1 citations · 1 across the 9 of their papers we have counts for
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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…
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…