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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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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.
Once a behavior is learned,...
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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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The barriers to effective communication also include cultural barriers, semantic barriers, gender barriers, and time constraints.
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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Comunicación robusta y eficiente en el aprendizaje por refuerzo multiagente

Zejiao Liu1, Yi Li2, Jiali Wang2

  • 1The School of Mathematics, East China University of Science and Technology, Shanghai 200237, China.

Chaos (Woodbury, N.Y.)
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Resumen
Este resumen es generado por máquina.

Esta encuesta explora la comunicación robusta para el aprendizaje por refuerzo multiagente (MARL) bajo restricciones del mundo real como retrasos y ancho de banda limitado. Destaca estrategias para sistemas MARL confiables en conducción autónoma y aprendizaje federado.

Palabras clave:
aprendizaje por refuerzo multiagentecomunicación robustaaprendizaje federadoconducción autónomaaprendizaje automático

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Área de la Ciencia:

  • Inteligencia Artificial
  • Robótica
  • Aprendizaje Automático

Sus antecedentes:

  • El aprendizaje por refuerzo multiagente (MARL) permite comportamientos coordinados de los agentes.
  • Los modelos de comunicación MARL existentes a menudo asumen condiciones poco realistas como ancho de banda instantáneo e ilimitado.

Objetivo del estudio:

  • Revisar sistemáticamente los avances en la comunicación robusta y eficiente para MARL bajo restricciones realistas.
  • Centrarse en aplicaciones prácticas e identificar direcciones futuras de investigación.

Principales métodos:

  • Revisión de la literatura reciente sobre estrategias de comunicación MARL.
  • Análisis de desafíos que incluyen perturbaciones de mensajes, retrasos en la transmisión y ancho de banda limitado.
  • Enfoque en aplicaciones en conducción autónoma cooperativa, SLAM distribuido y aprendizaje federado.

Principales resultados:

  • Identificación de los desafíos clave en la confiabilidad de baja latencia, el uso del ancho de banda y las compensaciones de privacidad.
  • Exploración de estrategias de comunicación MARL adaptadas para la implementación en el mundo real.
  • Síntesis de la investigación actual para cerrar la brecha entre la teoría y la práctica.

Conclusiones:

  • Existe la necesidad de codesarrollar la comunicación, el aprendizaje y la robustez en MARL.
  • La investigación futura debe centrarse en enfoques unificados para implementaciones prácticas de MARL.
  • Abordar las restricciones de comunicación realistas es crucial para avanzar en las aplicaciones de MARL.