Chevron Left
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
725件の評価

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

WP

2020年4月11日

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.

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.

フィルター:

Prediction and Control with Function Approximation: 76 - 100 / 133 レビュー

by Ola D

2022年6月15日

F​antastic course with fantastic instructors

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

by Teresa Y B

2020年5月11日

Very Useful and Highly Recommend !!!

by Stewart A

2019年10月31日

Simply the best course on this topic.

by Farzad E b

2022年8月4日

It was perfect, I really enjoyed it

by Junchao

2020年5月29日

Very good and self-oriented course!

by Fernando A S G

2021年3月26日

Excellent course! Thanks a lot!

by Wei J

2020年10月11日

It is a very perfect RL course.

by Antonis S

2020年5月30日

Really a well-prepared course!

by Ignacio O

2019年11月29日

Really good, I learned a lot.

by FREDERIC N

2020年5月2日

Great speakers and content!

by Majd W

2020年2月1日

Very practical course.

by 李谨杰

2020年6月17日

Excellent class !!!

by Mohamed A

2021年9月11日

v​ery good course

by Hugo T K

2020年8月18日

Excellent course.

by Murtaza K B

2020年4月25日

Excellent course

by Ivan M

2020年8月30日

Just brilliant

by Juan F L

2022年8月3日

great course!

by Oriol A L

2020年11月19日

Very good!

by Cheuk L Y

2020年7月8日

Very good!

by Jialong F

2021年2月23日

gooood!

by Justin O

2021年5月18日

Great

by ARTEM B

2021年2月27日

Super

by Ananthapadmanaban, J

2020年7月19日

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.