Using Deep Reinforcement Learning method and Attention model to solve the Multiobjectve TSP.

This code is the model with four-dimension input (Euclidean-type).

The model with three-dimension input (Mixed-type) is in the RL_3static_MOTSP.zip.

Matlab code for visualzing and comparisons in the paper is in the MOTSP_compare_EMO.zip.

• Trained model is available in the tsp_transfer_... dirs.

• To test the model, use the load_all_rewards in Post_process dir.

• To train the model, run train_motsp_transfer.py

• To visualize the obtained Pareto Front, the result should be visulaized using Matlab.

• matlab code is in the .zip file. It is in the " MOTSP_compare_EMO/Problems/Combinatorial MOPs/compare.m ". It is used to produce the figures in batch.

  First you need to run the train_motsp_transfer.py to train the model.

  Run the load_all_rewards.py to load and test the model. It also converts the obtained Pareto Front to the .mat file

  Run the Matlab code to visualize the Pareto Front and compare with NSGA-II and MOEA/D

A lot codes are inherited from https://github.com/mveres01/pytorch-drl4vrp