1 research outputs found
Learning to Perform Local Rewriting for Combinatorial Optimization
Search-based methods for hard combinatorial optimization are often guided by
heuristics. Tuning heuristics in various conditions and situations is often
time-consuming. In this paper, we propose NeuRewriter that learns a policy to
pick heuristics and rewrite the local components of the current solution to
iteratively improve it until convergence. The policy factorizes into a
region-picking and a rule-picking component, each parameterized by a neural
network trained with actor-critic methods in reinforcement learning.
NeuRewriter captures the general structure of combinatorial problems and shows
strong performance in three versatile tasks: expression simplification, online
job scheduling and vehicle routing problems. NeuRewriter outperforms the
expression simplification component in Z3; outperforms DeepRM and Google
OR-tools in online job scheduling; and outperforms recent neural baselines and
Google OR-tools in vehicle routing problems.Comment: Published in NeurIPS 201