Fundamentals of Reinforcement Learning
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.
Courses : 4
Specification: Fundamentals of Reinforcement Learning
71 reviews for Fundamentals of Reinforcement Learning
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Abhijeet A –
Really good course, and not for beginner.
Parsa V –
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
Dario J G P –
Antonio P –
Great introductional course on Reinforcement Learning
Raul D M –
One of the best courses I’ve had on Coursera
Babak B –
It provides a great introduction to RL and fundamental concepts in this area.
Shashidhara K –
I really sorry for giving 4 star, my only reason for giving 4 star is so you can read this review. Please include some exercise on calculating the equations by hand, with solutions(this is the only reason for 4 star). Thank you for the course Course deserves 5 stars.(pardon my 4 stars, sorry)
Payam M –
I learned a lot! Thanks
Atamert A –
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.
Umut Z –
Great content and instructors. Could use some more programming assignments.
Nikhil G –
Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding
Andre B –
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
David R –
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.
Prudhvinath R –
Everything is explained in detail. The theory behind each logistic is clearly explained by both the Professors.
Linggih S –
challenging and fun. I understand better from the programming assignment
Soran G –
The size of different variables has not been clearly spelled out so this makes the concept confusing and requires so much time to figure them out.
sachin k –
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.
Saeid G –
The good thing about this course is that it is based on the bible of reinforcement learning and it is thoughts by the experts in the field. However, the pace of the teaching is extremely fast and it is quite hard to keep with the pace even for someone with some background in the RL.
Jhonny I C O –
Last week of this course was nice, great programming assignment.
Holakou R –
Fairly comprehensive. Easy/fast to follow.
Christos P –
Great reading material and videos. The assignments really help to better understand the theory.
Anton P –
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.
Karel V –
The course is very well organised and professionally made. Although it follows the first four chapters of the Reinforcement Learning textbook, it provides a little bit different narrative and thus serves as a very nice complement to the textbook. Most importantly, interactive quizzes, programming exercises in Python and plenty of visualisations help to strengthen understanding of the concepts.
Chad R –
great course. follows a good RL textbook that covers the fundamentals
Enrique A –
Awesome. Concepts are easy–to–digest and the explanations are masterful!
Mohamed S R I –
The material in this course is of interest or me. It combines both theories and practical aspects of RL. The course follows the standard book in RL (Sutton & Barto Book). One improvement may be needed is to add more “modern” examples and programming assignments/modules to explain the concepts. Also, it would be nice if the instructors can sometimes reflect on their own experiences with RL, rather than exactly following the book.
Lukas S –
Very well structured, good examples, and helpful quizzes. I think (even) more programming assignments would make the course even better.
Hyeokjoon K –
It was a really nice lecture that helped me a lot to understand the fundamentals of reinforcement learning. Even though the lengths of the lectures are pretty short, they include the essence. So if you read enough and understand the textbook prior to the lectures, you would earn more from them. I’m so looking forward to learning real practical RL algorithms and applying them to my research.
Ron K –
The course was well taught! It utilized practical examples that helped bring the concepts and math to light! The instructors explained the math well without getting caught up in too much of the unnecessary minutiae. I struggled a bit in the programming exercises due more to my Python skills, but i was able to use the discussion boards to complete the assignments and understand the concepts.
nicole s –
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.
Lik M C –
It is a great course! The concepts are elaborated very clear. The materials are well prepared.
A great introduction to RL. It provides ton of materials and makes everything clear at the same time.
Tomasz M –
Great course, created with passion and respect for the students! Thanks guys.
Maxim V –
Good content, but most of it is in the textbook, not so much in the videos.
Qianbo Y –
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.
Anubhab G –
A solid foundation for RL and great pedagogical explanations!!! Highly recommended!!
PRASHANT K R –
Mathis V E –
Good course, very clear introduction to relatively difficult concepts.
Jay U –
Very well designed course on the fundamentals of reinforcement learning.
Youval D –
Good examples can simplify things greatly. there where several places where an extra step would add value. Some lessons, such as the problem with the trucks could go a little deeper. Assignment grading system is buggy. I spend hours (that I do not have) because I used “transition” as a variable. After I figured this out, I was no longer able to know if other error is due to some other things the Notebook does not like or if there are actual errors. I also posted some questions but never got any response to any of them.
Lee, J –
Very understandable explaination! I was able to understand the fundamental of RL.
Joaquin T –
Extremely easy to follow, yet full of information. The book is a fabulous bonus.
Puyuan L –
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.
Maximiliano B –
This course is excellent and it is a great introduction to reinforcement learning. I really liked that an electronic version of the book from Sutton and Barto is available for download as part of the course. However, it is fundamental to read the book in advance before watching the videos every week to have a better understanding of the concepts. Mr. and Mrs. White explain the content very well and it helped me a lot because the book is sometimes quite abstract if you are dealing with this subject for the first time. I definitely recommend this course to have a solid foundation in Reinforcement Learning and I am looking forward to start the next course of the specialization.
Quirin D –
Amey M M –
Very Good Course
Daxkumar J –
this is a basic course of the RL and its very great to learn with University Alberta.
Juan C E –
Excellent course. Excellent teachers. I love the introduction sections, in which you’re presented what you’ll learn in each video, and the summary section. The animated slides are also very professional. Very thorough coverage of the RL book. Congratulations!!!
MOHD F U –
Need a clear explanation of topics with a way to code as explained by Andrew NG in Neural networks and deep learning by deeplearning.ai
Arun R –
Great class and I learned a lot docking one star because the final programming assignment didn’t give a comprehensive enough checker inside the Notebook, so I had to keep submitting and look to discussions for help in solving (for really a minor issue that it looks like many students faced on an edge test case).
Matias A –
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.
Ekaterina R –
Mohamed H –
I think it will be perfect if the board and pen are used to drive equations.
Andis R –
Thank you very much for putting effort in this one !!! I was looking for such a course for some time already, thanks.
I found this course very interesting. The basic concepts are explained very nicely. This course is great when a RL noob like me : D
Russel C –
Really good introduction to Reinforcement Learning foundations. The lectures were great, and helped translate the theory from the RL book. I would like there to be a few more detailed walk thru of the update algorithms in week 4, but I was able to work through the programming assignments okay.
Kyle W –
I enjoy the programming assignments very much.
I had learned a clear understanding of terminology and the formulas of value function, action value function, optimal value function, Bellman’s equation, policy evaluation and iteration. It’s a must go through course for Reinforcement Learning
Siddharth Y –
Very good introduction to the world of reinforcement learning. Highly recommended.
Oren Z –
Sandro M A T –
Great introduction to Reinforcement Learning!!
Ruosen L –
Spent tooooooo much time on reading materials than imagine.
Zhuang J –
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!
Joost G –
Interesting lectures and good quizzes+programming exercises.
Roberto M –
I really appreciated the course. Explanations are very clear and the material is well thought.
Volodymyr F –
A perfect introductory course into Reinforcement Learning topic
Kashish T –
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
Prajwal T –
Alper A –
Course is fine, but there could be more coding practices then the theoretical part. There are two coding assignments which are hard to do only with the course. The course context could be extended to include more coding practices.