skypitcher/risk_aware_marl
Risk-aware multi-agent deep reinforcement learning for packet routing in ultra-dense LEO satellite networks
This project helps satellite network operators manage packet routing in large, complex Low Earth Orbit (LEO) satellite constellations. It takes in real-time network conditions and traffic, then outputs optimized routing decisions that minimize network congestion and delays. The primary users are engineers and operations managers responsible for maintaining robust and efficient LEO satellite communication networks.
Use this if you need to simulate and optimize packet routing strategies for ultra-dense LEO satellite networks, especially to reduce network congestion and ensure reliable, low-latency data delivery.
Not ideal if you are managing traditional terrestrial networks or require a physical network deployment for immediate real-world operation rather than a simulation and optimization framework.
Stars
16
Forks
—
Language
Python
License
MIT
Category
Last pushed
Dec 31, 2025
Commits (30d)
0
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