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e but there is plenty of issues with the automated grader. you spend most time dealing with the letter not on actual learning of the matter.

Programming assignments are not as good as andrew ng's courses. But still they are good enough to help you understand the concepts better by coding them

The lectures and quiz tests are perfect. Jupyter. Programming exercises can be a little confusing sometimes but are also great. A great course, overall.

definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world

Everything is great overall but It would be more better if DynaQ & DynaQ+ were explained more detail in the lecture instead of assignment.

Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.

A​ssignment 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

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.

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 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.

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.