用逆强化学习从历史数据中自动学出多模式控制策略
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
- 通过逆强化学习从闭环数据中反推奖励函数和控制策略
- 引入隐变量区分不同运行模式，实现模式自适应控制
- 适用于复杂工业过程的多工况智能控制，适合制造领域研究者
在工业4.0与智能制造背景下，过程系统工程需应对数字化转型挑战。尽管强化学习提供无模型的控制方法，但其应用受限于精确数字孪生和设计良好的奖励函数。为此，本文提出一种新框架，将逆强化学习（IRL）与多任务学习结合，实现数据驱动的多模式控制设计。利用历史闭环数据作为专家示范，IRL提取最优奖励函数与控制策略，并引入隐式上下文变量以区分不同运行模式，从而训练出针对各模式的专用控制器。在连续搅拌釜反应器与分批生物反应器上的案例研究验证了该框架在处理多模式数据及训练可适应控制器方面的有效性。
原文摘要 · Abstract (English)
In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design. Using historical closed-loop data as expert demonstrations, IRL extracts optimal reward functions and control policies. A latent-context variable is incorporated to distinguish modes, enabling the training of mode-specific controllers. Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling multi-mode data and training adaptable controllers.
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