UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecture

Data processing is a key challenge for computationally limited Ground Users (GUs) in various applications. Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers can assist GUs by offloading their computing tasks. However, existing work ignores fairness when multiple GUs compete for limited computing resources, which may result in UAV underserving certain GUs. In this paper, we investigate a flight trajectory optimization based on reinforcement learning for UAV selection of target GUs for task computation, which provides low latency and fair offloading computing services for GUs by jointly training UAV flight trajectories and task offloading decisions. We formulate UAV flight and offloading as a mixed integer non-convex optimization problem with high-dimensional state and action spaces. The problem is then transformed into a Markov Decision Processes (MDPs) problem and the Maximizing Service Efficiency Proximal Policy Optimization (MSE-PPO) algorithm is proposed to find the optimal solution. The algorithm adopts an actor-critic-based parallel architecture to handle the parameterized action space. Specifically, the UAV position sequence is updated while ensuring an optimal offloading policy between the UAV and the GUs. Simulation results verify that the average system rewards including computational energy efficiency and fairness index are improved by 35.06 \(\%\) $\%$ and 12.10 \(\%\) $\%$ respectively compared to DDPG and PPO algorithms.