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Opiniones y comentarios de aprendices correspondientes a Practical Reinforcement Learning por parte de National Research University Higher School of Economics

4.1
211 calificaciones
56 revisiones

Acerca del Curso

Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun!...

Principales revisiones

AK

May 28, 2019

This is one of the Best Course available on Reinforcement Learning. I have gone through various study material but the depth and practical knowledge given in the course is awesome.

FZ

Feb 14, 2019

A great course with very practical assignments to help you learn how to implement RL algorithms. But it also has some stupid quiz questions which makes you feel confusing.

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1 - 25 de 56 revisiones para Practical Reinforcement Learning

por Hamed N

Apr 23, 2019

I would give it -5 star if it was possible. The course material is so vague but still understandable if you sleep on them 10 times more than watching it. Maybe Andrew Ng courses or Python Course or Advanced ML course on google cloud (GCD ) spoiled me However statistically and self-judgement , this is not the case.

The instructor talking super fast and not understandable that could beat any translator machine I bet. What s more, the instructor talking about things which are not consistent with slides and also sometimes he does not explain some formulas or modelings.

The assignments are full of grammatical errors and they are super confusing. Very simple but super confusing leads you to have the grader failed you.

But , The worst part is if you take this course you will be all on your own and no body help you out as TA . If you check the forum discussion you see how many people complaining and how many questions left with no answer. I took this course as granted , but this is my responsibility to give back my feed back to potential learners.

Note that this is my feeling from the first week of class , I hope my idea change later.

por Pedro L A V

Nov 27, 2018

Pros:

-It is a pioneer RL course in Coursera.

-Great exercise templates with interesting applications of RL algorithms.

-There are always references to good papers and new developments in RL.

-Good sense of humor in the lecture and templates.

-The discussion forum addresses the the bugs of the course.

-The course is challenging in the right level.

Cons:

-The lectures are not in that level yet ... they do not explain the important parts in detail.

-The lecturers should improve their public speaking and storytelling skills.

-The course subverts the sequence of the RL topics (cross-entropy is the first method and the multi-armed bandits setting is in the last week). This could be good, but ended up being confusing.

-The quizzes and exercises still contain many bugs.

Overall:

This is a good course, but it has the potential to be much better. If you want to challenge yourself and solve really interesting problems, take this course. You will probably have to watch David Silver's lectures on YouTube and read some parts of Sutton and Barto's book to understand the concepts. However, if you feel frustrated dealing with bugs in the exercises or answering quizzes that are confusing, do not take this course.

por Xiao M

Aug 19, 2018

have to give a one star on this course, content hard to understand, speaker speaks too fast, programming assignment many mistakes, move on to david silver's youtube video for RL.

por Jay G

Oct 30, 2018

Course 4 of Advanced Machine Learning, Practical Reinforcement Learning, is harder than Course 1, Introduction to Deep Learning. (I jumped to Course 4 after Course 1). That is saying quite a lot because I would describe Course 1 as "fiendishly difficult".

There's a few reasons for why 4 is harder than 1.

One big reason is, the course is still "in beta". Not everything, and maybe not anything, works as a straightforward Coursera Notebook. My workaround was to download the courses as IPYNB files, and then upload them to Google Colab. I'm glad for the experience as I'm now very familiar with Google Colab and how to navigate a Coursera notebook environment to get at the grader.py and submit.py files needed.

If you are not at least somewhat skilled as a programmer, you may want to avoid this course until it is out of beta.

Second reason is the Quizzes. These quizzes, most of them, are difficult. I myself never resorted to "try every possible permutation" to pass a quiz, but I did have to retake quizzes, re-watch videos, Ctrl-F find words in the video Interactive Transcript area, and read the Forums for help. Get ready to have some "fun" (and by "fun" I mean the opposite of "fun") taking these quizzes.

Third reason is, Alexander Panin can occasionally be difficult to understand in English (that's as gently as I can put it). But this, too, I'm glad for the experience. The neural networks in my brain for translating "thick Russian accent" to "colloquial English" have improved greatly. But everyone should take it easy on Alexander, because...

This course of his is awesome! I dreaded the Videos. I hated the Quizzes. And the assignments? Until I had finished an assignment 100%, it was the bane of my existence. But when you solve the assignment? Exhilerating. The assignments are a treasure trove of HOW-TOs on different RL techniques. Have you got an RL problem you want to solve? Chances are at least one of these notebooks will either flat out give you the solution, or else at least point the way forward.

por Tomas L

Dec 28, 2018

Still needs a lot of work

por Roman P

Nov 05, 2018

The course is really in 'beta' state. Be prepared to struggle against not only the practical assignments themselves, but also against their bugs and assignment grading infrastructure problems.

But the course content itself is very useful and worth the trouble. Also, most of the bugs and problems are already solved by the community, you just need to visit the Discussion forums to find the solutions.

por maciej.osinski

Nov 02, 2018

Brilliant content but quite some bugs in assignments

por Kota M

Oct 04, 2018

The class is very immature as of September 2018. A good reason for taking this course is because it is one of few online courses where you can play with actual programming exercises of various reinforcement learning techniques, from dynamic programming to deep Q networks and actor critiques. Examples are mostly for environments of Open AI gym. You can also see examples where you use libraries such as tensorflow and pytorch used in the framework. However, the codes, including submission and grading system, have numerous bugs, which forces you to do extra debugging works unrelated to the course topics. Fortunately some early takers of the class left helpful comments on the forum, with which you can solve the most of issues if you read them carefully.

