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

Extensions to DQN: Rainbow

The Rainbow improvements bring in significant performance boost over the vanilla DQN and they have become standard in most Q-learning implementations. In this section, we discuss what those improvements are, how they help, and what their relative importance are. At the end, we talk how DQN and these extensions collectively overcome the deadly triad.

The extensions

There are six extensions to DQN included in the Rainbow algorithm. These are: i) double Q-learning, ii) prioritized replay, iii) dueling networks, iv) multi-step learning, v) distributional RL, and iv) noisy nets. Let's start describing them with double Q-learning.

Double Q-learning

One of the well-known issues in Q-learning is that the Q-value estimates we obtain during learning is higher than the true Q-values because of the maximization operation . This phenomenon is called maximization bias, and the reason we run into it is that we do a maximization operation over noisy observations...