  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).
