Chevron Left
Zurück zu Sample-based Learning Methods

Bewertung und Feedback des Lernenden für Sample-based Learning Methods von University of Alberta

1,041 Bewertungen
209 Bewertungen

Über den Kurs

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...


14. Feb. 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.

11. Aug. 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

Filtern nach:

126 - 150 von 206 Bewertungen für Sample-based Learning Methods

von MD M R S

4. März 2021


von Eleni F

15. März 2020

i really enjoy it!

von Mohamed A

19. Juli 2021

v​ery good course

von Guoxiang Z

7. März 2021

Very nice course!


7. Aug. 2020

Brilliant Course!

von Antoni S D S

1. Juli 2021

Curso muito bom!

von Julio E F

29. Juni 2020

Amazing course!

von Santiago M C

20. Mai 2020

excelent course

von Trần Q M

17. Feb. 2020

wondrous course

von Max L

29. Sep. 2020

great lecture


5. Sep. 2020

Great course

von Antonio P

13. Dez. 2019

Great Course

von John H

10. Nov. 2019

It was good.

von Marconi S G

20. Jan. 2022

Ótimo Curso

von Charles X

19. Juni 2021

Good course

von Oren

12. Apr. 2020

Fun course!

von Jialong F

25. Feb. 2021

learn much

von Sohail R

7. Okt. 2019


von LuSheng Y

10. Sep. 2019

Very good.

von Oriol A L

10. Nov. 2020

Very good

von Pouya E

28. Nov. 2020


von Artod d

27. Feb. 2021


von Justin O

2. Mai 2021


von chao p

29. Dez. 2019


von Alejandro S H

31. Aug. 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?