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Learner Reviews & Feedback for Decision Making and Reinforcement Learning by Columbia University

4.1
stars
10 ratings

About the Course

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods. Finally, we introduce the reinforcement learning problem and discuss two paradigms: Monte Carlo methods and temporal difference learning. We conclude the course by noting how the two paradigms lie on a spectrum of n-step temporal difference methods. An emphasis on algorithms and examples will be a key part of this course....

Top reviews

SH

Jul 9, 2023

Well-structured course that provides a great introduction to methodologies used in reinforcement learning. I am now eager to experiment more in my own time, to consolidate what I have learned.

QN

Jan 20, 2024

Very good introductory and basic to Reinforcement Learning. But programming assignments need more careful compilation and more attention to detail!

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1 - 3 of 3 Reviews for Decision Making and Reinforcement Learning

By Stefan H

Jul 10, 2023

Well-structured course that provides a great introduction to methodologies used in reinforcement learning. I am now eager to experiment more in my own time, to consolidate what I have learned.

By quy d n

Jan 21, 2024

Very good introductory and basic to Reinforcement Learning. But programming assignments need more careful compilation and more attention to detail!

By Christian B

Nov 16, 2023

Good Course, you will be teached a lot about Reinforcement learning and Decision making. The Quiz and the Programming Assignments help a lot with this. For me personally the Videos were sometimes a bit fast but thats not a problem because you can pause and rewatch them. The only critic I have to mention is that some of the programming tasks are not not sufficiently described or explained. I think you could learn faster if you dont bother with figuring out what to implement and what to do. All in all the course is good and I would recommend it for someone who wants to start to learn about Reinforcement Learning.