APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target...

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Bibliographic Details
Main Authors: Junqiang Lin, Po Zhang, Chengen Li, Yipeng Zhou, Hongjun Wang, Xiangjun Zou
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Machines
Subjects:
deep reinforcement learning
proximal policy optimization
autonomous driving
driving strategy
artificial potential field method
reward function
Online Access:https://www.mdpi.com/2075-1702/10/7/533

Internet

https://www.mdpi.com/2075-1702/10/7/533

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