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We propose deep reinforcement learning as a model-free method for exploring the landscape of string vacua.
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M. R. Douglas and W. Taylor, The Landscape of intersecting brane models , JHEP 01
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W. Taylor and Y.-N. Wang, The F-theory geometry with most flux vacua , JHEP 12
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D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez et al., Mastering the game of go without human knowledge , Nature 550
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M. Birck, U. Corrêa, P. Ballester, V. Andersson and R. Araujo, Multi-task reinforcement learning: An hybrid a3c domain approach , 01, 2017
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W. Taylor and Y.-N. Wang, Scanning the skeleton of the 4D F-theory landscape , JHEP 01
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Y.-N. Wang and Z. Zhang, Learning non-Higgsable gauge groups in 4D F-theory , JHEP 08
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2015
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V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare et al., Human-level control through deep reinforcement learning , Nature 518
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S. Tokui, K. Oono, S. Hido and J. Clayton, Chainer: a next-generation open source framework for deep learning , in Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS) , 2015, http://learningsys.org/papers/LearningSys_2015_paper_33.pdf
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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche et al., Mastering the game of go with deep neural networks and tree search , Nature 529
2016
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2016
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2016
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2016
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K. Bull, Y.-H. He, V. Jejjala and C. Mishra, Machine Learning CICY Threefolds , Phys. Lett. B785
2018
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2018
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2018
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M. Bukov, A. G. R. Day, D. Sels, P. Weinberg, A. Polkovnikov and P. Mehta, Reinforcement learning in different phases of quantum control , Phys. Rev. X 8
2018
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2018
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2019
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2019
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