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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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76 - 100 de 203 revisiones para Sample-based Learning Methods

por Martin P

30 de may. de 2020

A very interesting topic presented in an easy to consume form. It was fun learning with this course.

por 석박통합김한준

7 de abr. de 2020

The course is spectacular! I've learned countless material on Reinforcement learning! Thank you!

por Roberto M

28 de mar. de 2020

The course is well organized and teachers provide a lot of examples to facilitate comprehension.

por Chintan K

22 de jul. de 2020

the course videos were short and precise , which makes the learning content fun and informative

por Wang G

19 de oct. de 2019

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

por Sodagreenmario

18 de sep. de 2019

Great course, but there are still some little bugs that can be fixed in notebook assignments.

por Chris D

18 de abr. de 2020

Very good. Minor issues with inconsistency between parameter naming in different exercises.

por Sirusala N S

30 de jul. de 2020

The concepts were explained very clearly. The assignments were helpful in understanding.

por koji t

6 de oct. de 2019

I made a lot of mistakes, but I learned a lot because of that.

It ’s a wonderful course.

por Sérgio V C

15 de mar. de 2021

A good course to learn the basics of Monte Carlo methods for RL, as well as TD-methods!

por Jau-Jie Y

7 de jul. de 2021

I am happy of the history talking of Barto and Sutton.

The others teachers were good.

por Louis S

5 de jun. de 2020

Excellent content. The fact that it follows Sutton and Barto's TextBook is a must.

por Pruthvi J

7 de feb. de 2021

Excellent course, gives a decent theoretical and practical introduction to RL.

por ding l

24 de abr. de 2020

By taking the class, I learned much more than only reading the textbook.

por Ofir E

22 de mar. de 2020

Amazing course, truly academy-grade. And RL is such a fascinating topic!

por Fabrice L

14 de nov. de 2020

Things start to get interesting in this course of the specialization.

por Sourav G

10 de mar. de 2020

It was a very good course. All the concepts were explained very well.

por leftheory

6 de jul. de 2021

Very neat and clear. However, it just follows the RL book strictly.

por Varun K R K

15 de may. de 2021

The best course available on entire world for reinforcement learning

por Dan N

24 de oct. de 2021

I liked that this course had programming assignments for each week.

por Animesh

28 de may. de 2020

this course is very well designed and executed. wow! i loved it :D

por Li W

30 de mar. de 2020

Very good introductions and practices to the classic RL algorithms

por DEEP P

8 de jul. de 2020

Great learning Experience and really helpful lecturers and staff.

por Rudi C

21 de jul. de 2020

Wonderful course, highly instructive, and follows the textbook!

por Rajesh

2 de jul. de 2020

Please make assignments more explanatory and allow flexiblity