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



Key Features:

Understand how large-scale state-of-the-art RL algorithms and approaches work
Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more
Explore tips and best practices from experts that will enable you to overcome real-world RL challenges

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.

Reinforcement learning (RL) is a field of artificial intelligence (AI) used to create self-learning, autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach, using examples inspired by real-world industry problems to teach you state-of-the-art RL.



Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of classical RL techniques, including 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 randomisation and curiosity-driven learning.



As you advance, you'll explore many novel algorithms and their advanced implementations using modern Python libraries such as TensorFlow and Ray's RLlib. You'll also learn how to implement RL across robotics, supply chain management, marketing, finance, smart cities, and cybersecurity, while assessing trade-offs 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.



What You Will Learn:

Model and solve complex sequential decision-making problems using RL
Develop a solid understanding of how state-of-the-art RL methods work
Use Python and TensorFlow to code RL algorithms from scratch
Parallelise and scale up your RL implementations using Ray's RLlib package
Get in-depth knowledge of a wide variety of RL topics
Understand the trade-offs between different RL approaches
Discover and address the challenges of implementing RL in the real world