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. 18 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English
User
Learners taking this Course are
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Research Assistants
  • Scientists

What you will learn

  • Check

    Formalize problems as Markov Decision Processes

  • Check

    Understand basic exploration methods and the exploration / exploitation tradeoff

  • Check

    Understand value functions, as a general-purpose tool for optimal decision-making

  • Check

    Know how to implement dynamic programming as an efficient solution approach to an industrial control problem

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
User
Learners taking this Course are
  • Data Scientists
  • Machine Learning Engineers
  • Researchers
  • Research Assistants
  • Scientists

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. 18 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 Course!

4 videos (Total 20 min), 2 readings
4 videos
Course Introduction5m
Meet your instructors!8m
Your Specialization Roadmap3m
2 readings
Reinforcement Learning Textbook10m
Read Me: Pre-requisites and Learning Objectives10m
7 hours to complete

The K-Armed Bandit Problem

8 videos (Total 46 min), 3 readings, 2 quizzes
8 videos
Learning Action Values4m
Estimating Action Values Incrementally5m
What is the trade-off?7m
Optimistic Initial Values6m
Upper-Confidence Bound (UCB) Action Selection5m
Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8m
Week 1 Summary3m
3 readings
Module 2 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 practice exercise
Exploration/Exploitation45m
Week
2
4 hours to complete

Markov Decision Processes

7 videos (Total 36 min), 2 readings, 2 quizzes
7 videos
Examples of MDPs4m
The Goal of Reinforcement Learning3m
Michael Littman: The Reward Hypothesis12m
Continuing Tasks5m
Examples of Episodic and Continuing Tasks3m
Week 2 Summary1m
2 readings
Module 3 Learning Objectives10m
Weekly Reading30m
1 practice exercise
MDPs45m
Week
3
3 hours to complete

Value Functions & Bellman Equations

9 videos (Total 56 min), 3 readings, 2 quizzes
9 videos
Value Functions6m
Rich Sutton and Andy Barto: A brief History of RL7m
Bellman Equation Derivation6m
Why Bellman Equations?5m
Optimal Policies7m
Optimal Value Functions5m
Using Optimal Value Functions to Get Optimal Policies8m
Week 3 Summary4m
3 readings
Module 4 Learning Objectives10m
Weekly Reading30m
Chapter Summary13m
2 practice exercises
Value Functions and Bellman Equations45m
Value Functions and Bellman Equations45m
Week
4
7 hours to complete

Dynamic Programming

10 videos (Total 72 min), 3 readings, 2 quizzes
10 videos
Iterative Policy Evaluation8m
Policy Improvement4m
Policy Iteration8m
Flexibility of the Policy Iteration Framework4m
Efficiency of Dynamic Programming5m
Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)7m
Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)21m
Week 4 Summary2m
Congratulations!3m
3 readings
Module 5 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 practice exercise
Dynamic Programming45m
4.7
83 ReviewsChevron Right

Top reviews from Fundamentals of Reinforcement Learning

By ABSep 7th 2019

Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.\n\nSometimes, visualizing the problem is hard, so need to thoroghly get prepared.

By RDOct 16th 2019

An excellent introduction to the subject of Reinforcement Learning, accompanied by a very clear text book. The python assignments in Jupyter notebooks are both informative and helpful.

Instructors

Avatar

Martha White

Assistant Professor
Computing Science
Avatar

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.