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Opiniones y comentarios de aprendices correspondientes a Sample-based Learning Methods por parte de Universidad de Alberta

4.8
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798 calificaciones
160 reseña

Acerca del Curso

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...

Principales reseñas

KM
9 de ene. de 2020

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

KN
2 de oct. de 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

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26 - 50 de 156 revisiones para Sample-based Learning Methods

por Jesse W

14 de may. de 2020

This is an excellent course in reinforcement learning. They provide a PDF for a textbook which is very clear and readable, and the lectures do a great job at reinforcing the concepts. The programming assignments are pretty interesting as well.

por AhmadrezaSheibanirad

10 de nov. de 2019

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

por Luis G

21 de nov. de 2019

Great course!!! Even better than the 1st one. I tried to read the book before taking the course, and some algorithmics have not been clear to me until I saw the videos (DynaQ, DynaQ+). Same wrt some key concepts (on vs off policy learning).

por David R

10 de dic. de 2019

Course is not easy, videos presentation is a bit dull - but the material is cool and interesting, and the additional quizzes, videos and especially notebooks make it a great course - you learn a lot and see progress. Highly recommended.

por Shashidhara K

12 de dic. de 2019

This course required more work than the 1st in the series, (may be i took it lightly as the first was not that difficult). Request : Please include some worked examples (calculations) or include in graded/ungraded quiz, will be nice.

por Aze A

12 de oct. de 2020

The lectures videos are concise and clear. The labs offer the opportunity to put in practice the theory. Al in all very content with content and the way the material was explain. Watching the interviews with SME was very motivating.

por Rafael B M

16 de ago. de 2020

The course build up the knowledge required to fully understand the basis of Reinforcement Learning, in that way, the student become well prepared and ready to investigate broader approaches for RL such as Function Approximation.

por Jose M R F

21 de jul. de 2020

Phenomenal walk-through over Sutton & Barto's book. The programming exercises really help to dive deeper into the details of each algorithm, visualize their behavior and get dirty with the intricacies of the implementations.

por Lucas O S

21 de ene. de 2020

Awesome! It is a pitty n-steps and eligibility traces were not included - felt like a huge gap. All the future chapters have a reference to the n-steps, and your understanding won't be complete unless you learn that as well.

por Dani C

24 de ago. de 2020

The material discussed is very clear, and the graded quizzes and programming assignments force you to really understand what you have just heard. I enjoyed this course a lot, and learned even more.

por george p

15 de oct. de 2020

Well structured course with amazing mentoring and examples. Chapters of the book are easy to follow with meaningful applications. Coursework particularly interesting with high hands-on experience.

por Andreas_spanopoulos

12 de ago. de 2020

Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job

por Kinal M

10 de ene. de 2020

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

por Kyle N

3 de oct. de 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

por Gordon L W C

15 de feb. de 2020

The course is intermediate in difficulty. But it explains the concept very clearly for me to understand difference between different sample based learning methods.

por Art H

13 de abr. de 2020

Well done. Follows Reinforcement Learning (Sutton/Barto) closely and explains topics well. Graded notebooks are invaluable in understanding the material well.

por Karim D

20 de oct. de 2020

Excellent course. Really well taught. Good pace of videos and assignments, with the support of perfect reading material. thank you tot he teachers.

por Giulio C

13 de jul. de 2020

Excellent course and instructors! I'm very excited about this specialization. They are able to explain hard concepts from the book in an easy way.

por Umut Z

23 de nov. de 2019

Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.

por DOMENICO P

19 de abr. de 2020

One of most accurate, precise and well explained courses I have ever had with Coursera. Congratulations for teachers and course creators.

por 李谨杰

1 de may. de 2020

An excellent course!!!! This is the best course I have ever taken on Coursera! Thanks a lot to two supervisors and teaching assistants!

por S. K G P

11 de jun. de 2020

I think it was one of the best courses to cover this topic. Clear and crisp presentations. Great programming assignments as well!!

por Christian J R F

7 de may. de 2020

Excelent course, I would love to do some other exercises out of the grid world but in general the content is good and interesting.

por Antonis S

9 de may. de 2020

Very well prepared and interesting course! I will seek more for sure in the future! Thank you so much for offering this course!

por La W N

28 de jul. de 2020

I am really enjoying to learn reinforcement learning. The instructors are really good at explanation. Going for next course B)