Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem

Jie Chun, Wenyuan Yang, Xiaolu Liu (), Guohua Wu, Lei He and Lining Xing
Additional contact information
Jie Chun: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Wenyuan Yang: College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
Xiaolu Liu: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Guohua Wu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Lei He: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Lining Xing: College of Electronic Engineering, Xidian University, Xi’an 710126, China

Mathematics, 2023, vol. 11, issue 19, 1-20

Abstract: The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention network-based decision neural network (GDNN) to solve the AEOSSP. Specifically, we first represent the task and time-dependent attitude transition constraints by a graph. We then describe the problem as a Markov decision process and perform feature engineering. On this basis, we design a GDNN to guide the construction of the solution sequence and train it with proximal policy optimization (PPO). Experimental results show that the proposed method outperforms construction heuristics at scheduling profit by at least 45%. The proposed method can also calculate the approximate profits of the state-of-the-art method with an error of less than 7% and reduce scheduling time markedly. Finally, we demonstrate the scalability of the proposed method.

Keywords: agile satellite scheduling; task planning; graph attention network; deep reinforcement learning; proximal policy optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/19/4059/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/19/4059/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:19:p:4059-:d:1246998

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().