Optimizing collective behavior of communicating active particles with machine learning

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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 for maximizing 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.