Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Reinforcement Schedules01:24

Reinforcement Schedules

241
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,...
241
Cognitive Learning01:21

Cognitive Learning

517
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
517
Observational Learning01:12

Observational Learning

311
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...
311
Reinforcement01:23

Reinforcement

341
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:
341
Associative Learning01:27

Associative Learning

572
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
572
Introduction to Learning01:18

Introduction to Learning

530
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
530

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

<i>Amphibambusa fangchenggangensis</i> sp. nov., <i>Am. yunnanensis</i> sp. nov., and <i>Arecophila viscosa</i> sp. nov. (<i>Xylariales</i>, <i>Cainiaceae</i>) associated with bamboo from southwest China.

MycoKeys·2026
Same author

Synergistic and Antagonistic Controlled Charge Transfer in In<sub>2</sub>S<sub>3</sub>/Bi<sub>2</sub>O<sub>3</sub>/Mo<sub>2</sub>S<sub>3</sub> by Magnetic Field and P-Doping Strategies.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Trends in the Co-Occurrence of Alcohol Use and Cardiovascular Disease Among U.S. Adults, 1999 to 2023.

JACC. Advances·2026
Same author

Sleep Patterns of Chinese Aged 15 and Above with Different Characteristics - China, 2024.

China CDC weekly·2026
Same author

Demographic and socioeconomic inequalities in sleep quality among Chinese aged 15 years and above: a national population-based study.

The Lancet regional health. Western Pacific·2026
Same author

Associations of plant-based diets with all-cause and cause-specific mortality and life expectancy among participants with cardiometabolic disorders from UK, US, and China.

European journal of preventive cardiology·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Sep 10, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

612

Un nuevo marco de programación de tareas en la nube que utiliza el aprendizaje de refuerzo profundo jerárquico para

Delong Cui1, Zhiping Peng2, Kaibin Li1

  • 1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China.

PloS one
|August 21, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un marco jerárquico de aprendizaje de refuerzo profundo (DRL) para la programación eficiente de tareas en la nube. El programador DRL optimiza los costos y el rendimiento, mejora el equilibrio de carga y reduce las tareas atrasadas en un 10%.

Más Videos Relacionados

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Videos de Experimentos Relacionados

Last Updated: Sep 10, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

612
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Área de la Ciencia:

  • Ciencias de la computación
  • Inteligencia artificial
  • La computación en la nube

Sus antecedentes:

  • La programación de tareas de computación en la nube es NP-completa debido a cargas grandes y dinámicas.
  • Los métodos existentes luchan con la eficiencia y la adaptabilidad en entornos dinámicos de nube.

Objetivo del estudio:

  • Proponer un nuevo marco jerárquico de aprendizaje de refuerzo profundo (DRL) para la programación de tareas en la nube a gran escala.
  • Mejorar la adaptabilidad, la rentabilidad y el rendimiento en entornos dinámicos de nube.

Principales métodos:

  • Un enfoque de programación jerárquica que asigna tareas primero a grupos de máquinas virtuales y luego a máquinas virtuales individuales.
  • Un programador basado en DRL que aprende y adapta continuamente los parámetros de la red.

Principales resultados:

  • El marco DRL equilibra efectivamente el costo y el rendimiento, optimizando el equilibrio de carga, el costo y el tiempo de vencimiento.
  • Logró una mejora general del 10% en comparación con los algoritmos heurísticos clásicos.
  • Reducción de costes demostrada en escenarios de baja carga y mejora de la utilización de los recursos en escenarios de alta carga.

Conclusiones:

  • El marco de DRL jerárquico propuesto ofrece una solución prometedora para los desafíos complejos de programación de tareas en la nube.
  • Las limitaciones reconocidas incluyen sobrecarga computacional, latencia potencial y dependencia de datos.
  • Se necesita más investigación para abordar la complejidad y mejorar la eficiencia en tiempo real.