| 标题 |
 Efficient End–Edge–Cloud Task Offloading in 6G Networks Based on Multiagent Deep Reinforcement Learning 基于多智能体深度强化学习的6G网络端边云高效任务卸载
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| 其它 | 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 article proposes a deep reinforcement learning-based algorithm utilizing a multiagent approach in the end–edge–cloud architecture for 6G networks. The offloading issue can be reformulated to a decentralized partially observable Markov decision process, |
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