Chevron Left
返回到 Fundamentals of Reinforcement Learning

學生對 阿尔伯塔大学 提供的 Fundamentals of Reinforcement Learning 的評價和反饋

4.8
2,050 個評分
514 條評論

課程概述

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....

熱門審閱

AT
2020年7月6日

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
2020年4月7日

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!

篩選依據:

401 - Fundamentals of Reinforcement Learning 的 425 個評論(共 512 個)

創建者 Sachin U

2019年9月14日

Great course.

創建者 obadan s

2020年7月8日

GREAT COURSE

創建者 Cheuk L Y

2020年6月30日

Great intro!

創建者 Fintan K

2020年6月27日

Great course

創建者 Liuhui D

2019年10月11日

Nice lecture

創建者 Ruiwen W

2020年10月11日

Very clear!

創建者 Noah

2020年4月21日

good course

創建者 Oriol A L

2020年11月4日

Very good!

創建者 Oren

2020年3月22日

Thank you.

創建者 BRIGHTON S U

2021年5月16日

Very nice

創建者 Justin O

2021年3月24日

Fantastic

創建者 Alexander K

2019年11月6日

loved it

創建者 Puyuan L

2020年1月24日

not bad

創建者 최홍석

2020年4月18日

great!

創建者 Tobias S

2019年9月8日

Great!

創建者 JingZeng X

2020年9月25日

Good!

創建者 Yetao W

2020年4月23日

Good!

創建者 Yatin T

2020年4月11日

Nice

創建者 Hakan K

2020年3月1日

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...

創建者 Michael S

2020年5月21日

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.

創建者 Rohit K

2020年10月19日

Hi,

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.

Thanks:)

overall course very nice. A great effort !

創建者 Stefano P

2020年5月19日

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.

創建者 Laurence G

2021年5月3日

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.

創建者 Yashar S

2021年7月17日

T​his course enabled me to be familiar with core concepts of Reinforecement learning. I was able to understand how Markov Decision Process and Dynamic Programming help to solve the problems. the lectures were clear and assignements were good and helpfull. I just expect to go more with how we can code agen-envirnoment interactions which are missed in this course. By the way, thanks for all the efforts done by the teachers.

創建者 Hadrien H

2020年11月5日

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.