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

All Emails Subject: “[CSC498-F21]

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


Repository for code:




Priority will be given to students who meet prerequisites for the course. Knowledge of probability, multivariate calculus, and linear algebra is expected.



Algorithm implementation will be done mainly in Python. Please familiarize yourself with the language and common tools (git, cmd, and the frameworks numpy and pytorch).

Textbook & Resources

There is no required textbook.The course will provide all material in class and handouts.
The students can refer the following material for additional help:

Additional resources:

Reinforcement learning ressources:

Evaluation format

This course combines lectures with Tutorials, encouraging both fundamental knowledge acquisition as well as hands-on experience. Each student will be responsible for 4 individual assignments (40%), one take-home midterm (20%) and one project (20%). In addition, we will conduct 8 short online quizzes during the exercise slot on Fridays, the 4 best ones will count (20%). The quiz dates will be announced at least one week prior. FOr more information, see the syllabus.

Late penalties

Each student will have 3 grace days throughout the semester for late assignment submissions. Late submissions that exceed those grace days will lose 33% of their value for every late day beyond the allotted grace days. Late submissions that exceed three days of delay after the grace days have been used will unfortunately not be accepted. The official policy of the Registrar’s Office at UTM regarding missed exams can be found here.