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學生對 阿尔伯塔大学 提供的 Prediction and Control with Function Approximation 的評價和反饋

326 個評分
56 條評論


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...



Apr 12, 2020

Difficult but excellent and impressing. Human being is incredible creating such ideas. This course shows a way to the state when all such ingenious ideas will be created by self learning algorithms.


Nov 05, 2019

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.


26 - Prediction and Control with Function Approximation 的 50 個評論(共 57 個)

創建者 Andrew G

Jan 27, 2020

Did a good job of attaching a programming assignment to each lesson and giving clear and detailed instructions throughout

創建者 Alexander P

Dec 14, 2019

Great course on more advanced reinforcement learning techniques. Can't wait to apply these new skills in the wild.

創建者 Chang, W C

Oct 15, 2019

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

創建者 Kaustubh S

Dec 24, 2019

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

創建者 Max C

Nov 01, 2019

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


Jan 27, 2020

Everything is amazing in this course! Dont miss it!

創建者 Pachi C

Dec 31, 2019

Fantastic course and great content and teachers!!!

創建者 Han-June K

Apr 25, 2020

Excellent course! Never be replaced! Thank you!

創建者 Raktim P

Dec 17, 2019

Great Course! Highly recommended for beginners.

創建者 Teresa Y B

May 11, 2020

Very Useful and Highly Recommend !!!

創建者 Stewart A

Oct 31, 2019

Simply the best course on this topic.

創建者 Junchao

May 29, 2020

Very good and self-oriented course!

創建者 Ignacio O

Nov 30, 2019

Really good, I learned a lot.


May 02, 2020

Great speakers and content!

創建者 Majd W

Feb 01, 2020

Very practical course.

創建者 Murtaza K B

Apr 25, 2020

Excellent course

創建者 Luiz C

Oct 03, 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

創建者 Dmitry S

Jan 05, 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.

創建者 LOS

Jan 21, 2020

Great course, deserve 5 stars. It is a good complement to the book, it adds interesting visualizations to help parse the content. The only issues were in the exercises. There are technical issues with the notebook platform where it keeps disconnecting from time to time, with no warning, and you lose your unsaved work (seems like token expiration).

創建者 Hugo V

Jan 15, 2020

it was great to apply what I have learned from the book, but it was hard to find my mistakes in the course 3 notebook. I also misunderstood the alphas in the course 4 notebook at first glance, their indices look like they are powers (sorry for the bad english). Besides it, great course.

創建者 Navid H

Oct 16, 2019

The material is very good. But this course needs better instructors/ method of teaching. The book is also written in an unnecessarily technical way filled with jargon. explanations are not clear, simple stuff is presented in a very complicated manner for no obvious way.

創建者 Lik M C

Jan 19, 2020

The course is still good. But the assignment is not as good as course 1 and 2. In fact, the contents of the course are getting complicated and interesting as well. But the assignments are relatively simple.

創建者 Anton P

Apr 13, 2020

There is a lot of material covered in the course. Be aware the pace picks up considerably from the first two courses. This said, it is a worthwhile course to take.

創建者 Sharang P

Feb 27, 2020

more detailed explanation of some of the assignments and how state values are got with tile coding but overall a great experience!

創建者 Jerome b

Apr 09, 2020

Great course, based on the reference book about reinforcement learning. A must for anyone interested in machine learning.