Mathematical and Statistical Foundations
A dynamic primer on linear algebra, probability, and Markov chains, laying the groundwork for RL’s decision-making prowess.
Chapter 1: Mathematical Preliminaries (Linear algebra, calculus, optimization, differential equations) Chapter 2: Probability and Decision Theory (Distributions, expectation, Bayes’ theorem, utility theory) Chapter 3: Stochastic Processes (Markov chains, stationary distributions, ergodicity)