Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

【Author】 Hare, Ryan; Tang, Ying

【Source】IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

【影响因子】11.471

【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.

【Keywords】ACP; experience sharing; metaverse learning; reinforcement learning (RL)

【发表时间】

【收录时间】2023-01-17

【文献类型】实验仿真

【主题类别】

区块链应用-虚拟经济-元宇宙