Pointer Network-Based Reinforcement Learning for UAV-Assisted Mobile Edge Computing

Unmanned aerial vehicles (UAVs) have been extensively adopted in mobile edge computing (MEC) due to their high autonomy and powerful maneuverability. Although UAV-assisted MEC can achieve strong efficiency and flexibility enhancements for mobile users, it results in serious resource allocation and task scheduling problems. The computing, storage, and communication resources of UAVs should be efficiently allocated and scheduled so that the service quality at different positions is ensured even in a highly dynamic environment. In this work, we focus on the UAV-assisted MEC problem and design a pointer network-based reinforcement learning framework that effectively addresses the pain point of poor reusability of traditional reinforcement learning methods and optimizes the decision-making behavior of UAVs to improve the system performance. First, with the models of UAVs and mobile users, we analyze the collision avoidance, motion continuity, coverage, and energy consumption constraints of UAVs, and formulate the UAV-assisted MEC problem as a multi-constraint combinatorial optimization one. Then, inspired by the suitability of pointer networks in effectively handling variable-length sequences, we propose a pointer network-based reinforcement learning framework to seek an optimal service strategy for every UAV while minimizing the completion time of MEC tasks. Simulation experiments are conducted on randomly generated environments to quantitatively analyze the performance of our approach, and the experimental results indicate that compared to traditional methods, our approach can reduce the task completion time, flight path length, and energy consumption by more than 1.25%, 2.78%, and 1.12%, respectively.