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Hands-On Q-Learning with Python
Hands-On Q-Learning with Python

Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Hands-On Q-Learning with Python

Section 1: Q-Learning: A Roadmap

This section will introduces the reader to reinforcement learning and Q-learning, and the types of problem that can be solved with both. Readers will become familiar with OpenAI Gym as a tool for creating Q-learning projects and will build their first model-free Q-learning agent.

The following chapters are included in this section:

  • Chapter 1, Brushing Up on Reinforcement Learning Concepts
  • Chapter 2, Getting Started with the Q-Learning Algorithm
  • Chapter 3, Setting Up Your First Environment with OpenAI Gym
  • Chapter 4, Teaching a Smartcab to Drive Using Q-Learning
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Key benefits

  • Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)
  • Study practical deep reinforcement learning using Q-Networks
  • Explore state-based unsupervised learning for machine learning models

Description

Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.

Who is this book for?

If you are a machine learning developer, engineer, or professional who wants to explore the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.

What you will learn

  • Explore the fundamentals of reinforcement learning and the state-action-reward process
  • Understand Markov Decision Processes
  • Get well-versed with libraries such as Keras, and TensorFlow
  • Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym
  • Choose and optimize a Q-network's learning parameters and fine-tune its performance
  • Discover real-world applications and use cases of Q-learning

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 19, 2019
Length: 212 pages
Edition : 1st
Language : English
ISBN-13 : 9781789345759
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Product Details

Publication date : Apr 19, 2019
Length: 212 pages
Edition : 1st
Language : English
ISBN-13 : 9781789345759
Category :
Languages :
Tools :

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Frequently bought together


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Total AU$ 168.97
Python Reinforcement Learning
AU$68.99
Hands-On Deep Learning Architectures with Python
AU$45.99
Hands-On Q-Learning with Python
AU$53.99
Total AU$ 168.97 Stars icon
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Table of Contents

13 Chapters
Section 1: Q-Learning: A Roadmap Chevron down icon Chevron up icon
Brushing Up on Reinforcement Learning Concepts Chevron down icon Chevron up icon
Getting Started with the Q-Learning Algorithm Chevron down icon Chevron up icon
Setting Up Your First Environment with OpenAI Gym Chevron down icon Chevron up icon
Teaching a Smartcab to Drive Using Q-Learning Chevron down icon Chevron up icon
Section 2: Building and Optimizing Q-Learning Agents Chevron down icon Chevron up icon
Building Q-Networks with TensorFlow Chevron down icon Chevron up icon
Digging Deeper into Deep Q-Networks with Keras and TensorFlow Chevron down icon Chevron up icon
Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym Chevron down icon Chevron up icon
Decoupling Exploration and Exploitation in Multi-Armed Bandits Chevron down icon Chevron up icon
Further Q-Learning Research and Future Projects Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 0%
1 star 66.7%
SSV Jul 18, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was sent a copy of this book by the publisher to read and review.If you are an intermediate level python user and if you are passionate about artificial intelligence and neural networks and are looking to improve your programming skills with Python, then this book is a must purchase!Author Ms. Nazia Habib has created an outstanding textbook that is perfect for self-directed learning. It first begins with an extremely thorough and easy to understand explanation of theoretical concepts surrounding reinforcement learning, and provides extensive information on the coding process with Q learning, using easy to follow examples as well as companion coding exercises to help you integrate your newfound knowledge as you progress through the book.As your skills progress throughout the book, more complex examples including neural networks are introduced, with applications being endless!So if you want to start building your expertise in programming for artificial intelligence, then this book is a must-read!
Amazon Verified review Amazon
Dr. Mark Potter May 15, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Limited in scope, not a great read.
Amazon Verified review Amazon
roman575 Jun 30, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Introduction, repetitions, conclusions, summaries, installation instructions comprise 90% of the book. Essential material is very basic and could be find in any 20 pages blog
Amazon Verified review Amazon
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