为过程系统工程提供强化学习方法的综述与入门指南。
Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
- 梳理价值、策略与演员-评论家三类强化学习算法核心思路。
- 总结强化学习在连续/批次过程控制、优化及供应链中的应用实例。
- 适合希望了解强化学习在工业过程控制中应用的研究者。
不确定性下的序列决策是过程系统工程（PSE）的核心挑战，传统方法在控制与优化复杂随机系统时常受限。强化学习（RL）提供了一种数据驱动的方法来生成此类问题的控制策略。本文面向PSE领域，系统综述并撰写强化学习教程，涵盖价值型、策略型与演员-评论家方法的基本概念与关键算法家族。随后，我们调研了这些技术在连续与批次过程控制、过程优化及供应链等领域的实际应用。最后，结合PSE需求讨论专用技术与新兴方向。通过整合当前强化学习算法进展及其对PSE的启示，本工作识别出已有成果、现存挑战、发展态势，并指明两领域交叉研究的未来路径。
原文摘要 · Abstract (English)
Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a survey and tutorial on RL methods, tailored for the PSE community. We deliver a tutorial on RL, covering fundamental concepts and key algorithmic families including value-based, policy-based and actor-critic methods. Subsequently, we survey existing applications of these RL techniques across various PSE domains, such as in fed-batch and continuous process control, process optimization, and supply chains. We conclude with PSE focused discussion of specialized techniques and emerging directions. By synthesizing the current state of RL algorithm development and implications for PSE this work identifies successes, challenges, trends, and outlines avenues for future research at the interface of these fields.
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