Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices

by Enes Bilgin

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices 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. This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.

Genres: Technology, Technical, Artificial Intelligence

544 pages, Paperback

Published December 18, 2020

Reviews

[Dan Emery · Rating 3 out of 5]

I felt chapters 1-6 were really well done. The book makes a natural progression from simple to complex problems and the supporting code is well written.

I experienced many issues with the code in the later chapters, most of them due to outdated libraries. This was really frustrating and can be easily solved by including a requirements.txt file on the GitHub instead of the dependencies table.

Overall, I would say this is a good introduction to RL, but a reader will need to supplement the book with additional resources to become proficient.