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相关概念视频

Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Schwarzschild Radius and Event Horizon01:21

Schwarzschild Radius and Event Horizon

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No object with a finite mass can travel faster than the speed of light in a vacuum. This fact has an interesting consequence in the domain of extremely high gravitational fields.
The minimum speed required to launch a projectile from the surface of an object to which it is gravitationally bound so that it eventually escapes the object’s gravitational field is called the escape velocity. The escape velocity is independent of the mass of the object. Merging the idea of escape...
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Conservation of Mass in Finite Cotrol Volume01:16

Conservation of Mass in Finite Cotrol Volume

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The principle of conservation of mass is a fundamental law in fluid mechanics and is applied using the continuity equation. We apply the concept to a finite control volume to derive the continuity equation.
A system is defined as a collection of unchanging contents, and the conservation of mass states that a system's mass is constant.
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Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions03:03

Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions

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Unless individual gases chemically react with each other, the individual gases in a mixture of gases do not affect each other’s pressure. Each gas in a mixture exerts the same pressure that it would exert if it were present alone in the container. The pressure exerted by each individual gas in a mixture is called its partial pressure.
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Author Spotlight: Evaluating the Impact of Immediate Partial Removal of Cumulus-Oocyte Complexes on Fertilization Efficiency and Embryo Quality
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Q学习方法对有限地平线的追踪 H ∞ 跟踪与部分观察

Mingxiang Liu, Qianqian Cai, Wei Meng

    IEEE transactions on cybernetics
    |January 19, 2026
    PubMed
    概括

    本研究介绍了对具有部分观测的离散时间系统的新型无模型强化学习算法. 这些数据驱动的方法解决了有限地平线H-无限跟踪控制的挑战,而不需要初始的政策或折扣因子.

    科学领域:

    • 控制理论 控制理论
    • 强化学习是一种强化学习.
    • 游戏理论 游戏理论

    背景情况:

    • 现有的强化学习 (RL) 方法通常需要完整的状态信息,并且仅限于无限地平线,时间不变系统.
    • 有限地平线控制与部分观测和未知的动态提出了重大挑战,包括需要时间变化的里卡蒂方程.
    • 对于那些动态不明的系统来说,无模型方法是可取的,仅依赖输入-输出数据.

    研究的目的:

    • 调查有限地平线H-无限跟踪控制问题,用于有部分观测和未知动态的离散时间线性系统.
    • 开发无模型的强化学习算法,克服现有方法的局限性,特别是关于状态信息和系统视界的局限性.
    • 为解决时间变化的控制问题提供一个框架,而不需要最初允许的政策或折扣因子.

    主要方法:

    • 从历史的输入-输出轨迹重建系统状态,以创建数据驱动的系统表示.
    • 基于输入输出数据的时间变化的Q函数的定义.
    • 关于两种设计为无模型,数据驱动控制的最小化Q学习算法的建议.

    主要成果:

    • 开发的算法成功地重建了系统状态,并定义了基于输出输入的Q函数.
    • 最少的Q学习算法不需要最初可接受的政策,避免折扣因素,增强稳定性保证.

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  • 该框架表明可扩展到无限地平线和时间变化的系统,而无需进行结构变化.
  • 结论:

    • 拟议的数据驱动的,无模型的强化学习算法有效地解决了对有部分观测的离散时间系统的有限地平线H-无限跟踪控制问题.
    • 理论收已经被证明,模拟结果验证了算法的有效性.
    • 这项工作在强化学习控制方面取得了重大进展,特别是在具有未知动态和部分状态信息的系统中.