4 papers
Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks
Stefan Huber, Hannes Unger, Georg Schäfer +2
We analytically solve the Mountain Car problem, a canonical benchmark in RL, and derive an optimal control solution, closing a gap after 36 years. This enables us to reveal two sur…
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…
A Generative Model Based Honeypot for Industrial OPC UA Communication
Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter +1
Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era.…