REINFORCEMENT LEARNING

Catalog description

This course introduces the foundational concepts of reinforcement learning; a core area of Artificial Intelligence concerned with agents that learn to make decisions by interacting with an environment. Students will explore key ideas such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, and deep reinforcement learning, culminating in a hands-on final project. PREREQ: COMPSCI 218 AND MATH 355 AND STAT 342