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相关概念视频

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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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Reinforcement Schedules01:24

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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.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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通过整合掉落不确定性和轨迹采样,实现基于实践概率模型的强化学习.

Wenjun Huang, Yunduan Cui, Huiyun Li

    IEEE transactions on neural networks and learning systems
    |October 17, 2024
    PubMed
    概括

    本研究介绍了DPETS,这是一种新的强化学习方法,可以提高预测的稳定性和准确性. DPETS增强了机器人系统的控制能力,以更高的样本效率优于现有的方法.

    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 使用神经网络的基于概率模型的强化学习 (MBRL) 在预测稳定性,准确性和控制方面面临挑战.
    • 现有的方法在复杂的控制任务中难以有效管理系统不确定性.

    研究的目的:

    • 提出一种新的方法,DPETS (基于掉落的概率集与轨迹采样),以增强MBRL.
    • 为了提高基于神经网络的MBRL的预测稳定性,准确性和控制能力.

    主要方法:

    • DPETS将蒙特卡洛脱落 (MC脱落) 和轨迹采样相结合,用于稳定的系统不确定性预测.
    • 一个专门的损失函数纠正神经网络的拟合错误,以准确的概率模型预测.
    • 状态传播被扩展到过 aleatoric 不确定性,提高控制性能.

    主要成果:

    • 在Mujoco基准任务和机器人手臂操纵任务中,DPETS表现出卓越的表现.
    • 该方法在平均回归和收速度方面表现优于相关的MBRL方法.
    • DPETS取得了比无模型基线更好的结果,具有显著的采样效率.

    结论:

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    • DPETS提供了一个强大的解决方案,用于增强MBRL中的预测和控制.
    • 提出的方法在稳定性,准确性和样本效率方面取得了显著的改进.
    • 对于复杂的机器人控制应用,DPETS提供了一个有希望的进步.