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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Críticos de la élite de la pseudo-distribución: Mejorar la precisión en la estimación del valor del aprendizaje por

Yujia Zhang¹, Lin Li², Wei Wei²

¹School of Computer Science and Technology, North University of China, Taiyuan, 030051, Shanxi, China.

Neural networks : the official journal of the International Neural Network Society

|August 28, 2025

Resumen

Los críticos de élite de pseudo-distribución (PEC) mejoran el aprendizaje por refuerzo equilibrando los sesgos de valor Q. Este nuevo enfoque mejora la eficiencia de la muestra y el rendimiento del agente en entornos complejos.

Palabras clave:

Seudo-representación de la distribución Aprendizaje por refuerzo Medición de la incertidumbre Estimación del valor

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Área de la Ciencia:

Inteligencia artificial
Aprendizaje automático
Aprendizaje por refuerzo

Sus antecedentes:

Los agentes de aprendizaje por refuerzo (RL) sobresalen en entornos complejos, pero sufren sesgos en la estimación del valor de la acción del estado.
Los sesgos de sobreestimación y subestimación en las aproximaciones del valor Q limitan la eficiencia y el rendimiento de la muestra.

Objetivo del estudio:

Introducir el marco de Criticos de élite de la pseudo-distribución (PEC) para mejorar la eficiencia de la muestra RL.
Abordar y equilibrar los sesgos de sobreestimación y subestimación en las aproximaciones del valor Q.
Mejorar la precisión y fiabilidad de las estimaciones del valor Q en agentes inteligentes.

Principales métodos:

Utilice una representación de pseudo-distribución para enriquecer las aproximaciones de valor Q con características distributivas.
Incorporar una medición de incertidumbre para seleccionar el criterio más confiable para el cálculo del objetivo de diferencia temporal (TD).
Emplear una técnica de media recortada para equilibrar los sesgos optimistas y pesimistas en los objetivos de TD.

Principales resultados:

PEC demuestra mejoras estadísticamente significativas en las tareas de aprendizaje por refuerzo.
El marco muestra un rendimiento superior en comparación con las metodologías existentes en los escenarios de referencia.
El PEC mejora efectivamente la eficiencia de la muestra y refina las estimaciones del valor Q.

Conclusiones:

El marco de Pseudo-distribución Elite Critics (PEC) ofrece una solución sólida a los sesgos de estimación del valor Q en RL.
El PEC mejora el rendimiento del agente y la eficiencia de la muestra mediante el enriquecimiento distributivo y el equilibrio de sesgo.
Este enfoque innovador representa un avance significativo en el desarrollo de agentes inteligentes más hábiles.