Multi-Agents Service Migration Strategy Under Vehicle Edge Computing

As Intelligent Transport Systems (ITS) advance, the Internet of Vehicles (IoV) improves traffic efficiency and safety. Vehicle Edge Computing (VEC) provides strong computing and storage capabilities. However, high vehicle speeds require efficient service migration to maintain service continuity. This study proposes a real-time service migration strategy that optimizes both Quality of Service (QoS) and response latency. To support dynamic decision-making in the VEC framework, this study introduces Priority Experience Replay and Four-Trajectory exploration (PERFT), Recurrent Neural Networks (RNNs) and attention mechanisms into the Proximal Policy Optimization (PPO) algorithm. This leads to the PERFT-PPO, which addresses issues such as long sequence handling, sparse reward problems, and problems of limited exploration depth of long trajectories in single-agent scenarios. To address multi-agents resource scheduling in VEC, this study integrates Centralized Training and Distributed Execution (CTDE) into PERFT-PPO, creating the Prioritized Experience Replay Four-Trajectory Multi-Agent Proximal Policy Optimization (PERFT-MAPPO). The simulation results show that PERFT-MAPPO addresses the challenges of real-time decision-making and resource scheduling, achieving lower system cost. Compared to PERFT-PPO, PERFT-MAPPO demonstrates superior adaptability in dynamic network conditions, further optimizing system efficiency and Qos.