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

129
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,...
129
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

356
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
356
State Space Representation01:27

State Space Representation

162
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
162
Reinforcement01:23

Reinforcement

177
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:
177
Fixed Action Patterns01:06

Fixed Action Patterns

15.8K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
15.8K
Observational Learning01:12

Observational Learning

123
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...
123

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

Simulated microgravity weakens wheat root microbial network against pathogens.

NPJ microgravity·2026
Same author

Development of a multiplex real-time PCR method for detecting immunosuppressive viruses and its preliminary application in broilers.

Journal of virological methods·2026
Same author

Risk factors for anastomotic leakage after anterior resection of rectal cancer with preservation of the left colic artery.

Experimental and therapeutic medicine·2026
Same author

Gingiva-Derived Decellularised Extracellular Matrix Hydrogel Supports Osteogenic and Angiogenic Phenotypes of 3D STRO-1<sup>+</sup> GMSC/HUVEC Spheroids In Vitro.

International dental journal·2026
Same author

<i>Chryseobacterium electrogenes</i> sp. nov., an electrogenic bacterium isolated from the Mars-analogue Aiken Spring in the Qaidam Basin, Northwest China.

International journal of systematic and evolutionary microbiology·2026
Same author

A Lightweight and Versatile Prosthetic Hand for Daily Grasping.

Biomimetics (Basel, Switzerland)·2026

相关实验视频

Updated: May 29, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.5K

LazyAct:基于受约束的MDP的动态状态跳转的惰演员.

Hongjie Zhang1, Zhenyu Chen1, Hourui Deng1

  • 1College of Computer Science, Sichuan Normal University, Chengdu, China.

PloS one
|February 6, 2025
PubMed
概括

LazyAct通过智能跳过非关键状态来降低深度强化学习的计算成本. 这种算法大大减少了推断,为代理商节省了时间和计算资源.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度强化学习学习 (deep reinforcement learning) 是一种深度强化学习的方法.

背景情况:

  • 深度强化学习 (DRL) 在复杂的决策方面表现出色,但其计算成本很高.
  • 当前的DRL方法需要对每个决策进行完整的神经网络计算,从而增加与相互作用和代理相关的成本.

研究的目的:

  • 介绍LazyAct算法以减少DRL中的推理计算.
  • 保持政策性能,同时显著降低计算需求.

主要方法:

  • 开发了LazyAct,灵感来自于人类的推理,可以绕过非关键状态.
  • 整合了一个国家跳过分支机构进入演员网络.
  • 使用IMPALA和MAPPO框架为单代理和多代理推理建立了优化目标.
  • 为政策网络培训提供雇员预培训和微调.

主要成果:

  • LazyAct在单剂环境中减少了约80%的推断,在多剂环境中减少了约40%.
  • 与现有方法相比,政策绩效保持稳定.
  • 观察到任务完成时间和浮点操作 (FLOP) 显著减少.

更多相关视频

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

1.8K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K

相关实验视频

Last Updated: May 29, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.5K
Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

1.8K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K

结论:

  • 通过减少计算开销,LazyAct为DRL提供了一种高效的方法.
  • 该算法通过降低资源需求而不会牺牲性能来证明其实际适用性.