Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems

Tristan Cazenave

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arXiv · 2401.10431

Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems

Tristan Cazenave

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Abstract

Monte Carlo Search gives excellent results in multiple difficult combinatorial problems. Using a prior to perform non uniform playouts during the search improves a lot the results compared to uniform playouts. Handmade heuristics tailored to the combinatorial problem are often used as priors. We propose a method to automatically compute a prior. It uses statistics on solved problems. It is a simple and general method that incurs no computational cost at playout time and that brings large performance gains. The method is applied to three difficult combinatorial problems: Latin Square Completion, Kakuro, and Inverse RNA Folding.

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BibTeX RIS

Tristan Cazenave. 2024-01-19. Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems. https://arxiv.org/abs/2401.10431

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