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相关概念视频

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

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相关实验视频

Updated: Sep 10, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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用于云计算的层次深度强化学习的新型云任务调度框架

Delong Cui1, Zhiping Peng2, Kaibin Li1

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

PloS one
|August 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一个层次深度强化学习 (DRL) 框架,用于高效的云任务调度. 通过DRL调度器优化成本和性能,改善负载平衡,并减少10%的逾期任务.

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科学领域:

  • 计算机科学
  • 人工智能
  • 云计算

背景情况:

  • 由于大量的动态负载,云计算任务调度是NP完整的.
  • 现有的方法在动态云环境中难以实现效率和适应性.

研究的目的:

  • 为大规模的云任务安排提出一个新的层次深度强化学习 (DRL) 框架.
  • 在动态云环境中增强适应性,成本效益和性能.

主要方法:

  • 一种分层调度方法,首先将任务分配给虚拟机集群,然后分配给单个虚拟机.
  • 一个基于DRL的调度器,不断学习和调整网络参数.

主要成果:

  • 该DRL框架有效地平衡成本和性能,优化负载平衡,成本和延迟时间.
  • 与经典启发式算法相比,实现了10%的整体改进.
  • 在低负载场景中可证明成本降低,在高负载场景中可证明资源利用率提高.

结论:

  • 提出的等级DRL框架为复杂的云任务调度挑战提供了有希望的解决方案.
  • 已知限制包括计算开销,潜在延迟和数据依赖性.
  • 需要进一步的研究来解决复杂性和提高实时效率.