# Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control

arXiv: https://arxiv.org/abs/2505.07607 · HTML: https://mlsanity.com/paper/2505.07607/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Python-Based Reinforcement Learning on Simulink Models](https://mlsanity.com/paper/2405.08567/) (2024)
- [Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System](https://mlsanity.com/paper/2408.15633/) (2024)
- [The Crucial Role of Problem Formulation in Real-World Reinforcement Learning](https://mlsanity.com/paper/2503.20442/) (2025)
- [Energy and Age Pareto Optimal Trajectories in UAV-assisted Wireless Data Collection](https://mlsanity.com/paper/2106.03822/) (2021)
- [AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights](https://mlsanity.com/paper/2404.00204/) (2024)

## Built on by (most cited first, 2 of 2)

- [Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems](https://mlsanity.com/paper/2512.18317/) (2025)
- [Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization](https://mlsanity.com/paper/2607.03140/) (2026)

## 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.