Joint Optimization of Serial Task Offloading and UAV Position for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Driven by the proliferation of the Internet of Things (IoT), Mobile Edge Computing (MEC) is a key technology for meeting the low-latency and high-computational demands of future wireless networks. However, ground-based MEC servers suffer from limited coverage and inflexible deployment. Unmanned Aerial Vehicles (UAVs), with their high mobility, can serve as aerial edge servers to extend this coverage. This paper addresses the multi-user serial task offloading problem in cache-assisted UAV-MEC systems by proposing a joint optimization algorithm for service caching, UAV positioning, task offloading, and serial processing order. Under the constraints of physical resources such as UAV cache capacity, heterogeneous computing capabilities, and wireless channel bandwidth, an optimization problem is formulated to minimize the weighted sum of task completion time and user cost. The method first performs service caching based on task popularity and then utilizes the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize the UAV’s position, task offloading decisions, and serial processing order. The MADDPG algorithm consists of two collaborative agents: a UAV position agent responsible for selecting the optimal UAV position, and a task scheduling agent that determines the serial processing order and offloading decisions for all tasks. Simulation results demonstrate that the proposed algorithm can converge quickly to a stable solution, significantly reducing both task completion time and user cost.