DHLight：用于交通信号控制的基于多智能体策略的有向超图学习方法
DHLight: Multi-agent Policy-based Directed Hypergraph Learning for Traffic Signal Control
AI总结：
针对传统图结构难以捕捉交通动态高阶时空关联的问题，提出融合有向超图学习的多智能体交通信号控制框架DHLight，通过动态有向超图构建机制实现自适应决策，实验验证其性能优于现有最优基线。
摘要：
深度强化学习（DRL）与图神经网络（GNN）的最新进展在智能交通信号控制领域展现出显著应用潜力，可实现多交叉口间的协同管控。但传统方法依赖标准图结构，往往难以捕捉真实交通动态中固有的复杂高阶时空关联，标准图无法完整表征路网内的空间关系，限制了基于图的方法的有效性。相比之下，有向超图可更精准地表征空间信息，对多节点间的复杂有向关系建模。本文提出DHLight，这一新型基于多智能体策略的框架协同融合了有向超图学习模块，引入了全新的动态有向超图构建机制，可捕捉路网中交叉口间复杂且动态演化的时空关系。借助有向超图关系结构，DHLight支撑智能体实现交通信号控制的自适应决策。我们在多种路网数据集上开展大量实验，将DHLight与当前最优基线方法对比，验证了其有效性。我们已在https://github.com/LuckyVoasem/Traffic-Light-control发布代码以保障本研究可复现。
英文摘要：
Recent advancements in Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) have demonstrated notable promise in the realm of intelligent traffic signal control, facilitating the coordination across multiple intersections. However, the traditional methods rely on standard graph structures often fail to capture the intricate higher-order spatio-temporal correlations inherent in real-world traffic dynamics. Standard graphs cannot fully represent the spatial relationships within road networks, which limits the effectiveness of graph-based approaches. In contrast, directed hypergraphs provide more accurate representation of spatial information to model complex directed relationships among multiple nodes. In this paper, we propose DHLight, a novel multi-agent policy-based framework that synergistically integrates directed hypergraph learning module. This framework introduces a novel dynamic directed hypergraph construction mechanism, which captures complex and evolving spatio-temporal relationships among intersections in road networks. By leveraging the directed hypergraph relational structure, DHLight empowers agents to achieve adaptive decision-making in traffic signal control. The effectiveness of DHLight is validated against state-of-the-art baselines through extensive experiments in various network datasets. We release the code to support the reproducibility of this work at https://github.com/LuckyVoasem/Traffic-Light-control
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
