The Metaverse is gaining attention among academics as maturing technologies
empower the promises and envisagements of a multi-purpose, integrated virtual
environment. An interactive and immersive socialization experience between
people is one of the promises of the Metaverse. In spite of the rapid
advancements in current technologies, the computation required for a smooth,
seamless and immersive socialization experience in the Metaverse is
overbearing, and the accumulated user experience is essential to be considered.
The computation burden calls for computation offloading, where the integration
of virtual and physical world scenes is offloaded to an edge server. This paper
introduces a novel Quality-of-Service (QoS) model for the accumulated
experience in multi-user socialization on a multichannel wireless network. This
QoS model utilizes deep reinforcement learning approaches to find the
near-optimal channel resource allocation. Comprehensive experiments demonstrate
that the adoption of the QoS model enhances the overall socialization
experience.

该研究提出了一种全新的质量服务（Quality-of-Service）模型，利用深度强化学习方法来找到多用户社交中接近最优的信道资源分配。全面的实验证明采用该质量服务模型可以提升整体社交体验。