mlsanityData to 14 Oct 2026

Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control

arXiv 2505.07607 ↗First posted 12 May 2025eess.SY cs.LG cs.SYcited by 2

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

Abstract (arXiv)

Industrial automation increasingly demands energy-efficient control strategies to balance performance with environmental and cost constraints. In this work, we present a multi-objective reinforcement learning (MORL) framework for energy-efficient control of the Quanser Aero 2 testbed in its one-degree-of-freedom configuration. We design a composite reward function that simultaneously penalizes tracking error and electrical power consumption. Preliminary experiments explore the influence of varying the Energy penalty weight, alpha, on the trade-off between pitch tracking and energy savings. Our results reveal a marked performance shift for alpha values between 0.0 and 0.25, with non-Pareto optimal solutions emerging at lower alpha values, on both the simulation and the real system. We hypothesize that these effects may be attributed to artifacts introduced by the adaptive behavior of the Adam optimizer, which could bias the learning process and favor bang-bang control strategies. Future work will focus on automating alpha selection through Gaussian Process-based Pareto front modeling and transitioning the approach from simulation to real-world deployment.

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 6 of its 14 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

6 of 14 references.

References · 6

  1. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  2. 2024Python-Based Reinforcement Learning on Simulink Modelscited by 9
  3. 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter Systemcited by 6
  4. 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6
  5. 2021Energy and Age Pareto Optimal Trajectories in UAV-assisted Wireless Data Collectioncited by 2
  6. 2024AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flightscited by 2

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systemscited by 0
  2. 2026Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
  2. 2025A Survey of Reinforcement Learning for Optimization in Automation
  3. 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
  4. 2026Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency
  5. 2026Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
  6. 2026EARL: Energy-Aware Optimization of Liquid State Machines for Pervasive AI
  7. 2026Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
  8. 2025Preference-based Multi-Objective Reinforcement Learning