Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning

We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We focus on a backtracking search algorithm for quantified Boolean logics, 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.