mlsanityData to 14 Oct 2026

Hierarchical RL-MPC for Demand Response Scheduling

arXiv 2502.13714 ↗First posted 19 Feb 2025eess.SY cs.SYcited by 3

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

Abstract (arXiv)

This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive control (LMPC). We investigate two control architectures: a direct RL approach and a control-informed methodology where an RL agent provides setpoints to a lower-level LMPC. The proposed RL-LMPC framework demonstrates improved sample efficiency during training and better constraint satisfaction compared to direct RL control. Using an industrial ASU case study, we show that our approach successfully manages operational constraints while optimizing electricity costs under time-varying pricing. Results indicate that the RL-LMPC architecture achieves comparable economic performance to direct RL while providing better robustness and requiring fewer training samples to converge. The framework offers a practical solution for implementing flexible operation strategies in process industries, bridging the gap between data-driven methods and traditional control approaches.

Key facts. Cited by 3 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 3 of its 16 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

3 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Control-Informed Reinforcement Learning for Chemical Processes24 Aug

Other references · 2

  1. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
  2. 2020Chance Constrained Policy Optimization for Process Control and Optimizationcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  2. 2026Addressing Terminal Constraints in Data-Driven Demand Response Schedulingcited by 0
  3. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Should we use model-free or model-based control? A case study of battery management systems
  2. 2025Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
  3. 2026A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
  4. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
  5. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
  6. 2025Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review
  7. 2026Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
  8. 2025Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO