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End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

Abstract : Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving. We present a novel technique, coined implicit affor-dances, to effectively leverage RL for urban driving thus including lane keeping, pedestrians and vehicles avoidance, and traffic light detection. To our knowledge we are the first to present a successful RL agent handling such a complex task especially regarding the traffic light detection. Furthermore , we have demonstrated the effectiveness of our method by winning the Camera Only track of the CARLA challenge.
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Submitted on : Friday, March 20, 2020 - 5:39:27 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:08 PM


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  • HAL Id : hal-02513566, version 1


Marin Toromanoff, Emilie Wirbel, Fabien Moutarde. End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances. IEEE conference on Computer Vision and Pattern Recognition (CVPR'2020), Jun 2020, Seattle, United States. ⟨hal-02513566⟩



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