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

561 Bewertungen
99 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...


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!

13. Aug. 2020

Adam & Martha really make the walk through Sutton & Barto's book a real pleasure and easy to understand. The notebooks and the practice quizzes greatly help to consolidate the material.

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51 - 75 von 99 Bewertungen für Prediction and Control with Function Approximation

von Chang, W C

14. Okt. 2019

The course presentation is wonderful. I can't stop after I watch the first video.

von Rishi R

3. Aug. 2020

It has amazing content with no compromise on concepts yet holds simplicity.

von Kaustubh S

24. Dez. 2019

It was a wonderful course. To the point yet well-explained concepts.

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 MJ A

23. Jan. 2021

perfect and thank you for this course

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.