May 28, 2019
This is one of the Best Course available on Reinforcement Learning. I have gone through various study material but the depth and practical knowledge given in the course is awesome.
Feb 14, 2019
A great course with very practical assignments to help you learn how to implement RL algorithms. But it also has some stupid quiz questions which makes you feel confusing.
von Kapil d•
Feb 02, 2020
Great learning experience! Course material is highly relevant and balances handson programming with Quizzes
von BUFORT A•
Dec 25, 2019
Very good course. A lot of thing explained in details. And instructors allow you to go deeper.
von Juxihong J•
Sep 15, 2019
Fantastic class if you don't mind to overcome some code issues in the homework.
von Alex H•
Aug 17, 2018
Learned a lot. The pace is quick and the assignment is challenging sometimes
May 27, 2020
very practical and very well taught, especially the jokes :-)
von Tom C•
May 17, 2018
Great course. Best course so far on reinforcement learning.
von Сазонтов Ю Ю•
Jun 05, 2020
Great deep very interesting course. Thanks to the authors!
von Meet G•
Oct 28, 2019
Good Introduction to Reinforcement Learning
von Francesco Z•
Sep 02, 2019
Very interesting topic and well taught. THX
von Nimish S•
Jun 30, 2018
great course and fabulous exercises
von Dmitry I•
May 23, 2019
Very reinforcement, much learning
von Abhijeet R P•
Jul 21, 2018
Nice intro to RL and Deep RL!
von Meytal L•
Jan 16, 2019
Great course. Thank you!
von Faris G•
Jul 01, 2019
Loved the teaching.
Sep 29, 2018
Awesome. Worth it!
von Ahmed R A•
Dec 25, 2019
von Diego E P M•
Apr 21, 2020
von Keanu T•
Jan 08, 2020
von Nguyen, Q H (•
May 08, 2020
I learned a lot from this course despite the very strong accent (bro please speak slowly). Most of the time I had to watch David Silver's lectures to gain a better understanding of the subject. RL is a very challenging area from both theoretical and applied perspective (at least for me it is clearly not easy), so don't expect it be a piece of cake like many of Andrew's courses on coursera. I have taken courses in probability theory, computational inference, stochastic processes and algorithms analysis, which are extremely essential to fully understand the materials in this course and RL courses in general. Assignments are challenging and very interesting but most of the heavy lifting were taken cared of. My definition of learning is that I should not expect the lecturer to take care of everything for me, they're there to give me direction and the rest is my job to find the answer that matters most to me. The teaching in this course is no where compared to Andrew's level of teaching but it will give you a very clear roadmap to further deepen your curiosity in RL field. best of luck.
von Nahas P•
Apr 24, 2020
Good course that covers a lot of Reinforcement Learning concepts and methods in a format that is simple and non-intimidating. It touches upon the basics of RL and Q Learning, then follows it up with explanation of popular methods like REINFORCE and MCTS.
The assignments using partially completed Jupyter notebooks reinforce the theoretical knowledge while ensuring the students are not encumbered by environment setup or non-core issues.
Both the course content and assignments progress linearly, so it was easy to follow.
A important suggestion for improvement would be to tweak the presentation style to reduce monotony. Improving animation in the slides or highlighting sections being discussed might help here.
Considering the word 'Practical' in the title, a couple of real-world applications of RL should have been part of the course as a coding examples or assignments.
While the simplicity of the assignments help in easy understanding of the topic, completion of the assignments do not impart the required confidence for handling more complex problems.
von Thomas D•
Aug 14, 2018
The course is dense and is accompanied by quality support (references to other courses, articles,...). It is punctuated with quizzes (which are unfortunately often quite ambiguous) and exercises on jupyter (which are well guided). This course seems to me, alone, insufficient and it is necessary to go to consult some references proposed to have a better understanding of certain topics. It is regrettable that the course goes sometimes too fast (some examples described in detail would be very useful for understanding) and that teachers are not always easy to understand.
von Pavel C•
Dec 04, 2019
I'm very happy to accomplish this course! Now I have a much clearer picture of RL methods.
In order to pass this course you'll require a good knowledge of python and some nonzero experience with tensorflow. Some tasks are really hard to pass, once I even had to install environment and run training on my home computer for several hours.
I want to say thanks to course authors and a little suggestion: please add topic about curiosity in RL.
von Roland R•
Apr 15, 2020
Topic is very interesting and most of the content is presented in an understandable way. A lot of additional material is presented that helps to deepen your knowledge. The programming assignments also help a lot to grasp what really is going on. Some of the notebooks are a little bit broken (missing RL environments, broken submit scripts, some tasks not clear, ...). But all in all very good.
von Guy K•
Nov 04, 2018
great content !
administration could benefit from some improvements (some exercises required "hacking" but the course forum were helpful)
Also, would be great if the slides can be shared.
this is the 2nd course I take from HSE. very happy with the content and the level. exercises are excellent !
I will happily continue to the next course in this specialization :)
von Jonas B•
Jun 10, 2018
Content provides a good - and useful :) - overview of reinforcement learning. The hands-on exercises in the notebooks were the main reason I decided to do the course, and I enjoyed doing them. However, they did contain a lot of errors and broken code,. This would need to be fixed for the course to earn a 5/5.