Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization

2026-07-03

AI Summary

This work addresses the inefficiency and suboptimality of manually tuned weight assignments in multi-objective reinforcement learning for industrial automation. It proposes the first integration of multi-objective Bayesian optimization with reinforcement learning, leveraging a sampling strategy based on the expected hypervolume improvement (qEHVI) to efficiently explore the Pareto front between energy consumption and control performance. Evaluated on the Quanser Aero 2 platform in a one-degree-of-freedom pitch control task, the method significantly outperforms uniform grid search by achieving superior hypervolume metrics and broader policy distribution coverage with substantially fewer evaluations. This demonstrates a sample-efficient approach to automatically discovering diverse Pareto-optimal policies without manual trade-off tuning.

Abstract

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.

Problem

• Pareto front
• multi-objective optimization
• energy-aware reinforcement learning
• weight selection
• sample efficiency

Innovation

• Multi-objective Bayesian Optimization
• Pareto Front
• Energy-Aware Reinforcement Learning
• Expected Hypervolume Improvement
• Sample Efficiency