End-to-end driving with Deep Reinforcement Learning

Learning to drive directly from pixels

We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster convergence and more robust driving using only RGB image from a forward facing camera. An Asynchronous Actor Critic (A3C) framework is used to learn the car control in a physically and graphically realistic rally game, with the agents evolving simultaneously on tracks with a variety of road structures (turns, hills), graphics (seasons, location) and physics (road adherence). A thorough evaluation is conducted and generalization is proven on unseen tracks and using legal speed limits. Open loop tests on real sequences of images show some domain adaption capability of our method.

Articles

End-to-End Race Driving with Deep Reinforcement Learning

Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Pérot, Fawzi Nashashibi

International Conference on Robotics and Automation (ICRA 2018)

[PDF] – [video] – [poster]

End-to-End Driving in a Realistic Racing Game with Deep Reinforcement Learning

Etienne Pérot, Maximilian Jaritz, Marin Toromanoff, Raoul de Charette

Computer Vision and Pattern Recognition (CVPR 2017) Workshop

[PDF] – [video] – [poster]