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Mastering Reinforcement Learning with Python
Mastering Reinforcement Learning with Python

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

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Mastering Reinforcement Learning with Python

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 18, 2020
Length: 544 pages
Edition : 1st
Language : English
ISBN-13 : 9781838644147
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Product Details

Publication date : Dec 18, 2020
Length: 544 pages
Edition : 1st
Language : English
ISBN-13 : 9781838644147
Category :
Languages :
Tools :

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Table of Contents

23 Chapters
Section 1: Reinforcement Learning Foundations Chevron down icon Chevron up icon
Chapter 1: Introduction to Reinforcement Learning Chevron down icon Chevron up icon
Chapter 2: Multi-Armed Bandits Chevron down icon Chevron up icon
Chapter 3: Contextual Bandits Chevron down icon Chevron up icon
Chapter 4: Makings of a Markov Decision Process Chevron down icon Chevron up icon
Chapter 5: Solving the Reinforcement Learning Problem Chevron down icon Chevron up icon
Section 2: Deep Reinforcement Learning Chevron down icon Chevron up icon
Chapter 6: Deep Q-Learning at Scale Chevron down icon Chevron up icon
Chapter 7: Policy-Based Methods Chevron down icon Chevron up icon
Chapter 8: Model-Based Methods Chevron down icon Chevron up icon
Chapter 9: Multi-Agent Reinforcement Learning Chevron down icon Chevron up icon
Section 3: Advanced Topics in RL Chevron down icon Chevron up icon
Chapter 10: Introducing Machine Teaching Chevron down icon Chevron up icon
Chapter 11: Achieving Generalization and Overcoming Partial Observability Chevron down icon Chevron up icon
Chapter 12: Meta-Reinforcement Learning Chevron down icon Chevron up icon
Chapter 13: Exploring Advanced Topics Chevron down icon Chevron up icon
Section 4: Applications of RL Chevron down icon Chevron up icon
Chapter 14: Solving Robot Learning Chevron down icon Chevron up icon
Chapter 15: Supply Chain Management Chevron down icon Chevron up icon
Chapter 16: Personalization, Marketing, and Finance Chevron down icon Chevron up icon
Chapter 17: Smart City and Cybersecurity Chevron down icon Chevron up icon
Chapter 18: Challenges and Future Directions in Reinforcement Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(12 Ratings)
5 star 66.7%
4 star 25%
3 star 0%
2 star 0%
1 star 8.3%
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Top Reviews

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Ismail Kose Sep 04, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It’s a very good reference book for beginners and experienced engineers.
Amazon Verified review Amazon
Hossein Khadivi Heris Mar 15, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Verified review Amazon
Amazon Customer Jan 21, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Claus Horn Mar 07, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Matt Nov 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Must have if you are into applied RL.
Amazon Verified review Amazon
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