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

208
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:
208
Reinforcement Schedules01:24

Reinforcement Schedules

147
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,...
147
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

607
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
607
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
Observational Learning01:12

Observational Learning

173
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...
173
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K

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

Downregulation of circFASTKD1 ameliorates myocardial infarction by promoting angiogenesis.

Aging·2021
Same author

A Special Report on 2019 International Planning Competition and a Comprehensive Analysis of Its Results.

Frontiers in oncology·2020
Same author

Tomato protein phosphatase 2C influences the onset of fruit ripening and fruit glossiness.

Journal of experimental botany·2020
Same author

CT changes of severe coronavirus disease 2019 based on prognosis.

Scientific reports·2020
Same author

Meta-neural-network for real-time and passive deep-learning-based object recognition.

Nature communications·2020
Same author

Selective stress of antibiotics on microbial denitrification: Inhibitory effects, dynamics of microbial community structure and function.

Journal of hazardous materials·2020

相关实验视频

Updated: Jul 2, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K

针对安全增强学习的CVaR受限制的政策优化.

Qiyuan Zhang, Shu Leng, Xiaoteng Ma

    IEEE transactions on neural networks and learning systems
    |February 23, 2024
    PubMed
    概括
    此摘要是机器生成的。

    目前的安全关键强化学习 (RL) 方法无法保证安全. 我们引入CVaR受约束的政策优化 (CVaR-CPO),以确保高概率的约束满足更安全的RL决策.

    更多相关视频

    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    11.0K
    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
    08:59

    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

    Published on: March 3, 2023

    2.1K

    相关实验视频

    Last Updated: Jul 2, 2025

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
    08:18

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

    Published on: August 15, 2020

    5.0K
    Pavlovian Conditioned Approach Training in Rats
    06:57

    Pavlovian Conditioned Approach Training in Rats

    Published on: February 4, 2016

    11.0K
    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
    08:59

    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

    Published on: March 3, 2023

    2.1K

    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:

    • 有约束的强化学习 (RL) 通常只能在预期中保证安全.
    • 这种基于预期的安全性对于安全关键的应用程序是不够的,冒着高概率的约束违规的风险.
    • 确保高概率的约束满足对于安全的RL至关重要.

    研究的目的:

    • 开发一种新的安全强化学习算法,以解决基于预期的安全保证的局限性.
    • 为了最大限度地提高预期回报率,同时确保满足安全约束的高概率.
    • 专注于强大安全的约束成本的上部尾部.

    主要方法:

    • 提出CVaR受约束政策优化 (CVaR-CPO) 算法.
    • 在增强状态空间中使用状态扩展和信任区域方法制定问题.
    • 应用于政策更新的拉格朗日方法,并利用基于量子的估计用于CVaR相关的价值函数.

    主要成果:

    • 在遵守安全限制的同时,CVaR-CPO有效地最大化了预期回报.
    • 该方法证明了在实验中满足约束的高概率.
    • 性能与现有的最先进的安全RL方法相提并论.

    结论:

    • 通过直接解决有条件风险价值 (CVaR),CVaR-CPO提供了一种强大的安全强化学习方法.
    • 该算法为安全关键决策问题提供了实际解决方案.
    • 这项工作通过改善约束满足保证,推动了安全RL领域的发展.