東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Control systems and reinforcement le...

Meyn, Sean.

Linked to FindBook

Google Book

Amazon

博客來

Control systems and reinforcement learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Control systems and reinforcement learning/ Sean Meyn.
Author:	Meyn, Sean.
Published:	Cambridge :Cambridge University Press, : 2022.,
Description:	xv, 435 p. :ill., digital ;25 cm.
Notes:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1017/9781009051873
ISBN:	9781009051873

Control systems and reinforcement learning
Meyn, Sean.

Control systems and reinforcement learning[electronic resource] /Sean Meyn. - Cambridge :Cambridge University Press,2022. - xv, 435 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 07 Apr 2022).

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

ISBN: 9781009051873Subjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .M49 2022

Dewey Class. No.: 006.31

Control systems and reinforcement learning
LDR:01742nmm a2200241 a 4500 001 2324488
003 UkCbUP
005 20220609101243.0
006 m d
007 cr nn 008maaau
008 231215s2022 enk o 1 0 eng d
020 $a 9781009051873 $q (electronic bk.)
020 $a 9781316511961 $q (hardback)
035 $a CR9781009051873
040 $a UkCbUP $b eng $c UkCbUP $d GP
050 4 $a Q325.6 $b .M49 2022
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .M614 2022
100 1 $a Meyn, Sean. $3 3645769
245 1 0 $a Control systems and reinforcement learning $h [electronic resource] / $c Sean Meyn.
260 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a xv, 435 p. : $b ill., digital ; $c 25 cm.
500 $a Title from publisher's bibliographic system (viewed on 07 Apr 2022).
520 $a A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.
650 0 $a Reinforcement learning. $3 1006373
650 0 $a Mathematical optimization. $3 517763
650 0 $a Control theory. $3 535880
856 4 0 $u https://doi.org/10.1017/9781009051873