Ecole: A Library for Learning Inside MILP Solvers
This provides a tool for researchers and practitioners in optimization and AI to more easily incorporate learning into solving complex combinatorial problems, though it is incremental as it builds on existing solver frameworks.
The authors tackled the integration of machine learning into combinatorial optimization solvers by developing Ecole, a library that exposes sequential decision-making in solvers as Markov decision processes, enabling cooperation with a state-of-the-art mixed-integer linear programming solver.
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.