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

Learning a world model

In the introduction to this chapter, we reminded you how we departed from dynamic programming methods to avoid assuming that the model of the environment an agent is in is available and accessible. Now, coming back to talking about models, we need to also discuss how a world model can be learned when not available. In particular, in this section, we discuss what we aim to learn as a model, when we may want to learn it, a general procedure for learning a model, how to improve it by incorporating the model uncertainty into the learning procedure, and what to do when we have complex observations. Let's dive in!

Understanding what model means

From what we have done so far, a model of the environment could be equivalent to the simulation of the environment in your mind. On the other hand, model-based methods don't require the full fidelity of a simulation. Instead, what we expect to get from a model is the next state given the current state and action...