基于图注意力多智能体强化学习的月球延迟容忍网络分散式路由策略学习
Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks
AI总结：
针对月球延迟容忍网络中的多机器人探索，提出基于图注意力多智能体强化学习的分散式路由策略，采用集中训练分散执行，仅依赖局部观测，在蒙特卡洛模拟中实现更高投递率、无重复和更少丢包，并可扩展至更大团队。
摘要：
我们提出了一种完全分散式的路由框架，用于在月球延迟容忍网络（LDTN）约束下执行多机器人探索任务。在此场景中，自主漫游车必须在间歇性连接和未知移动模式下，将收集到的数据中继传输至着陆器。我们将该问题建模为部分可观测马尔可夫决策问题（POMDP），并提出一种基于图注意力的多智能体强化学习（GAT-MARL）策略，该策略采用集中式训练、分散式执行（CTDE）范式。与最短路径和受控泛洪等经典算法不同，我们的方法仅依赖局部观测，无需全局拓扑更新或数据包复制。通过在随机探索环境中的蒙特卡洛模拟，GAT-MARL实现了更高的投递率、无重复数据包和更少的丢包，并能利用短期移动性预测；该方法为未来行星探索的空间机器人系统提供了可扩展的解决方案，并成功泛化至更大规模的漫游车团队。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
