Deep Learning for Capacitated Arc Routing

Requirements:

Python 3.6
pytorch>=1.0
matplotlib

To install dependencies, navigate to this folder and run pip install -r requirements.txt

To train a new model

A new model can be trained by running the trainer.py file. Various arguments can be passed for training as needed:

seed : Random seed
checkpoint : Link to the actor/critic checkpoints
test : Flag to set the mode to Testing instead of Training.
nodes : Number of nodes to train for (Can be 10, 20, 50 or 100)
actor_lr : Learning rate for the actor
critic_lr : Learning rate for the critic
max_grad_norm : Value for clipping the gradient
batch_size : Training/Testing batch size
hidden : Number of hidden layers to be generated
dropout : Value for dropout between NN layers
layers : Number of layers in the NN
train_size : Number of examples to be trained on
valid_size : Number of examples to be validated on
epochs : Number of epochs to train for

To run the problem with the default set of arguments, run python trainer.py

However, based on the system used, various parameters may have to be changed. Try reducing the number of training examples (train-size), batch size (batch_size), validation size (valid-size), and epochs (epochs) if the system crashes or if it takes too long to run.

To test a pre-trained model

In order to get a near-optimal solution, the model will have to be trained for a very long time with a wide range of examples. During training, the model stores checkpoints for each epoch, as well as a checkpoint for the model that gives the best results.

Pre-trained checkpoints have to be stored with the names actor.pt and critic.pt.

A set of pre-trained checkpoints are provided for testing. This can be found in the example_cp directory. To load this model for testing, use the command: python tester.py --checkpoint=example_cp

Test cases

The model can be trained and tested on various combinations of input arguments. However, each would require re-training the model.

python trainer.py --train_size=10000 --batch_size=64 --nodes=20
python trainer.py --nodes=50 --actor_lr=1e-4
python trainer.py --train_size=10000 --batch_size=32 --nodes=100 --critic_lr=2e-4
python trainer.py --train_size=10000 --batch_size=32 --nodes=50 --critic_lr=2e-4 --actor_lr=1e-4
python trainer.py --train_size=10000 --batch_size=32 --nodes=20 --critic_lr=2e-4

Architecture Diagrams