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

Offline reinforcement learning

Offline reinforcement learning is about training agents using data recorded during some prior interactions of an agent (likely non-RL, such as a human agent) with the environment, as opposed to directly interacting with it. It is also called batch reinforcement learning. In this section, we look into some of the key components of offline RL. Let's get started with an overview of how it works.

An overview of how offline reinforcement learning works

In offline RL, the agent does not directly interact with the environment to explore and learn a policy. Figure 13.12 contrasts this to on-policy and off-policy settings.

Figure 13.12 – Comparison of on-policy, off-policy, and offline deep RL (adapted from Levine 2020).

Let's unpack what this figure illustrates:

  • In on-policy RL, the agent collects a batch of experiences with each policy. Then, it uses this batch to update the policy. This cycle repeats until...