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Вернуться к Practical Reinforcement Learning

Отзывы учащихся о курсе Practical Reinforcement Learning от партнера Национальный исследовательский университет "Высшая школа экономики"

Оценки: 246
Рецензии: 64

О курсе

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! Do you have technical problems? Write to us:

Лучшие рецензии


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.


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 из 64 отзывов о курсе Practical Reinforcement Learning

автор: 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.

автор: 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.

автор: Pedro L A V

Nov 27, 2018


-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.


-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.


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.

автор: 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 and 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.

автор: 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.

автор: maciej.osinski

Nov 02, 2018

Brilliant content but quite some bugs in assignments

автор: 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.

автор: 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.

автор: Tomas L

Dec 28, 2018

Still needs a lot of work

автор: 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).

автор: 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.

автор: Chua R R

Dec 24, 2018

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

автор: Sergey

Oct 13, 2018

Доведите ноутбуки и grader до ума, не позорьтесь пожалуйста!

автор: 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

автор: 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

автор: 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.

автор: Meytal L

Jan 16, 2019

Great course. Thank you!

автор: Anmol G

Jan 30, 2019

The content was tough but the efforts were appreciable even if there were some hiccups along the way. The best part of the course was the plethora of information you get, don't forget to check out the references at the end of notebooks ;)

автор: Milos V

Jan 30, 2019

This is my fourth AML course, and for now I would say it is the best one. It connects lectures and practice in the best way. On the other hand, there are mistakes all around, as it is beta-version. In my opinion, it is not fair to put the beta-version course into paid specialization.

автор: Vaibhav O

Mar 17, 2019

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

автор: Tom C

May 17, 2018

Great course. Best course so far on reinforcement learning.

автор: Ahmed R A

Jun 16, 2018

Excellent course

автор: Nimish S

Jun 30, 2018

great course and fabulous exercises

автор: Marcin G

May 30, 2018

Great practical assignments based on gym environment (from Open AI). Quizzes on the other hand tend to be very challenging and therefore might be a bit exhausting. Practical assignments are well designed and explanatory. I am convinced, that programming practices make it the best course on reinforcement learning currently available.

автор: Jose S

Jun 13, 2018

This is a great course. There are some technical issues with the assignments but we knew this going in since the class is still in Beta. I learned a lot working through sections of the honors track. I recommend you download the assignments, install all python requirements and work locally. It would be great if they had a TA for this class, fortunately people in the forums were super helpful.