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Opiniones y comentarios de aprendices correspondientes a Sample-based Learning Methods por parte de Universidad de Alberta

1,012 calificaciones
206 reseña

Acerca del Curso

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...

Principales reseñas

11 de ago. de 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 de ene. de 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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126 - 150 de 203 revisiones para Sample-based Learning Methods

por Eleni F

15 de mar. de 2020

i really enjoy it!

por Mohamed A

19 de jul. de 2021

v​ery good course

por Guoxiang Z

7 de mar. de 2021

Very nice course!


7 de ago. de 2020

Brilliant Course!

por Antoni S D S

1 de jul. de 2021

Curso muito bom!

por Julio E F

29 de jun. de 2020

Amazing course!

por Santiago M C

20 de may. de 2020

excelent course

por Trần Q M

17 de feb. de 2020

wondrous course

por Max L

29 de sep. de 2020

great lecture


5 de sep. de 2020

Great course

por Antonio P

13 de dic. de 2019

Great Course

por John H

10 de nov. de 2019

It was good.

por Charles X

19 de jun. de 2021

Good course

por Oren

12 de abr. de 2020

Fun course!

por Jialong F

25 de feb. de 2021

learn much

por Sohail R

7 de oct. de 2019


por LuSheng Y

10 de sep. de 2019

Very good.

por Oriol A L

10 de nov. de 2020

Very good

por Pouya E

28 de nov. de 2020



27 de feb. de 2021


por Justin O

2 de may. de 2021


por chao p

29 de dic. de 2019


por Alejandro S H

31 de ago. de 2020

The course material are great. You will learn a lot from the assignments and from the book. The videos are a good refresher of what you'll read in the book, sometimes with improved animated visuals. However, I've a few nitpicks that prevent me from giving it 5 stars. (1) The instructors do not interact much with the students in the forum (if at all). (2) There's an inaccuracy in one of the videos that (as of the instant I'm doing this review) hasn't been fixed yet. (3) The quizzes sometime ask for questions that are NOT in the assigned homework materials (I'm thinking now about a question about prioritized sweeping in the planning section, but there are others). This is not a big deal, the questions will ring a bell immediately and you will find the section of the book where the answer lies (or you will answer out of common sense). (4) There's a video about applying RL in continuous tasks in robotics (purely motivational, not part of the syllabus) that is missing the second part. I'm guessing it's in the next course?

por Oscar R R M

25 de abr. de 2021

I have to admit that the practical programming tasks are excellent. The discussions sections are well maintained by professors. The recommended readings are good. The exams are fine but some questions can be a bit confusing or not clear enough.

I consider the lectures to be the weakest part of this course. In lectures, they only provide a short summary of what is already written in the book, which may be useful for some people, but I prefer the old way of long lectures in a blackboard full of mathematical proofs and historical notes providing true understanding of the topic. The invited lecturers are pretty good, since they show some historical notes or cutting edge projects.

So far this is the best course I have found on this topic.

por Neil S

12 de sep. de 2019

This is THE course to go with Sutton & Barto's Reinforcement Learning: An Introduction.

It's great to be able to repeat the examples from the book and end up writing code that outputs the same diagrams for e.g. Dyna-Q comparisons for planning. The notebooks strike a good balance between hand-holding for new topics and letting you make your own msitakes and learn from them.

I would rate five stars, but decided to drop one for now as there are still some glitches in the coding of Notebook assignments, requiring work-arounds communicated in the course forums. I hope these will be worked on and the course materials polished to perfection in future.