Knowledge Commons of Institute of Automation,CAS
DDRL: A Decentralized Deep Reinforcement Learning Method for Vehicle Repositioning | |
Jinhao Xi1,2![]() ![]() ![]() ![]() | |
2021-10-25 | |
会议名称 | 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) |
会议日期 | 19-22 September 2021 |
会议地点 | Indianapolis, IN, USA |
出版者 | IEEE |
摘要 | Online Ride-hailing System improves the efficiency of vehicle utilization and the urban transportation. However, the imbalance between supply and demand is still a problem. To solve this problem and improve resource utilization efficiency, a Decentralized Deep Reinforcement Learning Method (DDRL) for vehicle repositioning is proposed. In DDRL, each vehicle is modeled as an independent agent and dispatched according to its own state to rebalance its local supply and demand. Thus, the global rebalance problem is divided into many small local rebalance problems. First, a new reward evaluation method is proposed and the long-term global reward in traditional reinforcement learning is transformed into many short-term local rewards. Second, a unified algorithm is designed by learning all the decentralized agents' sample data. Finally, the weight matrix of the state is introduced to magnify the differences between the states of adjacent vehicles. Experiments are carried out and the effectiveness of DDRL is verified. |
收录类别 | EI |
语种 | 英语 |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 人工智能+交通 |
国重实验室规划方向分类 | 多智能体决策 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52124 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Fenghua Zhu |
作者单位 | 1.The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049 China 2.The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China 3.iFLYTEK CO. LTD, Hefei 230088, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jinhao Xi,Fenghua Zhu,Yuanyuan Chen,et al. DDRL: A Decentralized Deep Reinforcement Learning Method for Vehicle Repositioning[C]:IEEE,2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
DDRL_A_Decentralized(1652KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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