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First Pass Effect01:12

First Pass Effect

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Presystemic elimination, or the first-pass effect, is the metabolism of drugs that reduces their effective concentration at the site of action. Apart from the first-pass effect, the systemic bioavailability of the drug is also reduced by other factors, including incomplete absorption or chemical degradation of drugs.
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Adjusting a Traverse01:12

Adjusting a Traverse

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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
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Crossover Experiments01:16

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Reflex Activity01:08

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Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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まず戻り,それから探検

Adrien Ecoffet1,2, Joost Huizinga3,4, Joel Lehman5,6

  • 1Uber AI Labs, San Francisco, CA, USA. adrienecoffet@gmail.com.

Nature
|February 25, 2021
PubMed
まとめ
この要約は機械生成です。

Go-Exploreアルゴリズムは,状態を記憶し,探索する前にその状態に戻すことで補強学習を改善します. このアプローチは,これまで解明されていないゲームを解決し,AIの探索能力を向上させます.

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Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
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関連する実験動画

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Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

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科学分野:

  • 人工知能
  • 機械学習
  • ロボット

背景:

  • 補強学習 (RL) は自律的な意思決定を目的としていますが,希少または欺瞞的な報酬信号に苦しんでいます.
  • 効果的な環境調査はRLにとって極めて重要ですが,依然として大きな課題です.
  • 既存の RL アルゴリズムは,以前訪問した状態を忘れ,新しい状態を探索する前に再訪問することに失敗します.

研究 の 目的:

  • 強化学習の探索における脱線と脱線という課題に取り組むこと
  • Go-Exploreという新しいアルゴリズムを導入し,より効果的な環境探索を目的としています.
  • Go-Exploreが 複雑なゲームやロボット作業に 有効性を示すために

主な方法:

  • Go-Exploreというアルゴリズムファミリーを導入し,有望な状態を記憶し,探索前にそれらに戻ることを強調しました.
  • Go-Exploreを未解決のアタリゲームとハード探索のベンチマークに適用した.
  • "Go-Explore"を 賞与の少ない ピック&プレイス ロボットでテストしました
  • 探査の効率をさらに高め ストキャスティシズムに対処するための 統合された目標条件付きの政策

主要な成果:

  • Go-Exploreはこれまで解明されていないすべてのアタリゲームを解決しました.
  • ハードな探検ゲームで最先端のパフォーマンスを達成し,モンテズマの復讐とピットフォールに大幅に改善しました.
  • 挑戦的な報酬の少ないロボティクスタスクの 実践的な適用性を示した.
  • Go-Exploreの探査効率と ストキャスティシティの強さを高めました

結論:

  • RLの探索には強力で一般的なアプローチです.
  • Go-Exploreは 優れたパフォーマンスを提供し よりインテリジェントな学習エージェントへの 重要な経路を示唆しています
  • RLの限界を克服するために,構造化された探査戦略の重要性を強調しています.