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    This study introduces a novel local communication learning method to address challenges in multi-agent reinforcement learning (MARL). The approach enhances coordination in large-scale agent systems, improving performance in tasks like adaptive traffic signal control.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Multi-agent reinforcement learning (MARL) faces significant challenges due to partial observability and limited real-time agent interactions.
    • Coordinating a large number of agents in MARL settings is computationally intensive and complex.

    Purpose of the Study:

    • To propose a new method for efficient coordination in large-scale MARL settings.
    • To develop a communication protocol that leverages local interactions and reduces computational complexity.

    Main Methods:

    • A novel communication protocol using depthwise convolution to learn local communication between neighboring agents.
    • Integration of mean-field approximation to scale down agent interactions.
    • Enhancement of mean-field approximation with a supervised policy rectification network (PRN) and a learnable compensation term for improved accuracy.

    Main Results:

    • The proposed method demonstrates efficient coordination capabilities in MARL.
    • The approach significantly outperforms baseline methods on benchmark tasks.
    • Successful application in adaptive traffic signal control (ATSC) and StarCraft II multi-agent challenge (SMAC).

    Conclusions:

    • The developed local communication learning method effectively tackles MARL challenges in large-scale systems.
    • The integration of PRN and compensation terms enhances the accuracy and coordination of agent behaviors.
    • The method offers a promising direction for advancing MARL research and applications.