Time and Place: Monday, Thursday 11:45am - 1:25pm, Snell Library 033
College of Computer and Information Science
Instructor: Robert Platt
This course will introduce the student to reinforcement learning. The bulk of what we will cover comes straight from the second edition of Sutton and Barto's book, Reinforcement Learning: An Introduction. However, we will also cover additional material drawn from the latest deep RL literature. The following list of topics is subject to revisions.
Reinforcment Learning: An Introduction, 2nd Ed., Sutton and Barto
Cheating and other acts of academic dishonesty will be referred to OSCCR (office of student conduct and conflict resolution) and the College of Computer Science. See this link.
Late assignments will be penalized by 10% for each day late. For example, if you turned in a perfect programming assignment two days late, you would receive an 80% instead of 100%.
Primary Instructor: Robert Platt ( r [dot] platt [at] neu [dot] edu )
Office hours: Thursday 3--4pm, 526 ISEC, or by Appt.
TA: Colin Kohler, kohler.c@husky.neu.edu
Office hours: Weds 3--4pm, Fri 11am--noon, 505 ISEC
Our Piazza page is here.
Required course work is:
Many of the weekly assignments will involve programming in Python. You must be proficient with python and able to install tensorflow on your computer.
We will assign approximately one problem set each Thursday that will be due on the following Tuesday.
The final project assignment can be found here. The final project can be on any topic related to RL. Students may work alone or in groups of two. Many people choose to work on a project applying a method studied in the class to some practical problem. The amount of project work should be equivalent to approximately three programming assignments.
We're using git. You should follow the instructions outlined here.