COMP.4240 Introduction to Reinforcement Learning

Id: 042128 Credits: 3-3

Description

This course provides a solid introduction to the field of Reinforcement Learning (RL) and Decision Making. The students will learn about the basic blocks, main approached, and core challenges of Reinforcement Learning including tabular methods, Finite Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference learning, policy search, function approximation, exploration, and generalization. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning. In addition, students will advance their understanding and the field of RL through a final project.

Prerequisites

COMP.2010 Computing III, and MATH.3860 Probability and Statistics I, or Permission of Instructor.(Students seeking this permission are expected to have knowledge of calculus, linear algebra, and Python).

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Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.