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This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.

Home Page: https://arxiv.org/abs/2310.18264

License: MIT License

Python 0.46% Jupyter Notebook 99.54%
deep-reinforcement-learning k-opt learning-to-optimize neural-combinatorial-optimization reinforcement-learning reward-shaping transformer tsp vehicle-routing-problem vrp

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neuopt's Issues

configuration on tsplib data

Hi, I am testing the pretrained TSP100 model on tsplib data. Using the default configuration, on a280 data, it gave a best value of 23508, which has gap of 800% compared to the optimal value. May I ask if there's a different set of configuration I should be using or did I do anything wrong? Thanks!

CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py --eval_only --no_saving --no_tb --init_val_met random --k 4 --problem tsp --val_m 1 --stall 10 --T_max 1000 --load_path pre-trained/tsp100.pt

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