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相关概念视频

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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相关实验视频

Updated: Sep 10, 2025

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:基于环境的线下元强化学习与因果表示

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
概括
此摘要是机器生成的。

因果COMRL通过使用因果表示学习来增强离线的元强化学习,以避免虚假的相关性. 这提高了强化学习对新任务的概括性和性能.

关键词:
基于上下文的超强化学习线下超强化学习强化学习

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科学领域:

  • 人工智能
  • 机器学习
  • 强化学习

背景情况:

  • 线下元强化学习 (OMRL) 使用线下数据集进行任务表示学习.
  • 现有的方法由于混因素而存在虚假的相关性,限制了通用性.
  • 当测试任务与培训任务不同时,混引发的相关性会降低政策性能.

研究的目的:

  • 提出一个新的基于背景的OMRL方法,将因果表示学习整合起来.
  • 解决虚假的相关性并提高强化学习剂的概括性.
  • 改进不同任务中的任务表示的区别.

主要方法:

  • 学习发现任务组件之间的因果关系.
  • 相互信息优化和对比学习以增强任务表示的独特性.
  • 使用因果任务表示来优化政策的软行为-关键 (SAC) 算法.

主要成果:

  • 与大多数元RL基准相比,因果COMRL表现优越.
  • 该方法有效地减轻了虚假相关性的负面影响.
  • 在强化学习中,因果任务表达带来了更好的概括性.

结论:

  • 因果COMRL通过利用因果推断提供了基于上下文的OMRL的强有力的方法.
  • 整合因果表示学习显著提高了代理的性能和通用性.
  • 这项工作通过提供一种方法来克服混引发的局限性,从而推动了OMRL领域的发展.