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Sym-NCO-IL: Imitation Learning with Symmetric Learning for Combinatorial Optimization.

The Sym-NCO (Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization) was deep reinforcement learning-based neural combinatorial optimization scheme by exploiting symmetric nature of combinatorial optimization, published at NeurIPS 2022.

Sym-NCO-IL is extension of Sym-NCO's motivation into imitation learning with sparse labeled data setting.

We think symmetric learning scheme can gives clear gain in sparse labeled data setting; therefore IL is more clear benchmark than DRL for making furtherwork of Sym-NCO.

This repository is for people who want to research symmetric learning for combinatorial optimization; feel free to use this code for your further research.

Source code implementation

This code is originally implemented based on Attention Model , which is source code of the paper Attention, Learn to Solve Routing Problems! which has been accepted at ICLR 2019, cite as follows:

@inproceedings{
    kool2018attention,
    title={Attention, Learn to Solve Routing Problems!},
    author={Wouter Kool and Herke van Hoof and Max Welling},
    booktitle={International Conference on Learning Representations},
    year={2019},
    url={https://openreview.net/forum?id=ByxBFsRqYm},
}

Dependencies

• Python>=3.8
• NumPy
• SciPy
• PyTorch>=1.7
• tqdm
• tensorboard_logger
• Matplotlib (optional, only for plotting)

How to generate expert labeled data?

We used LKH3 as expert, but feel free to use any expert if you want. We provide expert data at "expert_data/"

Quick Start

You can run quick start with:

python run.py --label_aug --consistancy_learning

More options

AM with IL (baseline without symmetricity)

python run.py

AM with symmetric data augmentation

python run.py --label_aug

AM with unsupervised symmetric consistancy learning

python run.py --consistancy_learning

AM with Semi-supervised symmetric learning

python run.py --label_aug --consistancy_learning

Change expert data path

--datapath expert_data/[DATA NAME].pkl

Change Problem Size

--graph_size 20

Important Remark

• Read code for AM first!! Attention Model
• Only code for TSP is implemented, but other problems can be easily implemented.
• I did not implement "eval.py". I just check performances via validation score at "run.py". However, it is recommented to modify "eval.py".

Reference

• If you want to make your paper based on this code, please consider to refer:

AM with Imitation Learning for Hardware Routing

@inproceedings{kim2021imitation,
  title={Imitation Learning for Simultaneous Escape Routing},
  author={Kim, Minsu and Park, Hyunwook and Son, Keeyoung and Kim, Seongguk and Kim, Haeyeon and Kim, Jihun and Song, Jinwook and Ku, Youngmin and Park, Jounggyu and Kim, Joungho},
  booktitle={2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)},
  pages={1--3},
  year={2021},
  organization={IEEE}
}

Sym-NCO

@article{kim2022sym,
  title={Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization},
  author={Kim, Minsu and Park, Junyoung and Park, Jinkyoo},
  journal={arXiv preprint arXiv:2205.13209},
  year={2022}
}

AM-IL with placement symmetricity for multi-task hardware placement

@inproceedings{kimcollaborative,
  title={Collaborative symmetricity exploitation for offline learning of hardware design solver},
  author={Kim, Haeyeon and Kim, Minsu and Kim, Joungho and Park, Jinkyoo},
  booktitle={3rd Offline RL Workshop: Offline RL as a''Launchpad''}
}