Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks

TL;DR

This paper introduces a decentralized multi-agent reinforcement learning approach using graph attention mechanisms for routing data in lunar delay-tolerant networks, improving delivery rates without global topology knowledge.

Contribution

It proposes a novel GAT-MARL framework that enables decentralized routing with local observations, scalable to larger rover teams, and does not rely on classical flooding or shortest path algorithms.

Findings

Higher delivery rates compared to classical methods

No packet duplications or losses in simulations

Effective generalization to larger rover teams

Abstract

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.