  Deep learning approaches have shown promising results in solving routing
problems. However, there is still a substantial gap in solution quality between
machine learning and operations research algorithms. Recently, another line of
research has been introduced that fuses the strengths of machine learning and
operational research algorithms. In particular, search perturbation operators
have been used to improve the solution. Nevertheless, using the perturbation
may not guarantee a quality solution. This paper presents "Learning to Guide
Local Search" (L2GLS), a learning-based approach for routing problems that uses
a penalty term and reinforcement learning to adaptively adjust search efforts.
L2GLS combines local search (LS) operators' strengths with penalty terms to
escape local optimals. Routing problems have many practical applications, often
presetting larger instances that are still challenging for many existing
algorithms introduced in the learning to optimise field. We show that L2GLS
achieves the new state-of-the-art results on larger TSP and CVRP over other
machine learning methods.
