Chevron Left
Retour à Sample-based Learning Methods

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

1,109 évaluations

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


14 févr. 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 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

Filtrer par :

1 - 25 sur 219 Avis pour Sample-based Learning Methods

par JD

22 sept. 2019

par Kaiwen Y

2 oct. 2019

par hope

25 janv. 2020

par Juan C E

7 mars 2020

par Rishi R

3 août 2020

par Mukund C

17 mars 2020

par Kinal M

10 janv. 2020

par Kyle A

3 oct. 2019

par Ivan S F

29 sept. 2019

par Manuel B

28 nov. 2019

par Amit J

27 févr. 2021

par Manuel V d S

4 oct. 2019

par Maxim V

12 janv. 2020

par Andrew G

24 déc. 2019

par Bernard C

22 mars 2020

par Maximiliano B

23 févr. 2020

par Jonathan B

9 mai 2020

par Steven W

11 mai 2021

par Sandesh J

8 juin 2020

par César S

9 juil. 2021

par Yover M C C

22 avr. 2020

par Alberto H

28 oct. 2019

par Karol P

9 avr. 2021

par Pars V

5 janv. 2020

par Surya K

12 avr. 2020