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Observational Learning01:12

Observational Learning

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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...
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Reinforcement01:23

Reinforcement

1.0K
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:
1.0K
Introduction to Learning01:18

Introduction to Learning

1.3K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.3K
Associative Learning01:27

Associative Learning

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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...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Cognitive Learning01:21

Cognitive Learning

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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...
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Decoding Natural Behavior from Neuroethological Embedding
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概念埋め込みを用いた階層型深層強化学習のフレームワーク

Yinglong Dai1, Zhi Yi2, Qiangfu Zhao3

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, 965-8580, Japan.

Neural networks : the official journal of the International Neural Network Society
|February 28, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、概念埋め込みを用いた階層型深層強化学習（HDRL）フレームワークを導入し、大規模な状態行動空間を管理します。このアプローチは、探索効率を向上させ、複雑な意思決定プロセスを簡素化します。

キーワード:
概念埋め込み階層型深層強化学習事前知識制約状態目標空間抽象化

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科学分野:

  • 人工知能
  • 機械学習
  • ロボット工学

背景:

  • 深層強化学習（DRL）は、大規模で組み合わせ的な状態行動空間に苦労しています。
  • 階層型DRL（HDRL）はスケーラビリティの解決策を提供しますが、効率的な構造設計が不足しています。
  • 現在のHDRL手法は、効果的な階層的ポリシーの作成に課題を抱えています。

研究 の 目的:

  • 探索空間を制約するために概念埋め込みを利用する一般的なHDRLフレームワークを提案すること。
  • 階層的ポリシー構造内で、認識と意思決定の分離を初めて形式化すること。
  • 透明な推論のために、抽象的な状態空間と目標空間の関係を明確にすること。

主な方法:

  • 概念埋め込みを組み込んだ新しいHDRLフレームワークを開発しました。
  • 階層的ポリシー内で認識と意思決定の分離を実装しました。
  • 提案されたフレームワークの下での探索空間の複雑さを定義および分析しました。

主要な成果:

  • フレームワークは概念埋め込みを通じて探索空間を効果的に制限します。
  • 構造化された推論と事前知識の統合を可能にする透明な推論パイプラインを実証しました。
  • 実験的検証により、フレームワークが探索効率を向上させる上で有効であることが確認されました。

結論:

  • 概念埋め込みを用いた提案されたHDRLフレームワークは、DRLのスケーラビリティの課題に対処します。
  • このアプローチは、抽象的な概念を活用することで、効率的なポリシー学習と探索を促進します。
  • この方法は、AIにおける複雑な意思決定への構造化された透明なアプローチを提供します。