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

1,100 calificaciones
217 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


14 de feb. de 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.


11 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

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26 - 50 de 215 revisiones para Sample-based Learning Methods

por Dinh-Son V

19 de jul. de 2020

Excellent course. The amount of information is suited for RL enthusiast. The intuition behind the equation are well introduced. The exercises are challenging, yet interesting. It would have been more enriching to introduce examples not only from the textbook but from other materials.

por Mark J

23 de sep. de 2019

In my opinion, this course strikes a comfortable balance between theory and practice. It is, essentially, a walk-through of the textbook by Sutton and Barto entitled, appropriately enough, 'Reinforcement Learning'. Sutton's appearances in some of the videos are an added treat.

por Antonio A

22 de mar. de 2021

It's a great course, which provides you with the fundamental aspects of RL. Additionally, all the video lessons are accompanied with references and reading to the textbook, which I recommend to anyone would like to dive deeper on some of the aspects coped with this module.

por Dustin Z

17 de abr. de 2021

A really good extension of the first course. While reading the textbook does take longer to complete the course, the textbook definitely ensures that I am learning more from this course than any other course I have taken. Really excellent course in Reinforcement Learning.

por Damian K

5 de oct. de 2019

Great balance between theory and demonstration of how all techniques works. Exercises are prepared so it is possible to focus on core part of concepts. And if you will you can take deep dive into exercise and how experiments are designed. Very recommended course.

por Yoel S

19 de abr. de 2020

Interesting topic, medium-advanced material, loved the programming assignments (despite technical difficulties with submissions), the textbook is excellent, and the online course is well organized, balanced, and well presented. Thank you to the whole team!

por İbrahim Y

29 de sep. de 2020

The course dives into the methods that are important for basic knowledge of RL intuition. Well designed examples and assignments. It seems somehow easy if a learner knows sth in advance, however, for a new learner of RL, this course is highly recommended.

por Majd W

6 de dic. de 2019

One of the amazing things this specialization stands out in is that it is based on a textbook. if you read from it and watch the lectures, you will have a very good understanding of the material. Also, the programming assignments are very beneficial.

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 D. 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 Haolong Z

13 de may. de 2022

I learned a lot and enjoyed the journey very much. Thank you for the clear explanation! Sometimes I felt some knowledge it's hard to digest when I read the book, but it suddenly made sense once I watched the video explanation!!!

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 Daniel P

14 de feb. de 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.

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 Andrew S

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 AR

14 de mar. de 2022

The videos are very clear and do a good job explaining the material from the textbook. The assignments are relevant and just right in terms of length and difficulty.

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 Raja S

19 de jun. de 2022

Great course - well paced, with the right material. And the professors deliver content in a structured way, which makes it easier to understand complex concepts.