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

Han Fang, Zhihao Song, Paul Weng, Yutong Ban
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:12973-12992, 2024.

Abstract

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 data augmentations. We demonstrate that the proposed INViT achieves a dominant generalization performance on both TSP and CVRP problems with various distributions and different problem scales. Our source code and datasets are available in supplementary materials.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-fang24c, title = {{INV}i{T}: A Generalizable Routing Problem Solver with Invariant Nested View Transformer}, author = {Fang, Han and Song, Zhihao and Weng, Paul and Ban, Yutong}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {12973--12992}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/fang24c/fang24c.pdf}, url = {https://proceedings.mlr.press/v235/fang24c.html}, abstract = {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 data augmentations. We demonstrate that the proposed INViT achieves a dominant generalization performance on both TSP and CVRP problems with various distributions and different problem scales. Our source code and datasets are available in supplementary materials.} }
Endnote
%0 Conference Paper %T INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer %A Han Fang %A Zhihao Song %A Paul Weng %A Yutong Ban %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-fang24c %I PMLR %P 12973--12992 %U https://proceedings.mlr.press/v235/fang24c.html %V 235 %X 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 data augmentations. We demonstrate that the proposed INViT achieves a dominant generalization performance on both TSP and CVRP problems with various distributions and different problem scales. Our source code and datasets are available in supplementary materials.
APA
Fang, H., Song, Z., Weng, P. & Ban, Y.. (2024). INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:12973-12992 Available from https://proceedings.mlr.press/v235/fang24c.html.

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