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Sample-based Learning Methods(으)로 돌아가기

앨버타 대학교의 Sample-based Learning Methods 학습자 리뷰 및 피드백

521개의 평가
104개의 리뷰

강좌 소개

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...

최상위 리뷰


Jan 10, 2020

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.


Apr 14, 2020

Well done. Follows Reinforcement Learning (Sutton/Barto) closely and explains topics well. Graded notebooks are invaluable in understanding the material well.

필터링 기준:

Sample-based Learning Methods의 101개 리뷰 중 51~75

교육 기관: koji t

Oct 07, 2019

I made a lot of mistakes, but I learned a lot because of that.

It ’s a wonderful course.

교육 기관: Louis S

Jun 05, 2020

Excellent content. The fact that it follows Sutton and Barto's TextBook is a must.

교육 기관: Ding L

Apr 24, 2020

By taking the class, I learned much more than only reading the textbook.

교육 기관: Ofir E

Mar 22, 2020

Amazing course, truly academy-grade. And RL is such a fascinating topic!

교육 기관: Sourav G

Mar 10, 2020

It was a very good course. All the concepts were explained very well.

교육 기관: Animesh

May 28, 2020

this course is very well designed and executed. wow! i loved it :D

교육 기관: Li W

Mar 30, 2020

Very good introductions and practices to the classic RL algorithms

교육 기관: David P

Nov 03, 2019

Really a wonderful course! Very professional and high level.

교육 기관: Teresa Y B

Apr 10, 2020

Very well structured course, Thanks for so nice preparing!!

교육 기관: Shi Y

Nov 10, 2019


교육 기관: Alex E

Nov 19, 2019

A fun an interesting course. Keep up the great work!

교육 기관: garcia b

Dec 31, 2019

very copacetic. excellent complement to the book

교육 기관: Ignacio O

Oct 13, 2019

Great, informative and very interesting course.

교육 기관: Ashish S

Sep 16, 2019

A good course with proper Mathematical insights


Jan 15, 2020

A nice course with well-designed homework:)

교육 기관: Jingxin X

May 27, 2020

Very helpful follow up tot he first one.

교육 기관: Sriram R

Oct 21, 2019

Well done mix of theory and practice!

교육 기관: Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

교육 기관: Alejandro D

Sep 19, 2019

Excellent content and delivery.

교육 기관: PRIYA S

Jun 01, 2020

Great Course by great faculty!

교육 기관: Pachi C

Dec 08, 2019

Great and fantastic course!!!

교육 기관: Rashid P

Nov 12, 2019

Best RL course ever done

교육 기관: Eleni F

Mar 15, 2020

i really enjoy it!

교육 기관: Santiago M C

May 21, 2020

excelent course