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An extensive Reinforcement Learning (RL) for Combinatorial Optimization (CO) benchmark. Our goal is to provide a unified framework for RL-based CO algorithms, and to facilitate reproducible research in this field, decoupling the science from the engineering.

RL4CO is built upon:

• TorchRL: official PyTorch framework for RL algorithms and vectorized environments on GPUs
• TensorDict: a library to easily handle heterogeneous data such as states, actions and rewards
• PyTorch Lightning: a lightweight PyTorch wrapper for high-performance AI research
• Hydra: a framework for elegantly configuring complex applications

We offer flexible and efficient implementations of the following policies:

• Constructive: learn to construct a solution from scratch
  • Autoregressive (AR): construct solutions one step at a time via a decoder
  • NonAutoregressive (NAR): learn to predict a heuristic, such as a heatmap, to then construct a solution
• Improvement: learn to improve a pre-existing solution

We provide several utilities and modularization. For example, we modularize reusable components such as environment embeddings that can easily be swapped to solve new problems.

Getting started

RL4CO is now available for installation on pip!

pip install rl4co

To get started, we recommend checking out our quickstart notebook or the minimalistic example below.

Install from source

This command installs the bleeding edge main version, useful for staying up-to-date with the latest developments - for instance, if a bug has been fixed since the last official release but a new release hasn’t been rolled out yet:

pip install -U git+https://github.com/ai4co/rl4co.git

Local install and development

If you want to develop RL4CO we recommend you to install it locally with pip in editable mode:

git clone https://github.com/ai4co/rl4co && cd rl4co
pip install -e .

We recommend using a virtual environment such as conda to install rl4co locally.

Usage

Train model with default configuration (AM on TSP environment):

python run.py

Tip

You may check out this notebook to get started with Hydra!

Change experiment settings

Train model with chosen experiment configuration from configs/experiment/

python run.py experiment=routing/am env=tsp env.num_loc=50 model.optimizer_kwargs.lr=2e-4

Here you may change the environment, e.g. with env=cvrp by command line or by modifying the corresponding experiment e.g. configs/experiment/routing/am.yaml.

Disable logging

python run.py experiment=routing/am logger=none '~callbacks.learning_rate_monitor'

Note that ~ is used to disable a callback that would need a logger.

Create a sweep over hyperparameters (-m for multirun)

python run.py -m experiment=routing/am  model.optimizer.lr=1e-3,1e-4,1e-5

Minimalistic Example

Here is a minimalistic example training the Attention Model with greedy rollout baseline on TSP in less than 30 lines of code:

from rl4co.envs.routing import TSPEnv, TSPGenerator
from rl4co.models import AttentionModelPolicy, POMO
from rl4co.utils import RL4COTrainer

# Instantiate generator and environment
generator = TSPGenerator(num_loc=50, loc_distribution="uniform")
env = TSPEnv(generator)

# Create policy and RL model
policy = AttentionModelPolicy(env_name=env.name, num_encoder_layers=6)
model = POMO(env, policy, batch_size=64, optimizer_kwargs={"lr": 1e-4})

# Instantiate Trainer and fit
trainer = RL4COTrainer(max_epochs=10, accelerator="gpu", precision="16-mixed")
trainer.fit(model)

Other examples can be found on our documentation!

Testing

Run tests with pytest from the root directory:

pytest tests

Known Bugs

Bugs installing PyTorch Geometric (PyG)

Installing PyG via Conda seems to update Torch itself. We have found that this update introduces some bugs with torchrl. At this moment, we recommend installing PyG with Pip:

pip install torch_geometric

Contributing

Have a suggestion, request, or found a bug? Feel free to open an issue or submit a pull request. If you would like to contribute, please check out our contribution guidelines here. We welcome and look forward to all contributions to RL4CO!

We are also on Slack if you have any questions or would like to discuss RL4CO with us. We are open to collaborations and would love to hear from you 🚀

Contributors

Citation

If you find RL4CO valuable for your research or applied projects:

@article{berto2024rl4co,
    title={{RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark}},
    author={Federico Berto and Chuanbo Hua and Junyoung Park and Laurin Luttmann and Yining Ma and Fanchen Bu and Jiarui Wang and Haoran Ye and Minsu Kim and Sanghyeok Choi and Nayeli Gast Zepeda and Andr\'e Hottung and Jianan Zhou and Jieyi Bi and Yu Hu and Fei Liu and Hyeonah Kim and Jiwoo Son and Haeyeon Kim and Davide Angioni and Wouter Kool and Zhiguang Cao and Jie Zhang and Kijung Shin and Cathy Wu and Sungsoo Ahn and Guojie Song and Changhyun Kwon and Lin Xie and Jinkyoo Park},
    year={2024},
    journal={arXiv preprint arXiv:2306.17100},
    note={\url{https://github.com/ai4co/rl4co}}
}

Note that a previous version of RL4CO has been accepted as an oral presentation at the NeurIPS 2023 GLFrontiers Workshop. Since then, the library has greatly evolved and improved!

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Join us

We invite you to join our AI4CO community, an open research group in Artificial Intelligence (AI) for Combinatorial Optimization (CO)!