Reinforcement Learning

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In this course, "Reinforcement Learning", you will learn about various reinforcement learning (RL) algorithms, a branch of machine learning and AI. This course covers everything you need to know about RL, including an overview of the basic concepts of RL, value-based methods, policy-based methods, and actor-critic algorithms. You will also learn how to leverage these algorithms to address specific real-world problems.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning RL enjoyable and rewarding. You will also be able to tackle real-world problems in discrete and continuous controls, and sequential decision-making. By the end of this course, you’ll have the skills and confidence to tackle any machine-learning challenge with RL.

This course is one of 6 courses in the Advanced AI Techniques pilot Micro-Credential pathway offered by the Translational AI Center at Iowa State University.

For more information regarding the course including: Learning Outcomes, Assessments, and a Course Outline please visit the Reinforcement Learning course page from Iowa State Online.

Prerequisite

Basic Python programming
Basic understanding of deep learning models
Basic understanding of control and sequential decision-making
Basic PyTorch programming

Intended Audience
The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a basic understanding of reinforcement learning and how to use these methods for a broad range of audiences.

Pages / Length:
Publication Date: 12/2024

Permanent link for this product: https://store.extension.iastate.edu/product/17179