Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
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...
5.7K
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

537
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
537
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Expected Value01:15

Expected Value

4.2K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
4.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Confidence Coefficient01:24

Confidence Coefficient

7.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.8K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

PeerJ·2025
Same author

A virulence protein activates SERK4 and degrades RNA polymerase IV protein to suppress rice antiviral immunity.

Developmental cell·2025
Same author

Enhanced accumulation of indole glucosinolate and resistance to insect and pathogen in flowering Chinese cabbage by overexpression of Arabidopsis CYP79B2 and CYP83B1.

Pest management science·2025
Same author

<i>Borrelia burgdorferi</i> Strain-Specific Differences in Mouse Infectivity and Pathology.

Pathogens (Basel, Switzerland)·2025
Same author

Transcriptomic analysis of wrinkled leaf development of Tai-cai (Brassica rapa var. tai-tsai) and its synthetic allotetraploid via RNA and miRNA sequencing.

Plant molecular biology·2025
Same author

Phenylpropanoid Metabolites Mediate Antiviral Defense and Vector Resistance in Rice Infected With RRSV, RGSV, and SRBSDV.

Plant, cell & environment·2025

関連する実験動画

Updated: Sep 9, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K

擬似分布のエリート批評家:補強学習の価値推定の精度向上

Yujia Zhang1, Lin Li2, Wei Wei2

  • 1School of Computer Science and Technology, North University of China, Taiyuan, 030051, Shanxi, China.

Neural networks : the official journal of the International Neural Network Society
|August 28, 2025
PubMed
まとめ

擬似分布エリートクリティクス (PEC) は,Q値バイアスをバランスすることで補強学習を改善します. この新しいアプローチは,複雑な環境でのサンプル効率とエージェントの性能を向上させます.

キーワード:
偽配布表示強化学習不確実性の測定価値見積もり

さらに関連する動画

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.6K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.5K

関連する実験動画

Last Updated: Sep 9, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.6K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.5K

科学分野:

  • 人工知能
  • 機械学習
  • 強化学習

背景:

  • 強化学習 (RL) エージェントは複雑な環境で優れているが,状態行動値推定バイアスに苦しんでいる.
  • Q値近似における過大評価と過小評価のバイアスは,サンプル効率とパフォーマンスを制限する.

研究 の 目的:

  • RLサンプルの効率性を高めるために,擬似分布エリートクリティクス (PEC) フレームワークを導入する.
  • Q値の近似で過大評価と過小評価のバイアスを解決し,バランスをとる.
  • インテリジェントエージェントにおけるQ値の推定の精度と信頼性を向上させる.

主な方法:

  • 擬似分布表現を用いて Q 値の近似を分布特性に富ませる.
  • 時間差 (TD) ターゲット計算のための最も信頼性の高いクリティカルを選択するために,不確実性測定を組み込む.
  • TDターゲットにおける楽観的・悲観的なバイアスのバランスをとるために,トリム平均のテクニックを使用します.

主要な成果:

  • PECは 強化学習の課題において 統計的に有意な改善を示しています
  • このフレームワークは,ベンチマークシナリオにおける既存の方法論と比較して優れたパフォーマンスを示しています.
  • PECはサンプル効率を効果的に高め,Q値の推定を精密にします.

結論:

  • 擬似分布エリートクリティクス (PEC) フレームワークは,RLにおけるQ値推定バイアスに対する堅固な解決策を提供します.
  • PECは,配分濃縮とバイアスバランスによって,エージェントの性能とサンプル効率を高めます.
  • この革新的なアプローチは,より熟練したインテリジェントエージェントの開発における重要な進歩を表しています.