Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education
提出一个带经验共享的分层多智能体强化学习技术框架，用于增强教育元宇宙中非玩家角色的智能，实现个性化学习，并通过游戏Gridlock和仿真验证效果。
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.