Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices

Enes Bilgin

$31.99 ~~$35.99~~

4.4 (12 Ratings)

eBook Dec 2020 544 pages 1st Edition

Enes Bilgin

$31.99 ~~$35.99~~

4.4 (12 Ratings)

eBook Dec 2020 544 pages 1st Edition

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Key benefits

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

Description

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, you'll 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, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll 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, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

Who is this book for?

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.

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 Ray s 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Ismail Kose Sep 04, 2021

It’s a very good reference book for beginners and experienced engineers.

Amazon Verified review

Hossein Khadivi Heris Mar 15, 2021

The book provides good level of details on foundation of RL, practical tools and libraries, and step by step guides on solving some applied problems. You need to have some knowledge in statistics and probability to understand the topics discussed in the book. Some programming skills in python is also required but you do not need to be an advanced python programmer to benefit from the book.There are links to many useful external resources and blog posts to help you gain deeper knowledge in topics discussed at each chapter. Many advanced topics such as Machine teaching is covered that has industrial relevance (example: Microsoft's project bonsai). Most importantly, the challenges of applying RL and the limitations of RL for some applications are discussed. Being aware of the limitations can save you a lot of time in your solution formulations.

Amazon Customer Jan 21, 2021

The flow of the book is great, it is easy to follow the book and the python codes concurrently. I strongly recommend the book everyone including the ones with no strong background in machine/reinforcement learning.

Claus Horn Mar 07, 2021

This is a book for practitioners. It is well written and covers a wide range of topics from the basics of RL and Markov decision processes to multi-agent systems. It focuses on modern methods of deep RL including model-based approaches, notably also an introduction to machine teaching. Very nice is also part 4, with a lot of application examples from robotics, supply chain management, marketing and cybersecurity. I definitely recommend it for everyone interested in developing their own real-world RL solutions.

Matt Nov 28, 2022

Must have if you are into applied RL.

Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices

What do you get with eBook?