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

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

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

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

Sensors (Basel, Switzerland)·2023
Same author

DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing.

Sensors (Basel, Switzerland)·2022
Same author

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters.

Sensors (Basel, Switzerland)·2022
Same author

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems.

Sensors (Basel, Switzerland)·2022
Same author

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework.

Sensors (Basel, Switzerland)·2019
Same author

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

503

基于深度强化学习的适应性调度,用于无线时间敏感网络.

Hanjin Kim1, Young-Jin Kim2, Won-Tae Kim1

  • 1Future Convergence Engineering Major, Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan-si 31253, Republic of Korea.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种无线时间敏感网络 (TSN) 模型和使用深度强化学习的新型调度器 (WISE). 智能有效地管理无线流量,确保关键应用程序的高可靠性和低延迟.

关键词:
深度强化学习的学习.时间意识的造型器.时间敏感的网络.无线网络 LAN 无线网络 LAN无线时间敏感网络.

更多相关视频

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

相关实验视频

Last Updated: Jun 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

503
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 网络化 网络化 网络化

背景情况:

  • 时间敏感网络 (TSN) 对于实时通信至关重要.
  • 将TSN调整为无线IEEE 802.11网络带来了诸如通道争议和动态条件之类的挑战.
  • 现有的TSN调度器在无线延迟和可变性方面扎.

研究的目的:

  • 为IEEE 802.11网络提供独家通道访问的无线TSN模型提出建议.
  • 开发一个新的时间敏感的交通调度器,WISE,利用深度强化学习.
  • 解决延迟问题,确保无线TSN的可靠性.

主要方法:

  • 开发了一个深度强化学习 (DRL) 框架,以建模时间敏感的交通模式.
  • 设计和实施了无线智能调度器 (WISE) 算法.
  • 进行了实验,以评估WISE在各种无线场景中的性能.

主要成果:

  • 拟议的WISE算法在各种无线场景中实现了高达99.9%的可靠性.
  • 处理延迟在指定的时间要求内被成功限制.
  • 提议的机制有效地保证了TSN流的可扩展性.

结论:

  • 无线TSN模型和WISE调度器为低延迟无线通信提供了强大的解决方案.
  • 深度强化学习有效地优化TSN在动态无线环境中的性能.
  • 该方法确保了高可靠性,并满足关键应用程序的严格时间限制.