# Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks

arXiv: https://arxiv.org/abs/2503.03391 · HTML: https://mlsanity.com/paper/2503.03391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing](https://mlsanity.com/paper/2009.11277/) (2020)
- [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020)
- [Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing](https://mlsanity.com/paper/1812.08076/) (2018)
- [Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs](https://mlsanity.com/paper/2202.06046/) (2022)
- [Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems](https://mlsanity.com/paper/2106.14928/) (2021)
- [Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach](https://mlsanity.com/paper/2308.12756/) (2023)
- [A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments](https://mlsanity.com/paper/1804.11001/) (2018)

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

- [On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process](https://mlsanity.com/paper/2601.03976/) (2026)

## Abstract (arXiv)

Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.