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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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485 reseña

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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

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.

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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376 - 400 de 482 revisiones para Fundamentals of Reinforcement Learning

por Sachin U

14 de sep. de 2019

Great course.

por obadan s

8 de jul. de 2020


por Cheuk L Y

30 de jun. de 2020

Great intro!

por Fintan K

27 de jun. de 2020

Great course

por Liuhui D

11 de oct. de 2019

Nice lecture

por Ruiwen W

11 de oct. de 2020

Very clear!

por Noah

21 de abr. de 2020

good course

por Oriol A

4 de nov. de 2020

Very good!

por Oren

22 de mar. de 2020

Thank you.


16 de may. de 2021

Very nice

por Justin O

24 de mar. de 2021


por Alexander K

6 de nov. de 2019

loved it

por Puyuan L

24 de ene. de 2020

not bad

por 최홍석

18 de abr. de 2020


por Tobias S

8 de sep. de 2019


por JingZeng X

25 de sep. de 2020


por Yetao W

23 de abr. de 2020


por Yatin T

11 de abr. de 2020


por Hakan K

1 de mar. de 2020

I enjoyed this introduction course in Reinforcement Learning (RL). It explained in detail the fundamentals of RL such as k-armed bandits, Contextual Bandits and - of course - Markov Decision Processes (MDP). The lectures explained the conceps with nice examples and as well as the math behind (Bellman equations). The coursebook was the great "RL bible" ("Reinforcement Learning - An Introduction", 2nd edition by Sutton & Bartto); the lectures followed the first 4 chapters of the book quite closely.

I liked the programming assignments. It took some time to understand the structure of the tools used (e.g. the little known RLGlue) but after that it was quite straight forward, especially since the Notebook had great support for testing the solutions before submitting the assignment.

It was also interesting to see the guest lectures talk about the world outside the simple example MDPs used as examples, such as RL in the real world (using Contextual Bandits as a foundation), and about solving huge Fleet Management problems with RL.

One thing I missed in this course was more details about MDP and linear programming, which was mentioned in passim by the lecturers, and was an essential tool for solving the Fleet Management Problem (using approximate linear programming). Perhaps some of the next courses will discuss linear programming more...

por Michael S

21 de may. de 2020

I thought that the course content was extremely interesting, and the tests and programming were informative.

I did think though that the lectures were a little terse and could have given more information and worked through more examples. I think the presenters of this course and the people who constructed it could learn a lot from how, say, Andrew Ing's Coursera courses and Geoffrey Hinton's Coursera courses are put together and presented.

Specifically, the actual video time was very short and huge dependence was placed on the text book (which is very good textbook). I found Jupyter note book buggy and had to reset it a few times, but that might be me: I am not familar with it. I think as well, in a preliminary section, there could have been more on the Jupyter notebook and programming - even if this was just a document. As a user inexperienced with the Jupyter notebook, I found debugging and running test code in the lecturer's notebook in order to find my errors really hard. I often had to reset the notebook. Some assistance would have been appreciated here. In other courses that I have done, the prgramming environment has been more flexible which has made debugging easier, but I accept that my concerns here may be due to my inexperience.

por Rohit K

19 de oct. de 2020


I don't know whether this feedback will reach the correct ears or not.

I have already completed the course before and now I am doing it again. One thing that I found is the coding assignments are using library and is not letting the student do the thing from scratch. Things will be very clear to the student if the build everything from scratch using the basic libraries. for eg. not using rl_glue, but coding up the environment, coding up the agent. Using abstraction is good, but for those who already know the things. Since this course is more about the fundamentals of RL, it should teach the basics of building environment, agent from scratch. Maybe we can use library once we have done it from scratch, like starting from week 3 or course 2. I persnally was not able to get the full understanding of the things untill I implemented the things from scratch.


overall course very nice. A great effort !

por Stefano P

19 de may. de 2020

The course is overall very good, and it actually introduces you to Reinforcement Learning from scratch. Lectures are very clear, quizzes are challenging and the course relies on a text book, provided when you enroll. The only weak point, but not a serious issue, is that most of the lectures do not add content to what is in the book. Since studying the book is in fact mandatory, they could have used the lectures to better explain some concepts, assuming people read the book. Sometimes they do, but not so often.

por Laurence G

3 de may. de 2021

Overall fairly satisfied with this course.

Good coverage of the fundamentals through textbook backed up by videos and labs. Some of the quiz questions are a bit outside the box and include weird multi choice options that feel like they could be right depending on interpretation. I wasn't a fan of how the textbook handled Week 2 and 3, and spent a lot the time thinking "but why" - could be improved by explaining the policy and value dance from chapter 4 prior to commencing.

por Hadrien H

5 de nov. de 2020

Very good course which goes very well with reading the book alongside. I found very useful to read the chatper first and then brush and check my understanding by watching the videos. The explainations are clear and good and the videos length is just very good for me. Only thing I would improve is more coding assignment. With a more step by step series of exercises where one is learning to implement more things.

por Sanat D

21 de jun. de 2020

The course material (the textbook in particular) is great. I'm not sure how much value the videos add to the readings, but everyone has their preferred style of learning. My one dissatisfaction with this course is that I feel the material is not conducive to multiple choice quizzes. I wish there were fewer of those, and many more programming assignments. The coding parts were where I learned the most.