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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Contents
O. Dogru, J. Xie, O. Prakash, R. Chiplunkar, J. Soesanto, H. Chen, K. Velswamy, F. Ibrahim, and  B. Huang,  “Reinforcement learning in process industries: Review and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 283–300, Feb. 2024. doi: 10.1109/JAS.2024.124227
Citation: O. Dogru, J. Xie, O. Prakash, R. Chiplunkar, J. Soesanto, H. Chen, K. Velswamy, F. Ibrahim, and  B. Huang,  “Reinforcement learning in process industries: Review and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 283–300, Feb. 2024. doi: 10.1109/JAS.2024.124227

Reinforcement Learning in Process Industries: Review and Perspective

doi: 10.1109/JAS.2024.124227
Funds:  This work was supported in part by the Natural Sciences Engineering Research Council of Canada (NSERC)
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  • This survey paper provides a review and perspective on intermediate and advanced reinforcement learning (RL) techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms, including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization, planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.


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    • Reviews the link between modern reinforcement learning techniques and process industries by considering the control hierarchy holistically
    • Presents the state-of-the-art theoretical advancements in the theory while presenting the relevant applications in numerous industries
    • Discusses limitations, advantages, trends, new applications, opportunities, and future prospects that can help the researchers and practitioners


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