RaffaeleGalliera/marlin-rlcc
Reinforcement Learning environment for Congestion Control with ContainerNet
This project offers a specialized environment for developing and evaluating Reinforcement Learning (RL) agents designed to optimize network congestion control. It allows researchers and network engineers to simulate various network conditions using ContainerNet and observe how different RL algorithms perform. You can feed network topology definitions and configuration parameters into the system, and it outputs performance metrics and trained RL agent models for congestion control.
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Use this if you are a network researcher or engineer developing and testing AI-driven congestion control strategies in a simulated containerized network environment.
Not ideal if you are looking for a pre-trained, ready-to-deploy congestion control solution for a production network or if you need to test agents on real physical network hardware without simulation.
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Python
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Last pushed
May 21, 2024
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