Quality of presentation is not as good as other courses I found in the Coursera. Most of the time, the lecturer seems to be just reading the scripts. To make it worse, the scripts are not written in spoken language.

por Zikai W

Jun 16, 2018

Indeed, this the 1st reinforcement learning course during May 2018. The topics and supporting materials are good for learning the course. Unfortunately, the course is not well-prepared in different aspects: 1) The assignments contained many bugs. One may spend half of the time to fix the bugs in the assignments. Sometimes, one may not be able to find tutor to ask for a help. The only thing one can do is helping herself or waiting for other classmates' feedbacks.2) Quiz is not designed for help one's learning. The questions in quiz are very confusing sometime. Also, one cannot get the correct answers after repeating the video several times. Sometime even one cannot find the topics in the lecture video. It takes you long time to try 'trail and error'.In all, it seem this course is not a well-prepared course in Coursera. I have paid and enrolled in many Coursera courses. Unfortunately, one might feel disappointed this time. A feedback from a PhD student (also a loyal customer of Coursera).

por MASSON

Apr 07, 2019

Interesting topic, however several things are not acceptable for a paid course:

+ Some assignments are a mess, it's crazy hard to get the environments working right, very little instructions and explanations

+ Assignment graders are broken and require you to fix them manually

+ No consistency between the notations of the different lecturers

+ Slides from videos are not provided (seriously ?!)

Overall, the course does not look serious, a kind of alpha version.

por Hany A

Feb 16, 2019

The course gives a good intro to reniforcement learning. I liked the fact the assignments here are shorter compared to other coursers. However, the quality of preparation of the material is very low. In many cases there are problems with the code and you cannot submit from coursera. I had to download the docker container locally and fix the bugs in order to submit. Quizes are not very nicely prepared and mathematical notation not very clear. I think I struggled a lot to get some of the quizes finished as the accepted score is quite high and some questions require multiple answers and you have to get them all right in order to get a score. I think the authors need to spend more time refining the quizes as well as the assignments

por Fan Z

Feb 14, 2019

A great course with very practical assignments to help you learn how to implement RL algorithms. But it also has some stupid quiz questions which makes you feel confusing.

por Keshav V J

Dec 27, 2018

This course was theoretically fulfilling, however i felt that the teachers failed to explain core principles with ease and felt a connection break in between their accent, their lectures and the slides in the background

por Chua R R

Dec 24, 2018

Great content! The python notebook submit problems leave a lot more to be desired.

por Sergey

Oct 13, 2018

Доведите ноутбуки и grader до ума, не позорьтесь пожалуйста!

por Michel C

Jun 12, 2019

Submission python code is very buggy. Instructors are hard to understand.

por Ajay K

May 28, 2019

This is one of the Best Course available on Reinforcement Learning. I have gone through various study material but the depth and practical knowledge given in the course is awesome.

por Dmitry I

May 23, 2019

Very reinforcement, much learning

por Mikhail V

May 23, 2019

The material covered in this course is very comprehensive, up-to-date, and broad. It goes far beyond typical RL courses/tutorials. BUT, at the moment the course is extremely raw:

1) For larger/longer assignment, it is impossible to work with coursera notebooks (keep disconnecting); It takes lost of efforts to set-up own environment (and you shouldn't really count on discussion forum for help).

2) The assignments have bugs / broken links and other issues.

3) Finally, I believe the main issue is that there is basically zero support from the course personnel/tutors. It looks like the course was just abandoned by their creators and they don't care about it anymore. Very sad, since the material is quite exciting and deep, and the course has lots of potential.

All in all: 5 stars for the content, 0 stars for the organization = rounding down to 2 overall.

por Tingting X

Apr 22, 2019

I really like the lectures and homework, especially the coding assignments, which help me play games with RL and also improve understanding of the typical RL algorithms. Also, the discussion forum is very helpful and I can usually get out of stuck by following mentors' and other students' advice. Great thanks to Pavel Shvechikov and Alexander Panin for making such a useful course available!

por Felix A

Mar 18, 2019

The course itself is great, but the assignments are a bit chaotic (so make sure to bring a lot of patience and willingness to bugfix)

por Vaibhav O

Mar 17, 2019

Well Prepared and taught course.. Will highly recommend as the primer for reinforcement learning

por Antony L

Mar 12, 2019

Course not ready and has installation prerequisites. Seems to use a libraries (Docker, Env).

I waste too much of my time trying to install libraries and dependencies for online courses, most of which become obsolete within a year or two.

Additionally, the logic embedded within the library is often the thing I want to learn, and abstracting it only teaches me about the bugs and shortcomings of that library.

por Xiaoahe X

Feb 20, 2019

The course is well organized. Reference and extra learning items is helpful to enhance the knowledge.

BUT! There are so many small bugs in the assignments that it really takes time to fix and make the course hard to get passed.

por Ashish J

Feb 19, 2019

Horrible graders starting from week 3. A lot of time wasted in fixing grader issues which is course provider's primary job. This is a paid course for goodness sake. No proper communication by course's staff/mentors even in the discussion forums.