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

Training tic-tac-toe agents through self-play

In this section, we will provide you with some key explanations of the code in our Github repo to get a better grasp of MARL with RLlib while training tic-tac-toe agents on a 3x3 board. For the full code, you can refer to

Figure 9.5 – A 3x3 tic-tac-toe. For the image credit and to learn how it is played, see

Figure 9.5 – A 3x3 tic-tac-toe. For the image credit and to learn how it is played, see

Let's started with designing the multi-agent environment.

Designing the multi-agent tic-tac-toe environment

In the game, we have two agents, X and O, playing the game. We will train four policies for the agents to pull their actions from, and each policy can play either an X or O. We construct the environment class as follows:


class TicTacToe(MultiAgentEnv):
    def __init__(self, config=None):