PyTSC统一平台加速交通信号多智能体强化学习研究
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
- 整合SUMO/CityFlow，提供简洁API支持多智能体训练
- 仿真速度更快，代码更易维护，提升实验效率
- 适合交通信号控制与智能交通系统研究者使用
多智能体强化学习（MARL）为解决城市环境中交通信号控制（TSC）的复杂性提供了有前景的方案。然而，现有的基于MARL的TSC研究平台存在仿真速度慢、代码结构复杂且难以维护等问题。为此，我们提出PyTSC，一个强大且灵活的仿真环境，支持MARL算法在TSC中的训练与评估。PyTSC集成SUMO和CityFlow等多个仿真器，提供简洁的API，使研究人员能高效探索多种MARL方法。该平台显著加速实验进程，为智能交通管理系统在真实场景中的应用提供新机遇。
原文摘要 · Abstract (English)
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.
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