An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks

no code implementations • 8 Jul 2024 • Federico Lozano-Cuadra, Mathias D. Thorsager, Israel Leyva-Mayorga, Beatriz Soret

This paper introduces an open source simulator for packet routing in Low Earth Orbit Satellite Constellations (LSatCs) considering the dynamic system uncertainties.

reinforcement-learning Reinforcement Learning +1

Continual Deep Reinforcement Learning for Decentralized Satellite Routing

no code implementations • 20 May 2024 • Federico Lozano-Cuadra, Beatriz Soret, Israel Leyva-Mayorga, Petar Popovski

First, an offline learning phase relies on decentralized decisions and a global Deep Neural Network (DNN) trained with global experiences.

Continual Learning Federated Learning +2

Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing

no code implementations • 27 Feb 2024 • Federico Lozano-Cuadra, Beatriz Soret

This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs).

Decision Making reinforcement-learning +1