Preference-based Deep Reinforcement Learning for Historical Route Estimation
Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang
Abstract
Recent Deep Reinforcement Learning (DRL) techniques have advanced solutions to Vehicle Routing Problems (VRPs). However, many of these methods focus exclusively on optimizing distance-oriented objectives (i.e., minimizing route length), often overlooking the implicit drivers' preferences for routes. These preferences, which are crucial in practice, are challenging to model using traditional DRL approaches. To address this gap, we propose a preference-based DRL method characterized by its reward design and optimization objective, which is specialized to learn historical route preferences. Our experiments demonstrate that the method aligns generated solutions more closely with human preferences. Moreover, it exhibits strong generalization performance across a variety of instances, offering a robust solution for different VRP scenarios.
BibTeX
@inproceedings{ijcai2025_preferencebasedd,
title = {Preference-based Deep Reinforcement Learning for Historical Route Estimation},
author = {Boshen Pan and Yaoxin Wu and Zhiguang Cao and Yaqing Hou and Guangyu Zou and Qiang Zhang},
booktitle = {IJCAI 2025},
year = {2025}
}