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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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176 - 200 von 205 Bewertungen für Sample-based Learning Methods

von judson g

21. Aug. 2020

Assignment problems needs to be clearly defined and content of the video needs to updated and expects more information

von Cristian V

30. März 2020

The course provides a lot of value. I only give 4 stars because the classes are scripted and feel unnatural to me.

von Max C

23. Okt. 2019

Some of the programming homeworks were difficult to debug due to the feedback from autograder being unhelpful.

von Raj P

8. Dez. 2020

Would recommend covering more examples to aid the understanding of concepts.

von Hugo T K

11. Aug. 2020

The course is excellent! Only missed some programming assignments on Week 2.

von Nicolas M

23. Sep. 2020

Great course, but some exercises would be better using concrete examples.

von Soren J

20. Juni 2020

Very good. Although the python skills are quite high to pass this course.

von Yu G

21. Jan. 2021

Tough, challenging course, very worthwhile taking!

von italo a d s o

7. Jan. 2022


von Sachin K

17. Aug. 2020

Passing notebook assignments is hellish due to strict decimal matching for numerical computations. You must do steps in one specific order or the assignments in autograder comparisons won't work. The course is itself fine and is more or less a rehash of the book so you may as well read that. There is no special intuition but the notebooks do provide a good experimental design strategy. Many of the experiments listed in the book are actually implemented in assignments which aids in learning. There is no technical support staff on Coursera anymore. So you are on your own when taking the course. Discussions forums are littered with discussion prompts and new ones are added every week so its not easy to find anything in there. Coursera has become substandard and the rating reflects a mixture of the course and coursera as a platform.

von Mark L

1. Juli 2020

This course has presented a large number of techniques/algorithms in addition to the ones presented in the first course. I find it hard to keep track of these. It would be most helpful if the techniques could be summarized in a table to lists the various attributes. In addition, I would like to see some examples of practical problems that can be solved with these techniques in addition to the explanatory "toy" problems. I also find the pace of the lectures a little "choppy", with a lot of very small lectures, each with its own introduction and summary.

von Hadrien H

13. Dez. 2020

Still very good course but I felt like this second unit covers less of the book than the first one. The classes are quite shorter than in the first part while the book content gets richer. The assignments are a bit more complete though

von Mukesh

11. Sep. 2020

There should be more examples on Q-learning and Expected SARSA. The course just compares different algorithms for different parameters. The autograder is annoying too. Really need some work on that. Otherwise the course is okay.

von Alessandro o

12. Juni 2020

To be honest I think that arguments quite complex are treated too quickly and basically it's up to you to figure it out. I think that some ideas would have been nice to have a more detailed explanation

von Juan A V G

13. Apr. 2021

It is required some mentoring on the Discussion forums. There is some part grading part that requires some improvement and it is too dependent on other students to work around some main issues.

von Pratik S

11. Sep. 2020

The duration of the lectures was very very short. They were for 5-7mins, in which 1-2 min was overview and summary. Had the lectures been more longer, more examples could have been explained.

von Liam M

26. März 2020

The assignments are an exercise in programming far more than they are a learning tool for RL. The course lectures are good, and I recommend auditing the course.

von Marwan F A

21. Juni 2020

The content is very helpful and clear, however, the notebook implementations are not so good and misleading sometimes.

von Chan Y F

4. Nov. 2019

The video content is not elaborated enough, need to read the book and search on the web to understand the idea

von Yetao W

5. Mai 2020

The course is good , however the submission of is inconvenient

von Jeel V

13. Juni 2020

Videos can have a little bit more technical details for the algorithms

von Duc H N

2. Feb. 2020

The last test is a little bit tricky

von Sanat D

29. Juli 2020

The reading material is great (as are the lectures), but frankly, the hypersensitive autograder is a real hinderance. Correct implementations don't get full points, and are sensitive to things like the order of random number generator calls, rather than looking for a correct range of solutions. To make things worse, the autograder has poor feedback - I often had to rely on assignment discussions with people who had received similarly unhelpful feedback to debug my solutions.

von Vasilis V

15. Juni 2020

Some explanations need should be broken down into smaller pieces

von Chungeon K

24. Mai 2020

너무 함축적입니다. 강의 시간이 늘어날 필요가 있을 것으로 보입니다.