To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi-agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software-defined networking technology. Subsequently, considering UAV load and fairness in user equipment-related services, we employ the multi-agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method’s effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users’ service requests while utilizing computing resources efficiently.

中文翻译：

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通过多智能体深度强化学习的多无人机辅助移动边缘计算任务卸载和路径优化


为了解决多无人机辅助移动边缘计算中的任务卸载和路径规划挑战，本文提出了一种通过多智能体深度强化学习的任务卸载和路径优化方法。主要目标是最大限度地减少系统的总体能耗并提高计算性能。最初，我们建立了多无人机辅助移动边缘计算系统的模型，通过软件定义网络技术集中管理无人机网络。随后，考虑到无人机负载和用户设备相关服务的公平性，我们采用多智能体深度确定性策略梯度算法来优化任务卸载和无人机路径管理，旨在实现负载平衡并降低整体系统能耗。仿真结果证明了与基准算法相比，我们的方法在降低能耗和计算延迟方面的有效性。保证系统高效、稳定、可靠，满足移动边缘用户的业务需求，同时高效利用计算资源。