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Learning to Drive in a Day

2018-07-01 · Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah

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.

📄 PDF Abstract BibTeX arXiv:1807.00412

Code (8)

B-C-WANG/ReinforcementLearningInAutoPilot

ZexinLi0w0/R3 pytorch

ankur-rc/autodrive_ddpg

araffin/learning-to-drive-in-5-minutes pytorch

bitsauce/Carla-ppo tf

bryonkucharski/Learning-to-Drive-with-Reinforcement-Learning-and-Variational-Autoencoders pytorch

bryonkucharski/learning-to-drive-in-a-day-reproduction pytorch

nautilusPrime/autodrive_ddpg

Tasks

Autonomous DrivingDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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