Decision Making and Reinforcement Learning
$49
ENROLL NOWCourse Overview
What You'll Learn
- 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.
- Finally, we introduce the reinforcement learning problem and discuss two paradigms: Monte Carlo methods and temporal difference learning.
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.
Course FAQs
Is this an accredited online course?
Accreditation for 'Decision Making and Reinforcement Learning' is determined by the provider, Columbia University. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
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How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'Decision Making and Reinforcement Learning' on the Columbia University online class platform, where you can complete your registration.
