Video Experimental Relacionado
Updated: Apr 16, 2026
05:41
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
Published on: February 6, 2020
10.0K
Control a nivel humano a través del aprendizaje de refuerzo profundo
Volodymyr Mnih1, Koray Kavukcuoglu1, David Silver1
1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|February 27, 2015
Resumen
Este estudio introduce una red Q profunda, un agente artificial que aprende de entradas sensoriales de alta dimensión utilizando aprendizaje de refuerzo de extremo a extremo. El agente logró un rendimiento a nivel humano en juegos de Atari, demostrando una generalización efectiva a partir de datos de píxeles sin procesar.
Área de la Ciencia:
- La inteligencia artificial es inteligencia artificial.
- Aprendizaje automático Aprendizaje automático.
- La neurociencia computacional es una neurociencia computacional.
Sus antecedentes:
- El aprendizaje por refuerzo (RL) optimiza el control del agente basado en principios psicológicos y neurocientíficos.
- La RL del mundo real requiere que los agentes deriven representaciones eficientes de entradas sensoriales de alta dimensión para la generalización.
- Los agentes de RL existentes se limitan a características hechas a mano o estados de baja dimensión y completamente observados.
Objetivo del estudio:
- Desarrollar un nuevo agente artificial capaz de aprender refuerzo de extremo a extremo a partir de entradas sensoriales de alta dimensión.
- Para superar las limitaciones de los agentes RL anteriores en escenarios complejos y reales.
- Para cerrar la brecha entre los datos sensoriales en bruto y la toma de decisiones efectiva en los agentes artificiales.
Principales métodos:
- Utilizó los avances en el entrenamiento de redes neuronales profundas para crear un agente de red Q profunda.
- Empleó aprendizaje de refuerzo de extremo a extremo, procesando solo píxeles sin procesar y puntuaciones de juegos como entradas.
- Probó el agente en un conjunto diverso de 49 juegos clásicos de Atari 2600.
Principales resultados:
- El agente de red Q profundo superó a todos los algoritmos anteriores en juegos de Atari 2600.
- Logró un rendimiento comparable al de los probadores profesionales de juegos humanos en los juegos probados.
- Aprendizaje y generalización exitosos demostrados directamente de la entrada visual de alta dimensión.
Conclusiones:
- La red Q profunda representa un avance significativo en la inteligencia artificial, permitiendo el aprendizaje a partir de datos sensoriales en bruto.
- Este enfoque cierra la brecha entre las entradas y acciones de alta dimensión, creando agentes versátiles.
- El éxito del agente en una variedad de tareas desafiantes destaca el potencial del aprendizaje por refuerzo profundo.
Videos de Conceptos Relacionados
01:23Reinforcement
1.2K
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:
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
1.2K
01:06Law of Effect
6.0K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
6.0K
01:12Observational Learning
1.4K
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...
1.4K
01:14Avoidance Learning and Learned Helplessness
3.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
3.9K
01:24Reinforcement Schedules
721
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,...
Once a behavior is learned,...
721
01:18Hierarchy of Motor Control
6.9K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
6.9K