Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges. While most RL-based TSC systems typically employ an online approach, facilitating frequent active interaction with the environment, learning such strategies in the real world is impractical due to safety and risk concerns. To tackle these challenges, this study introduces an innovative offline data-driven approach, called DataLight. DataLight employs effective state representations and reward function by capturing vehicular speed information within the environment. It then segments roads to capture spatial information and further enhances the spatially segmented state representations with sequential modeling. The experimental results demonstrate the effectiveness of DataLight, showcasing superior performance compared to both state-of-the-art online and offline TSC methods. Additionally, DataLight exhibits robust learning capabilities concerning real-world deployment issues. The code is available at https://github.com/LiangZhang1996/DataLight.

翻译：强化学习（RL）已成为解决交通信号控制（TSC）挑战的一种有前景的方案。尽管大多数基于RL的TSC系统通常采用在线方法，通过频繁主动与环境交互来学习策略，但由于安全与风险问题，在实际环境中部署此类策略并不现实。为应对这些挑战，本研究提出了一种创新的离线数据驱动方法，称为DataLight。DataLight通过捕获环境中车辆速度信息，采用了有效的状态表示和奖励函数。随后，其对道路进行分段以提取空间信息，并通过序列建模进一步强化空间分段状态表示。实验结果表明了DataLight的有效性，其性能优于当前最先进的在线与离线TSC方法。此外，DataLight在实际部署相关问题中展现出强大的学习能力。代码可参见https://github.com/LiangZhang1996/DataLight。