Introduction to Artificial Intelligence: Monte Carlo Reinforcement Learning Methods - Lecture 16

Explore Monte Carlo Reinforcement Learning methods in this comprehensive lecture from the Intro to Artificial Intelligence course at Memorial University. Delve into the fundamentals of Monte Carlo techniques, comparing actual vs. simulated experiences, and understand how MC methods utilize sampling. Examine Monte Carlo prediction and its application in games like Blackjack. Learn about generalized policy iteration, Monte Carlo policy iteration, and the Monte Carlo ES algorithm. Gain insights into the Matchbox Machine Learning concept and prepare for potential exam questions on the topic. This 49-minute lecture, delivered by Professor David Churchill, offers a thorough introduction to Monte Carlo RL methods as part of the broader AI curriculum.

Intro
Monte Carlo Methods
Actual vs. Simulated Experienc
MC Methods use Sampling
Monte Carlo Prediction
Syntax Note
MC Example: Blackjack
Ex: Blackjack Hand (Episode)
Blackjack Using DP?
Generalized Policy Iteration
MC Policy Iteration
Blackjack Policy
Monte Carlo ES
Monte Carlo Overview
Matchbox Machine Learning
Exam Questions