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

Reinforcement01:23

Reinforcement

969
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:
969
Observational Learning01:12

Observational Learning

1.0K
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...
1.0K
Actor-Observer Effect01:23

Actor-Observer Effect

421
The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
421
Reinforcement Schedules01:24

Reinforcement Schedules

538
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,...
538
The Two-State Receptor Model01:29

The Two-State Receptor Model

3.1K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
3.1K
Cognitive Learning01:21

Cognitive Learning

1.4K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.4K

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

Discovery of a Novel Phenyl Thiophene-3-carboxamide Derivative DZX19 as an Orally TRK Inhibitor with Potent Antitumor Effects.

Journal of medicinal chemistry·2026
Same author

Neighboring State-Aware Policy for Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2025
Same author

Design, synthesis and biological evaluation of novel 1H-indole-3-carbonitrile derivatives as potent TRK Inhibitors.

European journal of medicinal chemistry·2025
Same author

Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.

Sensors (Basel, Switzerland)·2023
Same author

An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification.

Journal of neuroscience methods·2023
Same author

Rheological Behaviors and Damage Mechanism of Asphalt Binder under the Erosion of Dynamic Pore Water Pressure Environment.

Polymers·2022

関連する実験動画

Updated: Feb 18, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

深層補強学習のための一般的競争的-協力的-アクター-批判的枠組み

Meng Xu, Zihao Wen, Xinhong Chen

    IEEE transactions on pattern analysis and machine intelligence
    |February 16, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,双重アクターによるディープ・レインフォースメント・ラーニング (DRL) の新しい方法を導入し,アクターの相互模倣を通じて政策学習を強化します. このアプローチは,探査とQ値の精度を向上させ,さまざまなDRLタスクにおけるパフォーマンスを大幅に向上させます.

    関連する実験動画

    Last Updated: Feb 18, 2026

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.9K

    科学分野:

    • 人工知能 (AI) とは,人工知能 (AI) のことです.
    • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
    • ディープ・インフォースメント・ラーニング (深層補強学習)

    背景:

    • 深層補強学習 (DRL) は,探索とQ値の推定における課題に直面しています.
    • ダブルアクターDRLの方法は有望だが,アクターのコラボレーションが欠如し,最適でない政策につながっている.

    研究 の 目的:

    • ダブルアクター DRL 方法における関係者間の相互学習と協力を促進するための汎用的な解決策を提案する.
    • 行動者の独立性に対処することによって,DRLの政策開発と全体的なパフォーマンスを改善します.

    主な方法:

    • 俳優による行動の出力の違いを最小限に抑える方法を導入し,相互の模倣を促進しました.
    • 批評家からのQ値の差異を最小限に抑えて,模倣されたアクションの一貫した価値推定を確実にするために組み込みました.
    • 2つの具体的な実装を開発し,他のDRL方法へのアプローチを拡張しました.

    主要な成果:

    • 提案された方法は,ダブルアクターアプローチを含む20の最先端 (SOTA) DRL方法を大幅に改善します.
    • 改善は,11のさまざまなタスクで観察され,リターンやその他の主要指標で測定されました.
    • ダブルアクターDRLを超えて方法を拡張することによって,より広範な適用性を実証しました.

    結論:

    • 提案された相互学習フレームワークは,ダブルアクターDRLにおける独立したアクターの限界を効果的に解決します.
    • このアプローチは,政策の最適化とパフォーマンスを改善することによって,DRLの重要な進歩を提供します.
    • このメソッドの汎用性と,他のDRLパラダイムへの成功拡張は,幅広い潜在的インパクトを示唆しています.