"Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions"

entries per page

Filter:

Showing 1 to 20 of 100 entries

Book	Author(s)	Year	Publisher
Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions	Warren B. Powell	2022	Wiley Data and Cybersecurity
Sequential Stochastic Optimization	R. Cairoli	1996	John Wiley & Sons
Sequential stochastic optimization /	Cairoli, R.; Dalang, Robert C.,	[1996], ©1996.	Wiley,
Statistical Machine Learning: A Unified Framework	Richard Golden	2020	Routledge
Learning Automata and Stochastic Optimization	A.S. Poznyak; K. Najim	1997	Springer Berlin Heidelberg
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning	Abhijit Gosavi	2003	Springer US
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning	Abhijit Gosavi	2nd ed. 2015	Springer US
Stochastic Optimization for Large-scale Machine Learning	Vinod Kumar Chauhan	2021	Routledge
Stochastic Learning and Optimization: A Sensitivity-Based Approach	Xi-Ren Cao	2007	Springer US
First-order and Stochastic Optimization Methods for Machine Learning	Guanghui Lan	1st ed. 2020	Springer International Publishing
Algorithms sequential and parallel: a unified approach	Russ Miller; Laurence Boxer	2005	Charles River Media
Constant access systems: a general framework for greedy optimization on stochastic networks	Michael P. Bailey	1989	Naval Postgraduate School;Available from National Technical Information Service
Algorithms sequential & parallel: a unified approach /	Miller, Russ.; Boxer, Laurence.	[2000], ©2000.	Prentice Hall,
Networks of Learning Automata: Techniques for Online Stochastic Optimization	M.A.L. Thathachar; P.S. Sastry	2004	Springer US
Networks of learning automata: techniques for online stochastic optimization /	Thathachar, Mandayam A. L.; Sastry, P. S.	[2004], ©2004.	Kluwer Academic,
Algorithms for Reinforcement Learning	Csaba Szepesvári		Springer International Publishing
Algorithms for reinforcement learning	Csaba Szepesvári	2010	Morgan & Claypool
Learning and Reinforcement	Stephen F. Walker	1975	Methuen
Convex Analysis for Optimization: A Unified Approach	Jan Brinkhuis	1st ed. 2020	Springer International Publishing
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems	Changsheng Hua	1st ed. 2021	Springer Fachmedien Wiesbaden
Boook	Author(s)	Year	Publisher

Showing 1 to 20 of 100 entries

entries per page

Filter:

