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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

123
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,...
123

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在分布式网络控制中用于无线调度的深度强化学习.

Gaoyang Pang, Kang Huang, Daniel E Quevedo

    IEEE transactions on cybernetics
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    概括
    此摘要是机器生成的。

    本研究引入了一种深度强化学习方法,用于优化无线网络控制系统 (WNCS) 调度. 拟议的方法确保了系统的稳定性,并优于现有的政策.

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    科学领域:

    • 控制系统工程 控制系统工程
    • 无线通信网络 无线通信网络
    • 随机系统理论 随机系统理论

    背景情况:

    • 无线网络控制系统 (WNCS) 面临着在有限频率通道的联合上下链路调度方面的挑战.
    • 确保WNCS中的系统稳定性需要考虑控制和通信参数.

    研究的目的:

    • 为完全分布的WNCS制定稳定和最佳的传输调度政策.
    • 为了解决WNCS调度的强化学习中大型行动空间的复杂性.

    主要方法:

    • 通过随机系统理论来推导WNCS的足够稳定条件.
    • 制定日程安排问题作为马尔科夫决策过程.
    • 开发一个深度强化学习 (DRL) 框架,采用新的行动空间缩小和嵌入技术.

    主要成果:

    • 一个静止和决定性的调度策略被证明可以在满足衍生稳定性条件时稳定WNCS.
    • 拟议的DRL框架有效地处理大型行动空间.
    • 数字结果表明,与基准政策相比,表现优越.

    结论:

    • 基于DRL的框架提供了一个有效的解决方案,用于WNCS.中联合上链/下链调度.
    • 拟议的行动空间管理技术提高了DRL对复杂的调度问题的适用性.
    • 该研究确定了系统稳定条件和WNCS.中可实现的调度策略之间的联系.