Model-based reinforcement learning : (Record no. 88734)

000 -LEADER
fixed length control field 04429cam a2200517 i 4500
001 - CONTROL NUMBER
control field 1352957082
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240923145219.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |n|||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221203s2023 xxum o u000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119808589
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119808588
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119808596
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119808596
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119808602
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 111980860X
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 111980857X
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9781119808572
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1002/9781119808602
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1352957082
037 ## - SOURCE OF ACQUISITION
Stock number 9979000
Source of stock number/acquisition IEEE
040 ## - CATALOGING SOURCE
Original cataloging agency YDX
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency YDX
Modifying agency IEEEE
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.6
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
Edition number 23/eng/20221227
100 1# - MAIN ENTRY--PERSONAL NAME
Preferred name for the person Farsi, Milad,
Authority record control number https://id.loc.gov/authorities/names/n2022058722
Relator term author.
245 10 - TITLE STATEMENT
Title Model-based reinforcement learning :
Remainder of title from data to actions with Python-based toolbox /
Statement of responsibility, etc Milad Farsi, Jun Liu.
264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc [United States] :
Name of publisher, distributor, etc Wiley-Blackwell,
Date of publication, distribution, etc 2023.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent.
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia.
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier.
520 ## - SUMMARY, ETC.
Summary, etc Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory-optimal control and dynamic programming - or on algorithms-most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework-from design to application-of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.
506 ## - RESTRICTIONS ON ACCESS NOTE
Terms governing access Available to OhioLINK libraries.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Reinforcement learning.
Authority record control number https://id.loc.gov/authorities/subjects/sh92000704.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
Authority record control number https://id.loc.gov/authorities/subjects/sh96008834.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Liu, Jun
Titles and other words associated with a name (Professor of applied mathematics),
Authority record control number https://id.loc.gov/authorities/names/n2022058723
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Ohio Library and Information Network.
Authority record control number https://id.loc.gov/authorities/names/no95058981.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
International Standard Book Number 111980857X
-- 9781119808572
Record control number (OCoLC)1237352176.
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Connect to resource
Uniform Resource Identifier https://rave.ohiolink.edu/ebooks/ebc2/9781119808602
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Connect to resource
Uniform Resource Identifier https://onlinelibrary.wiley.com/doi/book/10.1002/9781119808602
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Connect to resource (off-campus)
Uniform Resource Identifier https://go.ohiolink.edu/goto?url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119808602
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-- model_based_reinforcement_learning_from_data_tawpbtoolbox________2023_______wia_______________________________

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