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

Area of Science:

• Artificial Intelligence
• Transportation Engineering
• Computer Science

Background:

• Multi-Agent Reinforcement Learning (MARL) is a key technology for complex Traffic Signal Control (TSC) in urban settings.
• Current MARL-based TSC research platforms suffer from slow simulations and difficult codebases, hindering progress.
• There is a need for efficient and maintainable simulation environments to advance intelligent traffic management.

Purpose of the Study:

• To introduce PyTSC, a novel simulation environment designed to overcome the limitations of existing platforms for MARL-based TSC research.
• To provide a robust and flexible tool for training and evaluating MARL algorithms in traffic signal control scenarios.
• To accelerate research and development in intelligent traffic management systems.

Main Methods:

• Developed PyTSC, a simulation environment integrating multiple traffic simulators (e.g., SUMO, CityFlow).
• Implemented a streamlined API to simplify the exploration of diverse MARL approaches.
• Focused on enhancing simulation speed and code maintainability for MARL-based TSC.

Main Results:

• PyTSC offers a robust and flexible platform for MARL-based TSC research.
• The environment integrates multiple simulators, supporting a wide range of MARL algorithms.
• PyTSC significantly accelerates experimentation and facilitates the evaluation of traffic management strategies.

Conclusions:

• PyTSC addresses critical challenges in MARL-based TSC research, improving efficiency and flexibility.
• The platform enables broader exploration of MARL approaches for traffic signal control.
• PyTSC is poised to advance intelligent traffic management systems towards real-world applications.