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arXiv AI By Shreya K. Chari, John S. Vardakas, Kostas Ramantas, Christos Verikoukis

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

Read the original on arXiv AI →

RAGAM-AI is a hybrid framework that merges reinforcement learning (RL) with Generative AI to manage resource allocation in Multi-access Edge Computing (MEC) environments. It addresses reward generalization challenges by employing a Large Language Model (LLM) for feedback and uses a Multi-Agent Soft-Actor Critic (MASAC) with a surrogate reward model to guide resource orchestration across distributed edge servers. Compared to single-agent SAC, REINFORCE, and random policies, RAGAM-AI achieves lower deviations in CPU, memory, and bandwidth allocation while reducing latency and data rate costs.

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