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相关概念视频

Observational Learning01:12

Observational Learning

779
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...
779
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

366
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...
366
Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Reinforcement01:23

Reinforcement

777
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:
777
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
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...
2.5K
Decision Making01:20

Decision Making

858
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
858

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相关实验视频

为适应性投资组合优化提供基于注意力的异质多代理深度强化学习图表.

Bing Zhang1

  • 1School of Finance and Trade, Harbin Finance University, Harbin, 150030, Heilongjiang, China. 13352504766@163.com.

Scientific reports
|December 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的深度强化学习框架,使用图表注意力网络进行高级投资组合优化. 与传统方法相比,它实现了更高的回报率和风险管理.

关键词:
适应性优化适应性优化深度强化学习的学习.金融市场是金融市场.图表注意力网络的图表.多代理系统是多代理系统.投资组合优化 投资组合优化

相关实验视频

科学领域:

  • 计算金融是指计算金融.
  • 机器学习 机器学习
  • 金融建模金融建模

背景情况:

  • 传统的投资组合优化与复杂,动态的市场条件作斗争.
  • 现有的方法无法有效地捕捉复杂的资产相互依赖.

研究的目的:

  • 开发一个新的基于注意力的异构的多代理深度强化学习框架.
  • 通过建模时间变化的资产相关性和适应市场动态来增强投资组合优化.

主要方法:

  • 整合图形神经网络 (GNN) 和用于风险评估,回报预测和市场感知的专业代理.
  • 使用图表注意力网络来建模资产相关性和依赖性.
  • 实施基于实时市场状况的自适应优化策略.

主要成果:

  • 在标准普尔500,纳斯达克100和拉塞尔2000数据集上实现了16.8%的年化回报率,1.34的夏普比率和8.2%的最大下调.
  • 显著优于传统的平均差异优化,平等权重的投资组合和现有的深度学习方法.
  • 废弃和敏感性分析证实了框架组件的贡献和稳定性.

结论:

  • 拟议的框架为投资组合优化提供了计算金融方面的重大进展.
  • 在动态市场中表现出增强的适应能力和卓越的风险管理能力.
  • 代表了一种新的方法,将GNN和多代理强化学习集成到财务决策中.