A Survey of Reinforcement Learning for Optimization in Automation

摘要

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 present landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It delves into 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 an comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.