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Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper

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kevin031060/RL_TSP_4static

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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

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