Excellent material!

excellent course

Excellent course

Nice Course!

GOOGD GOOD

Enjoyable.

Very good

Excellent

awesome~

Great!

Great

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.

They mostly discuss the importance of real world experience and hyperparameter tuning in this class. The content it did have was solid and the instructors were great. The "capstone" was creating an agent to solve the Moon Lander problem, and much of the code was already written.

I would have really preferred getting experience with a real RL framework like RLLib or acme, rather than the toy libraries used by the book. It would have also been really nice to have a little more freedom and challenge, such as making us actually create an agent to solve an MDP of our own choosing and definition.

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.

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

I liked the project. I hoped it would be harder and enable the students to design the whole agent and environment code and be evaluated with a human grader. But overall, I was able to practice the concepts I have learnt throughout the specialization.

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.

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.