mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Process Control: A Primer for Beginners

arXiv 2004.05490 ↗First posted 11 Apr 2020eess.SY cs.SYcited by 15

Not on the map. Why?

Abstract (arXiv)

Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a common practice to monitor controller performance continuously and to initiate a remedial model re-identification procedure in the event of performance degradation. Such procedures are typically complicated and resource-intensive, and they often cause costly interruptions to normal operations. In this paper, we exploit recent developments in reinforcement learning and deep learning to develop a novel adaptive, model-free controller for general discrete-time processes. The DRL controller we propose is a data-based controller that learns the control policy in real time by merely interacting with the process. The effectiveness and benefits of the DRL controller are demonstrated through many simulations.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

15 papers.

Sort:

  1. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 18
  2. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 10
  3. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 10
  4. 2022Hyperparameter Tuning for Deep Reinforcement Learning Applicationscited by 7
  5. 2022Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Surveycited by 6
  6. 2024Machine learning for industrial sensing and control: A survey and practical perspectivecited by 6
  7. 2021A Meta-Reinforcement Learning Approach to Process Controlcited by 5
  8. 2025Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)cited by 2
  9. 2022Meta-Reinforcement Learning for Adaptive Control of Second Order Systemscited by 1
  10. 2023Timing Process Interventions with Causal Inference and Reinforcement Learningcited by 1
  11. 2024Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategiescited by 1
  12. 2022Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Controlcited by 0
3 more
  1. 2023Reinforcement Learning with Partial Parametric Model Knowledgecited by 0
  2. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectivescited by 0
  3. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0