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

arXiv 2404.00247 ↗First posted 30 Mar 2024eess.SY cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 19

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
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  10. 2019Safe Reinforcement Learning Using Robust MPCcited by 72
  11. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 55
  12. 2019Reinforcement Learning for Batch Bioprocess Optimizationcited by 24
7 more
  1. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 18
  2. 2022Physics-Informed Model-Based Reinforcement Learningcited by 17
  3. 2020Deep Reinforcement Learning for Process Control: A Primer for Beginnerscited by 15
  4. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 10
  5. 2023A Survey on Offline Model-Based Reinforcement Learningcited by 7
  6. 2021A Meta-Reinforcement Learning Approach to Process Controlcited by 5
  7. 2023Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Controlcited by 4

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