Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Ryan Hare, Ying Tang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

Original languageEnglish (US)
Pages (from-to)2047-2055
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume53
Issue number4
DOIs
StatePublished - Apr 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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