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

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 char...

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Autores principales: Bahrpeyma, Fouad, Reichelt, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751367/
https://www.ncbi.nlm.nih.gov/pubmed/36530498
http://dx.doi.org/10.3389/frobt.2022.1027340
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author Bahrpeyma, Fouad
Reichelt, Dirk
author_facet Bahrpeyma, Fouad
Reichelt, Dirk
author_sort Bahrpeyma, Fouad
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description 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.
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spelling pubmed-97513672022-12-16 A review of the applications of multi-agent reinforcement learning in smart factories Bahrpeyma, Fouad Reichelt, Dirk Front Robot AI Robotics and AI 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. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9751367/ /pubmed/36530498 http://dx.doi.org/10.3389/frobt.2022.1027340 Text en Copyright © 2022 Bahrpeyma and Reichelt. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Bahrpeyma, Fouad
Reichelt, Dirk
A review of the applications of multi-agent reinforcement learning in smart factories
title A review of the applications of multi-agent reinforcement learning in smart factories
title_full A review of the applications of multi-agent reinforcement learning in smart factories
title_fullStr A review of the applications of multi-agent reinforcement learning in smart factories
title_full_unstemmed A review of the applications of multi-agent reinforcement learning in smart factories
title_short A review of the applications of multi-agent reinforcement learning in smart factories
title_sort review of the applications of multi-agent reinforcement learning in smart factories
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751367/
https://www.ncbi.nlm.nih.gov/pubmed/36530498
http://dx.doi.org/10.3389/frobt.2022.1027340
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