6 juil. 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 avr. 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!
par Mohamed S R I•
22 déc. 2019
The material in this course is of interest or me. It combines both theories and practical aspects of RL. The course follows the standard book in RL (Sutton & Barto Book).
One improvement may be needed is to add more "modern" examples and programming assignments/modules to explain the concepts. Also, it would be nice if the instructors can sometimes reflect on their own experiences with RL, rather than exactly following the book.
par Tianwen M•
3 févr. 2021
This course provides me the fundamental principles of RL! I like the clarity of each module (because I tend to be lost in the textbook only). In addition, I really appreciate the programming assignments of this specialization which helps me gain a deeper understanding of the basic concepts. I used to be afraid of dynamic programming, but I think I am confident enough to study more complex problems using DP in the future.
par x y•
19 août 2020
I have been interested in RL for a while and have watched many videos taught by other researchers, but this one provides something unique that helped me really get a deeper understanding of RL and gain confidence, such as the graded exercises, the quiz, I look forward to continuing this sequence of the RL specilization! Thank you so much for making the complex concepts accessible and make the quizzes and assignments!
par Corey A•
27 mars 2022
Combined with the suggested reference, this course does a good job of giving an introduction to the fundamentals of RL. The videos are easy to follow and compliment the text well, the pacing is good, and the assignments were fairly easy to follow. I did find it helpful to supplement the assignments with additional independent exercises to fully understand the chapters covered, but I feel that was expected.
par Yuri F•
20 sept. 2021
Very good course, you can take it at any level, if you wish just to get familiar with reinforcement learning you can watch the video and quickly read the book, but if you would like to be an expert you can deep dive in the book. i really like that the course follow some book which made it an serious course. could be nice to add some more homework (optional) with more interesting problem (e.g. gym)
par Jan Z•
25 août 2020
The course was very fun and informative. I really enjoyed the presentation style with clear outlines and summaries. The explanations were useful and easy to follow. Suggestions for improvements:
1) Provide a kindle version of the book, reading on screen is very tiring for eyes.
2) I think the programming exercises could use some work from SE perspective, as some of the code is not really pythonic.
par Karel V•
16 déc. 2019
The course is very well organised and professionally made. Although it follows the first four chapters of the Reinforcement Learning textbook, it provides a little bit different narrative and thus serves as a very nice complement to the textbook. Most importantly, interactive quizzes, programming exercises in Python and plenty of visualisations help to strengthen understanding of the concepts.
25 avr. 2020
This course is the best course I have taken in Coursera! As a learner of RL in a non-English-speaking country, Sutton's book is too hard for me to accept a new idea very quickly. However, after watching the short videos in this course that summarize the core concepts explicitly, I can understand the contents of that book easily. Recommend for anyone who wants to study reinforcement learning！
par Christian C•
4 août 2019
Exceptional course, the fundamental of RL explanations are excellent! I in particular I found it insightful the focus on thinking about examples in real-life that can be modeled as Markov Decision process. Additionally, great quizzes questions and assignments all helped in deepening my understanding of topics such as Dynamic Programing, Bellman Optimality, and Generalized Policy Iteration.
par Justin S•
23 août 2019
Excellent Course! The level of difficulty is perfect. It is difficult but not impossible if you do the readings in the textbook and understand the lectures. I strongly suggest reading the book before watching the lectures. This helped my understanding significantly. The material and assignments are very interesting and informative.
Highly recommend this course to anyone interested in RL.
par Bhargav D P•
20 juin 2020
This course will give you the knowledge of most fundamental concepts of RL Like MDPs, Policy evaluation, policy improvement & value iteration algorithms. Even though you follow theory well, quiz and assignment will challenge your knowledge to think into bit more deeper level. frankly speaking, I took some quizzes three times and at the end I learned the concepts very well. :)
par La W N•
1 juil. 2020
So far so good. The course is really valuable. It'll be better if there are more explanations about mathematics used but there is discussion forums so not a big problem. It is ineffective in teaching the practicality, i.e, how real word problem can be related, what kind of problems can be solved by these methods. Overall, it is a great explanation about reinforcement learning.
par Joosung M•
2 juin 2020
The content was very interesting, the instructors made things very clear that they were a great help in understanding what was really happening in the textbook.
