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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

2,384 個評分


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....




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.



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!


451 - Fundamentals of Reinforcement Learning 的 475 個評論(共 569 個)



Very nice

創建者 Justin O



創建者 Alexander K


loved it

創建者 Puyuan L


not bad

創建者 최홍석



創建者 Tobias S



創建者 JingZeng X



創建者 Yetao W



創建者 Zhiming Z



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創建者 Hakan K


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


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



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 !

創建者 Allen C


T​his course is mostly about walking you through the first few chapters on the Sutton and Barto book, which is offered in a free pdf. You get some nice quizes and simple programming assignments and some nice animated graphics to go with the presentations. The exercises in the book are more challenging and open ended than in the course if you're interested in more work.

Y​ou don't need an extensive math background but must be comfortable parsing scary equations and be familiar with some probability/expected value ideas.

T​he videos are completely scripted which results in the lectures being a bit stiff and robotic. I prefer it when the professor just uses an outline and speaks from the heart.

創建者 Stefano P


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


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


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


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.

創建者 Sanat D


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.

創建者 Nikhil S


Great material! The course was very well taught and at an appropriate pace. I do think that the teaching style was a bit too formal, however. Also, the entire course, lectures, and order are centered around the book which is easy enough to understand on its own. It might be useful to discuss some practical tips and methods instead of only the book theory. Learned a lot anyway. Thank you!

創建者 Ananthapadmanaban, J


Reading all weeks' suggested sections from the book before going through the videos would make it easy to understand the concepts. I actually read after watching half the videos, but it makes more sense to read before the videos. The assignments are decent. Policy evaluation, policy iteration and policy improvement are the concepts the course is trying to explain.

創建者 Satish C R


I have definitely learned basics of reinforcement learning by taking the course. In my opinion, to really absorb the material, one needs to read the provided textbook carefully and do the exercises. I suggest doing the some of the textbook programming problems as well to really learn the material. The videos only provide an overview.

創建者 Rishi R


An amazing course with great insights that drive a new learner in this field want to know more. The only slight drawback I felt was in missing details in implementing the algorithm, which of course the assignments took care of. Yet a good elucidation of the algorithms step-by-step will give a better understanding.

創建者 Arun R


Great class and I learned a lot - docking one star because the final programming assignment didn't give a comprehensive enough checker inside the Notebook, so I had to keep submitting and look to discussions for help in solving (for really a minor issue that it looks like many students faced on an edge test case).