  This paper proposes a framework for training Reinforcement Learning agents
using Python in conjunction with Simulink models. Leveraging Python's superior
customization options and popular libraries like Stable Baselines3, we aim to
bridge the gap between the established Simulink environment and the flexibility
of Python for training bleeding edge agents. Our approach is demonstrated on
the Quanser Aero 2, a versatile dual-rotor helicopter. We show that policies
trained on Simulink models can be seamlessly transferred to the real system,
enabling efficient development and deployment of Reinforcement Learning agents
for control tasks. Through systematic integration steps, including C-code
generation from Simulink, DLL compilation, and Python interface development, we
establish a robust framework for training agents on Simulink models.
Experimental results demonstrate the effectiveness of our approach, surpassing
previous efforts and highlighting the potential of combining Simulink with
Python for Reinforcement Learning research and applications.
