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Learner Reviews & Feedback for Fundamentals of Reinforcement Learning by University of Alberta

1,455 ratings
368 reviews

About the Course

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization....

Top reviews


Jul 07, 2020

An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.


Apr 08, 2020

This course is one of the best I've learned so far in coursera. The explanations are clear and concise enough. It took a while for me to understand Bellman equation but when I did, it felt amazing!

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301 - 325 of 365 Reviews for Fundamentals of Reinforcement Learning

By Puyuan L

Jan 24, 2020

not bad

By 최홍석

Apr 18, 2020


By Tobias S

Sep 08, 2019


By Yetao W

Apr 23, 2020


By yatin a t

Apr 11, 2020


By Hakan K

Mar 01, 2020

I enjoyed this introduction course in Reinforcement Learning (RL). It explained in detail the fundamentals of RL such as k-armed bandits, Contextual Bandits and - of course - Markov Decision Processes (MDP). The lectures explained the conceps with nice examples and as well as the math behind (Bellman equations). The coursebook was the great "RL bible" ("Reinforcement Learning - An Introduction", 2nd edition by Sutton & Bartto); the lectures followed the first 4 chapters of the book quite closely.

I liked the programming assignments. It took some time to understand the structure of the tools used (e.g. the little known RLGlue) but after that it was quite straight forward, especially since the Notebook had great support for testing the solutions before submitting the assignment.

It was also interesting to see the guest lectures talk about the world outside the simple example MDPs used as examples, such as RL in the real world (using Contextual Bandits as a foundation), and about solving huge Fleet Management problems with RL.

One thing I missed in this course was more details about MDP and linear programming, which was mentioned in passim by the lecturers, and was an essential tool for solving the Fleet Management Problem (using approximate linear programming). Perhaps some of the next courses will discuss linear programming more...

By Michael S

May 21, 2020

I thought that the course content was extremely interesting, and the tests and programming were informative.

I did think though that the lectures were a little terse and could have given more information and worked through more examples. I think the presenters of this course and the people who constructed it could learn a lot from how, say, Andrew Ing's Coursera courses and Geoffrey Hinton's Coursera courses are put together and presented.

Specifically, the actual video time was very short and huge dependence was placed on the text book (which is very good textbook). I found Jupyter note book buggy and had to reset it a few times, but that might be me: I am not familar with it. I think as well, in a preliminary section, there could have been more on the Jupyter notebook and programming - even if this was just a document. As a user inexperienced with the Jupyter notebook, I found debugging and running test code in the lecturer's notebook in order to find my errors really hard. I often had to reset the notebook. Some assistance would have been appreciated here. In other courses that I have done, the prgramming environment has been more flexible which has made debugging easier, but I accept that my concerns here may be due to my inexperience.

By Stefano P

May 19, 2020

The course is overall very good, and it actually introduces you to Reinforcement Learning from scratch. 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 Sanat D

Jun 21, 2020

The course material (the textbook in particular) is great. I'm not sure how much value the videos add to the readings, but everyone has their preferred style of learning. My one dissatisfaction with this course is that I feel the material is not conducive to multiple choice quizzes. I wish there were fewer of those, and many more programming assignments. The coding parts were where I learned the most.

By Ananthapadmanaban, J

May 23, 2020

Reading all weeks' suggested sections from the book before going through the videos would make it easy to understand the concepts. I actually read after watching half the videos, but it makes more sense to read before the videos. The assignments are decent. Policy evaluation, policy iteration and policy improvement are the concepts the course is trying to explain.

By Rishi R

Aug 03, 2020

An amazing course with great insights that drive a new learner in this field want to know more. The only slight drawback I felt was in missing details in implementing the algorithm, which of course the assignments took care of. Yet a good elucidation of the algorithms step-by-step will give a better understanding.

By Arun R

Feb 12, 2020

Great class and I learned a lot - docking one star because the final programming assignment didn't give a comprehensive enough checker inside the Notebook, so I had to keep submitting and look to discussions for help in solving (for really a minor issue that it looks like many students faced on an edge test case).

By J B

Jun 15, 2020

A very well constructed course with two excellent lecturers leading it. A lovely introduction to RL although some may prefer a more mathematical treatment (in which case you need to find a longer course). No tutorial support during the course though so you need to be prepared to sort out your own problems.

By Sebastian T

Feb 22, 2020

Slightly too theoretical but clarified couple loose ideas and enabled me to work with python a bit. although a t the beginning of the course they speak that it is not about python, we actually get a chance using it although indeed we are not getting nice python code examples in course materials.

By Russel C

Feb 15, 2020

Really good introduction to Reinforcement Learning foundations. The lectures were great, and helped translate the theory from the RL book. I would like there to be a few more detailed walk-thru of the update algorithms in week 4, but I was able to work through the programming assignments okay.

By Shashidhara K

Nov 13, 2019

I really sorry for giving 4 star, my only reason for giving 4 star is so you can read this review. Please include some exercise on calculating the equations by hand, with solutions(this is the only reason for 4 star).

Thank you for the course

Course deserves 5 stars.(pardon my 4 stars, sorry)

By Dror L

Jul 31, 2020

Clear and pleasant recorded presentations. Very good and precise reading materials. Time estimate for reading materials are super optimistic. Guest lectures are at best inspiring. No real value. They are unfocused and all over the place.

By Ed J

Apr 26, 2020

I think the course was well put together and the labs were clear. My only real complaint is that the book and tests spent a lot of time proving and manipulating equations. I am mostly interested in using the formulas and programming.

By Alper A

Mar 29, 2020

Course is fine, but there could be more coding practices then the theoretical part. There are two coding assignments which are hard to do only with the course. The course context could be extended to include more coding practices.

By Ayse E G

Sep 28, 2019

The course is a very good introduction to RL but the concepts are handled a little too abstractly. However this provides an excellent fundamental for the rest of the courses. I would have liked more programming exercises.

By Aaron H

Sep 11, 2019

Great material, and awesome coding exercises. Some additional information or context around a few of the problems would have been great, but nonetheless the struggle allowed me to grow in my knowledge!

By Aidan M

Aug 24, 2020

Don't think it would be unreasonable to have more demanding coding assignments where all functions are made from scratch (though the function names and some comments might be provided as an outline.

By Christian J R F

Apr 02, 2020

Great course, I think theory is really well explained and book is great, but including more practice exercises is needed for this course to strengthen the learning of concepts.

By Narendra G

Jun 05, 2020

The course is well developed, reading the reference book is the most important thing that you will do while taking this course. The delivery of both instructors seems robotic.

By Nathaniel W

Aug 25, 2020

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.