About the Book
Video introductionWatch this short video to hear me introduce the book. I talk more about my reasons for writing this book, what you can expect from it and why you should read it too.
From the cover
Why?Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to perform the reinforcement process that allows a machine to learn by itself.
From the cover
The contentAuthor Dr. Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You’ll explore the current state of RL, focusing on industrial applications, and learn numerous algorithms, frameworks, and environments. This is no cookbook—it doesn’t shy away from math and expects familiarity with ML.
IndustrialLearn what RL is and how the algorithms help solve problems
FundamentalsBecome grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
Deep-diveDive deep into value methods and policy gradient methods
ApplyApply advanced RL implementations such as meta learning, hierarchical learning, evolutionary algorithms, and imitation learning
State-of-the-artUnderstand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
ExamplesGet practical examples through the accompanying Git repository
Inside the Book
Here's what's inside the book. Over 350 pages of up-to-date RL experience, explained in simple terms.
Become a Reinforcement Learning Ninja
Written from the perspective of an industrial engineer, this book has everything you need to know to get started with reinforcement learning.
Learn about all major reinforcement learning algorithms in one place. This book covers all major types of RL algorithm.
Packed full of industrial use cases, you won’t be short of ideas. All examples are real (no hypotheticals here!) and have accompanying code.
Complex models don’t need to be complicated. You will understand the pros and cons before accidents happen.
Intuitive experiments to demonstrate the how and the why.
Clear evolution of techniques from A/B testing to the state of the art.
Keep it Simple
Simple, concise language that packs in more content compared to other RL books.