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Learner Reviews & Feedback for Fundamentals of Reinforcement Learning by University of Alberta

4.8
stars
1,992 ratings
493 reviews

About the Course

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....

Top reviews

AT
Jul 6, 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
Apr 7, 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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426 - 450 of 487 Reviews for Fundamentals of Reinforcement Learning

By Ulf Ä

Jan 3, 2021

The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.

By Christian J R F

Apr 1, 2020

Great course, I think theory is really well explained and book is great, but including more practice exercises is needed for this course to strengthen the learning of concepts.

By Narendra G

Jun 5, 2020

The course is well developed, reading the reference book is the most important thing that you will do while taking this course. The delivery of both instructors seems robotic.

By Nils S

Oct 29, 2020

Very good an enjoyable course. It seemed like the explanations dwelled on the easier parts and skipped the parts that I would like to have seen in concrete numbers.

By Nathaniel W

Aug 25, 2020

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.

By David S

Sep 27, 2019

It will be good to include more detailed examples and more practice exercices in week 2 and 3. Also to repair the week 4 submission.

Although, It is a good course.

By Naresh T

Mar 28, 2020

Good understanding of the fundamentals and aptly paced. The programming assignments were very good if there were more like that the course could get better

By aysegul

Jun 9, 2021

It brings general understanding. The main focus is reading the book. Assignments are about the introduction, help to understand, but they can be improved.

By Romesh M P

May 21, 2020

I really enjoyed the course, especially the guest segments (I got to know important people from that). Presenters did a good job but can be more relaxed.

By Jo K

Jan 29, 2021

Very good introductory course for reinforcement learning. Good coding assignment, but could add more visual representation to understand the transition.

By Marcello M

Aug 13, 2020

Very good theoretical contents, pretty much in line with the textbook - practical coding parts are mostly exercises of conversion of equations into code

By Mahmmoud M

Sep 29, 2019

However, Missing the lectures of slide, the supported book is very good. The lectures are very simple and one can finish fast.

Thanks for teaching team.

By Prakhar J

Aug 28, 2020

The content was very well organized, but applications could have been better understood using more complex numerical algorithms and more assignments.

By krishna c

Dec 31, 2020

The guest lecture on truck fleet management was not great, the teacher tried to cover lot more material in a short time in the video then possible.

By Ramakrishnan.K

Jun 21, 2020

The fundamentals of Bandits and MDPs are well covered. A major plus is the way we are made to read the text book before attending the lectures.

By Slav K

Jan 4, 2021

A solid start with theoretical fundament. Assignment 2 was too cumbersome, lacking the description of actions encoded in the assignment.

By Petru R

Jan 20, 2021

More Python examples are needed throughout the lessons.

Not only at the final. No proper introduction to DL Python library is given.

By NEHUL B

Sep 9, 2020

I was hoping for a bit more practical application too, but this course does a solid job at teaching you the theory thoroughly.

By Matthew C

Aug 24, 2019

Auto-grading of programming exercises did not work that well, but other than that, it was very instructive and well presented.

By Muhammad U S

Oct 11, 2020

Highly recommended for the beginners. If you are new to RL then this course is the best along with Sutton and Barto Book.

By parham M

Jul 6, 2020

there is so many great fundamental stuff here, with deep theoric background, but it lacks some practical approach

By Christopher B C

Sep 8, 2019

I thought the lectures were informative, but the pacing could have been a bit faster to get through more content.

By Rafael V M

Jul 15, 2020

Excellent course, with several practical examples of the theory being explained. I found week 3 a little dense.

By Balsher S

Jul 10, 2020

Week 3 should be improved a bit. It is a bit confusing to understand. Btw Great course. Keep the good work up.

By Sharmili S

Apr 15, 2020

Quizzes and assignments are a bit challenging. It might be easier if questions are better elucidated.