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

Reinforcement01:23

Reinforcement

192
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:
192
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
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

498
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...
498
Reinforcement Schedules01:24

Reinforcement Schedules

138
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,...
138
Observational Learning01:12

Observational Learning

155
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...
155
Associative Learning01:27

Associative Learning

322
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...
322

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

Machine-learning-assisted comparative analysis of rice growth and yield formation in field and plant factory systems.

Frontiers in plant science·2026
Same author

An anti-swelling and wet-adhesive nanocellulose hydrogel sensor for underwater communication.

Materials horizons·2026
Same author

Model-informed safety management of tocilizumab for pediatric sJIA: a PBPK approach for dose-escalation and vaccination timing.

Frontiers in immunology·2026
Same author

Development and Interpretable Machine Learning-Based Prediction of Cardiovascular Disease Risk in Chinese COPD Patients: An Analysis of the CHARLS Database.

International journal of chronic obstructive pulmonary disease·2026
Same author

Comparative Evaluation of Functional Outcomes and Postoperative Complications After Minimally Invasive Fixation for Acromioclavicular Joint Injuries.

British journal of hospital medicine (London, England : 2005)·2026
Same author

Metabolic reconfiguration via bioenergetic repair of constructed wetlands: How magnesite transforms rhizosphere functionality in acid mine drainage treatment.

Journal of hazardous materials·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 16, 2025

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.5K

安全的增强学习与双强度的强度.

Zeyang Li, Chuxiong Hu, Yunan Wang

    IEEE transactions on pattern analysis and machine intelligence
    |August 15, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了强化学习 (RL) 代理的统一框架,使它们能够既安全又强大地应对对抗性干扰. 新的双重政策代方案确保代理人即使在最坏的情况下也保持业绩和安全.

    更多相关视频

    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
    07:05

    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

    Published on: September 10, 2018

    5.9K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K

    相关实验视频

    Last Updated: Jun 16, 2025

    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
    09:01

    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

    Published on: July 8, 2015

    12.5K
    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
    07:05

    Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

    Published on: September 10, 2018

    5.9K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K

    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:

    • 强化学习 (RL) 代理容易受到对抗性攻击,影响性能和安全.
    • 现有的安全RL和强大的RL方法单独解决这些问题,留下了统一解决方案的空白.
    • 挑战在于在竞争条件下平衡可行性和最佳性.

    研究的目的:

    • 开发一个系统的框架,统一安全和强大的强化学习.
    • 解决在对抗性环境中可行性和最佳性的相互交织的挑战.
    • 创建一个安全且在对抗性干扰下性能最佳的强化学习代理.

    主要方法:

    • 问题表述为受约束的两人零和马尔科夫游戏.
    • 关于双重政策代方案的建议,以同时优化任务和安全政策.
    • 开发一个实用的深度强化学习算法,双强的演员-关键 (DRAC),使用对抗网络.

    主要成果:

    • 双重政策代方案汇聚到对任务执行和安全的最佳政策.
    • DRAC在各种对抗场景 (没有对手,安全对手,性能对手) 中展示了高性能和持续的安全性.
    • 在安全关键的基准指标上,DRAC显著优于现有的基准方法.

    结论:

    • 拟议的框架有效地统一了安全和强大的强化学习.
    • DRAC提供了一个实用和有效的解决方案,用于开发在敌对环境中既安全又强大的代理.
    • 这项工作推动了安全关键应用中可靠的人工智能系统的开发.