Nonlinear Inverse Reinforcement Learning for Human Performance Enhancing Feedback

Rucker, Mark, Systems Engineering - School of Engineering and Applied Science, University of Virginia
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

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.

MS (Master of Science)
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