Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common approach to solving this multi-objective problem, particularly within the framework of reinforcement learning (RL), is to formulate a single, scalar reward function that linearly combines the competing objectives. However, the manual weighting of these different objectives is heavily reliant on domain intuition, incredibly time-consuming, prone to human bias, and frequently fails to uncover optimal trade-off solutions. This work addresses the critical challenge of automating the weight selection process to systematically and efficiently discover the Pareto front of optimal trade-off policies. We formulate the weight selection process as a multi-objective Bayesian optimization (MOBO) problem and evaluate its sample efficiency against a standard uniform grid search baseline. Using a physical Quanser Aero 2 testbed configured for 1-DoF pitch control, our results demonstrate that the MOBO approach, utilizing the expected hypervolume improvement (qEHVI) acquisition function, consistently outperforms uniform grid sampling. MOBO achieves superior hypervolume and maximum spread, successfully identifying high-quality, diverse trade-off policies with a reduced evaluation budget, thereby enabling highly efficient energy-aware control in complex mechatronic systems.
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 12 of its 20 references that are in the map. First posted on arXiv 3 Jul 2026.
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- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
- 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 242
- 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 179
- 2019Multi-objective Bayesian Optimization using Pareto-frontier Entropycited by 57
- 2024Pareto Front-Diverse Batch Multi-Objective Bayesian Optimizationcited by 13
- 2024Python-Based Reinforcement Learning on Simulink Modelscited by 9
- 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter Systemcited by 6
- 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6
- 2026Energy-Aware Reinforcement Learning for Robotic Manipulation of Articulated Components in Infrastructure Operation and Maintenancecited by 3
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- 2025A Survey of Reinforcement Learning for Optimization in Automation
- 2026A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects
- 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
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- 2026Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL