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


In this chapter, we covered model-based methods. We started the chapter by describing how we humans use the world models we have in our brains to plan our actions. Then, we introduced several methods that can be used to plan an agent's actions in an environment when a model is available. These were derivative-free search methods, and for the CEM and CMA-ES methods, we implemented parallelized versions. As a natural follow-up to this section, we then went into how a world model can be learned to be used for planning or developing policies. This section contained some important discussions about model uncertainty and how learned models can suffer from it. At the end of the chapter, we unified the model-free and model-based approaches in the Dyna framework.

As we conclude our discussion on model-based RL, we proceed to the next chapter for yet another exciting topic: multi-agent RL. Take a break, and we will see you soon!