mlsanityData to 14 Oct 2026

Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering

arXiv 2510.24272 ↗First posted 28 Oct 2025eess.SY cs.AI cs.SYcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a survey and tutorial on RL methods, tailored for the PSE community. We deliver a tutorial on RL, covering fundamental concepts and key algorithmic families including value-based, policy-based and actor-critic methods. Subsequently, we survey existing applications of these RL techniques across various PSE domains, such as in fed-batch and continuous process control, process optimization, and supply chains. We conclude with PSE focused discussion of specialized techniques and emerging directions. By synthesizing the current state of RL algorithm development and implications for PSE this work identifies successes, challenges, trends, and outlines avenues for future research at the interface of these fields.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 55 of its 144 references that are in the map. First posted on arXiv 28 Oct 2025.

Builds on

55 of 144 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactor8 Sep
  2. 2025Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses28 Mar
  3. 2025Hierarchical RL-MPC for Demand Response Scheduling19 Feb
  4. 2024PC-Gym: Benchmark Environments For Process Control Problems29 Oct
  5. 2024Control-Informed Reinforcement Learning for Chemical Processes24 Aug

Other references · 50

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  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2015Trust Region Policy Optimizationcited by 2,580
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
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  11. 2015Prioritized Experience Replaycited by 1,099
  12. 2017Constrained Policy Optimizationcited by 983
38 more
  1. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 773
  2. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  3. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  4. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  5. 2018Reward Constrained Policy Optimizationcited by 417
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  9. 2019Fully Parameterized Quantile Function for Distributional Reinforcement Learningcited by 88
  10. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  11. 2018An Analysis of Categorical Distributional Reinforcement Learningcited by 68
  12. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 68
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  14. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
  15. 2022OMLT: Optimization & Machine Learning Toolkitcited by 35
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  17. 2013On the Complexity of Solving Markov Decision Problemscited by 31
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  19. 2019Reinforcement Learning for Batch Bioprocess Optimizationcited by 28
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  21. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
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  24. 2019Stochastic data-driven model predictive control using Gaussian processescited by 19
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  28. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 12
  29. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 11
  30. 2023An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systemscited by 11
  31. 2024Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chainscited by 10
  32. 2020Constrained Model-Free Reinforcement Learning for Process Optimizationcited by 9
  33. 2022New Paradigms for Exploiting Parallel Experiments in Bayesian Optimizationcited by 9
  34. 2023Machine Learning-Assisted Discovery of Flow Reactor Designscited by 8
  35. 2025A view on learning robust goal-conditioned value functions: Interplay between RL and MPCcited by 7
  36. 2024EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimizationcited by 6
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  38. 2023Distributional constrained reinforcement learning for supply chain optimizationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Addressing Terminal Constraints in Data-Driven Demand Response Schedulingcited by 0
  2. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0

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  3. 2025A Survey of Reinforcement Learning for Optimization in Automation
  4. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
  5. 2026Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
  6. 2026Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning
  7. 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
  8. 2025An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring