RT Conference Proceedings
T1 Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks
A1 Lozano Cuadra, Federico
A1 Soret, Beatriz
A1 Sánchez Net, Marc
A1 Cauligi, Abhishek
A1 Rossi, Federico
K1 Aprendizaje automático
K1 Astronáutica - Sistemas de comunicaciones
AB 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, autonomous rovers must relay collected data to a lander under intermittent connectivity and unknown mobility patterns. We formulate the problem as a Partially Observable Markov Decision Problem (POMDP) and propose a Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL) policy that performs Centralized Training, Decentralized Execution (CTDE). Our method relies only on local observations and does not require global topology updates or packet replication, unlike classical approaches such as shortest path and controlled flooding-based algorithms. Through Monte Carlo simulations in randomized exploration environments, GAT-MARL provides higher delivery rates, no duplications, and fewer packet losses, and is able to leverage short-term mobility forecasts; offering a scalable solution for future space robotic systems for planetary exploration, as demonstrated by successful generalization to larger rover teams.
YR 2025
FD 2025-10-23
LK https://hdl.handle.net/10630/45373
UL https://hdl.handle.net/10630/45373
LA eng
NO Ministerio de Ciencia, Innovación y Universidades
NO ERDD: A way of making Europe
NO National Aeronautics and Space Administration (NASA)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026