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

Trust-region methods

One of the important developments in the world of policy-based methods has been the evolution of the trust-region methods. In particular, TRPO and PPO algorithms have led to significant improvement over the algorithms like A2C and A3C. For example, the famous Dota 2 AI agent which reached expert-level performance competitions was trained using PPO and GAE. In this section, we go into the details of those algorithms to help you gain a solid understanding of how they work.


Prof. Sergey Levine, who co-authored the TRPO and PPO papers, goes deep into the details of the math behind these methods in his online lecture more than we do in this section. That lecture is available at and I highly recommend you watch it to improve your theoretical understanding of these algorithms.

Without further ado, let's dive in!

Policy gradient as policy iteration

In the earlier chapters, we described how most of the RL algorithms...