DAN624 Reinforcement Learning

[3–0, 3 cr.]

This course provides students with a solid foundation in reinforcement learning (RL), a key branch of machine learning that enables systems to learn optimal behaviors through trial-and-error interactions with dynamic environments. The course covers core concepts such as Markov Decision Processes (MDPs), dynamic programming, Monte Carlo methods, temporal-difference learning, Q-learning, and policy gradient methods. Through real-world examples and practical applications, students will learn how to model decision-making problems and implement RL algorithms to solve complex challenges in fields like business optimization, operations, recommendation systems, and resource allocation. Emphasis will be placed on applying RL in data-driven business environments using Python-based libraries.