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相关概念视频

Reinforcement01:23

Reinforcement

830
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:
830
Reinforcement Schedules01:24

Reinforcement Schedules

458
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,...
458
Observational Learning01:12

Observational Learning

832
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...
832
Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

10.0K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.0K
Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K

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相关实验视频

通过使用预测在云中优化资源配置,实现了强化学习.

S Kayalvili1, R Senthilkumar2, S Yasotha3

  • 1Kongu Engineering College, Erode, Tamil Nadu, India. skayalvili10@gmail.com.

Scientific reports
|October 15, 2025
PubMed
概括

本研究介绍了一种使用强化学习的预测启用云资源分配 (PCRA) 框架. PCRA增强了适应性资源分配,降低了动态云环境中的成本和SLA违规.

关键词:
云计算是一种云计算.优化,强化学习 (RL),鱼优化算法资源分配资源的分配.

相关实验视频

科学领域:

  • 计算机科学 计算机科学
  • 云计算 云计算 云计算
  • 人工智能的人工智能

背景情况:

  • 云计算提供了可访问的,按需的资源共享.
  • 适应性资源分配 (RA) 对于服务质量 (QoS) 和降低云环境中的成本至关重要.
  • 现有的RA方法与动态的工作负载和波动的系统状态作斗争,往往缺乏适应性.

研究的目的:

  • 为云计算中的自适应性资源分配 (PCRA) 提出一个预测支持的反系统.
  • 为了应对动态工作负载的挑战,并提高RA的适应性.
  • 为了提高 QoS 和降低基于云的包装设施的资源成本.

主要方法:

  • 开发了一个基于强化学习的RA框架 (PCRA) 与Q值预测.
  • 利用Q学习进行准确的Q值预测,以预测管理价值.
  • 采用特征选择鱼优化算法 (FSWOA) 进行公正的资源分配.

主要成果:

  • PCRA实现了94.7%的Q值预测准确度.
  • 在服务级别协议 (SLA) 违规事件中显著减少了17.4%.
  • 与传统方法相比,减少了17.4%的资源成本.

结论:

  • PCRA框架有效地实现了云环境中的实时,自适应性资源分配.
  • PCRA显著提高了预测准确性,并减少了SLA违规和资源成本.
  • 拟议的系统提供了一个强大的解决方案,用于管理云计算中的动态工作负载.