Great class and I learned a lot - docking one star because the final programming assignment didn't give a comprehensive enough checker inside the Notebook, so I had to keep submitting and look to discussions for help in solving (for really a minor issue that it looks like many students faced on an edge test case).

A very well constructed course with two excellent lecturers leading it. A lovely introduction to RL although some may prefer a more mathematical treatment (in which case you need to find a longer course). No tutorial support during the course though so you need to be prepared to sort out your own problems.

Slightly too theoretical but clarified couple loose ideas and enabled me to work with python a bit. although a t the beginning of the course they speak that it is not about python, we actually get a chance using it although indeed we are not getting nice python code examples in course materials.

Really good introduction to Reinforcement Learning foundations. The lectures were great, and helped translate the theory from the RL book. I would like there to be a few more detailed walk-thru of the update algorithms in week 4, but I was able to work through the programming assignments okay.

I really sorry for giving 4 star, my only reason for giving 4 star is so you can read this review. Please include some exercise on calculating the equations by hand, with solutions(this is the only reason for 4 star).

Thank you for the course

Course deserves 5 stars.(pardon my 4 stars, sorry)

The course is enlightening. However, it requires some sort of pre-exposure to the subject and definitely not a course for novices. The major part of the learning is achieved by reading the book. Lectures are mostly a recap of important ideas in the book and for clarification purposes.

For me, math a bit harder and more opaque than other ML courses I've taken. Even though only a few lines, final programming assignment one of more challenging ones in taking book equations to python implementation. Explanations pretty clear in videos.

Great course with interesting material and good examples. The only reason for rating 4 and not 5 is because I feel that programming assignments are a little too easy. Maybe they could benefit from letting the student implement more parts.

Clear and pleasant recorded presentations. Very good and precise reading materials. Time estimate for reading materials are super optimistic. Guest lectures are at best inspiring. No real value. They are unfocused and all over the place.

I think the course was well put together and the labs were clear. My only real complaint is that the book and tests spent a lot of time proving and manipulating equations. I am mostly interested in using the formulas and programming.

The coding assignments are a bit confusing. If you expand on coding assignment and probably provide a more step by step instruction as how the functions are being defined, or how the environments are created it would be way better.

Course is fine, but there could be more coding practices then the theoretical part. There are two coding assignments which are hard to do only with the course. The course context could be extended to include more coding practices.

Good course. The bulk of the content learned is in the textbook. The quizzes were sometimes a bit tricky with wording but it might have been because I wasn't careful enough. I feel excited to start the next course in this series!

The course is a very good introduction to RL but the concepts are handled a little too abstractly. However this provides an excellent fundamental for the rest of the courses. I would have liked more programming exercises.

A very useful and also rather compact course. I can recommend to anyone interested in the subject matter. I did expect a little bit more hands-on action (ie. more concrete, yet still simple examples in the coding side).

This course covers fundamentals of reinforcement learning from a book, "Reinforcement Learning: An Introduction" and that is a good thing; however, the course asks you to study by yourself by reading the book.

A really good introductory course to RL. The instructors have structured the course in the same manner as in the specified textbook (which is also great), so it's easy to follow them both at the same time.

Great material, and awesome coding exercises. Some additional information or context around a few of the problems would have been great, but nonetheless the struggle allowed me to grow in my knowledge!

The video lectures were very short and just a repetition of the book itself. After we studied the book, the lectures didn't have anything new for us. They should have been different and more hands-on.

Don't think it would be unreasonable to have more demanding coding assignments where all functions are made from scratch (though the function names and some comments might be provided as an outline.

The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.

Great course, I think theory is really well explained and book is great, but including more practice exercises is needed for this course to strengthen the learning of concepts.

The course is well developed, reading the reference book is the most important thing that you will do while taking this course. The delivery of both instructors seems robotic.

Very good an enjoyable course. It seemed like the explanations dwelled on the easier parts and skipped the parts that I would like to have seen in concrete numbers.

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.