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相关概念视频

Reinforcement01:23

Reinforcement

786
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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Incentive Theory: Pull Theory of Motivation01:18

Incentive Theory: Pull Theory of Motivation

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Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
The theory differentiates between...
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Reinforcement Schedules01:24

Reinforcement Schedules

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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,...
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Autocrine Signaling01:01

Autocrine Signaling

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Autocrine signaling is one of the many signaling mechanisms that function inside multicellular organisms to carry out intercellular communication. In this type of signaling mechanism, the same cell that secretes an extracellular signaling molecule also expresses the receptors to bind and respond to that signaling molecule.
Autocrine Signaling in Macrophages
Under normal physiological conditions, autocrine signaling is essential for maintaining homeostasis. This process is well characterized in...
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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
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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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Pavlovian Conditioned Approach Training in Rats
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在动态环境中进行增强的多代理强化学习的信号驱动激励通信.

Kexing Peng, Pengyi Li, Jianye Hao

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    此摘要是机器生成的。

    本研究引入了多代理强化学习 (MARL) 的新框架,该框架可以提高代理协调和通信效率. 信号驱动激励沟通 (SDIC) 框架提高了复杂环境中的任务成功.

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    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:

    • 在多代理强化学习 (MARL) 中的集中培训和分散执行 (CTDE) 框架面临着因有限的可观测性和通信开销而导致代理协调的挑战.
    • 在MARL中现有的通信方法往往增加了复杂性,而没有适应动态的环境条件.

    研究的目的:

    • 为CTDE开发一个新的沟通框架,以提高多代理系统的协调和效率.
    • 通过实现更有针对性和自适应性的介质间信号,解决当前通信机制的局限性.

    主要方法:

    • 将马尔科夫信号游戏 (MSG) 集成到CTDE中,以创建信号驱动激励通信 (SDIC) 框架.
    • 利用以价值为基础的方法与稀疏的沟通,并将合作伙伴建模纳入适应性代理行为预测.
    • 在合作的多代理强化学习 (MARL) 环境中实施SDIC.

    主要成果:

    • 在复杂的环境中,SDIC表现出了卓越的协调和任务成功,例如StarCraft II和SUMO交通模拟.
    • 该框架在保持可管理的计算复杂度的同时,在通信效率方面取得了显著的改进.
    • 废弃性研究证实了SDIC组件在减少开销和调整代理政策方面的有效性.

    结论:

    • 信号驱动的激励通信 (SDIC) 框架为CTDE环境中的代理间通信提供了更高效和有效的方法.
    • 通过集成的合作伙伴建模和有针对性的信号,SDIC成功地平衡了通信效率和计算复杂性.
    • 这种新的方法显著提高了合作型多代理强化学习 (MARL) 的协调和任务性能.