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 7: Policy-Based Methods

Value-based methods that we covered in the previous chapter achieve great results in many environments with discrete control spaces. However, a lot of applications, such as robotics, require continuous control. In this chapter, we go into another important class of algorithms, called policy-based methods, which enable us to solve continuous-control problems. In addition, these methods directly optimize a policy network, and hence stand on a stronger theoretical foundation. Finally, policy-based methods are able to learn truly stochastic policies, which are needed in partially observable environments and games, which value-based methods could not learn. All in all, policy-based approaches complement value-based methods in many ways. This chapter goes into the details of policy-based methods to gain you a strong understanding of how they work.

In particular, we discuss the following topics in this chapter.

  • Need for policy-based methods
  • Vanilla...