Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning-Reference-Cited by-同舟云学术

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Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning

Published:2023-02-23 Issue:2 Volume:5 Page:145-158
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Cao Zhong^ORCID,Jiang Kun,Zhou Weitao^ORCID,Xu Shaobing,Peng Huei,Yang Diange^ORCID

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Beijing Municipal Science and Technology Commission

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-023-00610-y.pdf

Reference42 articles.

1. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 2018).

2. Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140–1144 (2018).

3. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

4. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

5. Ye, F., Zhang, S., Wang, P. & Chan, C.-Y. A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles. In 2021 IEEE Intelligent Vehicles Symposium (IV) 1073–1080 (IEEE, 2021).

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