Deep Reinforcement Learning
Frontiers of Artificial Intelligence
Authors: Sewak, Mohit
Free Preview- 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
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- About this book
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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.
- About the authors
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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.
- Table of contents (14 chapters)
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Introduction to Reinforcement Learning
Pages 1-18
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Mathematical and Algorithmic Understanding of Reinforcement Learning
Pages 19-27
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Coding the Environment and MDP Solution
Pages 29-49
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Temporal Difference Learning, SARSA, and Q-Learning
Pages 51-63
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Q-Learning in Code
Pages 65-74
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Table of contents (14 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Deep Reinforcement Learning
- Book Subtitle
- Frontiers of Artificial Intelligence
- Authors
-
- Mohit Sewak
- Copyright
- 2019
- Publisher
- Springer Singapore
- Copyright Holder
- Springer Nature Singapore Pte Ltd.
- eBook ISBN
- 978-981-13-8285-7
- DOI
- 10.1007/978-981-13-8285-7
- Hardcover ISBN
- 978-981-13-8284-0
- Softcover ISBN
- 978-981-13-8287-1
- Edition Number
- 1
- Number of Pages
- XVII, 203
- Number of Illustrations
- 8 b/w illustrations, 98 illustrations in colour
- Topics
