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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...

Top-Bewertungen

AC
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.

SJ
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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76 - 92 von 92 Bewertungen für Prediction and Control with Function Approximation

von Lucas O S

21. Jan. 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).

von Hugo V

15. Jan. 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.

von Lik M C

18. Jan. 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.

von Mark P

17. Aug. 2020

Solid intro course. Wish we covered more using neural nets. The neural net equations used very non-standard notation. Wish the assignments were a little more creative. Too much grid world.

von Anton P

12. Apr. 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.

von Vladyslav Y

8. Sep. 2020

I wish agents that are based on visual information (with the usage of CNN) would be included in the course. But overall that was really great!

von Sharang P

27. Feb. 2020

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

von Jerome b

9. Apr. 2020

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

von Rajesh M

17. Apr. 2020

I loved the course videos and programming assignments. The only suggestion would be to go a little deeper in the videos.

von SCOTT A

5. Aug. 2020

This was a good course but I really struggled to understand how each of the value functions translated into code.

von Rishabh K

19. Mai 2020

The average reward and differential return needs to be explained more thoroughly

von RJT

17. Okt. 2019

Course is great! Maybe some slides would be helpful not to forget.

von Prashant M

7. Juni 2020

great course material but you need read the RL book through out the course. Also assignments are bit difficult, oops concept is mandatory.

von Justin N

31. März 2020

Lectures are pretty good, but the programming exercises are extremely easy. All of the problems are rather contrived as well.

von Bakhti Y

4. Mai 2020

I think It must be more deep neural networks dedicated course and not focus on coarse and tile coding!!!

von Bernard C

24. Mai 2020

Course was good, but assignments were not well constructed. Problems with the unit tests were frequent.

von Vasileios V

11. Juli 2020

Needs more work in my opinion. It's not bad of course. I just believe that more intuition should be built with better examples, outside the text book rather than going through the actual mathematical proofs