The performance and efficiency of electric drives are fundamentally determined by their control methods and modulation schemes. While conventional approaches rely on simplified models and control structures, these limitations often restrict optimal performance in real-world applications. Reinforcement Learning (RL) has emerged as a promising solution, offering the potential to enhance performance through more sophisticated models and control structures, e.g. direct switching control which directly manipulates the switching time instants of the inverter terminals. However, RL agents trained in simulation environments using simplified models frequently experience performance gaps when deployed in real-world scenarios. The main objective of this thesis is the development of an innovative electric drive model suitable for a direct switching controller design using reinforcement learning. During your thesis you will conduct a comprehensive literature review on data-based modelling and control of electric drives. You will develop a concept for electric drive system excitation for generating training data capturing the switching behavior. Furthermore, you will elaborate an electric drive model that captures the switching behavior using physics-based and data-based modelling techniques. Optionally, you will train and evaluate a direct switching controller using reinforcement learning and the developed models. Finally, the documentation of your work also falls within your area of responsibility.