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

4.8
2,179 個評分
532 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....

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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!

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526 - Fundamentals of Reinforcement Learning 的 533 個評論(共 533 個)

創建者 KAUSHIKKUMAR K R

2020年9月27日

I automatically transferred to Auditing mode.

創建者 Vadim A

2020年4月14日

More explanations to theory would be nice.

創建者 Jeel V

2020年6月13日

More details in teaching concepts

創建者 Marju P

2021年7月30日

The course was disappointing for two reasons: poor instruction and poor content. I was expecting a high quality course from Coursera, but was instead finding myself with instructors that simply read a textbook to you. The instructors did not provide any added value. They read the book, even used the exact same examples and slides as in the book. Moreover, this was done in a a boring monotone way. The instructors seemed frozen still, eyes glazed over (with boredom?) with the exception of their lips that moved as they read the slides. Good instruction includes giving more value than just reading a book: new and different examples, different explanations, or at least different wording, personal commentary, sharing own intuition, and linking material to the broader world, making connections between ideas. All of this was missing. Furthermore, the course is not inclusive. The few examples that were chosen were applications to chess and golf. In other words, activities of the privileged few. RL is highly relevant in our world where AI solutions are springing up in all areas of life. There is a wealth of examples that are relatable to a wide variety of people. Instead, by choosing golf and chess, the instructors are alienating the majority of their students. This is in stark contract to Coursera's own mission of expanding and promoting access to high quality education for ALL people regardless of their background (including socio-economic background). The course could be improved by adding content (commentary, explanations, examples, discussions) that has not appeared in the book. Relating this content in a student friendly manner (not monotonically reading slides). In short, the instructors should follow the basics of modern provably effective teaching practices.

創建者 Simon S R

2020年9月1日

They put a lot of effort into it the course, however, they choose for some reason not to share the slides with their students. The accompanying book may be the standard, but yet it does not summarize the content as the slides do.

The programming examples are to simple and to few.

A vast amount of the video contains 'what we are going to cover' and 'what we have have'. This would make sense, if there are longer videos, but not if there is just one or two minutes of content.

創建者 Eli C

2020年9月15日

the first and only other coursera course I took was mathematics of machine learning from imperial university of london. I found it challenging and educational, with fantastic presentation. it may serve as a good model to improve this course

創建者 Amr K

2021年1月25日

A Lot of theoretical math and Too few code I recommend to show this complex mathematical equetion in code also

創建者 Jeon,Hyeon C

2021年4月6日

등록 취소가 안되서 1점 드립니다.