Reinforcement Learning-based Path Following Control for a Vehicle with Variable Delay in the Drivetrain

In this contribution we propose a reinforcement learning-based controller which is able to solve the path following
problem for vehicles with significant delay in the drivetrain. To efficiently train the controller, a control-oriented simulation model for a vehicle with combustion engine, automatic gear box and hydraulic brake system has been developed. In addition, to enhance the reinforcement learning-based controller, we have introduced preview information in the feedback state to better deal with the delays. We present our approach of designing a reward function which enables the reinforcement learning-based controller to solve the problem. The controller is trained using the Soft Actor-Critic algorithm by incorporating the developed simulation model. Finally, the performance and robustness is evaluated in simulation. Our controller is able to follow an unseen path and is robust against variations in the vehicle parameters, in our case an additional payload.

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