An UAVs-Assisted Edge Computing Network with Multi-agent Reinforcement Learning

After installing a wireless communication module and computing equipment on the UAV (Unmanned Aerial Vehicle), the UAV can provide better communication coverage and even edge computing services for ground nodes by taking advantage of its high maneuverability and altitude advantages. According to the characteristics of user mobility and energy sensitivity, the edge computing problem of an intensive intelligent terminal network is presented. In this paper path planning and task computing strategy optimization are studied. For multi-UAV scenarios, a multi-agent reinforcement learning algorithm combined with initial UAV deployment is proposed. Specifically, the GAK (Genetic Algorithm k-means) algorithm is used to optimize the initial deployment positions of all UAVs. Then, using the multi-agent reinforcement learning algorithm MADDPG solves the optimization problems of track planning and task calculation strategies in dynamic environments. The simulation results indicate that the GAK-MADDPG algorithm can effectively utilize the local computing power of UAV and intelligent terminal by reasonably planning the trajectory and task calculation strategy of UAV, thus saving energy consumption on the user side to the greatest extent.