MULTI-AGENT REINFORCEMENT LEARNING FOR TASK OFFLOADING AND RESOURCE ALLOCATION IN MEC SYSTEMS
TL;DR
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Abstract
Multi-Access Edge Computing (MEC) brings computation closer to end users to reduce latency and energy consumption for compute-intensive mobile applications. In this paper, we address the joint task offloading and resource allocation problem in multi-user MEC systems and propose a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL). Each user–base station link is modeled as an autonomous agent that decides whether to execute tasks locally or offload them to the edge, and how much computing resource to request. The learning framework follows a centralized training and decentralized execution paradigm and integrates Q-Learning, Deep Q-Network (DQN), and Double DQN (DDQN) algorithms. Extensive simulations show that the DDQN-based approach achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines. The results confirm that lightweight MARL is a practical and scalable solution for dynamic MEC environments under realistic resource constraints.