# Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks

arXiv: https://arxiv.org/abs/2412.10700 · HTML: https://mlsanity.com/paper/2412.10700/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 14 Dec 2024.

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

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing](https://mlsanity.com/paper/2009.11277/) (2020)
- [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016)
- [Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2212.02075/) (2022)
- [Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Control](https://mlsanity.com/paper/2408.02501/) (2024)
- [Aerial-Terrestrial Communications: Terrestrial Cooperation and Energy-Efficient Transmissions to Aerial-Base Stations](https://mlsanity.com/paper/1405.3941/) (2014)
- [Towards Symbiotic SAGIN Through Inter-operator Resource and Service Sharing: Joint Orchestration of User Association and Radio Resources](https://mlsanity.com/paper/2404.16611/) (2024)

## Built on by (most cited first, 3 of 3)

- [PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition](https://mlsanity.com/paper/2601.12798/) (2026)
- [Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion](https://mlsanity.com/paper/2506.21933/) (2025)
- [OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers](https://mlsanity.com/paper/2512.24334/) (2025)

## Abstract (arXiv)

The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. The low-altitude economy, leveraging the flexibility and multifunctionality of Unmanned Aerial Vehicles (UAVs) in SAGIN, holds significant potential for development in areas such as communication and sensing. However, effective coordination is needed to streamline information exchange and enable efficient system resource allocation. In this paper, we propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN. The CMADDPG algorithm leverages dynamic UAV clustering to partition UAVs into clusters, each managed by a Cluster Head (CH) UAV, facilitating a distributed-centralized control approach. Within each cluster, UAVs delegate offloading decisions to the CH UAV, reducing intra-cluster communication costs and decision conflicts, thereby enhancing task scheduling efficiency. Additionally, by employing a multi-agent reinforcement learning framework, the algorithm leverages the extensive coverage of satellites to achieve centralized training and distributed execution of multi-agent tasks, while maximizing overall system profit through optimized task offloading decision-making. Simulation results reveal that the CMADDPG algorithm effectively optimizes resource allocation, minimizes queue delays, maintains balanced load distribution, and surpasses existing methods by achieving at least a 25% improvement in system profit, showcasing its robustness and adaptability across diverse scenarios.