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相关概念视频

Observational Learning01:12

Observational Learning

213
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...
213
Associative Learning01:27

Associative Learning

452
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...
452
Reinforcement01:23

Reinforcement

282
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:
282
Cognitive Learning01:21

Cognitive Learning

433
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
433
Reinforcement Schedules01:24

Reinforcement Schedules

208
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,...
208
Introduction to Learning01:18

Introduction to Learning

478
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
478

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学习注意力沟通与一个共同的网络多代理强化学习学习.

Wenwu Yu1,2, Rui Wang2, Xiaohui Hu2

  • 1University of Chinese Academy of Sciences, Beijing 100049, China.

Computational intelligence and neuroscience
|July 7, 2023
PubMed
概括

本研究引入了一种新的多代理通信算法 (MAACCN),通过结合历史代理网络数据,增强在部分可观测环境中的合作. MAACCN显著提高了性能,特别是在具有挑战性的场景中.

科学领域:

  • 人工智能的人工智能
  • 多代理系统 多代理系统
  • 强化学习是一种强化学习.

背景情况:

  • 现有的多代理系统经常将信息限制在当前的网络状态上,阻碍了在部分可观测环境中的合作.
  • 有效的沟通和合作对于复杂的任务至关重要,但受到有限的信息来源的限制.

研究的目的:

  • 提出一种新的算法,与共同网络 (MAACCN) 进行多代理注意力通信,以扩大多代理通信的信息来源.
  • 通过将当前的观察与历史的共识知识相结合来改善决策.

主要方法:

  • 开发了MAACCN,结合了一个共识信息模块,利用历史表现最好的网络作为一个共同的网络.
  • 利用注意力机制将当前的观察与提取的共识知识相结合,以增强信息推断.
  • 在StarCraft多代理挑战 (SMAC) 基准上对MAACCN进行评估.

主要成果:

  • 与现有的基线算法相比,MAACCN表现出优越的性能.
  • 拟议的算法在一个特别困难的场景中实现了超过20%的性能改善.
  • 实验结果验证了MAACCN在加强多代理合作方面的有效性.

结论:

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  • 通过结合历史的共识知识,MAACCN有效地扩大了信息来源,从而改善了多代理沟通和合作.
  • 该算法显示了在部分可观测环境和复杂的多代理任务中推进研究的巨大潜力.
  • 未来的工作可以探索进一步增强共识机制及其在不同领域的应用.