Showing 1 to 20 of 100 entries

Chapter	Author(s)	Book	Book Authors	Year	Publisher
From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions	Warren B. Powell	Handbook of Reinforcement Learning and Control	Kyriakos G. Vamvoudakis; Yan Wan; Frank L. Lewis; Derya Cansever	1st ed. 2021	Springer International Publishing
A Unified Bayesian Framework for Evolutionary Learning and Optimization	Byoung-Tak Zhang	Advances in Evolutionary Computing: Theory and Applications	Ashish Ghosh; Shigeyoshi Tsutsui	2003	Springer Berlin Heidelberg
A Unified Framework for Planning and Learning	PAT LANGLEY; JOHN A. ALLEN	Machine Learning Methods for Planning	Steven Minton	1993	Morgan Kaufmann
A framework for distributed reinforcement learning	Pan Gu; Anthony B. Maddox	Adaptation and Learning in Multi-Agent Systems: IJCAI' 95 Workshop, Montreal, Canada, August 21, 1995. Proceedings.	Gerhard Weiß; Sandip Sen	1996	Springer Berlin Heidelberg
A Novel Deep Reinforcement Learning Framework for Stock Portfolio Optimization	Shaobo Hu; Hongying Zheng; Jianyong Chen	Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V	Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto	1st ed. 2021	Springer International Publishing
OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems	Weijian Zheng; Dali Wang; Fengguang Song	Computational Science – ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part V	Valeria V. Krzhizhanovskaya; Gábor Závodszky; Michael H. Lees; Jack J. Dongarra; Peter M. A. Sloot; Sérgio Brissos; João Teixeira	1st ed. 2020	Springer International Publishing
A Framework for Stochastic and Surrogate-Assisted Optimization using Sequential Modular Process Simulators	Alberto T. Penteado; Erik Esche; Joris Weigert; Jens-Uwe Repke	30 European Symposium on Computer Aided Process Engineering	Sauro Pierucci; Flavio Manenti; Giulia Luisa Bozzano; Davide Manca	2020	Elsevier
General Framework for Parameter Learning and Optimization in Stochastic Environments	Wen Jiang; Yan Yan; Hao Ge; Shenghong Li	Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems	Qilian Liang; Jiasong Mu; Wei Wang; Baoju Zhang	1st ed. 2016	Springer Berlin Heidelberg
Explanation-Based Learning and Reinforcement Learning: A Unified View	Thomas G. Dietterich; Nicholas S. Flann	Machine Learning Proceedings 1995	Armand Prieditis and Stuart J. Russell	1995	Morgan Kaufmann
A Unified Matrix-Based Stochastic Optimization Algorithm	Xinchao Zhao; Junling Hao	Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part I	Ying Tan; Yuhui Shi; Carlos Coello Coello	2014	Springer International Publishing
Selection and Reinforcement Learning for Combinatorial Optimization	A. Berny	Parallel Problem Solving from Nature-PPSN VI: 6th International Conference, Paris, France, September 18-20 2000 Proceedings	Marc Schoenauer; Kalyanmoy Deb; Günther Rudolph; Xin Yao; Evelyne Lutton; Juan Julian Merelo; Hans-Paul Schwefel	2000	Springer Berlin Heidelberg
Unified Sequential Optimization Directives in OpenMP	Brandon Neth; Thomas R. W. Scogland; Michelle Mills Strout; Bronis R. de Supinski	OpenMP: Portable Multi-Level Parallelism on Modern Systems: 16th International Workshop on OpenMP, IWOMP 2020, Austin, TX, USA, September 22–24, 2020, Proceedings	Kent Milfeld; Bronis R. de Supinski; Lars Koesterke; Jannis Klinkenberg	1st ed. 2020	Springer International Publishing
Stochastic Optimization and Worst-case Decisions	Nalan Gülpinar; Berç Rustem; Stanislav Žaković	Cooperative Systems: Control and Optimization	Don Grundel; Robert Murphey; Panos Pardalos; Oleg Prokopyev	2007	Springer Berlin Heidelberg
Sequential Constant Size Compressors for Reinforcement Learning	Linus Gisslén; Matt Luciw; Vincent Graziano; Jürgen Schmidhuber	Artificial General Intelligence: 4th International Conference, AGI 2011, Mountain View, CA, USA, August 3-6, 2011, Proceedings	Jürgen Schmidhuber; Kristinn R. Thorisson; Moshe Looks	2011	Springer Berlin Heidelberg
Multiagent Reinforcement Learning for Combinatorial Optimization	Yifan Gu; Qi Sun; Xinye Cai	Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings	Haijun Zhang; Zhao Zhang; Zhou Wu; Tianyong Hao	1st ed. 2020	Springer Nature Singapore
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks	Fatemeh Azimi; Federico Raue; Jörn Hees; Andreas Dengel	Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I	Igor V. Tetko; Věra Kůrková; Pavel Karpov; Fabian Theis	1st ed. 2019	Springer International Publishing
A framework for decisions		Teaching, Learning and Assessing Science 5 - 12	Wynne Harlen	December 2005	SAGE Publications Ltd
A Unified Framework for the Formal Verification of Sequential Circuits	Olivier Coudert; Jean Christophe Madre	The Best of ICCAD: 20 Years of Excellence in Computer-Aided Design	Andreas Kuehlmann	2003	Springer US
A Unified Framework for Trapdoor-Permutation-Based Sequential Aggregate Signatures	Craig Gentry; Adam O’Neill; Leonid Reyzin	Public-Key Cryptography – PKC 2018: 21st IACR International Conference on Practice and Theory of Public-Key Cryptography, Rio de Janeiro, Brazil, March 25-29, 2018, Proceedings, Part II	Michel Abdalla; Ricardo Dahab	1st ed. 2018	Springer International Publishing
A Framework for Reinforcement Learning with Autocorrelated Actions	Marcin Szulc; Jakub Łyskawa; Paweł Wawrzyński	Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II	Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King	1st ed. 2020	Springer International Publishing
Chapter	Author(s)	Book	Book Authors	Year	Publisher

Showing 1 to 20 of 100 entries

"Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions" - similar Books and Chapters