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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

664
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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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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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Reinforcement01:23

Reinforcement

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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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Observational Learning01:12

Observational Learning

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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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Cognitive Learning01:21

Cognitive Learning

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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...
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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相关实验视频

基于深度强化学习学习的云端资源调度和卸载优化.

Lili Yin1, Yunze Xie1, Ze Zhao1

  • 1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了用于智能制造的深度强化学习算法,显著减少工业物联网 (IoT) 环境中的任务中断和延迟. 该方法有效地管理动态边缘节点负载,用于实时处理.

关键词:
深度Q-网络是什么卷积神经网络是一种卷积神经网络.深度强化学习的学习.举报人 举报人 举报人 举报人资源规划 资源规划任务卸载 任务卸载

相关实验视频

科学领域:

  • 智能制造 智能制造是一种智能制造.
  • 物联网 (IoT) 的工业互联网.
  • 边缘计算 边缘计算

背景情况:

  • 智能制造依赖于工业物联网 (IoT) 设备,产生许多需要实时处理的延迟敏感任务.
  • 边缘节点负载的动态变化导致延迟增加和任务中断,这给云端边缘端协作带来了挑战.
  • 现有的任务卸载策略与未知的边缘节点负载和动态系统状态作斗争.

研究的目的:

  • 提出一种分布式算法,用于在智能制造环境中有效卸载任务.
  • 为了应对未知的边缘节点负载和动态系统状态变化的挑战.
  • 优化对延迟敏感任务的任务分配和执行顺序.

主要方法:

  • 基于深度强化学习的分布式算法,结合了卷积神经网络 (CNN) 和Informer架构.
  • CNN提取边缘节点负载的局部特征;Informer的自我注意力捕捉了长期负载趋势.
  • 集成决斗深度Q网络 (DQN) 和双DQN,用于精确的状态动作值函数近似.

主要成果:

  • 拟议的算法可以将任务中断率降低82.3-94%.
  • 与现有算法相比,平均延迟时间减少了28-39.2%.
  • 该方法在高负载,延迟敏感的制造场景中显示出显著的优势.

结论:

  • 开发的深度强化学习算法有效地处理动态边缘节点负载和系统不确定性.
  • 移动设备的独立任务卸载决策使动态任务分配和优化执行成为可能.
  • 该算法提供了一个强大的解决方案,用于实时处理与工业物联网的智能制造.