CSC 498 Fall 2021: Introduction to Reinforcement Learning

Weekly schedule

Lectures: online delivery, Tues 5:00 pm - 7:00 pm EST, Zoom

Tutorials: Fri 09:00 am - 10:00 am EST, Zoom

Animesh Garg office hours: Thurs 2:30 pm - 3:30 pm EST, Zoom

TA office hours: Thurs 10:00 am - 12:00 am EST, Zoom

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Accessing resources

Piazza: piazza Zoom: Link in Quercus Announcement

Online delivery: The lectures will be delivered live online in the lecture slot. During the Friday tutorial slot, we will have a small quiz every week (mandatory attendance) and discuss the material and exercises. For questions about the material or exercises, join the office hours or participate in the online offerings on Zoom.


Reinforcement learning is a powerful paradigm for modeling autonomous and intelligent agents interacting with the environment, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will study agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources. The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.

Learning objectives

At the end of this course, you will have gained both knowledge and system building abilities in:

List of Topics covered in this course (expected)

With a focus on AI as the design of agents learning from experience to predict and control their environment, topics will include