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相关概念视频

Reinforcement01:23

Reinforcement

178
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:
178
Reinforcement Schedules01:24

Reinforcement Schedules

130
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,...
130
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
105
Observational Learning01:12

Observational Learning

131
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...
131
Associative Learning01:27

Associative Learning

288
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...
288
Transformers in Distribution System01:27

Transformers in Distribution System

98
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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相关实验视频

Updated: Jun 4, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

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一个全价值的分布式深度强化学习框架,用于多代理合作.

Mingsheng Fu1, Liwei Huang1, Fan Li2

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.

Neural networks : the official journal of the International Neural Network Society
|December 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个全分布式多代理强化学习 (RL) 的新框架,该框架保证了个人-全球-最大原则. 拟议的全分布式多代理合作 (FDMAC) 模型显著提高了复杂的合作任务的性能.

关键词:
深度强化学习的学习.分布式强化学习的学习.多机构合作多机构合作神经网络的神经网络的神经网络

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相关实验视频

Last Updated: Jun 4, 2025

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 多代理系统 多代理系统

背景情况:

  • 分布强化学习 (RL) 模拟了整个回报分布,提供了比预期值更丰富的见解.
  • 现有的分布式多代理系统在使用传统的价值分解时,难以满足个人-全球-最大 (IGM) 原则.
  • 一个完全分布式的多代理系统需要个人和全球价值函数都以分布式形式存在.

研究的目的:

  • 提出一个全新的全价值分布式多代理框架,保证IGM原则.
  • 基于这个框架,引入一个实际的深度强化学习模型,完全分布式多代理合作 (FDMAC).
  • 在复杂的多代理合作场景中验证FDMAC的有效性.

主要方法:

  • 为完全分布式的多代理系统开发了一个新的价值分解框架.
  • 证明,拟议的框架确保了满足IGM原则的要求.
  • 实施了完全分布式多代理合作 (FDMAC) 深度强化学习模型.

主要成果:

  • 拟议的框架保证了完全分布式多代理系统中的IGM原则.
  • 在StarCraft多代理挑战赛中,FDMAC模型表现出卓越的性能.
  • 与最佳基线相比,FDMAC的中位数测试胜率平均提高了10.47%.

结论:

  • 新的框架有效地解决了现有的分布式多代理RL的局限性.
  • FDMAC代表了合作型多代理强化学习的重大进步.
  • 结果强调了在复杂的合作任务中完全分布式价值函数的好处.