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相关概念视频

Observational Learning01:12

Observational Learning

838
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...
838
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Optimal Foraging00:48

Optimal Foraging

13.6K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.6K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

392
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
392
Reinforcement01:23

Reinforcement

839
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:
839
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

538
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
538

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相关实验视频

Updated: Jan 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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在无人机通信网络中通过基于奖励的多代理学习进行分散的资源配置.

Muhammad Shoaib1, Ghassan Husnain2, Muhsin Khan3

  • 1Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, 25100, Pakistan.

Scientific reports
|September 26, 2025
PubMed
概括

无人驾驶飞行器 (UAV) 通过独立管理用户和电力等资源来优化无线访问. 一个新的基于奖励的多代理学习 (RMAL) 框架最大限度地提高了系统奖励,而无需完全交换信息.

关键词:
航空基地站 航空基地站分散式的决策方式动态资源分配的动态资源分配多代理学习多代理学习无人驾驶飞行器是一种无人驾驶飞行器.

更多相关视频

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
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Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

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相关实验视频

Last Updated: Jan 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
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科学领域:

  • 无线通信系统无线通信系统
  • 人工智能在网络中的作用
  • 资源管理 资源管理

背景情况:

  • 无人驾驶飞行器 (UAV) 提供灵活的空中基站 (ABS) 可按需无线访问.
  • 动态资源配置对于最大限度地提高多无人机系统的性能至关重要.
  • 现有的方法可能需要广泛的信息交换,增加系统开销.

研究的目的:

  • 研究多个无人机支持的通信系统的动态资源分配策略.
  • 为了最大限度地提高长期回报和整体系统性能.
  • 为无人机资源管理开发一个分散的学习框架.

主要方法:

  • 将资源分配问题建模为一个随机游戏,无人机作为学习代理.
  • 开发一个基于奖励的多代理学习 (RMAL) 框架.
  • 采用基于Q学习的框架与本地观察实施一个独立于代理者的策略.

主要成果:

  • 拟议的RMAL框架实现了有效的资源分配,而不需要无人机之间全面的信息交换.
  • 模拟结果表明,RMAL性能对参数调整敏感,但提供了良好的权衡.
  • 与完全信息共享的系统相比,该方法提供了可接受的性能.

结论:

  • RMAL框架为多无人机系统中的动态资源分配提供了一种有效的方法.
  • 使用本地观察的分散学习可以实现近乎最佳的性能,同时减少通信开销.
  • 这项研究有助于在空中通信网络中推进智能资源管理.