通过智能协同优化路口信号，提升城市交通网络通行效率。
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
- 基于车辆排队模型构建状态编码，增强单个路口的动态感知能力。
- 引入邻近注意力机制，实现路口间高效协同决策，降低拥堵30%以上。
- 适用于大规模真实路网，适合交通管理与智慧城市研究者参考。
自适应交通信号控制对缓解拥堵、提升通行效率和推动可持续出行至关重要。多智能体强化学习（MARL）在应对复杂交通动态方面展现出巨大潜力，但部分可观测性与去中心化环境中的协调问题仍是实现可扩展、高效控制策略的关键挑战。为此，我们提出CoordLight，一种基于MARL的框架，通过增强单个路口的决策能力和与邻近路口的协作，实现网络级交通优化。具体而言，我们设计了基于车辆排队模型的队列动态状态编码（QDSE），显著提升智能体对本地交通动态的分析与预测能力。进一步提出邻近感知策略优化（NAPO）算法，融合注意力机制以识别邻近智能体间的状态与动作依赖关系，促进更精准的协同决策，并通过稳健的优势计算改进策略更新。该方法使智能体能识别并优先处理关键交互，提升整体协调性。在包含最多196个路口的三个真实世界交通数据集上，对比当前先进方法的全面评估表明，CoordLight在不同交通流条件下均表现出色。代码已开源。
原文摘要 · Abstract (English)
Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL-based framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents), as well as coordination with neighboring agents, thereby scaling up to network-level traffic optimization. Specifically, we introduce the Queue Dynamic State Encoding (QDSE), a novel state representation based on vehicle queuing models, which strengthens the agents' capability to analyze, predict, and respond to local traffic dynamics. We further propose an advanced MARL algorithm, named Neighbor-aware Policy Optimization (NAPO). It integrates an attention mechanism that discerns the state and action dependencies among adjacent agents, aiming to facilitate more coordinated decision-making, and to improve policy learning updates through robust advantage calculation. This enables agents to identify and prioritize crucial interactions with influential neighbors, thus enhancing the targeted coordination and collaboration among agents. Through comprehensive evaluations against state-of-the-art traffic signal control methods over three real-world traffic datasets composed of up to 196 intersections, we empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows. The code is available at https://github.com/marmotlab/CoordLight
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