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Reinforcement Learning

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Unlock the power of AI with the ‘Reinforcement Learning’ course

Course Description

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.

Complete any 3 courses listed below to earn the Advanced AI Techniques badge:

Learn more about Micro-Credentials at Iowa State University!

Prerequisites

  • 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.

  • Learning Outcomes
  • Assessments
  • Course Outline

By the end of the course, you should be able to:

  • Formulate a reinforcement learning problem into a Markov Decision Process based on a specific task.
  • Develop basic value-based and policy-based reinforcement learning algorithms.
  • Develop Q learning algorithms with deep neural networks to address tasks.
  • Develop policy gradient algorithms with deep neural networks to address tasks.
  • 2 Quizzes to learn basic and advanced definitions and concepts of RL
  • 2 Coding exercise questions in which you will implement Python codes based on hands-on activities. This includes coding value-based and policy-based RL algorithms to solve classic control problems
  • Module 1: Introduction to Markov Decision Process and Reinforcement Learning
  • Module 2: Develop Value-based Reinforcement Learning Algorithms
  • Module 3: Develop Policy-based Reinforcement Learning Algorithms
  • Module 4: Advanced Deep Reinforcement Learning Methods

Course Procedures

The course starts on February 3, 2025. All coursework must be completed by March 31, 2025, in order to earn the micro-credential badge. You will continue to have access to the course materials until January 1, 2026. The approximate time to complete this course is 16 hours. 

This course has an instructional period from February 3 to March 2, 2025. During this instructional period, course materials will be released weekly and live synchronous sessions will be held. You may complete the course materials at your own pace. Live Zoom meetings will be conducted for interactive coding sessions. A suitable time for these live sessions will be determined through a group poll. The recordings of those sessions will be available soon after each meeting.

You will receive the Reinforcement Learning micro-credential badge upon successful completion of the course assessments.

Course Materials

Course materials are provided within the course. No additional purchase is required.

Contact Information

Contact isopd@iastate.edu for more information.

Course Developer

Translational_AI Center

The Translational AI Center breaks down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. For more information, visit Translational AI Center at Iowa State University

 

Red micro-credential badge for "Reinforcement Learning" offered by Iowa State University's Translational AI Center.

At a Glance

Registration Cost: $750.00 $500.00 USD (Initial Promo)
*$300.00 Student Discount Available

Course Hours: 16 hours

Course Start Date: February 3, 2025

Last Day to Register: January 26, 2025

Instructional Period & Live Sessions: February 3 – March 2, 2025

Last Day to Earn a Micro-Credential Badge: March 31, 2025

Time to Complete Badge: 56 Days

*At the time of registration, you’ll be asked to create an account for this course. Use “.edu” email address for a student discount. $250.00 will be immediately credit back after purchase.

Instructor

 

Zhanhong Jiang stands in front of a blackboard filled with mathematical equations, explaining the content.Zhanhong Jiang, Data Scientist

Zhanhong Jiang is a data scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests lie in decentralized deep learning, reinforcement learning, time-series prediction and applications to cyber-physical systems. Prior to that, he was a senior AI scientist at Johnson Controls and worked on smart and healthy building solutions using AI/ML technologies. He has numerous publications in prestigious journals and conferences and more than 10 patents.