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    Área de la Ciencia:

    • Robótica y sistemas de control de los sistemas de control.
    • La inteligencia artificial es inteligencia artificial.
    • Aprendizaje automático Aprendizaje automático.

    Sus antecedentes:

    • El aprendizaje por refuerzo multiagente (MARL) se enfrenta a desafíos en la exploración de espacios de alta dimensión con seguridad, arriesgando la inestabilidad del sistema y los estados inseguros.
    • La implementación en sistemas críticos para la seguridad se ve obstaculizada por la necesidad de garantías de seguridad sólidas y estabilidad bajo dinámicas inciertas.

    Objetivo del estudio:

    • Proponer una nueva capa de seguridad y estabilidad para los sistemas MARL que operan en entornos desconocidos con dinámicas inciertas.
    • Para integrar las funciones de barrera de control robusto (RCBF) y las funciones Lyapunov de control estable de entrada a estado (ISS-CLF) para una mayor seguridad y estabilidad.

    Principales métodos:

    • Se desarrolla una capa de seguridad y estabilidad, integrando RCBF y ISS-CLF para sistemas multiagente.
    • Las restricciones de seguridad y estabilidad se incorporan al marco MARL, separando la capacitación (logro de objetivos) y el despliegue (resultados de políticas filtradas).
    • Un mecanismo de compensación de acción desencadenado por eventos está diseñado para optimizar el uso de recursos computacionales basado en evaluaciones de seguridad.

    Principales resultados:

    • El método propuesto asegura el estricto cumplimiento de las restricciones de seguridad durante la ejecución de la política.
    • La eficiencia de ejecución de tareas de los sistemas multiagente se mejora significativamente.
    • La validación a través de simulaciones en entornos multiunicycle dinámicos confirma la efectividad del enfoque.

    Conclusiones:

    • La capa integrada de seguridad y estabilidad aborda efectivamente los desafíos de exploración en MARL para aplicaciones críticas de seguridad.
    • El método proporciona una solución robusta para equilibrar la exploración, la seguridad y la eficiencia en sistemas multiagente.
    • El enfoque demuestra la aplicabilidad práctica y el potencial para el despliegue en el mundo real en sistemas robóticos complejos.