  Local search algorithms are well-known methods for solving large, hard
instances of the satisfiability problem (SAT). The performance of these
algorithms crucially depends on heuristics for setting noise parameters and
scoring variables. The optimal setting for these heuristics varies for
different instance distributions. In this paper, we present an approach for
learning effective variable scoring functions and noise parameters by using
reinforcement learning. We consider satisfiability problems from different
instance distributions and learn specialized heuristics for each of them. Our
experimental results show improvements with respect to both a WalkSAT baseline
and another local search learned heuristic.
