标题： 促进过程控制中强化学习的迁移学习应用：概述与展望 显示英文标题

标题： Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives

摘要： 在工业4.0和智能制造的背景下，流程工业优化与控制领域也正在经历数字化转型。随着深度强化学习（DRL）的兴起，其在过程控制中的应用引起了广泛关注。然而，DRL极低的样本效率以及由探索引起的安全部署问题阻碍了其在工业环境中的实际应用。迁移学习为DRL提供了一种有效的解决方案，增强了其在多模态控制场景下的泛化能力和适应性。本文从迁移学习的角度探讨了DRL在过程控制中的应用。我们分析了在流程工业中应用DRL的挑战以及引入迁移学习的必要性。此外，还为未来的研究方向提供了建议和展望，讨论了如何将迁移学习与DRL结合以增强过程控制。本文旨在为流程工业领域的学者和工程师提供一系列有前景、用户友好、易于实施且可扩展的方法，以促进人工智能辅助的工业控制。 显示英文摘要

摘要： In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Reinforcement Learning (DRL), its application in process control has attracted widespread attention. However, the extremely low sample efficiency and the safety concerns caused by exploration in DRL hinder its practical implementation in industrial settings. Transfer learning offers an effective solution for DRL, enhancing its generalization and adaptability in multi-mode control scenarios. This paper provides insights into the use of DRL for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the process industry and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to enhance process control. This paper aims to offer a set of promising, user-friendly, easy-to-implement, and scalable approaches to artificial intelligence-facilitated industrial control for scholars and engineers in the process industry.