Efficient End–Edge–Cloud Task Offloading in 6G Networks Based on Multiagent Deep Reinforcement Learning
Hao She, Lixing Yan, Yongan Guo
Abstract
With the progressive evolution of the sixth-generation (6G) network, an array of diverse application tasks is experiencing a steady surge, consequently intensifying the computational pressure. However, even with highly optimized task offloading approaches, ensuring overall service quality for rapidly expanding network applications remains challenging due to hardware resource limitations. This paper proposes a deep reinforcement learning-based algorithm utilizing a multi-agent approach in the End-Edge-Cloud architecture for 6G networks. The offloading issue can be reformulated to a decentralized partially observable Markov decision process, which transfers the NP-hard problem. We design an efficient algorithm based on multi-agent deep deterministic policy gradient (MADDPG) to observe the states of user equipments (UEs), edge servers, and cloud servers, thereby reducing offloading delay and energy consumption. Numerical results demonstrate that our proposed algorithm demonstrates superior performance compared to conventional and state-of-the-art approaches.