Book description
Leverage the power of rewardbased training for your deep learning models with Python
Key Features
 Understand Qlearning algorithms to train neural networks using Markov Decision Process (MDP)
 Study practical deep reinforcement learning using QNetworks
 Explore statebased unsupervised learning for machine learning models
Book Description
Qlearning 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 Qlearning, 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 modelfree Qlearning and use deep Qnetworks and double deep Qnetworks to solve complex problems. This book will guide you in exploring use cases such as selfdriving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Qnetworks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving realworld problems. You will also explore how to use Qlearning 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 Qlearning algorithms with OpenAI Gym, Keras, and TensorFlow.
What you will learn
 Explore the fundamentals of reinforcement learning and the stateactionreward process
 Understand Markov decision processes
 Get wellversed with libraries such as Keras, and TensorFlow
 Create and deploy modelfree learning and deep Qlearning agents with TensorFlow, Keras, and OpenAI Gym
 Choose and optimize a Qnetwork’s learning parameters and finetune its performance
 Discover realworld applications and use cases of Qlearning
Who this book is 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 decisionmaking in reinforcement learning is assumed.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface
 Section 1: QLearning: A Roadmap
 Brushing Up on Reinforcement Learning Concepts
 Getting Started with the QLearning Algorithm
 Setting Up Your First Environment with OpenAI Gym
 Teaching a Smartcab to Drive Using QLearning
 Section 2: Building and Optimizing QLearning Agents
 Building QNetworks with TensorFlow
 Digging Deeper into Deep QNetworks with Keras and TensorFlow
 Section 3: Advanced QLearning Challenges with Keras, TensorFlow, and OpenAI Gym

Decoupling Exploration and Exploitation in MultiArmed Bandits
 Technical requirements
 Probability distributions and ongoing knowledge
 Revisiting a simple bandit problem
 Multiarmed bandit strategy overview
 Contextual bandits and state diagrams
 Thompson sampling and the Bayesian control rule
 Solving a multiarmed bandit problem in Python – user advertisement clicks
 Multiarmed bandits in experimental design
 Summary
 Questions
 Further reading
 Further QLearning Research and Future Projects

Assessments
 Chapter 1, Brushing Up on Reinforcement Learning Concepts
 Chapter 2, Getting Started with the QLearning Algorithm
 Chapter 3, Setting Up Your First Environment with OpenAI Gym
 Chapter 4, Teaching a Smartcab to Drive Using QLearning
 Chapter 5, Building QNetworks with TensorFlow
 Chapter 6, Digging Deeper into Deep QNetworks with Keras and TensorFlow
 Chapter 7, Decoupling Exploration and Exploitation in MultiArmed Bandits
 Chapter 8, Further QLearning Research and Future Projects
 Other Books You May Enjoy
Product information
 Title: HandsOn QLearning with Python
 Author(s):
 Release date: April 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789345803
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