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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Reinforcement Learning por parte de Universidad de Alberta

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488 reseña

Acerca del Curso

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization....

Principales reseñas

AT
6 de jul. de 2020

An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.

NH
7 de abr. de 2020

This course is one of the best I've learned so far in coursera. The explanations are clear and concise enough. It took a while for me to understand Bellman equation but when I did, it felt amazing!

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426 - 450 de 483 revisiones para Fundamentals of Reinforcement Learning

por Nils S

29 de oct. de 2020

Very good an enjoyable course. It seemed like the explanations dwelled on the easier parts and skipped the parts that I would like to have seen in concrete numbers.

por Nathaniel W

25 de ago. de 2020

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.

por David S

27 de sep. de 2019

It will be good to include more detailed examples and more practice exercices in week 2 and 3. Also to repair the week 4 submission.

Although, It is a good course.

por Naresh T

28 de mar. de 2020

Good understanding of the fundamentals and aptly paced. The programming assignments were very good if there were more like that the course could get better

por Romesh M P

21 de may. de 2020

I really enjoyed the course, especially the guest segments (I got to know important people from that). Presenters did a good job but can be more relaxed.

por Jo K

29 de ene. de 2021

Very good introductory course for reinforcement learning. Good coding assignment, but could add more visual representation to understand the transition.

por Marcello M

13 de ago. de 2020

Very good theoretical contents, pretty much in line with the textbook - practical coding parts are mostly exercises of conversion of equations into code

por Mahmmoud M

29 de sep. de 2019

However, Missing the lectures of slide, the supported book is very good. The lectures are very simple and one can finish fast.

Thanks for teaching team.

por Prakhar J

28 de ago. de 2020

The content was very well organized, but applications could have been better understood using more complex numerical algorithms and more assignments.

por krishna c

31 de dic. de 2020

The guest lecture on truck fleet management was not great, the teacher tried to cover lot more material in a short time in the video then possible.

por Ramakrishnan.K

21 de jun. de 2020

The fundamentals of Bandits and MDPs are well covered. A major plus is the way we are made to read the text book before attending the lectures.

por Slav K

4 de ene. de 2021

A solid start with theoretical fundament. Assignment 2 was too cumbersome, lacking the description of actions encoded in the assignment.

por Petru R

20 de ene. de 2021

More Python examples are needed throughout the lessons.

Not only at the final. No proper introduction to DL Python library is given.

por NEHUL B

9 de sep. de 2020

I was hoping for a bit more practical application too, but this course does a solid job at teaching you the theory thoroughly.

por Matthew C

24 de ago. de 2019

Auto-grading of programming exercises did not work that well, but other than that, it was very instructive and well presented.

por Muhammad U S

11 de oct. de 2020

Highly recommended for the beginners. If you are new to RL then this course is the best along with Sutton and Barto Book.

por parham M

6 de jul. de 2020

there is so many great fundamental stuff here, with deep theoric background, but it lacks some practical approach

por Christopher B C

8 de sep. de 2019

I thought the lectures were informative, but the pacing could have been a bit faster to get through more content.

por Rafael V M

15 de jul. de 2020

Excellent course, with several practical examples of the theory being explained. I found week 3 a little dense.

por Balsher S

10 de jul. de 2020

Week 3 should be improved a bit. It is a bit confusing to understand. Btw Great course. Keep the good work up.

por Sharmili S

15 de abr. de 2020

Quizzes and assignments are a bit challenging. It might be easier if questions are better elucidated.

por Arthur

24 de nov. de 2020

Great course, yet a bit superficial. If you want to understand details, you have grind on your own.

por Aze A

10 de dic. de 2020

I enjoyed the course, especially week 3 and week 4 materials. I would have like more examples.

por Daxkumar J

3 de feb. de 2020

this is a basic course of the RL and its very great to learn with University Alberta.

por Simon N

20 de dic. de 2020

Very good introduction. Helps you get through Sutton and Barto (free pdf supplied).