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相关概念视频

Reinforcement01:23

Reinforcement

273
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:
273
Observational Learning01:12

Observational Learning

207
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...
207
Associative Learning01:27

Associative Learning

434
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
434
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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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,...
202
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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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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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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阶层对抗逆向增强学习学习

Jiayu Chen, Tian Lan, Vaneet Aggarwal

    IEEE transactions on neural networks and learning systems
    |September 13, 2023
    PubMed
    概括

    层次模仿学习 (HIL) 通过具有子任务结构的学习策略来解决复杂的任务. 本研究介绍了层次对抗逆强化学习 (H-AIRL),以改善因果关系,并有效地学习政策,即使没有子任务注释.

    科学领域:

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

    背景情况:

    • 模仿学习 (IL) 旨在复制专家的行为,但与复杂的,需要等级政策的长期任务作斗争.
    • 现有的层次 IL (HIL) 方法往往无法捕捉因果关系或共同优化高层和低层政策,从而导致低于最佳性能.

    研究的目的:

    • 开发一种新的HIL算法,解决当前方法的局限性.
    • 通过明确建模子任务结构和因果关系来提高层次政策的学习.

    主要方法:

    • 提出分层对抗反向增强学习 (H-AIRL),扩展最先进的AIRL算法.
    • 重新定义扩展状态/动作空间的目标,并引入定向信息术语以增强因果关系.
    • 开发一个预期-最大化 (EM) 适应从未注释的演示学习.

    主要成果:

    • 与最先进的HIL基线相比,H-AIRL在具有挑战性的机器人控制任务上表现出卓越的性能.
    • 拟议的定向信息术语有效地增强了低层政策和子任务之间的因果关系.
    • 在EM适应允许从易于获得的,没有注释的专家演示学习.

    结论:

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  • H-AIRL在复杂任务的层次模仿学习中提供了显著的进步.
  • 算法的处理未注释数据和改进因果模型的能力提供了实际优势.
  • 这项工作有助于在机器人和人工智能领域更有效的政策学习.