This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditional RL-based traffic controllers that focus on step-by-step decision making, CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. A decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, SUMO. Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

翻译：本研究提出CycLight，一种新颖的基于周期级深度强化学习的网络级自适应交通信号控制方法。与大多数传统基于强化学习的交通控制器注重逐步决策不同，CycLight采用周期级策略，利用参数化深度Q网络算法同时优化周期时长与绿信比。该周期级方法有效降低了频繁数据通信带来的计算负担，同时增强了实际应用中的实用性与安全性。针对多智能体协同建立了分散式框架，并集成注意力机制以准确评估周围环境对当前交叉口的影响。CycLight在微观交通仿真工具SUMO构建的大型合成交通网格中进行了测试。实验结果不仅证明了CycLight相较于其他先进方法的优越性，还展示了其对信息传输延迟的鲁棒性。