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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

148
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,...
148

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基于深度渐进增强学习的灵活资源调度框架,用于IRS和无人机辅助MEC系统.

Li Dong, Feibo Jiang, Minjie Wang

    IEEE transactions on neural networks and learning systems
    |January 12, 2024
    PubMed
    概括

    本研究引入了灵活资源调度 (FRES) 框架,使用深度强化学习来最大限度地降低智能反射表面 (IRS) 和无人机 (UAV) 辅助移动边缘计算 (MEC) 系统的能源消耗.

    科学领域:

    • 无线通信网络是无线通信网络.
    • 边缘计算系统边缘计算系统
    • 人工智能应用的人工智能应用.

    背景情况:

    • 智能反射表面 (IRS) 和无人机 (UAV) 辅助的移动边缘计算 (MEC) 系统对于动态环境至关重要.
    • 在这些系统中优化能源消耗是具有挑战性的,因为无人机的数量和资源需求等参数是可变的.

    研究的目的:

    • 开发一种新的框架,以最大限度地减少IRS-UAV-MEC系统的能源消耗.
    • 为了解决联合优化无人机位置,IRS相位移,任务卸载和资源分配的复杂性.
    • 在无人机数量可变的系统中实现高效的资源安排.

    主要方法:

    • 提出了一个灵活的资源安排 (FRES) 框架,利用一种新的深度渐进的强化学习方法.
    • 引入了一个多任务代理,对离散 (卸载) 和连续 (资源分配) 变量具有不同的输出头,以解决混合整数非线性编程 (MINLP) 问题.
    • 实施了一种渐进式调度器,以使代理适应变化的无人机数量,防止灾难性的遗忘.
    • 集成了一个Light Tabu Search (LTS) 系统,以提高FRES框架的全球搜索能力.

    主要成果:

    • 与现有方法相比,FRES框架在降低能源消耗方面表现优越.
    • 多任务代理通过分离整数和连续变量优化来有效处理MINLP问题.

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  • 渐进式调度器允许系统适应无人机数量的动态变化.
  • LTS提高了整体优化效率和解决方案质量.

    • 结论:

    • 在动态的IRS-UAV-MEC系统中,FRES框架为实时和最佳的资源调度提供了有效的解决方案.
    • 建议使用多任务代理和渐进式调度器的深度渐进式强化学习方法具有高度的适应性和效率.
    • 这项研究有助于在临时和紧急情况下推进节能MEC系统.