无人机（unmanned aerial vehicle, UAV）系统具有部署灵活、视距传播等优势，成为辅助移动通信应对高密集数据处理、应急通信等业务的重要工具。然而，复杂环境下UAV的计算处理能力及续航问题是其长期面临的技术瓶颈。移动边缘计算（mobile edge computing, MEC）技术的发展为解决UAV计算与能耗问题提供了有效方法。针对MEC辅助的UAV系统，提出一种基于多智能体强化学习算法的分布式任务卸载策略。将UAV的任务卸载和资源分配过程建模为多个MEC节点马尔可夫博弈过程（Markov game process, MGP）。为了解决MGP问题，提出一种面向多智能体协作的分布式强化学习算法。该算法能够使智能体基于局部观测信息，通过在线协作学习方式寻找最优策略。在对比实验中，评估了所提方案的收敛性和系统性能。结果显示该方案在收敛速度、能耗和卸载率方面均优于对比方案。

The unmanned aerial vehicle (UAV) system, with its advantages of flexible deployment and line-of-sight propagation, has become an essential tool for assisting mobile communications in handling high-density data processing and emergency communications. However, the computational processing capabilities and endurance issues of UAVs under complex environments remain significant technological bottlenecks. The development of mobile edge computing (MEC) technology provides an effective solution to address UAVs' computational and energy consumption challenges. A distributed task offloading strategy based on a multi-agent reinforcement learning algorithm was proposed for MEC-assisted UAV systems. The task offloading and resource allocation process of UAVs was modelled as a Markov game process (MGP) involving multiple MEC nodes. To solve the MGP problem, a distributed reinforcement learning algorithm for multi-agent collaboration was proposed. The algorithm enabled agents to find the optimal strategies through online collaborative learning based on local observation information. In comparative experiments, the convergence and system performance of the proposed scheme were evaluated. The results show that the proposed scheme outperforms the comparison schemes in terms of convergence speed, energy consumption, and unloading rate.