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Fundamentals of Reinforcement Learning に戻る

アルバータ大学(University of Alberta) による Fundamentals of Reinforcement Learning の受講者のレビューおよびフィードバック



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.


Fundamentals of Reinforcement Learning: 1 - 25 / 119 レビュー

by 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.

by 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.

by 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

by 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

by 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.

by Apurva

Sep 17, 2019

Not much help available on forums

by 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.

by 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.

by 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.

by 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.

by 姚佳奇

Aug 06, 2019

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

by 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!

by Andrei C

Sep 27, 2019

The course is overall well structured and concise. I am sure that the instructors could do a better job of putting more emphasis on the difficult parts of the course (such as how to actually use the Bellman Equations and how to calculate the State/Action Value functions). More examples of calculation would have made things far easier. All in all, it was a decent introduction to RL and the videos cleared some of the confusion that arised just by reading the RL handbook by Sutton & Barto.

by Eric K

Sep 26, 2019

The lectures are not indicative of the problem sets. Both are very interesting and cover the materials well, but as a beginner with Dynamic Programming the bugs in the Notebooks are hard to distinguish from a lack of knowledge. The locked cells also make it hard to iterate slowly, to see the sensitivity of the algorithms to certain variables. Overall it is a great learning experience and the staff/mentors step in for support.

by K. S

Sep 22, 2019

Some of the sections seemed a bit rushed up. While the book provided a good source to clarify, I would prefer a slightly slower pace with emphasis on understanding during the video presentation. However, I have learnt significantly on reinforcement learning during the course. Thanks to the instructors who are highly accomplished, and have taken the time to create this video course.

by Akshay P

Oct 23, 2019

Good overall. Need to work with your assignments and their submission procedure. Lectures should be more interactive than just going through slides.

by Aaron H

Sep 11, 2019

Great material, and awesome coding exercises. Some additional information or context around a few of the problems would have been great, but nonetheless the struggle allowed me to grow in my knowledge!

by satheeshkumar v

Sep 12, 2019

What ever the content taught was really really good. but still more hands-on algorithms such as Monte Carlo would have been even better. overall worth studying the course

by Rahul

Sep 29, 2019

It was a good course, but I feel like there could have been programming assignments for week 2 and 3 to really help understand the bellman equations. Also, the jupyter notebook was pretty buggy sometimes.

by Julian S

Oct 22, 2019

Solid introduction, but materials could be better prepared, e.g., overview of important concepts / formulas. Furthermore I would have liked to have more programming assignments and also more quizzes to practice the theory.

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 Marvin F

Aug 03, 2019

Good introduction :-)

by 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.

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 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.