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Learner Reviews & Feedback for Sample-based Learning Methods by University of Alberta

965 ratings
200 reviews

About the Course

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...

Top reviews

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.

Aug 11, 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

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151 - 175 of 197 Reviews for Sample-based Learning Methods

By Qianbo Y

Jul 9, 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.

By Scott L

Sep 25, 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.

By Jimson T

May 10, 2021

The course includes very clear explanation of sample_based learning methods. However, if the program assignment can include more detail explanation per steps, it will help students quickly realize what they are going to implement. In addition, an clear content page that shows the purpose of each cell at the beginning of the assignment can help students understand whole structure of the code.

By 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.

By 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.


May 1, 2021

I like the structure of the course and it is in general well done. However, the programming assignment system is occasionally difficult to work with. I would suggest that the lectures be a little longer and a little more details, going into the nuts and bolts of how the algorithms work.

By Henry H

Jul 2, 2021

Overall I think the course is great. However, I wish the quizzes had less of the 'select all that are true' because if you miss one then you get a 0 for the whole question. Also, the variable names would change between programming assignments, ex. switching to self.gamma.

By Yicong H

Dec 4, 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.....

By Arun A

Sep 22, 2020

Mid way thru my course in week 5, Jupyter notebooks were revised. In general, new ones are better but lost valuable forum discussion Still one error in plot of notebook of week 5th. But in general course was good

By Matias x

Jun 8, 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.

By 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.

By 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.

By 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.

By Sebastian T

Feb 28, 2020



















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.

By Bruno L

May 21, 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.

By 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

By Bhargav D P

Jul 1, 2020

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

By 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.

By judson g

Aug 21, 2020

Assignment problems needs to be clearly defined and content of the video needs to updated and expects more information

By Cristian V

Mar 30, 2020

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

By Max C

Oct 23, 2019

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

By Rajvardhan P

Dec 8, 2020

Would recommend covering more examples to aid the understanding of concepts.

By Hugo T K

Aug 11, 2020

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

By Nicolas M

Sep 23, 2020

Great course, but some exercises would be better using concrete examples.

By Soren J

Jun 20, 2020

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