14. Feb. 2021
Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.
11. Aug. 2020
Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job
von Narendra G•
26. Juni 2020
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.
von Misael D C•
30. Juni 2020
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.
von István Z K•
21. Mai 2020
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.
von Sebastian T•
28. Feb. 2020
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.
von Muhammed A Ç•
10. Aug. 2021
Programming assignments are not as good as andrew ng's courses. But still they are good enough to help you understand the concepts better by coding them
von Bruno L•
21. Mai 2020
The lectures and quiz tests are perfect. Jupyter. Programming exercises can be a little confusing sometimes but are also great. A great course, overall.
von Navid H•
16. Okt. 2019
definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world
von Bhargav D P•
1. Juli 2020
Everything is great overall but It would be more better if DynaQ & DynaQ+ were explained more detail in the lecture instead of assignment.
von Wahyu G•
20. März 2020
Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.
von LI C Y•
14. Juni 2022
Assignment is a bit hard, expecially the last assignment of Dyna-Q and Dyna-Q+. It would be great if more hints can be provided.
von judson g•
21. Aug. 2020
Assignment problems needs to be clearly defined and content of the video needs to updated and expects more information
von Cristian V•
30. März 2020
The course provides a lot of value. I only give 4 stars because the classes are scripted and feel unnatural to me.
von Max C•
23. Okt. 2019
Some of the programming homeworks were difficult to debug due to the feedback from autograder being unhelpful.
von Raj P•
8. Dez. 2020
Would recommend covering more examples to aid the understanding of concepts.
von Hugo T K•
11. Aug. 2020
The course is excellent! Only missed some programming assignments on Week 2.
von Nicolas M•
23. Sep. 2020
Great course, but some exercises would be better using concrete examples.
von Soren J•
20. Juni 2020
Very good. Although the python skills are quite high to pass this course.
von Yu G•
21. Jan. 2021
Tough, challenging course, very worthwhile taking!
von italo a d s o•
7. Jan. 2022
von Sachin K•
17. Aug. 2020
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.
von Mark L•
1. Juli 2020
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.
von Hadrien H•
13. Dez. 2020
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
11. Sep. 2020
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.
von Alessandro o•
12. Juni 2020
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
von Juan A V G•
13. Apr. 2021
It is required some mentoring on the Discussion forums. There is some part grading part that requires some improvement and it is too dependent on other students to work around some main issues.