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

Chapter 9: Multi-Agent Reinforcement Learning

If there is something more exciting than training a reinforcement learning (RL) agent to exhibit intelligent behavior, it is to train multiple of them to collaborate or compete. Multi-agent RL (MARL) is where you will really feel the potential in artificial intelligence. Many famous RL stories, such as AlphaGo or OpenAI Five, stemmed from MARL, which we introduce you to in this chapter. Of course, there is no free lunch, and MARL comes with lots of challenges along with its opportunities, which we will also explore. At the end of the chapter, we will train a bunch of tic-tac-toe agents through competitive self-play. So, at the end, you will have some companions to play some game against.

This will be a fun chapter, and specifically we will cover the following topics:

  • Introducing multi-agent reinforcement learning,
  • Exploring the challenges in multi-agent reinforcement learning,
  • Training policies in multi-agent settings...