Fall 2021 Course Descriptions

CS 138-01 Reinforcement Learning

J. Sinapov
TR 1:30-2:45, Halligan Hall 111A
H+ Block

"Reinforcement learning problems involve learning what to do — how to map situations to actions — so as to maximize a numerical reward signal." - Sutton and Barto ("Reinforcement Learning: An Introduction", course textbook)

This course will focus on agents that much learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of Reinforcement Learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL) with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

There will be a programming component to the course in the form of a few short assignments and a final projects.

Approved as a category 2 elective in Data Science (analysis and interfaces).

Prerequisite: Students are expected to be proficient programmers in at least one of the following languages: C++, Java, or Python. Prior coursework (or experience) in Artificial Intelligence and/or Machine Learning is highly recommended, but not required.

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