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相关概念视频

Reinforcement01:23

Reinforcement

791
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:
791
Reinforcement Schedules01:24

Reinforcement Schedules

436
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,...
436
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

373
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
373
Observational Learning01:12

Observational Learning

795
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...
795
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

813
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...
813
Associative Learning01:27

Associative Learning

1.2K
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...
1.2K

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相关实验视频

Updated: Jan 10, 2026

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

9.1K

多代理强化学习系统的区块链增强的激励兼容机制.

Ke Tian1

  • 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA. ketian2@illinois.edu.

Scientific reports
|November 28, 2025
PubMed
概括

本研究介绍了一个区块链框架,其中包括智能合约和多代理强化学习 (MARL),以提高分散系统的信任和效率. 这种方法增强了协调,减少了勾结,并促进了代理互动中的公平性.

科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 区块链技术 区块链技术
  • 游戏理论 游戏理论

背景情况:

  • 由于战略操纵和勾结,分散的多代理系统面临着信任,公平和效率方面的挑战.
  • 现有的协调机制经常在部分竞争和分散的环境中扎.
  • 需要强大而透明的方法来使代理行为与全球目标保持一致.

研究的目的:

  • 提出一种新的区块链增强框架,用于在分散系统中进行激励兼容的代理协调.
  • 将多代理强化学习 (MARL) 与区块链和智能合约集成,用于自动执行协调机制.
  • 提高信任,公平和长期效率在多个代理协调.

主要方法:

  • 开发一个区块链框架,利用智能合约进行链上行为记录和自动化惩罚/奖励系统.
  • 将这些机制集成到多代理软行为者-关键 (MASAC) 算法中,以实现一致的决策.
  • 在自动化市场竞标和智能交通控制领域进行实验验证.

主要成果:

  • 在经过验证的领域中观察到社会福利和公平性的显著改善.
  • 在噪音条件下,共谋成功率的显著降低和行为强度的提高.
关键词:
区块链 区块链 区块链 区块链分散式的中心化协调.激励机制兼容的机制多个代理强化学习学习多个代理强化学习学习智能合约是一种智能合约.

相关实验视频

Last Updated: Jan 10, 2026

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

9.1K
  • 废弃性研究证实了每个框架组件的协同贡献.
  • 结论:

    • 拟议的区块链增强的MARL框架有效地解决了分散的代理协调方面的挑战.
    • 该系统在复杂的多代理环境中促进可扩展,透明和激励协调.
    • 这种方法为未来的分散型智能系统提供了一个基本模型.