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Updated: Sep 9, 2025

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Simulación proyectiva de energía libre (FEPS): inferencia activa con interpretabilidad

Joséphine Pazem1, Marius Krumm1, Alexander Q Vining1,2

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Resumen

Presentamos la Simulación Proyectiva de Energía Libre (FEPS), un modelo interpretable para agentes que aprenden sin redes neuronales profundas. Los agentes FEPS resuelven efectivamente la ambigüedad ambiental e inferen políticas óptimas al contextualizar las observaciones basadas en la precisión de la predicción.

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Área de la Ciencia:

  • Neurociencia computacional
  • Inteligencia artificial
  • Sistemas complejos

Sus antecedentes:

  • El Principio de la Energía Libre (FEP) y la Inferencia Activa (AIF) ofrecen un marco unificado para comprender el aprendizaje, la cognición, la percepción y la acción en los sistemas auto-organizados.
  • Los agentes de aprendizaje por refuerzo (RL), que a menudo emplean redes neuronales profundas, se han desarrollado para realizar tareas de inferencia activa, con esfuerzos recientes centrados en mejorar el rendimiento en entornos complejos.

Objetivo del estudio:

  • Desarrollar un enfoque de modelado de agentes interpretable dentro de las limitaciones de FEP y AIF, evitando las redes neuronales profundas.
  • Introducir la simulación proyectiva de energía libre (FEPS) como un nuevo método para el modelado de agentes y la optimización de políticas.

Principales métodos:

  • Los agentes FEPS utilizan recompensas internas para construir modelos mundiales de entornos parcialmente observables.
  • Las políticas se derivan de minimizar la energía libre esperada, un principio básico de AIF.
  • Se incorporan técnicas para gestionar los objetivos a largo plazo y mitigar los errores de predicción de la estimación del estado oculto.

Principales resultados:

  • Los agentes FEPS resolvieron con éxito la ambigüedad en dos entornos de RL (respuesta cronometrada y navegación parcialmente observable) inspirados en la biología del comportamiento.
  • Los agentes demostraron la capacidad de contextualizar las observaciones basadas únicamente en la precisión de la predicción.
  • Las políticas óptimas se inferían de manera flexible para diversas observaciones objetivo dentro de los entornos.

Conclusiones:

  • FEPS proporciona una alternativa interpretable a las redes neuronales profundas para los agentes de modelado bajo FEP y AIF.
  • El modelo maneja efectivamente la ambigüedad ambiental y optimiza las políticas a través de la contextualización basada en la precisión de la predicción.
  • FEPS es prometedor para el desarrollo de agentes adaptativos y dirigidos a objetivos en entornos complejos y parcialmente observables.