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This paper introduces a library for cross-simulator comparison of reinforcement learning models in trafc signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unifed crosssimulator evaluation metrics. It supports commonly-used simulators in trafc 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-theart RL algorithms across diferent datasets and simulators. This is the frst time that these methods have been compared fairly under the same datasets with diferent simulators.