Development and Challenges of Computing Power Routing in Space-Ground Integrated Networks Based on Reinforcement Learning

WANG Mingqian; WU Xiaomei; YANG Guang; LEI Guangwang; ZHANG Tingting; LIU Zishen

doi:10.11959/j.issn.1000-0801.2025034

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Development and Challenges of Computing Power Routing in Space-Ground Integrated Networks Based on Reinforcement Learning

Reviews | 更新时间：2026-01-09

- Development and Challenges of Computing Power Routing in Space-Ground Integrated Networks Based on Reinforcement Learning
- Space-Integrated-Ground Information Networks Vol. 6, Issue 4, Pages: 1-8(2025)
- 作者机构：
  
  1.北京理工大学，北京 100081
  2.中国铁塔股份有限公司，北京 100195
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62401047)
- DOI：10.11959/j.issn.1000-0801.2025034
  CLC： TP393
- Received：19 June 2025，
  
  Revised：2025-08-14，
  
  Published：20 December 2025
- 稿件说明：
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王明乾,吴晓梅,杨桄等.基于强化学习的天地融合网络算力路由发展与挑战[J].天地一体化信息网络,2025,06(04):1-8.

WANG Mingqian,WU Xiaomei,YANG Guang,et al.Development and Challenges of Computing Power Routing in Space-Ground Integrated Networks Based on Reinforcement Learning[J].Space-Integrated-Ground Information Networks,2025,06(04):1-8.
王明乾,吴晓梅,杨桄等.基于强化学习的天地融合网络算力路由发展与挑战[J].天地一体化信息网络,2025,06(04):1-8. DOI： 10.11959/j.issn.1000-0801.2025034.

WANG Mingqian,WU Xiaomei,YANG Guang,et al.Development and Challenges of Computing Power Routing in Space-Ground Integrated Networks Based on Reinforcement Learning[J].Space-Integrated-Ground Information Networks,2025,06(04):1-8. DOI： 10.11959/j.issn.1000-0801.2025034.

摘要

天地融合网络通过协同卫星、空中平台和地面设施构建立体化通信架构，其动态拓扑和异构算力资源对路由机制提出了全新挑战。面对这一复杂场景，强化学习因其强大的环境适应能力和智能决策优势成为解决算力路由问题的研究热点。首先，系统梳理并深入探究强化学习在算力路由中的应用实践；然后，从算法范式与优化目标两个维度解析其核心设计原理；最后，深入剖析并前瞻性地指出其在天地融合网络环境下所面临的关键挑战。

Abstract

Space-air-ground integrated networks (SAGIN) establish a multi-dimensional communication architecture by synergistically coordinating satellites

aerial platforms

and terrestrial facilities. The inherent characteristics of dynamic topologies and heterogeneous computing resources within this architecture present novel challenges for routing mechanisms. In response to this complex scenario

reinforcement learning (RL) has emerged as a prominent research focus for addressing computing-aware routing problems

owing to its robust environmental adaptability and intelligent decision-making capabilities. This paper systematically reviews and investigates the application of RL in computing-aware routing. It analyzes the core design principles from the dual perspectives of algorithmic paradigms and optimization objectives. Finally

this paper deeply analyzes and prospectively identifies the key challenges that RL faces in the context of SAGIN environments.

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references

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