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    Area of Science:

    • Artificial Intelligence
    • Computer Science

    Background:

    • Multi-agent reinforcement learning (MARL) relies on agent communication for group behavior.
    • Real-world MARL systems face bandwidth limitations, hindering timely message exchange and cooperation.
    • Existing methods inadequately reduce communication resource consumption.

    Purpose of the Study:

    • To enhance communication efficiency in multi-agent systems under bandwidth constraints.
    • To develop a novel communication network that minimizes bandwidth usage by communicating only when necessary.
    • To propose adaptable frameworks for learning efficient communication protocols.

    Main Methods:

    • Proposed an event-triggered communication network (ETCNet) with two paradigms: event-triggered sending network (ETSNet) and event-triggered receiving network (ETRNet).
    • Utilized information theory to translate limited bandwidth into a penalty threshold for event-triggered strategies.
    • Formulated the event-triggered strategy design as a constrained Markov decision problem, solved using reinforcement learning.

    Main Results:

    • ETCNet demonstrated superior performance in reducing bandwidth occupancy compared to existing methods.
    • The proposed framework effectively preserves the cooperative performance of multi-agent systems.
    • Experiments on typical multi-agent tasks validated the efficiency and effectiveness of ETCNet.

    Conclusions:

    • ETCNet offers a viable solution for efficient communication in bandwidth-limited multi-agent systems.
    • The event-triggered approach optimizes communication resource utilization without compromising collaborative task achievement.
    • This work advances MARL by enabling robust cooperation under practical communication constraints.