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関連する概念動画

Cognitive Learning01:21

Cognitive Learning

517
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...
517
Purposive Learning01:22

Purposive Learning

206
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
206
Reinforcement01:23

Reinforcement

341
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:
341
Observational Learning01:12

Observational Learning

311
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...
311
Reinforcement Schedules01:24

Reinforcement Schedules

241
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,...
241
Associative Learning01:27

Associative Learning

572
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
572

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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因果COMRL:因果表現による文脈ベースのオフラインのメタ強化学習

Zhengzhe Zhang1, Wenjia Meng1, Haoliang Sun1

  • 1School of Software, Shandong University, Jinan, 250101, China.

Neural networks : the official journal of the International Neural Network Society
|August 20, 2025
PubMed
まとめ
この要約は機械生成です。

因果COMRLは,偽の相関を避けるために因果表現学習を使用してオフラインのメタ強化学習を強化します. これは新しいタスクに対する 強化学習エージェントの汎用性とパフォーマンスを改善します

キーワード:
文脈ベースのメタ強化学習オフラインのメタ強化学習強化学習

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科学分野:

  • 人工知能
  • 機械学習
  • 強化学習

背景:

  • オフラインメタ強化学習 (OMRL) は,タスク表現学習のためのオフラインデータセットを使用します.
  • 既存の方法は混同因子により誤った相関関係があり,一般化が制限されています.
  • 混同によって引き起こされる相関は,テストタスクがトレーニングタスクと異なる場合,ポリシーパフォーマンスを低下させます.

研究 の 目的:

  • 原因表現学習を統合した新しい文脈ベースのOMRL方法であるCausalCOMRLを提案する.
  • 偽の相関関係に対処し,補強学習エージェントの一般化性を高める.
  • 異なるタスク間のタスク表現の区別を改善する.

主な方法:

  • タスクの構成要素間の因果関係を明らかにすることを学ぶ.
  • 相互情報最適化と対照的な学習により,タスク表現の特異性を高める.
  • 因果的なタスク表現を用いたポリシー最適化のためのソフトアクター・クリティック (SAC) アルゴリズム.

主要な成果:

  • 原因COMRLは,ほとんどのメタRLベンチマークにおいて,既存の方法よりも優れたパフォーマンスを示しています.
  • この方法は,偽の相関の負の影響を効果的に軽減します.
  • 因果的なタスク表現は,強化学習エージェントの一般化性を改善します.

結論:

  • 原因COMRLは,因果推論を活用して,文脈ベースのOMRLに堅実なアプローチを提供します.
  • 原因表現学習を統合することで,エージェントの性能と一般化性が著しく向上します.
  • この研究は,混同によって引き起こされる制限を克服する方法を提供することによって,OMRLの分野を前進させる.