HT-DRIVE: Heterogeneous-Temporal GNN-Aided Multi-Agent Reinforcement Learning for Resource Allocation in Platoon-Based V2X Networks

Platoon-based Vehicle-to-Everything (V2X) is a promising communication paradigm for intelligent vehicular networks. In such networks, each platoon leader (PL) engages in a high-throughput vehicle-to-infrastructure (V2I) link to deliver entertainment data, while maintaining low-latency and high-reliability vehicle-to-vehicle (V2V) links with multiple platoon members (PMs) to convey safety-critical information, thus posing conflicting performance requirements. Besides, the temporal variations and co-channel interferences further complicate the problem. To address these issues, we propose a heterogeneity-aware and dynamics-adaptive resource allocation framework, termed HT-DRIVE, which integrates an advanced heterogeneous temporal graph neural network (HTGNN) with a tailored multi-agent reinforcement learning (MARL) mechanism. Specifically, we design an HTGNN module to capture heterogeneous and temporal patterns within the platoon-based V2X network, generating structured embeddings that serve as state inputs to a modified multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for downstream decision-making. To facilitate end-to-end optimization, several enhancements are also incorporated, including a hybrid action design to jointly handle discrete subchannel selection and continuous power control, a dual-critics architecture with reduced computational complexity, and customized loss functions for collaborative training. Extensive simulations demonstrate that HT-DRIVE significantly outperforms MATD3, multi-agent deep deterministic policy gradient (MADDPG) and heuristic methods under various settings, validating its effectiveness and robustness.