# Monte Carlo Game Solver

arXiv: https://arxiv.org/abs/2001.05087 · HTML: https://mlsanity.com/paper/2001.05087/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Study and improvement of search algorithms in two-players perfect information games](https://mlsanity.com/paper/2505.09639/) (2025)
- [On some improvements to Unbounded Minimax](https://mlsanity.com/paper/2505.04525/) (2025)
- [Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem](https://mlsanity.com/paper/2505.08451/) (2025)

## Abstract (arXiv)

We present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and the information in the Monte Carlo tree are used to order moves in game solvers. They improve greatly the solving time for multiple games.