Python>=3.6
PyTorch=1.1
Code for running baselines.
Code, data, and model for small scale travelling salesman problem (TSP). To train the model, please run train.py via
python train.py --size=X --epoch=X --batch_size=X --train_size=X --val_size=X --lr=X
Here the parameter --size is the size of TSP instance, and --lr is the learning rate. To test the model with data generated on the fly, please run test_random.py via
python test_random.py --size=X --batch_size=X --test_size=X --test_steps=X
To test the model with heldout TSP data, please run test.py via
python test.py --size=X
In this experiment, we train the model with small instances and use the model to predict the routes for larger scale TSP, i.e. TSP250/500. Please run the ipython notebook.
In this experiment, we use hierarchical reinforcement learning to tackle TSP with Time Window. To train hierarchical model, please first train the lower model by
python tsptw_low.py
Then train higher model by
python tsptw_high.py
To train non-hierarchical model, use
python tsptw_non_hier.py
To test hierarchical model using greedy method, use
python test_hier.py
To test hierarchical model using sampling method, use
python test_hier_sampling.py
To test non-hierarchical model, use
python test.py