3 papers
eess.SY2025
Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
Georg Schäfer, Raphael Seliger, Jakob Rehrl +2
Industrial automation increasingly demands energy-efficient control strategies to balance performance with environmental and cost constraints. In this work, we present a multi-obje…
eess.SY2025
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning
Georg Schäfer, Tatjana Krau, Jakob Rehrl +2
Reinforcement Learning (RL) offers promising solutions for control tasks in industrial cyber-physical systems (ICPSs), yet its real-world adoption remains limited. This paper demon…
eess.SY2024
Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
This study conducts a comparative analysis of Model Predictive Control (MPC) and Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) algorithm, applied to a 1-D…