图神经网络与深度强化学习结合的算力网络资源分配方法

韩雪莹; 谢明熹; 禹可; 黄小红; 杜宗鹏; 姚惠娟

doi:10.1631/FITEE.2300009

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图神经网络与深度强化学习结合的算力网络资源分配方法

专题 | Updated：2024-06-03

- 图神经网络与深度强化学习结合的算力网络资源分配方法
- Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
- “在计算力网络领域，取得了新的研究突破。专家们开发了一种基于图神经网络的深度强化学习框架，可以优化网络资源和计算资源。这项创新为网络拓扑的动态特性带来的挑战提供了一种解决方案，显著推进了网络优化领域。”
- 信息与电子工程前沿（英文） 2024年25卷第5期页码：701-712
- Affiliations：
  
  1.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
  3.Department of Infrastructure Network Technology Research, China Mobile Research Institute, Beijing 100032, China
- Author bio：
  
  E-mail: hanxueying@bupt.edu.cn
  ‡Corresponding author
- Funds：
  
  Beijing University of Posts and Telecommunications - China Mobile Research Institute Joint Innovation Center
- DOI：10.1631/FITEE.2300009
  中图分类号： TP393
- 收稿：2023-01-05，
  
  录用：2023-04-24，
  
  纸质出版：2024-05-0
- Accepted：
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韩雪莹, 谢明熹, 禹可, 等. 图神经网络与深度强化学习结合的算力网络资源分配方法[J]. 信息与电子工程前沿（英文）, 2024,25(5):701-712.

Xueying HAN, Mingxi XIE, Ke YU, et al. Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 701-712.
韩雪莹, 谢明熹, 禹可, 等. 图神经网络与深度强化学习结合的算力网络资源分配方法[J]. 信息与电子工程前沿（英文）, 2024,25(5):701-712. DOI： 10.1631/FITEE.2300009.

Xueying HAN, Mingxi XIE, Ke YU, et al. Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 701-712. DOI： 10.1631/FITEE.2300009.

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图神经网络与深度强化学习结合的算力网络资源分配方法

Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

DOI：10.1631/FITEE.2300009