LEC 18:  Universal Function Approximation Theorem &  Parametric vs Non Parametric Regression
Channel: Abhishek Gupta
Date: 2020-03-23

In this lecture, we explore the connection between RL and regression problems in EECE 8851: Reinforcement Learning Lec 18.

We start by discussing the fundamental concept of a regression algorithm and how it relates to the Q-learning algorithm. From there, we explore the universal approximate property and how it allows us to use regression algorithms to solve complex RL problems.

We then dive into two critical representation theorems, the Stone-Weierstrass approximation theorem, and the Kolmogorov-Arnold's representation theorem. These theorems play a crucial role in our ability to use regression algorithms effectively in RL and are essential for designing algorithms that work effectively in complex environments.

Finally, we explore the critical difference between parametric and non-parametric regression and how they relate to RL algorithms. Understanding this difference is essential for choosing the right regression algorithm for a given RL problem and optimizing performance.