Feudal Steering: Hierarchical Learning for Steering Angle Prediction

@article{Johnson2020FeudalSH,
  title={Feudal Steering: Hierarchical Learning for Steering Angle Prediction},
  author={Faith Johnson and K. Dana},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2020},
  pages={4316-4325}
}
  • Faith Johnson, K. Dana
  • Published 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
We consider the challenge of automated steering angle prediction for self driving cars using egocentric road images. In this work, we explore the use of feudal networks, used in hierarchical reinforcement learning (HRL), to devise a vehicle agent to predict steering angles from first person, dash-cam images of the Udacity driving dataset. Our method, Feudal Steering, is inspired by recent work in HRL consisting of a manager network and a worker network that operate on different temporal scales… Expand
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