Difference between revisions of "Reinforcement Learning"

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* [[Robert Levinson]], [[Ryan Weber]] ('''2001'''). ''Chess Neighborhoods, Function Combinations and Reinforcements Learning''. In Computers and Games (eds. [[Tony Marsland]] and I. Frank). [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science],. Springer,. [http://users.soe.ucsc.edu/~levinson/Papers/CNFCRL.pdf pdf]
 
* [[Robert Levinson]], [[Ryan Weber]] ('''2001'''). ''Chess Neighborhoods, Function Combinations and Reinforcements Learning''. In Computers and Games (eds. [[Tony Marsland]] and I. Frank). [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science],. Springer,. [http://users.soe.ucsc.edu/~levinson/Papers/CNFCRL.pdf pdf]
 
* [[Marco Block-Berlitz]] ('''2003'''). ''Reinforcement Learning in der Schachprogrammierung''. Studienarbeit, Freie Universität Berlin, Dozent: [[Raúl Rojas|Prof. Dr. Raúl Rojas]], [http://page.mi.fu-berlin.de/block/Skripte/Reinforcement.pdf pdf] (German)
 
* [[Marco Block-Berlitz]] ('''2003'''). ''Reinforcement Learning in der Schachprogrammierung''. Studienarbeit, Freie Universität Berlin, Dozent: [[Raúl Rojas|Prof. Dr. Raúl Rojas]], [http://page.mi.fu-berlin.de/block/Skripte/Reinforcement.pdf pdf] (German)
* [[Henk Mannen]] ('''2003'''). ''Learning to play chess using reinforcement learning with database games''. Master’s thesis, [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University]
+
* [[Henk Mannen]] ('''2003'''). ''Learning to play chess using reinforcement learning with database games''. Master’s thesis, Cognitive Artificial Intelligence, [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.109.810&rep=rep1&type=pdf pdf]
 
* [[Joelle Pineau]], [[Geoffrey Gordon]], [[Sebastian Thrun]] ('''2003'''). ''Point-based value iteration: An anytime algorithm for POMDPs''. [[Conferences#IJCAI2003|IJCAI]], [http://www.fore.robot.cc/papers/Pineau03a.pdf pdf]
 
* [[Joelle Pineau]], [[Geoffrey Gordon]], [[Sebastian Thrun]] ('''2003'''). ''Point-based value iteration: An anytime algorithm for POMDPs''. [[Conferences#IJCAI2003|IJCAI]], [http://www.fore.robot.cc/papers/Pineau03a.pdf pdf]
 
* [https://dblp.uni-trier.de/pers/hd/k/Kerr:Amy_J= Amy J. Kerr], [[Todd W. Neller]], [https://dblp.uni-trier.de/pers/hd/p/Pilla:Christopher_J=_La Christopher J. La Pilla] , [https://dblp.uni-trier.de/pers/hd/s/Schompert:Michael_D= Michael D. Schompert] ('''2002'''). ''[https://www.semanticscholar.org/paper/Java-Resources-for-Teaching-Reinforcement-Learning-Kerr-Neller/3d84018eb8b8668c13d1d4f6efca4442af2915b4 Java Resources for Teaching Reinforcement Learning]''. [https://dblp.uni-trier.de/db/conf/pdpta/pdpta2003-3.html PDPTA 2003]
 
* [https://dblp.uni-trier.de/pers/hd/k/Kerr:Amy_J= Amy J. Kerr], [[Todd W. Neller]], [https://dblp.uni-trier.de/pers/hd/p/Pilla:Christopher_J=_La Christopher J. La Pilla] , [https://dblp.uni-trier.de/pers/hd/s/Schompert:Michael_D= Michael D. Schompert] ('''2002'''). ''[https://www.semanticscholar.org/paper/Java-Resources-for-Teaching-Reinforcement-Learning-Kerr-Neller/3d84018eb8b8668c13d1d4f6efca4442af2915b4 Java Resources for Teaching Reinforcement Learning]''. [https://dblp.uni-trier.de/db/conf/pdpta/pdpta2003-3.html PDPTA 2003]
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* [[David Silver]] ('''2009'''). ''Reinforcement Learning and Simulation-Based Search''. Ph.D. thesis, [[University of Alberta]]. [http://webdocs.cs.ualberta.ca/~silver/David_Silver/Publications_files/thesis.pdf pdf]
 
* [[David Silver]] ('''2009'''). ''Reinforcement Learning and Simulation-Based Search''. Ph.D. thesis, [[University of Alberta]]. [http://webdocs.cs.ualberta.ca/~silver/David_Silver/Publications_files/thesis.pdf pdf]
 
* [[Marcin Szubert]] ('''2009'''). ''Coevolutionary Reinforcement Learning and its Application to Othello''. M.Sc. thesis, [https://en.wikipedia.org/wiki/Pozna%C5%84_University_of_Technology Poznań University of Technology], supervisor [[Krzysztof Krawiec]], [https://mszubert.github.io/papers/Szubert_2009_MSC.pdf pdf]
 
