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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

Chapter 1: Introduction to Reinforcement Learning

Reinforcement Learning (RL) aims to create Artificial Intelligence (AI) agents that can make decisions in complex and uncertain environments, with the goal of maximizing their long-term benefit. These agents learn how to do it through interacting with their environments, which mimics the way we as humans learn from experience. As such, RL has an incredibly broad and adaptable set of applications, with the potential to disrupt and revolutionize global industries.

This book will give you an advanced level understanding of this field. We will go deeper into the theory behind the algorithms you may already know, and cover state-of-the art RL. Moreover, this is a practical book. You will see examples inspired by real-world industry problems and learn expert tips along the way. By its conclusion, you will be able to model and solve your own sequential decision-making problems using Python.

So, let's start our journey with refreshing your mind on RL concepts and get you set up for the advanced material upcoming in the following chapters. Specifically, this chapter covers:

  • Why reinforcement learning?
  • The three paradigms of ML
  • RL application areas and success stories
  • Elements of a RL problem
  • Setting up your RL environment

Why reinforcement learning?

Creating intelligent machines that make decisions at or superior to human level is a dream of many scientist and engineers, and one which is gradually becoming closer to reality. In the seven decades since the Turing test, AI research and development has been on a roller coaster. The expectations were very high initially: In the 1960s, for example, Herbert Simon (who later received the Nobel Prize in Economics) predicted that machines would be capable of doing any work humans can do within twenty years. It was this excitement that attracted big government and corporate funding flowing into AI research, only to be followed by big disappointments and a period called the "AI winter." Decades later, thanks to the incredible developments in computing, data, and algorithms, humankind is again very excited, more than ever before, in its pursuit of the AI dream. 

Note

If you're not familiar with Alan Turing's instrumental work on the foundations of AI in 1950, it's worth learning more about the Turing Test here: https://youtu.be/3wLqsRLvV-c

The AI dream is certainly one of grandiosity. After all, the potential in intelligent autonomous systems is enormous. Think about how we are limited in terms of specialist medical doctors in the world. It takes years and significant intellectual and financial resources to educate them, which many countries don't have at sufficient levels. In addition, even after years of education, it is nearly impossible for a specialist to stay up-to-date with all of the scientific developments in her field, learn from the outcomes of the tens of thousands of treatments around the world, and effectively incorporate all this knowledge into practice.

Conversely, an AI model could process and learn from all this data and combine it with a rich set of information about a patient (medical history, lab results, presenting symptoms, health profile) to make diagnosis and suggest treatments. Such a model could serve even in the most rural parts of the world (as far as an internet connection and computer are available) and direct the local health personnel about the treatment. No doubt that it would revolutionize international healthcare and improve the lives of millions of people.

Note

AI is already transforming the healthcare industry. In a recent article, Google published results from an AI system surpassing human experts in breast cancer prediction using mammography readings (McKinney et al. 2020). Microsoft is collaborating with one of India's largest healthcare providers to detect cardiac illnesses using AI (Agrawal, 2018). IBM Watson for Clinical Trial Matching uses natural language processing to recommend potential treatments for patients from medical databases (https://youtu.be/grDWR7hMQQQ).

On our quest to develop AI systems that are at or superior to human level, which is -sometimes controversially- called Artificial General Intelligence (AGI), it makes sense to develop a model that can learn from its own experience - without necessarily needing a supervisor. RL is the computational framework that enables us to create such intelligent agents. To better understand the value of RL, it is important to compare it with the other ML paradigms, which we'll look into next.

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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

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Publication date : Dec 18, 2020
Length: 544 pages
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Language : English
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Publication date : Dec 18, 2020
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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

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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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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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