  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.
