Chevron Left
Zurück zu Sample-based Learning Methods

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

1,108 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:

51 - 75 von 218 Bewertungen für Sample-based Learning Methods

von Art H

13. Apr. 2020

von Kees J d V

19. Dez. 2020

von Karim D

20. Okt. 2020

von Giulio C

13. Juli 2020

von Umut Z

23. Nov. 2019

von Danish A

4. Juli 2022

von Tianpei X

8. Aug. 2022


19. Apr. 2020

von 李谨杰

1. Mai 2020

von Leon Y

9. Jan. 2021

von S. K G P

11. Juni 2020

von Christian J R F

7. Mai 2020

von Pokman C

8. Apr. 2021

von Antonis S

9. Mai 2020

von La W N

28. Juli 2020

von Kiara O

7. Jan. 2020

von John J

28. Apr. 2020

von nicole s

2. Feb. 2020

von Nikhil G

25. Nov. 2019

von Johannes

10. Sep. 2021

von Nathaniel W

24. Dez. 2020

von Lik M C

10. Jan. 2020

von Zhang d

7. Apr. 2020

von Xingbei W

8. März 2020

von Mathew

7. Juni 2020