Yuxi (Hayden) Liu - PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python
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Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes
Key Features- Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models
- Implement RL algorithms to solve control and optimization challenges faced by data scientists today
- Apply modern RL libraries to simulate a controlled environment for your projects
Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use.
With this book, youll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. Youll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. Youll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, youll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.
By the end of this book, youll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.
What you will learn- Use Q-learning and the stateactionrewardstateaction (SARSA) algorithm to solve various Gridworld problems
- Develop a multi-armed bandit algorithm to optimize display advertising
- Scale up learning and control processes using Deep Q-Networks
- Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems
- Select and build RL models, evaluate their performance, and optimize and deploy them
- Use policy gradient methods to solve continuous RL problems
Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
Table of Contents- Getting started with reinforcement learning and PyTorch
- Markov Decision Process and Dynamic Programming
- Monte Carlo Methods for making numerical estimations
- Temporal Difference and Q-Learning
- Solving Multi Armed Bandit problems
- Scaling up Learning with Function Approximation
- Deep Q-Networks in Action
- Implementing Policy Gradients and Policy Optimization
- Capstone Project: Playing Flappy Bird with DQN
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Copyright 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
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First published: October 2019
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Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his expertise in reinforcement learning to computational problems. He is an education enthusiast and is the author of a series of machine learning books. His first book, Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python, published by Packt. He has also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.
Greg Walters has been involved with computers and computer programming since 1972. Currently, he is extremely well versed in Visual Basic, Visual Basic .NET, Python, and SQL using MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people in many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is currently retired and, in his spare time, is a musician and avid cook, but he is also open to working as a freelancer on various projects.
Robert Moni is a PhD student at Budapest University of Technology and Economics (BME) and is also a Deep Learning Expert at Continental's Deep Learning Competence Center in Budapest. He also manages a cooperation project established between and BME with the goal of supporting students in conducting research in the field of deep learning and autonomous driving. His research topic is deep reinforcement learning in complex environments, and his goal is to apply this technology to self-driving vehicles.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
The surge in interest in reinforcement learning is due to the fact that it revolutionizes automation by learning the optimal actions to take in an environment in order to maximize the notion of cumulative reward.
PyTorch 1.x Reinforcement Learning Cookbook introduces you to important reinforcement learning concepts and implementations of algorithms in PyTorch. Each chapter of the book walks you through a different type of reinforcement learning method and its industry-adopted applications. With the help of recipes that contain real-world examples, you will find it intriguing to enhance your knowledge and proficiency of reinforcement learning techniques in areas such as dynamic programming, Monte Carlo methods, temporal difference and Q-learning, multi-armed bandit, function approximation, deep Q-Networks, and policy gradientsthey are no more obscure than you thought. Interesting and easy-to-follow examples, such as Atari games, Blackjack, Gridworld environments, internet advertising, Mountain Car, and Flappy Bird, will keep you interested until you reach your goal.
By the end of this book, you will have mastered the implementation of popular reinforcement learning algorithms and learned the best practices of applying reinforcement learning techniques to solve other real-world problems.
Machine learning engineers, data scientists, and AI researchers looking for quick solutions to different problems in reinforcement learning will find this book useful. Prior exposure to machine learning concepts is required, while previous experience with PyTorch will be a bonus.
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