* [[Marcin Szubert]] ('''2009'''). ''Coevolutionary Reinforcement Learning and its Application to Othello''. M.Sc. thesis, [https://en.wikipedia.org/wiki/Pozna%C5%84_University_of_Technology Poznań University of Technology], supervisor [[Krzysztof Krawiec]], [https://mszubert.github.io/papers/Szubert_2009_MSC.pdf pdf]
 +
* [[Joelle Pineau]], [[Geoffrey Gordon]], [[Sebastian Thrun]] ('''2006, 2011'''). ''Anytime Point-Based Approximations for Large POMDPs''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 27, [https://arxiv.org/abs/1110.0027 arXiv:1110.0027]
 
==2010 ...==
 
==2010 ...==
 
* [[Joel Veness]], [[Kee Siong Ng]], [[Marcus Hutter]], [[David Silver]] ('''2010'''). ''Reinforcement Learning via AIXI Approximation''. Association for the Advancement of Artificial Intelligence (AAAI), [http://jveness.info/publications/veness_rl_via_aixi_approx.pdf pdf]
 
* [[Joel Veness]], [[Kee Siong Ng]], [[Marcus Hutter]], [[David Silver]] ('''2010'''). ''Reinforcement Learning via AIXI Approximation''. Association for the Advancement of Artificial Intelligence (AAAI), [http://jveness.info/publications/veness_rl_via_aixi_approx.pdf pdf]
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'''2018'''
 
'''2018'''
 
* [[Hui Wang]], [[Michael Emmerich]], [[Aske Plaat]] ('''2018'''). ''Monte Carlo Q-learning for General Game Playing''. [https://arxiv.org/abs/1802.05944 arXiv:1802.05944] » [[Monte-Carlo Tree Search|MCTS]], [[General Game Playing]]
 
* [[Hui Wang]], [[Michael Emmerich]], [[Aske Plaat]] ('''2018'''). ''Monte Carlo Q-learning for General Game Playing''. [https://arxiv.org/abs/1802.05944 arXiv:1802.05944] » [[Monte-Carlo Tree Search|MCTS]], [[General Game Playing]]
 +
* [[Vinícius Flores Zambaldi]], [[David Raposo]], [[Adam Santoro]], [[Victor Bapst]], [[Yujia Li]], [[Igor Babuschkin]], [[Karl Tuyls]], [[David P. Reichert]], [[Timothy Lillicrap]], [[Edward Lockhart]], [[Murray Shanahan]], [[Victoria Langston]], [[Razvan Pascanu]], [[Matthew Botvinick]], [[Oriol Vinyals]], [[Peter W. Battaglia]] ('''2018'''). ''Relational Deep Reinforcement Learning''. [https://arxiv.org/abs/1806.01830 arXiv:1806.01830]
 
* [[Hui Wang]], [[Michael Emmerich]], [[Aske Plaat]] ('''2018'''). ''Assessing the Potential of Classical Q-learning in General Game Playing''. [https://arxiv.org/abs/1810.06078 arXiv:1810.06078]
 
* [[Hui Wang]], [[Michael Emmerich]], [[Aske Plaat]] ('''2018'''). ''Assessing the Potential of Classical Q-learning in General Game Playing''. [https://arxiv.org/abs/1810.06078 arXiv:1810.06078]
 
* [https://scholar.google.com/citations?user=n12uNYcAAAAJ&hl=en Vincent Francois-Lavet], [https://scholar.google.com/citations?user=dy_JBs0AAAAJ&hl=en Peter Henderson], [https://scholar.google.ca/citations?user=2_4Rs44AAAAJ&hl=en Riashat Islam], [https://scholar.google.com/citations?user=uyYPun0AAAAJ&hl=en Marc G. Bellemare], [[Joelle Pineau]] ('''2018'''). ''An Introduction to Deep Reinforcement Learning''. [https://arxiv.org/abs/1811.12560 arXiv:1811.12560]
 
* [https://scholar.google.com/citations?user=n12uNYcAAAAJ&hl=en Vincent Francois-Lavet], [https://scholar.google.com/citations?user=dy_JBs0AAAAJ&hl=en Peter Henderson], [https://scholar.google.ca/citations?user=2_4Rs44AAAAJ&hl=en Riashat Islam], [https://scholar.google.com/citations?user=uyYPun0AAAAJ&hl=en Marc G. Bellemare], [[Joelle Pineau]] ('''2018'''). ''An Introduction to Deep Reinforcement Learning''. [https://arxiv.org/abs/1811.12560 arXiv:1811.12560]
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* [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'''). ''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]
 
* [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>
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* [[Marc Lanctot]], [[Edward Lockhart]], [[Jean-Baptiste Lespiau]], [[Vinícius Flores 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>
 
* [[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>
 
* [[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>
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* [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]
 
* [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]
 
* [[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>
+
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[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]
 
* [[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]
  
