Introduction to RL

Dive into the fundamentals of Reinforcement Learning with this comprehensive lecture covering key concepts such as Markov Decision Processes, policy optimization, and model-free evaluation. Explore important components of RL, categorize RL agents, and understand Bellman's Equation through practical examples like the Inverted Pendulum and a Toy Maze. Learn about Monte Carlo methods for evaluation and control, as well as Temporal Difference Control, providing a solid foundation for understanding and implementing RL algorithms.

Intro
Markov Decision Process
Reinforcement Learning Problem
Policy optimization
Example: Inverted Pendulum
Important Components in RL
Categorizing RL agents
Toy Maze Example
Model free evaluation
Monte Carlo Evaluation
Model Free Control
Bellman's Equation
Monte Carlo Control
Temporal Difference Control