Excellent course with excellent materials, but definitely not for the uninitiated. This prereq should have been clearly mentioned.

Assignment is a bit hard, expecially the last assignment of Dyna-Q and Dyna-Q+. It would be great if more hints can be provided.

Assignment problems needs to be clearly defined and content of the video needs to updated and expects more information

The course provides a lot of value. I only give 4 stars because the classes are scripted and feel unnatural to me.

Some of the programming homeworks were difficult to debug due to the feedback from autograder being unhelpful.

Would recommend covering more examples to aid the understanding of concepts.

The course is excellent! Only missed some programming assignments on Week 2.

Great course, but some exercises would be better using concrete examples.

Very good. Although the python skills are quite high to pass this course.

Tough, challenging course, very worthwhile taking!

Good

good

Passing notebook assignments is hellish due to strict decimal matching for numerical computations. You must do steps in one specific order or the assignments in autograder comparisons won't work. The course is itself fine and is more or less a rehash of the book so you may as well read that. There is no special intuition but the notebooks do provide a good experimental design strategy. Many of the experiments listed in the book are actually implemented in assignments which aids in learning. There is no technical support staff on Coursera anymore. So you are on your own when taking the course. Discussions forums are littered with discussion prompts and new ones are added every week so its not easy to find anything in there. Coursera has become substandard and the rating reflects a mixture of the course and coursera as a platform.

This course has presented a large number of techniques/algorithms in addition to the ones presented in the first course. I find it hard to keep track of these. It would be most helpful if the techniques could be summarized in a table to lists the various attributes. In addition, I would like to see some examples of practical problems that can be solved with these techniques in addition to the explanatory "toy" problems. I also find the pace of the lectures a little "choppy", with a lot of very small lectures, each with its own introduction and summary.

Overall course instruction is good. However, there are serious issues with the programming assignment where the implemented code can be correct but fails the autograder because the random numbers might have been drawn in a different order than when the instructors created the code. These issues need to be fixed but based on the discussion thread (Sample-based Learning Methods - Discussions | Coursera) have been present for at least 8 months

Still very good course but I felt like this second unit covers less of the book than the first one. The classes are quite shorter than in the first part while the book content gets richer. The assignments are a bit more complete though

There should be more examples on Q-learning and Expected SARSA. The course just compares different algorithms for different parameters. The autograder is annoying too. Really need some work on that. Otherwise the course is okay.

To be honest I think that arguments quite complex are treated too quickly and basically it's up to you to figure it out. I think that some ideas would have been nice to have a more detailed explanation

It is required some mentoring on the Discussion forums. There is some part grading part that requires some improvement and it is too dependent on other students to work around some main issues.

The theory is explained quite well and is understandable. Assignments need to made more clear and users should be allowed more engagement because it just feels like fill in the blanks for now.

The duration of the lectures was very very short. They were for 5-7mins, in which 1-2 min was overview and summary. Had the lectures been more longer, more examples could have been explained.

It is very sad that tutors (or somebody from staff) stopped answering questions in the discussion forums. Aside from this problem, the flow, the lecture quality is super

The assignments are an exercise in programming far more than they are a learning tool for RL. The course lectures are good, and I recommend auditing the course.

The content is very helpful and clear, however, the notebook implementations are not so good and misleading sometimes.

The video content is not elaborated enough, need to read the book and search on the web to understand the idea