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: {{#evu:https://www.youtube.com/watch?v=O_1Z63EDMvQ|alignment=left|valignment=top}}
 
: {{#evu:https://www.youtube.com/watch?v=O_1Z63EDMvQ|alignment=left|valignment=top}}
 
==OpenSpiel==
 
==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 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]], [[Vinícius Flores 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 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/algorithms/alpha_zero open_spiel/open_spiel/algorithms/alpha_zero at master · deepmind/open_spiel · GitHub]

Latest revision as of 09:26, 17 April 2021

Home * Learning * Reinforcement Learning

Reinforcement Learning,
a learning paradigm inspired by behaviourist psychology and classical conditioning - learning by trial and error, interacting with an environment to map situations to actions in such a way that some notion of cumulative reward is maximized. In computer games, reinforcement learning deals with adjusting feature weights based on results or their subsequent predictions during self play.

Reinforcement learning is indebted to the idea of Markov decision processes (MDPs) in the field of optimal control utilizing dynamic programming techniques. The crucial exploitation and exploration tradeoff in multi-armed bandit problems as also considered in UCT of Monte-Carlo Tree Search - between "exploitation" of the machine that has the highest expected payoff and "exploration" to get more information about the expected payoffs of the other machines - is also faced in reinforcement learning.

Q-Learning

Q-Learning, introduced by Chris Watkins in 1989, is a simple way for agents to learn how to act optimally in controlled Markovian domains [2]. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely [3]. Q-learning has been successfully applied to deep learning by a Google DeepMind team in playing some Atari 2600 games as published in Nature, 2015, dubbed deep reinforcement learning or deep Q-networks [4], soon followed by the spectacular AlphaGo and AlphaZero breakthroughs.

Temporal Difference Learning

see main page Temporal Difference Learning

Q-learning at its simplest uses tables to store data. This very quickly loses viability with increasing sizes of state/action space of the system it is monitoring/controlling. One solution to this problem is to use an (adapted) artificial neural network as a function approximator, as demonstrated by Gerald Tesauro in his Backgammon playing temporal difference learning research [5] [6].

Temporal Difference Learning is a prediction method primarily used for reinforcement learning. In the domain of computer games and computer chess, TD learning is applied through self play, subsequently predicting the probability of winning a game during the sequence of moves from the initial position until the end, to adjust weights for a more reliable prediction.

See also

UCT

Selected Publications

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2011

2012

István Szita (2012). Reinforcement Learning in Games. Chapter 17

2013

2014

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2016

2017

2018

2019

2020 ...

Postings

1995 ...

Re: Parameter Tuning by Don Beal, CCC, October 02, 1998

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2010 ...

2020 ...

External Links

Reinforcement Learning

MDP

Q-Learning

Courses

  1. Lecture 1: Introduction to Reinforcement Learning
  2. Lecture 2: Markov Decision Process
  3. Lecture 3: Planning by Dynamic Programming
  4. Lecture 4: Model-Free Prediction
  5. Lecture 5: Model Free Control
  6. Lecture 6: Value Function Approximation
  7. Lecture 7: Policy Gradient Methods
  8. Lecture 8: Integrating Learning and Planning
  9. Lecture 9: Exploration and Exploitation
  10. Lecture 10: Classic Games

OpenSpiel

References

  1. Example of a simple Markov decision processes with three states (green circles) and two actions (orange circles), with two rewards (orange arrows), image by waldoalvarez CC BY-SA 4.0, Wikimedia Commons
  2. Q-learning from Wikipedia
  3. Chris Watkins, Peter Dayan (1992). Q-learning. Machine Learning, Vol. 8, No. 2
  4. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis (2015). Human-level control through deep reinforcement learning. Nature, Vol. 518
  5. Q-learning from Wikipedia
  6. Gerald Tesauro (1995). Temporal Difference Learning and TD-Gammon. Communications of the ACM, Vol. 38, No. 3
  7. University of Bristol - Department of Computer Science - Technical Reports
  8. Ms. Pac-Man from Wikipedia
  9. Demystifying Deep Reinforcement Learning by Tambet Matiisen, Nervana, December 22, 2015
  10. Patent US20150100530 - Methods and apparatus for reinforcement learning - Google Patents
  11. DeepChess: Another deep-learning based chess program by Matthew Lai, CCC, October 17, 2016
  12. ICANN 2016 | Recipients of the best paper awards
  13. AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017
  14. AlphaZero: Shedding new light on the grand games of chess, shogi and Go by David Silver, Thomas Hubert, Julian Schrittwieser and Demis Hassabis, DeepMind, December 03, 2018
  15. open_spiel/contributing.md at master · deepmind/open_spiel · GitHub
  16. New DeepMind paper by GregNeto, CCC, November 21, 2019
  17. e: Board adaptive / tuning evaluation function - no NN/AI by Tony P., CCC, January 15, 2020
  18. MuZero: Mastering Go, chess, shogi and Atari without rules
  19. Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinícius Flores 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. arXiv:1908.09453

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