Parallel Task Offloading and Trajectory Optimization for UAV-Assisted Mobile Edge Computing via Hierarchical Reinforcement Learning

With the rapid growth of IoT data and increasing demands for low-latency computation, UAV-assisted mobile edge computing (MEC) offers a flexible solution to overcome the limitations of fixed MEC servers. To better reflect concurrent service scenarios, this paper innovatively develops a multi-channel task modeling method, enabling UAVs to simultaneously select multiple users and offload tasks via separate channels, thereby breaking the constraints of traditional sequential assumptions. To address task conflicts and resource waste caused by sequential service assumptions, this paper proposes a hierarchical reinforcement learning-based trajectory and offloading decision-making framework (H-TAOD) for UAVs with multi-channel parallel processing. The framework decouples UAV trajectory planning and task offloading into two sub-tasks optimized via appropriate reinforcement learning methods. An invalid action masking (IAM) mechanism is introduced to avoid channel conflicts. Simulation results verify the superiority of H-TAOD in reward, delay, and convergence.