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Observational Learning01:12

Observational Learning

158
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...
158
Cognitive Learning01:21

Cognitive Learning

237
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...
237
Introduction to Learning01:18

Introduction to Learning

359
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...
359
Purposive Learning01:22

Purposive Learning

108
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...
108
Associative Learning01:27

Associative Learning

329
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...
329
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
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...
1.7K

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相关实验视频

Updated: Jun 19, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

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课程学习为基于图表的投资组合管理提供强化学习:性能优化和全面分析.

Abdullah Ali Salamai1

  • 1Department of Management, Applied College, Jazan University, Jazan, KSA, Saudi Arabia.

Neural networks : the official journal of the International Neural Network Society
|July 25, 2024
PubMed
概括

本研究介绍了一种用于自动化投资组合管理 (PM) 的新型深度强化学习 (RL) 模型. 该框架通过纳入资产关系来增强传统的RL,从而改善了股票市场的风险最小化和回报最大化.

科学领域:

  • 计算金融是指计算金融.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 投资组合管理 (PM) 涉及在风险约束下优化资产配置以实现利能力.
  • 强化学习 (RL) 被广泛用于自动化PM,但现有的方法往往忽略了资产间的关系.
  • 市场动态需要先进的模型,以捕捉时间价格变化和资产相互依赖.

研究的目的:

  • 为投资组合管理开发一种新的深度学习模型,解决当前RL方法的局限性.
  • 将时间价格分析与学习资产间关系相结合,以加强决策.
  • 通过最大限度地降低风险和最大限度地提高累积回报来改进自动化投资组合管理.

主要方法:

  • 一个新的深度模型,它结合了历史价格的时间学习器和资产间关系的关系图学习器 (RGL).
  • 将这些学习者整合到课程强化学习 (RL) 计划中,制定PM作为课程的马尔科夫决策过程.
  • 制定适应性课程政策,使RL代理能够动态调整风险和回报目标.

主要成果:

  • 拟议的框架成功地学习了资产价格的时间表示及其相互关系.
  • 在S&P500,纽约证券交易所和纳斯达克数据上的实验表明,与现有的RL解决方案相比,性能优越.
关键词:
课程学习学习课程学习深度强化学习的学习.图形神经网络是一个神经网络.投资组合管理 组合管理变压器网络的变压器网络.

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  • 该模型有效地降低了风险价值,同时最大限度地提高了投资组合管理中的累积回报.
  • 结论:

    • 新的深度RL模型通过考虑资产关系,在自动化投资组合管理方面取得了重大进展.
    • 在课程RL框架内,时间和关系学习的综合方法被证明是有效的.
    • 这项研究为改善动态股票市场环境中的长期业绩提供了有价值的工具.