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Overview on game reinforcement learning methods for edge computing of low-orbit constellation
Surveys and Prospectives | 更新时间：2024-11-04
|
Overview on game reinforcement learning methods for edge computing of low-orbit constellation
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 3, Pages: 301-318(2024)
- 作者机构：
1.国防科技大学智能科学学院，湖南 长沙 410073
2.湖南先进技术研究院，湖南 长沙 410205
- 作者简介：
- 基金信息：The National Natural Science Foundation of China(92271108;62173336)
DOI：10.11959/j.issn.2096-6652.202432
CLC： TP181Received：22 May 2024，
Revised：2024-07-25，
Published：15 September 2024
- 稿件说明：
移动端阅览
谷学强,张万鹏,谭思雨等.面向低轨星座边缘计算的博弈强化学习方法综述[J].智能科学与技术学报,2024,06(03):301-318.
GU Xueqiang,ZHANG Wanpeng,TAN Siyu,et al.Overview on game reinforcement learning methods for edge computing of low-orbit constellation[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):301-318.
谷学强,张万鹏,谭思雨等.面向低轨星座边缘计算的博弈强化学习方法综述[J].智能科学与技术学报,2024,06(03):301-318. DOI： 10.11959/j.issn.2096-6652.202432.
GU Xueqiang,ZHANG Wanpeng,TAN Siyu,et al.Overview on game reinforcement learning methods for edge computing of low-orbit constellation[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):301-318. DOI： 10.11959/j.issn.2096-6652.202432.
Overview on game reinforcement learning methods for edge computing of low-orbit constellation
摘要
博弈强化学习作为人工智能领域的新兴范式，是当前解决低轨星座边缘计算问题的主流方法。融入博弈论的多智能体深度强化学习方法为复杂、动态、不确定性的星座边缘计算问题提供了新思路。通过梳理总结卫星组网、任务卸载以及资源调度3种卫星边缘计算主要研究方向，详细阐述了博弈强化学习范式基础，并从博弈模型、深度Q网络、深度确定性策略梯度以及近端策略优化等方面分别阐述了3种研究方向上的典型应用现状，最后对该领域的前沿挑战进行分析，期望为博弈强化学习范式与低轨星座边缘计算领域的交叉融合研究提供参考。
Abstract
As a new paradigm in the field of artificial intelligence
game reinforcement learning is an advanced mainstream method to solve the edge computing problem of low-orbit constellation. The multi-agent deep reinforcement learning integrated into the game perspective provides a new idea for dynamic
complex and uncertain constellation edge computing problems. By summarizing the three main research directions of satellite edge computing
namely satellite networking
task unloading and resource scheduling
the basis of game reinforcement learning paradigm is elaborated
and the typical applications in the three research directions are described respectively from the methods of game model
deep Q network
deep deterministic strategy gradient and near-end strategy optimization. In the end
the paper looks forward to the frontier challenges in this field
expected to provide a reference for the cross-fusion research of game reinforcement learning paradigm and low-orbit constellation edge computing.
关键词
Keywords
references
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