AI RESEARCH
Learning Ad Hoc Network Dynamics via Graph-Structured World Models
arXiv CS.LG
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ArXi:2604.14811v1 Announce Type: new Ad hoc wireless networks exhibit complex, innate and coupled dynamics: node mobility, energy depletion and topology change that are difficult to model analytically. Model-free deep reinforcement learning requires sustained online interaction whereas existing model based approaches use flat state representations that lose per node structure. Therefore we propose G-RSSM, a graph structured recurrent state space model that maintains per node latent states with cross node multi head attention to learn the dynamics jointly from offline trajectories.