Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.
Surely a level-up from the previous courses. This course adds to and extends what has been learned in courses 1 & 2 to a greater sphere of real-world problems. Great job Prof. Adam and Martha!
von Max C•
I had a much better experience with the autograder than in course 2.
Everything is amazing in this course! Dont miss it!
von Pachi C•
Fantastic course and great content and teachers!!!
Excellent course! Never be replaced! Thank you!
von Raktim P•
Great Course! Highly recommended for beginners.
von İbrahim Y•
the course is the intro for high level RL
von Teresa Y B•
Very Useful and Highly Recommend !!!
von Stewart A•
Simply the best course on this topic.
Very good and self-oriented course!
von Wei J•
It is a very perfect RL course.
von Antonis S•
Really a well-prepared course!
von Ignacio O•
Really good, I learned a lot.
von FREDERIC N•
Great speakers and content!
von Majd W•
Very practical course.
Excellent class !!!
von Hugo T K•
von Murtaza K B•
von Ivan M•
von Oriol A L•
von Cheuk L Y•
von Ananthapadmanaban, J•
I am disappointed with policy gradients being introduced on last week of the 3rd course. The instructors need to understand that 12 weeks is too much for introduction before starting a good project to implement the concepts with a hope to better understand them (course 4). Policy gradients should have been introduced in week 3/4 of course 2 itself. The content before that should be made more efficient (4 weeks to understand until q-learning/sarsa and 2 weeks to understand function approximation should be enough). I realized after course 2 that Andrew Ng has 3/4 videos on RL in the recently released ML class from Stanford. I am yet to go through them, but I feel they may explain these faster with same amount of rigour. However, the stanford class assignments are not public, which makes this course still useful because of the assignments. However, thanks to the instructors for this course.
von Luiz C•
Almost perfect, except two ~minor objections:
1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course
2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment
von Dmitry S•
Definitely a course to take to learn the ropes of RL. For this course, it is critical to follow and math. I'd love to give 5 stars to this course but will however take one away since the course could benefit a lot if the math was made a bit simpler to follow. The book referenced in the course is excellent and does help, but still, some more pedagogical repetition/rephrase, simplification of notation, a bit slower pace of narration would make the course even better. Having said that, this seems to be the best course available at this time. Many thanks to tutors.
von Narendra G•
This course is important for those who not just want to learn RL for mere sake but want to dive into various topics currently in research (for that reading textbook is of most importance). This specialization would have been even better if it had included some more complex topics from the textbook. To fully comprehend all the topics, guidance from experts is necessary.
von Nicolas M•
Very interesting course: I have learned many things. A translation to other languages would be great: sometimes I can't memorize everything as I would if it was in my mother tongue.
Using another paper to study ( Experiments with Reinforcement Learningin Problems with Continuous State and Action Space) was a great idea that should be done in other courses.