Papers › Learning Local Search Heuristics for Boolean Satisfiability

Learning Local Search Heuristics for Boolean Satisfiability

1 Dec 2019NeurIPS 2019 12archive 2025-07-28

Emre Yolcu, Barnabas Poczos

We present an approach to learn SAT solver heuristics from scratch through deep reinforcement learning with a curriculum. In particular, we incorporate a graph neural network in a stochastic local search algorithm to act as the variable selection heuristic. We consider Boolean satisfiability problems from different classes and learn specialized heuristics for each class. Although we do not aim to compete with the state-of-the-art SAT solvers in run time, we demonstrate that the learned heuristics allow us to find satisfying assignments in fewer steps compared to a generic heuristic, and we provide analysis of our results through experiments.

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Deep Reinforcement Learning Graph Neural Network Reinforcement Learning Reinforcement Learning (RL)Variable Selection reinforcement-learning

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Graph Neural Network

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