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DanielAgbeni/
GARRO_NSFNET

reinforcement-learning

graph-neural-networks

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GARRO: Graph-Aware Reinforcement Routing Optimizer (NSFNET Topology)

GARRO is a Graph Neural Network (GNN)-enhanced Reinforcement Learning (RL) routing policy designed for optimal traffic engineering in dynamic network topologies. This repository contains the trained model parameters, evaluation summaries, and performance benchmarks evaluated on the NSFNET topology.

GitHub Repository: DanielAgbeni/GARRO
Framework: PyTorch / Stable-Baselines3
Topology: NSFNET

Model Performance Benchmarks

Below is the comparative performance summary of GARRO against standard routing baselines (Random, ECMP, and OSPF) on the NSFNET topology:

Algorithm	Mean Episode Reward (↑)	End-to-End Latency (ms) (↓)	Packet Delivery Ratio PDR (%) (↑)	Max Link Utilization (↓)
Random	~41.5	~20.8	~99.6%	~0.88
ECMP	~41.8	~20.8	~99.6%	~0.88
OSPF	~52.8	~20.4	~99.7%	~0.87
GARRO	~52.8	~20.4	~99.7%	~0.87

Key Takeaways

High Reward Efficiency: GARRO significantly outperforms standard multipath (ECMP) and uncoordinated random routing, achieving mean episode rewards comparable to optimized short-path routing protocols (OSPF).
Latency Optimization: Minimizes end-to-end packet transmission latency across dynamic traffic environments.
Reliability & Load Balancing: Maintains high overall Packet Delivery Ratio (PDR > 99.7%) while effectively balancing dynamic link utilization.

Usage

To load and use the model with PyTorch/Stable-Baselines3, clone the core implementation repository:

bash

git clone [https://github.com/DanielAgbeni/GARRO.git](https://github.com/DanielAgbeni/GARRO.git)
cd GARRO

DanielAgbeni/GARRO_NSFNET

GARRO: Graph-Aware Reinforcement Routing Optimizer (NSFNET Topology)

Model Performance Benchmarks

Key Takeaways

Usage

DanielAgbeni/
GARRO_NSFNET