Multiagent Deep Reinforcement Learning for Task Offloading and Resource Allocation in Cybertwin-Based Networks
Wenjing Hou, Hong Wen, Huanhuan Song, Wenxin Lei, Wei Zhang
Abstract
In this article, a hierarchical task offloading strategy is presented for delay-tolerant and delay-sensitive missions by integrating edge computing and artificial intelligence into Cybertwin-based network to guarantee user Quality of Experience (QoE), low latency, and ultrareliable services, which are huge challenges to the Internet of Things (IoT) due to diverse application requirements, heterogeneous multidimensional resources, and time-varying network environments. The novel scheme achieves faster task processing, dynamic real-time allocation, and lower overhead by taking advantages of a multiagent deep deterministic policy gradient (MADDPG). Moreover, federated learning is used to train the MADDPG model. Numerical results demonstrate that the proposed algorithm improves system processing efficiency and task completion ratio compared to the benchmark schemes.