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

What you have achieved with this book

First of all, congratulations! You have come a long way to go beyond the fundamentals and to acquire the skills and the mindset to apply reinforcement learning in real-world. Here is what we have done together in this book:

  • We have spent a fair amount of time on bandit problems, which have tremendous number of applications in industry and academia.
  • We have gone deeper into the theory than a typical applied book to strengthen your foundation in RL.
  • We have covered many of the algorithms and architectures behind the most successful applications of RL.
  • We have discussed advanced training strategies to get the most out of the advanced RL algorithms.
  • We have done hands-on work with realistic case studies.
  • Throughout this journey, we have both implemented our versions of some of the algorithms, as well as utilized libraries, such as Ray and RLlib, which power many teams and platforms at the top tech companies for their reinforcement...