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Reinforcement Schedules01:24

Reinforcement Schedules

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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.
Once a behavior is learned,...
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Avoidance Learning and Learned Helplessness01:14

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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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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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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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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.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
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在DoS攻击下通过强化学习来对多代理系统进行规定的时间的人在循环中最佳同步控制.

Zongsheng Huang, Tieshan Li, Yue Long

    IEEE transactions on neural networks and learning systems
    |July 8, 2025
    PubMed
    概括

    本研究介绍了面临拒绝服务 (DoS) 攻击的多代理系统 (MAS) 的循环控制. 一个新的观察者和Q学习方法在规定的时间内实现最佳同步,确保系统的稳定性和性能.

    科学领域:

    • 控制系统工程 控制系统工程
    • 人工智能的人工智能
    • 网络安全 网络安全

    背景情况:

    • 多代理系统 (MAS) 容易受到拒绝服务 (DoS) 攻击的攻击,破坏通信链路.
    • 人在循环 (HiTL) 控制提供了增强的系统治理,但需要强大的同步策略.
    • 规定的时间 (PT) 控制旨在在用户定义的有限时间内实现趋同,这对于时间敏感的应用程序至关重要.

    研究的目的:

    • 针对基于链接的DoS攻击下MAS的规定的时间 (PT) 最佳同步控制.
    • 开发一个分布式观察器,能够在切换拓学下估计PT内部的领导输出.
    • 实现无模型的Q学习算法,以实现最佳的政策学习,并减少计算负载.

    主要方法:

    • 对于追随者代理来说,提出了一个完全分布的观察者,具有规定的有限时间函数.
    • 增强系统将追随者动态与观察者结合起来进行稳定性分析.
    • 一个单关键神经网络 (NN) Q学习算法,通过最小方形进行训练,用于政策优化.

    主要成果:

    • 拟议的观察员保证了全球实际的PT收与有限的收益,独立于全球拓.
    • 该Q学习算法证明了Q函数的融合,使得最优的同步政策学习.

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  • 模拟结果验证了开发的控制方案对DoS攻击的有效性.
  • 结论:

    • 该研究成功开发了一个PT HiTL最佳同步控制在DoS攻击下的MAS.
    • 拟议的观察者和Q学习方法提供了一个强大的和计算效率高的解决方案.
    • 这些发现有助于提高MAS在敌对环境中的弹性和性能.