The design of efficient and generic algorithms for solving combinatorial optimization problems has been an active field of research for many years. Standard exact solving approaches are based on a clever and complete enumeration of the solution set. A critical and non-trivial design choice with such methods is the branching strategy, directing how the search is performed. This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning.
2021: Félix Chalumeau, Ilan Coulon, Quentin Cappart, Louis-Martin Rousseau
https://arxiv.org/pdf/2102.09193v2.pdf
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