  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.
