Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

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Authors Edward A. Lee, Gil Lederman, Markus Rabe, Sanjit Seshia
Journal/Conference Name ICLR 2020 1
Paper Category
Paper Abstract We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.
Date of publication 2020
Code Programming Language Unspecified
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