Evaluating participant responses to a virtual reality experience using reinforcement learning

Joan Llobera; Alejandro Beacco; Ramon Oliva; Gizem Şenel; Domna Banakou; Mel Slater

doi:10.1098/rsos.210537

Evaluating participant responses to a virtual reality experience using reinforcement learning

R Soc Open Sci. 2021 Sep 15;8(9):210537. doi: 10.1098/rsos.210537. eCollection 2021 Sep.

Authors

Joan Llobera¹, Alejandro Beacco¹, Ramon Oliva¹, Gizem Şenel^{1

2}, Domna Banakou^{1

2}, Mel Slater^{1

2}

Affiliations

¹ Event Laboratory, Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain.
² Institute of Neurosciences of the University of Barcelona, Barcelona, Spain.

Abstract

Virtual reality applications depend on multiple factors, for example, quality of rendering, responsiveness, and interfaces. In order to evaluate the relative contributions of different factors to quality of experience, post-exposure questionnaires are typically used. Questionnaires are problematic as the questions can frame how participants think about their experience and cannot easily take account of non-additivity among the various factors. Traditional experimental design can incorporate non-additivity but with a large factorial design table beyond two factors. Here, we extend a previous method by introducing a reinforcement learning (RL) agent that proposes possible changes to factor levels during the exposure and requires the participant to either accept these or not. Eventually, the RL converges on a policy where no further proposed changes are accepted. An experiment was carried out with 20 participants where four binary factors were considered. A consistent configuration of factors emerged where participants preferred to use a teleportation technique for navigation (compared to walking-in-place), a full-body representation (rather than hands only), the responsiveness of virtual human characters (compared to being ignored) and realistic compared to cartoon rendering. We propose this new method to evaluate participant choices and discuss various extensions.

Keywords: factorial design; participant preference; reinforcement learning; rendering quality; virtual reality; walking-in-place.

Associated data

figshare/10.6084/m9.figshare.c.5600442