Papers Read on AI

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November 3, 2022  

SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning

November 3, 2022

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: FĂ©lix Chalumeau, Ilan Coulon, Quentin Cappart, Louis-Martin Rousseau

https://arxiv.org/pdf/2102.09193v2.pdf