Learning to Drive in a Day

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 r...

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Bibliographic Details
Published in:arXiv.org 2018-09
Main Authors: Kendall, Alex, Hawke, Jeffrey, Janz, David, Mazur, Przemyslaw, Reda, Daniele, John-Mark, Allen, Lam, Vinh-Dieu, Bewley, Alex, Shah, Amar
Format: Article
Language:English
Subjects:
Algorithms
Drivers
Machine learning
Mapping
Optimization
Online Access:Get full text
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Summary: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.
ISSN:2331-8422