A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems

doi:10.1109/TNNLS.2022.3222206

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 智能化团队

	A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems
	Wei, Qinglai 2,3,4; Yan, Yutian 2,3,4; Zhang, Jie 2,3,4; Xiao, Jun 3; Wang, Cong 1
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2022-11-30
页码	12
关键词	Automated guided vehicle (AGV) dispatching deep learning reinforcement learning (RL) self-attention
ISSN号	2162-237X
DOI	10.1109/TNNLS.2022.3222206
通讯作者	Zhang, Jie(jie.zhang@ia.ac.cn) ; Xiao, Jun(xiaojun@ucas.ac.cn)
英文摘要	The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign transportation tasks to certain vehicles. This article proposes a new deep reinforcement learning approach with a self-attention mechanism to dynamically dispatch the tasks to AGV. The AGV dispatching system is modeled as a less complicated Markov decision process (MDP) using vehicle-initiated rules to dispatch a workcenter to an idle AGV. In order to deal with the highly dynamical environment, the self-attention mechanism is introduced to calculate the importance of different information. The invalid action masking technique is performed to alleviate false actions. A multimodal structure is employed to mix the features of various sources. Comparative experiments are performed to show the effectiveness of the proposed method. The properties of the learned policies are also investigated under different environment settings. It is discovered that the policies explore and learn the properties of different systems, and also smooth the traffic congestion. Under certain environment settings, the policy converges to a heuristic rule that assigns the idle AGV to the workcenter with the shortest queue length, which shows the adaptiveness of the proposed method.
资助项目	National Key Research and Development Program of China[2021YFE0206100] ; National Natural Science Foundation of China[62073321] ; National Defense Basic Scientific Research Program of China[JCKY2019203C029] ; Science and Technology Development Fund, Macau SAR[0060/2021/A2] ; Science and Technology Development Fund, Macau SAR[0015/2020/AMJ]
WOS关键词	AUTOMATED GUIDED VEHICLES ; OPTIMIZATION ; DESIGN ; LEVEL
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000896603800001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; National Defense Basic Scientific Research Program of China ; Science and Technology Development Fund, Macau SAR
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50812]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
通讯作者	Zhang, Jie; Xiao, Jun
作者单位	1.Hong Kong Appl Sci & Technol Res Inst Co Ltd, Hong Kong, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Qinglai,Yan, Yutian,Zhang, Jie,et al. A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:12.
APA	Wei, Qinglai,Yan, Yutian,Zhang, Jie,Xiao, Jun,&Wang, Cong.(2022).A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Wei, Qinglai,et al."A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):12.