Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning

ICLR 2020 Vitaly KurinSaad GodilShimon WhitesonBryan Catanzaro

We present GQSAT, a branching heuristic in a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation. Solvers using GQSAT are complete SAT solvers that either provide a satisfying assignment or a proof of unsatisfiability, which is required for many SAT applications... (read more)

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