A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching Systems
文献类型：期刊论文
| 作者 | Wei, Qinglai2,3,4 ; Yan, Yutian2,3,4; Zhang, Jie2,3,4; Xiao, Jun3; Wang, Cong1
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 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. |
| WOS关键词 | AUTOMATED GUIDED VEHICLES ; OPTIMIZATION ; DESIGN ; LEVEL |
| 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研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000896603800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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