GAN-powered heterogeneous multi-agent reinforcement learning for UAV-assisted task offloading
GAN-powered heterogeneous multi-agent reinforcement learning for UAV-assisted task offloading
复制标题
基于GAN的异构多智能体强化学习无人机辅助任务卸载
DOI：
10.1016/j.adhoc.2023.103341
复制
发表时间：
2023-11
期刊：
影响因子：
4.8
通讯作者：
Yangyang Li;Lei Feng;Yang Yang-Yang;Wenjing Li
中科院分区：
文献类型：
--
作者：
Yangyang Li;Lei Feng;Yang Yang-Yang;Wenjing Li
The flexible and highly mobile unmanned aerial vehicle (UAV) with computing capabilities can improve the quality of experience (QoE) of ground users (GUs) according to real-time service requirements by performing flight maneuvers. In this study, we investigate a task offloading scheme and trajectory optimization in a multi-UAV-assisted system, where UAVs offload a portion of multiple GUs’ computational tasks. The optimization problem is formulated to jointly minimize the energy consumption of UAVs and the task latency of GUs by optimizing trajectory, task allocation and offloading proportion. This paper proposes a heterogeneous multi-agent reinforcement learning (MARL)-based approach to solve the issue in high dimensions and limited states, where UAV and GU are treated separately as two different types of agents. Due to the high cost and low sample efficiency of online training of RL algorithms, a generative adversarial network (GAN)-powered auxiliary training mechanism is proposed, which reduces the overhead of interacting with the real world and makes the agent’s policy appropriate for real-world execution environment via offline training with generated environment states. Numerical evaluation results demonstrate that the proposed algorithm outperforms other benchmark algorithms in terms of energy consumption and task latency.
