PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

TL;DR

PyTSC is a versatile simulation platform integrating SUMO and CityFlow, designed to streamline and accelerate MARL research for traffic signal control in urban environments.

Contribution

PyTSC introduces a unified, flexible environment that simplifies MARL-based TSC research and overcomes existing platform limitations.

Findings

Supports multiple simulators for comprehensive testing

Streamlines API for easier implementation

Accelerates research and experimentation

Abstract

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.