Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

Abstract

With the continuous development of the 6G mobile network, computing-intensive and delay-sensitive onboard applications generate task data traffic more frequently. Particularly, when multiple intelligent agents are involved in tasks, limited computational resources cannot meet the new Quality of Service (QoS) requirements. To provide a satisfactory task offloading strategy, combining Multi-Access Edge Computing (MEC) with artificial intelligence has become a potential solution. In this context, we have proposed a task offloading decision mechanism (TODM) based on cooperative game and deep reinforcement learning (DRL). A joint optimization problem is presented to minimize both the overall task processing delay (OTPD) and overall task energy consumption (OTEC). The approach considers task vehicles (TaVs) and service vehicles (SeVs) as participants in a cooperative game, jointly devising offloading strategies to achieve resource optimization. Additionally, a proximate policy optimization (PPO) algorithm is designed to ensure robustness. Simulation experiments confirm the convergence of the proposed algorithm. Compared with benchmark algorithms, the presented scheme effectively reduces delay and energy consumption while ensuring task completion.