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We present Graph-$Q$-SAT, a branching heuristic for a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation.
Fast graph representation learning with pytorch geometric
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Neural heuristics for SAT solving
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Restart strategy selection using machine learning techniques
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Look-ahead based SAT solvers
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O. Ohrimenko, P. J. Stuckey, and M. Codish · 2009
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Avatarsat: An auto-tuning boolean sat solver
R. Singh, J. P. Near, V. Ganesh, and M. Rinard · 2009
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Empirical study of the anatomy of modern sat solvers
H. Katebi, K. A. Sakallah, and J. P. M. Silva · 2011
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Satzilla: Portfolio-based algorithm selection for SAT
L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2011
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Perceptron learning of SAT
A. Flint and M. B. Blaschko · 2012
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Can machine learning learn a decision oracle for NP problems? A test on SAT
C. Grozea and M. Popescu · 2014
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Impact of community structure on SAT solver performance
Z. Newsham, V. Ganesh, S. Fischmeister, G. Audemard, and L. Simon · 2014
Rainbow: Combining improvements in deep reinforcement learning
M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. G. Azar, and D. Silver · 2018
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Graph convolutional reinforcement learning for multi-agent cooperation
J. Jiang, C. Dun, and Z. Lu · 2018
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Deep multi-agent reinforcement learning with relevance graphs
A. Malysheva, T. T. K. Sung, C. Sohn, D. Kudenko, and A. Shpilman · 2018
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Graph networks as learnable physics engines for inference and control
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. A. Riedmiller, R. Hadsell, and P. W. Battaglia · 2018
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From gameplay to symbolic reasoning: Learning SAT solver heuristics in the style of alpha(go) zero
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Understanding VSIDS branching heuristics in conflict-driven clause-learning SAT solvers
J. H. Liang, V. Ganesh, E. Zulkoski, A. Zaman, and K. Czarnecki · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Learning rate based branching heuristic for SAT solvers
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F. Wang and T. Rompf · 2018
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Nervenet: Learning structured policy with graph neural networks
T. Wang, R. Liao, J. Ba, and S. Fidler · 2018
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Learning to solve circuit-sat: An unsupervised differentiable approach
S. Amizadeh, S. Matusevych, and M. Weimer · 2019
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Structured agents for physical construction
V. Bapst, A. Sanchez-Gonzalez, C. Doersch, K. L. Stachenfeld, P. Kohli, P. W. Battaglia, and J. B. Hamrick · 2019
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Satlib: An online resource for research on sat
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Pytorch: An imperative style, high-performance deep learning library
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Guiding high-performance SAT solvers with unsat-core predictions
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Learning a SAT solver from single-bit supervision
D. Selsam, M. Lamm, B. Bünz, P. Liang, L. de Moura, and D. L. Dill · 2019
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Learning local search heuristics for boolean satisfiability
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Graph representations for higher-order logic and theorem proving
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