Ecole is looking for a new home. It is not being actively developed, only critical issues will be investigated.
Ecole (pronounced [ekɔl]) stands for Extensible Combinatorial Optimization Learning Environments and aims to expose a number of control problems arising in combinatorial optimization solvers as Markov Decision Processes (i.e., Reinforcement Learning environments). Rather than trying to predict solutions to combinatorial optimization problems directly, the philosophy behind Ecole is to work in cooperation with a state-of-the-art Mixed Integer Linear Programming solver that acts as a controllable algorithm.
The underlying solver used is SCIP, and the user facing API is meant to mimic the OpenAI Gym API (as much as possible).
import ecole
env = ecole.environment.Branching(
reward_function=-1.5 * ecole.reward.LpIterations() ** 2,
observation_function=ecole.observation.NodeBipartite(),
)
instances = ecole.instance.SetCoverGenerator()
for _ in range(10):
obs, action_set, reward_offset, done, info = env.reset(next(instances))
while not done:
obs, action_set, reward, done, info = env.step(action_set[0])Consult the user Documentation for tutorials, examples, and library reference.
Head to Github Discussions for interaction with the community: give and recieve help, discuss intresting envirnoment, rewards function, and instances generators.
conda install -c conda-forge ecoleAll dependencies are resolved by conda, no compiler is required.
Currently unavailable.
- Building from source requires:
- A C++17 compiler,
- A SCIP installation.
pip install ecoleCheckout the installation instructions in the documentation for more installation options.
- OR-Gym is a gym-like library providing gym-like environments to produce feasible solutions directly, without the need for an MILP solver;
- MIPLearn for learning to configure solvers.
If you use Ecole in a scientific publication, please cite the Ecole publication
@inproceedings{
prouvost2020ecole,
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 Ch{\'e}telat and Andrea Lodi},
booktitle={Learning Meets Combinatorial Algorithms at NeurIPS2020},
year={2020},
url={https://openreview.net/forum?id=IVc9hqgibyB}
}