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Deep reinforcement learning = fronti...

Sewak, Mohit.

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Deep reinforcement learning = frontiers of artificial intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep reinforcement learning/ by Mohit Sewak.
其他題名:	frontiers of artificial intelligence /
作者:	Sewak, Mohit.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xvii, 203 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction to Reinforcement Learning -- Mathematical and Algorithmic understanding of Reinforcement Learning -- Coding the Environment and MDP Solution -- Temporal Difference Learning, SARSA, and Q Learning -- Q Learning in Code -- Introduction to Deep Learning -- Implementation Resources -- Deep Q Network (DQN), Double DQN and Dueling DQN -- Double DQN in Code -- Policy-Based Reinforcement Learning Approaches -- Actor-Critic Models & the A3C -- A3C in Code -- Deterministic Policy Gradient and the DDPG -- DDPG in Code.
Contained By:	Springer eBooks
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-981-13-8285-7
ISBN:	9789811382857

Deep reinforcement learning = frontiers of artificial intelligence /
Sewak, Mohit.

Deep reinforcement learningfrontiers of artificial intelligence /[electronic resource] :by Mohit Sewak. - Singapore :Springer Singapore :2019. - xvii, 203 p. :ill. (some col.), digital ;24 cm.

Introduction to Reinforcement Learning -- Mathematical and Algorithmic understanding of Reinforcement Learning -- Coding the Environment and MDP Solution -- Temporal Difference Learning, SARSA, and Q Learning -- Q Learning in Code -- Introduction to Deep Learning -- Implementation Resources -- Deep Q Network (DQN), Double DQN and Dueling DQN -- Double DQN in Code -- Policy-Based Reinforcement Learning Approaches -- Actor-Critic Models & the A3C -- A3C in Code -- Deterministic Policy Gradient and the DDPG -- DDPG in Code.

This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds - deep learning and reinforcement learning - to tap the potential of 'advanced artificial intelligence' for creating real-world applications and game-winning algorithms.

ISBN: 9789811382857

Standard No.: 10.1007/978-981-13-8285-7doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .S49 2019

Dewey Class. No.: 005.11

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505 0 $a Introduction to Reinforcement Learning -- Mathematical and Algorithmic understanding of Reinforcement Learning -- Coding the Environment and MDP Solution -- Temporal Difference Learning, SARSA, and Q Learning -- Q Learning in Code -- Introduction to Deep Learning -- Implementation Resources -- Deep Q Network (DQN), Double DQN and Dueling DQN -- Double DQN in Code -- Policy-Based Reinforcement Learning Approaches -- Actor-Critic Models & the A3C -- A3C in Code -- Deterministic Policy Gradient and the DDPG -- DDPG in Code.
520 $a This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds - deep learning and reinforcement learning - to tap the potential of 'advanced artificial intelligence' for creating real-world applications and game-winning algorithms.
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