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Reinforcement01:23

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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.
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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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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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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Video Experimental Relacionado

Updated: Jan 8, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Aprendizaje por refuerzo multiagente basado en grafos con población evolutiva para la cooperación

Kexing Peng1, Hanwen Qi1, Tinghuai Ma2

  • 1School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China.

Neural networks : the official journal of the International Neural Network Society
|December 12, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta GDE, un nuevo marco de aprendizaje por refuerzo multiagente (MARL) que mejora la coordinación en tareas complejas. GDE combina la descomposición de valor basada en grafos con la optimización de políticas evolutivas por etapas para mejorar el rendimiento del agente.

Palabras clave:
algoritmos evolutivosred neuronal de grafosaprendizaje por refuerzo multiagente

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Área de la Ciencia:

  • Inteligencia Artificial
  • Robótica
  • Ciencias de la Computación

Sus antecedentes:

  • Los métodos existentes de aprendizaje por refuerzo multiagente (MARL) enfrentan desafíos en la escalabilidad a tareas de coordinación complejas debido a observaciones limitadas de los agentes e interacciones dinámicas.
  • La convergencia a políticas óptimas es difícil a medida que aumenta la complejidad de la tarea y el espacio de políticas, lo que afecta las evaluaciones estables de las políticas.

Objetivo del estudio:

  • Proponer GDE, un marco MARL diseñado para superar los problemas de escalabilidad y convergencia en sistemas multiagente cooperativos.
  • Mejorar la coordinación de agentes y la propagación de información en entornos dinámicos sin requerir el consenso del estado.

Principales métodos:

  • GDE integra la descomposición de valor basada en grafos con la optimización de políticas evolutivas por etapas.
  • Se utilizan algoritmos evolutivos (EAs) para la búsqueda aleatoria sin gradiente para mejorar la exploración de políticas y la convergencia.
  • Se emplean redes neuronales de grafos (GNN) para extender los campos receptivos de los agentes y facilitar la propagación de información, aprovechando la invarianza de permutación para una convergencia estable con datos dinámicos.

Principales resultados:

  • GDE demuestra un rendimiento superior en tareas de coordinación complejas, incluida la microgestión de StarCraft II, la cooperación de robots MAMuJoCo y la conducción autónoma SUMO.
  • El marco captura eficazmente la dinámica de coordinación compleja a través de la formación de equipos multiagente y las GNN.
  • Los resultados experimentales validan la efectividad y necesidad de cada módulo dentro del marco GDE.

Conclusiones:

  • GDE ofrece una solución robusta para mejorar la coordinación y la convergencia de políticas en MARL.
  • La combinación propuesta de descomposición basada en grafos y optimización evolutiva es eficaz para sistemas multiagente complejos.
  • El diseño modular y la adaptabilidad del marco lo hacen adecuado para diversas aplicaciones del mundo real.