I loved that this course provided a textbook with a lot of examples and case studies. I am willing to learn more about RL in the next set of courses.
Thank you so much for proving this wonderful specialization.
par Thomas G•
1 avr. 2020
Fundamentals of Reinforcement Learning is one of the best Online Courses I did on Coursera. I like that the course is based on a text book (Reinforcement Learning by Sutton), so you can really dig into the theory. Also the exercises are very helpful and ambitious which I like. I haven't found much advanced online courses which are so well explained like this one.
par Thong Q N•
14 févr. 2021
RL is not an easy topic, but the instructors made sure to illustrate the concepts with a lot of examples and visualizations, making it much easier to digest than reading the textbook. Guest lectures were fascinating. Programming assignments in Jupyter notebooks are super helpful with a lot of step-by-step instructions. An excellent course overall.
par Silvio M•
20 juil. 2021
Outstanding course. Instructors are great. The course alternates between important readings and well-crafted videos, quizzes and assignments. As you progress, concepts get tangible and you start figuring out possible applications. Do check out the required background knowledge. I've immediately enrolled in the second course of the specialization.
par D. R•
3 déc. 2019
I really liked this course. I think it was challenging and high quality. I don't understand complaints about it following the book - I found the videos, quizzes and exercises insightful and thought provoking. And besides courses are meant to follow some material and not re-invent the wheel. Am really excited for the rest of the specialization.
par Nicolas L•
20 nov. 2019
The course was great. Very clearly explained, with meaningful examples and backup material, such as the recommended book.
My only comment will be on the case study given on the final programming assignment. The parking scenario was not very intuitive or clear for me. It took me quite a bit to understand what it was we were trying to optimize.
par Jesse W•
9 mai 2020
Excellent course. Covers all the basics at just the right challenge level, assuming you've had some Python programming experience and know a thing or two about probabilities and expectation values. They provide a PDF for a course textbook which is extremely well-written, and the videos are high-quality and complement the readings well.
par Yanis C•
28 déc. 2020
This course was a great introduction to reinforcement learning. I found the material both accessible and applicable to a number of potential real-world problems. The combination of reading, video lectures, and example coding problems was an effective way to "reinforce" the course materials and build a solid foundational understanding.
par MOUAFEK A•
13 avr. 2020
After studying Classical Machine Learning and Deep Learning, and applying them in real-life cases with some startups and companies, some aspects of day to day problems did not seem to be fit while trying to use the previous methods, thus I dived into Reinforcement Learning looking for answers, and so far it's been very promising!
par Luis G•
25 oct. 2019
I started to read Sutton & Barto book this summer, and although I find it fantastic, some concepts were not 100% clear to me. This course has changed it dramatically. Now every concept is clear to me. This book is like reading a book with the support of very good explanations.
Let's go for the 2nd course in the specialitation!!!
par Jing Z•
24 mars 2020
You really need to understand fundamentals before kick start for any real world reinforcement learning problem. That's why this course is very essential. Plus it also provides programming tasks and multi-choice question sheet to deepen your understanding about theories. Great! Looking forward to move on for next series!
par Tom W•
14 nov. 2020
Really good course, and happily surprised and thankful it's based around Sutton and Barto textbook and with close links between instructors and those authors - I'd bought it a year ago with the best intentions of getting into RL, but needed something practical like this to help me get into it! Amazing work all involved
par Shashank S•
13 avr. 2020
This course was a great first introduction to reinforcement learning! The course instructors make the material very accessible and the course follows the textbook very closely. I'd definitely recommend it to anyone trying to understand reinforcement learning and I personally plan to complete the entire specialization.