Online Archive of University of Virginia Scholarship
Nonlinear Inverse Reinforcement Learning for Human Performance Enhancing Feedback464 views
Author
Rucker, Mark, Systems Engineering - School of Engineering and Applied Science, University of Virginia0000-0003-0705-6704
Advisors
Beling, Peter, En-Sys/Info Engr Dept, University of Virginia
Wang, Hongning, En-Comp Science Dept, University of Virginia
Gerber, Matthew, En-Sys/Info Engr Dept, University of Virginia
Abstract
Many of the today’s most wicked problems are rooted in human behavior: whether it is distributing professional skills, managing chronic health conditions or responsibly sharing a common resource. Previous generations have dealt with such challenges through methods such as market forces, natural consequences and governmental policies. In this paper we propose a new, potential approach. We hypothesize that Inverse Reinforcement Learning can learn multifaceted, nonlinear reward functions which can drive predictable behavior change. To test this theory we create a stochastic, online control task and modify the task rewards according to IRL. A quasi-experimental design with a pre- and post-test, 4 treatment levels (plus a control group) and 400 participants per group (n≈2000) suggests that IRL rewards can cause behavior changes in a predictable manner.
Degree
MS (Master of Science)
Language
English
Rights
All rights reserved (no additional license for public reuse)
Rucker, Mark. Nonlinear Inverse Reinforcement Learning for Human Performance Enhancing Feedback. University of Virginia, Systems Engineering - School of Engineering and Applied Science, MS (Master of Science), 2018-11-05, https://doi.org/10.18130/V3-KWQX-R350.
