A Deep Reinforcement Learning Heuristic for SAT based on Antagonist Graph Neural Networks

Author:

Fournier Thomas1,Lallouet Arnaud1,Cropsal Télio1,Glorian Gaël1,Papadopoulos Alexandre1,Petitet Antoine1,Perez Guillaume1,Sekar Suruthy1,Suijlen Wijnand1

Affiliation:

1. Huawei Technologies Ltd,CSI Paris / TCS Lab,Boulogne-Billancourt,France

Publisher

IEEE

Reference20 articles.

1. Learning variable ordering heuristics for solving constraint satisfaction problems;song;CoRR,2019

2. From gameplay to symbolic reasoning: Learning SAT solver heuristics in the style of alpha(go) zero;wang;CoRR,2018

3. Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

4. Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver?;kurin;Advances in Neural Information Processing Systems 33 Annual Conference on Neural Information Processing Systems 2020 NeurIPS 2020,2020

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