One of the best courses I finished on Coursera, I really like the structure of the course. Textbook is also provided which really helps. Looking forward to next course in the series.

Very good courses. It helps me to understand reinforcement learning a lot.

A great course to start learning Reinforcement Learning!

The course is overall well structured and concise. I am sure that the instructors could do a better job of putting more emphasis on the difficult parts of the course (such as how to actually use the Bellman Equations and how to calculate the State/Action Value functions). More examples of calculation would have made things far easier. All in all, it was a decent introduction to RL and the videos cleared some of the confusion that arised just by reading the RL handbook by Sutton & Barto.

Some of the sections seemed a bit rushed up. While the book provided a good source to clarify, I would prefer a slightly slower pace with emphasis on understanding during the video presentation. However, I have learnt significantly on reinforcement learning during the course. Thanks to the instructors who are highly accomplished, and have taken the time to create this video course.

What ever the content taught was really really good. but still more hands-on algorithms such as Monte Carlo would have been even better. overall worth studying the course

Good overall. Need to work with your assignments and their submission procedure. Lectures should be more interactive than just going through slides.

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

My rating and review is for the entire specialization. Big compliments to the course instructors for developing a truly insightful and challenging course. Out of all the specializations and courses I have done on Coursera, this definitely has been the most challenging and rewarding one. The use of a textbook in addition to the video and programming materials is a very useful approach - to get the most out of this course, I recommend everyone to study all the material in advance of the lectures. This really increases the added value of the course.

As has been said by others, the capstone project (course 4) is the least developed. It is mostly a reiteration of videos from previous courses, and a few programming assignments (numbers 1 and 3 of which are very easy, whereas number 2 is very challenging). A drawback is that as the courses progress, but in particular in course 4, the programming exercises become quite mechanical, and mostly involve following specific instructions with a bit of debugging, without keeping proper sight of the bigger picture. Then again, I've felt this is the case for many courses in ML, and not particular to this course.

At this point, I feel I have a pretty good understanding of the theoretical basis of RL, but I would definitely not be able to implement an RL agent independently. I guess with this basis in hand, it is now time for some more applied (self-)study in this domain. I have definitely become even more eager to do so.

Very well put together course. It does a good job of walking you through concepts in a way that's direct and accessible, while not dumbing things down. I had bought the Intro to RL textbook some months back but ran into problems getting its material to 'stick', but the ideas in chapters 1-4 are much more concrete now.

Assignments were reasonably difficult, but not overwhelmingly so. Homeworks are designed to make sure you understand key concepts moreso than being vigilantly 'rigorous' for their own sake. Emphasis of the class is making sure you understand fundamental concepts moreso than hacking your way into a working prototype of something.

While the class is designed to be easily digested, the material assumes a working knowledge of programming and mathematical formalism, so people without some background knowledge you might struggle to keep pace, even if the material is well designed.

Also, like others have mentioned.......this class follows the book pretty carefully. Don't expect anything to be covered that's not in the RL textbook the course is based off of. By the end of the class the book material will be more vivid and concrete in your mind, but you will not have branched into a direction not covered within it.

The course is very good, I already had some experience with Markov chains. I found the hardest part to understand was week 3 with the Bellman equations. The course should be reinforced in this part so that everyone can understand, and it would even be to pass some of the material in this section to the second week (since it is a lot of topic).

I had to do a lot of research with other sources to be able to understand the content of week 3, since the book is not very clear material, especially on this topic. It would be very interesting if you could explain us better how to create these environments that are mentioned (eg GridWorld, Pole Car, etc) in Python in order to improve the form and intuition in the application of these concepts, this as off-topic material.

The guest talks were very beneficial to me, they give some very interesting historical and practical perspectives on the application of the concepts. Professor Warren Powell's talk was the most interesting in my opinion and he left me wondering how we could apply these concepts to real life problems.

Thanks to the team for this course.

The same review for all 4 courses: This is the first time I have done a Coursera module building courses up rather than just individual courses. You really feel you have achieved something out of it. Some people have commented that it is just presenting material from the Sutton and Barto book. But that book is *the* text book in the subject. The course selected particular chapters from the book. I wouldn't have got as much just from trying to read the book on its own (I probably wouldn't have read as much as I did). It was good to have the supplementary videos with other experts - and great to watch Sutton and Barto just sat down being recorded having a retrospective conversation. The programming exercises would sometimes feel they weren't testing much (in fact, the challenges were largely due to my lack of skill in Python - my Python abilities have improved which is a side benefit) but they would actually get you into the weeds as it were. All in all, the best courses I've done. Great job Martha and Adam!

