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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....

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.

Sep 20, 2019

One of the best courses I finished on Coursera, I really like the structure of the course. Textbook is also provided which really helps. Looking forward to next course in the series.

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von Luiz C

•Aug 04, 2019

Fantastic Course. That's the RL MOOC I have been waiting for so long. No surprise it is from Students of RL guru R. Sutton at Uni of Alberta. Very clearly and simply explained. Exercise and Test difficulty spot on. Wouldn't change a yota from this Course. Can't wait to access the rest of this specialization

von Kota M

•Jul 30, 2019

Course material is standard and mostly follows Sutton and Barto textbook. Unfortunately, most contents overlap with the existing reinforcement learning course on Coursera and David Silver's youtube videos. The course will be much more useful if it covers more practical stuff instead.

I was very disappointed that the free trial period ends before my assignments are graded by peers. I would suggest that the course should be arranged so that students can finish it during the free period.

I am not sure if RLglue is an appropriate package to use in the exercise, as it is not as a standard tool as all practitioners are familiar with. If the instructors believe it is something useful in the future, they should explain it more in detail in the lecture.

von Sebastian P B

•Aug 25, 2019

Is a very good introduction to Reinforcement Learning. It also gives a very nice foundation of the basics of this area without being shy of showing some math. Could use more examples about modeling real world problems as MDPs but otherwise is a very complete course.

von Avinash K

•Aug 08, 2019

I found the explanations of theory of RL to replicate what was written in the book. Without examples the videos were no value add.

I had to go through the RL course by David Silver in youtube to understand the concepts.

von Andrei T

•Jul 31, 2019

Very clear and engaging presentation, well thought out and typical Coursera-style programming assignments. Definitely looking forward to taking the rest of the sequence.

von 姚佳奇

•Aug 06, 2019

Very good courses. It helps me to understand reinforcement learning a lot.

von Santiago M Z O

•Aug 20, 2019

I've just finished this course, it is really wonderful and I learnt a lot, as a professional Backend Developer without a formal background in Machine Learning. It has a lot of mathematical theory and exercises, derivations, really good explanations, and even some coding tasks to apply this knowledge.

At first I was doubtful I would make it to the end as I was feeling rusty on my maths since I didn't practice them much after university, but with effort and patience I was able to see how everything is built from the ground up and got a really good picture of how the fundamentals of RL work.

The course is based on the famous "Reinforcement Learning: An Introduction" by Sutton and Barto, the 2nd edition of which was only released recently, and which the Data Scientists I work with say is the go-to book for RL. The book is a magnificent resource available digitally for free, but I have enjoyed this course so much that I got the physical version, and after auditing the course for a week decided to jump in to do my best in the whole specialization.

von Caleb B

•Aug 07, 2019

I wish there was more chances to engage the instructors and TAs, but outstanding video presentations and good math coverage to develop insight for the algorithms.

von Jeremy O

•Aug 26, 2019

The content was pretty good. However, the final requirement on the final programing assignment was vague and required a very specific implimentation to match test cases. It was frustrating to have to search the forums for the exact sequence used to recreate a very specific dataset.

von Ritu P

•Aug 08, 2019

The main reason I enrolled in this course was to have an opportunity to have my questions answered. I had already gone through videos of RL lectures from different universities before this. Hence, the value of the course diminished for me when some of my questions were not always answered by the TAs or the Staff

von 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.

von Tomas L

•Aug 02, 2019

The course is very comprehensive and gave a very good introduction to and initial overview of reinforcement learning. It was a bit more theoretic than I expected (after doing the Machine Learning course by Prof Ng) and I did have some problems in completing the last programming assignment due to this. In the end it all turned out well though. The instructors were quite pedagogic and structured (if anything a bit too structured), and the assignments were well chosen. One could tell that this is a new course as there were still a few small quirks, but overall a very worthwhile course!

von Marvin F

•Aug 03, 2019

Good introduction :-)

von 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.

von 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.

von Christian C C

•Aug 04, 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.

von 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.

von Pawel P

•Aug 07, 2019

I've enjoyed it. It pushes me to do stuff and not to cut corners.

von Shaji, N

•Aug 13, 2019

It is the perfect course.

von Garrett S

•Aug 10, 2019

Explained in a very simple way, with helpful assignments.

von Md O H

•Aug 13, 2019

Excellent course

von Kyle N

•Aug 15, 2019

great course!! thanks Adam, Martha and team!!

von Alejandro D

•Aug 11, 2019

Excellent! Great content and delivery quality.

von Ashish S

•Sep 07, 2019

learned a lot, thanks !

von Tobias S

•Sep 08, 2019

Great!