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.

翻译：局部搜索算法是解决大规模、困难可满足性问题（SAT）的著名方法。这些算法的性能关键取决于设置噪声参数和变量评分的启发式方法。这些启发式方法的最优设置因不同实例分布而异。本文提出了一种通过强化学习来学习有效变量评分函数和噪声参数的方法。我们考虑了来自不同实例分布的可满足性问题，并为每种分布学习了专门的启发式方法。实验结果表明，与WalkSAT基线及另一种局部搜索学习型启发式方法相比，我们的方法均有所改进。