Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

Attention based model for learning to solve the Pickup and Delivery Problem (PDP) using heterogeneous attention mechanism. Training with REINFORCE with greedy rollout baseline.

Paper

For more details, please see our paper Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning which has been accepted at IEEE Transactions on Intelligent Transportation Systems. If this code is useful for your work, please cite our paper:

@article{li2021heterogeneous,
  title={Heterogeneous attentions for solving pickup and delivery problem via deep reinforcement learning},
  author={Li, Jingwen and Xin, Liang and Cao, Zhiguang and Lim, Andrew and Song, Wen and Zhang, Jie},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={23},
  number={3},
  pages={2306--2315},
  year={2021},
  publisher={IEEE}
}

Dependencies

• Python>=3.6
• NumPy
• SciPy
• PyTorch>=1.1
• tqdm
• tensorboard_logger
• Matplotlib (optional, only for plotting)

Quick start

For training PDP instances with 20 nodes and using rollout as REINFORCE baseline:

python run.py --graph_size 20 --baseline rollout --run_name 'PDP20_rollout'

Usage

Generating data

Training data is generated on the fly. To generate validation and test data with uniform distribution (same as used in the paper) for pdp, and sigma is meaningful when 'is_gaussian' is True.

python generate_data.py --name validation --seed 1234 --is_gaussian 0 --sigma 1.0
python generate_data.py --name test --seed 6666 --is_gaussian 0 --sigma 1.0

To generate test data with gaussian distribution with sigma=1.0 (same as used in the paper) for pdp:

python generate_data.py --name test --seed 6666 --is_gaussian 1 --sigma 1.0

Training

For training PDP instances with 20 nodes and using rollout as REINFORCE baseline and using the generated validation set:

python run.py --graph_size 20 --baseline rollout --run_name 'pdp20_rollout' --val_dataset data/pdp/pdp20_validation_seed1234.pkl

Multiple GPUs

By default, training will happen on all available GPUs. To disable CUDA at all, add the flag --no_cuda. Set the environment variable CUDA_VISIBLE_DEVICES to only use specific GPUs:

CUDA_VISIBLE_DEVICES=2,3 python run.py 

Note that using multiple GPUs has limited efficiency for small problem sizes (up to 50 nodes).

Warm start

The --load_path option can be used to load an earlier run, in which case also the optimizer state will be loaded:

python run.py --graph_size 20 --load_path 'outputs/pdp_20/pdp20_rollout_{datetime}/epoch-0.pt'

Evaluation

To evaluate a model, you can add the --eval-only flag to run.py, or use eval.py, which will additionally measure timing and save the results:

python eval.py data/pdp/pdp20_test_seed6666.pkl --model 'outputs/pdp_20/pdp20_rollout_{datetime}/epoch-{epoch_number}.pt' --decode_strategy greedy

If the epoch is not specified, by default the last one in the folder will be used.

Sampling

To report the best of 1280 sampled solutions, use

python eval.py data/pdp/pdp20_test_seed6666.pkl --model 'outputs/pdp_20/pdp20_rollout_{datetime}/epoch-{epoch_number}.pt' --decode_strategy sample --width 1280 --eval_batch_size 1

Other options and help

python run.py -h
python eval.py -h

Acknowledgements

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