Lecture 8_ Exploring Stochastic Approximation Theorem & ODE Proof
Channel: Abhishek Gupta
Date: 2020-02-01

In this ECE 8851: Reinforcement Learning lecture, we take a closer look at the Stochastic Approximation Theorem and the proof of Stochastic Approximation using the ODE approach.

We begin by discussing the Q-learning algorithm and its role in reinforcement learning. Then, we delve into the Martingale theory and explore how it can be applied to the stochastic approximation theorem. We also examine the differential equation and its importance in understanding the convergence of stochastic approximation algorithms.

Another key topic covered in this lecture is the use of Markovian arguments to prove the stochastic approximation theorem. We examine the intuition behind these arguments and discuss how they can be applied to real-world scenarios.

Throughout the lecture, we focus on the key moments in these algorithms, including the use of linear interpolation, martingale theory, and differential equations. We explore the advantages and disadvantages of each approach and discuss how they can be used to solve complex reinforcement learning problems.