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

837 calificaciones
170 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

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

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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51 - 75 de 166 revisiones para Sample-based Learning Methods

por Antonis S

9 de may. de 2020

Very well prepared and interesting course! I will seek more for sure in the future! Thank you so much for offering this course!

por La W N

28 de jul. de 2020

I am really enjoying to learn reinforcement learning. The instructors are really good at explanation. Going for next course B)

por Kiara O

7 de ene. de 2020

This course is well explained, easy to follow and made me understand much better the tabular RL methods. I liked it very much.

por John J

28 de abr. de 2020

This second instalment in the reinforcement learning journey is amazing. Although you can get stuck sometimes in some places.

por nicole s

2 de feb. de 2020

I like the teaching style the emphasis on understanding and the fruitful combination with the textbook. Highly recommended!

por Nikhil G

25 de nov. de 2019

Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding

por Nathaniel W

24 de dic. de 2020

Well done course that covers the different basic aspects of to do reinforcement learning and how models work into it.

por Lik M C

10 de ene. de 2020

Again, the course is excellent. The assignments are even better than Course 1. A really great course worth to take!

por Zhang d

7 de abr. de 2020

It is a wonderful and meanningful course, which can teach us the knowledge of Q-learning, expected Sarsa and so on.

por Xingbei W

8 de mar. de 2020

Although I have learned q learning and td, this course still give me a lot of new feeling and understanding on it.

por Mathew

7 de jun. de 2020

Very well structured and a great compliment to the Reinforcement Learning (2nd Edition) book by Sutton and Barto.

por Alaaeldin Z

10 de dic. de 2020

The course is amazing. The lectures are well organized. Quizes and assignments are very useful for learning.

por Stewart A

3 de sep. de 2019

Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.

por Martin P

30 de may. de 2020

A very interesting topic presented in an easy to consume form. It was fun learning with this course.

por 석박통합김한준

7 de abr. de 2020

The course is spectacular! I've learned countless material on Reinforcement learning! Thank you!

por Roberto M

28 de mar. de 2020

The course is well organized and teachers provide a lot of examples to facilitate comprehension.

por Chintan K

22 de jul. de 2020

the course videos were short and precise , which makes the learning content fun and informative

por Wang G

19 de oct. de 2019

Very Nice Explanation and Assignment! Look forward the next 2 courses in this specialization!

por Sodagreenmario

18 de sep. de 2019

Great course, but there are still some little bugs that can be fixed in notebook assignments.

por Chris D

18 de abr. de 2020

Very good. Minor issues with inconsistency between parameter naming in different exercises.

por Sirusala N S

30 de jul. de 2020

The concepts were explained very clearly. The assignments were helpful in understanding.

por koji t

6 de oct. de 2019

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

It ’s a wonderful course.

por Louis S

5 de jun. de 2020

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

por Ding L

24 de abr. de 2020

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

por Ofir E

22 de mar. de 2020

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