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

4.8
étoiles
311 évaluations
60 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

KM

Jan 10, 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.

KN

Oct 03, 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!!

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1 - 25 sur 59 Avis pour Sample-based Learning Methods

par Kaiwen Y

Oct 02, 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 Ivan S F

Sep 29, 2019

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

par Manuel B

Nov 28, 2019

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

par Manuel V d S

Oct 04, 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 Maximiliano B

Feb 23, 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 Alberto H

Oct 28, 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 Parsa V

Jan 06, 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 Mark J

Sep 23, 2019

In my opinion, this course strikes a comfortable balance between theory and practice. It is, essentially, a walk-through of the textbook by Sutton and Barto entitled, appropriately enough, 'Reinforcement Learning'. Sutton's appearances in some of the videos are an added treat.

par Damian K

Oct 05, 2019

Great balance between theory and demonstration of how all techniques works. Exercises are prepared so it is possible to focus on core part of concepts. And if you will you can take deep dive into exercise and how experiments are designed. Very recommended course.

par Majd W

Dec 06, 2019

One of the amazing things this specialization stands out in is that it is based on a textbook. if you read from it and watch the lectures, you will have a very good understanding of the material. Also, the programming assignments are very beneficial.

par AhmadrezaSheibanirad

Nov 10, 2019

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

par LUIS M G M

Nov 22, 2019

Great course!!! Even better than the 1st one. I tried to read the book before taking the course, and some algorithmics have not been clear to me until I saw the videos (DynaQ, DynaQ+). Same wrt some key concepts (on vs off policy learning).

par David R

Dec 10, 2019

Course is not easy, videos presentation is a bit dull - but the material is cool and interesting, and the additional quizzes, videos and especially notebooks make it a great course - you learn a lot and see progress. Highly recommended.

par Shashidhara K

Dec 12, 2019

This course required more work than the 1st in the series, (may be i took it lightly as the first was not that difficult). Request : Please include some worked examples (calculations) or include in graded/ungraded quiz, will be nice.

par LOS

Jan 21, 2020

Awesome! It is a pitty n-steps and eligibility traces were not included - felt like a huge gap. All the future chapters have a reference to the n-steps, and your understanding won't be complete unless you learn that as well.

par Kinal M

Jan 10, 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.

par Kyle N

Oct 03, 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 Gordon L W C

Feb 15, 2020

The course is intermediate in difficulty. But it explains the concept very clearly for me to understand difference between different sample based learning methods.

par Umut Z

Nov 23, 2019

Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.

par Kiara O

Jan 07, 2020

This course is well explained, easy to follow and made me understand much better the tabular RL methods. I liked it very much.

par nicole s

Feb 02, 2020

I like the teaching style the emphasis on understanding and the fruitful combination with the textbook. Highly recommended!

par Nikhil G

Nov 25, 2019

Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding

par Lik M C

Jan 10, 2020

Again, the course is excellent. The assignments are even better than Course 1. A really great course worth to take!

par Stewart A

Sep 03, 2019

Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.

par Wang G

Oct 19, 2019

Very Nice Explanation and Assignment! Look forward the next 2 courses in this specialization!