This paper investigates the multi-UAV multi-task coordination problem in infrastructure-less emergency scenarios, where UAVs collaboratively are required to jointly perform aerial image acquisition and ground-user communication. To tackle the challenge of balancing heterogeneous tasks within dynamic environments, we propose a hierarchical dynamic weighting Deep Reinforcement Learning (DRL) framework. Specifically, an episode-level module is introduced to capture global task preferences, while a step-level module adaptively adjusts the objective weights according to real-time system conditions. By integrating global and instantaneous weights, the proposed framework improves decision stability and responsiveness during task execution. Simulation results demonstrate that the proposed method achieves faster convergence, more stable training, and higher task completion efficiency than conventional works.

翻译：本文研究了基础设施缺失的应急场景下多无人机多任务协同问题，其中无人机需协同完成空中图像采集与地面用户通信的双重任务。为应对动态环境中异构任务平衡的挑战，我们提出了一种分层动态加权深度强化学习框架。具体而言，引入情节级模块捕获全局任务偏好，同时采用步进级模块根据实时系统状态自适应调整目标权重。通过融合全局权重与瞬时权重，该框架提升了任务执行过程中的决策稳定性与响应能力。仿真结果表明，与传统方法相比，所提方法实现了更快的收敛速度、更稳定的训练过程以及更高的任务完成效率。