14 de feb. de 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.
11 de ago. de 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
por Pouya E•
28 de nov. de 2020
por ARTEM B•
27 de feb. de 2021
por Justin O•
2 de may. de 2021
por chao p•
29 de dic. de 2019
por Alejandro S H•
31 de ago. de 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?
por Oscar R R M•
25 de abr. de 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.
por Neil S•
12 de sep. de 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.
por Jorge A C•
31 de ago. de 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.
por Arsham M•
22 de sep. de 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.
por Stefano P•
19 de may. de 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.
por Michael R•
7 de jun. de 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.
por Qianbo Y•
9 de jul. de 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.
por Scott L•
25 de sep. de 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.
por Jimson T•
10 de may. de 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.
por David C•
10 de oct. de 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.
por Marius L•
20 de sep. de 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.
por PHILIP C•
1 de may. de 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.
por Henry H•
2 de jul. de 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. self.discount switching to self.gamma.
por Yicong H•
4 de dic. de 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.....
por Arun A•
22 de sep. de 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
22 de nov. de 2021
There was one bug with the blackjack exercise. The Dyna-Q and Dyna-Q+ exercises were quite challenging. Overall, it is possible to complete the module without understanding what's going on. Pay attention!!!
por matias s•
8 de jun. de 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.
por Narendra G•
26 de jun. de 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.
por Misael D C•
30 de jun. de 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.
por István Z K•
21 de may. de 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.