Event-Triggered Deep Reinforcement Learning for Dynamic Task Scheduling in Multi-Satellite Resource Allocation
Author:
Affiliation:
1. MIIT Key Laboratory of Servo Motion System Drive and Control, School of Automation, Beijing Institute of Technology, Beijing, China
2. China Academy of Space Technology, Beijing, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Aerospace Engineering
Link
http://xplorestaging.ieee.org/ielx7/7/7778228/09998480.pdf?arnumber=9998480
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Single-Agent Reinforcement Learning for Scalable Earth-Observing Satellite Constellation Operations;Journal of Spacecraft and Rockets;2024-01
2. PTMB: An online satellite task scheduling framework based on pre-trained Markov decision process for multi-task scenario;Knowledge-Based Systems;2024-01
3. A Fast Approach to Satellite Range Rescheduling Using Deep Reinforcement Learning;IEEE Transactions on Aerospace and Electronic Systems;2023-12
4. A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning;Electronics;2023-09-22
5. A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments;IEEE Access;2023
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