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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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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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Aprendizaje de representaciones mediante similitud conductual basada en dinámicas para el aprendizaje por refuerzo

Dayang Liang1, Yunlong Liu1

  • 1Department of Automation, Xiamen University, Xiamen, 361005, China.

Neural networks : the official journal of the International Neural Network Society
|December 21, 2025
PubMed
Resumen

Introducimos el aprendizaje de representaciones con Similitud conductual basada en dinámicas (RDS) para mejorar el aprendizaje por refuerzo profundo. RDS mejora el aprendizaje de representaciones al eliminar la dependencia de la recompensa, logrando importantes mejoras de rendimiento en tareas complejas de manipulación.

Palabras clave:
métricas de similitud conductualaprendizaje por refuerzo profundorecompensa dispersarepresentaciones relevantes para la tarea

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Área de la Ciencia:

  • Inteligencia Artificial
  • Aprendizaje Automático
  • Robótica

Sus antecedentes:

  • El aprendizaje por refuerzo profundo requiere el aprendizaje de representaciones relevantes para la tarea a partir de datos visuales.
  • Las métricas de similitud conductual agrupan estados equivalentes pero sufren de colapso de representación debido a recompensas dispersas.
  • Esto limita la escalabilidad en aplicaciones complejas.

Objetivo del estudio:

  • Proponer un enfoque novedoso, Similitud conductual basada en dinámicas (RDS), para superar las limitaciones de los métodos de aprendizaje de representaciones existentes.
  • Desarrollar una métrica de similitud independiente de la recompensa que preserve la discriminabilidad conductual para mejorar el aprendizaje por refuerzo profundo.

Principales métodos:

  • Se introdujo una métrica de similitud impulsada por la dinámica que elimina la dependencia de la recompensa.
  • Se incorporaron distancias de transición dinámicas con ruido gaussiano entrenable para mitigar la degradación de la métrica.
  • Se utilizaron distancias de trayectorias latentes para cuantificar las diferencias de tareas y extraer características relevantes.

Principales resultados:

  • RDS demostró un rendimiento superior a los métodos de referencia en tareas complejas de manipulación de DeepMind Control, MetaWorld y Adroit.
  • Se lograron mejoras significativas del 43% y 30% sobre DrQ-v2 y los métodos de última generación, respectivamente.
  • Los estudios de ablación confirmaron la efectividad de los componentes individuales dentro del enfoque RDS.

Conclusiones:

  • La Similitud conductual basada en dinámicas (RDS) aborda eficazmente el colapso de la representación en el aprendizaje por refuerzo profundo.
  • El método propuesto mejora el aprendizaje de características relevantes para la tarea al aprovechar la similitud basada en dinámicas, mostrando un fuerte rendimiento en tareas robóticas desafiantes.