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相关概念视频

Observational Learning01:12

Observational Learning

213
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...
213
Reinforcement01:23

Reinforcement

282
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:
282
Reinforcement Schedules01:24

Reinforcement Schedules

208
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,...
208
State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
245
Purposive Learning01:22

Purposive Learning

146
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
146
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
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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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
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人在循环中强化学习在持续行动空间中的学习.

Biao Luo, Zhengke Wu, Fei Zhou

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    此摘要是机器生成的。

    这项研究引入了一个新的循环强化学习 (HRL) 算法,用于连续行动空间. Q值依赖政策 (QDP) -HRL方法通过选择性地使用专家建议来提高学习速度和绩效.

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    科学领域:

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

    背景情况:

    • 强化学习 (RL) 经常受到样本效率低下的影响.
    • 现有的人在循环RL (HRL) 方法主要针对离散的行动空间.
    • 持续行动空间为有效的学习和人类指导带来了独特的挑战.

    研究的目的:

    • 为连续行动空间提出基于Q值依赖政策 (QDP) 的HRL算法 (QDP-HRL).
    • 通过结合选择性的人类专家建议来解决RL的样本效率低下问题.
    • 提高代理人在持续控制任务中的学习速度和性能.

    主要方法:

    • 开发了一个QDP-HRL算法,适应双延迟深度决定性政策梯度 (TD3) 算法.
    • 根据双胞胎Q网络输出之间的差异实施了选择性人类咨询.
    • 引入了利用专家经验和代理政策指导关键网络更新的优势损失函数.

    主要成果:

    • 在各种连续行动空间任务中,QDP-HRL显著提高了学习速度.
    • 与基线方法相比,该算法实现了整体性能提升.
    • 选择性人类干预有效地降低了认知负载,同时最大限度地提高了学习效益.

    结论:

    • QDP-HRL是一种有效的方法,可以提高连续行动空间RL中的样本效率.
    • 拟议的方法提供了一种将人类专业知识纳入复杂学习环境的实用方法.
    • 这项工作通过将其适用于连续控制问题来扩展HRL的进步.