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関連する概念動画

Reinforcement Schedules01:24

Reinforcement Schedules

447
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,...
447
Reinforcement01:23

Reinforcement

816
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:
816
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Observational Learning01:12

Observational Learning

817
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...
817
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

773
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
773
Transformers in Distribution System01:27

Transformers in Distribution System

491
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
491

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関連する実験動画

Updated: Jan 14, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.1K

モバイル充電車両のディスパッチのための進化的戦略を用いたマルチエージェント深層強化学習

Hua Li, Bongju Jeong

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    モバイル充電車両（MCV）は、電気自動車（EV）の充電に柔軟なソリューションを提供します。進化的戦略（MARL-ES）を用いた新しいマルチエージェント深層強化学習フレームワークは、効率と収益性を向上させるためにMCVのディスパッチを最適化します。

    キーワード:
    モバイル充電車両電気自動車深層強化学習マルチエージェント強化学習進化的戦略ディスパッチング最適化運用研究人工知能インテリジェント輸送システム

    関連する実験動画

    Last Updated: Jan 14, 2026

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.1K

    科学分野:

    • インテリジェント輸送システム
    • 運用研究
    • 人工知能

    背景:

    • 固定された充電インフラストラクチャは、電気自動車（EV）サービスを制限します。モバイル充電車両（MCV）の動的なディスパッチは、有望なソリューションです。確率的な需要と高い運用コストは、現在のMCVディスパッチに課題をもたらします。

    主な方法:

    • MCVディスパッチ問題をマルコフ決定過程（MDP）として定式化しました。
    • 進化的戦略（ES）を統合した新しいマルチエージェント深層強化学習（MARL）フレームワーク（MARL-ES）を提案しました。
    • CTDE（Centralized Training with Decentralized Execution）を利用し、ミューテーションやセグメントベースのクロスオーバー演算子を含むアクションスペースESを統合しました。

    結論:

    • MARL-ESは、インテリジェントモバイル充電サービス（MCS）に実用的で適応性の高いディスパッチソリューションを提供します。
    • このフレームワークは、EV需要とMCVの状態の時空間的な変動を効果的に処理します。
    • このアプローチは、EV充電サービスの効率と経済的実行可能性を高めます。