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

Runze Lin, Junghui Chen, Lei Xie, Hongye Su

TL;DR

This paper addresses the practical barriers of deploying deep reinforcement learning in industrial process control—namely safety and sample efficiency—by exploring transfer learning as a unifying framework. It presents a structured view of how world models, Sim2Real transfer, imitation and apprenticeship learning, offline RL, meta/Multi-Task RL, multi-mode inverse RL, model-based RL, and physics-informed RL can be integrated to accelerate learning, enhance safety, and enable robust multi-mode operation. The proposed perspectives offer concrete pathways to leverage historical data, expert knowledge, and domain physics to enable rapid, safer deployment of RL controllers in chemical and process industries. The work emphasizes scalable, user-friendly strategies that align RL with existing industry practices like MPC and PID, aiming to bridge theory and real-world adoption in smart manufacturing.

Abstract

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.

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

TL;DR

This paper addresses the practical barriers of deploying deep reinforcement learning in industrial process control—namely safety and sample efficiency—by exploring transfer learning as a unifying framework. It presents a structured view of how world models, Sim2Real transfer, imitation and apprenticeship learning, offline RL, meta/Multi-Task RL, multi-mode inverse RL, model-based RL, and physics-informed RL can be integrated to accelerate learning, enhance safety, and enable robust multi-mode operation. The proposed perspectives offer concrete pathways to leverage historical data, expert knowledge, and domain physics to enable rapid, safer deployment of RL controllers in chemical and process industries. The work emphasizes scalable, user-friendly strategies that align RL with existing industry practices like MPC and PID, aiming to bridge theory and real-world adoption in smart manufacturing.

Abstract

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.
Paper Structure (14 sections, 4 equations, 2 figures)

This paper contains 14 sections, 4 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Reinforcement Learning for Process Control
  3. Practical Barriers of DRL in Process Control
  4. Perspectives on How Transfer Learning Facilitates Reinforcement Learning for Industrial Process Control
  5. World Model-Empowered RL Training Acceleration
  6. Sim2Real Transfer Learning of RL Agents Using Pre-training + Fine-tuning
  7. Imitation Learning-Inspired RL Controller Prior Learning
  8. Apprenticeship Learning for Abstracting Controllers from (Human) Experts
  9. Offline RL towards Safety-Critical Practical Applications
  10. Meta-RL or Multi-Task RL for Multi-Mode Control
  11. Multi-Task Inverse RL for Process Control Using Multi-Mode Historical Big Data
  12. Model-based RL or MPC-based RL for Faster Adaptation
  13. Physics-Informed RL for Incorporating Prior/Physical Knowledge into controllers
  14. Conclusion

Figures (2)

  • Figure 1: Framework of DRL-based Sim2Real control design RN3.
  • Figure 2: Multi-task inverse RL framework for multi-mode process control design.