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Model-based reinforcement learning :...

Farsi, Milad.

Model-based reinforcement learning :from data to continuous actions with a Python-based toolbox /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Model-based reinforcement learning :/ Milad Farsi and Jun Liu.
Reminder of title:	from data to continuous actions with a Python-based toolbox /
Author:	Farsi, Milad.
other author:	Liu, Jun.
Published:	Hoboken, NJ :Wiley ;c2023.
Description:	xxxv, 226 p. :ill. ;24 cm.
Subject:	Reinforcement learning. -
ISBN:	9781119808572
ISBN:	9781119808589
ISBN:	9781119808596

Model-based reinforcement learning :from data to continuous actions with a Python-based toolbox /
Farsi, Milad.

Model-based reinforcement learning :from data to continuous actions with a Python-based toolbox /Milad Farsi and Jun Liu. - Hoboken, NJ :Wiley ;c2023. - xxxv, 226 p. :ill. ;24 cm. - IEEE Press series on control systems theory and applications.

Includes bibliographical references and index.

"Whilst reinforcement learning has gained tremendous success and popularity in recent years, most research papers and books focus on either the theory (optimal control and dynamic programming) or the algorithms (mostly simulation-based). From a control systems perspective, this book will provide a model-based framework that bridges these two aspects to provide a holistic treatment of the topic of model-based online learning control. The aim is to develop a model-based framework for data-driven control that encompasses the topics of systems identification from data, model-based reinforcement learning and optimal control, and their applications. This will be done through reviewing the classical results in system identification from a new perspective to develop more efficient reinforcement learning techniques. Hence, the focus of this book will be on presenting an end to end framework from design to application of a more tractable model-based reinforcement learning technique. The tutorial aspects of the book are enhanced by the provision of a Python-based toolbox, accessible online"--

ISBN: 9781119808572NT4385

LCCN: 2022052831Subjects--Topical Terms:

1000128604
Reinforcement learning.

LC Class. No.: Q325.6 / .F37 2023

Dewey Class. No.: 006.3/1

Model-based reinforcement learning :from data to continuous actions with a Python-based toolbox /
LDR:01938cam a2200229 a 4500 001 1000134154
005 20240731164408.0
008 221101s2023 njua b 001 0 eng
010 $a 2022052831
020 $a 9781119808572 $q (bound) : $c NT4385
020 $a 9781119808589 $q (adobe pdf)
020 $a 9781119808596 $q (epub)
035 $a 22848762
040 $a DLC $b eng $c DLC
042 $a pcc
050 0 0 $a Q325.6 $b .F37 2023
082 0 0 $a 006.3/1 $2 23/eng20230117
100 1 $a Farsi, Milad. $3 1000154133
245 1 0 $a Model-based reinforcement learning : $b from data to continuous actions with a Python-based toolbox / $c Milad Farsi and Jun Liu.
260 $a Hoboken, NJ : $a Piscataway, NJ : $b Wiley ; $c c2023. $b IEEE Press,
300 $a xxxv, 226 p. : $b ill. ; $c 24 cm.
490 0 $a IEEE Press series on control systems theory and applications
504 $a Includes bibliographical references and index.
520 $a "Whilst reinforcement learning has gained tremendous success and popularity in recent years, most research papers and books focus on either the theory (optimal control and dynamic programming) or the algorithms (mostly simulation-based). From a control systems perspective, this book will provide a model-based framework that bridges these two aspects to provide a holistic treatment of the topic of model-based online learning control. The aim is to develop a model-based framework for data-driven control that encompasses the topics of systems identification from data, model-based reinforcement learning and optimal control, and their applications. This will be done through reviewing the classical results in system identification from a new perspective to develop more efficient reinforcement learning techniques. Hence, the focus of this book will be on presenting an end to end framework from design to application of a more tractable model-based reinforcement learning technique. The tutorial aspects of the book are enhanced by the provision of a Python-based toolbox, accessible online"-- $c Provided by publisher.
650 0 $a Reinforcement learning. $3 1000128604
650 0 $a Reinforcement learning $x Mathematical models. $3 1000154135
700 1 $a Liu, Jun. $3 1000154134

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