Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2024

Abstract

Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling the intricate interactions among subproblems, thus holding back the quality of the Pareto solutions. To counteract this limitation, we introduce a collaborative deep reinforcement learning method. We first propose a preference-based attention network (PAN) that allows the DRL agents to reason out solutions to subproblems in parallel, where a shared encoder learns the instance embedding and a decoder is tailored for each agent by preference intervention to construct respective solutions. Then, we design a collaborative active search (CAS) to further improve the solution quality, which updates only a part of the decoder parameters per instance during inference. In the CAS process, we also explicitly foster the interactions of neighboring DRL agents by imitation learning, empowering them to exchange insights of elite solutions to similar subproblems. Extensive results on random and benchmark instances verified the efficacy of PAN and CAS, which is particularly pronounced on the configurations (i.e., problem sizes or node distributions) beyond the training ones. Our code is available at https://github.com/marmotlab/PAN-CAS.

Keywords

Multi-objective vehicle routing problems, Deep reinforcement learning, Attention network, Collaborative active search

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, 2024 May 6-10

First Page

1956

Last Page

1965

ISBN

9798400704864

Identifier

10.5555/3635637.3663059

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.5555/3635637.3663059

Download

Share

COinS