Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, empowering researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications.

翻译：多智能体强化学习（MARL）为应对城市环境中交通信号控制（TSC）的复杂性提供了一种前景广阔的方法。然而，现有基于MARL的TSC研究平台面临仿真速度慢、代码库复杂且难以维护等挑战。为克服这些局限，我们推出了PyTSC——一个稳健且灵活的仿真环境，旨在促进面向TSC的MARL算法的训练与评估。PyTSC集成了SUMO、CityFlow等多种仿真器，并提供简洁的API，使研究人员能够高效探索广泛的MARL方法。PyTSC加速了实验进程，并为推动现实应用中智能交通管理系统的发展提供了新的机遇。