2026-05-30T01:48:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/319662026-02-03T12:17:53Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Lozano Cuadra, Federico author Soret, Beatriz author 2024 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. 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. https://hdl.handle.net/10630/31966 Comunicaciones vía satélite Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing.