DRLPG: Reinforced Opponent-Aware Order Pricing for Hub Mobility Services
Published in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025
Recommended citation: Zuohan Wu, Chen Jason Zhang, Han Yin, Rui Meng, Libin Zheng, Huaijie Zhu, Wei Liu. "DRLPG: Reinforced Opponent-Aware Order Pricing for Hub Mobility Services." IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3298-3311, 2025. https://doi.org/10.1109/TKDE.2025.3551147
Abstract
Building upon our previous work on opponent-aware pricing for hub mobility services, this paper develops a sophisticated deep reinforcement learning model with a novel “multi-policy fusion mechanism”. This approach enables more adaptive and profitable pricing strategies in dynamic competitive environments.
Key Contributions
- Proposed DRLPG (Deep Reinforcement Learning Pricing with multi-policy fusion mechanism), a novel RL-based pricing framework
- Developed a multi-policy fusion mechanism that adaptively combines different pricing strategies based on market conditions
- Demonstrated superior performance in dynamic competitive scenarios through comprehensive experiments
- Achieved higher profitability and better adaptation to opponent strategies compared to existing methods
Publication Details
- Journal: IEEE Transactions on Knowledge and Data Engineering (TKDE)
- Ranking: CCF-A, JCR Q1
- Volume: 37, Issue 6
- Pages: 3298-3311
- Year: 2025
- DOI: 10.1109/TKDE.2025.3551147
- DBLP: Link
Authors
Zuohan Wu, Chen Jason Zhang, Han Yin, Rui Meng, Libin Zheng, Huaijie Zhu, Wei Liu
Related Work
This work extends our ICDE 2023 paper “Opponent-aware Order Pricing towards Hub-oriented Mobility Services” by introducing deep reinforcement learning techniques and a multi-policy fusion mechanism for more sophisticated and adaptive pricing strategies.
BibTeX
@article{DBLP:journals/tkde/WuZYMZZL25,
author = {Zuohan Wu and
Chen Jason Zhang and
Han Yin and
Rui Meng and
Libin Zheng and
Huaijie Zhu and
Wei Liu},
title = {{DRLPG:} Reinforced Opponent-Aware Order Pricing for Hub Mobility
Services},
journal = {{IEEE} Transactions on Knowledge and Data Engineering},
volume = {37},
number = {6},
pages = {3298--3311},
year = {2025},
url = {https://doi.org/10.1109/TKDE.2025.3551147},
doi = {10.1109/TKDE.2025.3551147}
}
