This survey paper provides a review and perspective on intermediate and advanced reinforcement learning (RL) techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms, including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization, planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

中文翻译：

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过程工业中的强化学习：回顾与展望

本调查论文对流程工业中的中级和高级强化学习 (RL) 技术进行了回顾和展望。它提供了涵盖过程控制层次结构的所有级别的整体方法。该调查论文全面概述了 RL 算法，包括马尔可夫决策过程等基本概念和不同的 RL 方法（例如基于价值、基于策略和行动者批评方法），同时还讨论了经典控制和 RL 之间的关系。它还进一步回顾了强化学习在过程工业中的广泛应用，例如软传感器、低层控制、高层控制、分布式过程控制、故障检测和容错控制、优化、规划、调度和供应链。该调查论文讨论了强化学习在流程工业中的局限性和优势、趋势和新应用以及机遇和未来前景。此外，由于数字化在过程工业中的重要性日益增加，它强调了在复杂系统中采取整体方法的必要性。