Excellent course. The videos, quizzes, and especially the exercises add a lot of extra value to the text book (which is available for free - Sutton and Burto, 2nd edition). Of course it is not perfect - the videos are sometimes a bit dry, the NN part was brushed over too quickly for a beginner (luckily I had taken some courses about deep learning, so I was ok - but if you don't know the basics of NN, week 2 might be quite challenging for you). Other than that the biggest disadvantage is that the course forums are still quite empty - and so if you get stuck you can be on your own... But you shouldn't get stuck, and I guess this will improve over time.

This, the third in an exceptionally well-paced series of four courses on Reinforcement Learning, extends the scope of the subject to include parameterized functions (i.e., neural networks). The section on tiling methods is especially interesting. The course is taught under the auspices of professors who, quite literally, wrote the book on reinforcement learning, and includes several video lectures by leading practitioners and theorists in the field. The final programming assignment, in particular, made me feel like I did when I wrote my first computer program that actually did what it was supposed to way back when -- delight and amazement.

Great course and specialization. The teachers are great, the material well presented and balanced. I strongly recommend this course to anyone interested in the field of Reinforcement Learning. For maximum chance of success I suggest following all 3 courses in succession and investing the necessary amount of time to read the textbook chapters as specified at the beginning of each week.

Looking forward to completing the capstone project now!

An almost overwhelming amount of material, however we managed to navigate through the thicket. The labs were well maintained and provided robust tests so that one could have a high degree of confidence in the solution before submitting to the grader. I really appreciate this. I would recommend this course to anybody wanting a serious introduction to reinforcement learning.

This was a very good and though course. The content in this course is perfect to get yourself the necessary bases in order to start getting into deep RL. It doesn't really explain that far, but at the end you will have a basic idea of how deep learning can be used with RL. Enough to start reading papers about it or to watch other lectures focused on that topic.

Its got a great variety of very applicable examples, use cases, and assignments. May be tough if people don't quite understand how neural networks work, so I suggest having a basic understanding of NN for parts of this course.

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.

Good course with a lot of technical information. I would add another assignment or make current ones a little bit more extensive, as there are many concepts to learn.

A great and interactive course to learn about using function approximation for control. Great way to learn DRL and its alternatives.

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

Great course on more advanced reinforcement learning techniques. Can't wait to apply these new skills in the wild.

The course presentation is wonderful. I can't stop after I watch the first video.

It was a wonderful course. To the point yet well-explained concepts.

I had a much better experience with the autograder than in course 2.

Fantastic course and great content and teachers!!!

Great Course! Highly recommended for beginners.

Simply the best course on this topic.

Really good, I learned a lot.

Almost perfect, except two ~minor objections:

1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course

2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment

Definitely a course to take to learn the ropes of RL. For this course, it is critical to follow and math. I'd love to give 5 stars to this course but will however take one away since the course could benefit a lot if the math was made a bit simpler to follow. The book referenced in the course is excellent and does help, but still, some more pedagogical repetition/rephrase, simplification of notation, a bit slower pace of narration would make the course even better. Having said that, this seems to be the best course available at this time. Many thanks to tutors.

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.

The material is very good. But this course needs better instructors/ method of teaching. The book is also written in an unnecessarily technical way filled with jargon. explanations are not clear, simple stuff is presented in a very complicated manner for no obvious way.

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.

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