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

Vanilla policy gradient

We start discussing the policy-based methods with the most fundamental algorithm: a vanilla policy gradient approach. Although such an algorithm is rarely useful in realistic problem settings, it is very important to understand it to build a strong intuition and a theoretical background for the more complex algorithms we will cover later.

Objective in the policy gradient methods

In value-based methods, we focused on finding good estimates for action values, with which we then obtained policies. Policy gradient methods, on the other hand, directly focus on optimizing the policy with respect to the reinforcement learning objective - although we will still make use of value estimates. If you don't remember what this objective was, it is the expected discounted return:

This is a slightly more rigorous way of writing this objective compared to how we wrote it before. Let's unpack what we have here:

  • The objective...