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相关概念视频

Reinforcement01:23

Reinforcement

918
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Corrosion of Reinforcement01:27

Corrosion of Reinforcement

577
The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
577
Reinforcement Schedules01:24

Reinforcement Schedules

501
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.
Once a behavior is learned,...
501
Reinforcements in Concrete01:25

Reinforcements in Concrete

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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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Fiber Reinforced Concrete01:22

Fiber Reinforced Concrete

393
Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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Reinforced Brick Masonry01:15

Reinforced Brick Masonry

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Reinforced brick masonry is an advanced construction technique that enhances the structural integrity of brick walls by incorporating steel reinforcements. These reinforcements are either placed within the hollow cores of bricks or sandwiched between two layers of masonry, known as wythes, and are then secured in place with grout. Grout is a fluid mixture composed of Portland cement, aggregate, and water, providing the necessary bonding agent for the steel and brick.
To fortify brick walls...
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相关实验视频

Updated: Feb 1, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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强化操作员学习 (ROL):一个混合的DeepONet引导的强化学习框架,用于稳定Kuramoto-Sivashinsky方程.

Nadim Ahmed1, Md Ashraful Babu1, Muhammad Sajjad Hossain2

  • 1Department of Physical Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.

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PubMed
概括

增强操作员学习 (ROL) 是一种混合控制方法,可显著稳定混乱系统. 这种方法结合了深度运营商网络和双延迟深度决定性政策梯度,以获得卓越的性能和效率.

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科学领域:

  • 控制理论 控制理论
  • 机器学习 机器学习
  • 应用数学 应用数学 应用数学

背景情况:

  • 复杂的系统经常表现出混乱的行为,使得它们难以控制.
  • 传统的控制方法与高维,非线性动力学作斗争.

研究的目的:

  • 引入强化操作员学习 (ROL),一种新的混合控制范式.
  • 通过Kuramoto-Sivashinsky方程来证明ROL在稳定时空混乱中的有效性.

主要方法:

  • 罗尔集成深度运营商网络 (DeepONet) 来实现线下控制法律的获取.
  • 双延迟深度决定性政策梯度 (TD3) 余量用于在线适应.
  • 该框架在1D Kuramoto-Sivashinsky方程上进行了测试,这是空间时间混乱的基准.

主要成果:

  • 与LQR相比,ROL减少了99.1%的系统能量,与纯TD3.3相比,LQR减少了64.3%,纯TD3.
  • 在200个时代中,DeepONet实现了低训练损失 (7.8 × 10-6).
  • 罗尔限制状态振幅是TD3的三倍,并更快地稳定了混乱.

结论:

  • 将操作员学习与剩余策略优化相结合,提供了最先进的控制.
  • ROL提供了一个样本效率高的方法来稳定混乱的部分微分方程.
  • 这种方法对于流抑制和燃烧控制等应用具有可扩展性.