基于深度强化学习的低推力航天器轨道优化方法

IF 5.1 2区计算机科学 Q1 ENGINEERING, AEROSPACE

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2025-05-13 DOI:10.1109/taes.2025.3569615

Syed Muhammad Talha Zaidi, Adrian Arustei, Arslan Munir, Atri Dutta

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Single-Agent Attention Actor-Critic: A Deep Reinforcement Learning-Based Solution for Low-Thrust Spacecraft Trajectory Optimization

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来源期刊

IEEE Transactions on Aerospace and Electronic Systems 工程技术-电信学

CiteScore

7.80

自引率

13.60%

发文量

433

审稿时长

8.7 months

期刊介绍： IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.