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arXiv · 2104.02828

Ecole: A Library for Learning Inside MILP Solvers

Antoine Prouvost·Justin Dumouchelle·Maxime Gasse·Didier Chételat·Andrea Lodi
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Abstract

In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers. It exposes sequential decision making that must be performed in the process of solving as Markov decision processes. This means that, rather than trying to predict solutions to combinatorial optimization problems directly, Ecole allows machine learning to work in cooperation with a state-of-the-art a mixed-integer linear programming solver that acts as a controllable algorithm. Ecole provides a collection of computationally efficient, ready to use learning environments, which are also easy to extend to define novel training tasks. Documentation and code can be found at https://www.ecole.ai.

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Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi. 2021-04-06. Ecole: A Library for Learning Inside MILP Solvers. https://arxiv.org/abs/2104.02828

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