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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

4.8
660 個評分
139 條評論

課程概述

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...

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AA

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

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.

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101 - Sample-based Learning Methods 的 125 個評論(共 135 個)

創建者 LuSheng Y

Sep 10, 2019

Very good.

創建者 chao p

Dec 29, 2019

Great

創建者 Neil S

Sep 12, 2019

This is THE course to go with Sutton & Barto's Reinforcement Learning: An Introduction.

It's great to be able to repeat the examples from the book and end up writing code that outputs the same diagrams for e.g. Dyna-Q comparisons for planning. The notebooks strike a good balance between hand-holding for new topics and letting you make your own msitakes and learn from them.

I would rate five stars, but decided to drop one for now as there are still some glitches in the coding of Notebook assignments, requiring work-arounds communicated in the course forums. I hope these will be worked on and the course materials polished to perfection in future.

創建者 Stefano P

May 19, 2020

The course is overall very good: lectures are very clear, quizzes are challenging and the course relies on a text book, provided when you enroll. The only weak point, but not a serious issue, is that most of the lectures do not add content to what is in the book. Since studying the book is in fact mandatory, they could have used the lectures to better explain some concepts, assuming people read the book. Sometimes they do, but not so often.

創建者 Michael R

Jun 07, 2020

Lectures were good, but not as intuition building as in the first course. The biggest strength of this course is that it follows a good textbook and expects you to read it. Quizzes and programming assignments are good for learning, but all the programming assignments are very scripted/guided. As a result, I think that it would be very easy to finish this course and still not be able to set up a sample-based learning model on your own.

創建者 Qianbo Y

Jul 09, 2020

A very thorough and well-designed course. It covers almost all important topics of tabular methods of Reinforcement Learning and follows the RL textbook very well. The only imperfectness of this course is the way instructors explaining the concepts. It is obvious that the instructors are reading off the scripts and not particularly explaining with their own words, which makes the lecture part less comprehensible.

創建者 Scott L

Sep 26, 2019

This course series is an incredible introduction to the basics of reinforcement learning, full stop. The course ... style, if you will, is a bit weird at first, but it seems to have been done on purpose with the aim of making the course somewhat timeless; they are presenting maths that will not change, in a format that will (hilariously) be no more slightly corny and weird in 2030 as it is in 2019.

創建者 David C

Oct 10, 2019

A very good course. The lectures are brief and provide a quick overview of the topics. The quizzes require more in-depth reading to pass (covering material not discussed in the lectures) and the projects are difficult but rewarding and really help to cement the information. My only suggestion would be to lengthen the lectures to provide more discussion on the topics.

創建者 Marius L

Sep 20, 2019

Overall, I found the course well made, inspiring and balanced. The videos really helped me to understand the rather austere textbook. I give 4 stars because some of the coding exercises felt more like work in progress, without the help of other students I would not have been able to overcome these issues.

創建者 Yicong H

Dec 05, 2019

Jump for here to there, it's nice to have all these algorithms. My gut tells me something is not correct. Too much focus on experience, which means a lot of data. The model part is touched very little, and main focus is on when model is wrong.....

創建者 matias s

Jun 08, 2020

This is a very good course, the only thing to improve are the technical issues with the assignments and submission processes. I had problems on the half of the assignments and many others learners too.

創建者 Narendra G

Jun 26, 2020

It's an important course in understanding the working of reinforcement learning. Although some important and complex topics are not explored in this course which are mentioned in the textbook.

創建者 Misael D C

Jun 30, 2020

This course excellent, my only complaint is that there is a 5 attempts limits and a 4 months wait to retry. It seems excesive to me and adds extra pressure when taking on assignments.

創建者 István Z K

May 21, 2020

Overall a very nice course, well explained and presented.

Sometimes, it would be nice to see the slides 'full screen' rather than the small version in the corner.

創建者 Sebastian T

Feb 28, 2020

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e but there is plenty of issues with the automated grader. you spend most time dealing with the letter not on actual learning of the matter.

創建者 Bruno G C L

May 22, 2020

The lectures and quiz tests are perfect. Jupyter. Programming exercises can be a little confusing sometimes but are also great. A great course, overall.

創建者 Navid H

Oct 16, 2019

definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world

創建者 Bhargav D P

Jul 01, 2020

Everything is great overall but It would be more better if DynaQ & DynaQ+ were explained more detail in the lecture instead of assignment.

創建者 Wahyu G

Mar 20, 2020

Pretty clear explanations! Nice starting point if you want to deep dive into RL. It gives clear picture over some confusing terms in RL.

創建者 Cristian V

Mar 31, 2020

The course provides a lot of value. I only give 4 stars because the classes are scripted and feel unnatural to me.

創建者 Max C

Oct 24, 2019

Some of the programming homeworks were difficult to debug due to the feedback from autograder being unhelpful.

創建者 Hugo T K

Aug 11, 2020

The course is excellent! Only missed some programming assignments on Week 2.

創建者 Soren J

Jun 20, 2020

Very good. Although the python skills are quite high to pass this course.

創建者 Mark L

Jul 01, 2020

This course has presented a large number of techniques/algorithms in addition to the ones presented in the first course. I find it hard to keep track of these. It would be most helpful if the techniques could be summarized in a table to lists the various attributes. In addition, I would like to see some examples of practical problems that can be solved with these techniques in addition to the explanatory "toy" problems. I also find the pace of the lectures a little "choppy", with a lot of very small lectures, each with its own introduction and summary.

創建者 Alessandro o

Jun 12, 2020

To be honest I think that arguments quite complex are treated too quickly and basically it's up to you to figure it out. I think that some ideas would have been nice to have a more detailed explanation