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523 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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26 - 50 von 92 Bewertungen für Prediction and Control with Function Approximation

von Rafael B M

1. Sep. 2020

The course extends the foundations of Reinforcement Learning to function approximation, which allows the application of the previous learned method to tackle more complex and real world problems.

von Antonio C

2. 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.

von Sandesh J

25. 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!

von Jose M R F

14. 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.

von Ding L

1. Juni 2020

I had been reading the book of Reinforcement Learning An Introduction by myself. This class helped me to finish the study with a great learning environment. Thank you, Martha and Adam!

von Akash B

5. Nov. 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.

von Niju M N

24. Okt. 2020

The course was really good one with quizzes to make us remember the important lesson items and well polished Assignments are given which i haven't seen before in coursera

von Christos P

19. Jan. 2020

Good course with a lot of technical information. I would add another assignment or make current ones a little bit more extensive, as there are many concepts to learn.

von Roberto M

29. März 2020

I found the course quite tough but really interesting. I would say that reading the book's chapters more than once is necessary to optimally grasp the concepts.

von John J

28. Apr. 2020

This is the third instalment in reinforcement learning.so far so good. yeah, you can get stuck some times but it is okay you can make it out.

von Sandro M A T

28. Juli 2020

I consider the professors explain in a feasible way the main concepts of RL hence communicate effectively and concise in the course videos.

von Kinal M

12. Jan. 2020

A great and interactive course to learn about using function approximation for control. Great way to learn DRL and its alternatives.

von Ivan S F

9. Nov. 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

von Jicheng F

11. Juli 2020

Martha and Adam are excellent instructors. This course is so well organized and presented. I have learned a lot! Thanks very much!

von Wahyu G

27. März 2020

Give nive theoretical foundation. I found RL courses are abstract, but the programming assignment give a nice conceptualization.

von Andrew G

26. Jan. 2020

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

von Alexander P

14. Dez. 2019

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

von Mathew

7. Juni 2020

Very well structured and a great compliment to the Reinforcement Learning (2nd Edition) book by Sutton and Barto.

von Joosung M

14. Juni 2020

The course materials were very informative, the assignments were challenging enough. Highly recommended!

von J B

13. Okt. 2020

Very helpful course. Excellent delivery and practical labs. There's even someone helping in the forum!

von Ricardo A F S

21. Nov. 2020

A great course, I took a long time doing the assignments, but in the end I solved it

von Artur M

3. Nov. 2020

Great course! Wished to see more about policy gradient methods, but it was awesome.

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.