Model-Based Reinforcement Learning and Neural-Network-Based Policy Compression for Spacecraft Rendezvous on Resource-Constrained Embedded Systems-Reference-Cited by-同舟云学术

Model-Based Reinforcement Learning and Neural-Network-Based Policy Compression for Spacecraft Rendezvous on Resource-Constrained Embedded Systems

Published:2023-01 Issue:1 Volume:19 Page:1107-1116
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Yang Zhibin¹^ORCID,Xing Linquan¹,Gu Zonghua²^ORCID,Xiao Yingmin¹,Zhou Yong¹,Huang Zhiqiu¹^ORCID,Xue Lei³

Affiliation:

1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden

3. Shanghai Aerospace Electronic Technology Institute, Shanghai, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Aeronautical Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9944026/09832819.pdf?arnumber=9832819

Reference30 articles.

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3. Autonomous Rendezvous Guidance via Deep Reinforcement Learning

4. Machine Learning Techniques for Autonomous Spacecraft Guidance during Proximity Operations

5. Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

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