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

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 real-world applications such as scientific research. Toward the end, you’ll gain a sense of 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 frameworks 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

Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into 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.
Table of Contents

1. Brushing Up on Reinforcement Learning Concepts
2. Getting Started with the Q-Learning Algorithm
3. Setting Up Your First Environment with OpenAI Gym
4. Teaching a Smartcab to Drive Using Q-Learning
5. Building Q-Networks with TensorFlow
6. Digging Deeper into Deep Q-Networks with Keras and TensorFlow
7. Decoupling Exploration and Exploitation in Multi-Armed Bandits
8. Further Q-Learning Research and Future Projects
9. Assessments