1️⃣ First work to incorporate end-to-end vehicle routing model in a modern RL platform (CleanRL)
⚡ Speed up the training of Attention Model by 8 times (25hours
🔎 A flexible framework for developing model, algorithm, environment, and search for operation research
- 13/04/2023: We release web demo on Hugging Face 🤗!
- 24/03/2023: We release our paper on arxiv!
- 20/03/2023: We release jupyter lab demo and pretrained checkpoints!
- 10/03/2023: We release our codebase!
We provide inference demo on colab notebook:
Environment | Search | Demo |
---|---|---|
TSP | Greedy | |
CVRP | Multi-Greedy |
conda env create -n <env name> -f environment.yml
# The environment.yml was generated from
# conda env export --no-builds > environment.yml
It can take a few minutes.
wandb
Refer to their quick start guide for installation.
All the major implementations were under rlor folder.
./rlor
├── envs
│ ├── tsp_data.py # load pre-generated data for evaluation
│ ├── tsp_vector_env.py # define the (vectorized) gym environment
│ ├── cvrp_data.py
│ └── cvrp_vector_env.py
├── models
│ ├── attention_model_wrapper.py # wrap refactored attention model to cleanRL
│ └── nets # contains refactored attention model
└── ppo_or.py # implementaion of ppo with attention model for operation research problems