To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods and pure DRL methods, the proposed framework achieves improvements in performance metrics such as communication success rate, Average Age of Information (AoI), and policy convergence speed, effectively enabling resource management in complex environments. Moreover, the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.

With the advancement of Vehicle-to-Everything (V2X) technology, efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance. Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions. To address these challenges, this study presents an innovative framework that combines Graph Neural Networks (GNNs) with a Double Deep Q-Network (DDQN), utilizing dynamic graph structures and reinforcement learning. An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology, thereby improving decision accuracy and efficiency. Meanwhile, the framework models communication links as nodes and interference relationships as edges, effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information. Employing an aggregation mechanism based on the Graph Attention Network (GAT), it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance, ensuring more efficient and adaptive resource management. This design ensures reliable Vehicle-to-Vehicle (V2V) communication while maintaining high Vehicle-to-Infrastructure (V2I) throughput. The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment, allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions. Experimental results demonstrate that the proposed method significantly reduces computational overhead, mitigates latency, and improves resource utilization efficiency in vehicular networks under complex traffic scenarios. This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems, offering substantial theoretical significance and practical value.