東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Reinforcement learning." ]

切換: 標籤 | MARC模式 | ISBD

Deep reinforcement learning

Plaat, Aske.

FindBook

Google Book

Amazon

博客來

Deep reinforcement learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep reinforcement learning/ by Aske Plaat.
作者:	Plaat, Aske.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xv, 406 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-981-19-0638-1
ISBN:	9789811906381

Deep reinforcement learning
Plaat, Aske.

Deep reinforcement learning[electronic resource] /by Aske Plaat. - Singapore :Springer Nature Singapore :2022. - xv, 406 p. :ill., digital ;24 cm.

1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world's leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects' desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

ISBN: 9789811906381

Standard No.: 10.1007/978-981-19-0638-1doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .P53 2022

Dewey Class. No.: 006.31

Deep reinforcement learning
LDR:03097nmm a2200337 a 4500 001 2301795
003 DE-He213
005 20220610223153.0
006 m d
007 cr nn 008maaau
008 230409s2022 si s 0 eng d
020 $a 9789811906381 $q (electronic bk.)
020 $a 9789811906374 $q (paper)
024 7 $a 10.1007/978-981-19-0638-1 $2 doi
035 $a 978-981-19-0638-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6 $b .P53 2022
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 .P696 2022
100 1 $a Plaat, Aske. $3 1532118
245 1 0 $a Deep reinforcement learning $h [electronic resource] / $c by Aske Plaat.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xv, 406 p. : $b ill., digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
505 0 $a 1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.
520 $a Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world's leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects' desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
650 0 $a Reinforcement learning. $3 1006373
650 0 $a Artificial intelligence. $3 516317
650 0 $a Human-computer interaction. $3 560071
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Computer Science. $3 626642
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-0638-1
950 $a Computer Science (SpringerNature-11645)