Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - CMPT 729 G100

Reinforcement Learning (3)

Class Number: 6394

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 4 – Dec 3, 2024: Wed, 11:30 a.m.–12:20 p.m.
    Burnaby

    Sep 4 – Dec 3, 2024: Fri, 10:30 a.m.–12:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

Reinforcement learning is the branch of machine learning that studies learning to act. Agents observe, predict, and act to change their environment. Reinforcement learning has notable success in learning to play video & board games, improving robot performance, and task scheduling. Many recent successes have utilized neural nets, an approach known as deep reinforcement learning.

COURSE DETAILS:

Reinforcement learning is the branch of machine learning that studies learning to act. Agents observe, predict, and act to change their environment. Reinforcement learning has notable success in learning to play video & board games, improving robot performance, and task scheduling. Many recent successes have utilized neural nets, an approach known as deep reinforcement learning. In this course, we will discuss fundamental concepts and algorithms in reinforcement learning, and provide students with experience applying these algorithms to commonly used benchmarks.

Topics

  • Markov Decision Proccesses
  • Behavioral Cloning
  • Policy Search
  • Policy Gradients
  • Q-Learning
  • Model-Based Reiforcement Learning
  • On-Policy vs Off-Policy Algorithms
  • Imitation learning
  • Unsupervised Reinforcement Learning

Materials

RECOMMENDED READING:

Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto
Free online edition: 
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
ISBN: 9780262193986

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.