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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

By Nazia Habib
€28.99
Book Apr 2019 212 pages 1st Edition
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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.

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
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Product Details

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Publication date : Apr 19, 2019
Length 212 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789345803
Category :

What do you get with Print?

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Estimated delivery fee Deliver to Malta

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Product Details


Publication date : Apr 19, 2019
Length 212 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789345803
Category :

Table of Contents

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

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