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相关概念视频

Reinforcement01:23

Reinforcement

984
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:
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.5K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Observational Learning01:12

Observational Learning

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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...
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Reinforcement Schedules01:24

Reinforcement Schedules

547
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,...
547
Associative Learning01:27

Associative Learning

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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...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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相关实验视频

一种基于图表的安全增强学习方法,用于多代理合作.

Fandi Gou1, Haikuo Du1, Yunze Cai2

  • 1School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China.

Neural networks : the official journal of the International Neural Network Society
|February 20, 2026
PubMed
概括

本研究介绍了基于图的安全多代理增强学习 (GS-MARL),以提高多代理系统的安全性和可扩展性. 在通信有限的场景中,GS-MARL提高了性能,比现有方法取得更高的成功率.

关键词:
避免碰撞,避免碰撞.有限制的政策优化.图形神经网络是一个神经网络.多机构合作多机构合作安全的强化学习学习.

相关实验视频

科学领域:

  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术
  • 控制系统 控制系统

背景情况:

  • 多代理系统 (MAS) 面临着安全和通信方面的挑战.
  • 现有的多代理强化学习 (MARL) 方法由于奖励塑造和完全连接的通信而难以获得安全性和可扩展性.

研究的目的:

  • 提出一个新的框架,基于图形的安全MARL (GS-MARL),增强MAS的安全性和可扩展性.
  • 在实际应用中解决当前MARL算法的局限性.

主要方法:

  • 利用MAS固有的图形结构与图形神经网络 (GNN) 进行信息传递.
  • 实施受约束的联合政策优化方法,在当地观察下改善安全.

主要成果:

  • 与现有方法相比,GS-MARL显示出最佳性和安全性之间的优越权衡.
  • 在大规模,通信有限的场景中,至少实现了10%的成功率.
  • 通过模拟实验和硬件实现与Mecanum轮式车辆进行验证.

结论:

  • GS-MARL有效地提高了MARL中的安全性和可扩展性.
  • 该框架适用于现实世界的应用,包括机器人系统.