RT Conference Proceedings
T1 Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing.
A1 Lozano Cuadra, Federico
A1 Soret, Beatriz
K1 Comunicaciones vía satélite
AB This paper introduces a Multi-Agent Deep Rein- forcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs). Each satellite is an independent decision-making agent with a partial knowledge of the environment, and supported by feedback received from the nearby agents. Building on our previous work that introduced a Q-routing solution, the contribution of this paper is to extend it to a deep learning framework able to quickly adapt to the network and traffic changes, and based on two phases: (1) An offline exploration learning phase that relies on a global Deep Neural Network (DNN) to learn the optimal paths at each possible position and congestion level; (2) An online exploitation phase with local, on-board, pre-trained DNNs. Results show that MA- DRL efficiently learns optimal routes offline that are then loaded for an efficient distributed routing online.
PB IEEE
YR 2024
FD 2024
LK https://hdl.handle.net/10630/31966
UL https://hdl.handle.net/10630/31966
LA eng
NO F. Lozano-Cuadra and B. Soret, “Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing”, in Proc. IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 2024.
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
NO F. Lozano-Cuadra (flozano@ic.uma.es) and B. Soret are with the Telecom- munications Research Institute, University of Malaga, 29071, Malaga, Spain. This work is partially funded by ESA SatNEx V (prime contract no. 4000130962/20/NL/NL/FE), and by the Spanish Ministerio de Ciencia, Inno- vacio ́n y Universidades (PID2022-136269OB-I00).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026