Introduction to Artificial Intelligence: Reinforcement Learning - Lecture 13

Explore the fundamentals of Reinforcement Learning in this comprehensive lecture from the COMP3200 Intro to Artificial Intelligence course. Delve into key concepts such as the definition of RL, its relationship to machine learning, and how it functions as a problem specification. Examine the process of learning through interaction, visualize RL process diagrams, and study practical examples like the Cart Pole and Mountain Car problems. Investigate essential elements of RL problems, including policies, rewards, returns, values, and environmental models. Gain insights into the crucial balance between exploration and exploitation in RL algorithms. Benefit from Professor David Churchill's expertise in this 53-minute session, designed to provide a solid foundation in Reinforcement Learning for computer science students.

- Preroll
- Greetings
- Lecture Start
- RL Textbook
- What is RL?
- What is Machine Learning?
- RL is a Problem Specification
- Learning via Interaction
- RL Process Diagrams
- Example 1: Cart Pole
- Example 2: Mountain Car
- Elements of RL Problems
- Policy
- Rewards
- Returns
- Values
- Model of Environment
- Exploration vs Exploitation
- Concluding Remarks