Very interesting course: I have learned many things. A translation to other languages would be great: sometimes I can't memorize everything as I would if it was in my mother tongue.

Using another paper to study ( Experiments with Reinforcement Learningin Problems with Continuous State and Action Space) was a great idea that should be done in other courses.

Great course, deserve 5 stars. It is a good complement to the book, it adds interesting visualizations to help parse the content. The only issues were in the exercises. There are technical issues with the notebook platform where it keeps disconnecting from time to time, with no warning, and you lose your unsaved work (seems like token expiration).

Excellent instructors and good concepts and assignments help you learn by doing. My only reason for giving a 4 is that this courses uses some internal tool (RL Glue) and hence none of the Jupyter notebooks are implementable outside. Some well known reinforcement learning framework like tensorforce perhaps should have been used.

Very good lecture! I understand a lot about function approximation such as linear approximation, neural networks, etc. However, detail of video lectures were not perfect as the textbook. If you don't want to read a lot of text and listen to the lectures, you might not understand a lot of concepts.

it was great to apply what I have learned from the book, but it was hard to find my mistakes in the course 3 notebook. I also misunderstood the alphas in the course 4 notebook at first glance, their indices look like they are powers (sorry for the bad english). Besides it, great course.

Everything is great about this course except the policy gradient part, the need of policy gradients and why we should use policy gradients while we can use function approximation and (to compute policy gradients, we anyways have to compute function approximation).

Lecture quality could have been better. They look like practiced monologues rather than a class where a teacher is trying (hard) to explain a concept. If one has to wait for assignment to get the full grasp, it doesn't reflect too well on the instructors.

The course is still good. But the assignment is not as good as course 1 and 2. In fact, the contents of the course are getting complicated and interesting as well. But the assignments are relatively simple.

Solid intro course. Wish we covered more using neural nets. The neural net equations used very non-standard notation. Wish the assignments were a little more creative. Too much grid world.

There is a lot of material covered in the course. Be aware the pace picks up considerably from the first two courses. This said, it is a worthwhile course to take.

I wish agents that are based on visual information (with the usage of CNN) would be included in the course. But overall that was really great!

more detailed explanation of some of the assignments and how state values are got with tile coding but overall a great experience!

Great course, based on the reference book about reinforcement learning. A must for anyone interested in machine learning.

I loved the course videos and programming assignments. The only suggestion would be to go a little deeper in the videos.

Programming exercises are not self explaining. But instructors are explaining concept in a perfect way

Great overall. The content on policy gradient could be expanded, some details were delivered hastily.

The average reward and differential return needs to be explained more thoroughly

Course is great! Maybe some slides would be helpful not to forget.

Gets hard to understand.

Content is great, but the text is super dense -- slow read for me. The lectures are much clearer, although also a bit dense / quick paced to retain the information long term (especially if one wishes to skip the reading).

great course material but you need read the RL book through out the course. Also assignments are bit difficult, oops concept is mandatory.

Lectures are pretty good, but the programming exercises are extremely easy. All of the problems are rather contrived as well.

I think It must be more deep neural networks dedicated course and not focus on coarse and tile coding!!!

Course was good, but assignments were not well constructed. Problems with the unit tests were frequent.