# Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.05722 · HTML: https://mlsanity.com/paper/2507.05722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing](https://mlsanity.com/paper/2403.08323/) (2024)
- [Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs](https://mlsanity.com/paper/2202.06046/) (2022)
- [Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework](https://mlsanity.com/paper/2405.11281/) (2024)
- [FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing](https://mlsanity.com/paper/2305.01536/) (2023)
- [Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs](https://mlsanity.com/paper/2506.01972/) (2025)
- [Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2502.10731/) (2025)
- [Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles](https://mlsanity.com/paper/2502.08119/) (2025)

## Built on by (most cited first, 1 of 1)

- [Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents](https://mlsanity.com/paper/2511.22076/) (2025)

## Abstract (arXiv)

With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.