mlsanityData to 14 Oct 2026

Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes

arXiv 2605.21211 ↗First posted 20 May 2026eess.SY cs.LG cs.SYcited by 0

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

Abstract (arXiv)

In this work we present an efficient and practically implementable approach for the application of reinforcement learning (RL)-based control in chemical process systems. This is an area that has yet to widely adopt RL-based control largely due to inherent challenges in trusting RL algorithms and the time-consuming process of training reliable agents. To address these challenges, we leverage a class of RL algorithms termed Y-wise Affine Neural Network (YANN)- RL, which we have developed in our prior work (Braniff and Tian, 2025a). By strategically initializing actor and critic networks YANN-RL algorithms provide confident and interpretable starting points within control schemes. We apply this RL-based control approach to three different process engineering case studies publicly available on the PC-Gym library (Bloor et al., 2026): (i) a continuous stirred tank reactor (CSTR), (ii) a four-tank system, and (iii) a multistage extraction column. Our approach is compared to several popular RL algorithms (PPO, SAC, DDPG, and TD3) and is benchmarked against nonlinear model predictive control (NMPC). These case studies demonstrate that YANN-RL can greatly reduce the training time and data needed, can be deployed with confidence for chemical process systems, and can approach the performance of NMPC without the knowledge of a full nonlinear model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 21 references that are in the map. First posted on arXiv 20 May 2026.

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5 of 21 references.

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Same direction, newest first

  1. 2024PC-Gym: Benchmark Environments For Process Control Problems29 Oct

Other references · 4

  1. 2021Stabilizing Neural Control Using Self-Learned Almost Lyapunov Criticscited by 35
  2. 2023Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustnesscited by 2
  3. 2025YANNs: Y-wise Affine Neural Networks for Exact and Efficient Representations of Piecewise Linear Functionscited by 2
  4. 2025Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)cited by 2

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  1. 2025Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction
  2. 2024Control-Informed Reinforcement Learning for Chemical Processes
  3. 2025Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes
  4. 2026Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
  5. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
  6. 2025Nuclear Microreactor Control with Deep Reinforcement Learning
  7. 2025Sample-Efficient Reinforcement Learning of Koopman eNMPC
  8. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives