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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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在RKHS中使用梯度方法在连续空间上流数据的值代.

Jiamin Liu1, Wangli Xu2, Yue Wang3

  • 1School of Mathematics and Physics, University of Science and Technology Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|August 11, 2023
PubMed
概括

本研究建立了多项式样本复杂性,用于使用梯度下降在连续空间中进行强化学习. 该方法有效计算值函数,为流数据提供接近最佳的融合率.

关键词:
梯度下降是一种梯度下降.在RKHSHS中.强化学习是一种强化学习.国家价值函数的状态值函数.值代的价值代是一个过程.

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科学领域:

  • 机器学习 机器学习
  • 强化学习的学习理论
  • 计算统计学 计算统计学

背景情况:

  • 经典的强化学习理论仅限于表格或线性设置.
  • 概括到连续状态和动作空间需要先进的函数近似和复杂性分析.
  • 随机梯度下降 (SGD) 是一种常见的代更新方法.

研究的目的:

  • 在连续状态和行动空间中建立强化学习的理论保证.
  • 为了分析强化学习预测问题的样本复杂性,使用流数据.
  • 为了证明在这个设置中梯度下降的效率.

主要方法:

  • 使用复制内核希尔伯特空间 (RKHS) 来表示函数.
  • 在值函数更新中应用梯度下降.
  • 分析样本的复杂性,考虑到函数的顺性.

主要成果:

  • 已建立的多项式样本复杂性用于连续空间中的强化学习.
  • 证明梯度下降实现了值函数的高效计算.
  • 已证明的收率接近参数SGD的最佳1/N.

结论:

  • 梯度下降是一种高效和计算方便的算法,用于使用流数据进行强化学习.
  • 拟议的框架为在持续强化学习环境中使用梯度下降提供了理论支持.
  • 这些发现为理解复杂环境的强化学习理论提供了重大进展.