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Reinforcement Learning

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=Q-Learning=
Q-Learning, introduced by [[Chris Watkins]] in 1989, is a simple way for [https://en.wikipedia.org/wiki/Intelligent_agent agents] to learn how to act optimally in controlled Markovian domains <ref>[https://en.wikipedia.org/wiki/Q-learning Q-learning from Wikipedia]</ref>. 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 <ref>[[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2</ref>. Q-learning has been successfully applied to [[Deep Learning|deep learning]] by a [[Google]] [[DeepMind]] team in playing some [[Atari 8-bit|Atari 2600]] [https://en.wikipedia.org/wiki/List_of_Atari_2600_games games] as published in [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], 2015, dubbed ''deep reinforcement learning'' or ''deep Q-networks'' <ref>[[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Mathematician#AARusu|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'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518</ref>, soon followed by the spectacular [[AlphaGo]] and [[AlphaZero]] breakthroughs.
=Temporal Difference Learning=
=See also=
* [[AlphaZero]]
* [[Automated Tuning]]
* [[Deep Learning]]
* [[Dynamic Programming]]
* [[A. Harry Klopf]] ('''1972'''). ''Brain Function and Adaptive Systems - A Heterostatic Theory''. [https://en.wikipedia.org/wiki/Air_Force_Cambridge_Research_Laboratories Air Force Cambridge Research Laboratories], Special Reports, No. 133, [http://www.dtic.mil/dtic/tr/fulltext/u2/742259.pdf pdf]
* [[Mathematician#Holland|John H. Holland]] ('''1975'''). ''Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence''. [http://www.amazon.com/Adaptation-Natural-Artificial-Systems-Introductory/dp/0262581116 amazon.com]
* [[Ian H. Witten]] ('''1977'''). ''An Adaptive Optimal Controller for Discrete-Time Markov Environments''. [https://en.wikipedia.org/wiki/Information_and_Computation Information and Control], Vol. 34, No. 4, [https://core.ac.uk/download/pdf/82451748.pdf pdf]
==1980 ...==
* [[Richard Sutton]] ('''1984'''). ''[http://scholarworks.umass.edu/dissertations/AAI8410337/ Temporal Credit Assignment in Reinforcement Learning]''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/University_of_Massachusetts University of Massachusetts]
* [[Richard Sutton]], [[Andrew Barto]] ('''1990'''). ''Time Derivative Models of Pavlovian Reinforcement''. Learning and Computational Neuroscience: Foundations of Adaptive Networks: 497-537
* [[Jürgen Schmidhuber]] ('''1990'''). ''Reinforcement Learning in Markovian and Non-Markovian Environments''. [https://dblp.uni-trier.de/db/conf/nips/nips1990.html NIPS 1990], [ftp://ftp.idsia.ch/pub/juergen/nipsnonmarkov.pdf pdf]
* [[Peter Dayan]] ('''1991'''). ''[https://www.era.lib.ed.ac.uk/handle/1842/14754 Reinforcing Connectionism: Learning the Statistical Way]''. Ph.D. thesis, [[University of Edinburgh]]
* [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2
* [[Gerald Tesauro]] ('''1992'''). ''Temporal Difference Learning of Backgammon Strategy''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/ml1992.html#Tesauro92 ML 1992]
* [[Mathematician#LPKaelbling|Leslie Pack Kaelbling]], [[Michael L. Littman]], [[Mathematician#AWMoore|Andrew W. Moore]] ('''1996'''). ''[http://www.cs.washington.edu/research/jair/volume4/kaelbling96a-html/rl-survey.html Reinforcement Learning: A Survey]''. [http://www.jair.org/vol/vol4.html JAIR Vol. 4], [http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a.pdf pdf]
* [[Robert Levinson]] ('''1996'''). ''[http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.1996.tb00257.x/abstract General Game-Playing and Reinforcement Learning]''. [http://dblp.uni-trier.de/db/journals/ci/ci12.html#PellEL96 Computational Intelligence, Vol. 12], No. 1
* [[David E. Moriarty]], [[Risto Miikkulainen]] ('''1996'''). ''[https://link.springer.com/article/10.1023/A:1018004120707 Efficient Reinforcement Learning through Symbiotic Evolution]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 22
* [[Ronald Parr]], [[Stuart Russell]] ('''1997'''). ''Reinforcement Learning with Hierarchies of Machines.'' In Advances in Neural Information Processing Systems 10, MIT Press, [http://www.cs.berkeley.edu/~russell/papers/nips97-ham.ps.gz zipped ps]
* [[William Uther]], [[Manuela Veloso|Manuela M. Veloso]] ('''1997'''). ''Adversarial Reinforcement Learning''. [[Carnegie Mellon University]], [http://www.cse.unsw.edu.au/~willu/w/papers/Uther97a.ps ps]
* [[Andrew Ng]], [[Stuart Russell]] ('''2000'''). ''Algorithms for inverse reinforcement learning.'' In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, California: Morgan Kaufmann, [http://www.cs.berkeley.edu/~russell/papers/ml00-irl.pdf pdf]
* [http://www.cs.ou.edu/~hougen/ Dean F. Hougen], [http://www-users.cs.umn.edu/~gini/ Maria Gini], [[James R. Slagle]] ('''2000'''). ''[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.2633 An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control]''. ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
* [[Jonathan Baxter]], [[Mathematician#PBartlett|Peter Bartlett]] ('''2000'''). ''Reinforcement Learning on POMDPs via Direct Gradient Ascent''. [http://dblp.uni-trier.de/db/conf/icml/icml2000.html ICML 2000], [https://pdfs.semanticscholar.org/b874/98f0879d312c308889135203b17069aa0486.pdf pdf]
* [[Doina Precup]] ('''2000'''). ''Temporal Abstraction in Reinforcement Learning''. Ph.D. Dissertation, Department of Computer Science, [https://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst University of Massachusetts], [https://en.wikipedia.org/wiki/Amherst,_Massachusetts Amherst].
