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

• URL: http://arxiv.org/abs/2605.21211v1
• Date: Wed, 20 May 2026 14:07:02 GMT
• Title: Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
• Abstract summary: We present an efficient and practically implementable approach for the application of reinforcement learning (RL)-based control in chemical process systems.<n>We leverage a class of RL algorithms termed Y-wise Affine Neural Network (YANN)- RL.<n>By strategically initializing actor and critic networks YANN-RL algorithms provide confident and interpretable starting points within control schemes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning [63.432969627395686]
  RefineRL is a novel approach designed to unleash the self-refinement capabilities of large language models for competitive programming problems.<n>Skeptical-Agent is an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems.<n>A reinforcement learning solution incentivizes LLMs to self-refine with only standard RLVR data.
  arXiv  Detail & Related papers  (2026-04-01T11:54:57Z)
• Statistical and Algorithmic Foundations of Reinforcement Learning [45.707617428078585]
  sequential learning (RL) has received a flurry of attention in recent years.<n>We aim to introduce several important developments in RL, highlighting the connections between new ideas classical topics.
  arXiv  Detail & Related papers  (2025-07-19T02:42:41Z)
• Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs [51.21041884010009]
  Ring-lite is a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL)<n>Our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-06-17T17:12:34Z)
• Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning [93.00629872970364]
  Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks.<n>We introduce SPARKLE, a fine-grained analytic framework to dissect the effects of RL across three key dimensions.<n>We study whether difficult problems -- those yielding no RL signals and mixed-quality reasoning traces -- can still be effectively used for training.
  arXiv  Detail & Related papers  (2025-06-05T07:53:59Z)
• Scaling Offline RL via Efficient and Expressive Shortcut Models [13.050231036248338]
  offline reinforcement learning (RL) remains challenging due to the iterative nature of their noise sampling processes.<n>We introduce Scalable Offline Reinforcement Learning (SORL), a new offline RL algorithm that leverages shortcut models to scale both training and inference.<n>We demonstrate that SORL achieves strong performance across a range of offline RL tasks and exhibits positive scaling behavior with increased test-time compute.
  arXiv  Detail & Related papers  (2025-05-28T20:59:22Z)
• Continuous Control with Coarse-to-fine Reinforcement Learning [15.585706638252441]
  We present a framework that trains RL agents to zoom-into a continuous action space in a coarse-to-fine manner. We introduce a concrete, value-based algorithm within the framework called Coarse-to-fine Q-Network (CQN) CQN robustly learns to solve real-world manipulation tasks within a few minutes of online training.
  arXiv  Detail & Related papers  (2024-07-10T16:04:08Z)
• Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks [5.968716050740402]
  This paper focuses on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks. We design a Bias Exploiting (BE) mechanism to dynamically select the most advantageous estimation bias during training of the RL agent. Most State-of-the-art Deep RL algorithms can be equipped with the BE mechanism, without hindering performance or computational complexity.
  arXiv  Detail & Related papers  (2024-02-14T10:44:03Z)
• LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning [78.2286146954051]
  LCRL implements model-free Reinforcement Learning (RL) algorithms over unknown Decision Processes (MDPs) We present case studies to demonstrate the applicability, ease of use, scalability, and performance of LCRL.
  arXiv  Detail & Related papers  (2022-09-21T13:21:00Z)
• Jump-Start Reinforcement Learning [68.82380421479675]
  We present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-04-05T17:25:22Z)
• DRL-based Slice Placement Under Non-Stationary Conditions [0.8459686722437155]
  We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary process. We specifically propose two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. We show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence.
  arXiv  Detail & Related papers  (2021-08-05T10:05:12Z)
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning [56.17667147101263]
  In real-world tasks, reinforcement learning agents encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
  arXiv  Detail & Related papers  (2021-03-18T16:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.