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

Using function approximation for action

In our online advertising examples so far, we have assumed to have a fixed set of ads (actions/arms) to choose from. However, in many applications of contextual bandits, the set of available actions change over time. Take the example of a modern advertising network that uses an ad server to match ads to websites/apps. This is a very dynamic operation which involves, leaving the pricing aside, three major components:

  • Website/app content,
  • Viewer/user profile,
  • Ad inventory.

Previously, we considered only the user profile for the context. An ad server needs to take the website/app content into account additionally, but this does not really change the structure of problem we solved before. However, now, we cannot use a separate model for each ad since the ad inventory is dynamic. We handle this by using a single model to which we feed ad features. This is illustrated in Figure 5.

Figure 3.5 –...