INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer

Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems. Meanwhile, most of the solvers suffer from generalizing to an unseen distribution or distributions with different scales. To address this issue, we propose a novel architecture, called Invariant Nested View Transformer (INViT), which is designed to enforce a nested design together with invariant views inside the encoders to promote the generalizability of the learned solver. It applies a modified policy gradient algorithm enhanced with …

architecture cs.lg distribution heuristics issue novel reinforcement reinforcement learning routing solve solver transformer view