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学生对 University of Alberta 提供的 Sample-based Learning Methods 的评价和反馈

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1,253 个评分

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

DP

Feb 14, 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.

DC

Aug 23, 2020

The material discussed is very clear, and the graded quizzes and programming assignments force you to really understand what you have just heard. I enjoyed this course a lot, and learned even more.

筛选依据：

126 - Sample-based Learning Methods 的 150 个评论（共 244 个）

创建者 garcia b

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Dec 31, 2019

very copacetic. excellent complement to the book

创建者 Ignacio O

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Oct 12, 2019

Great, informative and very interesting course.

创建者 Ashish S

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Sep 15, 2019

A good course with proper Mathematical insights

创建者 Guruprasad S

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Jul 12, 2021

very intutive and the instructors are succinct

创建者 Jayadev H

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Apr 28, 2024

Great stuff for learning intro RL! Thanks!:)

创建者 Ben - C L Y

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Jul 3, 2020

Very good overall! It takes time to digest.

创建者 LIWANGZHI

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Jan 14, 2020

A nice course with well-designed homework:)

创建者 Jingxin X

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May 26, 2020

Very helpful follow up tot he first one.

创建者 Ryan Y

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Jan 17, 2021

Better than reading the textbook alone.

创建者 Sriram R

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Oct 20, 2019

Well done mix of theory and practice!

创建者 Luiz C

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Sep 13, 2019

Great Course. Every aspect top notch

创建者 David I

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Apr 19, 2020

very good course with good examples

创建者 Alejandro D

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Sep 19, 2019

Excellent content and delivery.

创建者 Bekay K

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Jul 4, 2020

Great resource to learning RL

创建者 PRIYA S

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Jun 1, 2020

Great Course by great faculty!

创建者 Daniel W

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Jul 18, 2020

Hard but a really good course

创建者 Pachi C

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Dec 8, 2019

Great and fantastic course!!!

创建者 Sergey M

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Oct 3, 2021

Very well organized course!

创建者 rashid K

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Nov 12, 2019

Best RL course ever done

创建者 Jackson J

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Jan 4, 2024

Love the assignments!

创建者 MD M R S

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Mar 4, 2021

Awesome!!!!!!!!!!!!!

创建者 Venkat k

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Feb 3, 2022

Excited to learn!

创建者 Eleni F

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Mar 15, 2020

i really enjoy it!

创建者 Mohamed A

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Jul 19, 2021

very good course

创建者 Guoxiang Z

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Mar 7, 2021

Very nice course!