Mohit Sewak: Deep Reinforcement Learning

Cover: 9789811382871 | Deep Reinforcement Learning | Frontiers of Artificial Intelligence

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Sprache: Englisch

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Beschreibung

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.

Über den Autor

Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others.

Zusammenfassung

Presents comprehensive insights into advanced deep learning concepts like the 'hard attention mechanism'

Introduces algorithms that are slated to become the future of artificial intelligence

Allows readers to gain an understanding of algorithms such as TD Learning and Deep Q Learning, and Asynchronous Advantage Actor-Critic Models

Inhaltsverzeichnis

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.

Details

Erscheinungsjahr:	2020
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xvii 203 S. 8 s/w Illustr. 98 farbige Illustr. 203 p. 106 illus. 98 illus. in color.
ISBN-13:	9789811382871
ISBN-10:	9811382875
Sprache:	Englisch
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Sewak, Mohit
Auflage:	1st ed. 2019
Hersteller:	Springer Singapore Springer Nature Singapore
Maße:	235 x 155 x 12 mm
Von/Mit:	Mohit Sewak
Erscheinungsdatum:	15.08.2020
Gewicht:	0,394 kg

Artikel-ID: 118757211