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Prediction and Control with Function Approximation に戻る

アルバータ大学(University of Alberta) による Prediction and Control with Function Approximation の受講者のレビューおよびフィードバック

4.8
699件の評価
127件のレビュー

コースについて

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...

人気のレビュー

AC
2019年12月1日

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
2020年6月24日

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!

フィルター:

Prediction and Control with Function Approximation: 51 - 75 / 127 レビュー

by Andrew G

2020年1月26日

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

by Alexander P

2019年12月14日

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

by Mathew

2020年6月7日

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

by Ayan S

2021年7月4日

I really liked the lectures and how they clearly explained all the necessary details of such difficult topic.

by Johannes

2021年9月13日

T​his course is as excellent as its predecessors! Well-structured, engaging and with clear explanations.

by Joosung M

2020年6月14日

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

by Tolga K

2020年12月25日

Great course, great material and notebooks like previous courses. It was a great experience. Thank you!

by J B

2020年10月13日

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

by Eduardo I L H

2021年1月14日

Excellent course. Focused in the theory of function approximation for reinforcement learning.

by Yitao H

2021年8月29日

Intellectually challenging experience to combine supervised learning into RL framework!

by Huang C

2022年1月25日

Great course to take for combining function approximations with reinforcement learning

by RICARDO A F S

2020年11月21日

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

by Artur M

2020年11月3日

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

by George M

2021年3月11日

Comprehensive and intensive course.

More challenging than the previous two courses.

by WC C

2019年10月14日

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

by Rishi R

2020年8月3日

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

by Kaustubh S

2019年12月24日

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

by Max C

2019年11月1日

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

by Sergey M

2021年10月15日

Very nice and helpful course, very well organized and explained.

by LIWANGZHI

2020年1月27日

Everything is amazing in this course! Dont miss it!

by Pachi C

2019年12月31日

Fantastic course and great content and teachers!!!

by 석박통합김한준

2020年4月25日

Excellent course! Never be replaced! Thank you!

by Raktim P

2019年12月17日

Great Course! Highly recommended for beginners.

by İbrahim Y

2020年10月5日

the course is the intro for high level RL

by MJ A

2021年1月23日

perfect and thank you for this course