Enhanced multi-agent deep reinforcement learning for efficient task offloading and resource allocation in vehicular networks

In response to the rising demand for low-latency, computation-intensive applications in vehicular networks, this paper proposes an adaptive task offloading approach for Vehicle-to-Everything (V2X) environments. Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with an attention mechanism, the proposed approach optimizes computation offloading and resource allocation, aiming to minimize energy consumption and service delay. In this paper, vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links. The adaptive attention mechanism enables the system to prioritize relevant state information, leading to faster convergence. Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG (AT-MADDPG) algorithm significantly improves performance, achieving notable reductions in both energy consumption and latency compared to baseline algorithms, especially in high-demand, dynamic scenarios.