It's a great course, and they cover the basics of function approximation. The instructors were clear and knowledgeable, and the content that was covered was solid.

However, they skip some content that I feel is really important for modern RL, specifically the "deadly triad" regarding the convergence of off-policy approximate TD methods. They also don't discuss or link to papers on PPO or other recent advancements in RL, and I was hoping to learn more about those in particular.

This course is important for those who not just want to learn RL for mere sake but want to dive into various topics currently in research (for that reading textbook is of most importance). This specialization would have been even better if it had included some more complex topics from the textbook. To fully comprehend all the topics, guidance from experts is necessary.

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).

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.

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.

This was a good course but I really struggled to understand how each of the value functions translated into code.

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.