Advances in Reinforcement Learning: A Comprehensive Review of Real-World Applications in Industry; Acta Scientific Computer Sciences; Vol. 5, iss. 5
| Parent link: | Acta Scientific Computer Sciences.— .— Hyderabad: Acta Scientific Vol. 5, iss. 5.— 2023.— P. 32-38 |
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| Riassunto: | This paper investigates the current feasibility of utilizing reinforcement learning algorithms in the industrial sector. Although many studies have showcased the success of these algorithms in simulations or on isolated real-world objects, there is a paucity of research examining their wider implementation in real-world systems. In this study, we identify the obstacles that must be surmounted to fully leverage the potential benefits of reinforcement learning algorithms in practical applications. Moreover, we present a thorough overview of existing literature aimed at tackling these challenges. Текстовый файл |
| Lingua: | inglese |
| Pubblicazione: |
2023
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| Accesso online: | https://actascientific.com/ASCS/ASCS-05-0441.php |
| Natura: | MixedMaterials Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=671038 |
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| 200 | 1 | |a Advances in Reinforcement Learning: A Comprehensive Review of Real-World Applications in Industry |f K. Ussenko and V. I. Goncharov |d Достижения в области обучения с подкреплением: Всесторонний обзор реальных применений в промышленности | |
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| 330 | |a This paper investigates the current feasibility of utilizing reinforcement learning algorithms in the industrial sector. Although many studies have showcased the success of these algorithms in simulations or on isolated real-world objects, there is a paucity of research examining their wider implementation in real-world systems. In this study, we identify the obstacles that must be surmounted to fully leverage the potential benefits of reinforcement learning algorithms in practical applications. Moreover, we present a thorough overview of existing literature aimed at tackling these challenges. | ||
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| 610 | 1 | |a Reinforcement Learning | |
| 610 | 1 | |a Deep Learning | |
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| 610 | 1 | |a Engineering | |
| 610 | 1 | |a Artificial Intelligence | |
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