A very good course. The lectures are brief and provide a quick overview of the topics. The quizzes require more in-depth reading to pass (covering material not discussed in the lectures) and the projects are difficult but rewarding and really help to cement the information. My only suggestion would be to lengthen the lectures to provide more discussion on the topics.

Overall, I found the course well made, inspiring and balanced. The videos really helped me to understand the rather austere textbook. I give 4 stars because some of the coding exercises felt more like work in progress, without the help of other students I would not have been able to overcome these issues.

Jump for here to there, it's nice to have all these algorithms. My gut tells me something is not correct. Too much focus on experience, which means a lot of data. The model part is touched very little, and main focus is on when model is wrong.....

Mid way thru my course in week 5, Jupyter notebooks were revised. In general, new ones are better but lost valuable forum discussion Still one error in plot of notebook of week 5th. But in general course was good

This is a very good course, the only thing to improve are the technical issues with the assignments and submission processes. I had problems on the half of the assignments and many others learners too.

It's an important course in understanding the working of reinforcement learning. Although some important and complex topics are not explored in this course which are mentioned in the textbook.

This course excellent, my only complaint is that there is a 5 attempts limits and a 4 months wait to retry. It seems excesive to me and adds extra pressure when taking on assignments.

Overall a very nice course, well explained and presented.

Sometimes, it would be nice to see the slides 'full screen' rather than the small version in the corner.

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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.

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.

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.

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