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 3: Contextual Bandits

A more advanced version of the multi-armed bandit is the contextual bandit (CB) problem, where decisions are tailored to the context they are made in. In the previous chapter, we identified the best performing ad in an online advertising scenario. In doing so, we did not use any information about, for instance, the user persona, age, gender, location, previous visits etc., which would have increased the likelihood of a click. Contextual bandits allow us to leverage such information, which makes them play a central role in commercial personalization and recommendation applications.

Context is similar to a state in a multi-step reinforcement learning (RL) problem, with one key difference. In a multi-step RL problem, the action an agent takes affects the states it is likely to visit in the subsequent steps. For example, while playing tic-tac-toe, an agent's action in the current state changes the board configuration (state) in a particular way, which...