I will write a longer review for the entire Specialization later, but this course does well to sum up all of the other progress you've had made thus far on the Specialization. However, you'll find that from Course 2 onwards (and this one especially), very little hand holding is given for the programming assignments. Command of numpy and python at good level are expected. Personally, having worked with OpenAI gyms before starting this specialization helped me immensely. As the instructors state, this course lays the foundation for future studies. The field of RL is simply so complex that even foundational work is challenging. Overall, a great course.

A decent course to wrap up the RL specialization, with a "project" that demonstrates a "real-world" application of RL.

The word "project" is in quotes because it is structured as a (short) series of fairly short assignments with very heavy hand-holding, so very similar to previous courses.

My only complaints with this course are that the project is a bit too hand-holdy and that the course overall is quite short and thin in content. I would estimate that this course is around 1/3 the length of the previous courses in this series.

The project is a decent example to go through in order to review what we learned from previous courses. However there are few key things supposed to be addressed as well: 1) What exactly the reward function is in the final project (C4M1 practice is badly designed); 2) How can we build an environment on our own; 3) Apart from Mean Squared Value Error to be minimized, what are other loss functions to choose from and what's the consideration behind.

I am a recently junior researcher in the Optimization field, approaching predictive and prescriptive online retail problems. Therefore, I truly believe this complete reinforcement learning specialization gave me the foundations to evolve my research in this domain. About the structure and contents of the specialization, I think it is very well organized in the 4 main courses. Thanks to the team.

Good course. Summarises and puts everything in context. But would benefit from having larger programming assignments (which would make it more challenging as well) when less things are provided out of the box, and from a bit more extended and systematic overview and walk-through of the material.

Great course, thanks a lot really. But I do hope if we did visualize the environment to see how my agent behaves and then saves the RL agent to use it offline after being trained. Really thank you so much for making RL clear to me and interesting too :) <3

A cherry on top of the cake. This course helped me understand how to think about a novel problem and formulate and build an RL system from scratch. I thank Course Instructors, University of Alberta, and Coursera for this beautiful specialization.

The project is interesting. But the implementation left as assignments is too simple. There are too many guidance running in assignments. If more flexibility is allowed in implementing the project, it should be even more interesting.

It gave a good general understanding of the different tasks and questions in a real RL experiment but the final assignment was a bit sloppy (not following the same standards of the previous courses) and the they could be improved upon.

In my opinion, the capstone should've included more development and or programming. I liked having to develop NN action-value function approximator, but the parameter study was a bit too simple (should've had more code content).

The capstone project was great, it helped gain insights for developing a full RL agent. The RL problem though was a simple one, a more complex problem real-world problem implementation would have made this course perfect.

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

Great course. A good conclusion to this great RL Specialization

Thank you

Very good exercises and good way to learn about Reinforcement Learning

good practice of RL in a simulated example

Good course