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Vehicle dynamic dispatching using curriculum-driven reinforcement learning

文献类型：期刊论文

作者Zhang, Xiaotong4,5; Xiong, Gang5; Ai, Yunfeng4; Liu, Kunhua3; Chen, Long1,2
刊名MECHANICAL SYSTEMS AND SIGNAL PROCESSING
出版日期2023-12-01
卷号204页码:16
关键词Reinforcement learning Curriculum learning Vehicle dispatching Network optimization
ISSN号0888-3270
DOI10.1016/j.ymssp.2023.110698
通讯作者Chen, Long(long.chen@ia.ac.cn)
英文摘要This study focuses on optimizing resource allocation problems in complex dynamic environ-ments, specifically vehicle dispatching in closed bipartite queuing networks. We present a novel curriculum-driven reinforcement learning (RL) approach that seamlessly incorporates domain knowledge and environmental feedback, effectively addressing the challenges associated with sparse reward scenarios in RL applications. This approach involves a scalable reinforcement learning framework for dynamic vehicle fleet size. We design dense artificial rewards using domain knowledge and incorporate artificial action-reward pairs into the original experience sequence forming the basic structure of the training instances. A difficulty momentum boosting strategy is proposed to produce a series of training instances with progressively increasing difficulty, ensuring that the RL agent learns decision strategies in an organized and smooth manner. Experimental results demonstrate that the proposed method significantly surpasses existing approaches in enhancing productivity and model learning efficiency for transport tasks in open-pit mines, while confirming the superiority of a flexible and automated curriculum learning process over a rigid setting. This approach has vast potential for application in dynamic resource allocation problems across industries, such as manufacturing and logistics.
WOS关键词ALLOCATION
资助项目National Key R&D Program of China[2022YFB4703700] ; National Natural Science Foundation of China[62006256] ; National Natural Science Foundation of China[U1909204] ; China State Railway Group Co., Ltd. (China Railway) under RD project[L2022X002] ; Center of National Railway Intelligent Transportation System Engineering and Technology[RITS2021KF03] ; China Academy of Railway Sciences
WOS研究方向Engineering
语种英语
WOS记录号WOS:001156212700001
出版者ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; China State Railway Group Co., Ltd. (China Railway) under RD project ; Center of National Railway Intelligent Transportation System Engineering and Technology ; China Academy of Railway Sciences
源URL[http://ir.ia.ac.cn/handle/173211/55499]  
专题多模态人工智能系统全国重点实验室
通讯作者Chen, Long
作者单位1.Waytous Inc, Qingdao 266109, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao 266520, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xiaotong,Xiong, Gang,Ai, Yunfeng,et al. Vehicle dynamic dispatching using curriculum-driven reinforcement learning[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2023,204:16.
APA Zhang, Xiaotong,Xiong, Gang,Ai, Yunfeng,Liu, Kunhua,&Chen, Long.(2023).Vehicle dynamic dispatching using curriculum-driven reinforcement learning.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,204,16.
MLA Zhang, Xiaotong,et al."Vehicle dynamic dispatching using curriculum-driven reinforcement learning".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 204(2023):16.

入库方式： OAI收割

来源：自动化研究所

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