This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.

翻译：摘要：本文介绍了一个用于交通信号控制任务中强化学习模型跨模拟器对比的库。该库通过可扩展接口与统一跨模拟器评估指标，实现了当前最先进的强化学习模型。它支持交通信号控制任务中常用的模拟器（包括SUMO与CityFlow）及多个基准数据集，以实现公平对比。我们通过实验验证了模型的实现，并对模拟器进行校准，使得一个模拟器的实验结果可对另一模拟器具有参考价值。基于已验证的模型与校准后的环境，本文对比并报告了当前最先进强化学习算法在不同数据集与模拟器上的性能。这是首次在相同数据集下，不同模拟器之间对这些方法实现公平对比。