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學生對 阿尔伯塔大学 提供的 Sample-based Learning Methods 的評價和反饋

734 個評分
154 條評論


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...



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.


51 - Sample-based Learning Methods 的 75 個評論(共 150 個)

創建者 Nikhil G

Nov 25, 2019

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

創建者 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!

創建者 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.

創建者 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.

創建者 Mathew

Jun 07, 2020

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

創建者 Stewart A

Sep 03, 2019

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

創建者 Martin P

May 30, 2020

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

創建者 석박통합김한준

Apr 07, 2020

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

創建者 Roberto M

Mar 28, 2020

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

創建者 Chintan K

Jul 22, 2020

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

創建者 Wang G

Oct 19, 2019

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

創建者 Sodagreenmario

Sep 18, 2019

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

創建者 Chris D

Apr 18, 2020

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

創建者 Sirusala N S

Jul 30, 2020

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

創建者 koji t

Oct 07, 2019

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

It ’s a wonderful course.

創建者 Louis S

Jun 05, 2020

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

創建者 Ding L

Apr 24, 2020

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

創建者 Ofir E

Mar 22, 2020

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

創建者 Sourav G

Mar 10, 2020

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

創建者 Animesh

May 28, 2020

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

創建者 Li W

Mar 30, 2020

Very good introductions and practices to the classic RL algorithms

創建者 DEEP P

Jul 08, 2020

Great learning Experience and really helpful lecturers and staff.

創建者 Rudi C

Jul 21, 2020

Wonderful course, highly instructive, and follows the textbook!

創建者 Rajesh

Jul 02, 2020

Please make assignments more explanatory and allow flexiblity

創建者 David P

Nov 03, 2019

Really a wonderful course! Very professional and high level.