mlsanityData to 14 Oct 2026

Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning

arXiv 2505.21026 ↗First posted 27 May 2025eess.SY cs.AI cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

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Abstract (arXiv)

In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design. Using historical closed-loop data as expert demonstrations, IRL extracts optimal reward functions and control policies. A latent-context variable is incorporated to distinguish modes, enabling the training of mode-specific controllers. Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling multi-mode data and training adaptable controllers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 8 of its 28 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

8 of 28 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables20 Sep

Other references · 7

  1. 2016Generative Adversarial Imitation Learningcited by 1,597
  2. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  3. 2017A Survey on Multi-Task Learningcited by 840
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  5. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 482
  6. 2019Reinforcement Learning for Batch Bioprocess Optimizationcited by 28
  7. 2023Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Controlcited by 5

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  1. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
  2. 2026Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
  3. 2026Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
  4. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
  5. 2026Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
  6. 2025Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction
  7. 2024Control-Informed Reinforcement Learning for Chemical Processes
  8. 2026Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View