2 mathematical foundations of reinforcement learning
In this chapter:
- You learn about the core components of reinforcement learning.
- You learn to represent sequential decision-making problems as reinforcement learning environments using a mathematical framework known as Markov Decision Processes.
- You 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.