Controlling an Autonomous Vehicle with Deep Reinforcement Learning

-- We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy optimization in combination with a simulated environment. Training from scratch takes five to nine hours. The resulting agent is evaluated within simulation and subsequently applied to control a full-size research vehicle. For this, the autonomous exploration of a parking lot is considered, including turning maneuvers and obstacle avoidance. Altogether, this work is among the first examples to successfully apply deep reinforcement learning to a real vehicle. I. INTRODUCTION Self driving cars have the potential to sustainably change modern societies which are heavily based on mobility. The benefits of such a technology range from self-providing car sharing to platooning approaches, which ultimately yield a much more effective usage of vehicles and roads [1]. In recent years, great progress has been made in the development of these systems, with a major factor being the results achieved through deep learning methods.