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The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.
An analysis of alpha-beta pruning
Knuth, D. E. and Moore, R. W · 1975
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The rating of chess players, past and present
Elo, A. E · 1978
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Deep blue
Campbell, M., Hoane Jr, A. J., and Hsu, F.-h · 2002
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
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Parallel monte-carlo tree search
Chaslot, G. M.-B., Winands, M. H., and van Den Herik, H. J · 2008
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Fuego: an open-source framework for board games and go engine based on monte carlo tree search
Enzenberger, M., Muller, M., Arneson, B., and Segal, R · 2010
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Multi-armed bandits with episode context
Rosin, C. D · 2011
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A survey of monte carlo tree search methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S · 2012
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http://tromp.github.io/go.html , 2014
Tromp, J · 2014
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Training deep convolutional neural networks to play go
Clark, C. and Storkey, A · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Move evaluation in go using deep convolutional neural networks
Maddison, C. J., Huang, A., Sutskever, I., and Silver, D · 2015
Cited alongside, same era.
Better computer go player with neural network and long-term prediction
Tian, Y. and Zhu, Y · 2015
Cited alongside, same era.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
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ELF: an extensive, lightweight and flexible research platform for real-time strategy games
Tian, Y., Gong, Q., Shang, W., Wu, Y., and Zitnick, C. L · 2017
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Balduzzi, D., Tuyls, K., Pérolat, J., and Graepel, T · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., and Hassabis, D · 2018
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Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Cited alongside, same era.
Dawnbench: An end-to-end deep learning benchmark and competition
Coleman, C., Narayanan, D., Kang, D., Zhao, T., Zhang, J., Nardi, L., Bailis, P., Olukotun, K., Ré, C., and Zaharia, M · 2017
Cited alongside, same era.
Demystifying alphago zero as alphago gan
Dong, X., Wu, J., and Zhou, L · 2017
Cited alongside, same era.
Leelazero
Pascutto, G.-C · 2017
Cited alongside, same era.
From gameplay to symbolic reasoning: Learning sat solver heuristics in the style of alpha (go) zero
Wang, F. and Rompf, T · 2018
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Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Wang, L., Zhao, Y., and Jinnai, Y · 2018
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Phoenixgo
Zeng, Q., Zhang, J., Zeng, Z., Li, Y., Chen, M., and Liu, S · 2018
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Understanding & generalizing alphago zero, 2019
Addanki, R., Alizadeh, M., Venkatakrishnan, S. B., Shah, D., Xie, Q., and Xu, Z · 2019
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