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Reinforcement Learning

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=See also=
* [[AlphaZero]]
* [[Automated Tuning]]
* [[Deep Learning]]
* [[Dynamic Programming]]
* [[Marc Lanctot]], [[Vinícius Flores Zambaldi]], [[Audrunas Gruslys]], [[Angeliki Lazaridou]], [[Karl Tuyls]], [[Julien Pérolat]], [[David Silver]], [[Thore Graepel]] ('''2017'''). ''A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning''. [https://arxiv.org/abs/1711.00832 arXiv:1711.00832]
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|Aja Huang]], [[Arthur Guez]], [[Thomas Hubert]], [[Lucas Baker]], [[Matthew Lai]], [[Adrian Bolton]], [[Yutian Chen]], [[Timothy Lillicrap]], [[Fan Hui]], [[Laurent Sifre]], [[George van den Driessche]], [[Thore Graepel]], [[Demis Hassabis]] ('''2017'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550, [https://www.gwern.net/docs/rl/2017-silver.pdf pdf] <ref>[https://deepmind.com/blog/alphago-zero-learning-scratch/ AlphaGo Zero: Learning from scratch] by [[Demis Hassabis]] and [[David Silver]], [[DeepMind]], October 18, 2017</ref>
* [http://www.peterhenderson.co/ Peter Henderson], [https://scholar.google.ca/citations?user=2_4Rs44AAAAJ&hl=en Riashat Islam], [[Philip Bachman]], [[Joelle Pineau]], [[Doina Precup]], [https://scholar.google.ca/citations?user=gFwEytkAAAAJ&hl=en David Meger] ('''2017'''). ''Deep Reinforcement Learning that Matters''. [https://arxiv.org/abs/1709.06560 arXiv:1709.06560] * [https://scholar.google.com/citations?user=tiE4g64AAAAJ&hl=en Maithra Raghu], [https://scholar.google.com/citations?user=ZZNxNAYAAAAJ&hl=en Alex Irpan], [[Mathematician#JAndreas|Jacob Andreas]], [[Mathematician#RKleinberg|Robert Kleinberg]], [[Quoc V. Le]], [[Jon Kleinberg]] ('''2017'''). ''Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?'' [https://arxiv.org/abs/1711.02301 arXiv:1711.02301] 
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
* [[Kei Takada]], [[Hiroyuki Iizuka]], [[Masahito Yamamoto]] ('''2017'''). ''Reinforcement Learning for Creating Evaluation Function Using Convolutional Neural Network in Hex''. [[TAAI 2017]] » [[Hex]], [[Neural Networks#Convolutional|CNN]]
'''2019'''
* [https://scholar.google.co.uk/citations?user=OAkRr-YAAAAJ&hl=en Sanjeevan Ahilan], [[Peter Dayan]] ('''2019'''). ''Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning''. [https://arxiv.org/abs/1901.08492 arXiv:1901.08492]
* [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [https://scholar.google.co.in/citations?user=nx4NlpsAAAAJ&hl=en Raghuram Bharadwaj Diddigi], [[Shalabh Bhatnagar]] ('''2019'''). ''Successive Over Relaxation Q-Learning''. [https://arxiv.org/abs/1903.03812 arXiv:1903.03812]
* [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [https://scholar.google.co.in/citations?user=nx4NlpsAAAAJ&hl=en Raghuram Bharadwaj Diddigi], [[Shalabh Bhatnagar]] ('''2019'''). ''Second Order Value Iteration in Reinforcement Learning''. [https://arxiv.org/abs/1905.03927 arXiv:1905.03927]
* [[Marc Lanctot]], [[Edward Lockhart]], [[Jean-Baptiste Lespiau]], [[Vinicius Zambaldi]], [[Satyaki Upadhyay]], [[Julien Pérolat]], [[Sriram Srinivasan]], [[Finbarr Timbers]], [[Karl Tuyls]], [[Shayegan Omidshafiei]], [[Daniel Hennes]], [[Dustin Morrill]], [[Paul Muller]], [[Timo Ewalds]], [[Ryan Faulkner]], [[János Kramár]], [[Bart De Vylder]], [[Brennan Saeta]], [[James Bradbury]], [[David Ding]], [[Sebastian Borgeaud]], [[Matthew Lai]], [[Julian Schrittwieser]], [[Thomas Anthony]], [[Edward Hughes]], [[Ivo Danihelka]], [[Jonah Ryan-Davis]] ('''2019'''). ''OpenSpiel: A Framework for Reinforcement Learning in Games''. [https://arxiv.org/abs/1908.09453 arXiv:1908.09453] <ref>[https://github.com/deepmind/open_spiel/blob/master/docs/contributing.md open_spiel/contributing.md at master · deepmind/open_spiel · GitHub]</ref>
