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* [[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]
* [[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>
* [[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]
* [[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]
* [[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]
==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]
* [[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]* [[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>
=Postings=
* [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
* [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
=External Links=
