English(EN) RAGAM-AI: A Hybrid Reinforcement Learning based Agentic approach powered by Generative AI for Edge Resource Management

新的RAGAM-AI框架使用生成式AI进行边缘资源管理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了RAGAM-AI，一个将强化学习（RL）与生成式AI相结合的新型框架，以增强多接入边缘计算（MEC）环境中的资源管理。这种混合方法利用大型语言模型（LLM）来解决复杂RL工作流中的奖励泛化挑战。该系统采用具有代理奖励模型的Multi-Agent Soft-Actor Critic（MASAC）来优化CPU、内存和带宽分配，旨在降低延迟和数据速率成本。 AI

影响该框架可以通过优化资源分配来提高分布式边缘网络的效率并降低成本。

排序理由该集群包含一篇详细介绍新AI框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Shreya K. Chari, John S. Vardakas, Kostas Ramantas, Christos Verikoukis · 2026-11-05 05:00

RAGAM-AI：一种基于生成式AI的混合强化学习智能体方法，用于边缘资源管理

arXiv:2611.06554v1 Announce Type: new Abstract: Resource management for distributed edge networks hosting different services has been handled efficiently using Reinforcement learning (RL). Reward generalization in complex workflows is still an open issue within the RL realm. Ther…

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RAGAM-AI：一种基于生成式AI的混合强化学习智能体方法，用于边缘资源管理

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