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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

392
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
392
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
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
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.3K
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

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

Safety, efficacy, and pharmacokinetics of eratrectinib (VC004) in solid tumors with NTRK fusions: phase 1 results from a phase 1/2 study.

Experimental hematology & oncology·2026
Same author

Wearable Sensor-Based Knee Joint Angle Estimation: Modalities, Modeling, and Applications.

IEEE journal of biomedical and health informatics·2026
Same author

Role and Molecular Mechanisms of Aerobic Glycolysis in Gastrointestinal Tumors.

Journal of Cancer·2026
Same author

Artificial intelligence-based evaluation of prognostic benefits from immunotherapy plus targeted therapy with or without radiotherapy or TACE in advanced hepatocellular carcinoma.

Frontiers in oncology·2025
Same author

Data-driven dual-channel dynamic event-triggered load frequency control for multiarea power systems with uniform quantizer.

Science progress·2025
Same author

Dynamic Event-Triggered Bipartite Formation for MIMO Multiagent Systems With Quantized Data.

IEEE transactions on cybernetics·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

基于经验的综合强化学习共识,用于未知的多代理系统.

Longquan Ma1, Huarong Zhao2, Yuhao Chen1

  • 1Engineering Research Center of Internet of Things Applications Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu, China.

Scientific reports
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了非线性多代理系统的综合强化学习算法,使得最优的共识控制,而不需要识别系统动态. 该方法确保了稳定的学习,并避免了改善性能的局部最佳值.

关键词:
共识控制共识控制整体强化学习是一种强化学习.多代理系统是多代理系统.

更多相关视频

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

14.0K

相关实验视频

Last Updated: Jan 16, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

14.0K

科学领域:

  • 控制理论 控制理论
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 多代理系统 (MAS) 面临复杂的控制挑战,特别是未知的动态.
  • 在MAS中达成共识 (协议) 对于协调任务至关重要.
  • 传统的政策代方法通常需要系统模型识别.

研究的目的:

  • 为具有未知动态的非线性MAS开发最佳的共识控制策略.
  • 使用在线整体强化学习实现政策代算法.
  • 解决和克服在线学习中的本地最佳的挑战.

主要方法:

  • 一个批评者-演员神经网络架构被整合到政策代中.
  • 在线综合强化学习被用来处理未知的系统动态.
  • 基于经验的重量调节法被引入,以确保持续的兴奋.

主要成果:

  • 拟议的算法成功实现了最佳的共识控制.
  • 该系统表现出了不对称的稳定性.
  • 神经网络的重量在学习过程中趋同.
  • 模拟研究验证了算法的有效性和正确性.

结论:

  • 批评者-演员神经网络方法有效地绕过了MAS控制中的动态识别需求.
  • 开发的算法为非线性系统的最佳共识提供了强大的解决方案.
  • 这些发现有助于多代理系统智能控制的进步.