* [[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]
* [[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]
* [[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]
* [[Yngvi Björnsson]], Vignir Hafsteinsson, Ársæll Jóhannsson, Einar Jónsson ('''2004'''). ''Efficient Use of Reinforcement Learning in a Computer Game''. In Computer Games: Artificial Intellignece, Design and Education (CGAIDE'04), pp. 379–383, 2004. [http://www.ru.is/faculty/yngvi/pdf/BjornssonHJJ04.pdf pdf]
* [http://imranontech.com/ Imran Ghory] ('''2004'''). ''Reinforcement learning in board games''. CSTR-04-004, [http://www.cs.bris.ac.uk/ Department of Computer Science], [https://en.wikipedia.org/wiki/University_of_Bristol University of Bristol]. [http://www.cs.bris.ac.uk/Publications/Papers/2000100.pdf pdf] <ref>[http://www.cs.bris.ac.uk/Publications/pub_master.jsp?type=117 University of Bristol - Department of Computer Science - Technical Reports]</ref>
* [[Marco Block-Berlitz|Marco Block]], Maro Bader, [http://page.mi.fu-berlin.de/tapia/ Ernesto Tapia], Marte Ramírez, Ketill Gunnarsson, Erik Cuevas, Daniel Zaldivar, [[Raúl Rojas]] ('''2008'''). ''Using Reinforcement Learning in Chess Engines''. CONCIBE SCIENCE 2008, [http://www.micai.org/rcs/ Research in Computing Science]: Special Issue in Electronics and Biomedical Engineering, Computer Science and Informatics, ISSN:1870-4069, Vol. 35, pp. 31-40, [https://en.wikipedia.org/wiki/Guadalajara Guadalajara], Mexico, [http://page.mi.fu-berlin.de/block/concibe2008.pdf pdf]
* [[Cécile Germain-Renaud]], [[Julien Pérez]], [[Balázs Kégl]], [[Charles Loomis]] ('''2008'''). ''Grid Differentiated Services: a Reinforcement Learning Approach''. In 8th [[IEEE]] Symposium on Cluster Computing and the Grid. Lyon, [http://hal.inria.fr/docs/00/28/78/26/PDF/RLccg08.pdf pdf]
* [[Balázs Csanád Csáji]], [https://dblp.dagstuhl.de/pers/hd/m/Monostori:L=aacute=szl=oacute= László Monostori] ('''2008'''). ''Value function based reinforcement learning in changing Markovian environments''. [https://en.wikipedia.org/wiki/Journal_of_Machine_Learning_Research Journal of Machine Learning Research], Vol. 9, [http://www.jmlr.org/papers/volume9/csaji08a/csaji08a.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]
==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]
* [[Julien Pérez]], [[Cécile Germain-Renaud]], [[Balázs Kégl]], [[Charles Loomis]] ('''2010'''). ''Multi-objective Reinforcement Learning for Responsive Grids''. In The Journal of Grid Computing. [http://hal.archives-ouvertes.fr/docs/00/49/15/60/PDF/RLGrid_JGC09_V7.pdf pdf]
* [[Csaba Szepesvári]] ('''2010'''). ''[https://sites.ualberta.ca/~szepesva/RLBook.html Algorithms for Reinforcement Learning]''. Morgan & Claypool
* [https://dblp.org/pers/hd/z/Zaragoza:Julio_H= Julio H. Zaragoza], [[Eduardo F. Morales]] ('''2010'''). ''Relational Reinforcement Learning with Continuous Actions by Combining Behavioral Cloning and Locally Weighted Regression''. Journal of Intelligent Systems and Applications, Vol. 2
'''2011'''
* [[Peter Auer]] ('''2011'''). ''Exploration and Exploitation in Online Learning''. [http://dblp.uni-trier.de/db/conf/icais/icais2011.html#Auer11 ICAIS 2011]
: [[István Szita]] ('''2012'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-642-27645-3_17 Reinforcement Learning in Games]''. Chapter 17
* [[Thomas J. Walsh]], [[István Szita]], [[Carlos Diuk]], [[Michael L. Littman]] ('''2012'''). ''Exploring compact reinforcement-learning representations with linear regression''. [https://arxiv.org/abs/1205.2606 arXiv:1205.2606]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''[https://papers.nips.cc/paper/4767-efficient-bayes-adaptive-reinforcement-learning-using-sample-based-search Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search]''. [httphttps://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012 NIPS 2012], [https://papers.nips.cc/paper/4767-efficient-bayes-adaptive-reinforcement-learning-using-sample-based-search.pdf pdf]
