Skolkovo Institute of Science and Technology, Moscow, Russia, 121205.
The University of Leicester, University Road, Leicester, LE1 7RH, UK.
Sci Rep. 2021 Oct 21;11(1):20843. doi: 10.1038/s41598-021-99982-7.
We report a possible solution for the long-standing problem of the biological function of swirling motion, when a group of animals orbits a common center of the group. We exploit the hypothesis that learning processes in the nervous system of animals may be modelled by reinforcement learning (RL) and apply it to explain the phenomenon. In contrast to hardly justified models of physical interactions between animals, we propose a small set of rules to be learned by the agents, which results in swirling. The rules are extremely simple and thus applicable to animals with very limited level of information processing. We demonstrate that swirling may be understood in terms of the escort behavior, when an individual animal tries to reside within a certain distance from the swarm center. Moreover, we reveal the biological function of swirling motion: a trained for swirling swarm is by orders of magnitude more resistant to external perturbations, than an untrained one. Using our approach we analyze another class of a coordinated motion of animals-a group locomotion in viscous fluid. On a model example we demonstrate that RL provides an optimal disposition of coherently moving animals with a minimal dissipation of energy.
我们提出了一种可能的解决方案，用以解释当一群动物围绕共同的群体中心旋转时，其旋转运动的生物学功能这一长期存在的问题。我们利用了动物神经系统中的学习过程可以通过强化学习（RL）来建模的假设，并将其应用于解释这一现象。与动物之间物理相互作用的几乎没有依据的模型相反，我们提出了一组由agents 学习的简单规则，这些规则导致了旋转运动。这些规则非常简单，因此适用于信息处理水平非常有限的动物。我们证明，通过护航行为可以理解旋转运动，即个体动物试图在与群体中心的一定距离内存在。此外，我们揭示了旋转运动的生物学功能：经过旋转训练的群体比未经训练的群体对外部干扰的抵抗力要强几个数量级。使用我们的方法，我们分析了动物的另一类协调运动——粘性流体中的群体运动。在一个模型示例中，我们证明 RL 为具有最小能量耗散的协调动物提供了最佳的配置。
