2018-2019 2nd term, IERG 6130

Reinforcement Learning
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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 also discussed.

Announcements

Jan 9, 2019: Example code

Example code of RL used in the lecture will be uploaded to this github repo. Keep calm and code on!

Jan 2, 2019: 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

Time and Classroom

Tuesday 2:30 pm - 4:15 pm, SHB 833
Thursday 1:30 pm - 2:15 pm, SHB 833

Office Hours

Tuesday 4:30 pm - 5:30 pm, SHB 717

Textbook (recommended)

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

Grading Policy

Student-led seminars: 40%
Course project: 60%