Publications Conference paper · Orbital computing · 2026
Distributed Reinforcement Learning for Resource Management in Satellite Edge Computing Systems
IEEE Aerospace Conference, pp. 1–8, 2026
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Abstract
The emergence of large-scale Low Earth Orbit (LEO) satellite constellations has renewed attention to satellite networks, with a vision to deliver global connectivity and performance levels comparable to terrestrial infrastructures. Despite this progress, such constellations are often treated as standalone systems rather than being fully integrated into next-generation communication architectures. To bridge this gap, extensive research has been devoted to the development of Non-Terrestrial Networks (NTN), aiming to enable unified operation across terrestrial and space segments within cellular infrastructures. Nonetheless, realizing this vision remains challenging due to persistent issues in latency, dynamic scheduling, and resource management. To tackle these challenges, we present the development of a Space Cloud, where Multi-access Edge Computing (MEC) services are deployed across satellite nodes and interconnected through Inter-Satellite Links (ISL), forming a distributed space-based data center. By enabling in-orbit computation, the Space Cloud reduces dependence on terrestrial infrastructure, as processing no longer needs to be offloaded to ground-based data centers. This architectural shift is key to meeting the lowlatency requirements of modern, latency-sensitive applications. This paper proposes a distributed Reinforcement Learning (RL) approach to manage computational resources in space-based MEC environments. The system leverages a scalable actorcritic framework, where neural network-based actors and critics are deployed on individual satellites. Each node operates autonomously and in a distributed manner, enabling the network to optimize actions locally while considering both instant rewards and long-term impact. The RL model leverages historical data and processing patterns to control the activation of on-orbit servers, aiming for efficient resource utilization. We evaluate the proposed strategy using a synthetic constellation designed with our in-house satellite emulation and MEC computation framework. Moreover, through Pareto-efficient analysis across key performance indicators (KPI), we benchmark our approach against conventional baselines and assess its applicability under the constraints of specific space missions. The results demonstrate that the RL controller achieves comparable task failure and latency rates to the baselines, while significantly reducing resource consumption.
Cite
DOI 10.1109/aero66936.2026.11519789
BibTeX
@inproceedings{milla2026distributed,
author = {Rojas Milla, Camilo and Badini, Nour and Patrone, Fabio and Fraire, Juan A. and Marchese, Mario},
title = {Distributed Reinforcement Learning for Resource Management in Satellite Edge Computing Systems},
booktitle = {IEEE Aerospace Conference},
pages = {1--8},
year = {2026},
doi = {10.1109/aero66936.2026.11519789}
}