Energy-Efficient Task Offloading Optimization Based on Meta-Learning in UAV-Assisted Edge Computing Networks

Offloading computing tasks to edge servers can provide better user experience. However, the deployment of a large number of distributed edge nodes brings challenges to the optimal management of network energy consumption. In this paper, deep reinforcement learning (DRL) is used to optimize unmanned aerial vehicle (UAV)-assisted edge computing task offloading to improve network energy efficiency. First, different edge nodes are treated as agents in the DRL network, so that the optimization of edge computing task offloading strategy is transformed into a multi-agent optimization problem. In addition, meta-learning is introduced into the multi-agent deep deterministic policy gradient algorithm to cope with the dynamics and uncertainty of the edge computing network environment. The simulation results show that the task offloading strategy based on meta-learning can not only quickly adapts to the dynamic changes of the environment and tasks, but also outperforms the benchmark algorithms in network energy efficiency.