综述强化学习在制造、能源和机器人中的优化应用与挑战
A Survey of Reinforcement Learning for Optimization in Automation
- 系统梳理强化学习在自动化领域的主流方法与应用场景
- 指出样本效率、安全性、可解释性等核心挑战
- 适合关注RL落地实践的研究者与工程人员
强化学习（RL）已成为自动化领域优化问题的关键工具，推动了制造、能源系统和机器人等多个方向的显著进展。本文综述了当前强化学习在自动化中的研究现状，重点分析其在各领域的角色、前沿方法、主要挑战与未来研究方向，强调了其解决复杂优化问题的能力。文章探讨了基于RL的优化方法在样本效率、可扩展性、安全性和鲁棒性、可解释性与可信度、迁移学习与元学习以及真实世界部署与集成等方面面临的普遍挑战，并提出潜在应对策略与未来研究路径。此外，本文还列出了相关研究论文的全面清单，为希望探索该领域的学者和从业者提供重要参考。
原文摘要 · Abstract (English)
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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