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相关概念视频

Instinctive Drift01:05

Instinctive Drift

163
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
163

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通过强化学习来定制活跃阴性缺陷之间的相互作用.

Carlos Floyd1,2, Aaron R Dinner1,2, Suriyanarayanan Vaikuntanathan1,2

  • 1Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, USA. csfloyd@uchicago.edu.

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概括
此摘要是机器生成的。

强化学习提供了一种无模型的方法来控制活跃的阴性磁场. 这种方法可以精确地操纵阴性缺陷,为活性物质系统中的设计动力学铺平了道路.

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科学领域:

  • 活动物质物理学 活动物质物理学
  • 柔软的凝聚物质 软的凝聚物质
  • 非平衡系统 非平衡系统

背景情况:

  • 活跃的体是复杂的系统,表现出微米级的动态模式和流动.
  • 分子电机的光学控制允许操纵非平衡活动场.
  • 由于复杂的动力学,为活跃的敌人构建有效的控制协议是具有挑战性的.

研究的目的:

  • 通过强化学习探索一种无模型的方法来控制使用强化学习的活体阴性场.
  • 为了证明能够设计 nematic 缺陷之间的相互作用的能力.
  • 评估使用低维系统可观的可行性,用于反控制.

主要方法:

  • 利用强化学习,一种机器学习技术,用于系统动态的试错探索.
  • 绕过了需要准确的参数化和明确的模型表现的需要.
  • 应用强化学习来控制本地活动场和影响内马特缺陷相互作用.

主要成果:

  • 成功证明,局部活动场可以诱导内马特缺陷之间的有效相互作用.
  • 通过工程活动场所,启用内马特缺陷来遵循设计者的动态规律.
  • 展示了低维系统可观测的足够性和精确控制的行动.

结论:

  • 强化学习提供了一种强大的,无模型的策略来控制活跃的阴性系统.
  • 这种方法允许工程特定的行为和互动的灵性缺陷.
  • 这些发现表明,在实验和生物系统中实施反控制循环的合理性.