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相关概念视频

Reinforcement01:23

Reinforcement

830
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:
830
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
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
Observational Learning01:12

Observational Learning

832
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...
832
Introduction to Learning01:18

Introduction to Learning

945
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...
945
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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相关实验视频

推基于价值函数的深度强化学习,考虑减少错误.

JinLian Zhou1,2, DeRong Shen3, Ying Guo4

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China. 543518214@qq.com.

Scientific reports
|October 7, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了Q-AD,这是一种用于深度强化学习推系统的新算法. Q-AD解决了深度Q网络 (DQN) 中的行动空间减少错误,提高了动态用户偏好的准确性.

关键词:
知识图表知识图表邻近政策优化 政策优化推系统是推系统.强化学习是一种强化学习.

相关实验视频

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 推系统是一个推系统.

背景情况:

  • 深度强化学习 (DRL) 在推系统中捕获动态用户偏好.
  • 深度Q网络 (DQN) 很受欢迎,但在冷启动场景中,它们在逐渐缩小的行动空间中扎.
  • 现有的DQN方法会导致差异,导致Q值估计错误.

研究的目的:

  • 为了解决基于DQN的推系统中的行动空间减少错误.
  • 在具有动态行动空间的场景中提高强化学习的准确性和效率.
  • 为了减轻由固定和收缩的作用空间之间的差异引起的Q值估计不准确性.

主要方法:

  • 推出了Q-AD (Q-learning 行动减少),这是一个基于DQN的新算法.
  • 在每次更新时,Q-AD缓冲Q值估计错误,以减轻减少错误.
  • 增强标准DQN与时间差 (TD) 更新的错误减少期.

主要成果:

  • 与标准的DQN相比,Q-AD显著减少了估值误差.
  • 拟议的算法在各种数据集中实现了更好的准确性和效率.
  • 在不同相互作用长度的长期推场景中表现得更好.

结论:

  • 在基于DQN的推系统中,Q-AD有效地减轻了行动空间减少错误.
  • 该算法提高了Q值估计的准确性和效率.
  • 对于用户冷启动场景,Q-AD提供了更强大的解决方案,并逐渐减少了行动空间.