A Survey of Reinforcement Learning for Optimization in Automation
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913718296641536 |
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| author | Farooq, Ahmad Iqbal, Kamran |
| author_facet | Farooq, Ahmad Iqbal, Kamran |
| contents | Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Reinforcement Learning for Optimization in Automation Farooq, Ahmad Iqbal, Kamran Machine Learning Artificial Intelligence Neural and Evolutionary Computing Robotics Systems and Control 68T05, 90C40, 49M37 I.2.6; I.2.8; I.2.9; G.1.6; C.4; J.6 Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain. |
| title | A Survey of Reinforcement Learning for Optimization in Automation |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Robotics Systems and Control 68T05, 90C40, 49M37 I.2.6; I.2.8; I.2.9; G.1.6; C.4; J.6 |
| url | https://arxiv.org/abs/2502.09417 |