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

134
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,...
134
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

83
In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
83

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: Jun 10, 2025

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.4K

一种延迟强大的方法,用于增强实时增强学习.

Bo Xia1, Haoyuan Sun1, Bo Yuan2

  • 1Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.

Neural networks : the official journal of the International Neural Network Society
|October 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了实时强化学习的新框架,解决了环境延迟问题. 拟议的最小信息状态马尔科夫决策流程 (MISMDP) 和MRAC算法提高了代理人在动态任务中的性能.

关键词:
延迟 延迟 延迟马尔科夫决策过程最少的信息集是最小的信息集.实时实时的时间.强化学习是一种强化学习.

更多相关视频

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

109
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

14.6K

相关实验视频

Last Updated: Jun 10, 2025

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.4K
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

109
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

14.6K

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:

  • 传统的强化学习 (RL) 使用阻断范式 (马尔科夫决策过程 - MDP),假设静态环境和代理行为,不适合实时应用.
  • 现有的处理实时延迟的方法,如插值或状态增强,通常需要精确的延迟测量,并与复杂的动态作斗争.

研究的目的:

  • 开发一种新的实时决策框架,以适应代理和环境动态的同时变化.
  • 为实时环境引入一个重新设计的MDP,即最小信息状态马尔科夫决策过程 (MISMDP).
  • 建议使用Actor-Critic (MRAC) 算法进行实时任务的最小信息集,以有效地管理延误.

主要方法:

  • 引入了一个最小的信息集,以捕获并发的代理-环境相互作用数据.
  • 将封锁模式的MDP改为MISMDP框架,以实现实时适应性.
  • 在MISMDP框架内开发了MRAC算法,包括Q函数趋同的理论分析.

主要成果:

  • 与最先进的算法相比,MRAC在离散和连续动作空间环境中展示了卓越的性能和概括能力.
  • 该MISMDP框架有效地处理实时任务固有的延迟.
  • 严格的理论分析支持MRAC方法中的Q函数的趋同.

结论:

  • MISMDP框架和MRAC算法为实时增强学习挑战提供了强大的解决方案,特别是涉及环境延迟的挑战.
  • 在动态实时系统中,MRAC显著提升了管理延迟的最先进技术,以提高代理性能.