1 citations · 1 across the 9 of their papers we have counts for
9 papers
Safe Reinforcement Learning using Ideas from Model Predictive Control
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persi…
Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common approach to solving this mul…
Anticipatory Reinforcement Learning for Trajectory Tracking
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error. To achieve anticipa…
Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
Julian Langschwert, Georg Schaefer, Jakob Rehrl +2
Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge…
Achieving fast and robust perfect entangling gates via reinforcement learning
Leander Grech, Matthias G. Krauss, Mirko Consiglio +4
Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits…
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…
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…
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…
Python-Based Reinforcement Learning on Simulink Models
Georg Schäfer, Max Schirl, Jakob Rehrl +2
This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and…