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

144
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,...
144
Behavior Modification01:21

Behavior Modification

142
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
142
Law of Effect01:06

Law of Effect

1.4K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.4K
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

56
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
56
Response Surface Methodology01:16

Response Surface Methodology

127
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
127
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

246
In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
Effective reinforcers for humans vary depending on the individual and the context. Primary reinforcers, such as food, water, sleep, shelter, and pleasure, have inherent value and satisfy basic biological...
246

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

Bayesian optimization with unknown constraints in graphical skill models for compliant manipulation tasks using an industrial robot.

Frontiers in robotics and AI·2022
Same author

A Concise and Geometrically Exact Planar Beam Model for Arbitrarily Large Elastic Deformation Dynamics.

Frontiers in robotics and AI·2021
Same author

A Hybrid Framework for Understanding and Predicting Human Reaching Motions.

Frontiers in robotics and AI·2021
Same author

Adaptation and Transfer of Robot Motion Policies for Close Proximity Human-Robot Interaction.

Frontiers in robotics and AI·2021
Same author

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models.

Scientific reports·2018
Same author

Global localization of 3D point clouds in building outline maps of urban outdoor environments.

International journal of intelligent robotics and applications·2017

相关实验视频

Updated: Jun 27, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.0K

基于最佳响应政策的分散的多代理强化学习.

Volker Gabler1, Dirk Wollherr1

  • 1Chair of Automatic Control Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.

Frontiers in robotics and AI
|May 1, 2024
PubMed
概括

这项研究引入了一种新型的去中心化行为者批判方法,用于在奖励稀疏的环境中进行合作的多代理强化学习 (MARL). 该方法通过使用任务奖励和代理成本的双重关键来增强机器人协调,优于现有的方法.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 多代理系统 多代理系统

背景情况:

  • 多代理系统涉及多个在共享环境中相互作用的代理.
  • 单剂强化学习的进步刺激了对多剂强化学习 (MARL) 的兴趣.
  • 集中式学习方案是常见的,但对于现实世界,分散式机器人部署来说不那么实用.

研究的目的:

  • 为合作MARL在奖励较少的领域提出一个去中心化的行为者-批评 (AC) 方法.
  • 为了使个人机器人能够在没有集中控制的情况下学习和部署.
  • 为了应对协调多个代理人具有有限反的挑战.

主要方法:

  • 为合作MARL.开发了一种新的演员-批评 (AC) 方法.
  • 将MARL问题解为分布式代理,将其他代理模拟为响应.
  • 每个代理人实施了两个关键点:一个是联合任务奖励,一个是代理人特定成本.
  • 利用Stackelberg游戏模型 (对抗自然的游戏,二元游戏) 来实现分散的执行和训练.

主要成果:

  • 在一个奖励稀薄的模拟多代理环境中评估了新的AC方法.
  • 与最先进的MARL学习者相比,提出的方法显示出更高的性能.
关键词:
斯塔克尔贝格山 (Stackelberg) 是一个山脉.演员关键算法 演员关键算法分散式学习计划 分散式学习计划深度学习,人工智能的人工智能游戏理论的游戏理论.多个代理的多个代理.多种代理强化学习的多种代理强化学习强化支倾斜的倾斜方式

更多相关视频

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.7K
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

9.3K

相关实验视频

Last Updated: Jun 27, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.0K
Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.7K
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

9.3K
  • 该分散的方案允许在单个机器人上进行有效的学习和执行.
  • 结论:

    • 新型的去中心化AC方法对合作MARL在稀疏奖励环境中有效.
    • 双关键系统成功地平衡了联合任务优化和特定代理人的成本降低.
    • 未来的研究可以建立在这个框架上,以应对更复杂的多代理协调挑战.