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
[1]   Cooperative zone-based rebalancing of idle overhead hoist transportations using multi-agent reinforcement learning with graph representation learning [J].
Ahn, Kyuree ;
Park, Jinkyoo .
IISE TRANSACTIONS, 2021, 53 (10) :1140-1156
[2]  
Ahn K, 2019, IEEE DECIS CONTR P, P132, DOI [10.1109/CDC40024.2019.9030245, 10.1109/cdc40024.2019.9030245]
[3]   Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning [J].
Alqahtani, Mohammed ;
Scott, Michael J. ;
Hu, Mengqi .
COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 169
[4]   Deep Reinforcement Learning A brief survey [J].
Arulkumaran, Kai ;
Deisenroth, Marc Peter ;
Brundage, Miles ;
Bharath, Anil Anthony .
IEEE SIGNAL PROCESSING MAGAZINE, 2017, 34 (06) :26-38
[5]  
Baer S., 2020, INT C MANUFACTURING, V5
[6]  
Bollinger L.A., 2016, 23 INT WORKSH EUR GR
[7]   A distributed approach solving partially flexible job-shop scheduling problem with a Q-learning effect [J].
Bouazza, W. ;
Sallez, Y. ;
Beldjilali, B. .
IFAC PAPERSONLINE, 2017, 50 (01) :15890-15895
[8]   Smart factory performance and Industry 4.0 [J].
Buchi, Giacomo ;
Cugno, Monica ;
Castagnoli, Rebecca .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2020, 150
[9]  
Busoniu L, 2010, STUD COMPUT INTELL, V310, P183
[10]   Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility [J].
Charbonnier, Flora ;
Morstyn, Thomas ;
McCulloch, Malcolm D. .
APPLIED ENERGY, 2022, 314