An AlphaZero-Inspired Approach to Solving Search Problems
Evgeny Dantsin, Vladik Kreinovich, and Alexander Wolpert

TL;DR
This paper explores adapting AlphaZero's reinforcement learning approach to solve search problems like SAT, by developing representations and a Monte Carlo tree search variant tailored for such problems.
Contribution
It introduces methods to represent search problems for AlphaZero-inspired solvers and adapts Monte Carlo tree search for these problems.
Findings
Proposes representations of search problems for AlphaZero-based solving
Develops a version of Monte Carlo tree search for search problems
Provides examples for the satisfiability problem
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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Taxonomy
TopicsArtificial Intelligence in Games · Sports Analytics and Performance · Reinforcement Learning in Robotics
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · Residual Connection · Residual Block · Prioritized Experience Replay · Monte-Carlo Tree Search · Convolution · Average Pooling · MuZero · AlphaZero
