Useability Evaluation of Reinforcement Learning Toolboxes for Electrical Drives
N. Szécsényi and P. Stumpf Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary 2026-01-20

Abstract

The current direction of development predicts thatReinforcement Learning based data driven control methods can become a next generation technology to control electrical drives instead of the classical model-based techniques. The paper aims to evaluate toolboxes that can be used to train agents for control approaches. The paper helps lay the theoretical bases and provides guidelines for using these toolboxes via a case study. This is done to highlight each toolbox’s key aspects and workflow patterns, shifting the comparison to useability and peak-performance.

Key words: Artificial Intelligence, Reinforcement Learning, Electrical Machines & Drives, Permanent Magnet Synchronous Machine, Power Electronics

References

[1] S. Zhao, F. Blaabjerg and H. Wang, "An Overview of Artificial Intelligence Applications for Power Electronics," in IEEE Transactions on Power Electronics, vol. 36, no. 4, pp. 4633-4658, April 2021, doi: 10.1109/TPEL.2020.3024914

[2] S. Zhang, O. Wallscheid and M. Porrmann, "Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends," in IEEE Open Journal of Industry Applications, vol. 4, pp. 188-214, 2023, doi: 10.1109/OJIA.2023.3284717

[3] D. Jakobeit, M. Schenke and O. Wallscheid, "Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes," in IEEE Transactions on Power Electronics, vol. 38, no. 7, pp. 8062-8074, July 2023, doi: 10.1109/TPEL.2023.3256424

[4] A. Traue, G. Book, W. Kirchgässner and O. Wallscheid, "Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 3, pp. 919-928, March 2022, doi: 10.1109/TNNLS.2020.30295

[5] S. Fujimoto, H. van Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods.” in International Conference on Machine Learning, pp. 1587–1596. PMLR, 2018.

[6] G. Book et al., "Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments," in IEEE Open Journal of Power Electronics, vol. 2, pp. 187-201, 2021, doi: 10.1109/OJPEL.2021.3065877

[7] D. Weber, M. Schenke and O. Wallscheid, "Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems," in IEEE Access, vol. 11, pp. 76524-76536, 2023, doi: 10.1109/ACCESS.2023.3297274