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

4.7
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996 calificaciones
204 reseña

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 reseñas

AA
11 de ago. de 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
9 de ene. de 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.

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101 - 125 de 201 revisiones para Sample-based Learning Methods

por Da

3 de nov. de 2019

Really a wonderful course! Very professional and high level.

por Teresa Y B

10 de abr. de 2020

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

por Shi Y

10 de nov. de 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

19 de nov. de 2019

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

por Jicheng F

11 de jul. de 2020

Martha and Adam are great instructors, great job!

por garcia b

31 de dic. de 2019

very copacetic. excellent complement to the book

por Ignacio O

13 de oct. de 2019

Great, informative and very interesting course.

por Ashish S

16 de sep. de 2019

A good course with proper Mathematical insights

por Guruprasad

13 de jul. de 2021

very intutive and the instructors are succinct

por Cheuk L Y

3 de jul. de 2020

Very good overall! It takes time to digest.

por LIWANGZHI

15 de ene. de 2020

A nice course with well-designed homework:)

por Jingxin X

26 de may. de 2020

Very helpful follow up tot he first one.

por Ryan

17 de ene. de 2021

Better than reading the textbook alone.

por Sriram R

20 de oct. de 2019

Well done mix of theory and practice!

por Luiz C

13 de sep. de 2019

Great Course. Every aspect top notch

por Alejandro D

19 de sep. de 2019

Excellent content and delivery.

por Bekay K

4 de jul. de 2020

Great resource to learning RL

por PRIYA S

1 de jun. de 2020

Great Course by great faculty!

por Daniel W

18 de jul. de 2020

Hard but a really good course

por Pachi C

8 de dic. de 2019

Great and fantastic course!!!

por Sergey M

3 de oct. de 2021

Very well organized course!

por rashid K

12 de nov. de 2019

Best RL course ever done

por MD M R S

4 de mar. de 2021

Awesome!!!!!!!!!!!!!

por Eleni F

15 de mar. de 2020

i really enjoy it!