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Opiniones y comentarios de aprendices correspondientes a A Complete Reinforcement Learning System (Capstone) por parte de Universidad de Alberta

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173 calificaciones
33 revisiones

Acerca del Curso

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....

Principales revisiones

CR

Feb 27, 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.

MI

Mar 27, 2020

Thanks a lot for offering this specialization! I really enjoyed watching the videos and working on the assignments while exploring various topics of RL.

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1 - 25 de 33 revisiones para A Complete Reinforcement Learning System (Capstone)

por Daniel M

Nov 07, 2019

A great course/specialization, and the one in reinforcement learning you were looking for. A lot of work has been put into creating this specialization. Maybe a bit less into this last course (capstone) which consists of a patchwork of lectures from previous courses and some new ones. The capstone project is not fundamentally different from assignments in previous courses. Be aware, even if you’ve made it through the whole specialization it most likely doesn’t mean that you will be ready to return to your own area of interest/expertise and implement an RL project from scratch. Still, I would highly recommend taking the full specialization if you meet the programming prerequisites.

por Justin S

Dec 06, 2019

This course changed my life! It was so good and I learned so much. I can't believe I'm now an astronaut. Next mission: go to Mars!

por Ivan S F

Dec 15, 2019

Very good course. Compared to the prior courses in the specialization, it appears to be still a course under development rather than a final product. I recommend that the instructors work more on this course (the other courses in the specialization are very very good).

Keep up the great work.

por David C

Nov 13, 2019

Very good lectures - very informative and on point when it comes to theory but lacking in actual application of the theory. However, the projects are TERRIBLE. They could actually be very good, but there is simply not enough information in the descriptions to be very useful. None of the lectures discuss the details of how to implement any of the topics and the projects basically set things up but provide no information on what is actually expected to be done. They need to either discuss the basics or provide pointers to resources that provide that description. Some of the forums are helpful in clarifying things, but the projects really need someone knowledgeable in this area to rework things extensively.

por Kayla S

Jan 14, 2020

I really liked the new videos ("Meeting with...") and the idea of using all the information learned through the other courses to tackle a project. However, this course seems to not be fully thought-through. I didn't love the re-inclusion of videos I had already seen in the past (which were sometimes only tangentially-related to that week's topic). The programming assignments were either way too easy (#1 and #3) or way too difficult/involved/long (#2). The pacing of this course was way off as well, I don't think it should be broken into 6 weeks. I finished the entire thing in about 1 week.

por Alberto H

Jan 04, 2020

You might, like me, have acquired some understanding on several areas of RL (Q-learning, Policy Gradient...) from available sources (selected papers, articles, blogs, tutorials...), and were waiting for "the right" course to come up, wrapping up all existing and missing bits into one solid foundation.

If that's your case, don't waste any more time or money somewhere else: this course is the course you are needing. It will take you step by step (always) from the basics of bandits to MDP solutions and from tabular algorithms to more sophisticated function approximation algorithms.

And if you're just starting to scratch on this great field... well, I don't think you'll currently find a better online course, and I've seen quite a few.

Thanks for putting this together, Martha and Adam!

por Stewart A

Nov 09, 2019

Excellent final course for the specialization. Moon Lander project was informative and fun.

por David R

Jan 02, 2020

Unlike the previous courses in this specialization, this course seems a bit unripe. There's very little material added here (perhaps the only thing new is the Replay Experience algorithm, which is introduced rather briefly). It's more like a general recap of the previous 3 courses. I kind of hoped for something more challenging and broad - but the scope here was rather limited.

por allonhammer

Dec 29, 2019

It is clear that a lot of effort has been put in this course. Excellent examples and very clear explanations of the theoretical material. The down side is the programming assignment is too easy, and we didn't actually implement the environment

por Maxim V

Jan 25, 2020

Good content, but considering the bugginess of graders and the necessity to submit results separately from notebook, this requirement is too extreme: "Retakes: You can attempt this assignment 5 times every 4 months."

por Mukund C

Apr 02, 2020

Absolute fantastic!! Thank you to everyone that put this course together. I loved the "behind the scenes" decision making on choices of approaches - I just wish there were some more them to "distinguish" the process for making different choices. The instructors are excellent teachers. Would love the opportunity to sit in a class live and interact and ask questions!! Was great fun digging into the code and understanding the data structures.

por Akash B

Dec 08, 2019

This capstone project is really amazing as how it gives the overall expertise understanding for experimentation and how to implement the algorithm. From MDP to scientific selection of meta-parameters are really important to decide about how should be make an agent, but there are lots of considerations.Overall, this was a great experience and would remember the instructors for all my life. Thanks.

por Walter O A

Jan 18, 2020

A solid introduction to the subject matter of Reinforcement Learning. Especially helpful navigating through the Sutton & Barto book. The programming labs all worked and included robust tests for correctness. I especially appreciated this as I have spent significant time in other courses banging my head on the wall because of an incorrect or vague lab assignment. Well worth the time invested.

por Chad R

Feb 27, 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.

por Gordon L W C

Apr 04, 2020

The course is applicative in real world projects. I think it is a very good choice for any one that is interested to learn how to apply reinforcement learning.

por Mohamed S R I

Mar 27, 2020

Thanks a lot for offering this specialization! I really enjoyed watching the videos and working on the assignments while exploring various topics of RL.

por koji t

Nov 18, 2019

This course was the best course for me as a beginner in reinforcement learning.

por Roberto M

Mar 29, 2020

The project is well structured and very helpful to connect all the dots

por Andrew D G

Nov 14, 2019

Excellent course and specialization

por Chang, W C

Nov 09, 2019

Enjoyable.

por A4

Jan 02, 2020

awesome~

por Yijie X

Apr 04, 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.

por 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.

por Ahmed S S A

Mar 05, 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

por 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.