I spend 1 hour learning the material and coding the assignment while 8 hours trying to debug it so that the grader will not complain. The grader sometimes insists on a particular order of the coding which does not really matter in the real world. Also, grader inconsistently gives 0 marks to a particular part of the problem while give a full mark on other part using the same function. (Like numpy.max) However, the forum is quite helpful and the staff is generally responsive.

Great course. Clear, concise, practical. Right amount of programming. Right amount of tests of conceptual knowledge. Almost perfect course.

Great course! Really powerful but simply ideas to solve sequential optimization problems based on learning how the environment works.

Course was amazing until I reached the final assignment. What a terrible way to grade the notebook part. Also, nobody around in the forums to help... I would still recommend this to anyone interested, unless you have no intention of doing the weekly readings.

The second course of the specialization is excellent and it provides a solid foundation on sample-based learning methods. The book and the videos complement each other making the learning experience rich and pleasant. The professors explain the content very well and the programming assignments are very interesting to consolidate the knowledge. I had a few issues with the grader and it just returns the score without any message that could help find out what is causing the unexpected behavior. As a suggestion, I would like to suggest that the grader could return any additional information and/or include new unit tests. I am looking forward to begin the next course of the specialization.

Excellent Course!! Reading the content and making notes ahead of time before watching the lectures is a MUST!!. The graphics/visuals in describing the concepts in the lectures were very good, especially for a visual learning such as myself. However, I wish there were a few more lectures and the lectures were a little - maybe another 3-5 minutes longer and delved into the derivations/concepts - for example - Bellman Equations to Sarsa/Q-learning/TD.

A great step towards the acquisition of basic and medium complexity RL concepts with a nice balance between theory and practice, similar to the first one.

[Note: the course requires mastering the concepts of the first one in the specialization, so don't start here unless you're sure you master its contents.]

This course is perfect.

You will learn everything about sample-based RL. The programming assignments are harder than the previous course, but you will understand all the algorithms better.

These two courses covered part 1 of the book, and you will build a strong foundation of RL for the future.

In my opinion, this course strikes a comfortable balance between theory and practice. It is, essentially, a walk-through of the textbook by Sutton and Barto entitled, appropriately enough, 'Reinforcement Learning'. Sutton's appearances in some of the videos are an added treat.

Great balance between theory and demonstration of how all techniques works. Exercises are prepared so it is possible to focus on core part of concepts. And if you will you can take deep dive into exercise and how experiments are designed. Very recommended course.

One of the amazing things this specialization stands out in is that it is based on a textbook. if you read from it and watch the lectures, you will have a very good understanding of the material. Also, the programming assignments are very beneficial.

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

Great course!!! Even better than the 1st one. I tried to read the book before taking the course, and some algorithmics have not been clear to me until I saw the videos (DynaQ, DynaQ+). Same wrt some key concepts (on vs off policy learning).

Course is not easy, videos presentation is a bit dull - but the material is cool and interesting, and the additional quizzes, videos and especially notebooks make it a great course - you learn a lot and see progress. Highly recommended.

This course required more work than the 1st in the series, (may be i took it lightly as the first was not that difficult). Request : Please include some worked examples (calculations) or include in graded/ungraded quiz, will be nice.

Awesome! It is a pitty n-steps and eligibility traces were not included - felt like a huge gap. All the future chapters have a reference to the n-steps, and your understanding won't be complete unless you learn that as well.

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

The course is intermediate in difficulty. But it explains the concept very clearly for me to understand difference between different sample based learning methods.

Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.

This course is well explained, easy to follow and made me understand much better the tabular RL methods. I liked it very much.

I like the teaching style the emphasis on understanding and the fruitful combination with the textbook. Highly recommended!

Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding

Again, the course is excellent. The assignments are even better than Course 1. A really great course worth to take!

Although I have learned q learning and td, this course still give me a lot of new feeling and understanding on it.