About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

Approx. 8 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

Approx. 8 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
1 hour to complete

Welcome to the Final Capstone Course!

2 videos (Total 10 min), 2 readings
2 videos
Meet your instructors!8m
2 readings
Reinforcement Learning Textbook10m
Pre-requisites and Learning Objectives10m
Week
2
1 hour to complete

Milestone 1: Formalize Word Problem as MDP

4 videos (Total 23 min)
4 videos
Andy Barto on What are Eligibility Traces and Why are they so named?9m
Let's Review: Markov Decision Processes6m
Let's Review: Examples of Episodic and Continuing Tasks3m
Week
3
1 hour to complete

Milestone 2: Choosing The Right Algorithm

7 videos (Total 40 min), 1 quiz
7 videos
Let's Review: Expected Sarsa3m
Let's Review: What is Q-learning?3m
Let's Review: Average Reward- A New Way of Formulating Control Problems10m
Let's Review: Actor-Critic Algorithm5m
Csaba Szepesvari on Problem Landscape8m
Andy and Rich: Advice for Students5m
1 practice exercise
Choosing the Right Algorithm
Week
4
1 hour to complete

Milestone 3: Identify Key Performance Parameters

4 videos (Total 25 min), 1 quiz
4 videos
Let's Review: Non-linear Approximation with Neural Networks4m
Drew Bagnell on System ID + Optimal Control6m
Susan Murphy on RL in Mobile Health7m
1 practice exercise
Impact of Parameter Choices in RL40m
4.6
24 Reviews

Top reviews from A Complete Reinforcement Learning System (Capstone)

By JSDec 6th 2019

This course changed my life! It was so good and I learned so much. I can't believe I'm now an astronaut. Next mission: go to Mars!

By SANov 9th 2019

Excellent final course for the specialization. Moon Lander project was informative and fun.

Instructors

Image of instructor, Martha White

Martha White

Assistant Professor
Computing Science
Image of instructor, Adam White

Adam White

Assistant Professor
Computing Science

About University of Alberta

UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences....

About Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

About the Reinforcement Learning Specialization

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more....
Reinforcement Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.