GEM provides a simulation toolkit for electric motor drive trains with components like converters, motors, and load models. It wraps the physics simulation in a Gymnasium-compatible interface, enabling RL algorithm benchmarking on motor control tasks. The environment supports both classical control and reinforcement learning approaches to motor control problems.