risk_aware_marl
Risk-aware multi-agent deep reinforcement learning for packet routing in ultra-dense LEO satellite networks
This repository presents PRIMAL (Principled Risk-aware Independent Multi-Agent Learning), a novel multi-agent deep reinforcement learning framework for packet routing in ultra-dense LEO satellite networks. Our approach addresses the unique challenges of massive scale (1584 satellites, i.e., the first shell of Starlink), dynamic topology, and significant propagation delays inherent in next-generation mega-constellations.
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