End-to-End Race Driving with Deep Reinforcement Learning
Open accessAbstract
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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Although continuous control with DRL is possible, the most common strategy for DRL in autonomous driving is based on discrete control.
“Although continuous control with DRL is possible, the most common strategy for DRL in autonomous driving is based on discrete control [26].”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [26]
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Cited by
Sorted by citations
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809 · see citing works
- Authors
- Jaritz, M., de Charette, R., Toromanoff, M., Perot, E., & Nashashibi, F.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 2070-2075
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2869375357
- Cited by
- 210
- References
- 25
- Citations by year
- 2026: 4 · 2025: 24 · 2024: 31 · 2023: 22 · 2022: 36 · 2021: 39 · 2020: 31 · 2019: 21 · 2018: 2
- FWCI
- 9.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- end-to-end driving, deep reinforcement learning, reward function design, RGB camera, forward-facing camera, vehicle control, domain adaptation
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