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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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通过通过多步骤的最大风险估计进行行动纠正来提高稳定性.

Qinglong Chen1, Kun Ding2, Xiaoxiong Zhang2

  • 1School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.

Neural networks : the official journal of the International Neural Network Society
|January 1, 2025
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概括
此摘要是机器生成的。

本研究引入了多步最大风险意识强大的深度增强学习 (MMRAR-RL),以提高控制系统的稳定性. 通过考虑未来的影响,MMRAR-RL提高了对动态对抗性攻击的稳定性,优于现有的方法.

关键词:
动态预算 动态预算意识到最大的风险意识.多阶段扰动多阶段扰动政策优化 政策优化风险价值估计 风险价值估计

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科学领域:

  • 人工智能的人工智能
  • 控制系统工程 控制系统工程
  • 机器学习 机器学习

背景情况:

  • 确保控制系统对外部不确定性的稳定性对于稳定性至关重要.
  • 现有的对抗式学习方法经常使用固定的干扰,导致对动态或预见攻击的脆弱性.

研究的目的:

  • 开发一种新的算法,MMRAR-RL,优化强化学习策略,以提高对抗干扰的强度.
  • 在强化学习中解决贪的局限性,固定力量的对抗性攻击.

主要方法:

  • MMRAR-RL采用两阶段的方法:风险评估和政策改进.
  • 风险评估涉及对手根据代理轨迹适应性地为多步骤扰动分配预算.
  • 政策改进利用最大风险贝尔曼操作员来估计和减轻政策风险.

主要成果:

  • 在动态攻击预算下,MMRAR-RL有效估计了最大的政策风险.
  • 该算法在竞争条件下展示了最先进的性能.
  • 实验证实了MMRAR-RL能够容忍显著的动作干扰的能力.

结论:

  • 在不确定的环境中,MMRAR-RL为深度强化学习提供了强大的解决方案.
  • 拟议的方法提高了稳定性和性能,可以抵御复杂的对抗性攻击.