5 papers
Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks
Federico Lozano-Cuadra, Beatriz Soret, Marc Sanchez Net +2
We present a fully decentralized routing framework for multi-robot exploration missions operating under the constraints of a Lunar Delay-Tolerant Network (LDTN). In this setting, a…
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks
Federico Lozano-Cuadra, Mathias D. Thorsager, Israel Leyva-Mayorga +1
This paper introduces an open source simulator for packet routing in Low Earth Orbit Satellite Constellations (LSatCs) considering the dynamic system uncertainties. The simulator,…
Continual Deep Reinforcement Learning for Decentralized Satellite Routing
Federico Lozano-Cuadra, Beatriz Soret, Israel Leyva-Mayorga +1
This paper introduces a full solution for decentralized routing in Low Earth Orbit satellite constellations based on continual Deep Reinforcement Learning (DRL). This requires addr…
Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing
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). Each satellite is an independent…
Q-learning for distributed routing in LEO satellite constellations
Beatriz Soret, Israel Leyva-Mayorga, Federico Lozano-Cuadra +1
End-to-end routing in Low Earth Orbit (LEO) satellite constellations (LSatCs) is a complex and dynamic problem. The topology, of finite size, is dynamic and predictable, the traffi…