Abstract (arXiv)

We propose Monte Carlo Permutation Search (MCPS), a general-purpose Monte Carlo Tree Search (MCTS) algorithm that improves upon the GRAVE algorithm. MCPS is relevant when deep reinforcement learning is not an option or when the computing power available before play is not substantial, such as in General Game Playing. The principle of MCPS is to include in the exploration term of a node the statistics on all the playouts that contain all the moves on the path from the root to the node. We test MCPS on a variety of games: Hex, Go, AtariGo, NoGo and a Wargame. MCPS almost always outperforms GRAVE. We also provide a mathematical derivation of the formulas used for weighting the three sources of statistics. These formulas are an improvement on the GRAVE formula since they no longer use the bias hyperparameter of GRAVE.