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

4.8
stars
1,213 ratings

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

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.

AS

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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201 - 225 of 237 Reviews for Sample-based Learning Methods

By LI C Y

Jun 14, 2022

Assignment is a bit hard, expecially the last assignment of Dyna-Q and Dyna-Q+. It would be great if more hints can be provided.

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 Raj 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.

By Yu G

Jan 21, 2021

Tough, challenging course, very worthwhile taking!

By Yasaman C

Jul 7, 2023

Good

By italo a d s o

Jan 7, 2022

good

By Sachin K

Aug 17, 2020

Passing notebook assignments is hellish due to strict decimal matching for numerical computations. You must do steps in one specific order or the assignments in autograder comparisons won't work. The course is itself fine and is more or less a rehash of the book so you may as well read that. There is no special intuition but the notebooks do provide a good experimental design strategy. Many of the experiments listed in the book are actually implemented in assignments which aids in learning. There is no technical support staff on Coursera anymore. So you are on your own when taking the course. Discussions forums are littered with discussion prompts and new ones are added every week so its not easy to find anything in there. Coursera has become substandard and the rating reflects a mixture of the course and coursera as a platform.

By Mark L

Jul 1, 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.

By Daniel D

Sep 28, 2022

Overall course instruction is good. However, there are serious issues with the programming assignment where the implemented code can be correct but fails the autograder because the random numbers might have been drawn in a different order than when the instructors created the code. These issues need to be fixed but based on the discussion thread (Sample-based Learning Methods - Discussions | CourseraOpens in a new tab) have been present for at least 8 months

By Hadrien H

Dec 13, 2020

Still very good course but I felt like this second unit covers less of the book than the first one. The classes are quite shorter than in the first part while the book content gets richer. The assignments are a bit more complete though

By Mukesh

Sep 11, 2020

There should be more examples on Q-learning and Expected SARSA. The course just compares different algorithms for different parameters. The autograder is annoying too. Really need some work on that. Otherwise the course is okay.

By 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

By Juan A V G

Apr 13, 2021

It is required some mentoring on the Discussion forums. There is some part grading part that requires some improvement and it is too dependent on other students to work around some main issues.

By Ahmed A

Jun 18, 2023

The theory is explained quite well and is understandable. Assignments need to made more clear and users should be allowed more engagement because it just feels like fill in the blanks for now.

By Pratik S

Sep 11, 2020

The duration of the lectures was very very short. They were for 5-7mins, in which 1-2 min was overview and summary. Had the lectures been more longer, more examples could have been explained.

By Akerke B

Dec 18, 2022

It is very sad that tutors (or somebody from staff) stopped answering questions in the discussion forums. Aside from this problem, the flow, the lecture quality is super

By Liam M

Mar 26, 2020

The assignments are an exercise in programming far more than they are a learning tool for RL. The course lectures are good, and I recommend auditing the course.

By Marwan F A

Jun 21, 2020

The content is very helpful and clear, however, the notebook implementations are not so good and misleading sometimes.

By Chan Y F

Nov 4, 2019

The video content is not elaborated enough, need to read the book and search on the web to understand the idea

By Yetao W

May 5, 2020

The course is good , however the submission of result.zip is inconvenient