4 papers indexed
Georg Schäfer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
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 persistent challenge, however, is ensuring strict, hard safety constraints during the active learning pha…
Georg Schäfer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common approach to solving this multi-objective problem, particularly within the framework of reinforcement learning (RL), is to formul…
Georg Schäfer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
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 anticipatory control without high computational overhead, we introduce a predictive formulation that augment…
Georg Schäfer, Jakob Rehrl, Stefan Huber
Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits. Typically, these constraints are managed through complex reward shaping. In this work-in-progres…