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Learner Reviews & Feedback for A Complete Reinforcement Learning System (Capstone) by University of Alberta

4.7
stars
630 ratings

About the Course

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution....

Top reviews

JJ

Apr 27, 2020

This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the reinforcement learning.

CR

Feb 26, 2020

Great course for learning the fundamentals. I liked that it tied into function approximation for deep reinforcement learning. The text book made the fundamental concepts more clear.

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101 - 125 of 128 Reviews for A Complete Reinforcement Learning System (Capstone)

By Adrian Y X

Apr 4, 2020

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.

By Henry C

Oct 16, 2021

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.

By Jing Z

Jun 2, 2020

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.

By Francisco M

Jul 12, 2022

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.

By Dmitry S

Jan 10, 2020

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.

By Ahmed S S A

Mar 5, 2020

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

By Surya K

May 3, 2020

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.

By Lik M C

Jan 23, 2020

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.

By Moeen T

Feb 3, 2024

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.

By Mateusz K

Nov 16, 2019

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).

By Narendra G

Jul 24, 2020

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.

By Fred A

Jun 18, 2020

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.

By Tri W G

Apr 4, 2020

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!

By Yichen W

Dec 4, 2019

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.

By Harold

Jan 13, 2022

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.

By Pradeep

Jun 5, 2020

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!

By Matt S

Feb 3, 2021

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.

By Yassine B

May 15, 2020

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

By Sérgio V C

Apr 3, 2021

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".

By Akinyele O

Jun 7, 2020

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

By Rafael B R

Oct 28, 2021

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

By Francois R

Sep 19, 2023

Great course. A good conclusion to this great RL Specialization

Thank you

By Oscar R R M

Sep 1, 2021

Very good exercises and good way to learn about Reinforcement Learning

By Tianpei X

Aug 15, 2022

good practice of RL in a simulated example

By Antonio P

Jan 21, 2020

Good course