Chevron Left
Volver a Sample-based Learning Methods

Opiniones y comentarios de aprendices correspondientes a Sample-based Learning Methods por parte de Universidad de Alberta

4.7
estrellas
711 calificaciones
149 revisiones

Acerca del Curso

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...

Principales revisiones

AA

Aug 12, 2020

Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job

KM

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.

Filtrar por:

76 - 100 de 145 revisiones para Sample-based Learning Methods

por Shi Y

Nov 10, 2019

最喜欢的Coursera课程之一,难度适中的RL课程,非常推荐,学习到了很多自学很难理解全面的知识。感谢老师和助教们!

por Alex E

Nov 19, 2019

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

por Jicheng F

Jul 11, 2020

Martha and Adam are great instructors, great job!

por garcia b

Dec 31, 2019

very copacetic. excellent complement to the book

por Ignacio O

Oct 13, 2019

Great, informative and very interesting course.

por Ashish S

Sep 16, 2019

A good course with proper Mathematical insights

por Cheuk L Y

Jul 03, 2020

Very good overall! It takes time to digest.

por LIWANGZHI

Jan 15, 2020

A nice course with well-designed homework:)

por Jingxin X

May 27, 2020

Very helpful follow up tot he first one.

por Sriram R

Oct 21, 2019

Well done mix of theory and practice!

por Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

por Alejandro D

Sep 19, 2019

Excellent content and delivery.

por Bekay K

Jul 05, 2020

Great resource to learning RL

por PRIYA S

Jun 01, 2020

Great Course by great faculty!

por Daniel W

Jul 18, 2020

Hard but a really good course

por Pachi C

Dec 08, 2019

Great and fantastic course!!!

por rashid K

Nov 12, 2019

Best RL course ever done

por Eleni F

Mar 15, 2020

i really enjoy it!

por ABHILASH N

Aug 07, 2020

Brilliant Course!

por Julio E F

Jun 29, 2020

Amazing course!

por Santiago M C

May 21, 2020

excelent course

por Tran Q M

Feb 17, 2020

wondrous course

por Ricardo A F S

Sep 06, 2020

Great course

por Antonio P

Dec 13, 2019

Great Course