Optimizing collective behavior of communicating active particles with machine learning

Jens Grauer, F. J. Schwarzendahl, Hartmut Löwen, B. Liebchen
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Abstract

Bacteria and other self-propelling microorganisms produce and respond to signaling molecules to communicate with each other (quorum sensing) and to direct their collective behavior. Here, we explore agents (active particles) which communicate with each other to coordinate their collective dynamics to optimize nutrient consumption. Using reinforcement learning and neural networks, we identify three different strategies: a "clustering strategy", where the agents accumulate in regions of high nutrient concentration; a "spreading strategy", where particles stay away from each other to avoid competing for sparse resources; and an "adaptive strategy", where the agents adaptively decide to either follow or stay away from others. Our work exemplifies the idea that machine learning can be used to determine parameters that are evolutionarily optimized in biological systems but often occur as unknown parameters in mathematical models describing their dynamics.
利用机器学习优化通信主动粒子的集体行为
细菌和其他自走微生物会产生信号分子并对其做出反应,从而相互交流(法定人数感应)并指导其集体行为。在这里,我们探讨了相互通信的代理(活性颗粒)如何协调它们的集体动态以优化营养消耗。利用强化学习和神经网络,我们确定了三种不同的策略:一种是 "聚类策略",即代理聚集在营养物质浓度高的区域;一种是 "扩散策略",即粒子之间相互远离,以避免争夺稀少的资源;还有一种是 "自适应策略",即代理自适应地决定跟随或远离其他代理。我们的工作体现了这样一种理念,即机器学习可用于确定生物系统中进化优化的参数,但这些参数在描述生物系统动态的数学模型中往往是未知参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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