XAI引导的保守分散执行用于离线多智能体网络切片

AI 摘要

该研究提出了一种名为X-CODE的可解释离线多智能体强化学习框架，用于网络切片中的资源分配。X-CODE在离线训练阶段利用可解释性感知奖励塑造来调整联合转移的偏好，从而在无需环境交互或智能体间通信的情况下实现保守的分散执行。仿真结果显示，该方法在测试片段中实现了零资源冲突事件，并将有效推理延迟降低了88%。

正文节选

[Page 1] 1 XAI-Guided Conservative Decentralized Execution for Offline Multi-Agent Network Slicing Eslam Eldeeb, Member, IEEE, Hatim Chergui, Senior Member, IEEE, and Merouane Debbah, Fellow, IEEE Abstract—The recent advances toward sixth-generation (6G) a physical network into multiple virtual networks (slices), and beyond-6G networks have accelerated the need for intelli- each operating independently to satisfy specific service level gen

发布时间：2026-08-17 12:00

抓取时间：2026-08-17 12:43

来源机构：arXiv