Overview
- 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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Table of contents (14 chapters)
Keywords
- Reinforcement Learning
- Deep Learning
- Artificial Intelligence
- Deep Q Learning
- A3C
- Actor-Critic
- Deep Mind
- AI Agents
- Alpha-Go
- Attention Mechanism
- Temporal Difference Learning
- TD Lambda
- SARSA
- Hard Attention
- Recurrent Attention Model
- Dynamic Programming
- Monte Carlo
- algorithm analysis and problem complexity
About this book
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.
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Deep Reinforcement Learning
Book Subtitle: Frontiers of Artificial Intelligence
Authors: Mohit Sewak
DOI: https://doi.org/10.1007/978-981-13-8285-7
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-13-8284-0Published: 11 July 2019
Softcover ISBN: 978-981-13-8287-1Published: 15 August 2020
eBook ISBN: 978-981-13-8285-7Published: 27 June 2019
Edition Number: 1
Number of Pages: XVII, 203
Number of Illustrations: 8 b/w illustrations, 98 illustrations in colour
Topics: Programming Techniques, Artificial Intelligence, Algorithm Analysis and Problem Complexity, Cryptology