I found the explanations of theory of RL to replicate what was written in the book. Without examples the videos were no value add.

I had to go through the RL course by David Silver in youtube to understand the concepts.

Course material is standard and mostly follows Sutton and Barto textbook. Unfortunately, most contents overlap with the existing reinforcement learning course on Coursera and David Silver's youtube videos. The course will be much more useful if it covers more practical stuff instead.

I was very disappointed that the free trial period ends before my assignments are graded by peers. I would suggest that the course should be arranged so that students can finish it during the free period.

I am not sure if RLglue is an appropriate package to use in the exercise, as it is not as a standard tool as all practitioners are familiar with. If the instructors believe it is something useful in the future, they should explain it more in detail in the lecture.

I give the course a low rating for several reasons, the first being the most important one: The instructors basically completely absent. Having issues or problems? They don't bother. Not a single reply from either instructor in the forums for months or years. Second: Flawed and inprecise notebooks. Well known issues with random numbers, but no updates. Incorrect book references which will let you implement formulas other than intended. Third: Tons of short videos with 30% summary and "what you will learn", which is ridiculous for 3 minute videos. Fourth reason: Mathematical depth missing after the first subcourse. Suggestion: Watch the David Silver and Stanford youtube lessons instead. For free and better explained. Compared to, for instance, Andrew NGs specialization, this one is really bad mostly thanks to the complete disinterest of the instructors.

Fantastic Course. That's the RL MOOC I have been waiting for so long. No surprise it is from Students of RL guru R. Sutton at Uni of Alberta. Very clearly and simply explained. Exercise and Test difficulty spot on. Wouldn't change a yota from this Course. Can't wait to access the rest of this specialization

Too much history and talks about who we are. Not efficient time spent.

Poor explanations with count on book. Not suitable for listening or on the go study.

Easy things made so complicated. (First you forced to get into math and other roots in the book and then video with some explanations when already not needed.) And could be explained better, not in 3 minutes. This is red, this is green and here we go - Malevich.

Got some insights but not happy about time spent.

The main reason I enrolled in this course was to have an opportunity to have my questions answered. I had already gone through videos of RL lectures from different universities before this. Hence, the value of the course diminished for me when some of my questions were not always answered by the TAs or the Staff

Not much help available on forums

Is a very good introduction to Reinforcement Learning. It also gives a very nice foundation of the basics of this area without being shy of showing some math. Could use more examples about modeling real world problems as MDPs but otherwise is a very complete course.

I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses

The lectures are not indicative of the problem sets. Both are very interesting and cover the materials well, but as a beginner with Dynamic Programming the bugs in the Notebooks are hard to distinguish from a lack of knowledge. The locked cells also make it hard to iterate slowly, to see the sensitivity of the algorithms to certain variables. Overall it is a great learning experience and the staff/mentors step in for support.

I've just finished this course, it is really wonderful and I learnt a lot, as a professional Backend Developer without a formal background in Machine Learning. It has a lot of mathematical theory and exercises, derivations, really good explanations, and even some coding tasks to apply this knowledge.

At first I was doubtful I would make it to the end as I was feeling rusty on my maths since I didn't practice them much after university, but with effort and patience I was able to see how everything is built from the ground up and got a really good picture of how the fundamentals of RL work.

The course is based on the famous "Reinforcement Learning: An Introduction" by Sutton and Barto, the 2nd edition of which was only released recently, and which the Data Scientists I work with say is the go-to book for RL. The book is a magnificent resource available digitally for free, but I have enjoyed this course so much that I got the physical version, and after auditing the course for a week decided to jump in to do my best in the whole specialization.

Very clear and engaging presentation, well thought out and typical Coursera-style programming assignments. Definitely looking forward to taking the rest of the sequence.

The content was pretty good. However, the final requirement on the final programing assignment was vague and required a very specific implimentation to match test cases. It was frustrating to have to search the forums for the exact sequence used to recreate a very specific dataset.

This course was super helpful. I had tried a couple other online introductions to RL, but this was the only one where I could really engage and learn the material effectively. Would recommend!

Solid introduction, but materials could be better prepared, e.g., overview of important concepts / formulas. Furthermore I would have liked to have more programming assignments and also more quizzes to practice the theory.

It was a good course, but I feel like there could have been programming assignments for week 2 and 3 to really help understand the bellman equations. Also, the jupyter notebook was pretty buggy sometimes.

Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.

Sometimes, visualizing the problem is hard, so need to thoroghly get prepared.

To be honest I didn't like videos in the course. Lectors read prepared text as robots. No pauses in places that are hard to understand. I had to do lots of replays to understand vids. Without reading the book I wouldn't be able to understand the material. Having read the book it's questionable if there is a value in watching videos. Also there are only 2 programming assignments and in each assignment it's required to write only a couple of functions while the rest of the code is already written. Programming assignments were like puzzles where you need to understand the code written and plug missing part. It's not creating my own program.

I wish there was more chances to engage the instructors and TAs, but outstanding video presentations and good math coverage to develop insight for the algorithms.

Course instructors should improve their teaching style by writing equations in hand and explaining point-by-point. There is no need to show their faces in the video while teaching. They sounded like 'radio' throughout the course.

Very low pedagogy in there. Just a bunch of slides (not all that good) being narrated by a standing person. You're better off reading a book.

Not recommended

The course is very comprehensive and gave a very good introduction to and initial overview of reinforcement learning. It was a bit more theoretic than I expected (after doing the Machine Learning course by Prof Ng) and I did have some problems in completing the last programming assignment due to this. In the end it all turned out well though. The instructors were quite pedagogic and structured (if anything a bit too structured), and the assignments were well chosen. One could tell that this is a new course as there were still a few small quirks, but overall a very worthwhile course!

The course was well taught! It utilized practical examples that helped bring the concepts and math to light! The instructors explained the math well without getting caught up in too much of the unnecessary minutiae. I struggled a bit in the programming exercises due more to my Python skills, but i was able to use the discussion boards to complete the assignments and understand the concepts.

It was a really nice lecture that helped me a lot to understand the fundamentals of reinforcement learning. Even though the lengths of the lectures are pretty short, they include the essence. So if you read enough and understand the textbook prior to the lectures, you would earn more from them. I'm so looking forward to learning real practical RL algorithms and applying them to my research.