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相关概念视频

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
Reinforcement Schedules01:24

Reinforcement Schedules

242
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,...
242
Cognitive Learning01:21

Cognitive Learning

519
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...
519
Observational Learning01:12

Observational Learning

312
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...
312
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

451
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
451
Associative Learning01:27

Associative Learning

575
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...
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在5G车辆物联网中智能优先级感知频谱访问:强化学习方法

Adeel Iqbal1, Tahir Khurshaid2, Yazdan Ahmad Qadri1

  • 1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的强化学习框架,用于车辆网络中的智能频谱管理. 它平衡了诸如吞吐量,延迟和公平性等性能指标,以满足各种交通需求.

关键词:
5G是什么意思? 5G是什么意思?物联网的物联网,就是物联网.优先意识的频谱管理强化学习是一种强化学习.资源分配的资源分配.获得频谱的权限.

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科学领域:

  • 无线通信网络是无线通信网络.
  • 车载物联网 (V-IoT) 是一种物联网.
  • 这是下一代蜂网络.

背景情况:

  • 有效的频谱接入对于车载物联网 (V-IoT) 系统至关重要.
  • 下一代蜂网络需要动态的服务质量 (QoS) 管理.
  • 现有的频谱管理解决方案与车辆环境的动态性质作斗争.

研究的目的:

  • 提出一种基于强化学习 (RL) 的新型优先意识频谱管理 (RL-PASM) 框架.
  • 为高优先级 (HP),低优先级 (LP) 和最佳努力 (BE) 交通类别动态分配频谱资源.
  • 在基于RSU的集中控制系统中评估不同RL算法的性能.

主要方法:

  • 将频谱管理环境建模为离散时间马尔科夫决策过程 (MDP).
  • 使用情境敏感的奖励函数来保护公平的决策 (访问,抢购,共存,移交).
  • 将四种RL算法进行比较:Q学习,双重Q学习,深度Q网络 (DQN) 和演员批评 (AC).

主要成果:

  • RL-PASM有效地平衡了吞吐量,延迟,公平性和能源效率.
  • DQN实现了最高的平均吞吐量,而Q-Learning提供了最低的平均延迟和最高的能源效率.
  • 双重Q学习和演员批评保持了高公平性和低中断概率.

结论:

  • 在车辆网络中,RL-PASM为智能,优先意识的频谱访问提供了强大而适应性的解决方案.
  • 该框架适用于可扩展和资源受限的部署,特别是边缘受限的车辆环境.
  • 选择RL算法允许根据特定的网络优先级 (例如,吞吐量与能源效率) 进行量身定制的优化.