An AlphaZero-Inspired Approach to Solving Search Problems
Abstract
AlphaZero and its extension MuZero are computer programs that use machine-learning techniques to play at a superhuman level in chess, go, and a few other games. They achieved this level of play solely with reinforcement learning from self-play, without any domain knowledge except the game rules. It is a natural idea to adapt the methods and techniques used in AlphaZero for solving search problems such as the Boolean satisfiability problem (in its search version). Given a search problem, how to represent it for an AlphaZero-inspired solver? What are the "rules of solving" for this search problem? We describe possible representations in terms of easy-instance solvers and self-reductions, and we give examples of such representations for the satisfiability problem. We also describe a version of Monte Carlo tree search adapted for search problems.
Keywords
Cite
@article{arxiv.2207.00919,
title = {An AlphaZero-Inspired Approach to Solving Search Problems},
author = {Evgeny Dantsin and Vladik Kreinovich and Alexander Wolpert},
journal= {arXiv preprint arXiv:2207.00919},
year = {2022}
}
Comments
11 pages, to appear in: Martine Ceberio and Vladik Kreinovich (eds.), "Decision Making under Uncertainty and Constraints: A Why-Book", Springer, Cham, Switzerland, 2022