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相关概念视频

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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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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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,...
133
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...
3.0K
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

82
In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
82
Reinforcement01:23

Reinforcement

181
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:
181

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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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历史决策规范化最大力强化学习学习

Botao Dong, Longyang Huang, Ning Pang

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    此摘要是机器生成的。

    新的历史决策规范化最大 (HDMRME) 算法在政策之外的强化学习 (RL) 中平衡了探索和利用. 这种方法可以提高复杂的控制任务中的政策性能和样本效率.

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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:

    • 勘探-开采困境是政策之外的强化学习 (RL) 中的一个关键挑战,阻碍了政策执行和样本效率.
    • 现有的RL算法很难有效地平衡探索新动作和利用已知的最佳动作.

    研究的目的:

    • 引入一个新的算法,历史决策规范化最大 (HDMRME) RL,旨在解决勘探-开采困境.
    • 在最大的框架内增强RL政策的利用能力.

    主要方法:

    • 开发了HDMRME RL算法,将历史决策规范化整合到最大的RL框架中.
    • 进行了理论分析,包括收证明,勘探开发权衡分析,Q函数差异检查和政策次优化分析.
    • 在使用 Mujoco 和 OpenAI Gym 平台的连续动作控制任务上评估性能.

    主要成果:

    • 与最先进的RL算法相比,HDMRME显示出更高的样本效率.
    • 该算法在各种连续动作控制任务中实现了更具竞争力的性能.
    • 理论分析提供了对算法的趋同和权衡特征的见解.

    结论:

    • HDMRME算法有效地平衡了在政策之外的RL中的勘探和开采.
    • HDMRME为复杂的控制任务提供了更好的样本效率和性能.
    • 这项工作为推进RL算法设计提供了一个有希望的新方向.