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Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

Edward A. Lee
Gil Lederman
Markus Rabe
Sanjit A. Seshia
Proceedings of ICLR (2020) (to appear)
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We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We consider search algorithms for quantified Boolean logics, that already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a representation which lends to making predictions in a scalable way. The heuristics learned through our approach significantly improve over the handwritten heuristics for several sets of formulas.