@inproceedings{a8af91ff204f4c63aff12ec407f512e5,
title = "Trajectory Planning Based on Continuous Decision Deep Reinforcement Learning for Stratospheric Airship",
abstract = "Aiming at the characteristics of stratospheric airships which are greatly influenced by continuous wind fields, this paper proposes a trajectory planning method based on continuous deep reinforcement learning. Firstly, the state space, action space and reward function are designed. After that, the twin delay deep determined policy gradient algorithm based on time series is used the trajectory planning. The algorithm can be used to output actions under continuous space. The experimental results show that the algorithm is stable and efficient, and the feasibility and generalizability of the algorithm are demonstrated.",
keywords = "TD3, airship, reinforcement learning, trajectory planning",
author = "Jiayu Hou and Ming Zhu and Baojin Zheng and Xiao Guo and Jiajun Ou",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 China Automation Congress, CAC 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2023",
doi = "10.1109/CAC59555.2023.10451705",
language = "英语",
series = "Proceedings - 2023 China Automation Congress, CAC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1508--1513",
booktitle = "Proceedings - 2023 China Automation Congress, CAC 2023",
address = "美国",
}