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

Introducing multi-agent reinforcement learning

All of the problems and algorithms we have covered in the book so far involved a single agent being trained in an environment. On the other hand, in many applications from games to autonomous vehicle fleets, there are multiple decision-makers, agents, which train concurrently, but execute local policies (i.e., without a central decision-maker). This leads us to MARL, which involves a much richer set of problems and challenges than single-agent RL does. In this section, we give an overview of MARL landscape.

Collaboration and competition between MARL agents

MARL problems can be classified into three different groups with respect to the structure of collaboration and competition between agents. Let's look into what those groups are and what types of applications fit into each group.

Fully cooperative environments

In this setting, all of the agents in the environment work towards a common long-term goal. The agents are credited...