About this Course

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Intermediate Level

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

Approx. 22 hours to complete
English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Shareable Certificate
Earn a Certificate upon completion
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. 22 hours to complete
English

Offered by

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University of Alberta

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Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

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Week
1

Week 1

1 hour to complete

Welcome to the Course!

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

Monte Carlo Methods for Prediction & Control

3 hours to complete
11 videos (Total 58 min), 3 readings, 2 quizzes
11 videos
Using Monte Carlo for Prediction6m
Using Monte Carlo for Action Values2m
Using Monte Carlo methods for generalized policy iteration2m
Solving the Blackjack Example3m
Epsilon-soft policies5m
Why does off-policy learning matter?4m
Importance Sampling4m
Off-Policy Monte Carlo Prediction5m
Emma Brunskill: Batch Reinforcement Learning12m
Week 1 Summary3m
3 readings
Module 1 Learning Objectives10m
Weekly Reading40m
Chapter Summary40m
1 practice exercise
Graded Quiz30m
Week
2

Week 2

5 hours to complete

Temporal Difference Learning Methods for Prediction

5 hours to complete
6 videos (Total 37 min), 2 readings, 2 quizzes
6 videos
Rich Sutton: The Importance of TD Learning6m
The advantages of temporal difference learning5m
Comparing TD and Monte Carlo5m
Andy Barto and Rich Sutton: More on the History of RL12m
Week 2 Summary2m
2 readings
Module 2 Learning Objectives10m
Weekly Reading40m
1 practice exercise
Practice Quiz30m
Week
3

Week 3

6 hours to complete

Temporal Difference Learning Methods for Control

6 hours to complete
9 videos (Total 30 min), 3 readings, 2 quizzes
9 videos
Sarsa in the Windy Grid World3m
What is Q-learning?3m
Q-learning in the Windy Grid World3m
How is Q-learning off-policy?4m
Expected Sarsa3m
Expected Sarsa in the Cliff World3m
Generality of Expected Sarsa1m
Week 3 Summary2m
3 readings
Module 3 Learning Objectives10m
Weekly Reading40m
Chapter summary40m
1 practice exercise
Practice Quiz30m
Week
4

Week 4

7 hours to complete

Planning, Learning & Acting

7 hours to complete
11 videos (Total 47 min), 4 readings, 2 quizzes
11 videos
Comparing Sample and Distribution Models2m
Random Tabular Q-planning3m
The Dyna Architecture5m
The Dyna Algorithm5m
Dyna & Q-learning in a Simple Maze5m
What if the model is inaccurate?3m
In-depth with changing environments5m
Drew Bagnell: self-driving, robotics, and Model Based RL7m
Week 4 Summary1m
Congratulations!2m
4 readings
Module 4 Learning Objectives10m
Weekly Reading40m
Chapter Summary40m
Text Book Part 1 Summary40m
1 practice exercise
Practice Assessment45m

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About the Reinforcement Learning Specialization

Reinforcement Learning

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