AM
Reviewed Jul 1, 2021
This course is great for people who are just starting out. The programming assignments are really great and practically introduce you to the basic concepts of reinforcement learning.
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.
Status: Agentic systems
Status: Machine Learning Algorithms
IntermediateCourse15 hours
Featured reviews
CS
Reviewed May 5, 2021
Fantastic course! I have been interested in Reinforcement Learning for a long time and this has been the best introduction I have found so far. It gave me the foundations on the field.
NS
Reviewed Aug 3, 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.
GJ
Reviewed Apr 25, 2020
The concepts are explained in a simple and illustrative manner which helps in getting a better understanding of the concepts. The assignments and quizzes are also really well designed.
RD
Reviewed Apr 25, 2020
I was so confused about the fundamental concepts, but doing this course has given me a solid foundation of RL.This is a must-do course if you are starting with Reinforcement Learning.
AB
Reviewed Sep 6, 2019
Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.Sometimes, visualizing the problem is hard, so need to thoroghly get prepared.
KS
Reviewed Aug 8, 2023
nice material. really breaks down hard concepts into easy to digest chunks. However, you will have to read the book to answer questions and delivery method of instructor could have been better
KL
Reviewed Dec 3, 2020
This course was super helpful. I had tried a couple other online introductions to RL, but this was the only one where I could really engage and learn the material effectively. Would recommend!
KS
Reviewed Sep 1, 2019
All the concepts were well explained and this course was perhaps the best I have found for RL.Great efforts have been put into making the course and It goes well in line with the suggested textbook.
TX
Reviewed Aug 2, 2022
This course is well-structured and helps graduate students to learn basic of reinforcement learning. It is suggested to learn while reading the book materials provided by scholars.
CS
Reviewed Feb 10, 2021
This is a relatively gentle introduction for the mathematically sophisticated, but does well to set the stage for the rest of the specialization and introduce the newcomer to the field.
AA
Reviewed Aug 16, 2021
This course is the best course for anyone who needs to enter into the field of RL. Content within the course is excellent and instructors have explained each and every topic very well.