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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Reinforcement Learning por parte de Universidad de Alberta

2,328 calificaciones
557 reseña

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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....

Principales reseñas


6 de jul. de 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.


7 de abr. de 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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526 - 550 de 562 revisiones para Fundamentals of Reinforcement Learning

por Eli K

31 de oct. de 2021

Programming exercises teach the material a lot better than quizzes

por Sriram S

17 de abr. de 2020

The course was cool but needed some more programming assignments.

por Francisco R

15 de jun. de 2020

Excellent in terms of learning the foundations of RL.

por 袁之日

29 de mar. de 2021

There could be more coding examples for each module.

por Jeroen v H

17 de oct. de 2019

Quite theoretical. But a good base of the concepts.

por Husam D

4 de nov. de 2019

I wished there were more coding assignments

por Shahram E

25 de jun. de 2020







































por Matin S

13 de ene. de 2022

it was a bit hard in code assignments

por Mark R

26 de oct. de 2019

Interesting course.

por Arnaud 3

10 de oct. de 2021

good course

por Abhishek U

21 de ene. de 2022


por 배병선

31 de oct. de 2019


por Arpan M

17 de oct. de 2020


por Austin H

19 de mar. de 2022

I found this course difficult to get through, even tedious towards the end; this is a fundamentals course after all so it being heavily theoretical was to be expected.

I found the practical assessments challenging and very good for developing the understanding of what had been taught; however one practical in the first week and one in the fourth week was too few. I was longing for the final assignment!

It remains to be seen how relevent this is to the upcoming modules (I do feel that I have a good grounding and understanding of the underlying process so maybe it was a necessary slog). I hope that they are more practical!

Very small observation: the use of bespoke Python packages with the online notebooks was also a bit frustrating. I like to be able to work off line (e.g. in Anaconda) and I also wanted to try and work out some of the challenges in R but without access to the bespoke packages it would have been too involved. I understand that you have a lot of students though and online notebooks are easier to manage.

por Youval D

21 de ene. de 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.

por Chandan R S

9 de may. de 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.

por Rafael C P

12 de may. de 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.

por tom

16 de dic. de 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.

por Vaddadi S R

10 de mar. de 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.

por Thomas T

26 de ene. de 2022

Course is rather poorly structured. Some videos explain concepts better than others but come later in the courses. There's not enough of a summary of terms, and seems to follow the suggested book almost word for word. The course should use the book as supplementary not complimentary.

por Saeid G

10 de dic. de 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.

por Iuri P B

3 de jul. de 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.

por Amr M

14 de mar. de 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

por Soran G

9 de dic. de 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.