From Comparison to Selection: Enhancing Cross-Scale Generalization of Pointer Networks Trained with Reinforcement Learning for Capacitated Vehicle Routing Problem
Read the original on arXiv AI →The paper investigates why attention-based Pointer Networks, though size‑agnostic, lose performance when applied to different sized Capacitated Vehicle Routing Problems (CVRP). It identifies three causes—node‑density shifts, attention dispersion, and diluted graph context—and evaluates architectural changes on both encoder and decoder sides, including a learned permutation‑invariant graph‑context aggregation. The authors show that entropy‑based attention scaling improves large‑to‑small transfer, while the new aggregation methods best aid small‑to‑large transfer, and they combine these insights into a selector that chooses the optimal model for each train‑test size pair, reducing costs by up to 3.2% across seen and many unseen sizes.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.