Python-Based Reinforcement Learning on Simulink Models
This addresses the gap between Simulink environments and Python flexibility for RL in control applications, but it is incremental as it builds on existing methods with systematic integration steps.
The paper tackles the problem of integrating Reinforcement Learning (RL) agents with Simulink models for control tasks, demonstrating that policies trained on Simulink can be transferred to real systems like the Quanser Aero 2 helicopter, enabling efficient development and deployment.
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.