Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

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.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

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. 


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:

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.


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 (

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.