论文标题
libsignal：用于流量信号控制的开放库
LibSignal: An Open Library for Traffic Signal Control
论文作者
论文摘要
本文介绍了一个库，用于交叉模拟器比较交通信号控制任务中的增强学习模型。该库的开发是为了实施具有可扩展接口和统一的跨模拟器评估指标的最新最新的强化学习模型。它支持流量信号控制任务中常用的模拟器，包括城市移动性（SUMO）和CityFlow的模拟以及多个基准数据集以进行公平比较。我们进行了实验，以验证模型的实现并校准模拟器，以便一个模拟器的实验可以参考另一个模拟器。基于经过验证的模型和校准环境，本文比较并报告了不同数据集和模拟器上最新的RL算法的性能。这是第一次在具有不同的模拟器的同一数据集中对这些方法进行了比较。
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.
