Preface

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.

Who this book is for

Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals who have a working knowledge of machine learning techniques and are looking to explore emerging fields within machine learning such as reinforcement learning. Individuals who want to work on hands-on implementation projects will also find this book useful.

What this book covers

Chapter 1, Up and Running with Reinforcement Learning, introduces AI, RL, deep learning, the history/applications of the field, and other relevant topics. It will also provide a high-level overview of fundamental deep learning and TensorFlow concepts, especially those relevant to RL.

Chapter 2, Balancing Cart Pole, will have you implement your first RL algorithms in Python and TensorFlow to solve the cart pole balancing problem.

Chapter 3, Playing Atari Games, will get you creating your first deep RL algorithm to play ATARI games.

Chapter 4, Simulating Control Tasks, provides a brief introduction to actor-critic algorithms for continuous control problems. You will learn how to simulate classic control tasks, look at how to implement basic actor-critic algorithms, and understand the state-of-the-art algorithms for control.

Chapter 5, Building Virtual Worlds in Minecraft, takes the advanced concepts covered in previous chapters and applies them to Minecraft, a game more complex than those found on ATARI.

Chapter 6, Learning to Play Go, has you building a model that can play Go, the popular Asian board game that is considered one of the world's most complicated games.

Chapter 7, Creating a Chatbot, will teach you how to apply deep RL in natural language processing. Our reward function will be a future-looking function, and you will learn how to think in terms of probability when creating this function.

Chapter 8, Generating a Deep Learning Image Classifier, introduces one of the latest and most exciting advancements in RL: generating deep learning models using RL. We explore the cutting-edge research produced by Google Brain and implement the algorithms introduced.

Chapter 9, Predicting Future Stock Prices, discusses building an agent that can predict stock prices.

Chapter 10, Looking Ahead, concludes the book by discussing some of the real-world applications of reinforcement learning and introducing potential areas of future academic work.

To get the most out of this book

The examples covered in this book can be run on Windows, Ubuntu, or macOS. All the installation instructions are covered. A basic knowledge of Python and machine learning is required. It's preferred that you have GPU hardware, but it's not necessary.

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We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The gym-minecraft package has the same interface as other Gym environments."

A block of code is set as follows:

import logging
import minecraft_py
logging.basicConfig(level=logging.DEBUG)

Any command-line input or output is written as follows:

python3 -m pip install gym
python3 -m pip install pygame

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Note

Warnings or important notes appear like this.

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