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

4.8
stars
1,250 ratings

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

DP

Feb 14, 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.

DC

Aug 23, 2020

The material discussed is very clear, and the graded quizzes and programming assignments force you to really understand what you have just heard. I enjoyed this course a lot, and learned even more.

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126 - 150 of 243 Reviews for Sample-based Learning Methods

By garcia b

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Dec 31, 2019

very copacetic. excellent complement to the book

By Ignacio O

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Oct 13, 2019

Great, informative and very interesting course.

By Ashish S

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Sep 16, 2019

A good course with proper Mathematical insights

By Guruprasad S

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Jul 13, 2021

very intutive and the instructors are succinct

By Jayadev H

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Apr 29, 2024

Great stuff for learning intro RL! Thanks!:)

By Ben - C L Y

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Jul 3, 2020

Very good overall! It takes time to digest.

By LIWANGZHI

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Jan 15, 2020

A nice course with well-designed homework:)

By Jingxin X

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May 26, 2020

Very helpful follow up tot he first one.

By Ryan Y

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Jan 17, 2021

Better than reading the textbook alone.

By Sriram R

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Oct 20, 2019

Well done mix of theory and practice!

By Luiz C

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Sep 13, 2019

Great Course. Every aspect top notch

By David I

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Apr 19, 2020

very good course with good examples

By Alejandro D

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Sep 19, 2019

Excellent content and delivery.

By Bekay K

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Jul 4, 2020

Great resource to learning RL

By PRIYA S

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Jun 1, 2020

Great Course by great faculty!

By Daniel W

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Jul 18, 2020

Hard but a really good course

By Pachi C

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Dec 8, 2019

Great and fantastic course!!!

By Sergey M

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Oct 3, 2021

Very well organized course!

By rashid K

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Nov 12, 2019

Best RL course ever done

By Jackson J

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Jan 4, 2024

Love the assignments!

By MD M R S

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Mar 4, 2021

Awesome!!!!!!!!!!!!!

By Venkat k

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Feb 3, 2022

Excited to learn!

By Eleni F

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Mar 15, 2020

i really enjoy it!

By Mohamed A

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Jul 19, 2021

very good course

By Guoxiang Z

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Mar 7, 2021

Very nice course!