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

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.

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

The course is good , however the submission of result.zip is inconvenient

Videos can have a little bit more technical details for the algorithms

The last test is a little bit tricky

The reading material is great (as are the lectures), but frankly, the hypersensitive autograder is a real hinderance. Correct implementations don't get full points, and are sensitive to things like the order of random number generator calls, rather than looking for a correct range of solutions. To make things worse, the autograder has poor feedback - I often had to rely on assignment discussions with people who had received similarly unhelpful feedback to debug my solutions.

Some explanations need should be broken down into smaller pieces

너무 함축적입니다. 강의 시간이 늘어날 필요가 있을 것으로 보입니다.

I give the course a low rating for several reasons, the first being the most important one: The instructors basically completely absent. Having issues or problems? They don't bother. Not a single reply from either instructor in the forums for months or years. Second: Flawed and unprecise notebooks. Well known issues with random numbers, but no updates. Incorrect book references which will let you implement formulas other than intended. Third: Tons of short videos with 30% summary and "what you will learn", which is ridiculous for 3 minute videos. Fourth reason: Mathematical depth missing after the first subcourse. Suggestion: Watch the David Silver and Stanford youtube lessons instead. For free and better explained. Compared to, for instance, Andrew NGs specialization, this one is really bad mostly thanks to the complete disinterest of the instructors.

This is one of the worst courses I have ever taken in my life. The videos don't contain much content and presenters just read them off with no clarification or explanation. Furthermore, the book is also shit (despite the fact that it's the gospel of RL). The writers of the book, who are two well-respected scientists, really suck at writing books. There is another course (or specialization) from the National Research University Higher School of Economics called "Practical Reinforcement Learning". You probably should check it out before you take this one.

This course has been reorganized that reduced the contents from 5 weeks to 4 weeks during my break section. After I come back the course to finish my last assignment, my grades of previously finished assignments and exams are disappeared. I have to redo them again to pass the course. It should not happen like this way. Coursera system seems not so reliable and is better to make a backup of the materials on your computer. Please solve this technical error.