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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

657 个评分
138 条评论


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...



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.


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!!


76 - Sample-based Learning Methods 的 100 个评论(共 135 个)

创建者 Bill F

Jul 11, 2020

Martha and Adam are great instructors, great job!

创建者 garcia b

Dec 31, 2019

very copacetic. excellent complement to the book

创建者 Ignacio O

Oct 13, 2019

Great, informative and very interesting course.

创建者 Ashish S

Sep 16, 2019

A good course with proper Mathematical insights

创建者 Cheuk L Y

Jul 03, 2020

Very good overall! It takes time to digest.


Jan 15, 2020

A nice course with well-designed homework:)

创建者 Jingxin X

May 27, 2020

Very helpful follow up tot he first one.

创建者 Sriram R

Oct 21, 2019

Well done mix of theory and practice!

创建者 Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

创建者 Alejandro D

Sep 19, 2019

Excellent content and delivery.

创建者 Bekay K

Jul 05, 2020

Great resource to learning RL


Jun 01, 2020

Great Course by great faculty!

创建者 Daniel W

Jul 18, 2020

Hard but a really good course

创建者 Pachi C

Dec 08, 2019

Great and fantastic course!!!

创建者 Rashid P

Nov 12, 2019

Best RL course ever done

创建者 Eleni F

Mar 15, 2020

i really enjoy it!


Aug 07, 2020

Brilliant Course!

创建者 Julio E F

Jun 29, 2020

Amazing course!

创建者 Santiago M C

May 21, 2020

excelent course

创建者 Tran Q M

Feb 17, 2020

wondrous course

创建者 Antonio P

Dec 13, 2019

Great Course

创建者 John H

Nov 10, 2019

It was good.

创建者 Oren Z

Apr 12, 2020

Fun course!

创建者 Sohail

Oct 07, 2019