Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in
  AIoT-enabled Vehicular Metaverses with Trajectory Prediction
Junlong Chen Jiawen Kang Minrui Xu Zehui Xiong Dusit Niyato Chuan Chen Abbas Jamalipour Shengli Xie
Avatars, as promising digital assistants in Vehicular Metaverses, can enable
drivers and passengers to immerse in 3D virtual spaces, serving as a practical
emerging example of Artificial Intelligence of Things (AIoT) in intelligent
vehicular environments. The immersive experience is achieved through seamless
human-avatar interaction, e.g., augmented reality navigation, which requires
intensive resources that are inefficient and impractical to process on
intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide
Units (RSUs) or cloud servers for remote execution can effectively reduce
resource consumption. However, the high mobility of vehicles, the dynamic
workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making
avatar migration decisions. To address these challenges, in this paper, we
propose a dynamic migration framework for avatar tasks based on real-time
trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL).
Specifically, we propose a model to predict the future trajectories of
intelligent vehicles based on their historical data, indicating the future
workloads of RSUs.Based on the expected workloads of RSUs, we formulate the
avatar task migration problem as a long-term mixed integer programming problem.
To tackle this problem efficiently, the problem is transformed into a Partially
Observable Markov Decision Process (POMDP) and solved by multiple DRL agents
with hybrid continuous and discrete actions in decentralized. Numerical results
demonstrate that our proposed algorithm can effectively reduce the latency of
executing avatar tasks by around 25% without prediction and 30% with prediction
and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse
(AeVeM).