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2019'''). ''Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model''. [https://arxiv.org/abs/1911.08265 arXiv:1911.08265] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72381 New DeepMind paper] by GregNeto, [[CCC]], November 21, 2019</ref>
* [[Mathematician#SrbhBose|Sourabh Bose]] ('''2019'''). ''[https://rc.library.uta.edu/uta-ir/handle/10106/28094 Learning Representations Using Reinforcement Learning]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Texas_at_Arlington University of Texas at Arlington], advisor [[Mathematician#MHuber|Manfred Huber]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=72810&start=6 e: Board adaptive / tuning evaluation function - no NN/AI] by Tony P., [[CCC]], January 15, 2020</ref>
* [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [https://scholar.google.co.in/citations?user=nx4NlpsAAAAJ&hl=en Raghuram Bharadwaj Diddigi], [[Shalabh Bhatnagar]] ('''2019'''). ''Successive Over Relaxation Q-Learning''. [https://arxiv.org/abs/1903.03812 arXiv:1903.03812]
* [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [https://scholar.google.co.in/citations?user=nx4NlpsAAAAJ&hl=en Raghuram Bharadwaj Diddigi], [[Shalabh Bhatnagar]] ('''2019'''). ''Second Order Value Iteration in Reinforcement Learning''. [https://arxiv.org/abs/1905.03927 arXiv:1905.03927]
==2020 ...==
* [[Hung Guei]], [[Ting-Han Wei]], [[I-Chen Wu]] ('''2020'''). ''2048-like games for teaching reinforcement learning''. [[ICGA Journal#42_1|ICGA Journal, Vol. 42, No. 1]]
* [https://dblp.org/pid/233/8144.html Indu John], [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [[Shalabh Bhatnagar]] ('''2020'''). ''Generalized Speedy Q-Learning''. [[IEEE#CSL|IEEE Control Systems Letters]], Vol. 4, No. 3, [https://arxiv.org/abs/1911.00397 arXiv:1911.00397]
* [[Takuya Hiraoka]], [https://dblp.org/pers/hd/i/Imagawa:Takahisa Takahisa Imagawa], [https://dblp.org/pers/hd/t/Tangkaratt:Voot Voot Tangkaratt], [https://dblp.org/pers/hd/o/Osa:Takayuki Takayuki Osa], [https://dblp.org/pers/hd/o/Onishi:Takashi Takashi Onishi], [https://dblp.org/pers/hd/t/Tsuruoka:Yoshimasa Yoshimasa Tsuruoka] ('''2020'''). ''Meta-Model-Based Meta-Policy Optimization''. [https://arxiv.org/abs/2006.02608 arXiv:2006.02608]
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhar]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref>
* [[Tristan Cazenave]], [[Yen-Chi Chen]], [[Guan-Wei Chen]], [[Shi-Yu Chen]], [[Xian-Dong Chiu]], [[Julien Dehos]], [[Maria Elsa]], [[Qucheng Gong]], [[Hengyuan Hu]], [[Vasil Khalidov]], [[Cheng-Ling Li]], [[Hsin-I Lin]], [[Yu-Jin Lin]], [[Xavier Martinet]], [[Vegard Mella]], [[Jeremy Rapin]], [[Baptiste Roziere]], [[Gabriel Synnaeve]], [[Fabien Teytaud]], [[Olivier Teytaud]], [[Shi-Cheng Ye]], [[Yi-Jun Ye]], [[Shi-Jim Yen]], [[Sergey Zagoruyko]] ('''2020'''). ''Polygames: Improved zero learning''. [[ICGA Journal#42_4|ICGA Journal, Vol. 42, No. 4]], [https://arxiv.org/abs/2001.09832 arXiv:2001.09832], [https://arxiv.org/abs/2001.09832 arXiv:2001.09832]
=Postings=
==1995 ...==
* [https://www.stmintz.com/ccc/index.php?id=28584 Parameter Tuning] by [[Jonathan Baxter]], [[CCC]], October 01, 1998 » [[KnightCap]]
: [https://www.stmintz.com/ccc/index.php?id=28819 Re: Parameter Tuning] by [[Don Beal]], [[CCC]], October 02, 1998
==2000 ...==
