Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Observational Learning01:12

Observational Learning

782
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...
782
Modeling in Therapy01:26

Modeling in Therapy

360
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
360
Steps in the Modeling Process01:14

Steps in the Modeling Process

591
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
591
Reinforcement01:23

Reinforcement

781
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:
781
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
Modeling and Similitude01:12

Modeling and Similitude

573
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
573

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

RxMap: an LLM-assisted tool for medication normalization.

JAMIA open·2026
Same author

Monocyte epigenetic age acceleration is linked to non-somatic depressive symptoms in women with and without HIV.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions.

Journal of machine learning research : JMLR·2026
Same author

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables.

Journal of machine learning research : JMLR·2025
Same author

Modeling Alzheimer's Disease Biomarkers' Trajectory in the Absence of a Gold Standard Using a Bayesian Approach.

Statistics in medicine·2025
Same author

Multiplexing Proteomic and Ingenuity Pathway Analysis of Attention/Working Memory in Virally Suppressed Women with HIV: A Feasibility Study.

Diagnostics (Basel, Switzerland)·2025

相关实验视频

Updated: Jan 7, 2026

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.9K

博物馆:基于模型的镜像登,用于离线增强学习.

Mao Hong1, Zhiyue Zhang1, Yue Wu1

  • 1Department of Applied Mathematics and Statistics, Johns Hopkins University.

Transactions on machine learning research
|December 29, 2025
PubMed
概括
此摘要是机器生成的。

基于模型的线下强化学习 (RL) 方法很强大,但受限于有限的政策空间. 我们介绍了MoMA,这是一个新的算法,使用不受限制的策略来改善线下RL的决策.

更多相关视频

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
11:29

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

Published on: April 20, 2019

10.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

相关实验视频

Last Updated: Jan 7, 2026

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.9K
Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
11:29

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

Published on: April 20, 2019

10.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 控制理论 控制理论

背景情况:

  • 基于模型的线下强化学习 (RL) 提供了样本效率和通用性.
  • 现有的方法经常使用受限的政策空间,限制其潜力.
  • 需要实用,基于模型的离线RL,有不受限制的政策.

研究的目的:

  • 开发基于模型的离线RL算法MoMA,该算法利用一般函数近似和不受限制的策略类.
  • 解决现有方法在利用基于模型的方法的全部优势方面的局限性.
  • 为拟议的算法提供理论保证和实际实施.

主要方法:

  • 开发了MoMA,这是一个基于模型的镜像上升算法,用于离线RL.
  • 用于政策更新的一般函数近似,超越了受限制的参数类.
  • 在过渡模型的置信集中包含保守的价值函数估计.
  • 建立了关于返回政策的次优限的理论保证.

主要成果:

  • 在基于模型的线下RL中,MoMA有效地利用了不受限制的政策类.
  • 与现有方法相比,该算法显示出更好的决策能力.
  • 理论分析为MoMA政策的次优化提供了上限.
  • 开发并验证了MoMA的实用,近似版本.

结论:

  • 通过实现不受限制的政策,MoMA代表了基于模型的线下RL的重大进步.
  • 该算法为复杂的决策问题提供了实用和理论上合理的方法.
  • 数学研究证实了MoMA在现实应用中的有效性和潜力.