DATA 448 - Reinforcement Learning

Credits: (3)
Prerequisite(s): DATA 301
This course introduces the fundamentals of reinforcement learning (RL), a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. The course will cover Markov decision processes, reinforcement learning, planning, and function
approximation. By the end of this course, the students will be able to (1) understand the fundamentals concepts of an RL problem, (2) know how to formalize a problem as a RL problem, and (3) learn classic RL algorithms such as Q-learning and policy gradient. There will be several hands-on projects throughout the course. Programming will be done in the Python language. By the end of the course, the students should be able to implement classical RL algorithms such as Q-learning and policy gradient methods.