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相关概念视频

Observational Learning01:12

Observational Learning

838
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...
838
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
Reinforcement Schedules01:24

Reinforcement Schedules

459
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,...
459
Reinforcement01:23

Reinforcement

839
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:
839
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
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

459
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
459

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相关实验视频

Updated: Jan 16, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

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离线到在线的强化学习,具有高效的不受约束的微调.

Jun Zheng1, Runda Jia2, Shaoning Liu1

  • 1College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.

Neural networks : the official journal of the International Neural Network Society
|September 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个高效的不受约束的微调框架,用于线下到线上强化学习. 该方法通过使离线数据集之外的彻底探索成为可能,从而提高政策绩效,从而实现更好的样本效率.

关键词:
隐藏空间模型 隐藏空间模型层规范化的层规范化.离线到在线的强化学习.代表性的学习学习.

相关实验视频

Last Updated: Jan 16, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.8K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 线下强化学习 (RL) 从固定的数据集学习,但受到数据质量和覆盖范围的限制.
  • 线下到线上RL旨在将线下和线上学习结合起来,以提高样本效率.
  • 由于分布式转移和保守的培训,现有的方法面临着适应在线学习的挑战.

研究的目的:

  • 开发一个高效的不受约束的微调框架,用于线下到线上强化学习.
  • 克服现有方法在适应在线环境和改进预先培训的政策方面的局限性.
  • 为了提高采样效率和减轻价值函数估计中的偏差.

主要方法:

  • 提出了一个高效的不受约束的微调框架,在政策更新期间消除了保守的约束.
  • 杆动态表示学习以捕捉有意义的特征并加速微调.
  • 使用层规范化来限制Q值并防止灾难性的分歧.
  • 增加了价值网络的更新频率,以提高抽样效率和减少估计偏差.

主要成果:

  • 与最先进的线下到线上RL算法相比,拟的框架显示出更高的性能.
  • 在D4RL基准的各种任务中取得了显著的改进.
  • 需要最小的在线交互才能超越现有方法.

结论:

  • 高效的无约束微调框架有效地解决了线下到线上强化学习的挑战.
  • 该方法可以进行彻底的探索,并通过高样本效率提高政策绩效.
  • 这种方法为在现实世界应用中推进强化学习提供了一个有希望的方向.