Densely rewarded reinforcement learning for robust low-thrust trajectory optimization-Reference-Cited by-同舟云学术

Densely rewarded reinforcement learning for robust low-thrust trajectory optimization

Published:2023-08 Issue:4 Volume:72 Page:964-981
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Hu Jincheng^ORCID,Yang Hongwei^ORCID,Li Shuang^ORCID,Zhao Yingjie

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Elsevier BV

Subject

Space and Planetary Science,Aerospace Engineering,General Earth and Planetary Sciences,Atmospheric Science,Geophysics,Astronomy and Astrophysics

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5. Contingency planning in complex dynamical environments via heuristically accelerated reinforcement learning;Das-Stuart;AAS/AIAA Astrodynamics Specialist Conf.,2019

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