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

Enriching agent experience via domain randomization

DR is simply about randomizing the parameters defining (part of) the environment during training to enrich the training data. It is a useful technique to obtain policies that are robust and generalizable, both in fully and partially observable environments. In this section, we first present a classification of such parameters, in other words, different dimensions of randomization. Then, we discuss two curriculum learning approaches to guide RL training along those dimensions.

Dimensions of randomization

Borrowed from (Rivlin, 2019), a useful categorization of how two environments belonging to the same problem class (e.g., autonomous driving) can differ is as follows.

Different observations for the same/similar states

In this case, two environments emit different observations although the underlying state and transition functions are the same or very similar. An example to this is the same Atari game scene but with different...