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Real-World Application of Classical Conditioning01:15

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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
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Reinforcement Schedules01:24

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Video Experimental Relacionado

Updated: May 2, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

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HARL-TRADE: Un marco jerárquico adaptativo de aprendizaje por refuerzo para el trading de alta frecuencia de segundo

Hao Shi1, Xinting Zhang2, Desheng Wu2

  • 1School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, China.

Chaos (Woodbury, N.Y.)
|February 18, 2026
PubMed
Resumen

Este estudio presenta un marco jerárquico adaptativo para el trading de alta frecuencia (HFT) utilizando un meta-agente basado en atención. Mejora la adaptabilidad en mercados volátiles, superando a los métodos existentes con rendimientos significativos.

Palabras clave:
finanzas cuantitativasinteligencia artificialcomercio algorítmicoaprendizaje por refuerzocomercio de alta frecuencia

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Last Updated: May 2, 2026

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Área de la Ciencia:

  • Finanzas Cuantitativas
  • Inteligencia Artificial
  • Comercio Algorítmico

Sus antecedentes:

  • El trading de alta frecuencia (HFT) requiere estrategias adaptativas para condiciones de mercado volátiles.
  • Los marcos discretos de sub-agentes existentes exhiben una adaptabilidad limitada debido a asignaciones rígidas de las condiciones del mercado.

Objetivo del estudio:

  • Proponer un marco jerárquico novedoso con un meta-agente basado en atención para la coordinación dinámica de sub-agentes en HFT.
  • Mejorar la adaptabilidad y el rendimiento en la navegación de diversos regímenes de mercado.

Principales métodos:

  • Desarrollo de un marco jerárquico que incorpora un meta-agente basado en atención.
  • Utilización de incrustaciones de mercado y aprendizaje por refuerzo para el ajuste óptimo del peso del sub-agente.
  • Implementación de asignación dinámica de sub-agentes y mecanismos de atención de múltiples cabezas.

Principales resultados:

  • El marco propuesto logró un retorno total del 42,15% y un ratio de Sharpe de 4,19 en datos históricos de HFT.
  • Demostró un rendimiento superior en comparación con los puntos de referencia de última generación.
  • Los estudios de ablación confirmaron la efectividad de los mecanismos de asignación dinámica y atención.

Conclusiones:

  • El marco jerárquico basado en atención ofrece una adaptabilidad y un rendimiento superiores en el trading de alta frecuencia.
  • La coordinación dinámica de sub-agentes a través de un meta-agente aborda eficazmente la volatilidad y las transiciones del mercado.