DataLight: Data-Centric Offline Reinforcement Learning for Traffic Signal Control

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 a 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 superior performance of DataLight compared to both state-of-the-art online and offline TSC methods. Additionally, DataLight exhibits robust learning capabilities concerning real-world deployment issues. Our source code is publicly available at https://github.com/LiangZhang1996/DataLight (The supplementary appendix for this paper is archived on Zenodo with DOI: https://doi.org/10.5281/zenodo.20635875 ).