Thursday Nov 03, 2022
SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
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
Version: 20240320
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