Volver a Fundamentals of Reinforcement Learning

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460 reseña

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....

AT

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.

NH

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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por Bhargav D P

•20 de jun. de 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. :)

por La W N

•1 de jul. de 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.

por Joosung M

•2 de jun. de 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.

por Thomas G

•1 de abr. de 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.

por Thong Q N

•14 de feb. de 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.

por David R

•3 de dic. de 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.

por Nicolas L

•20 de nov. de 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.

por Jesse W

•9 de may. de 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.

por Yanis C

•28 de dic. de 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.

por MOUAFEK A

•13 de abr. de 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!

por Luis G

•25 de oct. de 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!!!

por Jing Z

•24 de mar. de 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!

por Tom W

•14 de nov. de 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

por Shashank S

•13 de abr. de 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.

por Dmitry N

•24 de oct. de 2020

Sometimes it was hard to follow. In those cases re-reading the book helped. It is nice that in videos you, guys, have solved some of the exercises from the book. Also, it helped a lot to re-cap the material by re-doing the tests (and of course by reading a helpful notes, if the answer was incorrect). Thank you!

por Stelios S

•11 de may. de 2020

This is the BEST course I've taken from Coursera, period. The level of explanation, the usage of mathematically precise terminology, the walking through of the algorithms, the summaries were all top-notch. This course will be my reference when I forget something in the future. I can't thank the creators enough.

por Ali N

•1 de abr. de 2020

It was a very good course, I had read Sutton's book first. But I must say that after completing this course, I learned the concepts of the book well. Although the exercises were a bit tough, they covered the topics well and increased learning at a faster rate.

For anyone interested, I recommend this course.

por Artod d

•18 de feb. de 2021

Honestly, I would prefer to just lay back and consume knowledges from videos rather than reading the book full of scaring math.

Another issue is that the time allocated for reading and programming assessments are not fair: reading the book definitely takes longer, considering all that level of abstraction.

por Alejandro A Z

•20 de jul. de 2020

It was really cool! Although I think there should be a forum where students could ask and answer questions. I got stuck for a silly mistake before delivering the last python notebook and could have used some help.

Still, I learnt an incredible amount of concepts that I didn't imagine were so important!

por Иванов К С

•29 de ago. de 2019

It's difficult to estimate this course because it's based on the book. I mean, 90% of materials i've seen on videos were on the book. That's very unusual, but effective. However, i've learned necessary information and python tasks were useful and interesting. I'll take the next course and will see.

por Nicolas T

•26 de abr. de 2020

Great course! The idea of suggesting reading before the videos gives a huge boost to the depth of the class. This, with the "not-too-straightforward-quizzes", and the assignments, makes it a real deep class, from which I'll probably learn and retain more than most online courses. Good job!

por Anton P

•14 de dic. de 2019

It is a very well laid out and taught course. The instructors make the material accessible with a bit of a mathematics background, or a willingness to learn. I will be taking every machine learning course I can from AMII and the UofA if the rest of the courses are of the same quality.

por Dante K

•28 de dic. de 2020

Teachers were very clear and so was the book. The only thing I feel could be improved is adding some coding exercises on Week 2 and 3 (there's only one at Week 1 and one at Week 4, with a Peer Reviewed assignment on Week 2 which was fun, but didn't feel as useful as coding exercises)

por Sandesh J

•1 de jun. de 2020

One of the best available courses on Reinforcement Learning. The instructors have explained all the underlying topics elegantly. Good blend of theory and numerical in assignments and programming problems. Moreover, the assignments have covered different perspectives on these topics.

por Sara S

•28 de dic. de 2020

Excellent Course. Although it was only 4 weeks course, I learned more than reading an entirely dynamic programming book which might take more than 3 months for me. It was a well-presented course and I suggest this course to the ones that want to learn about Dynamic programming

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