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===DeepChess===
In 2016, [[Omid Eli David|Omid E. David]], [[Nathan S. Netanyahu]], and [[Lior Wolf]] introduced [[DeepChess]] obtaining a grandmaster-level chess playing performance using a learning method incorporating two deep neural networks, which are trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given [[Chess Position|chess position]], and the supervised training learns to compare two chess positions to select the more favorable one. In order to use DeepChess inside a chess program, a novel version of [[Alpha-Beta|alpha-beta]] is used that does not require [[Bound|bounds]] but positions α<span style="font-size: 80%;vertical-align: sub;">pos</span> and β<span style="font-size: 80%;vertical-align: sub;">pos</span> <ref>[[Omid Eli David|Omid E. David]], [[Nathan S. Netanyahu]], [[Lior Wolf]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-319-44781-0_11 DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf pdf preprint]</ref>.
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===Alpha Zero===
* [[Peter H. Jin]] , [[Kurt Keutzer]] ('''2016'''). ''Convolutional Monte Carlo Rollouts for Computer Go''. [[CG 2016]]
* [[Hung Guei]], [[Tinghan Wei]], [[Jin-Bo Huang]], [[I-Chen Wu]] ('''2016'''). ''An Empirical Study on Applying Deep Reinforcement Learning to the Game 2048''. [[CG 2016]]
* [[Omid Eli David|Omid E. David]], [[Nathan S. Netanyahu]], [[Lior Wolf]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-319-44781-0_11 DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf pdf preprint] » [[DeepChess]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61748 DeepChess: Another deep-learning based chess program] by [[Matthew Lai]], [[CCC]], October 17, 2016</ref> <ref>[http://icann2016.org/index.php/conference-programme/recipients-of-the-best-paper-awards/ ICANN 2016 | Recipients of the best paper awards]</ref>* [[Dror Sholomon]], [[Omid Eli David|Omid E. David]], [[Nathan S. Netanyahu]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007/978-3-319-44781-0_21 DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] <ref>[https://en.wikipedia.org/wiki/Jigsaw_puzzle Jigsaw puzzle from Wikipedia]</ref>
* [[Ian Goodfellow]], [[Yoshua Bengio]], [[Aaron Courville]] ('''2016'''). ''[http://www.deeplearningbook.org/ Deep Learning]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
