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Learner Reviews & Feedback for Sample-based Learning Methods by University of Alberta

829 ratings
167 reviews

About the Course

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...

Top reviews

Aug 11, 2020

Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job

Jan 9, 2020

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

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76 - 100 of 163 Reviews for Sample-based Learning Methods

By Fabrice L

Nov 14, 2020

Things start to get interesting in this course of the specialization.

By Sourav G

Mar 10, 2020

It was a very good course. All the concepts were explained very well.

By Animesh

May 27, 2020

this course is very well designed and executed. wow! i loved it :D

By Li W

Mar 30, 2020

Very good introductions and practices to the classic RL algorithms


Jul 8, 2020

Great learning Experience and really helpful lecturers and staff.

By Rudi C

Jul 21, 2020

Wonderful course, highly instructive, and follows the textbook!

By Rajesh

Jul 2, 2020

Please make assignments more explanatory and allow flexiblity

By David P

Nov 3, 2019

Really a wonderful course! Very professional and high level.

By Teresa Y B

Apr 10, 2020

Very well structured course, Thanks for so nice preparing!!

By Shi Y

Nov 10, 2019


By Alex E

Nov 18, 2019

A fun an interesting course. Keep up the great work!

By Jicheng F

Jul 11, 2020

Martha and Adam are great instructors, great job!

By garcia b

Dec 31, 2019

very copacetic. excellent complement to the book

By Ignacio O

Oct 12, 2019

Great, informative and very interesting course.

By Ashish S

Sep 16, 2019

A good course with proper Mathematical insights

By Cheuk L Y

Jul 3, 2020

Very good overall! It takes time to digest.


Jan 14, 2020

A nice course with well-designed homework:)

By Jingxin X

May 26, 2020

Very helpful follow up tot he first one.

By Sriram R

Oct 20, 2019

Well done mix of theory and practice!

By Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

By Alejandro D

Sep 19, 2019

Excellent content and delivery.

By Bekay K

Jul 4, 2020

Great resource to learning RL


Jun 1, 2020

Great Course by great faculty!

By Daniel W

Jul 18, 2020

Hard but a really good course