DATA 643 - Reinforcement Learning

Fall (3) Chen. Prerequisite(s): Background in Python, Statistics, Linear Algebra, Calculus, and introductory concepts in Machine Learning.

This course introduces the fundamentals of reinforcement learning (RL) and its applications in various domains. The students will be able to (1) understand the theoretical foundations of RL problems, (2) know how to formalize a problem as a RL problem, (3) understand a spectrum of existing RL algorithms such as Q-learning and policy gradient, and (4) how to implement a RL algorithm to the target problem of interest. 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 and apply the RL algorithms to solve example real-world problems.