Dynamic Computation Offloading Optimization Based on Meta-Reinforcement Learning in UAV-Assisted MEC Networks

Offloading computationally intensive tasks to edge nodes reduces latency and improves user experience. Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) can effectively address the limitations of the fixed deployment of traditional edge nodes, but the dynamic nature of UAVs brings challenges to the optimization of computation offloading. Methods based on deep reinforcement learning (DRL) can efficiently learn edge network dynamics to optimize computation offloading in UAV-assisted systems. In the system model, different edge nodes such as UAVs, roadside units, and user equipments (UEs) are treated as agents in the reinforcement learning network, so that the optimization of the edge computation offloading strategy can be transformed into a multi-agent optimization problem. In addition, meta-reinforcement learning is introduced into the multi-agent deep deterministic policy gradient (MADDPG) algorithm to cope with the dynamics and uncertainty of the UAV-assisted edge computing network environment. Simulation results show that the computation offloading strategy based on meta-reinforcement learning can not only quickly adapt to the dynamic changes of the network environment but also outperform other benchmark algorithms in terms of network energy efficiency.