TY - GEN
T1 - Reinforcement learning content generation for virtual reality applications
AU - Lopez, Christian E.
AU - Ashour, Omar
AU - Tucker, Conrad S.
N1 - Funding Information:
This research is funded by the National Science Foundation NSF DUE #183446. Any opinions, findings, or conclusions found in this paper are those of the authors and do not necessarily reflect the views of the sponsors. The authors would also like to thank Bradley Nulsen, Gerard Pugliese Jr., Adith Rai, and Matthew Rodgers for developing and implementing the Unity application.
Publisher Copyright:
Copyright © 2019 ASME.
PY - 2019
Y1 - 2019
N2 - This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL agent using a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications.
AB - This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL agent using a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications.
UR - http://www.scopus.com/inward/record.url?scp=85076390556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076390556&partnerID=8YFLogxK
U2 - 10.1115/DETC2019-97711
DO - 10.1115/DETC2019-97711
M3 - Conference contribution
AN - SCOPUS:85076390556
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 39th Computers and Information in Engineering Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Y2 - 18 August 2019 through 21 August 2019
ER -