Multi-context generation in virtual reality environments using deep reinforcement learning

James Cunningham, Christian Lopez, Omar Ashour, Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, a Deep Reinforcement Learning (RL) approach is proposed for Procedural Content Generation (PCG) that seeks to automate the generation of multiple related virtual reality (VR) environments for enhanced personalized learning. This allows for the user to be exposed to multiple virtual scenarios that demonstrate a consistent theme, which is especially valuable in an educational context. RL approaches to PCG offer the advantage of not requiring training data, as opposed to other PCG approaches that employ supervised learning approaches. This work advances the state of the art in RL-based PCG by demonstrating the ability to generate a diversity of contexts in order to teach the same underlying concept. A case study is presented that demonstrates the feasibility of the proposed RL-based PCG method using examples of probability distributions in both manufacturing facility and grocery store virtual environments. The method demonstrated in this paper has the potential to enable the automatic generation of a variety of virtual environments that are connected by a common concept or theme.

Original languageEnglish (US)
Title of host publication40th Computers and Information in Engineering Conference (CIE)
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791883983
DOIs
StatePublished - 2020
EventASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020 - Virtual, Online
Duration: Aug 17 2020Aug 19 2020

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume9

Conference

ConferenceASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020
CityVirtual, Online
Period8/17/208/19/20

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'Multi-context generation in virtual reality environments using deep reinforcement learning'. Together they form a unique fingerprint.

Cite this