2 Mathematical foundations of reinforcement learning
In this chapter
- You will learn about the core components of reinforcement learning.
- You will learn to represent sequential decision-making problems as reinforcement learning environments using a mathematical framework known as Markov decision processes.
- You will build from scratch environments that reinforcement learning agents learn to solve in later chapters.
Mankind’s history has been a struggle against a hostile environment. We finally have reached a point where we can begin to dominate our environment. ... As soon as we understand this fact, our mathematical interests necessarily shift in many areas from descriptive analysis to control theory.
— Richard Bellman American applied mathematician, an IEEE medal of honor recipient
You pick up this book and decide to read one more chapter despite having limited free time. A coach benches their best player for tonight’s match ignoring the press criticism. A parent invests long hours of hard work and unlimited patience in teaching their child good manners. These are all examples of complex sequential decision-making under uncertainty.