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MARLens: Understanding Multi-Agent Reinforcement Learning for Traffic Signal Control via Visual Analytics.

Yutian Zhang, Guohong Zheng, Zhiyuan Liu

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    Summary

    This study introduces MARLens, a visual analytics system for understanding multi-agent reinforcement learning in traffic signal control. It enhances interpretability and aids in developing efficient traffic management strategies.

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    Area of Science:

    • Artificial Intelligence
    • Urban Planning
    • Data Visualization

    Background:

    • Traffic congestion hinders urban development, with intelligent traffic signal control (TSC) offering a solution.
    • Reinforcement learning (RL) shows promise for TSC, but current evaluation metrics lack depth.
    • Existing visual analysis tools are insufficient for multi-agent reinforcement learning (MARL) in complex traffic systems.

    Purpose of the Study:

    • To address the interpretability challenge in MARL for TSC.
    • To introduce MARLens, a visual analytics system designed for MARL-based TSC.
    • To provide researchers with a tool for exploring MARL decision-making and agent interactions in traffic management.

    Main Methods:

    • Developed MARLens, a visual analytics system with multiple visualization views.
    • Integrated a traffic simulation module for replaying training scenarios.
    • Conducted case studies, expert interviews, and a user study for validation.

    Main Results:

    • MARLens provides a versatile platform for exploring MARL features from multiple perspectives.
    • The system reveals decision-making processes and inter-agent interactions in TSC.
    • Validation through case studies and user feedback confirms the system's utility.

    Conclusions:

    • MARLens enhances the understanding of MARL-based TSC systems.
    • The system facilitates more informed and efficient traffic management strategies.
    • MARLens supports both RL and TSC researchers in practical implementation and further development.