Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education
Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education
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DOI：
10.1109/tsmc.2022.3227919
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发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Ryan Hare;Ying Tang
中科院分区：
文献类型：
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作者：
Ryan Hare;Ying Tang
Metverse对教育的兴趣与日俱增，许多文献都聚焦于它在提高个人和社会学习方面的巨大潜力。然而，在提供有意义的Metverse学习背后的系统和技术方面，几乎没有做过什么工作。本文提出了一个技术框架来弥补这一研究空白，其中提出了一种具有经验共享的分层多智能体强化学习方法，以增强Metverse学习中的非玩家角色的智能，从而实现个性化。在Metverse学习游戏Gridlock中，以及通过广泛的模拟，展示了所提出的框架和方法的实用性和益处。
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.