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

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.

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.

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.

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.

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!

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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

Reinforcement Learning has added a whole new paradigm to my thinking. The course + book combination is perfect. The instructors are extremely good :D

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