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Reinforcement Schedules01:24

Reinforcement Schedules

447
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,...
447
Reinforcement01:23

Reinforcement

816
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:
816
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Observational Learning01:12

Observational Learning

817
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...
817
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

773
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
773
Transformers in Distribution System01:27

Transformers in Distribution System

491
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
491

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Video Experimental Relacionado

Updated: Jan 14, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.1K

Aprendizaje profundo de agentes múltiples por refuerzo con estrategia evolutiva para la distribución de vehículos de

Hua Li, Bongju Jeong

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Los vehículos de carga móviles (VCM) ofrecen una solución flexible para la carga de vehículos eléctricos (VE). Un nuevo marco de aprendizaje profundo de agentes múltiples por refuerzo con estrategia evolutiva (MARL-ES) optimiza la distribución de VCM para mejorar la eficiencia y la rentabilidad.

    Palabras clave:
    vehículos de carga móvilescarga de vehículos eléctricosaprendizaje profundo por refuerzo de agentes múltiplesestrategia evolutivaoptimización de la distribución

    Videos de Experimentos Relacionados

    Last Updated: Jan 14, 2026

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.1K

    Área de la Ciencia:

    • Sistemas de transporte inteligentes
    • Investigación de operaciones
    • Inteligencia artificial

    Sus antecedentes:

    • La infraestructura de carga fija limita los servicios de vehículos eléctricos (VE).
    • El despacho dinámico de vehículos de carga móviles (VCM) es una solución prometedora.
    • La demanda estocástica y los altos costos operativos desafían el despacho actual de VCM.

    Objetivo del estudio:

    • Desarrollar una estrategia de despacho de VCM adaptativa y eficiente.
    • Abordar las limitaciones de los métodos de despacho de VCM estáticos y convencionales.
    • Mejorar el equilibrio entre la oferta y la demanda en los servicios de carga de VE en tiempo real.

    Principales métodos:

    • Se formuló el problema de despacho de VCM como un proceso de decisión de Markov (MDP).
    • Se propuso un novedoso marco de aprendizaje profundo de agentes múltiples por refuerzo (MARL) con estrategia evolutiva (ES) (MARL-ES).
    • Se utilizó entrenamiento centralizado con ejecución descentralizada (CTDE) y se integraron ES de espacio de acción, incluidos operadores de mutación y cruce basado en segmentos.

    Principales resultados:

    • MARL-ES superó significativamente la optimización estática y los enfoques convencionales de MARL.
    • Demostró mejoras en el beneficio total, la reducción de los costos operativos y la minimización de la distancia de desplazamiento de los VCM.
    • Mostró una escalabilidad y adaptabilidad robustas en diferentes tamaños de flota y en condiciones inciertas.

    Conclusiones:

    • MARL-ES proporciona una solución de despacho práctica y adaptativa para servicios móviles de carga inteligentes (MCS).
    • El marco maneja eficazmente las variaciones espacio-temporales en la demanda de VE y los estados de los VCM.
    • Este enfoque mejora la eficiencia y la viabilidad económica de los servicios de carga de VE.