Fundamentals of Reinforcement Learning に戻る

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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....

Nov 10, 2019

I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses

Sep 07, 2019

Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.\n\nSometimes, visualizing the problem is hard, so need to thoroghly get prepared.

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by nicole s

•Jan 02, 2020

Very well designed, it is clear that a lot of thought was put into the course. Also, I really liked the clarity regarding the learning objectives and the emphasis on understanding.

by Neil S

•Aug 04, 2019

The ideal course to go with the book Reinforcement Learning: An Introduction. The quizzes and coding workshops are pitched just right in my opinion, neither too easy nor too hard.

by sachin k

•Dec 10, 2019

A great introduction to RL. Credit goes to the instructors Mr and Mrs white for keep in it as simple as possible. Understanding the math behind RL is the key for the RL adventure.

by Peter L

•Aug 24, 2019

This is a nice course. I think for people with no background in RL the pace might be a little fast. A few more examples could help them understanding the concepts more easily.

by Ivan S F

•Aug 31, 2019

Very practical and learning-oriented. Providing the textbook in PDF is a big plus. I think there should be more programming exercises. Great course anyway. Worth taking it.

by André B

•Dec 01, 2019

I really enjoyed this course. The examples and the infrastructure provided (jupyter notebooks as assingments) made this course one of the best MOOCs that I have ever taken

by Matias A

•Feb 13, 2020

Great course in general. Very clear step-by-step explanations of the theory needed to understand, plus practical examples to be able to fully understand the concepts.

by Aditya J

•Sep 16, 2019

Impressed by the knowledge of professors in the video and inspite of that they took so much interest in teaching minor concepts to students which are trivial to them.

by Gowtham.R

•Jan 26, 2020

Amazing course! This course goes through the fundamentals of RL covering both theory and practicals(through programming assignments). The book is also great to read.

by Qianbo Y

•Jan 08, 2020

A very good course integrated with Sutton and Barto textbook. A good foundation of RL can be learned from this class. It also balances well with theory and practice.

by Saikat M

•Aug 08, 2019

Good course following the classic book but it is kept at an easy pace for diverse people to be able to understand and apply the concepts of reinforcement learning.

by Max C

•Oct 24, 2019

Got me kickstarted with RL pretty well. I tried reading the RL book myself previously, but having complementary lectures and assignments made all the difference.

by Christian S

•Sep 02, 2019

great course; well explained and exercises reinforce the learnt material. also, great that this course uses Sutton's and Barto's book on reinforcement learning.

by Shengjian C

•Sep 01, 2019

This is a very helpful courses to help me walk through the Reinforcement Learning book with different kind of practices. Looking forward to taking course2!

by June X

•Aug 06, 2019

I love their way of teaching, they ask you to read, understand firstly, and then start to give a lecture about what it is, which helps a lot to understand.

by braghadeesh

•Jul 31, 2019

Great course and awesome instructors. Wish this course should have been announced much earlier. Thanks for offering such a wonderful course.

by Lukas S

•Dec 23, 2019

Very well structured, good examples, and helpful quizzes. I think (even) more programming assignments would make the course even better.

by Animesh

•Feb 15, 2020

I found this course very interesting. The basic concepts are explained very nicely. This course is great when a RL-noob like me : D

by Mert İ

•Aug 19, 2019

The concepts are explained in a very simple manner. Reading book then watching videos helps a lot to understand the essential ideas.

by Nikhil G

•Nov 25, 2019

Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding

by Dylan R

•Feb 22, 2020

Great course, reading the textbook was difficult at times, but the professors really helped me in understanding it all!

by AhmadrezaSheibanirad

•Oct 27, 2019

This course is one of the great online course in Coursera which help people dig into reinforcement learning correctly.

by Andis R

•Feb 15, 2020

Thank you very much for putting effort in this one !!! I was looking for such a course for some time already, thanks.

by Prudhvinath R

•Dec 03, 2019

Everything is explained in detail. The theory behind each logistic is clearly explained by both the Professors.

by Seyyed M

•Sep 28, 2019

To have the best performance in the course, read the related textbook sections at the beginning of each week.