Fantastic course! Despite the challenging content, this course actually is taught at least at the same level as the ones by Andrew Ng, Daphne Koller, and Geoffrey Hinton. Congratulations Martha and Adam! You are awesome and are my heroes! Thanks a lot! George

Excellent Course and Lectures. Loved it!! So important to read the chapters in the book ahead of time. Book is also excellent!! I liked the way the instructors explained the equations and broke them down. Nicely done!! I wish some more of the questions in the quiz reflected the data structures we use in the programming exercise, which will be super-helpful to reinforce the concepts when we do the programming exercises. In other words, an intermediate step of a worked example between the Pseudo-Code Algorithm in the Texbook/Lectures and the Programming Exercise. For example, more of the Feature_Vector -> Action_Value Calculation - even if we have to do some matrix manipulation by hand, that'd be wonderful. One of the quizzes has something like that (but more simplified) - which was perfect.

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!

The course is very comprehensive on the content. But I think the difficulty of this course is in some sense too high for most people who don't have a background in engineering degree due to the extensive use of advanced mathematics. I think it might be a better idea if you are focusing on a few critical algorithms that trying to cover too much algorithms which is quite overwhelming

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.

I found the course quite tough but really interesting. I would say that reading the book's chapters more than once is necessary to optimally grasp the concepts.

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.

Give nive theoretical foundation. I found RL courses are abstract, but the programming assignment give a nice conceptualization.

Did a good job of attaching a programming assignment to each lesson and giving clear and detailed instructions throughout

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.

Everything is amazing in this course! Dont miss it!

Fantastic course and great content and teachers!!!

Great Course! Highly recommended for beginners.

Simply the best course on this topic.