Transfer Learning for Joint Trajectory Control and Task Offloading in Large-Scale Partially Observable UAV-Assisted MEC

Existing joint trajectory control and task offloading (JTCTO) algorithms offer ultra-low latency services for smart devices (SDs) in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC). However, these JTCTO algorithms typically require large training datasets to learn the optimal policies, leading to low learning efficiency. Additionally, most existing JTCTO algorithms are difficult to scale to environments with more than a few UAVs, as their complexity increases exponentially with the number of UAVs. In this paper, we propose a decentralized JTCTO algorithm based on the Policy Transfer and Mean Field-based Multi-Agent Actor-Critic (PTMF-MAAC). First, a novel policy transfer algorithm is proposed to determine which UAV's JTCTO strategy is helpful for each UAV and when to terminate the strategy to accelerate the learning efficiency of the UAV. Second, we propose a partially observable mean field algorithm that significantly reduces the model space by replacing the influence of all other UAVs on a particular UAV with an average value, thereby adapting to large-scale UAV scenarios. Experiments have shown that compared to the baseline, PTMF-MAAC reduces the system cost by 18.44%<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>28.57% and improves the model learning efficiency and adaptability to partially observable large-scale UAV-assisted MEC.