Title: Learning to Drive in a Day,
Abstract: We demonstrate the first application of deep reinforcement learning to
autonomous driving. From randomly initialised parameters, our model is able to
learn a policy for lane following in a handful of training episodes using a
single monocular image as input. We provide a general and easy to obtain
reward: the distance travelled by the vehicle without the safety driver taking
control. We use a continuous, model-free deep reinforcement learning algorithm,
with all exploration and optimisation performed on-vehicle. This demonstrates a
new framework for autonomous driving which moves away from reliance on defined
logical rules, mapping, and direct supervision. We discuss the challenges and
opportunities to scale this approach to a broader range of autonomous driving
tasks.