Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept. - Maximilian Schenke; Wilhelm Kirchgässner; Oliver Wallscheid (IEEE Trans. Ind. Informatics, 2020)

Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. - Maximilian Schenke; Oliver Wallscheid (CoRR, 2021)

gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems. - Praneeth Balakrishna; Gerrit Book; Wilhelm Kirchgässner; Maximilian Schenke; Arne Traue; Oliver Wallscheid (J. Open Source Softw., 2021)

Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control. - Daniel Weber 004; Maximilian Schenke; Oliver Wallscheid (CoRR, 2022)

Controller Design for Electrical Drives by Deep Reinforcement Learning - a Proof of Concept (Supplementary Material). - Maximilian Schenke; Wilhelm Kirchgässner; Oliver Wallscheid (IEEE DataPort, 2022)

Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems. - Daniel Weber 004; Maximilian Schenke; Oliver Wallscheid (IEEE Access, 2023)

Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum. - Maximilian Schenke; Shalbus Bukarov (CoRR, 2024)