Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning for Vehicle Repositioning
文献类型：期刊论文
| 作者 | Xi, Jinhao1,2 ; Zhu, Fenghua1,2; Ye, Peijun1,2; Lv, Yisheng1,2; Xiong, Gang1,3,4; Wang, Fei-Yue1,2
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2024-04-10 |
| 页码 | 13 |
| 关键词 | Mobility-on-demand system vehicle repositioning hierarchical graph reinforcement learning auxiliary graph reinforcement learning |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2024.3383720 |
| 通讯作者 | Zhu, Fenghua(fenghua.zhu@ia.ac.cn) ; Ye, Peijun(peijun_ye@hotmail.com) |
| 英文摘要 | Affected by people's dynamic social activities, the imbalance between vehicle supply and demand in the Mobility-On-Demand(MOD) system is a common phenomenon. To improve traffic efficiency, an Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning (AHGRL) method is proposed for vehicle repositioning. Firstly, a hierarchical graph reinforcement learning (HGRL) framework is designed. The complex vehicle repositioning problem in real road networks is divided into many sub-tasks and multiple reinforcement learning algorithms are designed to solve decision problems of different levels. Traffic congestion is also considered and road nodes are clustered dynamically. And then an auxiliary graph reinforcement learning (AGRL) algorithm is designed for the actuator. It contains the prediction branch and the repositioning branch. States and rewards of agents could be designed accurately with the support of the prediction branch. The two branches cooperate in an auxiliary way to achieve excellent forecasting and repositioning effects. Finally, to enable efficient multi-vehicle coordination, a discrete Soft Actor-Critic algorithm is adopted in the repositioning branch, which learns multiple optimal actions for vehicles in the same area. Comparative experiments with real data demonstrate the effectiveness of our method. And ablation experiments verify the effectiveness and universality of the HGRL framework and the AGRL algorithm. |
| WOS关键词 | DEMAND |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001201928800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58127]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Zhu, Fenghua; Ye, Peijun |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent M, Dongguan 523808, Peoples R China 4.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xi, Jinhao,Zhu, Fenghua,Ye, Peijun,et al. Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning for Vehicle Repositioning[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2024:13. |
| APA | Xi, Jinhao,Zhu, Fenghua,Ye, Peijun,Lv, Yisheng,Xiong, Gang,&Wang, Fei-Yue.(2024).Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning for Vehicle Repositioning.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13. |
| MLA | Xi, Jinhao,et al."Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning for Vehicle Repositioning".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024):13. |
入库方式： OAI收割
来源：自动化研究所
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