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Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
Combinatorial optimization has found applications in numerous fields, fr...
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Improving the Asymmetric TSP by Considering Graph Structure
Recent works on cost based relaxations have improved Constraint Programm...
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Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks
While there are optimal TSP solvers as well as recent learning-based app...
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Machine learning for constraint solver design -- A case study for the alldifferent constraint
Constraint solvers are complex pieces of software which require many des...
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Using machine learning to make constraint solver implementation decisions
Programs to solve so-called constraint problems are complex pieces of so...
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A General Large Neighborhood Search Framework for Solving Integer Programs
This paper studies how to design abstractions of large-scale combinatori...
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Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem
The job-shop scheduling is one of the most studied optimization problems...
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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. The last decade has shown an increasing interest in the design of machine learning-based heuristics to solve combinatorial optimization problems. The goal is to leverage knowledge from historical data to solve similar new instances of a problem. Used alone, such heuristics are only able to provide approximate solutions efficiently, but cannot prove optimality nor bounds on their solution. Recent works have shown that reinforcement learning can be successfully used for driving the search phase of constraint programming (CP) solvers. However, it has also been shown that this hybridization is challenging to build, as standard CP frameworks do not natively include machine learning mechanisms, leading to some sources of inefficiencies. 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. Support for modeling the learning component is also provided. We illustrate the modeling and solution performance of this new solver on two problems. Although not yet competitive with industrial solvers, SeaPearl aims to provide a flexible and open-source framework in order to facilitate future research in the hybridization of constraint programming and machine learning.
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