Adaptive Cooperative Task Offloading Decision for Multiple Unmanned Aerial Vehicles

This study investigates adaptive cooperative task offloading and allocation in a multiple Unmanned Aerial Vehicles (UAVs) collaborative mobile edge computing network. To enhance collaboration among UAVs in a time-varying environment and improve the efficiency of task execution, this study constructs a UAV task queuing model in a time-varying environment and establishes a UAVs task offloading decision model based on the Markov Decision Process (MDP). Moreover, this study proposes a Cooperative-based Deep Deterministic Policy Gradient (CODDPG) algorithm to address the optimization problem of multiple UAVs offloading. The CODDPG algorithm, which integrates CommNet with the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, facilitates the sharing of environmental observations among all UAVs. This approach effectively extends the UAVs' perception of the environment and enhances their collaborative decision capability. It also addresses the issue of local optima in the MADDPG algorithm caused by its sole dependence on local information during agent training, thereby minimizing total computation delay. Experimental results demonstrate that the CODDPG algorithm not only significantly reduces task computation delay effectively but also converges faster than the traditional MADDPG algorithm.