A review of the applications of multi-agent reinforcement learning in smart factories

被引:22
作者
Bahrpeyma, Fouad [1 ]
Reichelt, Dirk [1 ]
机构
[1] HTW Dresden, Smart Prod Syst, Dresden, Germany
关键词
Industry; 4.0; multi-agent reinforcement learning; smart factory; smart manufacturing; smart production systems; reinforcement learning; Artificial Intelligence; SYSTEM; TIME; OPTIMIZATION; MODEL;
D O I
10.3389/frobt.2022.1027340
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.
引用
收藏
页数:21
相关论文
共 78 条
[21]  
Ji H., 2022, DESIGN COMPUTING COG, P667
[22]   Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell [J].
Johnson, Dazzle ;
Chen, Gang ;
Lu, Yuqian .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (03) :7684-7691
[23]   A reference activity model for smart factory design and improvement [J].
Jung, Kiwook ;
Choi, SangSu ;
Kulvatunyou, Boonserm ;
Cho, Hyunbo ;
Morris, K. C. .
PRODUCTION PLANNING & CONTROL, 2017, 28 (02) :108-122
[24]  
Karapantelakis A, 2021, IEEE INTERNET THINGS
[25]   Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system [J].
Kim, Yun Geon ;
Lee, Seokgi ;
Son, Jiyeon ;
Bae, Heechul ;
Chung, Byung Do .
JOURNAL OF MANUFACTURING SYSTEMS, 2020, 57 :440-450
[26]   Towards Pick and Place Multi Robot Coordination Using Multi-agent Deep Reinforcement Learning [J].
Lan, Xi ;
Qiao, Yuansong ;
Lee, Brian .
2021 7TH INTERNATIONAL CONFERENCE ON AUTOMATION, ROBOTICS AND APPLICATIONS (ICARA 2021), 2021, :85-89
[27]  
Lau TT, 2022, arXiv
[28]   Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards [J].
Li, Mengyuan ;
Guo, Bin ;
Zhang, Jiangshan ;
Liu, Jiaqi ;
Liu, Sicong ;
Yu, Zhiwen ;
Li, Zhetao ;
Xiang, Liyao .
2021 IEEE 18TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2021), 2021, :482-489
[29]   Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing [J].
Liu, Changchun ;
Zhu, Haihua ;
Tang, Dunbing ;
Nie, Qingwei ;
Zhou, Tong ;
Wang, Liping ;
Song, Yejia .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2022, 77
[30]   Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems [J].
Liu, Chien-Liang ;
Chang, Chuan-Chin ;
Tseng, Chun-Jan .
IEEE ACCESS, 2020, 8 :71752-71762