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

Understanding the importance of the simulation in reinforcement learning

As we mentioned multiple times so far, and especially in the first chapter when we talked about RL success stories, RL's hunger for data is orders of magnitude greater than that of deep supervised learning. That is why it takes many months to train some complex RL agents, over millions and billions of iterations. Since it is often impractical to collect such data in a physical environment, we heavily rely on simulation models in training RL agents. This brings some challenges along with it:

  • Many businesses don't have a simulation model of their processes. This makes it challenging to bring the RL technology to the use of such companies.
  • When a simulation model exists, it is often too simplistic to capture the real-world dynamics. As a result, RL models could easily overfit to the simulation environment and may fail in deployment. It takes significant time and resources to calibrate and validate...