RAMA：用情景记忆增强多智能体强化学习优化 LEO 卫星网络路由
RAMA: Retrieval-Augmented Multi-Agent Routing with Episodic Memory for LEO Satellite Networks
卫星网络拓扑变化太快，传统 DRL 只看当下。RAMA 让每颗卫星带记忆检索历史经验，Starlink 场景时延降了 93%，做网络方向的朋友可以看看。
论文提出 RAMA，一个结合检索增强与近似信息状态（AIS）的多智能体 DRL 路由框架，每颗卫星可从本地情景记忆中检索历史经验辅助决策。在 140 颗卫星的 Kepler 星座上冻结权重测试，RAMA 较 DDQN 基线将平均端到端时延降低 13.4%，99 分位时延降低 66.7%。在 1,584 颗卫星的 Starlink shell 1 场景中，平均时延降幅达 93.2%，在线更新还能再降 25.5%。
RAMA: Retrieval-Augmented Multi-Agent Routing with Episodic Memory for LEO Satellite Networks
Low-Earth orbit (LEO) satellite networks present a challenging routing environment due to rapidly changing topology, traffic, and link conditions. Existing deep reinforcement learning (DRL) routing methods typically rely on current observations and information encoded in network weights, limiting their ability to exploit successful past experiences. We propose RAMA, a retrieval-augmented multi-agent DRL framework with Approximate Information State (AIS), in which each satellite combines its local observation, recent routing history, neighboring state information, and retrieved entries from a per-satellite episodic memory. Retrieved experiences are integrated through a Transformer-based decision network trained with AIS-based auxiliary objectives. With weights frozen in a 140-satellite Kepler constellation, RAMA reduces mean end-to-end latency by 13.4% and 99th-percentile latency by 66.7% relative to the state-of-theart double deep Q-network (DDQN) routing method, mainly by avoiding long detours. Updated online, RAMA remains stable while DDQN degrades. On the 1,584-satellite Starlink shell 1, the reduction in mean latency grows to 93.2%, and updating RAMA online lowers its mean latency by a further 25.5%. These results demonstrate the effectiveness of retrieval-augmented memory for scalable decentralized routing in LEO satellite networks.