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Kursteilnehmer-Bewertung und -Feedback für Prediction and Control with Function Approximation von University of Alberta

524 Bewertungen
92 Bewertungen

Über den Kurs

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment...


1. Dez. 2019

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.

24. Juni 2020

Surely a level-up from the previous courses. This course adds to and extends what has been learned in courses 1 & 2 to a greater sphere of real-world problems. Great job Prof. Adam and Martha!

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51 - 75 von 92 Bewertungen für Prediction and Control with Function Approximation

von Max C

1. Nov. 2019

I had a much better experience with the autograder than in course 2.


26. Jan. 2020

Everything is amazing in this course! Dont miss it!

von Pachi C

31. Dez. 2019

Fantastic course and great content and teachers!!!

von 석박통합김한준

25. Apr. 2020

Excellent course! Never be replaced! Thank you!

von Raktim P

17. Dez. 2019

Great Course! Highly recommended for beginners.

von İbrahim Y

5. Okt. 2020

the course is the intro for high level RL

von Teresa Y B

11. Mai 2020

Very Useful and Highly Recommend !!!

von Stewart A

31. Okt. 2019

Simply the best course on this topic.

von Junchao

29. Mai 2020

Very good and self-oriented course!

von Wei J

11. Okt. 2020

It is a very perfect RL course.

von Antonis S

30. Mai 2020

Really a well-prepared course!

von Ignacio O

29. Nov. 2019

Really good, I learned a lot.


2. Mai 2020

Great speakers and content!

von Majd W

1. Feb. 2020

Very practical course.

von 李谨杰

17. Juni 2020

Excellent class !!!

von Hugo T K

18. Aug. 2020

Excellent course.

von Murtaza K B

25. Apr. 2020

Excellent course

von Ivan M

30. Aug. 2020

Just brilliant

von Oriol A L

19. Nov. 2020

Very good!

von Cheuk L Y

8. Juli 2020

Very good!

von Ananthapadmanaban, J

19. Juli 2020

I am disappointed with policy gradients being introduced on last week of the 3rd course. The instructors need to understand that 12 weeks is too much for introduction before starting a good project to implement the concepts with a hope to better understand them (course 4). Policy gradients should have been introduced in week 3/4 of course 2 itself. The content before that should be made more efficient (4 weeks to understand until q-learning/sarsa and 2 weeks to understand function approximation should be enough). I realized after course 2 that Andrew Ng has 3/4 videos on RL in the recently released ML class from Stanford. I am yet to go through them, but I feel they may explain these faster with same amount of rigour. However, the stanford class assignments are not public, which makes this course still useful because of the assignments. However, thanks to the instructors for this course.

von Luiz C

3. Okt. 2019

Almost perfect, except two ~minor objections:

1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course

2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment

von Dmitry S

5. Jan. 2020

Definitely a course to take to learn the ropes of RL. For this course, it is critical to follow and math. I'd love to give 5 stars to this course but will however take one away since the course could benefit a lot if the math was made a bit simpler to follow. The book referenced in the course is excellent and does help, but still, some more pedagogical repetition/rephrase, simplification of notation, a bit slower pace of narration would make the course even better. Having said that, this seems to be the best course available at this time. Many thanks to tutors.

von Narendra G

19. Juli 2020

This course is important for those who not just want to learn RL for mere sake but want to dive into various topics currently in research (for that reading textbook is of most importance). This specialization would have been even better if it had included some more complex topics from the textbook. To fully comprehend all the topics, guidance from experts is necessary.

von Nicolas M

24. Okt. 2020

Very interesting course: I have learned many things. A translation to other languages would be great: sometimes I can't memorize everything as I would if it was in my mother tongue.

Using another paper to study ( Experiments with Reinforcement Learningin Problems with Continuous State and Action Space) was a great idea that should be done in other courses.