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Related Experiment Video

Updated: Jul 23, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Communication-Efficient and Resilient Distributed Q-Learning.

Yijing Xie, Shaoshuai Mou, Shreyas Sundaram

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces event-triggered communication for multi-agent reinforcement learning (MARL) to reduce data exchange. It also develops a resilient algorithm for MARL systems facing adversarial agents, ensuring reliable learning.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Distributed Systems

    Background:

    • Multi-agent reinforcement learning (MARL) involves multiple agents learning optimal policies in a shared environment.
    • Existing MARL algorithms often require extensive communication between agents, limiting scalability and efficiency.
    • Networked systems introduce challenges in coordination and information sharing among agents.

    Purpose of the Study:

    • To develop communication-efficient and resilient MARL algorithms for networked agents.
    • To address scenarios with adversarial agents (Byzantine attacks) in MARL.
    • To reduce communication overhead while maintaining learning performance.

    Main Methods:

    • An event-triggered learning rule was proposed, enabling communication only when specific conditions are met.
    • A resilient MARL algorithm was designed to handle Byzantine agents.
    • Analysis of convergence properties for reliable agents under network constraints.

    Main Results:

    • The event-triggered approach significantly reduces communication frequency without compromising learning.
    • A trade-off between optimality and resilience was identified in the presence of Byzantine agents.
    • The proposed resilient algorithm demonstrates almost sure convergence of value functions for reliable agents.

    Conclusions:

    • Event-triggered communication offers an efficient alternative for MARL in networked settings.
    • Resilient MARL algorithms are crucial for robust performance against adversarial behavior.
    • The developed methods provide a foundation for practical, secure, and efficient MARL systems.