'''2013'''
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2013'''). ''Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 48, [https://www.jair.org/media/4117/live-4117-7507-jair.pdf pdf]
* [[Marcin Szubert]] ('''2014'''). ''Coevolutionary Shaping for Reinforcement Learning''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Pozna%C5%84_University_of_Technology Poznań University of Technology], supervisor [[Krzysztof Krawiec]], co-supervisor [[Wojciech Jaśkowski]], [http://www.cs.put.poznan.pl/mszubert/pub/phdthesis.pdf pdf]
==2015 ...==
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Mathematician#AARusu|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'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518
* [[Tobias Graf]], [[Marco Platzner]] ('''2015'''). ''Adaptive Playouts in Monte Carlo Tree Search with Policy Gradient Reinforcement Learning''. [[Advances in Computer Games 14]]
* [[Arun Nair]], [[Praveen Srinivasan]], [[Sam Blackwell]], [[Cagdas Alcicek]], [[Rory Fearon]], [[Alessandro De Maria]], [[Veda Panneershelvam]], [[Mustafa Suleyman]], [[Charles Beattie]], [[Stig Petersen]], [[Shane Legg]], [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]] ('''2015'''). ''Massively Parallel Methods for Deep Reinforcement Learning''. [http://arxiv.org/abs/1507.04296 arXiv:1507.04296]
* [[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]]
* [[Ari Weinstein]], [[Matthew Botvinick]] ('''2017'''). ''Structure Learning in Motor Control: A Deep Reinforcement Learning Model''. [https://arxiv.org/abs/1706.06827 arXiv:1706.06827]
* [[Takuya Hiraoka]], [https://dblp.org/pers/hd/t/Tsuchida:Masaaki Masaaki Tsuchida], [https://dblp.org/pers/hd/w/Watanabe:Yotaro Yotaro Watanabe] ('''2017'''). ''Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings''. [https://arxiv.org/abs/1708.00667 arXiv:1708.00667]
* [[William Uther]] ('''2017'''). ''[https://link.springer.com/referenceworkentry/10.1007/978-1-4899-7687-1_512 Markov Decision Processes]''. in [https://en.wikipedia.org/wiki/Claude_Sammut Claude Sammut], [https://en.wikipedia.org/wiki/Geoff_Webb Geoffrey I. Webb] (eds) ('''2017'''). ''[https://link.springer.com/referencework/10.1007%2F978-1-4899-7687-1 Encyclopedia of Machine Learning and Data Mining]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [https://scholar.google.com/citations?user=zLksndkAAAAJ&hl=en Jayvant Anantpur], [https://dblp.org/pid/09/10702.html Nagendra Gulur Dwarakanath], [https://dblp.org/pid/16/4410.html Shivaram Kalyanakrishnan], [[Shalabh Bhatnagar]], [https://dblp.org/pid/45/3592.html R. Govindarajan] ('''2017'''). ''RLWS: A Reinforcement Learning based GPU Warp Scheduler''. [https://arxiv.org/abs/1712.04303 arXiv:1712.04303]
'''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'''). ''Assessing the Potential of Classical Q-learning in General Game Playing''. [https://arxiv.org/abs/1810.06078 arXiv:1810.06078]
* [[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]] ('''2018'''). ''[http://science.sciencemag.org/content/362/6419/1140 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 362, No. 6419 <ref>[https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ 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</ref>
* [[Tianhe Wang]], [[Tomoyuki Kaneko]] ('''2018'''). ''Application of Deep Reinforcement Learning in Werewolf Game Agents''. [[TAAI 2018]]
* [[Taichi Nakayashiki]], [[Tomoyuki Kaneko]] ('''2018'''). ''Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search''. [[TAAI 2018]]
* [[Hung Guei]], [[Ting-Han Wei]], [[I-Chen Wu]] ('''2018'''). ''Using 2048-like games as a pedagogical tool for reinforcement learning''. [[CG 2018]], [[ICGA Journal#40_3|ICGA Journal, Vol. 40, No. 3]]
'''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>
==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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