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Trained model is available in the tsp_transfer_... dirs.
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To test the model, use the load_all_rewards in Post_process dir.
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To train the model, run train_motsp_transfer.py
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To visualize the obtained Pareto Front, the result should be visulaized using Matlab.
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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
ZhangWei1993/RL_TSP_4static
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper
Python