Chevron Left
Вернуться к Fundamentals of Reinforcement Learning

Отзывы учащихся о курсе Fundamentals of Reinforcement Learning от партнера Альбертский университет

Оценки: 2,381

О курсе

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....

Лучшие рецензии


6 июля 2020 г.

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.


7 апр. 2020 г.

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!

Фильтр по:

501–525 из 568 отзывов о курсе Fundamentals of Reinforcement Learning

автор: aysegul

9 июня 2021 г.

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

автор: Romesh M P

21 мая 2020 г.

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.

автор: Jo K

29 янв. 2021 г.

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

автор: Marcello M

13 авг. 2020 г.

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

автор: Mahmmoud M

29 сент. 2019 г.

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.

автор: Prakhar J

28 авг. 2020 г.

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

автор: krishna c

31 дек. 2020 г.

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.

автор: Ramakrishnan.K

21 июня 2020 г.

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.

автор: Slav K

4 янв. 2021 г.

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

автор: Petru R

20 янв. 2021 г.

More Python examples are needed throughout the lessons.

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

автор: NEHUL B

9 сент. 2020 г.

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

автор: Matthew C

24 авг. 2019 г.

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

автор: Mark C

27 сент. 2021 г.

The course is mostly a repeat of the text book. Fortunately the text book is free. Regardless, the material is interesting.

автор: Muhammad U S

11 окт. 2020 г.

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

автор: Matthew W

17 июля 2021 г.

pretty good course for RL basics, not as in depth as the book and programming assignments were too easy, but good intro

автор: parham M

6 июля 2020 г.

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

автор: Christopher C

8 сент. 2019 г.

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

автор: Rafael V M

15 июля 2020 г.

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

автор: Balsher S

10 июля 2020 г.

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

автор: sharmili s

15 апр. 2020 г.

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

автор: Avishai B

15 февр. 2022 г.

This is a good course, the problem I see is that it is not covering enough material for the cost

автор: Arthur

24 нояб. 2020 г.

Great course, yet a bit superficial. If you want to understand details, you have grind on your own.

автор: Aze A

10 дек. 2020 г.

I enjoyed the course, especially week 3 and week 4 materials. I would have like more examples.

автор: Fateme S

28 июля 2022 г.

Thanks for the amazing course! It would be great if we had access to the lecture slides.

автор: Nga T

27 дек. 2021 г.

I dont understand the two games in this course. I have no idea how to mark them as done.