Reinforcement Learning: A Comprehensive Open-Source Course
A.H.A. Abdelwanis, B. Haucke-Korber, D. Jakobeit, W. Kirchgässner, M. Meyer, M. Schenke, H. Vater, O. Wallscheid, D. Weber, Journal of Open Source Education 9 (2026).

Safe Reinforcement Learning Direct Torque Control for Continuous Control Set Permanent Magnet Synchronous Motor Drives
M. Schenke, B. Haucke-Korber, O. Wallscheid, IEEE Access (2026) 1–1.

Structural Optimization of Meta-Reinforcement Learning-based Finite-Control-Set Direct Torque Control of Permanent Magnet Synchronous Motors
D. Jakobeit, M. Peña López, M. Schenke, B. Haucke-Korber, O. Wallscheid, in: 2025 IEEE International Electric Machines &; Drives Conference (IEMDC), IEEE, 2025.

Reinforcement Learning-based Direct Torque Control of Externally Excited Synchronous Motors: a Proof of Concept
B. Haucke-Korber, N.N. Aung, M. Schenke, M. Peña López, D. Jakobeit, O. Wallscheid, in: 2025 IEEE International Electric Machines &; Drives Conference (IEMDC), IEEE, 2025.

Reinforcement Learning Control of Three-Level Converter Permanent Magnet Synchronous Machine Drives
M. Peña López, M. Schenke, D. Jakobeit, B. Haucke-Korber, O. Wallscheid, in: 2025 IEEE International Electric Machines &; Drives Conference (IEMDC), IEEE, 2025.

Finite-Set Direct Torque Control via Edge Computing-Assisted Safe Reinforcement Learning for a Permanent Magnet Synchronous Motor
M. Schenke, B. Haucke-Korber, O. Wallscheid, IEEE Transactions on Power Electronics (2023) 1–16.

Deep Q Direct Torque Control with a Reduced Control Set Towards Six-Step Operation of Permanent Magnet Synchronous Motors
B. Haucke-Korber, M. Schenke, O. Wallscheid, in: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC), IEEE, 2023.

Gym-Electric-Motor (GEM) Control: An Automated Open-Source Controller Design Suite for Drives
F. Book, A. Traue, M. Schenke, B. Haucke-Korber, O. Wallscheid, in: 2023 IEEE International Electric Machines &;amp; Drives Conference (IEMDC), IEEE, 2023.

Reinforcement Learning-Based Deep Q Direct Torque Control with Adaptable Switching Frequency Towards Six-Step Operation of Permanent Magnet Synchronous Motors
B. Haucke-Korber, M. Schenke, O. Wallscheid, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments
G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021) 187–201.

gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems
P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid, Journal of Open Source Software (2021).

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors
M. Schenke, O. Wallscheid, IEEE Open Journal of the Industrial Electronics Society (2021) 388–400.

Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning
M. Schenke, O. Wallscheid, ArXiv Preprint ArXiv:2105.08990 (2021).

Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept
M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial Informatics (2019) 4650–4658.

Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines
O. Wallscheid, M. Schenke, J. Böcker, in: 2018 21st International Conference on Electrical Machines and Systems (ICEMS), 2018, pp. 1181–1186.

A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation
O. Wallscheid, M. Schenke, J. Böcker, in: 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), 2018, pp. 793–799.