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- •Implement Q-learning and Markov models with Python and OpenAI
- •Explore the power of TensorFlow to build self-learning models
- •Eight AI projects to gain confidence in building self-trained applications

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.
In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.
By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.

•Train and evaluate neural networks built using TensorFlow for RL
•Use RL algorithms in Python and TensorFlow to solve CartPole balancing
•Create deep reinforcement learning algorithms to play Atari games
• Deploy RL algorithms using OpenAI Universe
•Develop an agent to chat with humans
•Implement basic actor-critic algorithms for continuous control
•Apply advanced deep RL algorithms to games such as Minecraft
•Autogenerate an image classifier using RL

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Publication date :
Sep 29, 2018

Length
296 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781788991612

Category :

Languages :

Concepts :

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Up and Running with Reinforcement Learning

2. Balancing CartPole

3. Playing Atari Games

4. Simulating Control Tasks

5. Building Virtual Worlds in Minecraft

6. Learning to Play Go

7. Creating a Chatbot

8. Generating a Deep Learning Image Classifier

9. Predicting Future Stock Prices

10. Looking Ahead

1. Other Books You May Enjoy

Index

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