马尔:一个多代理强化重置器,用于重定向步行
IEEE transactions on visualization and computer graphics
|February 21, 2024
概括
重定向步行 (RDW) 可以破坏沉浸. 一个新的多代理增强重置器 (MARR) 通过考虑障碍物和多个用户,最大限度地减少虚拟现实中的重置,增强用户体验.
科学领域:
- 虚拟现实 (VR) 和人机交互
- 强化学习和多代理系统
背景情况:
- 重定向步行 (RDW) 是一种在有限的物理空间内浏览虚拟环境的技术.
- 过度的RDW重置可以减少用户的沉浸和存在.
- 现有的RDW优化方法主要侧重于单用户场景.
研究的目的:
- 在多用户虚拟环境中开发和评估RDW的新型重置技术.
- 为了尽量减少重置的次数,同时保持用户沉浸.
- 为了考虑静态障碍和动态用户移动.
主要方法:
- 拟议的多代理增强重置 (MARR) 算法.
- 训练有素的MARR使用多代理强化学习来优化重置方向.
- 通过模拟和用户研究对现有RDW技术进行评估MARR.
主要成果:
- 在最小化重置方面,MARR显著优于现有的RDW重置技术.
- MARR有效地适应了各种环境环境,包括动态的多用户交互.
- 该系统学习了针对不同RDW技术量身定制的最佳重置方向.
结论:
- 在多用户虚拟环境中,MARR是一种有效的RDW重置技术.
- 该方法通过减少沉浸破坏重置来增强用户体验.
- 马尔显示了与未来的RDW进步集成的潜力.
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