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

2024/10/23 by Rohit Bokade, Bokade, Rohit, Xiaoning Jin +1
Engineering · Decision Sciences · #Traffic control and management #Traffic Prediction and Management Techniques #Simulation Techniques and Applications

paper · pdf · doi:10.48550/arxiv.2410.18202

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.

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