This course provides an excellent start. It could have been a little better, though by incorporating some more deep neural nets probably and touching on some of the state-of-the-art. Anyhow, I'm glad that I enrolled.

Not as complex as previous courses in the specialization but gives a nice refresher and lets us see the bigger picture of how the algorithms learned in the previous courses fit and differ. Amazing course!

The comments given by the auto grader is not informative of the errors causing problem, and not sensitive enough to capture problems with action selection steps based on current state.

It may have been useful to provide less guidance to the students to make sure they develop the required skills. Overall, it was a nice exercise to implement a TD(0) network.

Project could be better designed and could be made more fun. The first 3 courses were brilliant. I finished the entire capstone in less than 26-hours to save money!

Good project as a capstone. Wish there would have been more work needed from our side of things in terms of coding, but very solid final course for RL.

Great Course. But, it would be much more fun if the programming assignments were implemented in for instance tensorflow or pytorch!

I give 4 stars because this last course is not as good as the previous ones. No real complaints, but it's not as "complete".

The courses in this specialization are very essential to obtain basic knowledge on reinforcement learning.

My unique (possible) critic is the absence of more industry standard packages

V​ery good exercises and good way to learn about Reinforcement Learning

Good course

I wish that this course contained more practical advice.

The video describing the problem and the notebook describing the problem contradict each other in many details.

Also, the name of the course is a bit misleading. Instead of building "A Complete Reinforcement Learning System" and covering all of the building blocks of an RL project, the homeworks were mainly focused on training the agent, very similar to most previous assignments. So it was more about doing the same one thing in more depth rather than doing more different things.

The idea of dedicating a whole course to a practical project was indeed very good. However I think that this idea was not exploited as deeply as it could have been. The project itself is actually a notebook just a little longer than usual. I would have left more to do to students, and maybe they could have used lectures to give more explanations and hints for the practical part, or to do some programming together. Anyway, the course is overall fairly good, and it also introduces some new concepts, like experience replay.

I would have expected to do more of the implementation in the capstone project.

It is still quite useful to go through the details of what is implemented by the instructor for us, but not as effective as having to implement more of it ourselves.

Thanks

A good review of the material covered in the specialization. I would have liked to see a more thorough parameter study and deeper coverage of the environment wrapper around the OpenAI lunar lander environment.

Review videos were tiring for people that had watched previous courses!

Could be more detailed in the environment setup

Not worth it.