"An Information-Spectrum Approach to Analysis of Return Maximization in Reinforcement Learning"

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Book	Author(s)	Year	Publisher
Reinforcement Learning: An Introduction	Richard S. Sutton; Andrew G. Barto	1998	MIT Press
Reinforcement Learning - An Introduction	Richard S. Sutton, Andrew G. Barto	1998	Mit Press
Reinforcement Learning: An Introduction	Richard S. Sutton; Andrew G Barto	2018	Bradford Book
Reinforcement Learning Algorithms: Analysis and Applications	Boris Belousov; Hany Abdulsamad; Pascal Klink; Simone Parisi; Jan Peters	1st ed. 2021	Springer International Publishing
Multi-Agent Coordination: A Reinforcement Learning Approach	Arup Kumar Sadhu; Amit Konar	2021	Wiley-IEEE Press
Multi-Agent Machine Learning: A Reinforcement Approach	Howard M. Schwartz	2014	John Wiley & Sons
Reinforcement Learning	Richard S. Sutton	1992	Springer US
Reinforcement Learning	Sutton, Barto	2018
Analysis of algorithms: an active learning approach	Jeffrey J. McConnell	2008	Jones and Bartlett Publishers
Advances in Reinforcement Learning	Abdelhamid Mellouk	2011-01-14	IntechOpen
An entropy maximization approach to the description of urban spatial organization	R. M. Ray	1977	Center for Advanced Computation, University of Illinois at Urbana-Champaign
Vibration spectrum analysis: a practical approach /	Goldman, Steve.	[1999], ©1999.	Industrial Press,
Vibration spectrum analysis: a practical approach	Steve Goldman	1991	Industrial Press Inc.
Reinforcement Learning. An Introduction [2nd ed. ]	Richard S. Sutton, Andrew G. Barto	2018	MIT
Learning to Play: Reinforcement Learning and Games	Aske Plaat	1st ed. 2020	Springer International Publishing
Deep Reinforcement Learning	Aske Plaat	1st ed. 2022	Springer Nature Singapore
Learning and Reinforcement	Stephen F. Walker	1975	Methuen
Practical Reinforcement Learning	Farrukh Akhtar	2017	Packt Publishing
Learning: reinforcement theory	Fred Simmons Keller
An introduction to the study of spectrum analysis	William Marshall Watts	1904	Longmans, Green, and co.
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
An Information-Spectrum Approach to Analysis of Return Maximization in Reinforcement Learning	Kazunori Iwata	Neural Information Processing. Theory and Algorithms: 17th International Conference, ICONIP 2010, Sydney, Australia, November 21-25, 2010, Proceedings, Part I	Kok Wai Wong; B. Sumudu U. Mendis; Abdesselam Bouzerdoum	2011	Springer Berlin Heidelberg
An Information-Spectrum Approach to Analysis of Return Maximization in Reinforcement Learni		Neural Information Processing. Theory and Algorithms: 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part I	Haruo Hosoya (auth.), Kok Wai Wong, B. Sumudu U. Mendis, Abdesselam Bouzerdoum (eds.)	2010	Springer-Verlag Berlin Heidelberg
Stochastic Processes for Return Maximization in Reinforcement Learning	Kazunori Iwata; Hideaki Sakai; Kazushi Ikeda	Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part II	Wlodzislaw Duch; Erkki Oja; Slawomir Zadrozny	2005	Springer Berlin Heidelberg
An Expectation-Maximization Approach to Nonlinear Component Analysis			MIT Press	2001	MIT Press
Using Expectation-Maximization for Reinforcement Learning			MIT Press	1997	MIT Press
An Approach of Reinforcement Learning Use in Tutoring Systems	Abdellah Bennane	Intelligent Tutoring Systems: 6th International Conference, ITS 2002, Biarritz, France and San Sebastian, Spain, June 2-7, 2002. Proceedings	Stefano A. Cerri; Guy Gouarderes; Fabio Paraguacu	2002	Springer Berlin Heidelberg
From Reinforcement Learning to Deep Reinforcement Learning: An Overview	Forest Agostinelli; Guillaume Hocquet; Sameer Singh; Pierre Baldi	Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emmanuil Braverman' s Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks	Lev Rozonoer; Boris Mirkin; Ilya Muchnik	1st ed. 2018	Springer International Publishing
An Introduction to Reinforcement Learning	Leslie Pack Kaelbling; Michael L. Littman; Andrew W. Moore	The Biology and Technology of Intelligent Autonomous Agents	Luc Steels	1995	Springer Berlin Heidelberg
An Introduction to Reinforcement Learning	David Paper	State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem	David Paper	1st ed.	Apress
An Information-Maximization Approach to Blind Separation and Blind Deconvolution			MIT Press	1995	MIT Press
Reinforcement Learning Performance Evaluation: An Evolutionary Approach		Intelligent Autonomous Systems 9	T. Arai, R. Pfeifer, T. Balch, H. Yokoi	2006	IOS Press
The Machine-Learning Approach of Reinforcement Learning		How the Brain Makes Decisions	Thomas Boraud	2020	Oxford University Press
Reinforcement Learning-Based Cooperative Spectrum Sensing	Wenli Ning; Xiaoyan Huang; Fan Wu; Supeng Leng; Lixiang Ma	IoT as a Service: 4th EAI International Conference, IoTaaS 2018, Xi’an, China, November 17–18, 2018, Proceedings	Bo Li; Mao Yang; Hui Yuan; Zhongjiang Yan	1st ed. 2019	Springer International Publishing
61 A different approach to reinforcement learning		Deep Reinforcement Learning in Action	Alexander Zai, Brandon Brown	2020	Manning Publications
A Reinforcement Learning Approach to Inventory Management	Apoorva Gokhale; Chirag Trasikar; Ankit Shah; Arpita Hegde; Sowmiya Raksha Naik	Advances in Artificial Intelligence and Data Engineering: Select Proceedings of AIDE 2019	Niranjan N. Chiplunkar; Takanori Fukao	1st ed. 2021	Springer Nature Singapore
A Reinforcement Learning Approach to Online Clustering			MIT Press	1999	MIT Press
8: An Information-Maximization Approach to Blind Separation and Blind Deconvolution		Unsupervised Learning: Foundations of Neural Computation	Geoffrey Hinton; Terrence J. Sejnowski	1999	MIT Press,
8. An Information-Maximization Approach to Blind Separation and Blind Deconvolution	Anthony J. Bell and Terrence J. Sejnowski	Unsupervised learning: foundations of neural computation /	Hinton, Geoffrey E.; Sejnowski, Terrence J.	1999.	MIT Press,
An Additive Reinforcement Learning	Takeshi Mori; Shin Ishii	Artificial Neural Networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part I	Cesare Alippi; Marios M. Polycarpou; Christos Panayiotou; Georgios Ellinas	2009	Springer Berlin Heidelberg
Intraday FX Trading: An Evolutionary Reinforcement Learning Approach	M. A. H. Dempster; Y. S. Romahi	Intelligent Data Engineering and Automated Learning - IDEAL 2002: Third International Conference, Manchester, UK, August 12-14 Proceedings	Hujun Yin; Nigel Allinson; Richard Freeman; John Keane; Simon Hubbard	2002	Springer Berlin Heidelberg
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