Proceedings

Preference-based Deep Reinforcement Learning for Historical Route Estimation

Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 8591-8599.

https://doi.org/10.24963/ijcai.2025/955

Keywords

  • Machine Learning: ML: Reinforcement learning
  • Planning and Scheduling: PS: Routing
  • Machine Learning: ML: Learning preferences or rankings

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.

Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, and Qiang Zhang. 2025. Preference-based Deep Reinforcement Learning for Historical Route Estimation. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 8591–8599. https://doi.org/10.24963/ijcai.2025/955
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