國立高雄大學圖資館 |

登入

回首頁

Fundamentals of reinforcement learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamentals of reinforcement learningby Rafael Ris-Ala.
作者:	Ris-Ala, Rafael.
出版者:	Cham :Springer Nature Switzerland :2023.
面頁冊數:	xv, 88 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning.
電子資源:	https://doi.org/10.1007/978-3-031-37345-9
ISBN:	9783031373459$q(electronic bk.)

Fundamentals of reinforcement learning
Ris-Ala, Rafael.

Fundamentals of reinforcement learning[electronic resource] /by Rafael Ris-Ala. - Cham :Springer Nature Switzerland :2023. - xv, 88 p. :ill. (some col.), digital ;24 cm.

Chapter. 1. Introduction -- Chapter. 2. Concepts -- Chapter. 3. Q-Learning algorithm -- Chapter. 4. Development tools -- Chapter. 5. Practice with code -- Chapter. 6. Recent applications and future research -- Index.

Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions.

ISBN: 9783031373459$q(electronic bk.)

Standard No.: 10.1007/978-3-031-37345-9doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Fundamentals of reinforcement learning
LDR:02222nmm a2200325 a 4500 001 647425
003 DE-He213
005 20230814161712.0
006 m d
007 cr nn 008maaau
008 240116s2023 sz s 0 eng d
020 $a 9783031373459$q(electronic bk.)
020 $a 9783031373442$q(paper)
024 7 $a 10.1007/978-3-031-37345-9 $2 doi
035 $a 978-3-031-37345-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .R595 2023
100 1 $a Ris-Ala, Rafael. $3 956817
245 1 0 $a Fundamentals of reinforcement learning $h [electronic resource] / $c by Rafael Ris-Ala.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xv, 88 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter. 1. Introduction -- Chapter. 2. Concepts -- Chapter. 3. Q-Learning algorithm -- Chapter. 4. Development tools -- Chapter. 5. Practice with code -- Chapter. 6. Recent applications and future research -- Index.
520 $a Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions.
650 0 $a Reinforcement learning. $3 349131
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Software Engineering. $3 274511
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-37345-9
950 $a Computer Science (SpringerNature-11645)

筆 0 讀者評論

多媒體

多媒體檔案
https://doi.org/10.1007/978-3-031-37345-9

評論

新增評論分享你的心得

Export

取書館別