2021-2022 Term 1, IERG 5350

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 discussed. Some other topics such as unsupervised learning and generative modeling will also be introduced.

Course Information

Course Instructor

Teaching Assistants

Time and Classroom

Monday 10:30 am - 12:15 pm, SHB 801
Tuesday 10:30 am - 11:15 am, SHB 801

Office Hours

Instructor: Tuesday 11:15 am - 12:00 pm, SHB 717
TA: Thursday 5:45 pm - 6:45 pm, MMW 715

Textbook (recommended but optional)

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

Grading Policy

Attendance: 10%
Assignments: 40%
Quiz: 20%
Course project: 30%