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

4.7
stars
966 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

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.

AA
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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126 - 150 of 197 Reviews for Sample-based Learning Methods

By Antoni S D S

Jul 1, 2021

Curso muito bom!

By Julio E F

Jun 29, 2020

Amazing course!

By Santiago M C

May 20, 2020

excelent course

By Tran Q M

Feb 17, 2020

wondrous course

By Jung-Yup L

Sep 29, 2020

great lecture

By Ricardo A F S

Sep 5, 2020

Great course

By Antonio P

Dec 13, 2019

Great Course

By John H

Nov 10, 2019

It was good.

By Charles X

Jun 19, 2021

Good course

By Oren

Apr 12, 2020

Fun course!

By Jialong F

Feb 25, 2021

learn much

By Sohail

Oct 7, 2019

Fantastic!

By LuSheng Y

Sep 10, 2019

Very good.

By Oriol A L

Nov 10, 2020

Very good

By Pouya E

Nov 28, 2020

Amazing!

By Artod d

Feb 27, 2021

Perfect

By Justin O

May 2, 2021

Great

By chao p

Dec 29, 2019

Great

By Alejandro S H

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?

By Oscar R R M

Apr 25, 2021

I have to admit that the practical programming tasks are excellent. The discussions sections are well maintained by professors. The recommended readings are good. The exams are fine but some questions can be a bit confusing or not clear enough.

I consider the lectures to be the weakest part of this course. In lectures, they only provide a short summary of what is already written in the book, which may be useful for some people, but I prefer the old way of long lectures in a blackboard full of mathematical proofs and historical notes providing true understanding of the topic. The invited lecturers are pretty good, since they show some historical notes or cutting edge projects.

So far this is the best course I have found on this topic.

By 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.

By Jorge A C

Aug 31, 2020

Excellent course, it complements very well the textbook by Sutton and Barto. The quizzes focus on conceptual issues, some of them not covered in the video lectures but presented in the textbook, which should be read carefully and in-depth. The programming assignments are based on the textbook examples and they are very effective in reinforcing what the course teaches despite not being that difficult nor time consuming. Although I have been able to navigate the course on my own I am taking one star off because there has been almost no feedback from the instructors in the discussion forums when I took the course in August 2020.

By Arsham M

Sep 22, 2020

The course content is of high quality and it would not be trevial even for people with a background in computer science and machine learning. The course works are very well defined, structured and clear. The only thing I guess could be improved is the way that content is delivered by the instructors. Overall, I do recommend taking this course to people who want to start exploring RL or who wants to gain better proficiency in Python programming for RL.

By 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.

By Michael R

Jun 7, 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.