Dec 1, 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.
Jun 24, 2020
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!
By PHILIP C•
Jun 18, 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.
By Luiz C•
Oct 3, 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
By Luka K•
Jan 4, 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.
By Dmitry S•
Jan 5, 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.
By Hadrien H•
Feb 4, 2021
I really appreciate that this course gives more hands on and assignments exercises. Really helped a lot in the understanding of the theory. As the books gets deeper into concepts and complexity so does the class, which is nice, but I felt like the depth and complexity in which the online class goes does not really keep up with the book content. Not only by skipping chapters but also by staying a bit at a too high level sometimes. Still a very good course again and really accessible, entertaining and resourceful material and instructors.
By Steven W•
May 11, 2021
It's a great course, and they cover the basics of function approximation. The instructors were clear and knowledgeable, and the content that was covered was solid.
However, they skip some content that I feel is really important for modern RL, specifically the "deadly triad" regarding the convergence of off-policy approximate TD methods. They also don't discuss or link to papers on PPO or other recent advancements in RL, and I was hoping to learn more about those in particular.
By Narendra G•
Jul 19, 2020
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.
By Nicolas M•
Oct 24, 2020
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.
By Lucas O S•
Jan 21, 2020
Great course, deserve 5 stars. It is a good complement to the book, it adds interesting visualizations to help parse the content. The only issues were in the exercises. There are technical issues with the notebook platform where it keeps disconnecting from time to time, with no warning, and you lose your unsaved work (seems like token expiration).
By Anirban D•
Jul 24, 2022
Excellent instructors and good concepts and assignments help you learn by doing. My only reason for giving a 4 is that this courses uses some internal tool (RL Glue) and hence none of the Jupyter notebooks are implementable outside. Some well known reinforcement learning framework like tensorforce perhaps should have been used.
Jan 17, 2021
Very good lecture! I understand a lot about function approximation such as linear approximation, neural networks, etc. However, detail of video lectures were not perfect as the textbook. If you don't want to read a lot of text and listen to the lectures, you might not understand a lot of concepts.
By Hugo V•
Jan 15, 2020
it was great to apply what I have learned from the book, but it was hard to find my mistakes in the course 3 notebook. I also misunderstood the alphas in the course 4 notebook at first glance, their indices look like they are powers (sorry for the bad english). Besides it, great course.
By Amit J•
Mar 17, 2021
Lecture quality could have been better. They look like practiced monologues rather than a class where a teacher is trying (hard) to explain a concept. If one has to wait for assignment to get the full grasp, it doesn't reflect too well on the instructors.
By Lik M C•
Jan 18, 2020
The course is still good. But the assignment is not as good as course 1 and 2. In fact, the contents of the course are getting complicated and interesting as well. But the assignments are relatively simple.
By Mark P•
Aug 17, 2020
Solid intro course. Wish we covered more using neural nets. The neural net equations used very non-standard notation. Wish the assignments were a little more creative. Too much grid world.
By Anton P•
Apr 12, 2020
There is a lot of material covered in the course. Be aware the pace picks up considerably from the first two courses. This said, it is a worthwhile course to take.
By Vladyslav Y•
Sep 8, 2020
I wish agents that are based on visual information (with the usage of CNN) would be included in the course. But overall that was really great!
By Sharang P•
Feb 27, 2020
more detailed explanation of some of the assignments and how state values are got with tile coding but overall a great experience!
By Jerome b•
Apr 9, 2020
Great course, based on the reference book about reinforcement learning. A must for anyone interested in machine learning.
By Rajesh M•
Apr 17, 2020
I loved the course videos and programming assignments. The only suggestion would be to go a little deeper in the videos.
By SCOTT A•
Aug 5, 2020
This was a good course but I really struggled to understand how each of the value functions translated into code.
By Muhammed A Ç•
Sep 4, 2021
Programming exercises are not self explaining. But instructors are explaining concept in a perfect way
By Pouya E•
Dec 2, 2020
Great overall. The content on policy gradient could be expanded, some details were delivered hastily.
By Rishabh K•
May 19, 2020
The average reward and differential return needs to be explained more thoroughly
By Ramaz J•
Oct 17, 2019
Course is great! Maybe some slides would be helpful not to forget.