糖心vlog官网观看

Chevron Left
Back to Prediction and Control with Function Approximation

Learner Reviews & Feedback for Prediction and Control with Function Approximation by University of Alberta

4.8
stars
836 ratings

About the Course

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 reviews

AC

Dec 2, 2019

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you 糖心vlog官网观看 and Univ. of Alberta for the masterclass.

WP

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.

Filter by:

76 - 100 of 147 Reviews for Prediction and Control with Function Approximation

By WC C

鈥�

Oct 15, 2019

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

By Rishi R

鈥�

Aug 3, 2020

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

By Ali N

鈥�

Mar 26, 2024

so good and useful! the value of this course is more of all previouses!

By Kaustubh S

鈥�

Dec 25, 2019

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

By Max C

鈥�

Nov 2, 2019

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

By Sergey M

鈥�

Oct 16, 2021

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

By helia

鈥�

Jun 9, 2023

This course was one of the best courses I have ever taken :)

By Saulo A G S

鈥�

Aug 13, 2022

The contents are so important for applications based on AI

By Seyed K M Z

鈥�

Jun 5, 2023

Well taught! I recommend to anyone who seeks to learn RL.

By LIWANGZHI

鈥�

Jan 27, 2020

Everything is amazing in this course! Dont miss it!

By Pachi C

鈥�

Jan 1, 2020

Fantastic course and great content and teachers!!!

By 旯�頃滌

鈥�

Apr 25, 2020

Excellent course! Never be replaced! Thank you!

By Raktim P

鈥�

Dec 18, 2019

Great Course! Highly recommended for beginners.

By Ola D

鈥�

Jun 15, 2022

Fantastic course with fantastic instructors

By 陌brahim Y

鈥�

Oct 5, 2020

the course is the intro for high level RL

By MJ A

鈥�

Jan 23, 2021

perfect and thank you for this course

By Teresa Y B

鈥�

May 12, 2020

Very Useful and Highly Recommend !!!

By Stewart A

鈥�

Oct 31, 2019

Simply the best course on this topic.

By Farzad E b

鈥�

Aug 5, 2022

It was perfect, I really enjoyed it

By Junchao

鈥�

May 30, 2020

Very good and self-oriented course!

By Fernando A S G

鈥�

Mar 26, 2021

Excellent course! Thanks a lot!

By Wei J

鈥�

Oct 12, 2020

It is a very perfect RL course.

By Antonis S

鈥�

May 30, 2020

Really a well-prepared course!

By Ignacio O

鈥�

Nov 30, 2019

Really good, I learned a lot.

By FREDERIC N

鈥�

May 3, 2020

Great speakers and content!