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Avis et commentaires pour d'étudiants pour Fundamentals of Reinforcement Learning par Université de l'Alberta

2,381 évaluations

À propos du cours

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....

Meilleurs avis


6 juil. 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 avr. 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 sur 568 Avis pour Fundamentals of Reinforcement Learning

par Sebastian T

22 févr. 2020

Slightly too theoretical but clarified couple loose ideas and enabled me to work with python a bit. although a t the beginning of the course they speak that it is not about python, we actually get a chance using it although indeed we are not getting nice python code examples in course materials.

par Russel C

15 févr. 2020

Really good introduction to Reinforcement Learning foundations. The lectures were great, and helped translate the theory from the RL book. I would like there to be a few more detailed walk-thru of the update algorithms in week 4, but I was able to work through the programming assignments okay.

par Shashidhara K

13 nov. 2019

I really sorry for giving 4 star, my only reason for giving 4 star is so you can read this review. Please include some exercise on calculating the equations by hand, with solutions(this is the only reason for 4 star).

Thank you for the course

Course deserves 5 stars.(pardon my 4 stars, sorry)


29 avr. 2022

The course is enlightening. However, it requires some sort of pre-exposure to the subject and definitely not a course for novices. The major part of the learning is achieved by reading the book. Lectures are mostly a recap of important ideas in the book and for clarification purposes.

par bob n

22 déc. 2020

For me, math a bit harder and more opaque than other ML courses I've taken. Even though only a few lines, final programming assignment one of more challenging ones in taking book equations to python implementation. Explanations pretty clear in videos.

par Lucas L

8 avr. 2021

Great course with interesting material and good examples. The only reason for rating 4 and not 5 is because I feel that programming assignments are a little too easy. Maybe they could benefit from letting the student implement more parts.

par Dror L

31 juil. 2020

Clear and pleasant recorded presentations. Very good and precise reading materials. Time estimate for reading materials are super optimistic. Guest lectures are at best inspiring. No real value. They are unfocused and all over the place.

par Ed J

25 avr. 2020

I think the course was well put together and the labs were clear. My only real complaint is that the book and tests spent a lot of time proving and manipulating equations. I am mostly interested in using the formulas and programming.

par Aresh B

13 janv. 2021

The coding assignments are a bit confusing. If you expand on coding assignment and probably provide a more step by step instruction as how the functions are being defined, or how the environments are created it would be way better.

par Alper A

29 mars 2020

Course is fine, but there could be more coding practices then the theoretical part. There are two coding assignments which are hard to do only with the course. The course context could be extended to include more coding practices.

par Ayse E G

28 sept. 2019

The course is a very good introduction to RL but the concepts are handled a little too abstractly. However this provides an excellent fundamental for the rest of the courses. I would have liked more programming exercises.

par Mauri K

23 nov. 2020

A very useful and also rather compact course. I can recommend to anyone interested in the subject matter. I did expect a little bit more hands-on action (ie. more concrete, yet still simple examples in the coding side).

par Jihun Y

13 mars 2022

This course covers fundamentals of reinforcement learning from a book, "Reinforcement Learning: An Introduction" and that is a good thing; however, the course asks you to study by yourself by reading the book.

par Aravind M

26 oct. 2020

A really good introductory course to RL. The instructors have structured the course in the same manner as in the specified textbook (which is also great), so it's easy to follow them both at the same time.

par Aaron H

10 sept. 2019

Great material, and awesome coding exercises. Some additional information or context around a few of the problems would have been great, but nonetheless the struggle allowed me to grow in my knowledge!

par Aboozar R

28 oct. 2020

The video lectures were very short and just a repetition of the book itself. After we studied the book, the lectures didn't have anything new for us. They should have been different and more hands-on.

par Aidan M

24 août 2020

Don't think it would be unreasonable to have more demanding coding assignments where all functions are made from scratch (though the function names and some comments might be provided as an outline.

par Ulf Ä

3 janv. 2021

The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.

par Christian J R F

1 avr. 2020

Great course, I think theory is really well explained and book is great, but including more practice exercises is needed for this course to strengthen the learning of concepts.

par Narendra G

5 juin 2020

The course is well developed, reading the reference book is the most important thing that you will do while taking this course. The delivery of both instructors seems robotic.

par Nils S

29 oct. 2020

Very good an enjoyable course. It seemed like the explanations dwelled on the easier parts and skipped the parts that I would like to have seen in concrete numbers.

par Nathaniel W

25 août 2020

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.

par David S

27 sept. 2019

It will be good to include more detailed examples and more practice exercices in week 2 and 3. Also to repair the week 4 submission.

Although, It is a good course.

par Muhammed A Ç

15 juil. 2021

Without reading the recommended book, course material would not be sufficient. Coding exercises quite good and also quizzes' are suitable for beginner level

par Naresh T

28 mars 2020

Good understanding of the fundamentals and aptly paced. The programming assignments were very good if there were more like that the course could get better