Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
While performing favourably on the independent and identically distributed (i.i.d.) instances, most of the existing neural methods for vehicle routing problems (VRPs) struggle to generalize in the presence of a distribution shift. To tackle this issue, we propose an ensemble-based deep reinforcement learning method for VRPs, which learns a group of diverse sub-policies to cope with various instance distributions. In particular, to prevent convergence of the parameters to the same one, we enforce diversity across sub-policies by leveraging Bootstrap with random initialization. Moreover, we also explicitly pursue inequality between sub-policies by exploiting regularization terms during training to further enhance diversity. Experimental results show that our method is able to outperform the state-of-the-art neural baselines on randomly generated instances of various distributions, and also generalizes favourably on the benchmark instances from TSPLib and CVRPLib, which confirmed the effectiveness of the whole method and the respective designs.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14
First Page
1
Last Page
14
Publisher
Neural information processing systems foundation
City or Country
California
Citation
JIANG, Yuan; CAO, Zhiguang; WU, Yaoxin; SONG, Wen; and ZHANG, Jie.
Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift. (2023). Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/8400
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.