Multi-Agent Deep Reinforcement Learning-Based Distributed Task Assignment in Multi-UAV Cooperative Edge Computing

Leveraging flexible deployment and wide-area coverage, Unmanned Aerial Vehicles (UAVs) assisted edge computing (EC) extends computation capability to the network edge and has become a key paradigm for improving the quality of service in the Internet of Things. However, constrained by onboard resources, UAVs struggle to independently process massive heterogeneous tasks in real time. Existing studies mainly focus on cooperation between UAVs and ground devices or the cloud, while the coupled cooperative relationships among UAVs and the performance scalability in large-scale UAV networks remain insufficiently characterized. To this end, we propose a distributed task assignment framework for a multi-UAV cooperative EC system, where each UAV explicitly accounts for cooperation with other UAVs and, leveraging controllable mobility, assigns a portion of local tasks to other UAVs or base stations to minimize the long-term system-wide total latency. To address the resulting joint trajectory control and task assignment optimization problem, we first formulate it as a Markov decision process. We then propose a Multi-head self-Attention critic-assisted MADDPG (MA$^{2}$DDPG) algorithm to train local online decision models for UAVs. Under the fully cooperative setting, we employ a centralized Critic to exploit global information and reduce parameter redundancy; concurrently, a multi-head self-attention mechanism is incorporated into the Critic to aggregate multi-UAV interaction information, delineate implicit cooperative dependencies, and alleviate the network input dimensionality curse arising from increasing UAV scale. Finally, extensive simulation experiments validate the effectiveness of the proposed method.