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arXiv · 2502.09417

A Survey of Reinforcement Learning for Optimization in Automation

Ahmad Farooq·Kamran Iqbal
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Abstract

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.

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BibTeXRIS

Ahmad Farooq, Kamran Iqbal. 2025-02-13. A Survey of Reinforcement Learning for Optimization in Automation. https://doi.org/10.1109/case59546.2024.10711718

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