The reset technique of Redirected Walking (RDW) forcibly reorients the user's direction overtly to avoid collisions with boundaries, obstacles, or other users in the physical space. However, excessive resetting can decrease the user's sense of immersion and presence. Several RDW studies have been conducted to address this issue. Among them, much research has been done on reset techniques that reduce the number of resets by devising reset direction rules (e.g.,~ 2:1-turn, reset-to-center) or optimizing them for a given environment. However, existing optimization studies on reset techniques have mainly focused on a single-user environment. In a multi-user environment, the dynamic movement of other users and static obstacles in the physical space increase the possibility of resetting. In this study, we propose a multi-user reset controller (MRC) that resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MRC is trained using multi-agent reinforcement learning to determine the optimal reset direction in different environments. This approach enables MRC to effectively account for different environmental contexts, including arbitrary physical obstacles and the dynamic movements of other users in the same physical space. We compared MRC with other reset techniques through simulation tests and user studies, and our results show that MRC reduces the mean number of resets by up to 55\%. Overall, our study confirmed that MRC is an effective reset technique in multi-user environments. Supplemental materials are available at an anonymous link: (https://osf.io/rpftu/?view_only=8230f344502f4013af2a5229db5e21c3).

翻译：重定向行走（RDW）的复位技术通过强制显式地重新调整用户行走方向，以避免与物理空间中的边界、障碍物或其他用户发生碰撞。然而，过度复位会降低用户的沉浸感和临场感。为解决此问题，学界已开展多项RDW研究，其中大量工作聚焦于通过设计复位方向规则（如2:1转向、复位至中心）或针对给定环境进行优化来减少复位次数的复位技术。然而，现有复位技术优化研究主要针对单用户环境。在多用户环境下，其他用户的动态移动和物理空间中的静态障碍物会增加复位可能性。本研究提出一种多用户复位控制器（MRC），该控制器综合考虑物理障碍物和多用户移动因素对用户进行复位，旨在最小化复位次数。MRC通过多智能体强化学习进行训练，以确定不同环境下的最优复位方向。该方法使MRC能有效适应包含任意物理障碍物和同一物理空间中其他用户动态移动的各种环境场景。通过仿真测试和用户研究将MRC与其他复位技术进行比较，结果表明MRC最多能将平均复位次数降低55%。总体而言，本研究证实MRC是多用户环境中的一种有效复位技术。补充材料可通过匿名链接获取：(https://osf.io/rpftu/?view_only=8230f344502f4013af2a5229db5e21c3)。