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

755 calificaciones
156 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


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


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.

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51 - 75 de 152 revisiones para Sample-based Learning Methods

por John J

Apr 28, 2020

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

por nicole s

Feb 02, 2020

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

por Nikhil G

Nov 25, 2019

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

por Lik M C

Jan 10, 2020

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

por Zhang d

Apr 07, 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

Mar 09, 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

Jun 07, 2020

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

por Stewart A

Sep 03, 2019

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

por Martin P

May 30, 2020

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

por 석박통합김한준

Apr 07, 2020

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

por Roberto M

Mar 28, 2020

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

por Chintan K

Jul 22, 2020

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

por Wang G

Oct 19, 2019

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

por Sodagreenmario

Sep 18, 2019

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

por Chris D

Apr 18, 2020

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

por Sirusala N S

Jul 30, 2020

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

por koji t

Oct 07, 2019

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

It ’s a wonderful course.

por Louis S

Jun 05, 2020

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

por Ding L

Apr 24, 2020

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

por Ofir E

Mar 22, 2020

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

por Sourav G

Mar 10, 2020

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

por Animesh

May 28, 2020

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

por Li W

Mar 30, 2020

Very good introductions and practices to the classic RL algorithms

por DEEP P

Jul 08, 2020

Great learning Experience and really helpful lecturers and staff.

por Rudi C

Jul 21, 2020

Wonderful course, highly instructive, and follows the textbook!