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Deep Reinforcement Learning

Overview of attention for book
Cover of 'Deep Reinforcement Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Deep Learning
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    Chapter 2 Introduction to Reinforcement Learning
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    Chapter 3 Taxonomy of Reinforcement Learning Algorithms
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    Chapter 4 Deep Q-Networks
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    Chapter 5 Policy Gradient
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    Chapter 6 Combine Deep Q -Networks with Actor-Critic
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    Chapter 7 Challenges of Reinforcement Learning
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    Chapter 8 Imitation Learning
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    Chapter 9 Integrating Learning and Planning
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    Chapter 10 Hierarchical Reinforcement Learning
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    Chapter 11 Multi-Agent Reinforcement Learning
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    Chapter 12 Parallel Computing
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    Chapter 13 Learning to Run
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    Chapter 14 Robust Image Enhancement
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    Chapter 15 AlphaZero
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    Chapter 16 Robot Learning in Simulation
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    Chapter 17 Arena Platform for Multi-Agent Reinforcement Learning
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    Chapter 18 Tricks of Implementation
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    Chapter 19 Algorithm Table
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    Chapter 20 Algorithm Cheatsheet
Attention for Chapter 9: Integrating Learning and Planning
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Citations

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Chapter title
Integrating Learning and Planning
Chapter number 9
Book title
Deep Reinforcement Learning
Published by
Springer, Singapore, January 2020
DOI 10.1007/978-981-15-4095-0_9
Book ISBNs
978-9-81-154094-3, 978-9-81-154095-0
Authors

Huaqing Zhang, Ruitong Huang, Shanghang Zhang, Zhang, Huaqing, Huang, Ruitong, Zhang, Shanghang

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Student > Master 12 20%
Researcher 10 16%
Student > Bachelor 3 5%
Student > Postgraduate 2 3%
Other 4 7%
Unknown 16 26%
Readers by discipline Count As %
Computer Science 23 38%
Engineering 11 18%
Agricultural and Biological Sciences 2 3%
Neuroscience 2 3%
Mathematics 1 2%
Other 1 2%
Unknown 21 34%

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