Energy Consumption Optimization of UAV-Assisted Edge Computing Network via Tiny-MADDPG

With the rapidly growing demand for mobile edge computing (MEC), unmanned aerial vehicles (UAVs) are increasingly used in task offloading and area coverage. However, high energy consumption and computational complexity become the key challenges limiting their development. In this paper, we propose the tiny multi-agent deep deterministic policy gradient (Tiny-MADDPG) algorithm based on deep reinforcement learning (DRL), which aims to optimize the energy consumption of multi-UAV-assisted MEC systems. By constructing a multi-agent deep deterministic policy gradient (MADDPG) framework and combining it with a lightweight network design, the algorithm achieves joint optimization of flight paths, task offloading and resource allocation. For the multi-UAV collaboration scenario, a comprehensive reward function containing energy consumption, delay and coverage is designed to ensure the overall improvement of system performance. Simulation experiments show that TinyMADDPG achieves significant results in minimizing energy consumption compared with other benchmark algorithms. The results validate the dual advantages of multi-agent collaboration and lightweight design, and provide a valuable reference for energy consumption optimization of multi-UAV systems.