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arXiv · 2207.00919

An AlphaZero-Inspired Approach to Solving Search Problems

Evgeny Dantsin·Vladik Kreinovich·Alexander Wolpert
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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.

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Evgeny Dantsin, Vladik Kreinovich, Alexander Wolpert. 2022-07-02. An AlphaZero-Inspired Approach to Solving Search Problems. https://arxiv.org/abs/2207.00919

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