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Avis et commentaires pour d'étudiants pour Sample-based Learning Methods par Université de l'Alberta

1,011 évaluations
206 avis

À propos du cours

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...

Meilleurs avis

11 août 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

9 janv. 2020

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.

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1 - 25 sur 203 Avis pour Sample-based Learning Methods

par JD

22 sept. 2019

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.

par Kaiwen Y

2 oct. 2019

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.

par hope

25 janv. 2020

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.

par Juan C E

7 mars 2020

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.

par Rishi R

3 août 2020

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.

par Mukund C

17 mars 2020

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.

par Kyle N

3 oct. 2019

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!!

par Ivan S F

29 sept. 2019

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

par Manuel B

28 nov. 2019

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

par Amit J

27 févr. 2021

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.

par Manuel V d S

4 oct. 2019

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.

par Maxim V

12 janv. 2020

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

par Andrew G

24 déc. 2019

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.

par Bernard C

22 mars 2020

Course was good but assignment graders were terrible.

par Maximiliano B

23 févr. 2020

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.

par Jonathan B

9 mai 2020

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!

par Steven W

11 mai 2021

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.

par Sandesh J

8 juin 2020

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.

par César S

9 juil. 2021

Clear and well organized content, based on one of the best books on the topic. Both programming exercises and exams have been crafted to be challenging but achievable, and show an incremental level of difficulty throughout the weeks. In simple words, a wonderful course I would recommend to anyone willing to learn about Reinforcement Learning!

par Yover M C C

22 avr. 2020

Excelente curso, la calidad de las lecturas y tareas de programación son muy buenas, un curso que no solo te ayuda a mejorar tus habilidades matemáticas y de programación en el tema de aprendizaje por refuerzo, sino también a entender parte del proceso de aprendizaje mediante TD. Un curso que se disfruta mucho! Gracias!.

par Alberto H

28 oct. 2019

A great step towards the acquisition of basic and medium complexity RL concepts with a nice balance between theory and practice, similar to the first one.

[Note: the course requires mastering the concepts of the first one in the specialization, so don't start here unless you're sure you master its contents.]

par Karol P

9 avr. 2021

A well thought and delivered course. The videos really help to understand the concepts that can be later on in depth investigated in the book. The programming assignments are challenging but not prohibitively difficult. They just build additional confidence. Thank you for this wonderful course!

par Pars V

5 janv. 2020

This course is perfect.

You will learn everything about sample-based RL. The programming assignments are harder than the previous course, but you will understand all the algorithms better.

These two courses covered part 1 of the book, and you will build a strong foundation of RL for the future.

par Surya K

12 avr. 2020

One of the more technically challenging courses I've done. Extremely fulfilling, a very good course to go along with the Book - "Reinforcement Learning, An Introduction". The assignments are very engaging, same with the videos, concise and to the point. The forums were incredibly useful.

par Dinh-Son V

19 juil. 2020

Excellent course. The amount of information is suited for RL enthusiast. The intuition behind the equation are well introduced. The exercises are challenging, yet interesting. It would have been more enriching to introduce examples not only from the textbook but from other materials.