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End-to-End Race Driving with Deep Reinforcement Learning

Abstract : 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.
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Contributor : Maximilian Jaritz Connect in order to contact the contributor
Submitted on : Tuesday, July 24, 2018 - 11:45:27 AM
Last modification on : Wednesday, June 8, 2022 - 12:50:05 PM

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  • HAL Id : hal-01848067, version 1
  • ARXIV : 1807.02371



Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Perot, Fawzi Nashashibi. End-to-End Race Driving with Deep Reinforcement Learning. ICRA 2018 - IEEE International Conference on Robotics and Automation, May 2018, Brisbane, Australia. ⟨hal-01848067⟩



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