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.

I believe that, as of now, this is the most educational and informative resource available online to learn the fundamentals of RL from scratch. the instructors use Sutton’s book as reference material (which is freely available online), guide you to details that no one would know are important when studying RL alone and prepare you to venture further into the area, with a solid foundation. I definitely recommend this as a starting point for anyone who wants to dig deep into RL.

This course gives a strong foundation for understanding and applying RL, particularly I like the way the material summarizes the content detailed in books like Sutton's and Barto's, and courses like those given by David Silver at UCL / Deepmind / Youtube / David's site. Also programming assignments let me understand the concepts I didn't grasp initially by implementing them in code, I wish the course would have had more of those though. Overall, I recommend taking this course.

This is the best RL course I have ever attended. Even before starting this course I had brought the textbook (the one which course instructors also recommend) and was through the first 4 chapters. I understood most of the material but when I attended the class, everything was crystal clear. I hope instructors follow up and create the remaining courses as well. Please increase prgramming assignments in number as well. Thumbs up. Thanks for this course, very grateful.

I really like this course. This course introduces the basic mathematical background needed in RL, as well as provided algorithms and hands-on programming practices in translating algorithms into actual code, which is a well-blended material for students to learn! The quizzes are very helpful as well, which helps me understand the concepts better. All the methods discussed here are quite practical and intuitive. Thanks Martha and Adam making this course fun!

wow....It was the best course for the basic of reinforcement learning. Before enrollment I was roaming all over internet for getting started but every other courses I visited from You Tube were not on order and often were too vast so early with proper background of fundamentals on reinforcement learning. But this courses from University of Alberta was just amazing.

Thank you for this course. It will be huge boost for preparing my college minor project.

Honestly, I would prefer to just lay back and consume knowledge from videos rather than reading a book full of scary math :)

Another issue is that the time allocated for reading and programming assessments is not fair: reading the book definitely takes longer, considering all that level of abstraction.

I would recommend to watch David Silver's course on Youtube after this course for generalization and a deeper understanding of the topic.

The material in this course is of interest or me. It combines both theories and practical aspects of RL. The course follows the standard book in RL (Sutton & Barto Book).

One improvement may be needed is to add more "modern" examples and programming assignments/modules to explain the concepts. Also, it would be nice if the instructors can sometimes reflect on their own experiences with RL, rather than exactly following the book.

This course provides me the fundamental principles of RL! I like the clarity of each module (because I tend to be lost in the textbook only). In addition, I really appreciate the programming assignments of this specialization which helps me gain a deeper understanding of the basic concepts. I used to be afraid of dynamic programming, but I think I am confident enough to study more complex problems using DP in the future.

I have been interested in RL for a while and have watched many videos taught by other researchers, but this one provides something unique that helped me really get a deeper understanding of RL and gain confidence, such as the graded exercises, the quiz, I look forward to continuing this sequence of the RL specilization! Thank you so much for making the complex concepts accessible and make the quizzes and assignments!

Combined with the suggested reference, this course does a good job of giving an introduction to the fundamentals of RL. The videos are easy to follow and compliment the text well, the pacing is good, and the assignments were fairly easy to follow. I did find it helpful to supplement the assignments with additional independent exercises to fully understand the chapters covered, but I feel that was expected.

Very good course, you can take it at any level, if you wish just to get familiar with reinforcement learning you can watch the video and quickly read the book, but if you would like to be an expert you can deep dive in the book. i really like that the course follow some book which made it an serious course. could be nice to add some more homework (optional) with more interesting problem (e.g. gym)

The course was very fun and informative. I really enjoyed the presentation style with clear outlines and summaries. The explanations were useful and easy to follow. Suggestions for improvements:

1) Provide a kindle version of the book, reading on screen is very tiring for eyes.

2) I think the programming exercises could use some work from SE perspective, as some of the code is not really pythonic.

The course is very well organised and professionally made. Although it follows the first four chapters of the Reinforcement Learning textbook, it provides a little bit different narrative and thus serves as a very nice complement to the textbook. Most importantly, interactive quizzes, programming exercises in Python and plenty of visualisations help to strengthen understanding of the concepts.

This course is the best course I have taken in Coursera! As a learner of RL in a non-English-speaking country, Sutton's book is too hard for me to accept a new idea very quickly. However, after watching the short videos in this course that summarize the core concepts explicitly, I can understand the contents of that book easily. Recommend for anyone who wants to study reinforcement learning！

Exceptional course, the fundamental of RL explanations are excellent! I in particular I found it insightful the focus on thinking about examples in real-life that can be modeled as Markov Decision process. Additionally, great quizzes questions and assignments all helped in deepening my understanding of topics such as Dynamic Programing, Bellman Optimality, and Generalized Policy Iteration.

Excellent Course! The level of difficulty is perfect. It is difficult but not impossible if you do the readings in the textbook and understand the lectures. I strongly suggest reading the book before watching the lectures. This helped my understanding significantly. The material and assignments are very interesting and informative.

Highly recommend this course to anyone interested in RL.

This course will give you the knowledge of most fundamental concepts of RL Like MDPs, Policy evaluation, policy improvement & value iteration algorithms. Even though you follow theory well, quiz and assignment will challenge your knowledge to think into bit more deeper level. frankly speaking, I took some quizzes three times and at the end I learned the concepts very well. :)