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相关概念视频

Reinforcement01:23

Reinforcement

202
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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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.
In the absence...
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Reinforcement Schedules01:24

Reinforcement Schedules

144
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,...
144
Observational Learning01:12

Observational Learning

166
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...
166
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
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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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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强大的多目标强化学习 考虑到环境的不确定性

Xiangkun He, Jianye Hao, Xu Chen

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    此摘要是机器生成的。

    本研究引入了强大的多目标强化学习 (RMORL),以解决决策中的环境不确定性. RMORL培养了一个单一的模型,用于强大的帕雷托最佳政策,在复杂的场景中提高绩效.

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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
    • 优化优化 优化优化

    背景情况:

    • 现实世界中的问题往往涉及到多个相互冲突的目标,需要对偏好进行权衡.
    • 环境的不确定性,如变化或噪音,可以导致低于最佳的政策,尽管目标是帕雷托最佳性.

    研究的目的:

    • 提出一种新的,强大的多目标强化学习 (RMORL) 范式.
    • 培养一个能够在不同偏好空间中接近强大的帕雷托最佳政策的单一模型.

    主要方法:

    • 模拟环境干扰作为零和游戏中的对抗性代理,集成到多目标马尔科夫决策过程 (MOMDP) 中.
    • 开发了一种对抗性防御技术,以限制在特定偏好的政策变化下对观察性扰动进行防御.

    主要成果:

    • 拟议的RMORL技术在五个具有连续行动空间的多目标环境中进行了评估.
    • 通过与经典和最先进的基线方法进行比较来证明有效性.

    结论:

    • 实际上,RMORL有效地提高了对环境不确定性和观测干扰的政策稳定性.
    • 该方法使单一模型能够在整个偏好空间中实现强大的帕雷托最佳性.