Meta-Learning-based Deep Reinforcement Learning for Multiobjective Optimization Problems (VRPTW)

Dependencies

• Python>=3.6
• NumPy
• PyTorch>=1.4

Meta-Learning

For training meta-model on MOCVRPTW-50 instances:

python run.py --graph_size 50 --CUDA_VISIBLE_ID "0" --is_train --meta_iterations 2000

Fine-tuning

For fine-tuning the trained meta-model (ML-DAM) on MOCVRPTW-50 instances with 10-step per subproblem:

python run.py --graph_size 50 --is_load --load_path "meta-model-VRPTW50.pt" --CUDA_VISIBLE_ID "0" --is_test --update_step_test 10

For fine-tuning the random-model (DAM) on MOCVRPTW-50 instances with 10-step per subproblem:

python run.py --graph_size 50 --CUDA_VISIBLE_ID "0" --is_test --update_step_test 10

Transfer-Learning

For training all the submodels with transfer-learning by loading the well trained 1st-submodel (DAM(transfer-obj2)) on MOCVRPTW-50 instances with 10-step per subproblem:

python run.py --graph_size 50 --is_load --load_path "model-obj2.pt" --CUDA_VISIBLE_ID "0" --is_transfer --is_test --update_step_test 10

For training all the submodels with transfer-learning by loading the well trained 100th-submodel (DAM(transfer-obj1)) on MOCVRPTW-50 instances with 10-step per subproblem:

python run.py --graph_size 50 --is_load --load_path "model-obj1.pt" --CUDA_VISIBLE_ID "0" --is_transfer --is_test --update_step_test 10

Acknowledgements

Thanks to wouterkool/attention-learn-to-route for getting me started with the code for the Attention Model.