After being laid off from my big-tech job I decided to use that opportunity to learn about new areas in machine learning. I had long wanted to explore the field of reinforcement learning. Reinforcement Learning is one of the most beautiful discoveries in Machine Learning. It's fascinating to see how a simple idea such as the reward hypothesis is so powerful in practice. Hearing the dialogue between Rich Sutton and Andrew Barto was very inspiring. I look forward to taking more courses in this specialization and learning more about RL. I would also like to request the instructors to create a course on the more recent trends in RL such as Deep Reinforcement Learning. There isn't a very good resource online to learn about Deep RL methods and reproducing them reliably.

I think it's really good course, probably the best that I have done on Coursera (and I have done a few). I like the approach of combining independent reading with high-quality video content. They do a great job of breaking down complicated topics in reinforcement learning, and the programming problems help gain a deeper and intuitive understanding of the underlying maths. I think some of the proofs in the book could be more detailed. For one or two I went on line to find more step-by-step proofs. This is in principle more an issue with the book and less with the course. But I think it could be helpful if the course would provide a reference to some more in-detail proofs. That's it though, I am really enjoying this specialization :)

This course teaches you by having you read the textbook chapters (free pdf) followed by complementary videos that help you gain intuition. The quizzes focus on you truly understanding the material and are not easy. Quizzes plus two programming assignments help you actively learn rather than just passively reading or watching videos. I do wish the Discussion Forums kept a longer history of Q&A and had more responses from the instructors and mentors. For example, I had a question that someone else had already asked (so it wasn't an unusual question), but there was no response, and enough time had passed where the post became locked from any responses. So, I would really rate this course as 4.5 stars.

Phenomenal Course. Very nicely done. Wish there were more active mentor engagement, however, since the student community for this course is not as large as at the time of this writing, so not much material to search through in the discussion forums. It will be good if some of the videos are consistent with the book - e.g. the notion of "control" is not in the text, but is introduced in Week4 for DP. Also, it'd be great to have some more lectures that dig deeper into "alternate" representation of Bellman equations (we are thrown this question in the quiz, but some working professionals, like myself can be quite rusty in English<=>Notation mindset, but that's a "very" small nitpick item.

This course is excellent and it is a great introduction to reinforcement learning. I really liked that an electronic version of the book from Sutton and Barto is available for download as part of the course. However, it is fundamental to read the book in advance before watching the videos every week to have a better understanding of the concepts. Mr. and Mrs. White explain the content very well and it helped me a lot because the book is sometimes quite abstract if you are dealing with this subject for the first time. I definitely recommend this course to have a solid foundation in Reinforcement Learning and I am looking forward to start the next course of the specialization.

The course is vary good constructed. Very clear. And the homework is relatively easy compared to the excercises in the textbook, which is a good intro for coming back for those excercises.

A deep insight can not be built by this fundamentals course, but i don't mean that in a bad way. It's not easy for instructors to lead beginners through these fundamental concepts without losing well explaination of the basic therom and illustrating meaning and purposes of them straightforwardly. But Instructors here did a good job. Great respect for the high quality of this lectur. Thx again. I will keep up with the rest courses.

I've taken a few Coursera courses on machine learning/AI, and this is by far my favorite one. I love how the course is theoretically rigorous while still providing you with hands-on practice. Note: the short lecture videos don't contain all useful details, reading the (free) recommended textbook is a wise thing to do. Sometimes the quiz questions are drawn from the textbook, with slight modifications, and you'd be glad that you've worked through them prior.

No need to fret over reading every page of the textbook either, because recommended page ranges are given and they help.

I found the course really helpful. I have been learning RL for some time and it was hear that almost finally i can say that a lot of the concepts that were vague in my head became clearer. Also it made me look at the book of Sutton and Barto and found that it was a good experience. Maybe more examples and questions in between videos as in deeplearning.ai of Andrew NG could be good for keeping with the attention could be nice. Also maybe doing more programming exercises in between the ones we did in order to implement each step would be great. Thank you very much!!!!

One of my best, if not THE best online course I have taken yet. Martha and Adam have deep expertise in this area, and they are excellent instructors. The topics are well selected, the quizzes and assignments require you to think through the answers. By the end of the course you start to get an intuitive understanding of the subject. The course follows the excellent Reinforcement Learning book by Sutton and Barto, a landmark achievement in itself. All in all, a great entry level intro to the area of reinforcement learning.

Pros: - the time required to complete this course is reasonable and flexible

-the teaching videos explain unclarity from just reading the textbook.

-the practical examples and programming exercises help learners to relate the learned knowledge to a greater context

Cons: - some part it is not clear what do the variables of the equation meant and how it is related to real-world variables. (nevertheless, the user can find resources online to better understand the mathematics behind reinforcement learning)

Hi

This is a very good course to get familiar with the concepts of reinforcement learning that I passed and a hardworking team managed this course.

The concepts were taught very well and the coding tests included in the course were very helpful.

For those who want to pass the course, I recommend that they have knowledge of Python coding, if they are at an intermediate level, that is great.

Finally, I would like to thank and thank all the staff of the course and the coursera's team for this training.