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相关概念视频

Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
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基于序列掩盖的动态分销网络重新配置的深度强化学习方法.

Ruoheng Wang, Xiaowen Bi, Siqi Bu

    IEEE transactions on neural networks and learning systems
    |June 11, 2025
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    概括

    本研究介绍了使用深度强化学习 (DRL) 来进行动态分布网络重新配置 (DDNR) 的新型顺序掩盖策略. 该方法有效地处理复杂的行动空间,提高可扩展性和性能,以确保安全和经济的电网运行.

    科学领域:

    • 电气工程 电气工程
    • 人工智能的人工智能
    • 电力系统 电力系统

    背景情况:

    • 动态配电网络重新配置 (DDNR) 对于安全和经济的电力配电网络 (PDN) 运行至关重要,特别是高可再生能源 (RES) 透率.
    • 数据驱动的解决方案,特别是深度强化学习 (DRL),由于PDN中的数据可用性提高,正在为DDNR获得吸引力.
    • 现有的DRL方法在DDNR的广和稀疏的行动空间中扎,这通常是由于辐射性约束,限制了可扩展性和最佳性.

    研究的目的:

    • 解决基于DRL的DDNR的可扩展性和最佳性的挑战.
    • 提出一种新的顺序掩盖策略,以分解DDNR复杂的行动空间.
    • 为DDNR.开发一个数据效率高,安全性保证,可扩展的DDRL解决方案.

    主要方法:

    • 提出了一种连续的掩盖策略,将DDNR问题的复杂行动空间分解为可管理的子行动空间.
    • 一个基于封闭的循环单位 (GRU) 的代理被设计用于处理顺序数据.
    • 适应软演员批评 (SAC) 算法用于处理分解的动作空间.

    主要成果:

    • 与现有的数据驱动方法相比,拟议的方法显示出优越的算法性能和可扩展性.
    • 案例研究证实了顺序掩盖策略在处理辐射约束方面的有效性.

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  • 基于GRU的代理和适应的SAC算法提供了一个数据效率高和安全保证的DRL解决方案.

    • 结论:

    • 开发的DRL方法,利用一个顺序掩盖策略,在解决DDNR问题方面取得了重大进展.
    • 这种方法克服了现有的DRL技术在扩展性和大规模发电网的最佳性方面的局限性.
    • 调查结果强调了这种方法在增强高可再生能源集成的现代电网的安全和经济运行方面的潜力.