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

From tabular Q-learning to deep Q-learning

When we covered the tabular Q-learning method in Chapter 5, Solving the Reinforcement Learning Problem, it should have been obvious that we cannot really extend those methods to most real-life scenarios. Think about an RL problem which uses images as input. A image with three 8-bit color channels would lead to possible images, a number that your calculator won't be able to calculate. For this very reason, we need to use function approximators to represent the value function. Given their success in supervised and unsupervised learning, neural networks / deep learning emerges as the clear choice here. On the other hand, as we mentioned in the introduction, the convergence guarantees of tabular Q-learning fall apart when function approximators come in. This section introduces two deep Q-learning algorithms, the Neural Fitted Q-iteration and online Q-learning, and then discusses what does not go so well with them. With that, we set the...