Implementation of the Policy Gradient algorithm for learning 2-opt improvement heuristics, following: http://proceedings.mlr.press/v129/costa20a/costa20a.pdf
https://arxiv.org/abs/2004.01608
Dependencies:
- Python 3.6.4
- Torch
- Numpy
- Matplotlib
- Apex
- tqdm
- pyconcorde
To train the model you can run:
For TSP instances with 20 nodes:
python PGTSP20.py
For TSP instances with 50/100 nodes (default 50 nodes):
python PGTSP50_100.py
To use the learned polcies reported in the paper you can run:
python TestLearnedAgent.py --load_path best_policy/policy-TSP20-epoch-189.pt --n_points 20 --test_size 1 --render
where load_path can be replaced with one of the policies in /best_policy.
Learned policy on a TSP with 50 nodes:
