Performance Improvement for UAV-Assisted Mobile Edge Computing with Multi-Agent Deep Reinforcement Learning

In this paper, we propose a method for mobile edge computing (MEC) using unmanned aerial vehicles (UAVs) to enhance wireless connectivity in areas afflicted by natural disasters or obstructed by tall buildings. Despite the active research on deep reinforcement learning in recent years, challenges persist in its application to the optimization of cooperative and coordinated behavior among multi-agents. MEC aims to improve efficiency and fairness in offloading from user terminals (UTs) to UAVs while minimizing energy consumption. Therefore, UAV groups must operate independently within designated areas to facilitate connections between UTs and servers. We introduce multi-agent deep deterministic policy gradient to MEC and improve the reward design to achieve high-performance MEC with efficient cooperative behavior only using limited local data. Experimental results demonstrate that our approach significantly enhances MEC efficiency through effective cooperative behavior. Specifically, it offloads more tasks/applications to UAVs compared to baseline methods while reducing energy consumption per offloading.