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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：

151 - Sample-based Learning Methods 的 175 个评论（共 244 个）

创建者 ABHILASH N

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Aug 7, 2020

Brilliant Course!

创建者 amirhossein s

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Apr 5, 2023

very nice course

创建者 Antoni S D S

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Jul 1, 2021

Curso muito bom!

创建者 Julio E F

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Jun 29, 2020

Amazing course!

创建者 Santiago M C

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May 20, 2020

excelent course

创建者 Trần Q M

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Feb 16, 2020

wondrous course

创建者 Max L

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Sep 29, 2020

great lecture

创建者 RICARDO A F S

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Sep 5, 2020

Great course

创建者 Antonio P

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Dec 13, 2019

Great Course

创建者 John H

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Nov 10, 2019

It was good.

创建者 Marconi S G

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Jan 20, 2022

Ótimo Curso

创建者 Charles X

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Jun 19, 2021

Good course

创建者 Oren Z B M

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Apr 11, 2020

Fun course!

创建者 Jialong F

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Feb 25, 2021

learn much

创建者 Sohail R

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Oct 7, 2019

Fantastic!

创建者 Deleted A

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Sep 10, 2019

Very good.

创建者 Nithiroj T

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Dec 21, 2023

Very good

创建者 Marc-Elie C

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Aug 25, 2022

Thank you

创建者 Oriol A L

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Nov 10, 2020

Very good

创建者 Pouya E

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Nov 28, 2020

Amazing!

创建者 Artem

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Feb 26, 2021

Perfect

创建者 Priyansh S

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Jul 26, 2023

Great!

创建者 Justin O

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May 2, 2021

Great

创建者 chao p

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Dec 29, 2019

Great

创建者 Alejandro S H

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Aug 31, 2020

The course material are great. You will learn a lot from the assignments and from the book. The videos are a good refresher of what you'll read in the book, sometimes with improved animated visuals. However, I've a few nitpicks that prevent me from giving it 5 stars. (1) The instructors do not interact much with the students in the forum (if at all). (2) There's an inaccuracy in one of the videos that (as of the instant I'm doing this review) hasn't been fixed yet. (3) The quizzes sometime ask for questions that are NOT in the assigned homework materials (I'm thinking now about a question about prioritized sweeping in the planning section, but there are others). This is not a big deal, the questions will ring a bell immediately and you will find the section of the book where the answer lies (or you will answer out of common sense). (4) There's a video about applying RL in continuous tasks in robotics (purely motivational, not part of the syllabus) that is missing the second part. I'm guessing it's in the next course?