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Final Project for ECE517 and modification of "Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks"

Makefile 1.44% Python 80.56% C 1.00% C++ 15.82% Shell 0.27% MATLAB 0.91%

efficient-graphs's Introduction

To start the training loop, run: cd floyd_net python dqn.py

Code directory: common: backend API functions to translate graphs between formats pytorch_structure2vec: pytorch implementation of S2V floyd_net: dqn.py: code for main training loop (i.e. get actions, update states, calculate loss, etc.) q_net.py: definition of DQN and full network pipeline message.py: random graph generation and efficiency calculation

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