Well paced lectures and exercises, clear explanations and fun programming tasks. I hope to use some of these tools in the real world.

This is really a good course to deeply understand and apply the reinforcement learning. Thanks to instructers...

Great learning experience! Course material is highly relevant and balances handson programming with Quizzes

Very good course. A lot of thing explained in details. And instructors allow you to go deeper.

Fantastic class if you don't mind to overcome some code issues in the homework.

Learned a lot. The pace is quick and the assignment is challenging sometimes

very practical and very well taught, especially the jokes :-)

Great course. Best course so far on reinforcement learning.

Great deep very interesting course. Thanks to the authors!

Super interesting, well structured, and clear.

Good Introduction to Reinforcement Learning

Very interesting topic and well taught. THX

great course and fabulous exercises

Very reinforcement, much learning

Very good lecturers and projects

Nice intro to RL and Deep RL!

Great course. Thank you!

Loved the teaching.

Awesome. Worth it!

Excellent course

yes

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.

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.

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.

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.