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Learner Reviews & Feedback for Fundamentals of Reinforcement Learning by University of Alberta

4.8
stars
2,046 ratings
514 reviews

About the Course

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization....

Top reviews

AT
Jul 6, 2020

An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.

NH
Apr 7, 2020

This course is one of the best I've learned so far in coursera. The explanations are clear and concise enough. It took a while for me to understand Bellman equation but when I did, it felt amazing!

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476 - 500 of 512 Reviews for Fundamentals of Reinforcement Learning

By Aze A

Dec 10, 2020

I enjoyed the course, especially week 3 and week 4 materials. I would have like more examples.

By Daxkumar J

Feb 3, 2020

this is a basic course of the RL and its very great to learn with University Alberta.

By Simon N

Dec 20, 2020

Very good introduction. Helps you get through Sutton and Barto (free pdf supplied).

By Anirudh B

May 13, 2020

Needs more coding implementation according to me. But overall theory was good.

By Zia M U D

May 4, 2020

Tutors are fantastic, but should also focus on programming not just on theory.

By Mohamed H

Feb 14, 2020

I think it will be perfect if the board and pen are used to drive equations.

By Maxim V

Jan 6, 2020

Good content, but most of it is in the textbook, not so much in the videos.

By Sri R R

Apr 17, 2020

The course was cool but needed some more programming assignments.

By Francisco R

Jun 15, 2020

Excellent in terms of learning the foundations of RL.

By YUAN Z

Mar 29, 2021

There could be more coding examples for each module.

By Jeroen v H

Oct 17, 2019

Quite theoretical. But a good base of the concepts.

By Husam D

Nov 4, 2019

I wished there were more coding assignments

By Shahram E

Jun 25, 2020

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By Mark R

Oct 26, 2019

Interesting course.

By 배병선

Oct 31, 2019

Good!

By Arpan M

Oct 17, 2020

good

By Youval D

Jan 21, 2020

Good examples can simplify things greatly. there where several places where an extra step would add value. Some lessons, such as the problem with the trucks could go a little deeper. Assignment grading system is buggy. I spend hours (that I do not have) because I used "transition" as a variable. After I figured this out, I was no longer able to know if other error is due to some other things the Notebook does not like or if there are actual errors. I also posted some questions but never got any response to any of them.

By Chandan R S

May 9, 2020

Not much satisfied with the course structure...

To successfully understand and complete this course, you constantly need to refer the reference book.

Most of the students are referring to online courses so that they can learn more efficiently than reading,

any casual book reader can easily complete this course but for the person who like to learn from videos rather than book reading (like me), it was not so great experience.

By Rafael C P

May 12, 2020

The content is there and it is good, but teachers lack good teaching skills and lessons feel rushed (Ng lectures come to mind as positive examples of good practices). Also, lessons aren't self-contained, as you need to read the book if you want to get good grades on the tests. I was looking for a smoother experience than the book, not to be told to read the book, which I can do without a course.

By tom

Dec 16, 2020

I would have learned more if the course had a coding assignment each week, or at least example code available for similar problems. I had a good theoretical understanding of everything we needed to do but very poor practical understanding.

The course did serve as a good introduction to the theory of reinforcement learning, and certainly acts as a good starting point.

By Vaddadi S R

Mar 10, 2021

The programming exercises are quite tough and difficult to code on our own. Concepts were explained nicely, however, lacks examples. Working out examples would have given an even better insight. Another video that could have proven useful is how to convert a real-world problem into an MDP.

By Saeid G

Dec 10, 2019

The good thing about this course is that it is based on the bible of reinforcement learning and it is thoughts by the experts in the field. However, the pace of the teaching is extremely fast and it is quite hard to keep with the pace even for someone with some background in the RL.

By Iuri P B

Jul 3, 2020

It needs more explanation about the fundamentals, examples and sections that demonstrate how each, for instance, Policy Iteration and Value Iteration differ. Despite that, the course is really good and I would recommend for a friend.

By Amr M

Mar 14, 2021

The material needs to be easier and more intuitive. Last assignment shall have some additional steps to help the student to solve it. and also to involve him more

By soran q

Dec 9, 2019

The size of different variables has not been clearly spelled out so this makes the concept confusing and requires so much time to figure them out.