GE-DDRL-ROUTER

The source code for "GE-DDRL: Graph Embedding and DeepDistributional Reinforcement Learning for ReliableShortest Path: A Universal and Scale Free Solution".

Table of Contents

• GE-DDRL-Router
  • Requirements
  • Framework of GE-DDRL-Router
  • How to use
  • Template

1.Requirements

numpy, cvxopt, scipy, networkx, gensim, pytorch and other common packages.

2.Framework of GE-DDRL-Router

2. How to use

step 1: Create a simulative environment(ENV), you must choose one transaction data or make a data by yourself, and specify one RSP objective. For some transaction data without the sigma of link travel time, you can generate them through the funtions in func_new.py, you can find the method you want in func_new.py or you can make your own method to generate sigma of link travel time;

step 2: Create a graph embedding module(GE) base on the environment that is created on step 1, some parameters are required to set;

step 3: Create a Agent based on the ENV and GE that are created on step 1 and step2;

step 4: Configure the parameter of the Agent that is created on step 3;

(Optional) step 5: Using dijkstra to pretrain the Agent(Imitation Learning), which is a warm start for DRL-Router.

step 6: Start the training of the Agent, we need to set the training parameters num_iterations, When the training is finished, we got a Policy. The more training times, the better performance of results will be.

3. Running

You can start your tranning in the main.py.