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

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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

DP

14 de feb. de 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.

AA

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

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76 - 100 de 216 revisiones para Sample-based Learning Methods

por George M

24 de feb. de 2021

Very well defined course.

Exercises are fairly challenging and provide useful intuition into common problems.

por Alaaeldin Z

10 de dic. de 2020

The course is amazing. The lectures are well organized. Quizes and assignments are very useful for learning.

por Maryam T

16 de nov. de 2021

A very good course for understanding basic concepts of RL. It is not enough for doing projects with coding.

por Stewart A

3 de sep. de 2019

Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.

por Casey S S

11 de feb. de 2021

I thought this was an excellent sequel, introducing the reader to the fundamental innovations of RL.

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 Floris v R

4 de ene. de 2022

Very clear explanations in the videos, good tests & asignments. Complex stuff well explained

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 Corey A

19 de abr. de 2022

A​wesome course. Fun examples and exercises and great compliment to the book.

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 Deleted A

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.