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相关概念视频

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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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 Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
106
Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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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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Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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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...
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随机微积分指导的强化学习:为最佳决策提供概率框架.

Raghavendra M Devadas1, Vani Hiremani2, K R Bhavya3

  • 1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.

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概括

随机计算引导的强化学习 (SCRL) 提高了不确定性下的决策. 这种新方法的性能优于传统的随机强化学习 (SRL),风险较低,适应性更好.

关键词:
决策 决策是做出决定的.深度学习是一种深度学习.机器学习是机器学习.强化学习是一种强化学习.随机计算指导的强化学习学习随机微积分 随机微积分 随机微积分

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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
  • 决策理论 决策理论

背景情况:

  • 在不确定的环境中做出决策是具有挑战性的.
  • 传统的随机增强学习 (SRL) 在复杂的场景中存在局限性.

研究的目的:

  • 介绍随机计算引导的强化学习 (SCRL) 作为一个先进的决策框架.
  • 将SCRL的性能和风险概况与传统SRL方法进行比较.

主要方法:

  • 通过将随机微积分原理整合到强化学习中,开发了SCRL.
  • 进行实证测试以评估SCRL与SRL相比的适应性,性能和风险.
  • 评估指标包括培训奖励,学习进展和滚动平均值.

主要成果:

  • 在各种指标上,SCRL表现优于SRL.
  • SCRL的分散值 (63.49) 比SRL的分散值 (65.96) 更低.
  • 与SRL相比,SCRL的短期 (0.64) 和长期 (0.78) 风险值明显较低 (分别为18.64和10.41).

结论:

  • 在不确定的和复杂的情况中,SCRL提供了一种更强大,更低风险的决策方法.
  • 这些发现凸显了SCRL在需要谨慎决策的现实应用中的潜力.
  • 与传统的SRL方法相比,SCRL是一种显著的进步.