arXiv cs.AI· Shreya K. Chari, John S. Vardakas, Kostas Ramantas, Christos Verikoukis·· 12 小时前AI 评分22
RAGAM-AI：基于混合强化学习与生成式 AI 的边缘资源管理智能体框架
RAGAM-AI: A Hybrid Reinforcement Learning based Agentic approach powered by Generative AI for Edge Resource Management
AI 导读
RAGAM-AI 提出一种混合框架，将强化学习与生成式 AI 结合，用 LLM 提供反馈以解决复杂工作流中的奖励泛化问题。该框架在 MEC 单元上实现基于 MathΣtral 作为奖励预言机的多智能体 Soft-Actor Critic（MASAC），并引入代理奖励模型降低复杂度。
正文 · 原文
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. Therefore, this work proposes RAGAM-AI, a hybrid framework that combines the capabilities of RL algorithms and Generative-AI (Gen-AI) for powering an Agentic AI workflow that optimizes resource allocation at the Multi-access Edge Computing (MEC) units. This framework further resolves the reward generalization issue by utilizing Large Language Model (LLM) for feedback. A Multi- Agent Soft-Actor Critic (MASAC) using Math{\Sigma}tral as a reward oracle is implemented for managing resource allocations at the MEC. RAGAM-AI interprets system state information and evaluates multi-objective trade-offs guiding the resource orchestration loop over distributed edge server agents. The complexity of the framework is further brought down by incorporating surrogate reward model. The overall performance is evaluated against the single agent SAC, REINFORCE and random policies for benchmarking. On using RAGAM-AI, lower allocation deviations for CPU, memory and bandwidth resources are reported while minimizing the latency and data rate costs. A scalable AI as a feedback (AIAF) solution optimizing network resources with significant performance improvement is thus presented.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06554 [cs.AI] |
| (or arXiv:2611.06554v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06554 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shreya Krishnama Chari [view email]
[v1]
Wed, 4 Nov 2026 14:18:07 UTC (866 KB)
来源：arXiv cs.AI · arxiv.org