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学生对 阿尔伯塔大学 提供的 Sample-based Learning Methods 的评价和反馈

4.8
657 个评分
138 条评论

课程概述

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...

热门审阅

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.

KN

Oct 03, 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

筛选依据:

51 - Sample-based Learning Methods 的 75 个评论(共 135 个)

创建者 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.

创建者 Mathew

Jun 07, 2020

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

创建者 Stewart A

Sep 03, 2019

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

创建者 Martin P

May 30, 2020

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

创建者 Han-June K

Apr 07, 2020

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

创建者 Roberto M

Mar 28, 2020

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

创建者 Chintan K

Jul 22, 2020

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

创建者 Wang G

Oct 19, 2019

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

创建者 Sodagreenmario

Sep 18, 2019

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

创建者 Chris D

Apr 18, 2020

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

创建者 Sirusala N S

Jul 30, 2020

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

创建者 koji t

Oct 07, 2019

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

It ’s a wonderful course.

创建者 Louis S

Jun 05, 2020

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

创建者 Ding L

Apr 24, 2020

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

创建者 Ofir E

Mar 22, 2020

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

创建者 Sourav G

Mar 10, 2020

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

创建者 Animesh

May 28, 2020

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

创建者 Li W

Mar 30, 2020

Very good introductions and practices to the classic RL algorithms

创建者 DEEP P

Jul 08, 2020

Great learning Experience and really helpful lecturers and staff.

创建者 Rudi C

Jul 21, 2020

Wonderful course, highly instructive, and follows the textbook!

创建者 Rajesh

Jul 02, 2020

Please make assignments more explanatory and allow flexiblity

创建者 David P

Nov 03, 2019

Really a wonderful course! Very professional and high level.

创建者 Teresa Y B

Apr 10, 2020

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

创建者 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!

创建者 Alex E

Nov 19, 2019

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