Rating 4.3 stars – so far (first two classes combined)

Lectures: 4.0stars

Quizes: 4.0stars

Programming assignments: 4.5stars

Book (Sutton and Barto): 4.5stars

In the spectrum from the theoretical to practical where you have, very roughly,...

(1) “Why”: Why you are doing what you are doing

(2) “What”: What you are doing

(3) “How”: How to implement it (eg programming)…

...this is a “what-how” class.

To cover the “why-what” I strongly recommend augmenting this class with David Silver’s lectures (on Youtube) and notes from a class he gave at UCL. This covers more of the theory/math behind RL but covers less on the coding. Combined together with this class it probably comprises the best RL education you can get *anywhere*, creating a 5-star combo.

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

I spend 1 hour learning the material and coding the assignment while 8 hours trying to debug it so that the grader will not complain. The grader sometimes insists on a particular order of the coding which does not really matter in the real world. Also, grader inconsistently gives 0 marks to a particular part of the problem while give a full mark on other part using the same function. (Like numpy.max) However, the forum is quite helpful and the staff is generally responsive.

This course is ok if you're reading the Sutton & Barto RL book and would like to have some quizzes to follow along. The programming assignments are not really "programming" because you're constrained to type a handful of lines in a few places into a solution that is largely has been prepared for you. With "hints" like "# given the state, select the action using self.choose_action_egreedy(),

# and save current state and action (~2 lines)

### self.past_state = ?

### self.past_action = ?"

it is impossible to get them wrong. These exercises are ok as labs (comparing various algorithms, etc), but the programming part can be done by rote. Coursera has classes with more intense and creative programming assignments and the learning there seems to be much deeper.

Many mistakes with grading and 100% penalty applied for tasks not completed on time, when the rules say that you can submit your assignments and do your quizzes after the deadlines without any penalty.

Good content, but a lot of annoying issues with grader.

Course was good but assignment graders were terrible.

Excellent Course!! Reading the content and making notes ahead of time before watching the lectures is a MUST!!. The graphics/visuals in describing the concepts in the lectures were very good, especially for a visual learning such as myself. However, I wish there were a few more lectures and the lectures were a little - maybe another 3-5 minutes longer and delved into the derivations/concepts - for example - Bellman Equations to Sarsa/Q-learning/TD.

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

The course needs more support and / or error message output for the programming assignments. Code that seems correct can easily fail the autograder, and the only method of recourse is posting in the forums, which may or may not be received by a moderator.

The second course of the specialization is excellent and it provides a solid foundation on sample-based learning methods. The book and the videos complement each other making the learning experience rich and pleasant. The professors explain the content very well and the programming assignments are very interesting to consolidate the knowledge. I had a few issues with the grader and it just returns the score without any message that could help find out what is causing the unexpected behavior. As a suggestion, I would like to suggest that the grader could return any additional information and/or include new unit tests. I am looking forward to begin the next course of the specialization.

Very good class. Has much of the same qualities as its predecessor in the specialization. The methods you learn about though are more exciting, since they go beyond the introductory academic stuff that is not really used in production. I can easily see how Q-Learning, SARSA, and DynaQ architectures are usable in the real world.

Programming assignments are also very similar, but just a **little** bit more challenging. Each assignment has just a touch less handholding than the one before it, although there's still a lot of boilerplate included.

Looking forward to the next class!

There are simply no words to explain how well the instructors have constructed and delivered this. The algorithms were beautifully explained ( unlike in the first course where it was missed) and good intuition was given to the subject. The course is amazing in itself.

Yet if permitted I would like to have an addition. It would be way better if the research papers from which these ideas are introduced are also mentioned, also what other future developments have occurred in that direction if that concept is not visited again.

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

Great course. Clear, concise, practical. Right amount of programming. Right amount of tests of conceptual knowledge. Almost perfect course.

Great course! Really powerful but simply ideas to solve sequential optimization problems based on learning how the environment works.

Course material is good but I'm slightly disappointed by the quality of lectures. They sound just short monologues based on snippets from the book. I wish they were slightly longer and more original going into various aspects and examples of the very interesting subject material.

Course was amazing until I reached the final assignment. What a terrible way to grade the notebook part. Also, nobody around in the forums to help... I would still recommend this to anyone interested, unless you have no intention of doing the weekly readings.

Solid course covering more advanced tabular learning. They follow the Sutton and Barto book pretty closely. The instructors were clear and knowledgeable, and the programming assignments gave a lot more clarity on RL in practice.

The programming assignments are a little rough sometimes because the library they use for RL doesn't have the best API, but they're using the standard library made by the book authors and you get used to it.

Especializado em Aprendizagem por Reforço na University of Alberta. Dominei técnicas como aprendizagem de diferenças temporais, Monte Carlo, e controle on/off-policy. Implementei algoritmos TD, Expected Sarsa, Q-learning e Dyna, enfatizando a exploração e eficiência da amostragem. Realizei estudos empíricos para avaliar a eficácia de abordagens baseadas em modelos.

The course involves several popular Sample-based RL algorithms with relatable graphical visualizations making it an even smoother transition from the previous course. The lecturers have done a great job of explaining the underlying concepts and highlighting the subtleties of the same. Programming assignments were great which solidified the lessons learned.