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eess.SY2026
Container Unloading via Reinforcement Learning: Picking Order, Deadlock Avoidance, and Proof-of-Concept Simulation
Jan Rüdiger, Max Schenke, Daniel Weber
Unloading containers in the courier, express and parcel industry is a physically demanding and labor-intensive work. Automatizing this process is an important step towards increasi…
eess.SY2024
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum
Maximilian Schenke, Shalbus Bukarov
The discipline of automatic control is making increased use of concepts that originate from the domain of machine learning. Herein, reinforcement learning (RL) takes an elevated ro…
eess.SY2022★ 1 cited
Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control
Daniel Weber, Maximilian Schenke, Oliver Wallscheid
Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven cont…