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関連する概念動画

Reinforcement01:23

Reinforcement

765
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:
765
Observational Learning01:12

Observational Learning

769
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...
769
Reinforcement Schedules01:24

Reinforcement Schedules

414
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,...
414
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

358
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Updated: Jan 4, 2026

Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice
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マルチエージェント強化学習によるスタークラフトIIのグランドマスターレベル

Oriol Vinyals1, Igor Babuschkin2, Wojciech M Czarnecki2

  • 1DeepMind, London, UK. vinyals@google.com.

Nature
|November 1, 2019
PubMed
まとめ
この要約は機械生成です。

アルファスターは,マルチエージェント強化学習を使用して,スタークラフトIIでグランドマスターレベルを達成した. このAIは 複雑な現実世界の 戦略ゲームで高度な能力を発揮します

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科学分野:

  • 人工知能
  • コンピュータゲーム理論
  • 多エージェントシステム

背景:

  • スタークラフトは現実世界のアプリケーションに 関連する複雑なマルチエージェントの課題を提示します
  • スタークラフトの以前のAIエージェントは ゲームの簡素化や超人能力に頼っていました
  • これまでの人工知能は スタークラフトのトッププレイヤーに匹敵しなかった

研究 の 目的:

  • 複雑なリアルタイム戦略ゲーム"スタークラフトII"で 人と対戦できるAIエージェントを開発する
  • 他の挑戦的な分野に適用できる汎用的な学習方法を活用する.

主な方法:

  • マルチエージェント強化学習アルゴリズムを使用した.
  • 適応戦略と対抗戦略を備えた 深いニューラルネットワークを活用した
  • スタークラフト2の人間とエージェントの両方のゲームからデータで訓練されています.

主要な成果:

  • アルファスターのエージェントは,スタークラフトIIの3つのレースでグランドマスターレベルを達成しました.
  • アルファスターのパフォーマンスは 人間選手の99.8%を超えました
  • 複雑な戦略ゲームにおける AI 能力の有意な進歩を示した.

結論:

  • 一般的な学習方法,特にマルチエージェント強化学習は,StarCraft IIのような複雑な戦略環境で人間レベルのパフォーマンスを得ることができます.
  • アルファスターはリアルタイム戦略ゲームにおける人工知能研究における重要なマイルストーンです.
  • このアプローチは,複雑な意思決定と調整を必要とする他の分野にも適用できます.