WP
11 de abr de 2020
Difficult but excellent and impressing. Human being is incredible creating such ideas. This course shows a way to the state when all such ingenious ideas will be created by self learning algorithms.
AC
1 de dez de 2019
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.
por MJ A
•23 de jan de 2021
perfect and thank you for this course
por Teresa Y B
•11 de mai de 2020
Very Useful and Highly Recommend !!!
por Stewart A
•31 de out de 2019
Simply the best course on this topic.
por Junchao
•29 de mai de 2020
Very good and self-oriented course!
por Fernando A S G
•26 de mar de 2021
Excellent course! Thanks a lot!
por Wei J
•11 de out de 2020
It is a very perfect RL course.
por Antonis S
•30 de mai de 2020
Really a well-prepared course!
por Ignacio O
•29 de nov de 2019
Really good, I learned a lot.
por FREDERIC N
•2 de mai de 2020
Great speakers and content!
por Majd W
•1 de fev de 2020
Very practical course.
por 李谨杰
•17 de jun de 2020
Excellent class !!!
por Mohamed A
•11 de set de 2021
very good course
por Hugo T K
•18 de ago de 2020
Excellent course.
por Murtaza K B
•25 de abr de 2020
Excellent course
por Ivan M
•30 de ago de 2020
Just brilliant
por Oriol A L
•19 de nov de 2020
Very good!
por Cheuk L Y
•8 de jul de 2020
Very good!
por Jialong F
•23 de fev de 2021
gooood!
por Justin O
•18 de mai de 2021
Great
por ARTEM B
•27 de fev de 2021
Super
por Ananthapadmanaban, J
•19 de jul de 2020
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.
por PHILIP C
•18 de jun de 2021
This is a good course, but I continue to be disappointed in the lack of detail in the lectures. I fill in the detail with the Deep Mind lectures on Reinforcement Learning by David Silver. The programming assignments are difficult, not because they are challenging, but because the data structures are not well explained and the conceptual connections between the equations in the book and the code structures used for the implementation are not clear. It's like being given somebody's not-very-well-documented code and trying to figure out what they were thinking. All that said, I think that the course offers a lot and I have learned a lot from it so far.
por Luiz C
•3 de out de 2019
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
por Luka K
•4 de jan de 2021
It is a good introduction to prediction and control with function approximation. Combining book and instructros results in a simple and nice explanation. What keeps it from the perfect grade are the examples. It would be nice if there are more examples and explained in a more detailed way why and how the example works. For example sometimes instructors would just say that the robot can use this, and that is mostly it. The other thing is more interactive project work. For example I would like to see how is my pendulum moving after N number of episodes. I would feel more satisfactory then.
por Dmitry S
•5 de jan de 2020
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.