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!
By Avinash K•
Aug 08, 2019
I found the explanations of theory of RL to replicate what was written in the book. Without examples the videos were no value add.
I had to go through the RL course by David Silver in youtube to understand the concepts.
By Kota M•
Jul 30, 2019
Course material is standard and mostly follows Sutton and Barto textbook. Unfortunately, most contents overlap with the existing reinforcement learning course on Coursera and David Silver's youtube videos. The course will be much more useful if it covers more practical stuff instead.
I was very disappointed that the free trial period ends before my assignments are graded by peers. I would suggest that the course should be arranged so that students can finish it during the free period.
I am not sure if RLglue is an appropriate package to use in the exercise, as it is not as a standard tool as all practitioners are familiar with. If the instructors believe it is something useful in the future, they should explain it more in detail in the lecture.
By Luiz C•
Aug 04, 2019
Fantastic Course. That's the RL MOOC I have been waiting for so long. No surprise it is from Students of RL guru R. Sutton at Uni of Alberta. Very clearly and simply explained. Exercise and Test difficulty spot on. Wouldn't change a yota from this Course. Can't wait to access the rest of this specialization
By Ritu P•
Aug 08, 2019
The main reason I enrolled in this course was to have an opportunity to have my questions answered. I had already gone through videos of RL lectures from different universities before this. Hence, the value of the course diminished for me when some of my questions were not always answered by the TAs or the Staff
By Sebastian P B•
Aug 25, 2019
Is a very good introduction to Reinforcement Learning. It also gives a very nice foundation of the basics of this area without being shy of showing some math. Could use more examples about modeling real world problems as MDPs but otherwise is a very complete course.
By Santiago M Z O•
Aug 20, 2019
I've just finished this course, it is really wonderful and I learnt a lot, as a professional Backend Developer without a formal background in Machine Learning. It has a lot of mathematical theory and exercises, derivations, really good explanations, and even some coding tasks to apply this knowledge.
At first I was doubtful I would make it to the end as I was feeling rusty on my maths since I didn't practice them much after university, but with effort and patience I was able to see how everything is built from the ground up and got a really good picture of how the fundamentals of RL work.
The course is based on the famous "Reinforcement Learning: An Introduction" by Sutton and Barto, the 2nd edition of which was only released recently, and which the Data Scientists I work with say is the go-to book for RL. The book is a magnificent resource available digitally for free, but I have enjoyed this course so much that I got the physical version, and after auditing the course for a week decided to jump in to do my best in the whole specialization.
By Andrei T•
Jul 31, 2019
Very clear and engaging presentation, well thought out and typical Coursera-style programming assignments. Definitely looking forward to taking the rest of the sequence.
By Eric K•
Sep 26, 2019
The lectures are not indicative of the problem sets. Both are very interesting and cover the materials well, but as a beginner with Dynamic Programming the bugs in the Notebooks are hard to distinguish from a lack of knowledge. The locked cells also make it hard to iterate slowly, to see the sensitivity of the algorithms to certain variables. Overall it is a great learning experience and the staff/mentors step in for support.
By Jeremy O•
Aug 26, 2019
The content was pretty good. However, the final requirement on the final programing assignment was vague and required a very specific implimentation to match test cases. It was frustrating to have to search the forums for the exact sequence used to recreate a very specific dataset.
Sep 17, 2019
Not much help available on forums
By Caleb B•
Aug 07, 2019
I wish there was more chances to engage the instructors and TAs, but outstanding video presentations and good math coverage to develop insight for the algorithms.
By Муратов А В•
Feb 24, 2020
Too much history and talks about who we are. Not efficient time spent.
Poor explanations with count on book. Not suitable for listening or on the go study.
Easy things made so complicated. (First you forced to get into math and other roots in the book and then video with some explanations when already not needed.) And could be explained better, not in 3 minutes. This is red, this is green and here we go - Malevich.
Got some insights but not happy about time spent.
By Julian S•
Oct 22, 2019
Solid introduction, but materials could be better prepared, e.g., overview of important concepts / formulas. Furthermore I would have liked to have more programming assignments and also more quizzes to practice the theory.
Sep 29, 2019
It was a good course, but I feel like there could have been programming assignments for week 2 and 3 to really help understand the bellman equations. Also, the jupyter notebook was pretty buggy sometimes.
By Andreas B•
Aug 22, 2020
I give the course a low rating for several reasons, the first being the most important one: The instructors basically completely absent. Having issues or problems? They don't bother. Not a single reply from either instructor in the forums for months or years. Second: Flawed and inprecise notebooks. Well known issues with random numbers, but no updates. Incorrect book references which will let you implement formulas other than intended. Third: Tons of short videos with 30% summary and "what you will learn", which is ridiculous for 3 minute videos. Fourth reason: Mathematical depth missing after the first subcourse. Suggestion: Watch the David Silver and Stanford youtube lessons instead. For free and better explained. Compared to, for instance, Andrew NGs specialization, this one is really bad mostly thanks to the complete disinterest of the instructors.
By Tomas L•
Aug 02, 2019
The course is very comprehensive and gave a very good introduction to and initial overview of reinforcement learning. It was a bit more theoretic than I expected (after doing the Machine Learning course by Prof Ng) and I did have some problems in completing the last programming assignment due to this. In the end it all turned out well though. The instructors were quite pedagogic and structured (if anything a bit too structured), and the assignments were well chosen. One could tell that this is a new course as there were still a few small quirks, but overall a very worthwhile course!
By Ron K•
Jan 01, 2020
The course was well taught! It utilized practical examples that helped bring the concepts and math to light! The instructors explained the math well without getting caught up in too much of the unnecessary minutiae. I struggled a bit in the programming exercises due more to my Python skills, but i was able to use the discussion boards to complete the assignments and understand the concepts.
By Hyeokjoon K•
Dec 31, 2019
It was a really nice lecture that helped me a lot to understand the fundamentals of reinforcement learning. Even though the lengths of the lectures are pretty short, they include the essence. So if you read enough and understand the textbook prior to the lectures, you would earn more from them. I'm so looking forward to learning real practical RL algorithms and applying them to my research.
By Niraj S•
May 23, 2020
This is by far the most comprehensible RL course available online. It does not mean easy but the way instructor take you each concept one at a time makes it easy to grasp the concepts which I think are confusing at times.
By Parsa V•
Nov 10, 2019
I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses
By Akash B•
Sep 07, 2019
Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.
Sometimes, visualizing the problem is hard, so need to thoroghly get prepared.
By Robert D•
Oct 16, 2019
An excellent introduction to the subject of Reinforcement Learning, accompanied by a very clear text book. The python assignments in Jupyter notebooks are both informative and helpful.
By Harshit S•
Sep 20, 2019
One of the best courses I finished on Coursera, I really like the structure of the course. Textbook is also provided which really helps. Looking forward to next course in the series.
Aug 06, 2019
Very good courses. It helps me to understand reinforcement learning a lot.
By Andrei C•
Sep 27, 2019
The course is overall well structured and concise. I am sure that the instructors could do a better job of putting more emphasis on the difficult parts of the course (such as how to actually use the Bellman Equations and how to calculate the State/Action Value functions). More examples of calculation would have made things far easier. All in all, it was a decent introduction to RL and the videos cleared some of the confusion that arised just by reading the RL handbook by Sutton & Barto.