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

Unifying model-based and model-free approaches

When we went from dynamic programming-based approaches to Monte Carlo and temporal-difference methods in Chapter 5, Solving the Reinforcement Learning Problem, our motivation was that it is limiting to assume that the environment transition probabilities are known. Now that we know how to learn the environment dynamics, we will leverage that to find a middle ground. It turns out that with a learned model of the environment, the learning with model-free methods can be accelerated. To that end, in this section, we first refresh our minds on Q-learning, then introduce a class of methods called Dyna.

Refresher on Q-learning

Let's start with remembering the definition of the action-value function:

The expectation operator here is because the transition into the next state is probabilistic, so is a random variable along with . On the other hand, if we know the probability distribution of and , we can...