2019-2020 2nd term, IERG 6130

Reinforcement Learning and Beyond
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Course Overview

This course covers fundamental topics relevant to reinforcement learning, a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment. Recent progress for deep reinforcement learning and its applications will be discussed. Some other topics such as unsupervised learning and generative modeling will be introduced.


Jan 8, 2020: Example code of RL!

Educational example code will be uploaded to this github repo.

Jan 6, 2020: Welcome to IERG 6130!

The course page is being updated, more information will come soon. The prerequisites of undergraduate level courses in linear algebra, probability, and machine learning are recommended.

Course Information

Course Instructor

Teaching Assistant

Time and Classroom

Tuesday 2:30 pm - 4:15 pm, SHB 833
Wednesday 3:30 pm - 4:15 pm, SHB 833

Office Hours

Instructor: Wednesday 4:30 pm - 5:30 pm, SHB 717
TA: Friday 1:00 pm - 2:00 pm, SHB 702a, or by email appointment

Textbook (recommended)

Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction 2nd edition. link

Grading Policy

Attendance: 10%
Assignments: 40%
Course project: 50%