mlsanityData to 7 Oct 2026

Safe Chance Constrained Reinforcement Learning for Batch Process Control

arXiv 2104.11706 ↗First posted 23 Apr 2021cs.LG cs.SY eess.SYcited by 10

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Abstract (arXiv)

Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the development of safe RL controllers. Previous works have proposed approaches to account for constraint satisfaction through constraint tightening from the domain of stochastic model predictive control. Here, we extend these approaches to account for plant-model mismatch. Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch. The method is benchmarked against nonlinear model predictive control via case studies. The results demonstrate the ability of the methodology to account for process uncertainty, enabling satisfaction of joint chance constraints even in the presence of plant-model mismatch.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 33 of its 92 references that are in the map. First posted on arXiv 23 Apr 2021.

Builds on

33 of 92 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 2.

10 papers.

  1. 2024A Survey of Constraint Formulations in Safe Reinforcement Learningcited by 42
  2. 2022Flowsheet synthesis through hierarchical reinforcement learning and graph neural networkscited by 10
  3. 2023Anytime-Constrained Reinforcement Learningcited by 5
  4. 2022When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Developmentcited by 4
  5. 2023Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam Warcited by 4
  6. 2024Flipping-based Policy for Chance-Constrained Markov Decision Processescited by 3
  7. 2022Distributional Reinforcement Learning for Scheduling of Chemical Production Processescited by 2
  8. 2025Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)cited by 2
  9. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  10. 2022Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Controlcited by 0