DQN Method Analysis for Network Routing of Electric Optical Communication Network

Abstract

Route planning of electric optical communication network play crucial role for communication reliability and performance. For the purpose to carry out enforcement learning and obtain optimized routing result, Deep Q Network (DQN), which has been approved to be a high performance neural network model, is analyzed for electric optical network routing. Depend on network function and structure, large scale electric optical communication network can be divided into several sub networks for better training speed. Advanced DQN model is analysis and trained for a 200 nodes communication network and a 700 nodes communication network. The training results of different scale networks, which can prove the effectiveness of this method, are given with reward data and running time for comparison. This method can be used for dynamic route planning of a large scale electric communication network.