Awesome course! If you can follow explanations (it is not an introductory course) it is an excellent course with a lot of detail and really good explanations. Programming exercises are also very good.
this is by far an AMAZING in-depth course! i enjoyed every second of it. It's challenging in a way that makes you improve. TOTALLY RECOMMEND IT. Great work guys 👏 well done and thanks for the effort!
par Hamed N•
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.
par Xiao M•
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.
par Pedro L A V•
-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.
par Jay G•
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.
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.
par Tomas L•
Still needs a lot of work
par Ajay K•
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.
par Fan Z•
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.
par Luke J•
Challenging (unlike many other courses on Coursera, it does not baby you and does not seem to be targeting as high a pass rate as possible), but very very rewarding.
par Kota M•
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.
par Zikai W•
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).
par Roman P•
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.
Brilliant content but quite some bugs in assignments
par Simon V L•
I've done about 14 courses on coursera and this was the worst. The teachers are so obsolete. They just rattle off a pre written text without any intonation. Instead of the videos it's easier to just read a book on reinforcement learning. I still gave it two stars because the programming exercises were interesting and usefull.
par Tingting X•
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!
par Mikhail V•
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.
par Alan P•
The lecture guy is a terrible terrible speaker - please get someone decent
par Sahil J•
Had a lot of fun doing this course. Although some of my fellow classmates are complaining that there are a few bugs in assignments, fixing those bugs itself can be a learning experience. The assignments,in general, are fun, particularly the honor's assignments.
par Sergey F•
At times it felt like a bit more video material would be helpful to better understand the subject/gain deeper understanding.
And fixing some of the notebooks would be helpful.
Very practical lecture. I strongly recommend this lecture. Programming assignments are little difficult, but not impossible :) Just do it!
par Vaibhav O•
Well Prepared and taught course.. Will highly recommend as the primer for reinforcement learning
par Thomas F•
Course was very challenging what is good! Did several courses that were too easy. Quizzes were sometimes difficult to pass because of the way the answers are evaluated (all answers have to be correct) and even after watching the video several times the answers were not obvious.
Small things in the notebooks e.g. in mtc code was needed at a place but there was no comment saying that it is needed. In another notebook the wrong environment was loaded per default and had to be changed based on the notes given at the end of the page.
par Chua R R•
Great content! The python notebook submit problems leave a lot more to be desired.
Доведите ноутбуки и grader до ума, не позорьтесь пожалуйста!
par Keshav V J•
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