BNN-UPC/ENERO
Code used in the paper "ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning". In this paper, the DRL agent is implemented with the PPO algorithm
This project helps network operators optimize Wide Area Network (WAN) routing in real-time. It takes information about network traffic and connectivity, even when conditions change dynamically (like link failures), and provides an optimized routing strategy. The goal is to ensure high Quality of Service for customers and meet Service Level Agreements for Internet Service Providers.
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Use this if you are an Internet Service Provider or network operator needing to efficiently manage and optimize WAN traffic in dynamic, real-world scenarios.
Not ideal if you are looking for a general-purpose reinforcement learning library or need to optimize network types other than WANs.
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33
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5
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 06, 2023
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