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Bewertung und Feedback des Lernenden für Sample-based Learning Methods von University of Alberta

1,038 Bewertungen
208 Bewertungen

Über den Kurs

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...


14. Feb. 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. Aug. 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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151 - 175 von 205 Bewertungen für Sample-based Learning Methods

von Neil S

12. Sep. 2019

This is THE course to go with Sutton & Barto's Reinforcement Learning: An Introduction.

It's great to be able to repeat the examples from the book and end up writing code that outputs the same diagrams for e.g. Dyna-Q comparisons for planning. The notebooks strike a good balance between hand-holding for new topics and letting you make your own msitakes and learn from them.

I would rate five stars, but decided to drop one for now as there are still some glitches in the coding of Notebook assignments, requiring work-arounds communicated in the course forums. I hope these will be worked on and the course materials polished to perfection in future.

von Jorge A C

31. Aug. 2020

Excellent course, it complements very well the textbook by Sutton and Barto. The quizzes focus on conceptual issues, some of them not covered in the video lectures but presented in the textbook, which should be read carefully and in-depth. The programming assignments are based on the textbook examples and they are very effective in reinforcing what the course teaches despite not being that difficult nor time consuming. Although I have been able to navigate the course on my own I am taking one star off because there has been almost no feedback from the instructors in the discussion forums when I took the course in August 2020.

von Arsham M

22. Sep. 2020

The course content is of high quality and it would not be trevial even for people with a background in computer science and machine learning. The course works are very well defined, structured and clear. The only thing I guess could be improved is the way that content is delivered by the instructors. Overall, I do recommend taking this course to people who want to start exploring RL or who wants to gain better proficiency in Python programming for RL.

von Stefano P

19. Mai 2020

The course is overall very good: lectures are very clear, quizzes are challenging and the course relies on a text book, provided when you enroll. The only weak point, but not a serious issue, is that most of the lectures do not add content to what is in the book. Since studying the book is in fact mandatory, they could have used the lectures to better explain some concepts, assuming people read the book. Sometimes they do, but not so often.

von Michael R

7. Juni 2020

Lectures were good, but not as intuition building as in the first course. The biggest strength of this course is that it follows a good textbook and expects you to read it. Quizzes and programming assignments are good for learning, but all the programming assignments are very scripted/guided. As a result, I think that it would be very easy to finish this course and still not be able to set up a sample-based learning model on your own.

von Qianbo Y

9. Juli 2020

A very thorough and well-designed course. It covers almost all important topics of tabular methods of Reinforcement Learning and follows the RL textbook very well. The only imperfectness of this course is the way instructors explaining the concepts. It is obvious that the instructors are reading off the scripts and not particularly explaining with their own words, which makes the lecture part less comprehensible.

von Scott L

25. Sep. 2019

This course series is an incredible introduction to the basics of reinforcement learning, full stop. The course ... style, if you will, is a bit weird at first, but it seems to have been done on purpose with the aim of making the course somewhat timeless; they are presenting maths that will not change, in a format that will (hilariously) be no more slightly corny and weird in 2030 as it is in 2019.

von Jimson T

10. Mai 2021

The course includes very clear explanation of sample_based learning methods. However, if the program assignment can include more detail explanation per steps, it will help students quickly realize what they are going to implement. In addition, an clear content page that shows the purpose of each cell at the beginning of the assignment can help students understand whole structure of the code.

von David C

10. Okt. 2019

A very good course. The lectures are brief and provide a quick overview of the topics. The quizzes require more in-depth reading to pass (covering material not discussed in the lectures) and the projects are difficult but rewarding and really help to cement the information. My only suggestion would be to lengthen the lectures to provide more discussion on the topics.

von Marius L

20. Sep. 2019

Overall, I found the course well made, inspiring and balanced. The videos really helped me to understand the rather austere textbook. I give 4 stars because some of the coding exercises felt more like work in progress, without the help of other students I would not have been able to overcome these issues.


1. Mai 2021

I like the structure of the course and it is in general well done. However, the programming assignment system is occasionally difficult to work with. I would suggest that the lectures be a little longer and a little more details, going into the nuts and bolts of how the algorithms work.

von Henry H

2. Juli 2021

Overall I think the course is great. However, I wish the quizzes had less of the 'select all that are true' because if you miss one then you get a 0 for the whole question. Also, the variable names would change between programming assignments, ex. switching to self.gamma.

von Yicong H

4. Dez. 2019

Jump for here to there, it's nice to have all these algorithms. My gut tells me something is not correct. Too much focus on experience, which means a lot of data. The model part is touched very little, and main focus is on when model is wrong.....

von Arun A

22. Sep. 2020

Mid way thru my course in week 5, Jupyter notebooks were revised. In general, new ones are better but lost valuable forum discussion Still one error in plot of notebook of week 5th. But in general course was good

von Harold

22. Nov. 2021

There was one bug with the blackjack exercise. The Dyna-Q and Dyna-Q+ exercises were quite challenging. Overall, it is possible to complete the module without understanding what's going on. Pay attention!!!

von matias s

8. Juni 2020

This is a very good course, the only thing to improve are the technical issues with the assignments and submission processes. I had problems on the half of the assignments and many others learners too.

von Narendra G

26. Juni 2020

It's an important course in understanding the working of reinforcement learning. Although some important and complex topics are not explored in this course which are mentioned in the textbook.

von Misael D C

30. Juni 2020

This course excellent, my only complaint is that there is a 5 attempts limits and a 4 months wait to retry. It seems excesive to me and adds extra pressure when taking on assignments.

von István Z K

21. Mai 2020

Overall a very nice course, well explained and presented.

Sometimes, it would be nice to see the slides 'full screen' rather than the small version in the corner.

von Sebastian T

28. Feb. 2020



















e but there is plenty of issues with the automated grader. you spend most time dealing with the letter not on actual learning of the matter.

von Muhammed A Ç

10. Aug. 2021

Programming assignments are not as good as andrew ng's courses. But still they are good enough to help you understand the concepts better by coding them

von Bruno L

21. Mai 2020

The lectures and quiz tests are perfect. Jupyter. Programming exercises can be a little confusing sometimes but are also great. A great course, overall.

von Navid H

16. Okt. 2019

definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world

von Bhargav D P

1. Juli 2020

Everything is great overall but It would be more better if DynaQ & DynaQ+ were explained more detail in the lecture instead of assignment.

von Wahyu G

20. März 2020

Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.