Enes Bilgin - Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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- Book:Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
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Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices
Key Features- Understand how large-scale state-of-the-art RL algorithms and approaches work
- Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more
- Explore tips and best practices from experts that will enable you to overcome real-world RL challenges
Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.
Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, youll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.
As you advance, youll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Rays RLlib package. Youll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.
By the end of this book, youll have mastered how to train and deploy your own RL agents for solving RL problems.
What you will learn- Model and solve complex sequential decision-making problems using RL
- Develop a solid understanding of how state-of-the-art RL methods work
- Use Python and TensorFlow to code RL algorithms from scratch
- Parallelize and scale up your RL implementations using Rays RLlib package
- Get in-depth knowledge of a wide variety of RL topics
- Understand the trade-offs between different RL approaches
- Discover and address the challenges of implementing RL in the real world
This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
Table of Contents- Introduction to Reinforcement Learning
- Multi-armed Bandits
- Contextual Bandits
- Makings of the Markov Decision Process
- Solving the Reinforcement Learning Problem
- Deep Q-Learning at Scale
- Policy Based Methods
- Model-Based Methods
- Multi-Agent Reinforcement Learning
- Machine Teaching
- Generalization and Domain Randomization
- Meta-reinforcement learning
- Other Advanced Topics
- Autonomous Systems
- Supply Chain Management
- Marketing, Personalization and Finance
- Smart City and Cybersecurity
- Challenges and Future Directions in Reinforcement Learning
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Build next-generation, self-learning models using reinforcement learning techniques and best practices
Enes Bilgin
BIRMINGHAMMUMBAI
Copyright 2020 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: December 2020
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ISBN 978-1-83864-414-7
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Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
Juan Toms Oliva Ramos is an environmental engineer from University of Guanajuato, Mexico, with a master's degree in administrative engineering and quality. He is an expert in technologies used to improve processes and project management. He also works in statistical studies and develop processes around it. He has been part of the design of surveillance and technology alert models as support for education, and the development of new products and processes. His specialities include the design of professional, engineering, and postgraduate updating programs in face-to-face and virtual environments.
I want to thank God for giving me the wisdom and humility to review this book. I want to thank my family for staying with me all this time (Brenda, Regina, Renata and Tadeo), I love you.
Satwik Kansal is a freelance software developer by profession. He has been working on data science and Python projects since 2014. Satwik has a keen interest in content creation and runs DevWriters, a tech content creation agency. A couple of his authored works are the What the f*ck Python project and the Hands-on Reinforcement Learning with TensorFlow course by Packt.
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.
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