TrAC Reinforcement Learning
The Reinforcement Learning digital badge is designed to provide basic concepts of RL, value-based methods, policy-based methods, and actor-critic algorithms. 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 Reinforcement Learning course is offered by Iowa State University's Translational AI Center (TrAC) and is part of a larger Advanced AI Techniques pathway program.
Skills / Knowledge
- Artificial Intelligence
- Reinforcement Learning
- Deep Learning
- Q Learning
Earning Criteria
Required
The Reinforcement Learning Badge is earned after successful completion of a 4-week, asynchronous, self-paced online course consisting of 4 modules. The 4 modules cover essential topics of various reinforcement learning (RL) algorithms, a branch of machine learning and AI with application to real-world problems.
This course offers a blend of hands-on activities, assignments, video lectures and tutorials.
Learning Outcomes:
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
Assessment:
Participants will be assessed on:
Engagement with each module
Two coding exercises that include implementing python codes and value-based and policy-based RL algorithms to solve classic control problems
2 Quizzes assessing basic and advanced knowledge and concepts of Reinforcement Learning
About TrAC
The Translational AI Center will break down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. The center will initially focus on conducting core artificial intelligence research, as well as pursuing five application areas of artificial intelligence:
Materials design and manufacturing
Biology, healthcare, and quality of life
Autonomy, intelligent transportation, and smart infrastructure
Food, energy, and water
Ethics, fairness, and adoption.
In addition to serving as a scientific hub for translational artificial intelligence, the center will organize research seminars, host workshops, training, and onboarding programs, offer seed funding for research projects, and serve as an intermediary between private industry partners seeking research services and appropriate university faculty.