* [https://www.stmintz.com/ccc/index.php?id=117970 Pseudo-code for TD learning] by [[Daniel Homan]], [[CCC]], July 06, 2000 » [[Temporal Difference Learning]]
* [https://www.stmintz.com/ccc/index.php?id=147377 any good experiences with genetic algos or temporal difference learning?] by [[Rafael B. Andrist]], [[CCC]], January 01, 2001
* [https://www.stmintz.com/ccc/index.php?id=401974 Temporal Differences] by [[Peter Fendrich]], [[CCC]], December 21, 2004
==2010 ...==
* [http://talkchess.com/forum/viewtopic.php?t=56913 *First release* Giraffe, a new engine based on deep learning] by [[Matthew Lai]], [[CCC]], July 08, 2015 » [[Deep Learning]], [[Giraffe]]
* [http://www.nervanasys.com/demystifying-deep-reinforcement-learning/ Demystifying Deep Reinforcement Learning] by [http://www.nervanasys.com/author/tambet/ Tambet Matiisen], [http://www.nervanasys.com/ Nervana], December 22, 2015
* [http://www.talkchess.com/forum/viewtopic.php?t=65909 Google's AlphaGo team has been working on chess] by [[Peter Kappler]], [[CCC]], December 06, 2017 » [[AlphaZero]]
* [http://www.talkchess.com/forum/viewtopic.php?t=65990 Understanding the power of reinforcement learning] by [[Michael Sherwin]], [[CCC]], December 12, 2017
==2020 ...==
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=72810 Board adaptive / tuning evaluation function - no NN/AI] by Moritz Gedig, [[CCC]], January 14, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75411 Unsupervised reinforcement tuning from zero] by Madeleine Birchfield, [[CCC]], October 16, 2020 » [[Automated Tuning]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75606 Transhuman Chess with NN and RL...] by [[Srdja Matovic]], [[CCC]], October 30, 2020 » [[Neural Networks|NN]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76465 Reinforcement learning project] by [[Harm Geert Muller]], [[CCC]], January 31, 2021 » [[Texel's Tuning Method]]
=External Links=
* [http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/ Introduction to Reinforcement Learning] by [[Joelle Pineau]], [[McGill University]], 2016, [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=O_1Z63EDMvQ|alignment=left|valignment=top}}
==OpenSpiel==
* [https://github.com/deepmind/open_spiel GitHub - deepmind/open_spiel: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games] <ref>[[Marc Lanctot]], [[Edward Lockhart]], [[Jean-Baptiste Lespiau]], [[Vinicius Zambaldi]], [[Satyaki Upadhyay]], [[Julien Pérolat]], [[Sriram Srinivasan]], [[Finbarr Timbers]], [[Karl Tuyls]], [[Shayegan Omidshafiei]], [[Daniel Hennes]], [[Dustin Morrill]], [[Paul Muller]], [[Timo Ewalds]], [[Ryan Faulkner]], [[János Kramár]], [[Bart De Vylder]], [[Brennan Saeta]], [[James Bradbury]], [[David Ding]], [[Sebastian Borgeaud]], [[Matthew Lai]], [[Julian Schrittwieser]], [[Thomas Anthony]], [[Edward Hughes]], [[Ivo Danihelka]], [[Jonah Ryan-Davis]] ('''2019'''). ''OpenSpiel: A Framework for Reinforcement Learning in Games''. [https://arxiv.org/abs/1908.09453 arXiv:1908.09453]</ref>
** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/algorithms open_spiel/open_spiel/algorithms at master · deepmind/open_spiel · GitHub]
*** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/algorithms/alpha_zero open_spiel/open_spiel/algorithms/alpha_zero at master · deepmind/open_spiel · GitHub]
** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/games open_spiel/open_spiel/games at master · deepmind/open_spiel · GitHub]
*** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/games/chess open_spiel/open_spiel/games/chess at master · deepmind/open_spiel · GitHub]
=References=

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