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延迟强化学习趋于间歇控制,用于人类安静的姿态

Yongkun Zhao¹, Balint K Hodossy¹, Shibo Jing²

¹Department of Bioengineering, Faculty of Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.

Medical engineering & physics

|August 19, 2024

概括

人类的平衡控制可能不是连续的. 这项研究使用学习算法来建模人体,发现间歇的神经信号,而不是恒定信号,是保持直立姿势的最佳选择.

科学领域:

神经科学是一个神经科学.
生物物理学的生物物理.
控制理论控制理论

背景情况:

人类安静姿势的神经控制受到争论,人们对大脑在保持直立姿势中的作用有不同的看法.
当考虑神经时间延迟时,诸如连续阻抗控制等经典模型面临不稳定性问题.

研究的目的:

通过使用计算模型,研究人类安静姿势的最佳神经控制策略.
为了确定连续或间歇的神经激活是否更有效地维持平衡.

主要方法:

使用了一个代表人体的一段倒置摆形模型.
开发了一个延迟强化学习算法,以发现没有预先实施的策略的最佳控制策略.

主要成果:

开发的算法确定了一个最佳的神经控制器,显示间歇性的,而不是连续的激活模式.
模拟结果表明,间歇性控制是维持平衡的可行策略.

结论:

这些发现支持人类安静姿势的间歇性控制模型.
中枢神经系统可能间歇地提供神经反扭矩以保持直立姿势,挑战连续控制理论.

关键词:

延迟的强化学习延迟的强化学习间歇性控制的间歇性控制神经控制机制的神经控制机制保持安静的姿态,保持静静的姿态.

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