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

Introduction to machine teaching

Machine teaching is the name of a general approach and collection of methods to efficiently transfer knowledge from a teacher, a subject matter expert, to a machine learning model. With that, we aim to make the training much more efficient, and even feasible for tasks that would be impossible to achieve otherwise. Let's talk about what MT is in more detail, why we need it, and what its components are.

Understanding the need for machine teaching

Did you know that the United States is expected to spend about 1.25 trillion dollars, around 5% of its gross domestic product, on education in 2021? This should speak to the existential significance of education for our society and civilization (and many would argue that we should spend more).

We humans have built such a giant system, which we expect people to spend many years in, because we don't expect ourselves to decipher the alphabet or math on our own. And it is not just that, we continuously...