We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes. Its interface mimics the popular OpenAI Gym library and is both extensible and intuitive to use. We aim at making this library a standardized platform that will lower the bar of entry and accelerate innovation in the field. Documentation and code can be found at https://www.ecole.ai.
@article{arxiv.2011.06069,
title = {Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers},
author = {Antoine Prouvost and Justin Dumouchelle and Lara Scavuzzo and Maxime Gasse and Didier Chételat and Andrea Lodi},
journal= {arXiv preprint arXiv:2011.06069},
year = {2020}
}
Comments
Published at the 1st Workshop on Learning Meets Combinatorial Algorithms @ NeurIPS 2020, Vancouver, Canada