Papers › Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

20 Jul 2018arXiv:1807.08058archive 2025-07-28

Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia

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.

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Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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