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

Chapter 10: Introducing Machine Teaching

The great excitement about reinforcement learning is, to a significant extent, due to its similarities to human learning: An RL agent learns from experience. This is also why many consider it as the path to artificial general intelligence. On the other hand, if you think about it, reducing human learning to just trial and error would be a gross underestimation. We don't discover everything we know, in science, art, engineering, from scratch when we are born! Instead, we build on the knowledge and civilization that have evolved over thousands of years! We transfer this knowledge among us through various, structured or unstructured forms of teaching. This capability makes it possible for us to gain skills relatively quickly and advance the common knowledge.

When we think from this perspective, what we are doing with machine learning looks quite inefficient: We dump bunch of raw data to algorithms, or expose them to an environment in the...